diff --git a/spaces/123Kumar/vits-uma-genshin-honkai123/Docker/vits.sh b/spaces/123Kumar/vits-uma-genshin-honkai123/Docker/vits.sh
deleted file mode 100644
index 2b87f26eda96d3800b73b4a21b210c78888a2299..0000000000000000000000000000000000000000
--- a/spaces/123Kumar/vits-uma-genshin-honkai123/Docker/vits.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/bin/bash
-run() {
-    echo -e "\033[32m已完成初始化，启动服务...\033[0m"
-    python3 /app/vits-uma-genshin-honkai/app.py
-}
-install() {
-    echo -e "\033[33m正在初始化：安装依赖....\033[0m"
-    pip install -r /app/vits-uma-genshin-honkai/requirements.txt -i https://mirrors.ustc.edu.cn/pypi/web/simple
-    echo -e "\033[33m正在下载模型....\033[0m"
-    rm -f /app/vits-uma-genshin-honkai/model/G_953000.pth
-    wget -O /app/vits-uma-genshin-honkai/model/G_953000.pth https://huggingface.co/spaces/ikechan8370/vits-uma-genshin-honkai/resolve/main/model/G_953000.pth
-    echo -e "\033[32m初始化完成！\033[0m"
-    run
-}
-
-if [ ! -f "/app/vits-uma-genshin-honkai/model/G_953000.pth" ] || [ "$(stat -c%s "/app/vits-uma-genshin-honkai/model/G_953000.pth")" -lt 10000 ]; then
-    install
-else
-    run
-fi
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deleted file mode 100644
index 17ae82b77ee054d35f34c6777bd25571a42c92da..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Badmash No.1 movie free download kickass torrent Find out why this movie is a must-watch for action lovers.md	
+++ /dev/null
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Descargar Gateway B2 Teacher Book Pdf LINK.md b/spaces/1gistliPinn/ChatGPT4/Examples/Descargar Gateway B2 Teacher Book Pdf LINK.md
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index 31d64c158a4eb1ed686e4ae9593140350c314197..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Descargar Gateway B2 Teacher Book Pdf LINK.md	
+++ /dev/null
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Download Film Si Doel Anak Sekolahan Full 14 BEST.md b/spaces/1gistliPinn/ChatGPT4/Examples/Download Film Si Doel Anak Sekolahan Full 14 BEST.md
deleted file mode 100644
index fcbdeebac209cc5ac65e9f428e734747a0b83064..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Download Film Si Doel Anak Sekolahan Full 14 BEST.md	
+++ /dev/null
@@ -1,18 +0,0 @@
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Become the Drift King with Drift Clash Online Racing Mod APK Android 1.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Become the Drift King with Drift Clash Online Racing Mod APK Android 1.md
deleted file mode 100644
index 31e69dd98cb4197565abac36a61c4534677ccd55..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Become the Drift King with Drift Clash Online Racing Mod APK Android 1.md	
+++ /dev/null
@@ -1,116 +0,0 @@
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-<h1>Drift Clash Online Racing Mod APK Android 1: A Review</h1>
-<p>If you are a fan of drift racing games, you might have heard of Drift Clash Online Racing, a game that offers real-time battles and realistic physics. But did you know that you can enjoy this game even more with the modded version from Mod APK Android 1? In this article, we will review Drift Clash Online Racing Mod APK Android 1 and tell you why you should try it.</p>
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-<li>Real-time multiplayer mode with up to 10 players</li>
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Traffic Racer MOD APK for Android The Ultimate Racing Experience.md b/spaces/1phancelerku/anime-remove-background/Download Traffic Racer MOD APK for Android The Ultimate Racing Experience.md
deleted file mode 100644
index f669117fb0eff412a21dd6de54ecfe4259d18096..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Traffic Racer MOD APK for Android The Ultimate Racing Experience.md	
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-<ul>
-<li>You can have unlimited money and cars in the game, which means you can buy any car you want, upgrade it to the max level, and customize it as you like.</li>
-<li>You can unlock all the game modes and environments without having to complete any missions or challenges.</li>
-<li>You can enjoy the game without any ads or interruptions.</li>
-<li>You can have more fun and excitement in the game, as you can drive faster, perform more stunts, and crash more cars.</li>
-<li>You can challenge yourself and your friends by competing on the online leaderboards and achievements.</li>
-</ul>
- <h2>How to download Traffic Racer Mod Apk done?</h2>
- <p>If you are interested in downloading Traffic Racer Mod Apk done, you need to follow these simple steps:</p>
- <h3>Step 1: Find a reliable source</h3>
- <p>The first thing you need to do is to find a reliable source that offers the mod apk file for download. There are many websites that claim to provide the mod apk file, but not all of them are trustworthy. Some of them might contain viruses, malware, or spyware that can harm your device or steal your personal information. Therefore, you need to be careful when choosing a source. One of the sources that we recommend is [AN1.com](^1^), which is a reputable website that provides various mod apk files for free.</p>
- <h3>Step 2: Enable unknown sources</h3>
- <p>The next thing you need to do is to enable unknown sources on your device. This is because the mod apk file is not from the official Google Play Store, so you need to allow the installation of apps from unknown sources. To do this, you need to go to your device settings, then security, then enable unknown sources. This will allow you to install the mod apk file without any problems.</p>
- <h3>Step 3: Download and install the mod apk file</h3>
- <p>The third thing you need to do is to download and install the mod apk file on your device. To do this, you need to go to the website that you chose in step 1, then find the download link for the Traffic Racer Mod Apk file. Click on the download link and wait for the file to be downloaded on your device. Once the file is downloaded, you need to locate it in your device storage, then tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to be completed.</p>
- <h3>Step 4: Launch the game and enjoy</h3>
- <p>The last thing you need to do is to launch the game and enjoy it. To do this, you need to find the game icon on your device home screen or app drawer, then tap on it to open the game. You will see that you have unlimited money and cars in the game, and you can access all the game modes and environments. You can also play the game without any ads or interruptions. You can now enjoy the game as much as you want and have fun.</p>
- <h2>Features of Traffic Racer Mod Apk</h2>
- <p>Traffic Racer Mod Apk has many features that make it different from the original game. Here are some of them:</p>
- <h3>Unlimited money</h3>
- <p>One of the main features of Traffic Racer Mod Apk is that it gives you unlimited money in the game. You can use this money to buy any car you want, upgrade it to the max level, and customize it as you like. You can also use this money to unlock all the game modes and environments without having to complete any missions or challenges. You can have as much money as you want and spend it as you wish.</p>
- <h3>Unlimited cars</h3>
- <p>Another feature of Traffic Racer Mod Apk is that it gives you unlimited cars in the game. You can choose from 35 different cars, ranging from sedans, sports cars, trucks, buses, police cars, and more. You can also unlock all the cars without having to earn cash or complete any missions or challenges. You can have as many cars as you want and switch between them as you like.</p>
- <h3>No ads</h3>
- <p>A third feature of Traffic Racer Mod Apk is that it removes all the ads from the game. You can play the game without any ads or interruptions. You can also save your data and battery by not having to watch any ads or videos. You can enjoy the game without any distractions or annoyances.</p>
- <h3>High-quality graphics and sound</h3>
- <p>A fourth feature of Traffic Racer Mod Apk is that it improves the graphics and sound quality of the game. You can experience realistic 3D graphics and smooth animations in the game. You can also hear realistic sound effects and music in the game. You can immerse yourself in the game and feel like you are driving a real car on a real highway.</p>
- <h2>Conclusion</h2>
- <p>Traffic Racer is a fun and addictive racing game that lets you drive your car through highway traffic, earn cash, upgrade your car and buy new ones. But if you want to enjoy the game without any limitations or restrictions, you should download Traffic Racer Mod Apk done. This mod apk file gives you access to unlimited money and cars in the game, as well as removes all the ads from the game. You can also unlock all the game modes and environments without having to complete any missions or challenges. With Traffic Racer Mod Apk, you can have more fun and excitement in the game, as well as challenge yourself and your friends by competing on the online leaderboards and achievements. If you are interested in downloading Traffic Racer Mod Apk done, you can follow the simple steps that we have explained in this article. We hope that you have found this article helpful and informative. Thank you for reading and happy racing!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Traffic Racer Mod Apk:</p>
- <h3>Is Traffic Racer Mod Apk safe to download and install?</h3>
- <p>Yes, Traffic Racer Mod Apk is safe to download and install, as long as you use a reliable source that offers the mod apk file for free. We recommend using [AN1.com], which is a reputable website that provides various mod apk files for free. However, you should always scan the mod apk file with an antivirus or anti-malware program before installing it on your device, just to be on the safe side.</p>
- <h3>Is Traffic Racer Mod Apk compatible with my device?</h3>
- <p>Traffic Racer Mod Apk is compatible with most Android devices that run on Android 4.1 or higher. However, some devices might not support the mod apk file due to different specifications or settings. Therefore, you should always check the compatibility of the mod apk file with your device before downloading and installing it. You can also contact the developer of the mod apk file if you encounter any problems or issues with the compatibility.</p>
- <h3>Will Traffic Racer Mod Apk affect my game progress or account?</h3>
- <p>No, Traffic Racer Mod Apk will not affect your game progress or account, as it does not require any root access or login credentials to work. You can play the game as usual, with or without the mod apk file installed on your device. However, you should be aware that using the mod apk file might violate the terms and conditions of the original game, and you might face some consequences or risks if you use it online or with other players. Therefore, you should use the mod apk file at your own discretion and responsibility.</p>
- <h3>Can I update Traffic Racer Mod Apk to the latest version?</h3>
- <p>Yes, you can update Traffic Racer Mod Apk to the latest version, as long as the developer of the mod apk file releases a new version that matches the original game version. You can check for updates on the website that you used to download the mod apk file, or on other websites that offer similar mod apk files. However, you should always backup your game data before updating the mod apk file, just in case something goes wrong or you lose your game progress.</p>
- <h3>Can I uninstall Traffic Racer Mod Apk if I don't like it?</h3>
- <p>Yes, you can uninstall Traffic Racer Mod Apk if you don't like it or if you want to switch back to the original game. To do this, you need to go to your device settings, then apps, then find and select Traffic Racer Mod Apk, then tap on uninstall. This will remove the mod apk file from your device and restore the original game. You can also delete the mod apk file from your device storage if you want to free up some space.</p> 401be4b1e0<br />
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-<h1>FIFA Mobile v18.1.01 Mod Apk: The Ultimate Guide</h1>
-<p>If you are a fan of soccer games, you probably have heard of <strong>FIFA Mobile</strong>, the official mobile game of EA Sports that lets you build your ultimate team of soccer stars and compete in various modes, including the FIFA World Cup 2022™. But did you know that there is a way to make your gaming experience even more exciting and rewarding? That's right, we are talking about <strong>FIFA Mobile v18.1.01 mod apk</strong>, a modified version of the game that gives you access to unlimited money, unlocked features, and more.</p>
-<p>In this article, we will tell you everything you need to know about FIFA Mobile v18.1.01 mod apk, including its benefits, how to download and install it, and how to use it to dominate the soccer field. Whether you want to build your dream team, relive the world's greatest soccer tournament, score big with soccer icons and heroes, experience immersive next-level soccer simulation, or be the soccer manager of your own dream team, FIFA Mobile v18.1.01 mod apk has something for you.</p>
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- <h2>What is FIFA Mobile and what are its features?</h2>
-<p>FIFA Mobile is a free-to-play soccer game for iOS and Android devices that lets you build your ultimate team of over 15,000 authentic soccer stars from over 600 teams across over 30 leagues. You can choose from world-class talent like Kylian Mbappé, Christian Pulisic, Vinicius Jr, and Son Heung-min, as well as legends like Paolo Maldini, Ronaldinho, and more. You can also customize your team's kits, badges, formation, tactics, and chemistry.</p>
-<p>FIFA Mobile also offers various modes for you to enjoy, such as:</p>
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-<li><strong>Head-to-Head</strong>: Play real-time 11v11 matches against other players from around the world and climb the leaderboards.</li>
-<li><strong>VS Attack</strong>: Take turns to score goals in fast-paced matches where every attack counts.</li>
-<li><strong>Manager Mode</strong>: Be the soccer manager of your own dream team and plan your strategy and adjust your tactics in real time or choose auto-play.</li>
-<li><strong>FIFA World Cup 2022™ Mode</strong>: Relive the world's greatest soccer tournament with any of the 32 qualified national teams or rewrite history with 15 non-qualified national teams. Play in authentic World Cup stadiums with official kits, badges, and match ball.</li>
-<li><strong>Events</strong>: Participate in live events that correspond with the real-world tournaments throughout the soccer season and earn special rewards.</li>
-<li><strong>Campaigns</strong>: Complete challenges and earn players from different leagues and regions.</li>
-<li><strong>The Academy</strong>: Learn the basics of the game and improve your skills with drills and tutorials.</li>
-</ul>
-<p>FIFA Mobile also features stunning graphics, realistic animations, and immersive sound effects that make you feel like you are on the pitch. You can also chat with your friends, join a league, or create your own league and compete with other players. FIFA Mobile is constantly updated with new content and features to keep you engaged and entertained.</p>
- <h2>What is FIFA Mobile v18.1.01 mod apk and what are its benefits?</h2>
-<p>FIFA Mobile v18.1.01 mod apk is a modified version of the original FIFA Mobile game that gives you some extra advantages and perks that are not available in the official version. Some of the benefits of FIFA Mobile v18.1.01 mod apk are:</p>
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-<li><strong>Unlocked features</strong>: You can access all the features and modes of the game without any restrictions or limitations.</li>
-<li><strong>No ads</strong>: You can enjoy the game without any annoying ads or pop-ups that interrupt your gameplay.</li>
-<li><strong>No root required</strong>: You can install and run FIFA Mobile v18.1.01 mod apk on your device without rooting it or risking its security.</li>
-<li><strong>Easy to use</strong>: You can easily download and install FIFA Mobile v18.1.01 mod apk on your device and start playing right away without any complicated steps or procedures.</li>
-</ul>
-<p>FIFA Mobile v18.1.01 mod apk is a great way to enhance your gaming experience and have more fun with FIFA Mobile. You can enjoy all the features and modes of the game without any limitations or costs, and build your ultimate team of soccer stars with ease.</p>
- <h2>How to download and install FIFA Mobile v18.1.01 mod apk?</h2>
-<p>Downloading and installing FIFA Mobile v18.1.01 mod apk is very simple and straightforward. Just follow these steps:</p>
-<ol>
-<li><strong>Download the FIFA Mobile v18.1.01 mod apk file from a trusted source</strong>. You can find many websites that offer the mod apk file for free, but make sure you choose a reliable and safe one. Alternatively, you can use this link to download the file directly: [FIFA Mobile v18.1.01 mod apk].</li>
-<li><strong>Allow unknown sources on your device</strong>. Before you can install the mod apk file, you need to enable the option to allow unknown sources on your device settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li><strong>Locate and install the mod apk file</strong>. After you have downloaded the file, go to your file manager and find the folder where you saved it. Tap on the file and follow the instructions to install it on your device.</li>
-<li><strong>Launch the game and enjoy</strong>. Once you have installed the mod apk file, you can launch the game from your app drawer or home screen and start playing with unlimited money, unlocked features, and no ads.</li>
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- <h2>How to build your ultimate team with star players from the biggest leagues and top teams?</h2> test your team's skills and abilities in various modes, such as Head-to-Head, VS Attack, Manager Mode, FIFA World Cup 2022™ Mode, Events, Campaigns, and The Academy. You can also chat with your friends, join a league, or create your own league and compete with other players. FIFA Mobile is the ultimate soccer game for mobile devices.</p>
- <h2>How to relive the world's greatest soccer tournament with FIFA World Cup 2022™ mode?</h2>
-<p>One of the most exciting modes in FIFA Mobile is the FIFA World Cup 2022™ mode, where you can relive the world's greatest soccer tournament with any of the 32 qualified national teams or rewrite history with 15 non-qualified national teams. You can play in authentic World Cup stadiums with official kits, badges, and match ball. You can also earn exclusive rewards and players from the World Cup events and campaigns. But how do you relive the world's greatest soccer tournament with FIFA World Cup 2022™ mode? Here are some steps to help you out:</p>
-<ol>
-<li><strong>Choose your national team</strong>. You can choose from any of the 32 qualified national teams or 15 non-qualified national teams to represent in the World Cup. You can also customize your team's kits, badges, formation, tactics, and chemistry.</li>
-<li><strong>Play the group stage</strong>. You can play against other national teams in your group and try to qualify for the knockout stage. You can earn points for winning or drawing matches and advance to the next round based on your ranking.</li>
-<li><strong>Play the knockout stage</strong>. You can play against other national teams that qualified from their groups and try to reach the final. You can win matches by scoring more goals than your opponent or by winning a penalty shootout if the score is tied after extra time.</li>
-<li><strong>Play the final</strong>. You can play against the other finalist and try to win the World Cup trophy. You can celebrate your victory with your team and fans and earn exclusive rewards and players.</li>
-</ol>
-<p>By following these steps, you can relive the world's greatest soccer tournament with FIFA World Cup 2022™ mode in FIFA Mobile. You can also play friendly matches against other national teams or challenge yourself with special scenarios and objectives. FIFA World Cup 2022™ mode is a great way to experience the thrill and excitement of the World Cup on your mobile device.</p>
- <h2>How to score big with soccer icons and heroes?</h2>
-<p>Another amazing feature of FIFA Mobile is the ability to score big with soccer icons and heroes, who are legendary players that have made history in the soccer world. You can choose from over 100 icons and heroes, such as Cristiano Ronaldo, Lionel Messi, Neymar Jr, Zinedine Zidane, David Beckham, Pele, Maradona, and more. You can also unlock their stories and learn about their careers and achievements. But how do you score big with soccer icons and heroes? Here are some tips and tricks to help you out:</p>
-<ul>
-<li><strong>Earn icons and heroes from events and campaigns</strong>. You can earn icons and heroes from various events and campaigns that are available throughout the soccer season. You can complete challenges and objectives to earn players or tokens that can be exchanged for players. You can also buy players from the Market or use coins or points to open packs that contain players.</li>
-<li><strong>Train and rank up your icons and heroes to boost their OVR and stats</strong>. You can train and rank up your icons and heroes using Training XP, coins, Rank Up Tokens, and coins. Training XP can be obtained from events, campaigns, rewards, or by using other players as training material. Rank Up Tokens can be obtained from events or by using duplicate players as rank up material.</li>
-<li><strong>Use skill boosts to enhance your icons' and heroes' attributes</strong>. You can use skill boosts to boost specific attributes of your icons and heroes, such as pace, shooting, passing, defending, or physical. You can apply skill boosts using Skill Boosts Tokens and coins. Skill Boosts Tokens can be obtained from events, rewards, or by using other skill boosts as skill boost material.</li>
-<li><strong>Add icons and heroes to your team to increase chemistry</strong>. Icons and heroes have a special ability to increase chemistry among your players. Icons have a base chemistry of 5 with any player regardless of league, team, or nation. Heroes have a base chemistry of 10 with any player from their league or nation. You can also increase chemistry by using players with the same skill boost or position link.</li>
-<li><strong>Use icons' and heroes' special traits and skills to score goals and win matches</strong>. Icons and heroes have special traits and skills that make them stand out from other players. Traits are passive abilities that affect the player's performance, such as finesse shot, speed dribbler, or long shot taker. Skills are active abilities that the player can use during matches, such as rainbow flick, roulette, or heel to heel. You can use these traits and skills to score goals and win matches with your icons and heroes.</li>
-</ul>
-<p>By following these tips and tricks, you can score big with soccer icons and heroes in FIFA Mobile. You can also unlock their stories and learn about their careers and achievements. Icons and heroes are the ultimate players to have in your team.</p>
- <h2>How to experience immersive next-level soccer simulation with upgraded stadiums and realistic audio?</h2>
-<p>FIFA Mobile is not only a game of skills and strategy, but also a game of immersion and realism. You can experience immersive next-level soccer simulation with upgraded stadiums and realistic audio that make you feel like you are on the pitch. You can play in authentic stadiums from around the world, such as Wembley Stadium, Camp Nou, Santiago Bernabéu, Allianz Arena, and more. You can also hear the roar of the crowd, the chants of the fans, the commentary of the announcers, and the sound of the ball hitting the net. But how do you experience immersive next-level soccer simulation with upgraded stadiums and realistic audio? Here are some steps to help you out:</p>
-<ol>
-<li><strong>Choose your preferred stadium</strong>. You can choose from various stadiums from different leagues and regions to play in. You can also unlock more stadiums by completing events and campaigns. You can change your stadium by going to Settings > Team > Stadium.</li>
-<li><strong>Adjust your graphics and sound settings</strong>. You can adjust your graphics and sound settings to optimize your gaming experience. You can change your graphics quality by going to Settings > Graphics Quality. You can change your sound settings by going to Settings > Sound Settings. You can also enable or disable music, sound effects, commentary, or crowd noise.</li>
-<li><strong>Enjoy the game</strong>. You can enjoy the game with upgraded stadiums and realistic audio that make you feel like you are on the pitch. You can see the details of the stadiums, such as the grass, the lights, the banners, and the fans. You can also hear the sounds of the game, such as the whistle, the ball, the players, and the crowd.</li>
-</ol>
-<p>By following these steps, you can experience immersive next-level soccer simulation with upgraded stadiums and realistic audio in FIFA Mobile. You can also switch between different camera angles and zoom levels to get a better view of the action. FIFA Mobile is a game that brings you closer to the real soccer world.</p>
- <h2>How to be the soccer manager of your own dream team with manager mode?</h2>
-<p>One of the most challenging and rewarding modes in FIFA Mobile is the manager mode, where you can be the soccer manager of your own dream team and plan your strategy and adjust your tactics in real time or choose auto-play. You can choose from over 600 teams across over 30 leagues or create your own custom team with your favorite players. You can also compete in various tournaments and leagues or play friendly matches against other teams. But how do you be the soccer manager of your own dream team with manager mode? Here are some tips and tricks to help you out:</p>
-<ul>
-<li><strong>Select your team</strong>. You can select your team by going to Manager Mode > Select Team. You can choose from any of the available teams or create your own custom team by going to Manager Mode > Create Team. You can also edit your team's name, logo, kit, formation, tactics, chemistry, and players by going to Manager Mode > Edit Team.</li>
-<li><strong>Play matches</strong>. You can play matches by going to Manager Mode > Play Match. You can choose from various tournaments and leagues or play friendly matches against other teams. You can also select your difficulty level, match length, weather condition, stadium, ball type, and referee by going to Manager Mode > Match Settings.</li>
-<li><strong>Manage your team</strong>. You can manage your team by going to Manager Mode > Manage Team. You can plan your strategy and adjust your tactics in real time or choose auto-play. You can also make substitutions, change formations, switch players' positions, or give instructions to your players during matches.</li>
-<li><strong>Earn rewards</strong>. You can earn rewards by playing matches in manager mode. You can earn coins, points , players, skill boosts, rank up tokens, and more by winning matches, completing objectives, and ranking up in the leaderboards. You can also unlock more teams, stadiums, balls, and kits by playing matches in manager mode.</li>
-</ul>
-<p>By following these tips and tricks, you can be the soccer manager of your own dream team with manager mode in FIFA Mobile. You can also compare your team's performance and stats with other teams and players by going to Manager Mode > Stats. Manager mode is a great way to test your soccer knowledge and skills.</p>
- <h2>Conclusion</h2>
-<p>FIFA Mobile v18.1.01 mod apk is a modified version of the original FIFA Mobile game that gives you access to unlimited money, unlocked features, and more. It is a great way to enhance your gaming experience and have more fun with FIFA Mobile. You can build your ultimate team with star players from the biggest leagues and top teams, relive the world's greatest soccer tournament with FIFA World Cup 2022™ mode, score big with soccer icons and heroes, experience immersive next-level soccer simulation with upgraded stadiums and realistic audio, or be the soccer manager of your own dream team with manager mode. FIFA Mobile v18.1.01 mod apk has something for everyone.</p>
-<p>So what are you waiting for? Download and install FIFA Mobile v18.1.01 mod apk on your device and start playing right away. You will not regret it.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about FIFA Mobile v18.1.01 mod apk:</p>
-<h3>What are the requirements for FIFA Mobile v18.1.01 mod apk?</h3>
-<p>FIFA Mobile v18.1.01 mod apk requires Android 4.4 or higher and at least 1 GB of RAM and 100 MB of free storage space on your device.</p>
-<h3>Is FIFA Mobile v18.1.01 mod apk safe and legal?</h3>
-<p>FIFA Mobile v18.1.01 mod apk is safe to use as long as you download it from a trusted source and scan it for viruses before installing it on your device. However, it is not legal to use FIFA Mobile v18.1.01 mod apk as it violates the terms and conditions of EA Sports and Google Play Store. You may face some risks or consequences if you use FIFA Mobile v18.1.01 mod apk, such as account suspension, data loss, or legal action.</p>
-<h3>How to update FIFA Mobile v18.1.01 mod apk?</h3>
-<p>To update FIFA Mobile v18.1.01 mod apk, you need to download the latest version of the mod apk file from a trusted source and install it on your device over the existing version. You may also need to uninstall the original FIFA Mobile game before installing the mod apk file.</p>
-<h3>How to get unlimited coins and points in FIFA Mobile v18.1.01 mod apk?</h3>
-<p>To get unlimited coins and points in FIFA Mobile v18.1.01 mod apk, you just need to launch the game and check your balance. You will see that you have unlimited coins and points to spend on players, upgrades, features, and more.</p>
-<h3>How to contact EA Sports for support or feedback on FIFA Mobile?</h3>
-<p>To contact EA Sports for support or feedback on FIFA Mobile, you can go to Settings > Help & Support > Contact Us and choose your preferred option to reach out to them. You can also visit their official website or social media pages for more information.</p> 197e85843d<br />
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diff --git a/spaces/7hao/bingo/Dockerfile b/spaces/7hao/bingo/Dockerfile
deleted file mode 100644
index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000
--- a/spaces/7hao/bingo/Dockerfile
+++ /dev/null
@@ -1,36 +0,0 @@
-FROM node:18
-
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME
-
-# Switch to the "user" user
-USER user
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Install app dependencies
-# A wildcard is used to ensure both package.json AND package-lock.json are copied
-# where available (npm@5+)
-COPY --chown=user package*.json $HOME/app/
-
-RUN npm install
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app/
-
-RUN npm run build
-
-ENV PORT 7860
-EXPOSE 7860
-
-CMD npm start
diff --git a/spaces/801artistry/RVC801/diffq/__init__.py b/spaces/801artistry/RVC801/diffq/__init__.py
deleted file mode 100644
index 2b997ee4ed99a90cc43db7812383927e6fe1a3e8..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/diffq/__init__.py
+++ /dev/null
@@ -1,18 +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.
-
-# flake8: noqa
-"""
-This package implements different quantization strategies:
-
-- `diffq.uniform.UniformQuantizer`: classic uniform quantization over n bits.
-- `diffq.diffq.DiffQuantizer`: differentiable quantizer based on scaled noise injection.
-
-Also, do check `diffq.base.BaseQuantizer` for the common methods of all Quantizers.
-"""
-
-from .uniform import UniformQuantizer
-from .diffq import DiffQuantizer
diff --git a/spaces/A00001/bingothoo/src/components/header.tsx b/spaces/A00001/bingothoo/src/components/header.tsx
deleted file mode 100644
index dc298b722154d1ac6d7a7e148204605562d6cc58..0000000000000000000000000000000000000000
--- a/spaces/A00001/bingothoo/src/components/header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import * as React from 'react'
-import { UserMenu } from './user-menu'
-
-export async function Header() {
-  return (
-    <header className="sticky top-0 z-50 flex items-center justify-between w-full h-16 px-4 border-b shrink-0 bg-gradient-to-b from-background/10 via-background/50 to-background/80 backdrop-blur-xl">
-      <div className="flex items-center justify-end space-x-2 w-full">
-        <UserMenu />
-      </div>
-    </header>
-  )
-}
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/__init__.py b/spaces/AIConsultant/MusicGen/audiocraft/__init__.py
deleted file mode 100644
index 6ab346075f1b35366e7231054513097b87552c6f..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/__init__.py
+++ /dev/null
@@ -1,26 +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.
-"""
-AudioCraft is a general framework for training audio generative models.
-At the moment we provide the training code for:
-
-- [MusicGen](https://arxiv.org/abs/2306.05284), a state-of-the-art
-    text-to-music and melody+text autoregressive generative model.
-    For the solver, see `audiocraft.solvers.musicgen.MusicGenSolver`, and for the model,
-    `audiocraft.models.musicgen.MusicGen`.
-- [AudioGen](https://arxiv.org/abs/2209.15352), a state-of-the-art
-    text-to-general-audio generative model.
-- [EnCodec](https://arxiv.org/abs/2210.13438), efficient and high fidelity
-    neural audio codec which provides an excellent tokenizer for autoregressive language models.
-    See `audiocraft.solvers.compression.CompressionSolver`, and `audiocraft.models.encodec.EncodecModel`.
-- [MultiBandDiffusion](TODO), alternative diffusion-based decoder compatible with EnCodec that
-    improves the perceived quality and reduces the artifacts coming from adversarial decoders.
-"""
-
-# flake8: noqa
-from . import data, modules, models
-
-__version__ = '1.0.0'
diff --git a/spaces/AIFILMS/generate_human_motion/pyrender/setup.py b/spaces/AIFILMS/generate_human_motion/pyrender/setup.py
deleted file mode 100644
index c3b5ba0da2b0f17b759e5556597981096a80bda8..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/generate_human_motion/pyrender/setup.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""
-Setup of pyrender Python codebase.
-
-Author: Matthew Matl
-"""
-import sys
-from setuptools import setup
-
-# load __version__
-exec(open('pyrender/version.py').read())
-
-def get_imageio_dep():
-    if sys.version[0] == "2":
-        return 'imageio<=2.6.1'
-    return 'imageio'
-
-requirements = [
-    'freetype-py',                # For font loading
-    get_imageio_dep(),            # For Image I/O
-    'networkx',                   # For the scene graph
-    'numpy',                      # Numpy
-    'Pillow',                     # For Trimesh texture conversions
-    'pyglet>=1.4.10',             # For the pyglet viewer
-    'PyOpenGL~=3.1.0',            # For OpenGL
-#    'PyOpenGL_accelerate~=3.1.0', # For OpenGL
-    'scipy',                      # Because of trimesh missing dep
-    'six',                        # For Python 2/3 interop
-    'trimesh',                    # For meshes
-]
-
-dev_requirements = [
-    'flake8',            # Code formatting checker
-    'pre-commit',        # Pre-commit hooks
-    'pytest',            # Code testing
-    'pytest-cov',        # Coverage testing
-    'tox',               # Automatic virtualenv testing
-]
-
-docs_requirements = [
-    'sphinx',            # General doc library
-    'sphinx_rtd_theme',  # RTD theme for sphinx
-    'sphinx-automodapi'  # For generating nice tables
-]
-
-
-setup(
-    name = 'pyrender',
-    version=__version__,
-    description='Easy-to-use Python renderer for 3D visualization',
-    long_description='A simple implementation of Physically-Based Rendering '
-                       '(PBR) in Python. Compliant with the glTF 2.0 standard.',
-    author='Matthew Matl',
-    author_email='matthewcmatl@gmail.com',
-    license='MIT License',
-    url = 'https://github.com/mmatl/pyrender',
-    classifiers = [
-        'Development Status :: 4 - Beta',
-        'License :: OSI Approved :: MIT License',
-        'Operating System :: POSIX :: Linux',
-        'Operating System :: MacOS :: MacOS X',
-        'Programming Language :: Python :: 2.7',
-        'Programming Language :: Python :: 3.5',
-        'Programming Language :: Python :: 3.6',
-        'Natural Language :: English',
-        'Topic :: Scientific/Engineering'
-    ],
-    keywords = 'rendering graphics opengl 3d visualization pbr gltf',
-    packages = ['pyrender', 'pyrender.platforms'],
-    setup_requires = requirements,
-    install_requires = requirements,
-    extras_require={
-        'dev': dev_requirements,
-        'docs': docs_requirements,
-    },
-    include_package_data=True
-)
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/predict.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/predict.py
deleted file mode 100644
index e9d13f30153cd43a4a8bcfe2da4b9a53846bf1eb..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/predict.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import os
-from torch.utils.data import DataLoader
-import torchvision
-from tqdm import tqdm
-from dataset import VGGSound
-import torch
-import torch.nn as nn
-from metrics import metrics
-from omegaconf import OmegaConf
-from model import VGGishish
-from transforms import Crop, StandardNormalizeAudio, ToTensor
-
-
-if __name__ == '__main__':
-    cfg_cli = OmegaConf.from_cli()
-    print(cfg_cli.config)
-    cfg_yml = OmegaConf.load(cfg_cli.config)
-    # the latter arguments are prioritized
-    cfg = OmegaConf.merge(cfg_yml, cfg_cli)
-    OmegaConf.set_readonly(cfg, True)
-    print(OmegaConf.to_yaml(cfg))
-
-    # logger = LoggerWithTBoard(cfg)
-    transforms = [
-        StandardNormalizeAudio(cfg.mels_path),
-        ToTensor(),
-    ]
-    if cfg.cropped_size not in [None, 'None', 'none']:
-        transforms.append(Crop(cfg.cropped_size))
-    transforms = torchvision.transforms.transforms.Compose(transforms)
-
-    datasets = {
-        'test': VGGSound('test', cfg.mels_path, transforms),
-    }
-
-    loaders = {
-        'test': DataLoader(datasets['test'], batch_size=cfg.batch_size,
-                           num_workers=cfg.num_workers, pin_memory=True)
-    }
-
-    device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu')
-    model = VGGishish(cfg.conv_layers, cfg.use_bn, num_classes=len(datasets['test'].target2label))
-    model = model.to(device)
-
-    optimizer = torch.optim.Adam(model.parameters(), lr=cfg.learning_rate)
-    criterion = nn.CrossEntropyLoss()
-
-    # loading the best model
-    folder_name = os.path.split(cfg.config)[0].split('/')[-1]
-    print(folder_name)
-    ckpt = torch.load(f'./logs/{folder_name}/vggishish-{folder_name}.pt', map_location='cpu')
-    model.load_state_dict(ckpt['model'])
-    print((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}'))
-
-    # Testing the model
-    model.eval()
-    running_loss = 0
-    preds_from_each_batch = []
-    targets_from_each_batch = []
-
-    for i, batch in enumerate(tqdm(loaders['test'])):
-        inputs = batch['input'].to(device)
-        targets = batch['target'].to(device)
-
-        # zero the parameter gradients
-        optimizer.zero_grad()
-
-        # forward + backward + optimize
-        with torch.set_grad_enabled(False):
-            outputs = model(inputs)
-            loss = criterion(outputs, targets)
-
-        # loss
-        running_loss += loss.item()
-
-        # for metrics calculation later on
-        preds_from_each_batch += [outputs.detach().cpu()]
-        targets_from_each_batch += [targets.cpu()]
-
-    # logging metrics
-    preds_from_each_batch = torch.cat(preds_from_each_batch)
-    targets_from_each_batch = torch.cat(targets_from_each_batch)
-    test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch)
-    test_metrics_dict['avg_loss'] = running_loss / len(loaders['test'])
-    test_metrics_dict['param_num'] = sum(p.numel() for p in model.parameters() if p.requires_grad)
-
-    # TODO: I have no idea why tboard doesn't keep metrics (hparams) in a tensorboard when
-    # I run this experiment from cli: `python main.py config=./configs/vggish.yaml`
-    # while when I run it in vscode debugger the metrics are present in the tboard (weird)
-    print(test_metrics_dict)
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-coslr-preciseBN_in1k.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-coslr-preciseBN_in1k.py
deleted file mode 100644
index 01fefbbf2852eeceddb0ad026fb5098e763e0710..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-coslr-preciseBN_in1k.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = 'resnet50_8xb32-coslr_in1k.py'
-
-# Precise BN hook will update the bn stats, so this hook should be executed
-# before CheckpointHook(priority of 'VERY_LOW') and
-# EMAHook(priority of 'NORMAL') So set the priority of PreciseBNHook to
-# 'ABOVENORMAL' here.
-custom_hooks = [
-    dict(
-        type='PreciseBNHook',
-        num_samples=8192,
-        interval=1,
-        priority='ABOVE_NORMAL')
-]
diff --git a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/create_configs.py b/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/create_configs.py
deleted file mode 100644
index cd47aacb01ee07c8bc673ff33daff334fe85d0f2..0000000000000000000000000000000000000000
--- a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/create_configs.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import yaml
-
-fname = "config/gpt-cls-tash-proc.yml"
-
-stream = open(fname, 'r')
-data = yaml.load(stream,  Loader=yaml.FullLoader)
-
-for i in range(0, 10):
-    data['n_layer'] = i
-    data['log_directory'] = f'log_dir_cls_{i}_tash_proc'
-    data['max_steps'] = 5000
-    with open(f"config/gpt-cls-{i}-tash-proc.yml", 'w') as yaml_file:
-        yaml_file.write( yaml.dump(data, default_flow_style=False))
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/Factory.js
deleted file mode 100644
index 7b1ce6aefc4c185f293b84af6fe7cdaf6eb1c009..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/Factory.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import Chart from './Chart.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('chart', function (x, y, width, height, config) {
-    var gameObject = new Chart(this.scene, x, y, width, height, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.Chart', Chart);
-
-export default Chart;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/YAMLMake.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/YAMLMake.js
deleted file mode 100644
index 211c152887986b5d45be7c8469d3e8f9445d1031..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/YAMLMake.js
+++ /dev/null
@@ -1,35 +0,0 @@
-import ParseYAML from './utils/ParseYAML.js';
-import Make from './Make.js';
-
-var YAMLMake = function (scene, data, view, styles, customBuilders) {
-    data = ParseYAML(data);
-    if (Array.isArray(data)) {
-        // Parsing result of YAML data might be an array, 
-        // Only last item will be used to create game object, others are references
-        data = data[data.length - 1];
-    } else if (data.$root) {
-        // Parsing result of YAML data might be an object, with $root key, 
-        // data.$root will be used to create game object, others are default styles
-        var defaultStyles = data;
-        data = data.$root;
-        delete defaultStyles.$root;
-
-        if (styles === undefined) {
-            styles = defaultStyles;
-        } else {
-            for (var key in defaultStyles) {
-                if (!styles[key]) {
-                    styles[key] = defaultStyles[key];
-                }
-            }
-        }
-    }
-
-    styles = ParseYAML(styles);
-
-    var gameObject = Make(scene, data, view, styles, customBuilders);
-
-    return gameObject;
-}
-
-export default YAMLMake;
\ No newline at end of file
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.py
deleted file mode 100644
index 5fa95018f961f1aaa8013befcae7471995eee505..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.py
+++ /dev/null
@@ -1,409 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# 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.
-
-"""Custom PyTorch ops for efficient resampling of 2D images."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import traceback
-
-from .. import custom_ops
-from .. import misc
-from . import conv2d_gradfix
-
-# ----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-
-
-def _init():
-    global _inited, _plugin
-    if not _inited:
-        sources = ['upfirdn2d.cpp', 'upfirdn2d.cu']
-        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
-        try:
-            _plugin = custom_ops.get_plugin(
-                'upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
-        except:
-            warnings.warn(
-                'Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
-    return _plugin is not None
-
-
-def _parse_scaling(scaling):
-    if isinstance(scaling, int):
-        scaling = [scaling, scaling]
-    assert isinstance(scaling, (list, tuple))
-    assert all(isinstance(x, int) for x in scaling)
-    sx, sy = scaling
-    assert sx >= 1 and sy >= 1
-    return sx, sy
-
-
-def _parse_padding(padding):
-    if isinstance(padding, int):
-        padding = [padding, padding]
-    assert isinstance(padding, (list, tuple))
-    assert all(isinstance(x, int) for x in padding)
-    if len(padding) == 2:
-        padx, pady = padding
-        padding = [padx, padx, pady, pady]
-    padx0, padx1, pady0, pady1 = padding
-    return padx0, padx1, pady0, pady1
-
-
-def _get_filter_size(f):
-    if f is None:
-        return 1, 1
-    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-    fw = f.shape[-1]
-    fh = f.shape[0]
-    with misc.suppress_tracer_warnings():
-        fw = int(fw)
-        fh = int(fh)
-    misc.assert_shape(f, [fh, fw][:f.ndim])
-    assert fw >= 1 and fh >= 1
-    return fw, fh
-
-# ----------------------------------------------------------------------------
-
-
-def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
-    r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
-
-    Args:
-        f:           Torch tensor, numpy array, or python list of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable),
-                     `[]` (impulse), or
-                     `None` (identity).
-        device:      Result device (default: cpu).
-        normalize:   Normalize the filter so that it retains the magnitude
-                     for constant input signal (DC)? (default: True).
-        flip_filter: Flip the filter? (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        separable:   Return a separable filter? (default: select automatically).
-
-    Returns:
-        Float32 tensor of the shape
-        `[filter_height, filter_width]` (non-separable) or
-        `[filter_taps]` (separable).
-    """
-    # Validate.
-    if f is None:
-        f = 1
-    f = torch.as_tensor(f, dtype=torch.float32)
-    assert f.ndim in [0, 1, 2]
-    assert f.numel() > 0
-    if f.ndim == 0:
-        f = f[np.newaxis]
-
-    # Separable?
-    if separable is None:
-        separable = (f.ndim == 1 and f.numel() >= 8)
-    if f.ndim == 1 and not separable:
-        f = f.ger(f)
-    assert f.ndim == (1 if separable else 2)
-
-    # Apply normalize, flip, gain, and device.
-    if normalize:
-        f /= f.sum()
-    if flip_filter:
-        f = f.flip(list(range(f.ndim)))
-    f = f * (gain ** (f.ndim / 2))
-    f = f.to(device=device)
-    return f
-
-# ----------------------------------------------------------------------------
-
-
-def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Pad, upsample, filter, and downsample a batch of 2D images.
-
-    Performs the following sequence of operations for each channel:
-
-    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
-
-    2. Pad the image with the specified number of zeros on each side (`padding`).
-       Negative padding corresponds to cropping the image.
-
-    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
-       so that the footprint of all output pixels lies within the input image.
-
-    4. Downsample the image by keeping every Nth pixel (`down`).
-
-    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
-    The fused op is considerably more efficient than performing the same calculation
-    using standard PyTorch ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        up:          Integer upsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        down:        Integer downsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the upsampled image. Can be a single number
-                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert impl in ['ref', 'cuda']
-    if impl == 'cuda' and x.device.type == 'cuda' and _init():
-        return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
-    return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
-    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
-    """
-    # Validate arguments.
-    assert isinstance(x, torch.Tensor) and x.ndim == 4
-    if f is None:
-        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-    assert f.dtype == torch.float32 and not f.requires_grad
-    batch_size, num_channels, in_height, in_width = x.shape
-    upx, upy = _parse_scaling(up)
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
-    # Upsample by inserting zeros.
-    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
-    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
-    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
-
-    # Pad or crop.
-    x = torch.nn.functional.pad(
-        x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
-    x = x[:, :, max(-pady0, 0): x.shape[2] - max(-pady1, 0),
-          max(-padx0, 0): x.shape[3] - max(-padx1, 0)]
-
-    # Setup filter.
-    f = f * (gain ** (f.ndim / 2))
-    f = f.to(x.dtype)
-    if not flip_filter:
-        f = f.flip(list(range(f.ndim)))
-
-    # Convolve with the filter.
-    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
-    if f.ndim == 4:
-        x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
-    else:
-        x = conv2d_gradfix.conv2d(
-            input=x, weight=f.unsqueeze(2), groups=num_channels)
-        x = conv2d_gradfix.conv2d(
-            input=x, weight=f.unsqueeze(3), groups=num_channels)
-
-    # Downsample by throwing away pixels.
-    x = x[:, :, ::downy, ::downx]
-    return x
-
-# ----------------------------------------------------------------------------
-
-
-_upfirdn2d_cuda_cache = dict()
-
-
-def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
-    """Fast CUDA implementation of `upfirdn2d()` using custom ops.
-    """
-    # Parse arguments.
-    upx, upy = _parse_scaling(up)
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
-    # Lookup from cache.
-    key = (upx, upy, downx, downy, padx0, padx1,
-           pady0, pady1, flip_filter, gain)
-    if key in _upfirdn2d_cuda_cache:
-        return _upfirdn2d_cuda_cache[key]
-
-    # Forward op.
-    class Upfirdn2dCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, f):  # pylint: disable=arguments-differ
-            assert isinstance(x, torch.Tensor) and x.ndim == 4
-            if f is None:
-                f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-            assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-            y = x
-            if f.ndim == 2:
-                y = _plugin.upfirdn2d(
-                    y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
-            else:
-                y = _plugin.upfirdn2d(y, f.unsqueeze(
-                    0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain))
-                y = _plugin.upfirdn2d(y, f.unsqueeze(
-                    1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain))
-            ctx.save_for_backward(f)
-            ctx.x_shape = x.shape
-            return y
-
-        @staticmethod
-        def backward(ctx, dy):  # pylint: disable=arguments-differ
-            f, = ctx.saved_tensors
-            _, _, ih, iw = ctx.x_shape
-            _, _, oh, ow = dy.shape
-            fw, fh = _get_filter_size(f)
-            p = [
-                fw - padx0 - 1,
-                iw * upx - ow * downx + padx0 - upx + 1,
-                fh - pady0 - 1,
-                ih * upy - oh * downy + pady0 - upy + 1,
-            ]
-            dx = None
-            df = None
-
-            if ctx.needs_input_grad[0]:
-                dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(
-                    not flip_filter), gain=gain).apply(dy, f)
-
-            assert not ctx.needs_input_grad[1]
-            return dx, df
-
-    # Add to cache.
-    _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
-    return Upfirdn2dCuda
-
-# ----------------------------------------------------------------------------
-
-
-def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Filter a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape matches the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        padding:     Padding with respect to the output. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + fw // 2,
-        padx1 + (fw - 1) // 2,
-        pady0 + fh // 2,
-        pady1 + (fh - 1) // 2,
-    ]
-    return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-# ----------------------------------------------------------------------------
-
-
-def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Upsample a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape is a multiple of the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        up:          Integer upsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the output. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    upx, upy = _parse_scaling(up)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + (fw + upx - 1) // 2,
-        padx1 + (fw - upx) // 2,
-        pady0 + (fh + upy - 1) // 2,
-        pady1 + (fh - upy) // 2,
-    ]
-    return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)
-
-# ----------------------------------------------------------------------------
-
-
-def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Downsample a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape is a fraction of the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        down:        Integer downsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the input. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + (fw - downx + 1) // 2,
-        padx1 + (fw - downx) // 2,
-        pady0 + (fh - downy + 1) // 2,
-        pady1 + (fh - downy) // 2,
-    ]
-    return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/transformer_temporal.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/transformer_temporal.md
deleted file mode 100644
index d67cf717f92b20791bf00214bdf5627ccc34003f..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/transformer_temporal.md
+++ /dev/null
@@ -1,11 +0,0 @@
-# Transformer Temporal
-
-A Transformer model for video-like data.
-
-## TransformerTemporalModel
-
-[[autodoc]] models.transformer_temporal.TransformerTemporalModel
-
-## TransformerTemporalModelOutput
-
-[[autodoc]] models.transformer_temporal.TransformerTemporalModelOutput
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/zh/quicktour.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/zh/quicktour.md
deleted file mode 100644
index 68ab56c55a85a53c6b444d7831a059f7bed745f4..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/zh/quicktour.md
+++ /dev/null
@@ -1,331 +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.
--->
-
-[[open-in-colab]]
-
-# 快速上手
-
-训练扩散模型，是为了对随机高斯噪声进行逐步去噪，以生成令人感兴趣的样本，比如图像或者语音。
-
-扩散模型的发展引起了人们对生成式人工智能的极大兴趣，你可能已经在网上见过扩散生成的图像了。🧨 Diffusers库的目的是让大家更易上手扩散模型。
-
-无论你是开发人员还是普通用户，本文将向你介绍🧨 Diffusers 并帮助你快速开始生成内容！
-
-🧨 Diffusers 库的三个主要组件：
-
-
-无论你是开发者还是普通用户，这个快速指南将向你介绍🧨 Diffusers，并帮助你快速使用和生成！该库三个主要部分如下：
-
-* [`DiffusionPipeline`]是一个高级的端到端类，旨在通过预训练的扩散模型快速生成样本进行推理。
-* 作为创建扩散系统做组件的流行的预训练[模型](./api/models)框架和模块。
-* 许多不同的[调度器](./api/schedulers/overview)：控制如何在训练过程中添加噪声的算法，以及如何在推理过程中生成去噪图像的算法。
-
-快速入门将告诉你如何使用[`DiffusionPipeline`]进行推理，然后指导你如何结合模型和调度器以复现[`DiffusionPipeline`]内部发生的事情。
-
-<Tip>
-
-快速入门是🧨[Diffusers入门](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)的简化版，可以帮助你快速上手。如果你想了解更多关于🧨 Diffusers的目标、设计理念以及关于它的核心API的更多细节，可以点击🧨[Diffusers入门](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)查看。
-
-</Tip>
-
-在开始之前，确认一下你已经安装好了所需要的库：
-
-```bash
-pip install --upgrade diffusers accelerate transformers
-```
-
-- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index) 在推理和训练过程中加速模型加载。
-- [🤗 Transformers](https://huggingface.co/docs/transformers/index) 是运行最流行的扩散模型所必须的库，比如[Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview).
-
-## 扩散模型管道
-
-[`DiffusionPipeline`]是用预训练的扩散系统进行推理的最简单方法。它是一个包含模型和调度器的端到端系统。你可以直接使用[`DiffusionPipeline`]完成许多任务。请查看下面的表格以了解一些支持的任务，要获取完整的支持任务列表，请查看[🧨 Diffusers 总结](./api/pipelines/overview#diffusers-summary) 。
-
-| **任务**                     | **描述**                                                                                              | **管道**
-|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|
-| Unconditional Image Generation          | 从高斯噪声中生成图片 | [unconditional_image_generation](./using-diffusers/unconditional_image_generation) |
-| Text-Guided Image Generation | 给定文本提示生成图像 | [conditional_image_generation](./using-diffusers/conditional_image_generation) |
-| Text-Guided Image-to-Image Translation     | 在文本提示的指导下调整图像 | [img2img](./using-diffusers/img2img) |
-| Text-Guided Image-Inpainting          | 给出图像、遮罩和文本提示，填充图像的遮罩部分 | [inpaint](./using-diffusers/inpaint) |
-| Text-Guided Depth-to-Image Translation | 在文本提示的指导下调整图像的部分内容，同时通过深度估计保留其结构 | [depth2img](./using-diffusers/depth2img) |
-
-首先创建一个[`DiffusionPipeline`]的实例，并指定要下载的pipeline检查点。
-你可以使用存储在Hugging Face Hub上的任何[`DiffusionPipeline`][检查点](https://huggingface.co/models?library=diffusers&sort=downloads)。
-在教程中，你将加载[`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)检查点，用于文本到图像的生成。
-
-首先创建一个[DiffusionPipeline]实例，并指定要下载的管道检查点。
-您可以在Hugging Face Hub上使用[DiffusionPipeline]的任何检查点。
-在本快速入门中，您将加载stable-diffusion-v1-5检查点，用于文本到图像生成。
-
-<Tip warning={true}>。
-
-对于[Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion)模型，在运行该模型之前，请先仔细阅读[许可证](https://huggingface.co/spaces/CompVis/stable-diffusion-license)。🧨 Diffusers实现了一个[`safety_checker`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py)，以防止有攻击性的或有害的内容，但Stable Diffusion模型改进图像的生成能力仍有可能产生潜在的有害内容。
-
-</Tip>
-
-用[`~DiffusionPipeline.from_pretrained`]方法加载模型。
-
-```python
->>> from diffusers import DiffusionPipeline
-
->>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-```
-[`DiffusionPipeline`]会下载并缓存所有的建模、标记化和调度组件。你可以看到Stable Diffusion的pipeline是由[`UNet2DConditionModel`]和[`PNDMScheduler`]等组件组成的：
-
-```py
->>> pipeline
-StableDiffusionPipeline {
-  "_class_name": "StableDiffusionPipeline",
-  "_diffusers_version": "0.13.1",
-  ...,
-  "scheduler": [
-    "diffusers",
-    "PNDMScheduler"
-  ],
-  ...,
-  "unet": [
-    "diffusers",
-    "UNet2DConditionModel"
-  ],
-  "vae": [
-    "diffusers",
-    "AutoencoderKL"
-  ]
-}
-```
-
-我们强烈建议你在GPU上运行这个pipeline，因为该模型由大约14亿个参数组成。
-
-你可以像在Pytorch里那样把生成器对象移到GPU上：
-
-```python
->>> pipeline.to("cuda")
-```
-
-现在你可以向`pipeline`传递一个文本提示来生成图像，然后获得去噪的图像。默认情况下，图像输出被放在一个[`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class)对象中。
-
-```python
->>> image = pipeline("An image of a squirrel in Picasso style").images[0]
->>> image
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/image_of_squirrel_painting.png"/>
-</div>
-
-
-调用`save`保存图像:
-
-```python
->>> image.save("image_of_squirrel_painting.png")
-```
-
-### 本地管道
-
-你也可以在本地使用管道。唯一的区别是你需提前下载权重：
-
-```
-git lfs install
-git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
-```
-
-将下载好的权重加载到管道中:
-
-```python
->>> pipeline = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
-```
-
-现在你可以像上一节中那样运行管道了。
-
-### 更换调度器
-
-不同的调度器对去噪速度和质量的权衡是不同的。要想知道哪种调度器最适合你，最好的办法就是试用一下。🧨 Diffusers的主要特点之一是允许你轻松切换不同的调度器。例如，要用[`EulerDiscreteScheduler`]替换默认的[`PNDMScheduler`]，用[`~diffusers.ConfigMixin.from_config`]方法加载即可：
-
-```py
->>> from diffusers import EulerDiscreteScheduler
-
->>> pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
->>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-```
-
-
-试着用新的调度器生成一个图像，看看你能否发现不同之处。
-
-在下一节中，你将仔细观察组成[`DiffusionPipeline`]的组件——模型和调度器，并学习如何使用这些组件来生成猫咪的图像。
-
-## 模型
-
-大多数模型取一个噪声样本，在每个时间点预测*噪声残差*（其他模型则直接学习预测前一个样本或速度或[`v-prediction`](https://github.com/huggingface/diffusers/blob/5e5ce13e2f89ac45a0066cb3f369462a3cf1d9ef/src/diffusers/schedulers/scheduling_ddim.py#L110)），即噪声较小的图像与输入图像的差异。你可以混搭模型创建其他扩散系统。
-
-模型是用[`~ModelMixin.from_pretrained`]方法启动的，该方法还在本地缓存了模型权重，所以下次加载模型时更快。对于快速入门，你默认加载的是[`UNet2DModel`]，这是一个基础的无条件图像生成模型，该模型有一个在猫咪图像上训练的检查点：
-
-
-```py
->>> from diffusers import UNet2DModel
-
->>> repo_id = "google/ddpm-cat-256"
->>> model = UNet2DModel.from_pretrained(repo_id)
-```
-
-想知道模型的参数，调用 `model.config`:
-
-```py
->>> model.config
-```
-
-模型配置是一个🧊冻结的🧊字典，意思是这些参数在模型创建后就不变了。这是特意设置的，确保在开始时用于定义模型架构的参数保持不变，其他参数仍然可以在推理过程中进行调整。
-
-一些最重要的参数：
-
-* `sample_size`：输入样本的高度和宽度尺寸。
-* `in_channels`：输入样本的输入通道数。
-* `down_block_types`和`up_block_types`：用于创建U-Net架构的下采样和上采样块的类型。
-* `block_out_channels`：下采样块的输出通道数；也以相反的顺序用于上采样块的输入通道数。
-* `layers_per_block`：每个U-Net块中存在的ResNet块的数量。
-
-为了使用该模型进行推理，用随机高斯噪声生成图像形状。它应该有一个`batch`轴，因为模型可以接收多个随机噪声，一个`channel`轴，对应于输入通道的数量，以及一个`sample_size`轴，对应图像的高度和宽度。
-
-
-```py
->>> import torch
-
->>> torch.manual_seed(0)
-
->>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
->>> noisy_sample.shape
-torch.Size([1, 3, 256, 256])
-```
-
-对于推理，将噪声图像和一个`timestep`传递给模型。`timestep` 表示输入图像的噪声程度，开始时噪声更多，结束时噪声更少。这有助于模型确定其在扩散过程中的位置，是更接近开始还是结束。使用 `sample` 获得模型输出：
-
-
-```py
->>> with torch.no_grad():
-...     noisy_residual = model(sample=noisy_sample, timestep=2).sample
-```
-
-想生成实际的样本，你需要一个调度器指导去噪过程。在下一节中，你将学习如何把模型与调度器结合起来。
-
-## 调度器
-
-调度器管理一个噪声样本到一个噪声较小的样本的处理过程，给出模型输出 —— 在这种情况下，它是`noisy_residual`。
-
-
-
-<Tip>
-
-🧨 Diffusers是一个用于构建扩散系统的工具箱。预定义好的扩散系统[`DiffusionPipeline`]能方便你快速试用，你也可以单独选择自己的模型和调度器组件来建立一个自定义的扩散系统。
-
-</Tip>
-
-在快速入门教程中，你将用它的[`~diffusers.ConfigMixin.from_config`]方法实例化[`DDPMScheduler`]：
-
-```py
->>> from diffusers import DDPMScheduler
-
->>> scheduler = DDPMScheduler.from_config(repo_id)
->>> scheduler
-DDPMScheduler {
-  "_class_name": "DDPMScheduler",
-  "_diffusers_version": "0.13.1",
-  "beta_end": 0.02,
-  "beta_schedule": "linear",
-  "beta_start": 0.0001,
-  "clip_sample": true,
-  "clip_sample_range": 1.0,
-  "num_train_timesteps": 1000,
-  "prediction_type": "epsilon",
-  "trained_betas": null,
-  "variance_type": "fixed_small"
-}
-```
-
-<Tip>
-
-
-💡 注意调度器是如何从配置中实例化的。与模型不同，调度器没有可训练的权重，而且是无参数的。
-
-</Tip>
-
-* `num_train_timesteps`：去噪过程的长度，或者换句话说，将随机高斯噪声处理成数据样本所需的时间步数。
-* `beta_schedule`：用于推理和训练的噪声表。
-* `beta_start`和`beta_end`：噪声表的开始和结束噪声值。
-
-要预测一个噪音稍小的图像，请将 模型输出、`timestep`和当前`sample` 传递给调度器的[`~diffusers.DDPMScheduler.step`]方法：
-
-
-```py
->>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample
->>> less_noisy_sample.shape
-```
-
-这个 `less_noisy_sample` 去噪样本 可以被传递到下一个`timestep` ，处理后会将变得噪声更小。现在让我们把所有步骤合起来，可视化整个去噪过程。
-
-首先，创建一个函数，对去噪后的图像进行后处理并显示为`PIL.Image`：
-
-```py
->>> import PIL.Image
->>> import numpy as np
-
-
->>> def display_sample(sample, i):
-...     image_processed = sample.cpu().permute(0, 2, 3, 1)
-...     image_processed = (image_processed + 1.0) * 127.5
-...     image_processed = image_processed.numpy().astype(np.uint8)
-
-...     image_pil = PIL.Image.fromarray(image_processed[0])
-...     display(f"Image at step {i}")
-...     display(image_pil)
-```
-
-将输入和模型移到GPU上加速去噪过程：
-
-```py
->>> model.to("cuda")
->>> noisy_sample = noisy_sample.to("cuda")
-```
-
-现在创建一个去噪循环，该循环预测噪声较少样本的残差，并使用调度程序计算噪声较少的样本：
-
-```py
->>> import tqdm
-
->>> sample = noisy_sample
-
->>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
-...     # 1. predict noise residual
-...     with torch.no_grad():
-...         residual = model(sample, t).sample
-
-...     # 2. compute less noisy image and set x_t -> x_t-1
-...     sample = scheduler.step(residual, t, sample).prev_sample
-
-...     # 3. optionally look at image
-...     if (i + 1) % 50 == 0:
-...         display_sample(sample, i + 1)
-```
-
-看！这样就从噪声中生成出一只猫了！😻
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/diffusion-quicktour.png"/>
-</div>
-
-## 下一步
-
-希望你在这次快速入门教程中用🧨Diffuser 生成了一些很酷的图像! 下一步你可以:
-
-* 在[训练](./tutorials/basic_training)教程中训练或微调一个模型来生成你自己的图像。
-* 查看官方和社区的[训练或微调脚本](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples)的例子，了解更多使用情况。
-* 在[使用不同的调度器](./using-diffusers/schedulers)指南中了解更多关于加载、访问、更改和比较调度器的信息。
-* 在[Stable Diffusion](./stable_diffusion)教程中探索提示工程、速度和内存优化，以及生成更高质量图像的技巧。
-* 通过[在GPU上优化PyTorch](./optimization/fp16)指南，以及运行[Apple (M1/M2)上的Stable Diffusion](./optimization/mps)和[ONNX Runtime](./optimization/onnx)的教程，更深入地了解如何加速🧨Diffuser。
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/transformer_temporal.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/transformer_temporal.py
deleted file mode 100644
index cfafdb055bcfedc911b0a19d1e5da8089a18b215..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/transformer_temporal.py
+++ /dev/null
@@ -1,179 +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.
-from dataclasses import dataclass
-from typing import Optional
-
-import torch
-from torch import nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
-from .attention import BasicTransformerBlock
-from .modeling_utils import ModelMixin
-
-
-@dataclass
-class TransformerTemporalModelOutput(BaseOutput):
-    """
-    The output of [`TransformerTemporalModel`].
-
-    Args:
-        sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`):
-            The hidden states output conditioned on `encoder_hidden_states` input.
-    """
-
-    sample: torch.FloatTensor
-
-
-class TransformerTemporalModel(ModelMixin, ConfigMixin):
-    """
-    A Transformer model for video-like data.
-
-    Parameters:
-        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
-        in_channels (`int`, *optional*):
-            The number of channels in the input and output (specify if the input is **continuous**).
-        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
-        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
-            This is fixed during training since it is used to learn a number of position embeddings.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
-        attention_bias (`bool`, *optional*):
-            Configure if the `TransformerBlock` attention should contain a bias parameter.
-        double_self_attention (`bool`, *optional*):
-            Configure if each `TransformerBlock` should contain two self-attention layers.
-    """
-
-    @register_to_config
-    def __init__(
-        self,
-        num_attention_heads: int = 16,
-        attention_head_dim: int = 88,
-        in_channels: Optional[int] = None,
-        out_channels: Optional[int] = None,
-        num_layers: int = 1,
-        dropout: float = 0.0,
-        norm_num_groups: int = 32,
-        cross_attention_dim: Optional[int] = None,
-        attention_bias: bool = False,
-        sample_size: Optional[int] = None,
-        activation_fn: str = "geglu",
-        norm_elementwise_affine: bool = True,
-        double_self_attention: bool = True,
-    ):
-        super().__init__()
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-        inner_dim = num_attention_heads * attention_head_dim
-
-        self.in_channels = in_channels
-
-        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-        self.proj_in = nn.Linear(in_channels, inner_dim)
-
-        # 3. Define transformers blocks
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BasicTransformerBlock(
-                    inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    dropout=dropout,
-                    cross_attention_dim=cross_attention_dim,
-                    activation_fn=activation_fn,
-                    attention_bias=attention_bias,
-                    double_self_attention=double_self_attention,
-                    norm_elementwise_affine=norm_elementwise_affine,
-                )
-                for d in range(num_layers)
-            ]
-        )
-
-        self.proj_out = nn.Linear(inner_dim, in_channels)
-
-    def forward(
-        self,
-        hidden_states,
-        encoder_hidden_states=None,
-        timestep=None,
-        class_labels=None,
-        num_frames=1,
-        cross_attention_kwargs=None,
-        return_dict: bool = True,
-    ):
-        """
-        The [`TransformerTemporal`] forward method.
-
-        Args:
-            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
-                Input hidden_states.
-            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
-                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
-                self-attention.
-            timestep ( `torch.long`, *optional*):
-                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
-            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
-                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
-                `AdaLayerZeroNorm`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
-                If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
-                returned, otherwise a `tuple` where the first element is the sample tensor.
-        """
-        # 1. Input
-        batch_frames, channel, height, width = hidden_states.shape
-        batch_size = batch_frames // num_frames
-
-        residual = hidden_states
-
-        hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, channel, height, width)
-        hidden_states = hidden_states.permute(0, 2, 1, 3, 4)
-
-        hidden_states = self.norm(hidden_states)
-        hidden_states = hidden_states.permute(0, 3, 4, 2, 1).reshape(batch_size * height * width, num_frames, channel)
-
-        hidden_states = self.proj_in(hidden_states)
-
-        # 2. Blocks
-        for block in self.transformer_blocks:
-            hidden_states = block(
-                hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                timestep=timestep,
-                cross_attention_kwargs=cross_attention_kwargs,
-                class_labels=class_labels,
-            )
-
-        # 3. Output
-        hidden_states = self.proj_out(hidden_states)
-        hidden_states = (
-            hidden_states[None, None, :]
-            .reshape(batch_size, height, width, channel, num_frames)
-            .permute(0, 3, 4, 1, 2)
-            .contiguous()
-        )
-        hidden_states = hidden_states.reshape(batch_frames, channel, height, width)
-
-        output = hidden_states + residual
-
-        if not return_dict:
-            return (output,)
-
-        return TransformerTemporalModelOutput(sample=output)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
deleted file mode 100644
index 7a69a7908efa96f21ca57d0fed1814147cd72078..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
+++ /dev/null
@@ -1,1932 +0,0 @@
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...models import ModelMixin
-from ...models.activations import get_activation
-from ...models.attention import Attention
-from ...models.attention_processor import (
-    AttentionProcessor,
-    AttnAddedKVProcessor,
-    AttnAddedKVProcessor2_0,
-    AttnProcessor,
-)
-from ...models.dual_transformer_2d import DualTransformer2DModel
-from ...models.embeddings import (
-    GaussianFourierProjection,
-    ImageHintTimeEmbedding,
-    ImageProjection,
-    ImageTimeEmbedding,
-    TextImageProjection,
-    TextImageTimeEmbedding,
-    TextTimeEmbedding,
-    TimestepEmbedding,
-    Timesteps,
-)
-from ...models.transformer_2d import Transformer2DModel
-from ...models.unet_2d_condition import UNet2DConditionOutput
-from ...utils import is_torch_version, logging
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def get_down_block(
-    down_block_type,
-    num_layers,
-    in_channels,
-    out_channels,
-    temb_channels,
-    add_downsample,
-    resnet_eps,
-    resnet_act_fn,
-    num_attention_heads,
-    resnet_groups=None,
-    cross_attention_dim=None,
-    downsample_padding=None,
-    dual_cross_attention=False,
-    use_linear_projection=False,
-    only_cross_attention=False,
-    upcast_attention=False,
-    resnet_time_scale_shift="default",
-    resnet_skip_time_act=False,
-    resnet_out_scale_factor=1.0,
-    cross_attention_norm=None,
-):
-    down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
-    if down_block_type == "DownBlockFlat":
-        return DownBlockFlat(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif down_block_type == "CrossAttnDownBlockFlat":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockFlat")
-        return CrossAttnDownBlockFlat(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            cross_attention_dim=cross_attention_dim,
-            num_attention_heads=num_attention_heads,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    raise ValueError(f"{down_block_type} is not supported.")
-
-
-def get_up_block(
-    up_block_type,
-    num_layers,
-    in_channels,
-    out_channels,
-    prev_output_channel,
-    temb_channels,
-    add_upsample,
-    resnet_eps,
-    resnet_act_fn,
-    num_attention_heads,
-    resnet_groups=None,
-    cross_attention_dim=None,
-    dual_cross_attention=False,
-    use_linear_projection=False,
-    only_cross_attention=False,
-    upcast_attention=False,
-    resnet_time_scale_shift="default",
-    resnet_skip_time_act=False,
-    resnet_out_scale_factor=1.0,
-    cross_attention_norm=None,
-):
-    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
-    if up_block_type == "UpBlockFlat":
-        return UpBlockFlat(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif up_block_type == "CrossAttnUpBlockFlat":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockFlat")
-        return CrossAttnUpBlockFlat(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            cross_attention_dim=cross_attention_dim,
-            num_attention_heads=num_attention_heads,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    raise ValueError(f"{up_block_type} is not supported.")
-
-
-# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel with UNet2DConditionModel->UNetFlatConditionModel, nn.Conv2d->LinearMultiDim, Block2D->BlockFlat
-class UNetFlatConditionModel(ModelMixin, ConfigMixin):
-    r"""
-    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
-    shaped output.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
-    for all models (such as downloading or saving).
-
-    Parameters:
-        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
-            Height and width of input/output sample.
-        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
-        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
-        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
-        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
-            Whether to flip the sin to cos in the time embedding.
-        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "DownBlockFlat")`):
-            The tuple of downsample blocks to use.
-        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlockFlatCrossAttn"`):
-            Block type for middle of UNet, it can be either `UNetMidBlockFlatCrossAttn` or
-            `UNetMidBlockFlatSimpleCrossAttn`. If `None`, the mid block layer is skipped.
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat")`):
-            The tuple of upsample blocks to use.
-        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
-            Whether to include self-attention in the basic transformer blocks, see
-            [`~models.attention.BasicTransformerBlock`].
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
-        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
-        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
-        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
-        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
-            If `None`, normalization and activation layers is skipped in post-processing.
-        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
-        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
-            The dimension of the cross attention features.
-        transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
-            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
-            [`~models.unet_2d_blocks.CrossAttnDownBlockFlat`], [`~models.unet_2d_blocks.CrossAttnUpBlockFlat`],
-            [`~models.unet_2d_blocks.UNetMidBlockFlatCrossAttn`].
-        encoder_hid_dim (`int`, *optional*, defaults to None):
-            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
-            dimension to `cross_attention_dim`.
-        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
-            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
-            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
-        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
-        num_attention_heads (`int`, *optional*):
-            The number of attention heads. If not defined, defaults to `attention_head_dim`
-        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
-            for ResNet blocks (see [`~models.resnet.ResnetBlockFlat`]). Choose from `default` or `scale_shift`.
-        class_embed_type (`str`, *optional*, defaults to `None`):
-            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
-            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
-        addition_embed_type (`str`, *optional*, defaults to `None`):
-            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
-            "text". "text" will use the `TextTimeEmbedding` layer.
-        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
-            Dimension for the timestep embeddings.
-        num_class_embeds (`int`, *optional*, defaults to `None`):
-            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
-            class conditioning with `class_embed_type` equal to `None`.
-        time_embedding_type (`str`, *optional*, defaults to `positional`):
-            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
-        time_embedding_dim (`int`, *optional*, defaults to `None`):
-            An optional override for the dimension of the projected time embedding.
-        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
-            Optional activation function to use only once on the time embeddings before they are passed to the rest of
-            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
-        timestep_post_act (`str`, *optional*, defaults to `None`):
-            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
-        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
-            The dimension of `cond_proj` layer in the timestep embedding.
-        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
-        conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
-        projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
-            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
-        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
-            embeddings with the class embeddings.
-        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
-            Whether to use cross attention with the mid block when using the `UNetMidBlockFlatSimpleCrossAttn`. If
-            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
-            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
-            otherwise.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        sample_size: Optional[int] = None,
-        in_channels: int = 4,
-        out_channels: int = 4,
-        center_input_sample: bool = False,
-        flip_sin_to_cos: bool = True,
-        freq_shift: int = 0,
-        down_block_types: Tuple[str] = (
-            "CrossAttnDownBlockFlat",
-            "CrossAttnDownBlockFlat",
-            "CrossAttnDownBlockFlat",
-            "DownBlockFlat",
-        ),
-        mid_block_type: Optional[str] = "UNetMidBlockFlatCrossAttn",
-        up_block_types: Tuple[str] = (
-            "UpBlockFlat",
-            "CrossAttnUpBlockFlat",
-            "CrossAttnUpBlockFlat",
-            "CrossAttnUpBlockFlat",
-        ),
-        only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
-        layers_per_block: Union[int, Tuple[int]] = 2,
-        downsample_padding: int = 1,
-        mid_block_scale_factor: float = 1,
-        act_fn: str = "silu",
-        norm_num_groups: Optional[int] = 32,
-        norm_eps: float = 1e-5,
-        cross_attention_dim: Union[int, Tuple[int]] = 1280,
-        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
-        encoder_hid_dim: Optional[int] = None,
-        encoder_hid_dim_type: Optional[str] = None,
-        attention_head_dim: Union[int, Tuple[int]] = 8,
-        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
-        dual_cross_attention: bool = False,
-        use_linear_projection: bool = False,
-        class_embed_type: Optional[str] = None,
-        addition_embed_type: Optional[str] = None,
-        addition_time_embed_dim: Optional[int] = None,
-        num_class_embeds: Optional[int] = None,
-        upcast_attention: bool = False,
-        resnet_time_scale_shift: str = "default",
-        resnet_skip_time_act: bool = False,
-        resnet_out_scale_factor: int = 1.0,
-        time_embedding_type: str = "positional",
-        time_embedding_dim: Optional[int] = None,
-        time_embedding_act_fn: Optional[str] = None,
-        timestep_post_act: Optional[str] = None,
-        time_cond_proj_dim: Optional[int] = None,
-        conv_in_kernel: int = 3,
-        conv_out_kernel: int = 3,
-        projection_class_embeddings_input_dim: Optional[int] = None,
-        class_embeddings_concat: bool = False,
-        mid_block_only_cross_attention: Optional[bool] = None,
-        cross_attention_norm: Optional[str] = None,
-        addition_embed_type_num_heads=64,
-    ):
-        super().__init__()
-
-        self.sample_size = sample_size
-
-        if num_attention_heads is not None:
-            raise ValueError(
-                "At the moment it is not possible to define the number of attention heads via `num_attention_heads`"
-                " because of a naming issue as described in"
-                " https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing"
-                " `num_attention_heads` will only be supported in diffusers v0.19."
-            )
-
-        # If `num_attention_heads` is not defined (which is the case for most models)
-        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
-        # The reason for this behavior is to correct for incorrectly named variables that were introduced
-        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
-        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
-        # which is why we correct for the naming here.
-        num_attention_heads = num_attention_heads or attention_head_dim
-
-        # Check inputs
-        if len(down_block_types) != len(up_block_types):
-            raise ValueError(
-                "Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`:"
-                f" {down_block_types}. `up_block_types`: {up_block_types}."
-            )
-
-        if len(block_out_channels) != len(down_block_types):
-            raise ValueError(
-                "Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`:"
-                f" {block_out_channels}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
-            raise ValueError(
-                "Must provide the same number of `only_cross_attention` as `down_block_types`."
-                f" `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
-            raise ValueError(
-                "Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`:"
-                f" {num_attention_heads}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
-            raise ValueError(
-                "Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`:"
-                f" {attention_head_dim}. `down_block_types`: {down_block_types}."
-            )
-
-        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
-            raise ValueError(
-                "Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`:"
-                f" {cross_attention_dim}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
-            raise ValueError(
-                "Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`:"
-                f" {layers_per_block}. `down_block_types`: {down_block_types}."
-            )
-
-        # input
-        conv_in_padding = (conv_in_kernel - 1) // 2
-        self.conv_in = LinearMultiDim(
-            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
-        )
-
-        # time
-        if time_embedding_type == "fourier":
-            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
-            if time_embed_dim % 2 != 0:
-                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
-            self.time_proj = GaussianFourierProjection(
-                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
-            )
-            timestep_input_dim = time_embed_dim
-        elif time_embedding_type == "positional":
-            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
-
-            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
-            timestep_input_dim = block_out_channels[0]
-        else:
-            raise ValueError(
-                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
-            )
-
-        self.time_embedding = TimestepEmbedding(
-            timestep_input_dim,
-            time_embed_dim,
-            act_fn=act_fn,
-            post_act_fn=timestep_post_act,
-            cond_proj_dim=time_cond_proj_dim,
-        )
-
-        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
-            encoder_hid_dim_type = "text_proj"
-            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
-            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
-
-        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
-            raise ValueError(
-                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
-            )
-
-        if encoder_hid_dim_type == "text_proj":
-            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
-        elif encoder_hid_dim_type == "text_image_proj":
-            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
-            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
-            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
-            self.encoder_hid_proj = TextImageProjection(
-                text_embed_dim=encoder_hid_dim,
-                image_embed_dim=cross_attention_dim,
-                cross_attention_dim=cross_attention_dim,
-            )
-        elif encoder_hid_dim_type == "image_proj":
-            # Kandinsky 2.2
-            self.encoder_hid_proj = ImageProjection(
-                image_embed_dim=encoder_hid_dim,
-                cross_attention_dim=cross_attention_dim,
-            )
-        elif encoder_hid_dim_type is not None:
-            raise ValueError(
-                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
-            )
-        else:
-            self.encoder_hid_proj = None
-
-        # class embedding
-        if class_embed_type is None and num_class_embeds is not None:
-            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
-        elif class_embed_type == "timestep":
-            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
-        elif class_embed_type == "identity":
-            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
-        elif class_embed_type == "projection":
-            if projection_class_embeddings_input_dim is None:
-                raise ValueError(
-                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
-                )
-            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
-            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
-            # 2. it projects from an arbitrary input dimension.
-            #
-            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
-            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
-            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
-            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
-        elif class_embed_type == "simple_projection":
-            if projection_class_embeddings_input_dim is None:
-                raise ValueError(
-                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
-                )
-            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
-        else:
-            self.class_embedding = None
-
-        if addition_embed_type == "text":
-            if encoder_hid_dim is not None:
-                text_time_embedding_from_dim = encoder_hid_dim
-            else:
-                text_time_embedding_from_dim = cross_attention_dim
-
-            self.add_embedding = TextTimeEmbedding(
-                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
-            )
-        elif addition_embed_type == "text_image":
-            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
-            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
-            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
-            self.add_embedding = TextImageTimeEmbedding(
-                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
-            )
-        elif addition_embed_type == "text_time":
-            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
-            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
-        elif addition_embed_type == "image":
-            # Kandinsky 2.2
-            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
-        elif addition_embed_type == "image_hint":
-            # Kandinsky 2.2 ControlNet
-            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
-        elif addition_embed_type is not None:
-            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
-
-        if time_embedding_act_fn is None:
-            self.time_embed_act = None
-        else:
-            self.time_embed_act = get_activation(time_embedding_act_fn)
-
-        self.down_blocks = nn.ModuleList([])
-        self.up_blocks = nn.ModuleList([])
-
-        if isinstance(only_cross_attention, bool):
-            if mid_block_only_cross_attention is None:
-                mid_block_only_cross_attention = only_cross_attention
-
-            only_cross_attention = [only_cross_attention] * len(down_block_types)
-
-        if mid_block_only_cross_attention is None:
-            mid_block_only_cross_attention = False
-
-        if isinstance(num_attention_heads, int):
-            num_attention_heads = (num_attention_heads,) * len(down_block_types)
-
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(down_block_types)
-
-        if isinstance(cross_attention_dim, int):
-            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
-
-        if isinstance(layers_per_block, int):
-            layers_per_block = [layers_per_block] * len(down_block_types)
-
-        if isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
-
-        if class_embeddings_concat:
-            # The time embeddings are concatenated with the class embeddings. The dimension of the
-            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
-            # regular time embeddings
-            blocks_time_embed_dim = time_embed_dim * 2
-        else:
-            blocks_time_embed_dim = time_embed_dim
-
-        # down
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = get_down_block(
-                down_block_type,
-                num_layers=layers_per_block[i],
-                transformer_layers_per_block=transformer_layers_per_block[i],
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=blocks_time_embed_dim,
-                add_downsample=not is_final_block,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim[i],
-                num_attention_heads=num_attention_heads[i],
-                downsample_padding=downsample_padding,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                resnet_skip_time_act=resnet_skip_time_act,
-                resnet_out_scale_factor=resnet_out_scale_factor,
-                cross_attention_norm=cross_attention_norm,
-                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        if mid_block_type == "UNetMidBlockFlatCrossAttn":
-            self.mid_block = UNetMidBlockFlatCrossAttn(
-                transformer_layers_per_block=transformer_layers_per_block[-1],
-                in_channels=block_out_channels[-1],
-                temb_channels=blocks_time_embed_dim,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                cross_attention_dim=cross_attention_dim[-1],
-                num_attention_heads=num_attention_heads[-1],
-                resnet_groups=norm_num_groups,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                upcast_attention=upcast_attention,
-            )
-        elif mid_block_type == "UNetMidBlockFlatSimpleCrossAttn":
-            self.mid_block = UNetMidBlockFlatSimpleCrossAttn(
-                in_channels=block_out_channels[-1],
-                temb_channels=blocks_time_embed_dim,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                cross_attention_dim=cross_attention_dim[-1],
-                attention_head_dim=attention_head_dim[-1],
-                resnet_groups=norm_num_groups,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                skip_time_act=resnet_skip_time_act,
-                only_cross_attention=mid_block_only_cross_attention,
-                cross_attention_norm=cross_attention_norm,
-            )
-        elif mid_block_type is None:
-            self.mid_block = None
-        else:
-            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
-
-        # count how many layers upsample the images
-        self.num_upsamplers = 0
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        reversed_num_attention_heads = list(reversed(num_attention_heads))
-        reversed_layers_per_block = list(reversed(layers_per_block))
-        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
-        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
-        only_cross_attention = list(reversed(only_cross_attention))
-
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(up_block_types):
-            is_final_block = i == len(block_out_channels) - 1
-
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            # add upsample block for all BUT final layer
-            if not is_final_block:
-                add_upsample = True
-                self.num_upsamplers += 1
-            else:
-                add_upsample = False
-
-            up_block = get_up_block(
-                up_block_type,
-                num_layers=reversed_layers_per_block[i] + 1,
-                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
-                in_channels=input_channel,
-                out_channels=output_channel,
-                prev_output_channel=prev_output_channel,
-                temb_channels=blocks_time_embed_dim,
-                add_upsample=add_upsample,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=reversed_cross_attention_dim[i],
-                num_attention_heads=reversed_num_attention_heads[i],
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                resnet_skip_time_act=resnet_skip_time_act,
-                resnet_out_scale_factor=resnet_out_scale_factor,
-                cross_attention_norm=cross_attention_norm,
-                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        # out
-        if norm_num_groups is not None:
-            self.conv_norm_out = nn.GroupNorm(
-                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
-            )
-
-            self.conv_act = get_activation(act_fn)
-
-        else:
-            self.conv_norm_out = None
-            self.conv_act = None
-
-        conv_out_padding = (conv_out_kernel - 1) // 2
-        self.conv_out = LinearMultiDim(
-            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
-        )
-
-    @property
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "set_processor"):
-                processors[f"{name}.processor"] = module.processor
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    def set_default_attn_processor(self):
-        """
-        Disables custom attention processors and sets the default attention implementation.
-        """
-        self.set_attn_processor(AttnProcessor())
-
-    def set_attention_slice(self, slice_size):
-        r"""
-        Enable sliced attention computation.
-
-        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
-        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
-
-        Args:
-            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
-                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
-                `"max"`, maximum amount of memory is saved by running only one slice at a time. 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`.
-        """
-        sliceable_head_dims = []
-
-        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
-            if hasattr(module, "set_attention_slice"):
-                sliceable_head_dims.append(module.sliceable_head_dim)
-
-            for child in module.children():
-                fn_recursive_retrieve_sliceable_dims(child)
-
-        # retrieve number of attention layers
-        for module in self.children():
-            fn_recursive_retrieve_sliceable_dims(module)
-
-        num_sliceable_layers = len(sliceable_head_dims)
-
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = [dim // 2 for dim in sliceable_head_dims]
-        elif slice_size == "max":
-            # make smallest slice possible
-            slice_size = num_sliceable_layers * [1]
-
-        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
-
-        if len(slice_size) != len(sliceable_head_dims):
-            raise ValueError(
-                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
-                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
-            )
-
-        for i in range(len(slice_size)):
-            size = slice_size[i]
-            dim = sliceable_head_dims[i]
-            if size is not None and size > dim:
-                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
-
-        # Recursively walk through all the children.
-        # Any children which exposes the set_attention_slice method
-        # gets the message
-        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
-            if hasattr(module, "set_attention_slice"):
-                module.set_attention_slice(slice_size.pop())
-
-            for child in module.children():
-                fn_recursive_set_attention_slice(child, slice_size)
-
-        reversed_slice_size = list(reversed(slice_size))
-        for module in self.children():
-            fn_recursive_set_attention_slice(module, reversed_slice_size)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (CrossAttnDownBlockFlat, DownBlockFlat, CrossAttnUpBlockFlat, UpBlockFlat)):
-            module.gradient_checkpointing = value
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        timestep: Union[torch.Tensor, float, int],
-        encoder_hidden_states: torch.Tensor,
-        class_labels: Optional[torch.Tensor] = None,
-        timestep_cond: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
-        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
-        mid_block_additional_residual: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-    ) -> Union[UNet2DConditionOutput, Tuple]:
-        r"""
-        The [`UNetFlatConditionModel`] forward method.
-
-        Args:
-            sample (`torch.FloatTensor`):
-                The noisy input tensor with the following shape `(batch, channel, height, width)`.
-            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
-            encoder_hidden_states (`torch.FloatTensor`):
-                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
-            encoder_attention_mask (`torch.Tensor`):
-                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
-                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
-                which adds large negative values to the attention scores corresponding to "discard" tokens.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
-                tuple.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
-            added_cond_kwargs: (`dict`, *optional*):
-                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
-                are passed along to the UNet blocks.
-
-        Returns:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
-                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
-                a `tuple` is returned where the first element is the sample tensor.
-        """
-        # By default samples have to be AT least a multiple of the overall upsampling factor.
-        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
-        # However, the upsampling interpolation output size can be forced to fit any upsampling size
-        # on the fly if necessary.
-        default_overall_up_factor = 2**self.num_upsamplers
-
-        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
-        forward_upsample_size = False
-        upsample_size = None
-
-        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
-            logger.info("Forward upsample size to force interpolation output size.")
-            forward_upsample_size = True
-
-        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
-        # expects mask of shape:
-        #   [batch, key_tokens]
-        # adds singleton query_tokens dimension:
-        #   [batch,                    1, key_tokens]
-        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
-        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
-        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
-        if attention_mask is not None:
-            # assume that mask is expressed as:
-            #   (1 = keep,      0 = discard)
-            # convert mask into a bias that can be added to attention scores:
-            #       (keep = +0,     discard = -10000.0)
-            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
-            attention_mask = attention_mask.unsqueeze(1)
-
-        # convert encoder_attention_mask to a bias the same way we do for attention_mask
-        if encoder_attention_mask is not None:
-            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
-            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
-
-        # 0. center input if necessary
-        if self.config.center_input_sample:
-            sample = 2 * sample - 1.0
-
-        # 1. time
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
-            # This would be a good case for the `match` statement (Python 3.10+)
-            is_mps = sample.device.type == "mps"
-            if isinstance(timestep, float):
-                dtype = torch.float32 if is_mps else torch.float64
-            else:
-                dtype = torch.int32 if is_mps else torch.int64
-            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
-        elif len(timesteps.shape) == 0:
-            timesteps = timesteps[None].to(sample.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timesteps = timesteps.expand(sample.shape[0])
-
-        t_emb = self.time_proj(timesteps)
-
-        # `Timesteps` does not contain any weights and will always return f32 tensors
-        # but time_embedding might actually be running in fp16. so we need to cast here.
-        # there might be better ways to encapsulate this.
-        t_emb = t_emb.to(dtype=sample.dtype)
-
-        emb = self.time_embedding(t_emb, timestep_cond)
-        aug_emb = None
-
-        if self.class_embedding is not None:
-            if class_labels is None:
-                raise ValueError("class_labels should be provided when num_class_embeds > 0")
-
-            if self.config.class_embed_type == "timestep":
-                class_labels = self.time_proj(class_labels)
-
-                # `Timesteps` does not contain any weights and will always return f32 tensors
-                # there might be better ways to encapsulate this.
-                class_labels = class_labels.to(dtype=sample.dtype)
-
-            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
-
-            if self.config.class_embeddings_concat:
-                emb = torch.cat([emb, class_emb], dim=-1)
-            else:
-                emb = emb + class_emb
-
-        if self.config.addition_embed_type == "text":
-            aug_emb = self.add_embedding(encoder_hidden_states)
-        elif self.config.addition_embed_type == "text_image":
-            # Kandinsky 2.1 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires"
-                    " the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
-                )
-
-            image_embs = added_cond_kwargs.get("image_embeds")
-            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
-            aug_emb = self.add_embedding(text_embs, image_embs)
-        elif self.config.addition_embed_type == "text_time":
-            # SDXL - style
-            if "text_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires"
-                    " the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
-                )
-            text_embeds = added_cond_kwargs.get("text_embeds")
-            if "time_ids" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires"
-                    " the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
-                )
-            time_ids = added_cond_kwargs.get("time_ids")
-            time_embeds = self.add_time_proj(time_ids.flatten())
-            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
-
-            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
-            add_embeds = add_embeds.to(emb.dtype)
-            aug_emb = self.add_embedding(add_embeds)
-        elif self.config.addition_embed_type == "image":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the"
-                    " keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
-                )
-            image_embs = added_cond_kwargs.get("image_embeds")
-            aug_emb = self.add_embedding(image_embs)
-        elif self.config.addition_embed_type == "image_hint":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires"
-                    " the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
-                )
-            image_embs = added_cond_kwargs.get("image_embeds")
-            hint = added_cond_kwargs.get("hint")
-            aug_emb, hint = self.add_embedding(image_embs, hint)
-            sample = torch.cat([sample, hint], dim=1)
-
-        emb = emb + aug_emb if aug_emb is not None else emb
-
-        if self.time_embed_act is not None:
-            emb = self.time_embed_act(emb)
-
-        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
-            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
-            # Kadinsky 2.1 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which"
-                    " requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires"
-                    " the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
-        # 2. pre-process
-        sample = self.conv_in(sample)
-
-        # 3. down
-
-        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
-        is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None
-
-        down_block_res_samples = (sample,)
-        for downsample_block in self.down_blocks:
-            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
-                # For t2i-adapter CrossAttnDownBlockFlat
-                additional_residuals = {}
-                if is_adapter and len(down_block_additional_residuals) > 0:
-                    additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0)
-
-                sample, res_samples = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                    **additional_residuals,
-                )
-            else:
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
-
-                if is_adapter and len(down_block_additional_residuals) > 0:
-                    sample += down_block_additional_residuals.pop(0)
-
-            down_block_res_samples += res_samples
-
-        if is_controlnet:
-            new_down_block_res_samples = ()
-
-            for down_block_res_sample, down_block_additional_residual in zip(
-                down_block_res_samples, down_block_additional_residuals
-            ):
-                down_block_res_sample = down_block_res_sample + down_block_additional_residual
-                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
-
-            down_block_res_samples = new_down_block_res_samples
-
-        # 4. mid
-        if self.mid_block is not None:
-            sample = self.mid_block(
-                sample,
-                emb,
-                encoder_hidden_states=encoder_hidden_states,
-                attention_mask=attention_mask,
-                cross_attention_kwargs=cross_attention_kwargs,
-                encoder_attention_mask=encoder_attention_mask,
-            )
-
-        if is_controlnet:
-            sample = sample + mid_block_additional_residual
-
-        # 5. up
-        for i, upsample_block in enumerate(self.up_blocks):
-            is_final_block = i == len(self.up_blocks) - 1
-
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
-            # if we have not reached the final block and need to forward the
-            # upsample size, we do it here
-            if not is_final_block and forward_upsample_size:
-                upsample_size = down_block_res_samples[-1].shape[2:]
-
-            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    upsample_size=upsample_size,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                )
-            else:
-                sample = upsample_block(
-                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
-                )
-
-        # 6. post-process
-        if self.conv_norm_out:
-            sample = self.conv_norm_out(sample)
-            sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        if not return_dict:
-            return (sample,)
-
-        return UNet2DConditionOutput(sample=sample)
-
-
-class LinearMultiDim(nn.Linear):
-    def __init__(self, in_features, out_features=None, second_dim=4, *args, **kwargs):
-        in_features = [in_features, second_dim, 1] if isinstance(in_features, int) else list(in_features)
-        if out_features is None:
-            out_features = in_features
-        out_features = [out_features, second_dim, 1] if isinstance(out_features, int) else list(out_features)
-        self.in_features_multidim = in_features
-        self.out_features_multidim = out_features
-        super().__init__(np.array(in_features).prod(), np.array(out_features).prod())
-
-    def forward(self, input_tensor, *args, **kwargs):
-        shape = input_tensor.shape
-        n_dim = len(self.in_features_multidim)
-        input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_features)
-        output_tensor = super().forward(input_tensor)
-        output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_features_multidim)
-        return output_tensor
-
-
-class ResnetBlockFlat(nn.Module):
-    def __init__(
-        self,
-        *,
-        in_channels,
-        out_channels=None,
-        dropout=0.0,
-        temb_channels=512,
-        groups=32,
-        groups_out=None,
-        pre_norm=True,
-        eps=1e-6,
-        time_embedding_norm="default",
-        use_in_shortcut=None,
-        second_dim=4,
-        **kwargs,
-    ):
-        super().__init__()
-        self.pre_norm = pre_norm
-        self.pre_norm = True
-
-        in_channels = [in_channels, second_dim, 1] if isinstance(in_channels, int) else list(in_channels)
-        self.in_channels_prod = np.array(in_channels).prod()
-        self.channels_multidim = in_channels
-
-        if out_channels is not None:
-            out_channels = [out_channels, second_dim, 1] if isinstance(out_channels, int) else list(out_channels)
-            out_channels_prod = np.array(out_channels).prod()
-            self.out_channels_multidim = out_channels
-        else:
-            out_channels_prod = self.in_channels_prod
-            self.out_channels_multidim = self.channels_multidim
-        self.time_embedding_norm = time_embedding_norm
-
-        if groups_out is None:
-            groups_out = groups
-
-        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=self.in_channels_prod, eps=eps, affine=True)
-        self.conv1 = torch.nn.Conv2d(self.in_channels_prod, out_channels_prod, kernel_size=1, padding=0)
-
-        if temb_channels is not None:
-            self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels_prod)
-        else:
-            self.time_emb_proj = None
-
-        self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels_prod, eps=eps, affine=True)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels_prod, out_channels_prod, kernel_size=1, padding=0)
-
-        self.nonlinearity = nn.SiLU()
-
-        self.use_in_shortcut = (
-            self.in_channels_prod != out_channels_prod if use_in_shortcut is None else use_in_shortcut
-        )
-
-        self.conv_shortcut = None
-        if self.use_in_shortcut:
-            self.conv_shortcut = torch.nn.Conv2d(
-                self.in_channels_prod, out_channels_prod, kernel_size=1, stride=1, padding=0
-            )
-
-    def forward(self, input_tensor, temb):
-        shape = input_tensor.shape
-        n_dim = len(self.channels_multidim)
-        input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_channels_prod, 1, 1)
-        input_tensor = input_tensor.view(-1, self.in_channels_prod, 1, 1)
-
-        hidden_states = input_tensor
-
-        hidden_states = self.norm1(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-        hidden_states = self.conv1(hidden_states)
-
-        if temb is not None:
-            temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
-            hidden_states = hidden_states + temb
-
-        hidden_states = self.norm2(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.conv2(hidden_states)
-
-        if self.conv_shortcut is not None:
-            input_tensor = self.conv_shortcut(input_tensor)
-
-        output_tensor = input_tensor + hidden_states
-
-        output_tensor = output_tensor.view(*shape[0:-n_dim], -1)
-        output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_channels_multidim)
-
-        return output_tensor
-
-
-# Copied from diffusers.models.unet_2d_blocks.DownBlock2D with DownBlock2D->DownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
-class DownBlockFlat(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_downsample=True,
-        downsample_padding=1,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    LinearMultiDim(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states, temb=None):
-        output_states = ()
-
-        for resnet in self.resnets:
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                if is_torch_version(">=", "1.11.0"):
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
-                    )
-                else:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb
-                    )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-            output_states = output_states + (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states = output_states + (hidden_states,)
-
-        return hidden_states, output_states
-
-
-# Copied from diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D with CrossAttnDownBlock2D->CrossAttnDownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
-class CrossAttnDownBlockFlat(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        transformer_layers_per_block: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        num_attention_heads=1,
-        cross_attention_dim=1280,
-        output_scale_factor=1.0,
-        downsample_padding=1,
-        add_downsample=True,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        only_cross_attention=False,
-        upcast_attention=False,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.num_attention_heads = num_attention_heads
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        num_attention_heads,
-                        out_channels // num_attention_heads,
-                        in_channels=out_channels,
-                        num_layers=transformer_layers_per_block,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        only_cross_attention=only_cross_attention,
-                        upcast_attention=upcast_attention,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        num_attention_heads,
-                        out_channels // num_attention_heads,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    LinearMultiDim(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        additional_residuals=None,
-    ):
-        output_states = ()
-
-        blocks = list(zip(self.resnets, self.attentions))
-
-        for i, (resnet, attn) in enumerate(blocks):
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    **ckpt_kwargs,
-                )
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(attn, return_dict=False),
-                    hidden_states,
-                    encoder_hidden_states,
-                    None,  # timestep
-                    None,  # class_labels
-                    cross_attention_kwargs,
-                    attention_mask,
-                    encoder_attention_mask,
-                    **ckpt_kwargs,
-                )[0]
-            else:
-                hidden_states = resnet(hidden_states, temb)
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                    return_dict=False,
-                )[0]
-
-            # apply additional residuals to the output of the last pair of resnet and attention blocks
-            if i == len(blocks) - 1 and additional_residuals is not None:
-                hidden_states = hidden_states + additional_residuals
-
-            output_states = output_states + (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states = output_states + (hidden_states,)
-
-        return hidden_states, output_states
-
-
-# Copied from diffusers.models.unet_2d_blocks.UpBlock2D with UpBlock2D->UpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim
-class UpBlockFlat(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_upsample=True,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                if is_torch_version(">=", "1.11.0"):
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
-                    )
-                else:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb
-                    )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-        return hidden_states
-
-
-# Copied from diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D with CrossAttnUpBlock2D->CrossAttnUpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim
-class CrossAttnUpBlockFlat(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        prev_output_channel: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        transformer_layers_per_block: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        num_attention_heads=1,
-        cross_attention_dim=1280,
-        output_scale_factor=1.0,
-        add_upsample=True,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        only_cross_attention=False,
-        upcast_attention=False,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.num_attention_heads = num_attention_heads
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        num_attention_heads,
-                        out_channels // num_attention_heads,
-                        in_channels=out_channels,
-                        num_layers=transformer_layers_per_block,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        only_cross_attention=only_cross_attention,
-                        upcast_attention=upcast_attention,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        num_attention_heads,
-                        out_channels // num_attention_heads,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        upsample_size: Optional[int] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-    ):
-        for resnet, attn in zip(self.resnets, self.attentions):
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    **ckpt_kwargs,
-                )
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(attn, return_dict=False),
-                    hidden_states,
-                    encoder_hidden_states,
-                    None,  # timestep
-                    None,  # class_labels
-                    cross_attention_kwargs,
-                    attention_mask,
-                    encoder_attention_mask,
-                    **ckpt_kwargs,
-                )[0]
-            else:
-                hidden_states = resnet(hidden_states, temb)
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                    return_dict=False,
-                )[0]
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-        return hidden_states
-
-
-# Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DCrossAttn with UNetMidBlock2DCrossAttn->UNetMidBlockFlatCrossAttn, ResnetBlock2D->ResnetBlockFlat
-class UNetMidBlockFlatCrossAttn(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        transformer_layers_per_block: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        num_attention_heads=1,
-        output_scale_factor=1.0,
-        cross_attention_dim=1280,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        upcast_attention=False,
-    ):
-        super().__init__()
-
-        self.has_cross_attention = True
-        self.num_attention_heads = num_attention_heads
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlockFlat(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-            )
-        ]
-        attentions = []
-
-        for _ in range(num_layers):
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        num_attention_heads,
-                        in_channels // num_attention_heads,
-                        in_channels=in_channels,
-                        num_layers=transformer_layers_per_block,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        upcast_attention=upcast_attention,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        num_attention_heads,
-                        in_channels // num_attention_heads,
-                        in_channels=in_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            hidden_states = attn(
-                hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                cross_attention_kwargs=cross_attention_kwargs,
-                attention_mask=attention_mask,
-                encoder_attention_mask=encoder_attention_mask,
-                return_dict=False,
-            )[0]
-            hidden_states = resnet(hidden_states, temb)
-
-        return hidden_states
-
-
-# Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DSimpleCrossAttn with UNetMidBlock2DSimpleCrossAttn->UNetMidBlockFlatSimpleCrossAttn, ResnetBlock2D->ResnetBlockFlat
-class UNetMidBlockFlatSimpleCrossAttn(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attention_head_dim=1,
-        output_scale_factor=1.0,
-        cross_attention_dim=1280,
-        skip_time_act=False,
-        only_cross_attention=False,
-        cross_attention_norm=None,
-    ):
-        super().__init__()
-
-        self.has_cross_attention = True
-
-        self.attention_head_dim = attention_head_dim
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        self.num_heads = in_channels // self.attention_head_dim
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlockFlat(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                skip_time_act=skip_time_act,
-            )
-        ]
-        attentions = []
-
-        for _ in range(num_layers):
-            processor = (
-                AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor()
-            )
-
-            attentions.append(
-                Attention(
-                    query_dim=in_channels,
-                    cross_attention_dim=in_channels,
-                    heads=self.num_heads,
-                    dim_head=self.attention_head_dim,
-                    added_kv_proj_dim=cross_attention_dim,
-                    norm_num_groups=resnet_groups,
-                    bias=True,
-                    upcast_softmax=True,
-                    only_cross_attention=only_cross_attention,
-                    cross_attention_norm=cross_attention_norm,
-                    processor=processor,
-                )
-            )
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    skip_time_act=skip_time_act,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-    ):
-        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
-
-        if attention_mask is None:
-            # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.
-            mask = None if encoder_hidden_states is None else encoder_attention_mask
-        else:
-            # when attention_mask is defined: we don't even check for encoder_attention_mask.
-            # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.
-            # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.
-            #       then we can simplify this whole if/else block to:
-            #         mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask
-            mask = attention_mask
-
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            # attn
-            hidden_states = attn(
-                hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                attention_mask=mask,
-                **cross_attention_kwargs,
-            )
-
-            # resnet
-            hidden_states = resnet(hidden_states, temb)
-
-        return hidden_states
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/vfnet/vfnet_r50_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/vfnet/vfnet_r50_fpn_1x_coco.py
deleted file mode 100644
index 76566bdb0fe827f222924142c22c846a86fd1d32..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/vfnet/vfnet_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,108 +0,0 @@
-_base_ = [
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-# model settings
-model = dict(
-    type='VFNet',
-    pretrained='torchvision://resnet50',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        start_level=1,
-        add_extra_convs=True,
-        extra_convs_on_inputs=False,  # use P5
-        num_outs=5,
-        relu_before_extra_convs=True),
-    bbox_head=dict(
-        type='VFNetHead',
-        num_classes=80,
-        in_channels=256,
-        stacked_convs=3,
-        feat_channels=256,
-        strides=[8, 16, 32, 64, 128],
-        center_sampling=False,
-        dcn_on_last_conv=False,
-        use_atss=True,
-        use_vfl=True,
-        loss_cls=dict(
-            type='VarifocalLoss',
-            use_sigmoid=True,
-            alpha=0.75,
-            gamma=2.0,
-            iou_weighted=True,
-            loss_weight=1.0),
-        loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
-        loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0)),
-    # training and testing settings
-    train_cfg=dict(
-        assigner=dict(type='ATSSAssigner', topk=9),
-        allowed_border=-1,
-        pos_weight=-1,
-        debug=False),
-    test_cfg=dict(
-        nms_pre=1000,
-        min_bbox_size=0,
-        score_thr=0.05,
-        nms=dict(type='nms', iou_threshold=0.6),
-        max_per_img=100))
-
-# data setting
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='DefaultFormatBundle'),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-
-# optimizer
-optimizer = dict(
-    lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=0.1,
-    step=[8, 11])
-runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/voc.py b/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/voc.py
deleted file mode 100644
index abd4cb8947238936faff48fc92c093c8ae06daff..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/voc.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from collections import OrderedDict
-
-from mmcv.utils import print_log
-
-from mmdet.core import eval_map, eval_recalls
-from .builder import DATASETS
-from .xml_style import XMLDataset
-
-
-@DATASETS.register_module()
-class VOCDataset(XMLDataset):
-
-    CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
-               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
-               'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
-               'tvmonitor')
-
-    def __init__(self, **kwargs):
-        super(VOCDataset, self).__init__(**kwargs)
-        if 'VOC2007' in self.img_prefix:
-            self.year = 2007
-        elif 'VOC2012' in self.img_prefix:
-            self.year = 2012
-        else:
-            raise ValueError('Cannot infer dataset year from img_prefix')
-
-    def evaluate(self,
-                 results,
-                 metric='mAP',
-                 logger=None,
-                 proposal_nums=(100, 300, 1000),
-                 iou_thr=0.5,
-                 scale_ranges=None):
-        """Evaluate in VOC protocol.
-
-        Args:
-            results (list[list | tuple]): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated. Options are
-                'mAP', 'recall'.
-            logger (logging.Logger | str, optional): Logger used for printing
-                related information during evaluation. Default: None.
-            proposal_nums (Sequence[int]): Proposal number used for evaluating
-                recalls, such as recall@100, recall@1000.
-                Default: (100, 300, 1000).
-            iou_thr (float | list[float]): IoU threshold. Default: 0.5.
-            scale_ranges (list[tuple], optional): Scale ranges for evaluating
-                mAP. If not specified, all bounding boxes would be included in
-                evaluation. Default: None.
-
-        Returns:
-            dict[str, float]: AP/recall metrics.
-        """
-
-        if not isinstance(metric, str):
-            assert len(metric) == 1
-            metric = metric[0]
-        allowed_metrics = ['mAP', 'recall']
-        if metric not in allowed_metrics:
-            raise KeyError(f'metric {metric} is not supported')
-        annotations = [self.get_ann_info(i) for i in range(len(self))]
-        eval_results = OrderedDict()
-        iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr
-        if metric == 'mAP':
-            assert isinstance(iou_thrs, list)
-            if self.year == 2007:
-                ds_name = 'voc07'
-            else:
-                ds_name = self.CLASSES
-            mean_aps = []
-            for iou_thr in iou_thrs:
-                print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}')
-                mean_ap, _ = eval_map(
-                    results,
-                    annotations,
-                    scale_ranges=None,
-                    iou_thr=iou_thr,
-                    dataset=ds_name,
-                    logger=logger)
-                mean_aps.append(mean_ap)
-                eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3)
-            eval_results['mAP'] = sum(mean_aps) / len(mean_aps)
-        elif metric == 'recall':
-            gt_bboxes = [ann['bboxes'] for ann in annotations]
-            recalls = eval_recalls(
-                gt_bboxes, results, proposal_nums, iou_thr, logger=logger)
-            for i, num in enumerate(proposal_nums):
-                for j, iou in enumerate(iou_thr):
-                    eval_results[f'recall@{num}@{iou}'] = recalls[i, j]
-            if recalls.shape[1] > 1:
-                ar = recalls.mean(axis=1)
-                for i, num in enumerate(proposal_nums):
-                    eval_results[f'AR@{num}'] = ar[i]
-        return eval_results
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x512_160k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x512_160k_ade20k.py
deleted file mode 100644
index 22aaf857c3212d0b36b0b04e7990616025a3ef9b..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/danet_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/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
deleted file mode 100644
index d6ade67b76ce04e1ede3ff99aab4863705cff446..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './encnet_r50-d8_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Andy1621/uniformerv2_demo/transforms.py b/spaces/Andy1621/uniformerv2_demo/transforms.py
deleted file mode 100644
index 2483fdf8569e25978b922774e84cc2244315fe61..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformerv2_demo/transforms.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import torchvision
-import random
-from PIL import Image, ImageOps
-import numpy as np
-import numbers
-import math
-import torch
-
-
-class GroupRandomCrop(object):
-    def __init__(self, size):
-        if isinstance(size, numbers.Number):
-            self.size = (int(size), int(size))
-        else:
-            self.size = size
-
-    def __call__(self, img_group):
-
-        w, h = img_group[0].size
-        th, tw = self.size
-
-        out_images = list()
-
-        x1 = random.randint(0, w - tw)
-        y1 = random.randint(0, h - th)
-
-        for img in img_group:
-            assert(img.size[0] == w and img.size[1] == h)
-            if w == tw and h == th:
-                out_images.append(img)
-            else:
-                out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
-
-        return out_images
-
-
-class MultiGroupRandomCrop(object):
-    def __init__(self, size, groups=1):
-        if isinstance(size, numbers.Number):
-            self.size = (int(size), int(size))
-        else:
-            self.size = size
-        self.groups = groups
-
-    def __call__(self, img_group):
-
-        w, h = img_group[0].size
-        th, tw = self.size
-
-        out_images = list()
-
-        for i in range(self.groups):
-            x1 = random.randint(0, w - tw)
-            y1 = random.randint(0, h - th)
-
-            for img in img_group:
-                assert(img.size[0] == w and img.size[1] == h)
-                if w == tw and h == th:
-                    out_images.append(img)
-                else:
-                    out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
-
-        return out_images
-
-
-class GroupCenterCrop(object):
-    def __init__(self, size):
-        self.worker = torchvision.transforms.CenterCrop(size)
-
-    def __call__(self, img_group):
-        return [self.worker(img) for img in img_group]
-
-
-class GroupRandomHorizontalFlip(object):
-    """Randomly horizontally flips the given PIL.Image with a probability of 0.5
-    """
-
-    def __init__(self, is_flow=False):
-        self.is_flow = is_flow
-
-    def __call__(self, img_group, is_flow=False):
-        v = random.random()
-        if v < 0.5:
-            ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group]
-            if self.is_flow:
-                for i in range(0, len(ret), 2):
-                    # invert flow pixel values when flipping
-                    ret[i] = ImageOps.invert(ret[i])
-            return ret
-        else:
-            return img_group
-
-
-class GroupNormalize(object):
-    def __init__(self, mean, std):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, tensor):
-        rep_mean = self.mean * (tensor.size()[0] // len(self.mean))
-        rep_std = self.std * (tensor.size()[0] // len(self.std))
-
-        # TODO: make efficient
-        for t, m, s in zip(tensor, rep_mean, rep_std):
-            t.sub_(m).div_(s)
-
-        return tensor
-
-
-class GroupScale(object):
-    """ Rescales the input PIL.Image to the given 'size'.
-    'size' will be the size of the smaller edge.
-    For example, if height > width, then image will be
-    rescaled to (size * height / width, size)
-    size: size of the smaller edge
-    interpolation: Default: PIL.Image.BILINEAR
-    """
-
-    def __init__(self, size, interpolation=Image.BILINEAR):
-        self.worker = torchvision.transforms.Resize(size, interpolation)
-
-    def __call__(self, img_group):
-        return [self.worker(img) for img in img_group]
-
-
-class GroupOverSample(object):
-    def __init__(self, crop_size, scale_size=None, flip=True):
-        self.crop_size = crop_size if not isinstance(
-            crop_size, int) else (crop_size, crop_size)
-
-        if scale_size is not None:
-            self.scale_worker = GroupScale(scale_size)
-        else:
-            self.scale_worker = None
-        self.flip = flip
-
-    def __call__(self, img_group):
-
-        if self.scale_worker is not None:
-            img_group = self.scale_worker(img_group)
-
-        image_w, image_h = img_group[0].size
-        crop_w, crop_h = self.crop_size
-
-        offsets = GroupMultiScaleCrop.fill_fix_offset(
-            False, image_w, image_h, crop_w, crop_h)
-        oversample_group = list()
-        for o_w, o_h in offsets:
-            normal_group = list()
-            flip_group = list()
-            for i, img in enumerate(img_group):
-                crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h))
-                normal_group.append(crop)
-                flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT)
-
-                if img.mode == 'L' and i % 2 == 0:
-                    flip_group.append(ImageOps.invert(flip_crop))
-                else:
-                    flip_group.append(flip_crop)
-
-            oversample_group.extend(normal_group)
-            if self.flip:
-                oversample_group.extend(flip_group)
-        return oversample_group
-
-
-class GroupFullResSample(object):
-    def __init__(self, crop_size, scale_size=None, flip=True):
-        self.crop_size = crop_size if not isinstance(
-            crop_size, int) else (crop_size, crop_size)
-
-        if scale_size is not None:
-            self.scale_worker = GroupScale(scale_size)
-        else:
-            self.scale_worker = None
-        self.flip = flip
-
-    def __call__(self, img_group):
-
-        if self.scale_worker is not None:
-            img_group = self.scale_worker(img_group)
-
-        image_w, image_h = img_group[0].size
-        crop_w, crop_h = self.crop_size
-
-        w_step = (image_w - crop_w) // 4
-        h_step = (image_h - crop_h) // 4
-
-        offsets = list()
-        offsets.append((0 * w_step, 2 * h_step))  # left
-        offsets.append((4 * w_step, 2 * h_step))  # right
-        offsets.append((2 * w_step, 2 * h_step))  # center
-
-        oversample_group = list()
-        for o_w, o_h in offsets:
-            normal_group = list()
-            flip_group = list()
-            for i, img in enumerate(img_group):
-                crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h))
-                normal_group.append(crop)
-                if self.flip:
-                    flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT)
-
-                    if img.mode == 'L' and i % 2 == 0:
-                        flip_group.append(ImageOps.invert(flip_crop))
-                    else:
-                        flip_group.append(flip_crop)
-
-            oversample_group.extend(normal_group)
-            oversample_group.extend(flip_group)
-        return oversample_group
-
-
-class GroupMultiScaleCrop(object):
-
-    def __init__(self, input_size, scales=None, max_distort=1,
-                 fix_crop=True, more_fix_crop=True):
-        self.scales = scales if scales is not None else [1, .875, .75, .66]
-        self.max_distort = max_distort
-        self.fix_crop = fix_crop
-        self.more_fix_crop = more_fix_crop
-        self.input_size = input_size if not isinstance(input_size, int) else [
-            input_size, input_size]
-        self.interpolation = Image.BILINEAR
-
-    def __call__(self, img_group):
-
-        im_size = img_group[0].size
-
-        crop_w, crop_h, offset_w, offset_h = self._sample_crop_size(im_size)
-        crop_img_group = [
-            img.crop(
-                (offset_w,
-                 offset_h,
-                 offset_w +
-                 crop_w,
-                 offset_h +
-                 crop_h)) for img in img_group]
-        ret_img_group = [img.resize((self.input_size[0], self.input_size[1]), self.interpolation)
-                         for img in crop_img_group]
-        return ret_img_group
-
-    def _sample_crop_size(self, im_size):
-        image_w, image_h = im_size[0], im_size[1]
-
-        # find a crop size
-        base_size = min(image_w, image_h)
-        crop_sizes = [int(base_size * x) for x in self.scales]
-        crop_h = [
-            self.input_size[1] if abs(
-                x - self.input_size[1]) < 3 else x for x in crop_sizes]
-        crop_w = [
-            self.input_size[0] if abs(
-                x - self.input_size[0]) < 3 else x for x in crop_sizes]
-
-        pairs = []
-        for i, h in enumerate(crop_h):
-            for j, w in enumerate(crop_w):
-                if abs(i - j) <= self.max_distort:
-                    pairs.append((w, h))
-
-        crop_pair = random.choice(pairs)
-        if not self.fix_crop:
-            w_offset = random.randint(0, image_w - crop_pair[0])
-            h_offset = random.randint(0, image_h - crop_pair[1])
-        else:
-            w_offset, h_offset = self._sample_fix_offset(
-                image_w, image_h, crop_pair[0], crop_pair[1])
-
-        return crop_pair[0], crop_pair[1], w_offset, h_offset
-
-    def _sample_fix_offset(self, image_w, image_h, crop_w, crop_h):
-        offsets = self.fill_fix_offset(
-            self.more_fix_crop, image_w, image_h, crop_w, crop_h)
-        return random.choice(offsets)
-
-    @staticmethod
-    def fill_fix_offset(more_fix_crop, image_w, image_h, crop_w, crop_h):
-        w_step = (image_w - crop_w) // 4
-        h_step = (image_h - crop_h) // 4
-
-        ret = list()
-        ret.append((0, 0))  # upper left
-        ret.append((4 * w_step, 0))  # upper right
-        ret.append((0, 4 * h_step))  # lower left
-        ret.append((4 * w_step, 4 * h_step))  # lower right
-        ret.append((2 * w_step, 2 * h_step))  # center
-
-        if more_fix_crop:
-            ret.append((0, 2 * h_step))  # center left
-            ret.append((4 * w_step, 2 * h_step))  # center right
-            ret.append((2 * w_step, 4 * h_step))  # lower center
-            ret.append((2 * w_step, 0 * h_step))  # upper center
-
-            ret.append((1 * w_step, 1 * h_step))  # upper left quarter
-            ret.append((3 * w_step, 1 * h_step))  # upper right quarter
-            ret.append((1 * w_step, 3 * h_step))  # lower left quarter
-            ret.append((3 * w_step, 3 * h_step))  # lower righ quarter
-
-        return ret
-
-
-class GroupRandomSizedCrop(object):
-    """Random crop the given PIL.Image to a random size of (0.08 to 1.0) of the original size
-    and and a random aspect ratio of 3/4 to 4/3 of the original aspect ratio
-    This is popularly used to train the Inception networks
-    size: size of the smaller edge
-    interpolation: Default: PIL.Image.BILINEAR
-    """
-
-    def __init__(self, size, interpolation=Image.BILINEAR):
-        self.size = size
-        self.interpolation = interpolation
-
-    def __call__(self, img_group):
-        for attempt in range(10):
-            area = img_group[0].size[0] * img_group[0].size[1]
-            target_area = random.uniform(0.08, 1.0) * area
-            aspect_ratio = random.uniform(3. / 4, 4. / 3)
-
-            w = int(round(math.sqrt(target_area * aspect_ratio)))
-            h = int(round(math.sqrt(target_area / aspect_ratio)))
-
-            if random.random() < 0.5:
-                w, h = h, w
-
-            if w <= img_group[0].size[0] and h <= img_group[0].size[1]:
-                x1 = random.randint(0, img_group[0].size[0] - w)
-                y1 = random.randint(0, img_group[0].size[1] - h)
-                found = True
-                break
-        else:
-            found = False
-            x1 = 0
-            y1 = 0
-
-        if found:
-            out_group = list()
-            for img in img_group:
-                img = img.crop((x1, y1, x1 + w, y1 + h))
-                assert(img.size == (w, h))
-                out_group.append(
-                    img.resize(
-                        (self.size, self.size), self.interpolation))
-            return out_group
-        else:
-            # Fallback
-            scale = GroupScale(self.size, interpolation=self.interpolation)
-            crop = GroupRandomCrop(self.size)
-            return crop(scale(img_group))
-
-
-class ConvertDataFormat(object):
-    def __init__(self, model_type):
-        self.model_type = model_type
-
-    def __call__(self, images):
-        if self.model_type == '2D':
-            return images
-        tc, h, w = images.size()
-        t = tc // 3
-        images = images.view(t, 3, h, w)
-        images = images.permute(1, 0, 2, 3)
-        return images
-
-
-class Stack(object):
-
-    def __init__(self, roll=False):
-        self.roll = roll
-
-    def __call__(self, img_group):
-        if img_group[0].mode == 'L':
-            return np.concatenate([np.expand_dims(x, 2)
-                                   for x in img_group], axis=2)
-        elif img_group[0].mode == 'RGB':
-            if self.roll:
-                return np.concatenate([np.array(x)[:, :, ::-1]
-                                       for x in img_group], axis=2)
-            else:
-                #print(np.concatenate(img_group, axis=2).shape)
-                # print(img_group[0].shape)
-                return np.concatenate(img_group, axis=2)
-
-
-class ToTorchFormatTensor(object):
-    """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255]
-    to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """
-
-    def __init__(self, div=True):
-        self.div = div
-
-    def __call__(self, pic):
-        if isinstance(pic, np.ndarray):
-            # handle numpy array
-            img = torch.from_numpy(pic).permute(2, 0, 1).contiguous()
-        else:
-            # handle PIL Image
-            img = torch.ByteTensor(
-                torch.ByteStorage.from_buffer(
-                    pic.tobytes()))
-            img = img.view(pic.size[1], pic.size[0], len(pic.mode))
-            # put it from HWC to CHW format
-            # yikes, this transpose takes 80% of the loading time/CPU
-            img = img.transpose(0, 1).transpose(0, 2).contiguous()
-        return img.float().div(255) if self.div else img.float()
-
-
-class IdentityTransform(object):
-
-    def __call__(self, data):
-        return data
-
-
-if __name__ == "__main__":
-    trans = torchvision.transforms.Compose([
-        GroupScale(256),
-        GroupRandomCrop(224),
-        Stack(),
-        ToTorchFormatTensor(),
-        GroupNormalize(
-            mean=[.485, .456, .406],
-            std=[.229, .224, .225]
-        )]
-    )
-
-    im = Image.open('../tensorflow-model-zoo.torch/lena_299.png')
-
-    color_group = [im] * 3
-    rst = trans(color_group)
-
-    gray_group = [im.convert('L')] * 9
-    gray_rst = trans(gray_group)
-
-    trans2 = torchvision.transforms.Compose([
-        GroupRandomSizedCrop(256),
-        Stack(),
-        ToTorchFormatTensor(),
-        GroupNormalize(
-            mean=[.485, .456, .406],
-            std=[.229, .224, .225])
-    ])
-    print(trans2(color_group))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/start_wsl.bat b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/start_wsl.bat
deleted file mode 100644
index d7bacead6b0ea94656ecacd8bccede01d7d53cc8..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/start_wsl.bat
+++ /dev/null
@@ -1,11 +0,0 @@
-@echo off
-
-cd /D "%~dp0"
-
-set PATH=%PATH%;%SystemRoot%\system32
-
-@rem sed -i 's/\x0D$//' ./wsl.sh converts newlines to unix format in the wsl script
-call wsl -e bash -lic "sed -i 's/\x0D$//' ./wsl.sh; source ./wsl.sh %*"
-
-:end
-pause
diff --git a/spaces/Arnx/MusicGenXvAKN/tests/common_utils/wav_utils.py b/spaces/Arnx/MusicGenXvAKN/tests/common_utils/wav_utils.py
deleted file mode 100644
index d3a563ee1749a58217ece55c9a08b8d93c0fc386..0000000000000000000000000000000000000000
--- a/spaces/Arnx/MusicGenXvAKN/tests/common_utils/wav_utils.py
+++ /dev/null
@@ -1,32 +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 typing as tp
-
-import torch
-import torchaudio
-
-
-def get_white_noise(chs: int = 1, num_frames: int = 1):
-    wav = torch.randn(chs, num_frames)
-    return wav
-
-
-def get_batch_white_noise(bs: int = 1, chs: int = 1, num_frames: int = 1):
-    wav = torch.randn(bs, chs, num_frames)
-    return wav
-
-
-def save_wav(path: str, wav: torch.Tensor, sample_rate: int):
-    fp = Path(path)
-    kwargs: tp.Dict[str, tp.Any] = {}
-    if fp.suffix == '.wav':
-        kwargs['encoding'] = 'PCM_S'
-        kwargs['bits_per_sample'] = 16
-    elif fp.suffix == '.mp3':
-        kwargs['compression'] = 320
-    torchaudio.save(str(fp), wav, sample_rate, **kwargs)
diff --git a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/tuneavideo_text2video.py b/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/tuneavideo_text2video.py
deleted file mode 100644
index 774f7ee6d259953ea8716b05b1f6b99c92b0e9bb..0000000000000000000000000000000000000000
--- a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/tuneavideo_text2video.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import gradio as gr
-import torch
-
-from video_diffusion.tuneavideo.models.unet import UNet3DConditionModel
-from video_diffusion.tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
-from video_diffusion.tuneavideo.util import save_videos_grid
-from video_diffusion.utils.model_list import stable_model_list
-
-video_diffusion_model_list = [
-    "Tune-A-Video-library/a-man-is-surfing",
-    "Tune-A-Video-library/mo-di-bear-guitar",
-    "Tune-A-Video-library/redshift-man-skiing",
-]
-
-
-class TunaVideoText2VideoGenerator:
-    def __init__(self):
-        self.pipe = None
-        self.unet = None
-
-    def load_model(self, video_diffusion_model_list, stable_model_list):
-        if self.pipe is None:
-            if self.unet is None:
-                self.unet = UNet3DConditionModel.from_pretrained(
-                    video_diffusion_model_list, subfolder="unet", torch_dtype=torch.float16
-                ).to("cuda")
-
-            self.pipe = TuneAVideoPipeline.from_pretrained(
-                stable_model_list, unet=self.unet, torch_dtype=torch.float16
-            )
-            self.pipe.to("cuda")
-            self.pipe.enable_xformers_memory_efficient_attention()
-            
-        return self.pipe
-
-    def generate_video(
-        self,
-        video_diffusion_model: str,
-        stable_model_list: str,
-        prompt: str,
-        negative_prompt: str,
-        video_length: int,
-        height: int,
-        width: int,
-        num_inference_steps: int,
-        guidance_scale: int,
-        fps: int,
-    ):
-        pipe = self.load_model(video_diffusion_model, stable_model_list)
-        video = pipe(
-            prompt,
-            negative_prompt=negative_prompt,
-            video_length=video_length,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-        ).videos
-
-        save_videos_grid(videos=video, path="output.gif", fps=fps)
-        return "output.gif"
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    tunevideo_video_diffusion_model_list = gr.Dropdown(
-                        choices=video_diffusion_model_list,
-                        label="Video Diffusion Model",
-                        value=video_diffusion_model_list[0],
-                    )
-                    tunevideo_stable_model_list = gr.Dropdown(
-                        choices=stable_model_list,
-                        label="Stable Model List",
-                        value=stable_model_list[0],
-                    )
-                    with gr.Row():
-                        with gr.Column():
-                            tunevideo_prompt = gr.Textbox(
-                                lines=1,
-                                placeholder="Prompt",
-                                show_label=False,
-                            )
-                            tunevideo_video_length = gr.Slider(
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=10,
-                                label="Video Length",
-                            )
-                            tunevideo_num_inference_steps = gr.Slider(
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=50,
-                                label="Num Inference Steps",
-                            )
-                            tunevideo_fps = gr.Slider(
-                                minimum=1,
-                                maximum=60,
-                                step=1,
-                                value=5,
-                                label="Fps",
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                tunevideo_negative_prompt = gr.Textbox(
-                                    lines=1,
-                                    placeholder="Negative Prompt",
-                                    show_label=False,
-                                )
-                                tunevideo_guidance_scale = gr.Slider(
-                                    minimum=1,
-                                    maximum=15,
-                                    step=1,
-                                    value=7.5,
-                                    label="Guidance Scale",
-                                )
-                                tunevideo_height = gr.Slider(
-                                    minimum=1,
-                                    maximum=1280,
-                                    step=32,
-                                    value=512,
-                                    label="Height",
-                                )
-                                tunevideo_width = gr.Slider(
-                                    minimum=1,
-                                    maximum=1280,
-                                    step=32,
-                                    value=512,
-                                    label="Width",
-                                )
-                    tunevideo_generate = gr.Button(value="Generator")
-
-                with gr.Column():
-                    tunevideo_output = gr.Video(label="Output")
-
-            tunevideo_generate.click(
-                fn=TunaVideoText2VideoGenerator().generate_video,
-                inputs=[
-                    tunevideo_video_diffusion_model_list,
-                    tunevideo_stable_model_list,
-                    tunevideo_prompt,
-                    tunevideo_negative_prompt,
-                    tunevideo_video_length,
-                    tunevideo_height,
-                    tunevideo_width,
-                    tunevideo_num_inference_steps,
-                    tunevideo_guidance_scale,
-                    tunevideo_fps,
-                ],
-                outputs=tunevideo_output,
-            )
diff --git a/spaces/AsakuraMizu/moe-tts/text/mandarin.py b/spaces/AsakuraMizu/moe-tts/text/mandarin.py
deleted file mode 100644
index ff71de9788e4f20c897b971a775d1ecfbfe1c7b7..0000000000000000000000000000000000000000
--- a/spaces/AsakuraMizu/moe-tts/text/mandarin.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import os
-import sys
-import re
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba
-import cn2an
-import logging
-
-logging.getLogger('jieba').setLevel(logging.WARNING)
-jieba.initialize()
-
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'ㄟˉ'),
-    ('b', 'ㄅㄧˋ'),
-    ('c', 'ㄙㄧˉ'),
-    ('d', 'ㄉㄧˋ'),
-    ('e', 'ㄧˋ'),
-    ('f', 'ㄝˊㄈㄨˋ'),
-    ('g', 'ㄐㄧˋ'),
-    ('h', 'ㄝˇㄑㄩˋ'),
-    ('i', 'ㄞˋ'),
-    ('j', 'ㄐㄟˋ'),
-    ('k', 'ㄎㄟˋ'),
-    ('l', 'ㄝˊㄛˋ'),
-    ('m', 'ㄝˊㄇㄨˋ'),
-    ('n', 'ㄣˉ'),
-    ('o', 'ㄡˉ'),
-    ('p', 'ㄆㄧˉ'),
-    ('q', 'ㄎㄧㄡˉ'),
-    ('r', 'ㄚˋ'),
-    ('s', 'ㄝˊㄙˋ'),
-    ('t', 'ㄊㄧˋ'),
-    ('u', 'ㄧㄡˉ'),
-    ('v', 'ㄨㄧˉ'),
-    ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-    ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-    ('y', 'ㄨㄞˋ'),
-    ('z', 'ㄗㄟˋ')
-]]
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'p⁼wo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p⁼'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't⁼'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k⁼'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'h'),
-    ('ㄐ', 'ʧ⁼'),
-    ('ㄑ', 'ʧʰ'),
-    ('ㄒ', 'ʃ'),
-    ('ㄓ', 'ʦ`⁼'),
-    ('ㄔ', 'ʦ`ʰ'),
-    ('ㄕ', 's`'),
-    ('ㄖ', 'ɹ`'),
-    ('ㄗ', 'ʦ⁼'),
-    ('ㄘ', 'ʦʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ə'),
-    ('ㄝ', 'e'),
-    ('ㄞ', 'ai'),
-    ('ㄟ', 'ei'),
-    ('ㄠ', 'au'),
-    ('ㄡ', 'ou'),
-    ('ㄧㄢ', 'yeNN'),
-    ('ㄢ', 'aNN'),
-    ('ㄧㄣ', 'iNN'),
-    ('ㄣ', 'əNN'),
-    ('ㄤ', 'aNg'),
-    ('ㄧㄥ', 'iNg'),
-    ('ㄨㄥ', 'uNg'),
-    ('ㄩㄥ', 'yuNg'),
-    ('ㄥ', 'əNg'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'ɥ'),
-    ('ˉ', '→'),
-    ('ˊ', '↑'),
-    ('ˇ', '↓↑'),
-    ('ˋ', '↓'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-# List of (romaji, ipa) pairs:
-_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('ʃy', 'ʃ'),
-    ('ʧʰy', 'ʧʰ'),
-    ('ʧ⁼y', 'ʧ⁼'),
-    ('NN', 'n'),
-    ('Ng', 'ŋ'),
-    ('y', 'j'),
-    ('h', 'x')
-]]
-
-# List of (bopomofo, ipa) pairs:
-_bopomofo_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'p⁼wo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p⁼'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't⁼'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k⁼'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'x'),
-    ('ㄐ', 'tʃ⁼'),
-    ('ㄑ', 'tʃʰ'),
-    ('ㄒ', 'ʃ'),
-    ('ㄓ', 'ts`⁼'),
-    ('ㄔ', 'ts`ʰ'),
-    ('ㄕ', 's`'),
-    ('ㄖ', 'ɹ`'),
-    ('ㄗ', 'ts⁼'),
-    ('ㄘ', 'tsʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ə'),
-    ('ㄝ', 'ɛ'),
-    ('ㄞ', 'aɪ'),
-    ('ㄟ', 'eɪ'),
-    ('ㄠ', 'ɑʊ'),
-    ('ㄡ', 'oʊ'),
-    ('ㄧㄢ', 'jɛn'),
-    ('ㄩㄢ', 'ɥæn'),
-    ('ㄢ', 'an'),
-    ('ㄧㄣ', 'in'),
-    ('ㄩㄣ', 'ɥn'),
-    ('ㄣ', 'ən'),
-    ('ㄤ', 'ɑŋ'),
-    ('ㄧㄥ', 'iŋ'),
-    ('ㄨㄥ', 'ʊŋ'),
-    ('ㄩㄥ', 'jʊŋ'),
-    ('ㄥ', 'əŋ'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'ɥ'),
-    ('ˉ', '→'),
-    ('ˊ', '↑'),
-    ('ˇ', '↓↑'),
-    ('ˋ', '↓'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-# List of (bopomofo, ipa2) pairs:
-_bopomofo_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'pwo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'h'),
-    ('ㄐ', 'tɕ'),
-    ('ㄑ', 'tɕʰ'),
-    ('ㄒ', 'ɕ'),
-    ('ㄓ', 'tʂ'),
-    ('ㄔ', 'tʂʰ'),
-    ('ㄕ', 'ʂ'),
-    ('ㄖ', 'ɻ'),
-    ('ㄗ', 'ts'),
-    ('ㄘ', 'tsʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ɤ'),
-    ('ㄝ', 'ɛ'),
-    ('ㄞ', 'aɪ'),
-    ('ㄟ', 'eɪ'),
-    ('ㄠ', 'ɑʊ'),
-    ('ㄡ', 'oʊ'),
-    ('ㄧㄢ', 'jɛn'),
-    ('ㄩㄢ', 'yæn'),
-    ('ㄢ', 'an'),
-    ('ㄧㄣ', 'in'),
-    ('ㄩㄣ', 'yn'),
-    ('ㄣ', 'ən'),
-    ('ㄤ', 'ɑŋ'),
-    ('ㄧㄥ', 'iŋ'),
-    ('ㄨㄥ', 'ʊŋ'),
-    ('ㄩㄥ', 'jʊŋ'),
-    ('ㄥ', 'ɤŋ'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'y'),
-    ('ˉ', '˥'),
-    ('ˊ', '˧˥'),
-    ('ˇ', '˨˩˦'),
-    ('ˋ', '˥˩'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-
-def number_to_chinese(text):
-    numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-    for number in numbers:
-        text = text.replace(number, cn2an.an2cn(number), 1)
-    return text
-
-
-def chinese_to_bopomofo(text):
-    text = text.replace('、', '，').replace('；', '，').replace('：', '，')
-    words = jieba.lcut(text, cut_all=False)
-    text = ''
-    for word in words:
-        bopomofos = lazy_pinyin(word, BOPOMOFO)
-        if not re.search('[\u4e00-\u9fff]', word):
-            text += word
-            continue
-        for i in range(len(bopomofos)):
-            bopomofos[i] = re.sub(r'([\u3105-\u3129])$', r'\1ˉ', bopomofos[i])
-        if text != '':
-            text += ' '
-        text += ''.join(bopomofos)
-    return text
-
-
-def latin_to_bopomofo(text):
-    for regex, replacement in _latin_to_bopomofo:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_romaji(text):
-    for regex, replacement in _bopomofo_to_romaji:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_ipa(text):
-    for regex, replacement in _bopomofo_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_ipa2(text):
-    for regex, replacement in _bopomofo_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def chinese_to_romaji(text):
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_romaji(text)
-    text = re.sub('i([aoe])', r'y\1', text)
-    text = re.sub('u([aoəe])', r'w\1', text)
-    text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
-                  r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
-    text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
-    return text
-
-
-def chinese_to_lazy_ipa(text):
-    text = chinese_to_romaji(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def chinese_to_ipa(text):
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_ipa(text)
-    text = re.sub('i([aoe])', r'j\1', text)
-    text = re.sub('u([aoəe])', r'w\1', text)
-    text = re.sub('([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
-                  r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
-    text = re.sub('([s][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
-    return text
-
-
-def chinese_to_ipa2(text):
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_ipa2(text)
-    text = re.sub(r'i([aoe])', r'j\1', text)
-    text = re.sub(r'u([aoəe])', r'w\1', text)
-    text = re.sub(r'([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)', r'\1ʅ\2', text)
-    text = re.sub(r'(sʰ?)([˩˨˧˦˥ ]+|$)', r'\1ɿ\2', text)
-    return text
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/glibc.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/glibc.py
deleted file mode 100644
index 7bd3c20681d865cb4fa42617cf939b5512c7663f..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/glibc.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import os
-import sys
-from typing import Optional, Tuple
-
-
-def glibc_version_string() -> Optional[str]:
-    "Returns glibc version string, or None if not using glibc."
-    return glibc_version_string_confstr() or glibc_version_string_ctypes()
-
-
-def glibc_version_string_confstr() -> Optional[str]:
-    "Primary implementation of glibc_version_string using os.confstr."
-    # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
-    # to be broken or missing. This strategy is used in the standard library
-    # platform module:
-    # https://github.com/python/cpython/blob/fcf1d003bf4f0100c9d0921ff3d70e1127ca1b71/Lib/platform.py#L175-L183
-    if sys.platform == "win32":
-        return None
-    try:
-        # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17":
-        _, version = os.confstr("CS_GNU_LIBC_VERSION").split()
-    except (AttributeError, OSError, ValueError):
-        # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
-        return None
-    return version
-
-
-def glibc_version_string_ctypes() -> Optional[str]:
-    "Fallback implementation of glibc_version_string using ctypes."
-
-    try:
-        import ctypes
-    except ImportError:
-        return None
-
-    # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
-    # manpage says, "If filename is NULL, then the returned handle is for the
-    # main program". This way we can let the linker do the work to figure out
-    # which libc our process is actually using.
-    process_namespace = ctypes.CDLL(None)
-    try:
-        gnu_get_libc_version = process_namespace.gnu_get_libc_version
-    except AttributeError:
-        # Symbol doesn't exist -> therefore, we are not linked to
-        # glibc.
-        return None
-
-    # Call gnu_get_libc_version, which returns a string like "2.5"
-    gnu_get_libc_version.restype = ctypes.c_char_p
-    version_str = gnu_get_libc_version()
-    # py2 / py3 compatibility:
-    if not isinstance(version_str, str):
-        version_str = version_str.decode("ascii")
-
-    return version_str
-
-
-# platform.libc_ver regularly returns completely nonsensical glibc
-# versions. E.g. on my computer, platform says:
-#
-#   ~$ python2.7 -c 'import platform; print(platform.libc_ver())'
-#   ('glibc', '2.7')
-#   ~$ python3.5 -c 'import platform; print(platform.libc_ver())'
-#   ('glibc', '2.9')
-#
-# But the truth is:
-#
-#   ~$ ldd --version
-#   ldd (Debian GLIBC 2.22-11) 2.22
-#
-# This is unfortunate, because it means that the linehaul data on libc
-# versions that was generated by pip 8.1.2 and earlier is useless and
-# misleading. Solution: instead of using platform, use our code that actually
-# works.
-def libc_ver() -> Tuple[str, str]:
-    """Try to determine the glibc version
-
-    Returns a tuple of strings (lib, version) which default to empty strings
-    in case the lookup fails.
-    """
-    glibc_version = glibc_version_string()
-    if glibc_version is None:
-        return ("", "")
-    else:
-        return ("glibc", glibc_version)
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/Misc/mmdet_mask_rcnn_R_50_FPN_1x.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/Misc/mmdet_mask_rcnn_R_50_FPN_1x.py
deleted file mode 100644
index 0f2464be744c083985898a25f9e71d00104f689d..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/Misc/mmdet_mask_rcnn_R_50_FPN_1x.py
+++ /dev/null
@@ -1,151 +0,0 @@
-# An example config to train a mmdetection model using detectron2.
-
-from ..common.data.coco import dataloader
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.optim import SGD as optimizer
-from ..common.train import train
-
-from detectron2.modeling.mmdet_wrapper import MMDetDetector
-from detectron2.config import LazyCall as L
-
-model = L(MMDetDetector)(
-    detector=dict(
-        type="MaskRCNN",
-        pretrained="torchvision://resnet50",
-        backbone=dict(
-            type="ResNet",
-            depth=50,
-            num_stages=4,
-            out_indices=(0, 1, 2, 3),
-            frozen_stages=1,
-            norm_cfg=dict(type="BN", requires_grad=True),
-            norm_eval=True,
-            style="pytorch",
-        ),
-        neck=dict(type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5),
-        rpn_head=dict(
-            type="RPNHead",
-            in_channels=256,
-            feat_channels=256,
-            anchor_generator=dict(
-                type="AnchorGenerator",
-                scales=[8],
-                ratios=[0.5, 1.0, 2.0],
-                strides=[4, 8, 16, 32, 64],
-            ),
-            bbox_coder=dict(
-                type="DeltaXYWHBBoxCoder",
-                target_means=[0.0, 0.0, 0.0, 0.0],
-                target_stds=[1.0, 1.0, 1.0, 1.0],
-            ),
-            loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
-            loss_bbox=dict(type="L1Loss", loss_weight=1.0),
-        ),
-        roi_head=dict(
-            type="StandardRoIHead",
-            bbox_roi_extractor=dict(
-                type="SingleRoIExtractor",
-                roi_layer=dict(type="RoIAlign", output_size=7, sampling_ratio=0),
-                out_channels=256,
-                featmap_strides=[4, 8, 16, 32],
-            ),
-            bbox_head=dict(
-                type="Shared2FCBBoxHead",
-                in_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type="DeltaXYWHBBoxCoder",
-                    target_means=[0.0, 0.0, 0.0, 0.0],
-                    target_stds=[0.1, 0.1, 0.2, 0.2],
-                ),
-                reg_class_agnostic=False,
-                loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
-                loss_bbox=dict(type="L1Loss", loss_weight=1.0),
-            ),
-            mask_roi_extractor=dict(
-                type="SingleRoIExtractor",
-                roi_layer=dict(type="RoIAlign", output_size=14, sampling_ratio=0),
-                out_channels=256,
-                featmap_strides=[4, 8, 16, 32],
-            ),
-            mask_head=dict(
-                type="FCNMaskHead",
-                num_convs=4,
-                in_channels=256,
-                conv_out_channels=256,
-                num_classes=80,
-                loss_mask=dict(type="CrossEntropyLoss", use_mask=True, loss_weight=1.0),
-            ),
-        ),
-        # model training and testing settings
-        train_cfg=dict(
-            rpn=dict(
-                assigner=dict(
-                    type="MaxIoUAssigner",
-                    pos_iou_thr=0.7,
-                    neg_iou_thr=0.3,
-                    min_pos_iou=0.3,
-                    match_low_quality=True,
-                    ignore_iof_thr=-1,
-                ),
-                sampler=dict(
-                    type="RandomSampler",
-                    num=256,
-                    pos_fraction=0.5,
-                    neg_pos_ub=-1,
-                    add_gt_as_proposals=False,
-                ),
-                allowed_border=-1,
-                pos_weight=-1,
-                debug=False,
-            ),
-            rpn_proposal=dict(
-                nms_pre=2000,
-                max_per_img=1000,
-                nms=dict(type="nms", iou_threshold=0.7),
-                min_bbox_size=0,
-            ),
-            rcnn=dict(
-                assigner=dict(
-                    type="MaxIoUAssigner",
-                    pos_iou_thr=0.5,
-                    neg_iou_thr=0.5,
-                    min_pos_iou=0.5,
-                    match_low_quality=True,
-                    ignore_iof_thr=-1,
-                ),
-                sampler=dict(
-                    type="RandomSampler",
-                    num=512,
-                    pos_fraction=0.25,
-                    neg_pos_ub=-1,
-                    add_gt_as_proposals=True,
-                ),
-                mask_size=28,
-                pos_weight=-1,
-                debug=False,
-            ),
-        ),
-        test_cfg=dict(
-            rpn=dict(
-                nms_pre=1000,
-                max_per_img=1000,
-                nms=dict(type="nms", iou_threshold=0.7),
-                min_bbox_size=0,
-            ),
-            rcnn=dict(
-                score_thr=0.05,
-                nms=dict(type="nms", iou_threshold=0.5),
-                max_per_img=100,
-                mask_thr_binary=0.5,
-            ),
-        ),
-    ),
-    pixel_mean=[123.675, 116.280, 103.530],
-    pixel_std=[58.395, 57.120, 57.375],
-)
-
-dataloader.train.mapper.image_format = "RGB"  # torchvision pretrained model
-train.init_checkpoint = None  # pretrained model is loaded inside backbone
diff --git a/spaces/Axolotlily/Interpolate/README.md b/spaces/Axolotlily/Interpolate/README.md
deleted file mode 100644
index c6400b55c51292975a56d5de3261d8d9d255c7fa..0000000000000000000000000000000000000000
--- a/spaces/Axolotlily/Interpolate/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Interpolate
-emoji: 🌖
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.17
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/BasToTheMax/22h-vintedois-diffusion-v0-1/README.md b/spaces/BasToTheMax/22h-vintedois-diffusion-v0-1/README.md
deleted file mode 100644
index 9188d339026237beaba58ca5510cd43bf9170a1a..0000000000000000000000000000000000000000
--- a/spaces/BasToTheMax/22h-vintedois-diffusion-v0-1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: 22h Vintedois Diffusion V0 1
-emoji: 🦀
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Benebene/Chat-question-answering/app.py b/spaces/Benebene/Chat-question-answering/app.py
deleted file mode 100644
index 7fec02c7643da5cbcde94ffad0ce5fd81012379b..0000000000000000000000000000000000000000
--- a/spaces/Benebene/Chat-question-answering/app.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from utils import Stuff
-from test import test, test_bench
-from interface import launch_gradio
-
-s = Stuff()
-
-#test(test_bench, s)
-
-launch_gradio(s)
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Buscar En La Lista De Miembros.md b/spaces/Benson/text-generation/Examples/Buscar En La Lista De Miembros.md
deleted file mode 100644
index 86732f917063d9cd74fed45980d5c367c62e9663..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Buscar En La Lista De Miembros.md	
+++ /dev/null
@@ -1,82 +0,0 @@
-
-<h1>Ghost Rider 3 Dawn of Darkness: Todo lo que necesitas saber</h1>
-<p>Si usted es un fan del antihéroe de fuego Ghost Rider, es posible que se pregunte si hay una tercera película en las obras. La respuesta no es tan simple, ya que ha habido rumores, especulaciones y remolques hechos por fans sobre Ghost Rider 3 Dawn of Darkness, pero no hay confirmación oficial de Marvel Studios o cualquier otra compañía de producción. En este artículo, exploraremos todo lo que necesitas saber sobre Ghost Rider 3 Dawn of Darkness, incluyendo qué es, quién está en el reparto, cuál es la trama y cómo se relaciona con las películas anteriores de Ghost Rider y el Universo Cinematográfico de Marvel (MCU). </p>
-<h2>Buscar en la lista de miembros</h2><br /><p><b><b>Download File</b> &#127383; <a href="https://bltlly.com/2v6M7b">https://bltlly.com/2v6M7b</a></b></p><br /><br />
- <h2>Introducción</h2>
-<p>Ghost Rider es un personaje de cómics estadounidenses publicado por Marvel Comics. Es un ser sobrenatural que monta una motocicleta en llamas y tiene un cráneo por cabeza. También es conocido como el Espíritu de Venganza, ya que castiga a los malvados con sus poderes de fuego infernal. Ha habido varias versiones de Ghost Rider en los cómics, pero el más famoso es Johnny Blaze, un motociclista especialista que vendió su alma al diablo para salvar la vida de su padre. </p>
-<p>Ghost Rider ha aparecido en dos películas de acción en vivo hasta ahora, ambas protagonizadas por Nicolas Cage como Johnny Blaze. El primero fue lanzado en 2007 y fue dirigido por Mark Steven Johnson. El segundo fue lanzado en 2011 y fue dirigido por Mark Neveldine y Brian Taylor. Ambas películas recibieron críticas mixtas a negativas de críticos y fans, pero tuvieron éxito comercial, recaudando más de $400 millones en todo el mundo combinados. </p>
- <h3>¿Qué es Ghost Rider 3 Dawn of Darkness? </h3>
-
-<p>Uno de los trailers más populares de Ghost Rider 3 Dawn of Darkness fue subido a YouTube por Mega Movie Trailer en 2017. Presenta clips de varias películas y programas, como Blade, Constantine, Supernatural y Agents of S.H.I.E.L.D., para crear una historia mash-up que involucra a Wesley Snipes como Blade, Idris Elba como Moreau y Nicolas Cage como Johnny Blaze/ Ghost Rider. El tráiler tiene más de 2 millones de visitas y ha recibido comentarios positivos de los espectadores. </p>
-<p>Otro trailer hecho por fans para Ghost Rider 3 Dawn of Darkness fue subido a YouTube por End Of The Galaxy en 2020. Presenta clips de varias películas y programas, como Doctor Strange, Thor: Ragnarok, Avengers: Endgame y Lucifer, para crear una historia de mash-up que involucra a Benedict Cumberbatch como Doctor Strange, Chris Hemsworth como Thor, Tom Ellis como Lucifer Morningstar, y Nicolas Cage como Johnny Blaze/Ghost Rider. El trailer tiene más de 300 mil visitas y ha recibido comentarios positivos de los espectadores. </p>
- <h3>¿Quién está en el elenco de Ghost Rider 3 Dawn of Darkness? </h3>
-<p>Como Ghost Rider 3 Dawn of Darkness no es una película oficial, no hay un elenco oficial para ella. Sin embargo, en base a los trailers y carteles hechos por los fans, algunos de los actores que les gustaría ver en la película son:</p>
-<p></p>
-<ul>
-<li>Nicolas Cage como Johnny Blaze/ Ghost Rider: Cage jugó el papel en las dos primeras películas y ha expresado interés en repetirlo en el futuro. </li>
-<li>Wesley Snipes as Blade: Snipes jugó el papel en las tres primeras películas de Blade y está programado para regresar en el próximo reinicio del personaje en MCU. </li>
-<li>Idris Elba como Moreau: Elba jugó el papel en Ghost Rider: Spirit of Vengeance y también es conocido por sus papeles en el MCU, Luther y The Dark Tower.</li>
-
-<li>Chris Hemsworth como Thor: Hemsworth jugó el papel en Thor, Los Vengadores, Thor: El Mundo Oscuro, Avengers: Age of Ultron, Thor: Ragnarok, Avengers: Infinity War, Avengers: Endgame, y lo hará en Thor: Love and Thunder.<li>
-<li>Tom Ellis como Lucifer Morningstar: Ellis interpretó el papel en Lucifer, una serie de televisión basada en el personaje de DC Comics del mismo nombre. </li>
-</ul>
-<p>Por supuesto, estos son solo deseos de los fans y no miembros del reparto confirmados. Es poco probable que todos estos actores aparezcan en una película de Ghost Rider 3, especialmente porque algunos de ellos pertenecen a diferentes franquicias y estudios. Sin embargo, es divertido imaginar cómo sería una película de crossover como esta. </p>
- <h3>¿Cuál es la trama de Ghost Rider 3 Dawn of Darkness? </h3>
-<p>De nuevo, ya que Ghost Rider 3 Dawn of Darkness no es una película oficial, no hay ninguna trama oficial para ella. Sin embargo, sobre la base de los remolques y carteles hechos por fans, algunos de los posibles elementos de la trama son:</p>
-<ul>
-Johnny Blaze/Ghost Rider sigue huyendo de sus enemigos y su maldición. Es contactado por Moreau, un antiguo monje que lo ayudó en Ghost Rider: Spirit of Vengeance. Moreau le dice que hay una manera de terminar su sufrimiento y liberar su alma del diablo. </li>
-<li>La manera de hacer eso es encontrar y destruir el Libro de Cagliostro, un tomo antiguo que contiene oscuros secretos y hechizos. El libro está escondido en algún lugar de Europa y está custodiado por un culto de vampiros dirigido por Blade, un medio vampiro mitad humano que caza a su propia especie. </li>
-Johnny Blaze/ Ghost Rider se une a Moreau y otros aliados, como el Doctor Strange, Thor y Lucifer Morningstar, para encontrar el libro y enfrentar a Blade y sus secuaces. En el camino, se encuentran con varias amenazas y desafíos de fuerzas sobrenaturales y enemigos. </li>
-
-</ul>
-<p>Por supuesto, esto es solo una trama hecha por fans y no una historia oficial. Es poco probable que una película de Ghost Rider 3 siga esta historia exacta, especialmente porque involucra personajes y elementos de diferentes franquicias y estudios. Sin embargo, es divertido imaginar cómo sería una película de crossover como esta. </p>
- <h2>La historia de las películas de Ghost Rider</h2>
-<p>Antes de sumergirnos en el futuro de Ghost Rider en el MCU, echemos un vistazo a la historia de las películas de Ghost Rider. Aquí hay algunos breves resúmenes y reseñas de las dos primeras películas protagonizadas por Nicolas Cage como Johnny Blaze/Ghost Rider.</p>
- <h3>Jinete fantasma (2007)</h3>
-<h4>Sinopsis</h4>
-<p>Ghost Rider es una película de superhéroes de 2007 basada en el personaje de Marvel Comics del mismo nombre. Fue dirigida por Mark Steven Johnson y protagonizada por Nicolas Cage como Johnny Blaze/Ghost Rider, Eva Mendes como Roxanne Simpson, Wes Bentley como Blackheart, Sam Elliott como Carter Slade/Caretaker, Peter Fonda como Mephistopheles, y Donal Logue como Mack.</p>
-<p>La película cuenta la historia de origen de Johnny Blaze/ Ghost Rider, un motociclista especialista que vendió su alma a Mefistófeles para salvar la vida de su padre. Años más tarde, es llamado por Mefistófeles para detener a Blackheart, su hijo rebelde que planea desatar el infierno en la tierra. En el camino, se reúne con su amor de la infancia Roxanne Simpson, que ahora es periodista. </p>
- <h4>Recepción</h4>
-<p>Ghost Rider recibió críticas mixtas a negativas de críticos y fans. Tiene una calificación del 26% en Rotten Tomatoes basada en 173 comentarios. El consenso dice: "Ghost Rider es una mezcla amarga de triste, [asistente] (#mensaje) comedia y efectos especiales, y no puede estar a la altura de su material de origen." </p>
-<p>Algunas de las críticas de la película fueron su débil guion, mala actuación, diálogo cursi, falta de humor y tono inconsistente. Algunas de las alabanzas de la película fueron sus efectos visuales, escenas de acción y la actuación de Cage como Ghost Rider.</p>
-
- <h3>Jinete fantasma: Espíritu de venganza (2011)</h3>
-<h4>Sinopsis</h4>
-<p>Ghost Rider: Spirit of Vengeance es una película de superhéroes de 2011 basada en el personaje de Marvel Comics del mismo nombre. Fue dirigida por Mark Neveldine y Brian Taylor y protagonizada por Nicolas Cage como Johnny Blaze/Ghost Rider, Ciarán Hinds como Roarke/Mephisto, Violante Placido como Nadya Ketch, Johnny Whitworth como Ray Carrigan/Blackout, Christopher Lambert como Methodius, e Idris Elba como Moreau</p>p.
-<p>La película es una secuela de Ghost Rider, pero también un reinicio suave que ignora algunos de los eventos y personajes de la primera película. Sigue a Johnny Blaze/ Ghost Rider, que se esconde en Europa del Este y trata de controlar su maldición. Es reclutado por Moreau, un miembro de una orden religiosa secreta, para proteger a un joven llamado Danny Ketch de Roarke/ Mephisto, que quiere usarlo como un recipiente para su poder. </p>
- <h4>Recepción</h4>
-<p>Ghost Rider: Spirit of Vengeance recibió críticas negativas de críticos y fans. Tiene una calificación del 19% en Rotten Tomatoes basado en 121 comentarios. El consenso dice: "Con un guion débil, un trabajo de CG desigual, y una actuación de Nic Cage tan predeciblemente loco que ya no es divertido, Ghost Rider: Spirit of Vengeance tiene como objetivo ser diversión basura pero termina como basura." </p>
-<p>Algunas de las críticas de la película fueron su trama sin sentido, personajes sosos, acción aburrida, efectos baratos y violencia excesiva. Algunas de las alabanzas de la película fueron su tono más oscuro, su estilo más atrevido y el compromiso de Cage con el papel. </p>
-<p>La película fue un fracaso de taquilla, sin embargo, recaudando solo $ 132 millones en todo el mundo con un presupuesto de $ 57 millones. Fue una de las películas menos taquilleras basadas en un personaje de Marvel Comics. </p>
- <h2>El futuro de Ghost Rider en el MCU</h2>
-
-<p>Desde entonces, ha habido varios rumores y especulaciones sobre la participación de Ghost Rider en el MCU. Aquí están algunos de los más notables:</p>
- <h3>Ryan Gosling como Ghost Rider? </h3>
-<p>En 2016, hubo un rumor de que Ryan Gosling estaba en conversaciones para interpretar a Johnny Blaze/Ghost Rider en una nueva película que sería parte de la Fase 4 del UCM. El rumor afirmaba que Gosling estaba interesado en trabajar con Marvel Studios después de ver al Doctor Strange y que se había reunido con Kevin Feige para discutir el papel. El rumor también afirmaba que la película sería dirigida por Neil Marshall (The Descent) y que incluiría a Doctor Strange como personaje secundario. </p>
-<p>Sin embargo, este rumor nunca fue confirmado o negado por Marvel Studios o el propio Gosling. Es posible que solo fuera un deseo de los fans o un informe falso. A partir de ahora, no hay noticias oficiales o anuncio sobre Gosling jugando Ghost Rider en el MCU.</p>
- <h3>Cómo Ghost Rider podría caber en el MCU</h3>
-<p>Incluso si Gosling no está jugando Ghost Rider en el MCU, todavía hay otras formas en que el personaje podría encajar en la franquicia. Estos son algunos de ellos:</p>
-<ul>
-<li>Ghost Rider podría aparecer en Doctor Strange en el Multiverso de la Locura. Se espera que esta película explore diferentes realidades y dimensiones dentro del UCM, que podría incluir una donde exista Ghost Rider. Ghost Rider también podría tener una conexión con Scarlet Witch, que se confirma que aparece en la película y que tiene poderes de deformación de la realidad. </li>
-<li>Ghost Rider podría aparecer en Blade. Esta película está programada para reiniciar Blade como parte de la Fase 5 del UCM y la estrella Mahershala Ali como el cazador de vampiros titular. Ghost Rider podría tener un cameo o un papel secundario en esta película, ya que se ha cruzado con Blade en los cómics antes. Ghost Rider y Blade podrían unirse para luchar contra vampiros y otras amenazas sobrenaturales. </li>
-
-<li>Ghost Rider podría aparecer en su propia película en solitario o serie de televisión. Esta es la opción más obvia y deseada para muchos fans, ya que daría a Ghost Rider la oportunidad de explorar su origen, sus poderes, sus enemigos y sus aliados. Una película o serie de televisión en solitario también podría introducir una nueva versión de Ghost Rider, como Danny Ketch, Robbie Reyes o Alejandra Jones, que tienen diferentes antecedentes e historias de Johnny Blaze.</li>
-</ul>
-<p>Por supuesto, estas son solo algunas de las posibles formas en que Ghost Rider podría caber en el MCU. Hay muchos otros escenarios y conexiones potenciales que podrían ser explorados. Lo único seguro es que Ghost Rider es un personaje popular e icónico que merece la oportunidad de brillar en el MCU.</p>
- <h2>Conclusión</h2>
-<p>En conclusión, Ghost Rider 3 Dawn of Darkness no es una película oficial, sino un título y concepto hecho por fans que ha estado circulando en Internet durante años. No hay confirmación o anuncio de que tal película exista o esté en desarrollo. Sin embargo, hay muchos remolques y carteles hechos por fans que han creado algo de bombo y curiosidad entre los fans de Ghost Rider. </p>
-<p>Ghost Rider ha aparecido en dos películas de acción en vivo hasta ahora, ambas protagonizadas por Nicolas Cage como Johnny Blaze/ Ghost Rider. El primero fue lanzado en 2007 y el segundo en 2011. Ambas películas recibieron críticas mixtas a negativas de críticos y fans, pero tuvieron éxito comercial. </p>
-<p>Los derechos de Ghost Rider volvieron a Marvel Studios en 2013, abriendo nuevas posibilidades para el futuro del personaje. Ha habido varios rumores y especulaciones sobre la participación de Ghost Rider en el UCM, pero nada ha sido confirmado o anunciado todavía. Sin embargo, hay muchas maneras en que Ghost Rider podría caber en el MCU, ya sea como un cameo, un papel de apoyo, o una estrella en solitario. </p>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Ghost Rider 3 Dawn of Darkness:</p>
-<ul>
-¿Es Ghost Rider 3 Dawn of Darkness real? </b><br>No, Ghost Rider 3 Dawn of Darkness no es una película real, sino un título hecho por fans y un concepto que ha estado circulando en Internet durante años. No hay confirmación o anuncio de que tal película exista o esté en desarrollo. </li>
-<li><b> ¿Quién está jugando Ghost Rider en Ghost Rider 3 Dawn of Darkness? </b><br>Como Ghost Rider 3 Dawn of Darkness no es una película real, no hay un elenco oficial para ella. Sin embargo, basados en los trailers y carteles hechos por fans, algunos de los actores que los fans quisieran ver en la película son Nicolas Cage como Johnny Blaze/ Ghost Rider, Wesley Snipes como Blade, Idris Elba como Moreau, Benedict Cumberbatch como Doctor Strange, Chris Hemsworth como Thor, y Tom Ellis como Lucifer Morningstar.</li>
-¿Cuál es la trama de Ghost Rider 3 Dawn of Darkness? </b><br>Como Ghost Rider 3 Dawn of Darkness no es una película real, no hay ningún argumento oficial para ello. Sin embargo, sobre la base de los trailers y carteles hechos por fans, algunos de los posibles elementos de la trama son Johnny Blaze/ Ghost Rider haciendo equipo con Moreau y otros aliados para encontrar y destruir el Libro de Cagliostro, un antiguo tomo que contiene oscuros secretos y hechizos; Johnny Blaze/ Ghost Rider frente a Blade y su culto de vampiros que guardan el libro; Johnny Blaze/ Ghost Rider destruyendo el libro y liberándose de las garras del diablo. </li>
-<li><b>¿Cuándo se lanzará Ghost Rider 3 Dawn of Darkness? </b><br>Como Ghost Rider 3 Dawn of Darkness no es una película real, no hay fecha oficial para su lanzamiento. Sin embargo, basado en los remolques y carteles hechos por fans, algunas de las posibles fechas de lanzamiento son 2023, 2024 o 2025. </li>
-
-</ul>
-<p>Espero que este artículo haya respondido a sus preguntas y satisfecho su curiosidad sobre Ghost Rider 3 Dawn of Darkness. Si eres un fan de Ghost Rider, también puedes ver los cómics, los programas de televisión, los videojuegos y la mercancía relacionada con el personaje. Gracias por leer y tener un gran día! </p> 64aa2da5cf<br />
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-<br />
-<h1>Clash Mini: Un juego de mesa divertido y estratégico en el universo de choque</h1>
- <p>¿Te encanta el universo Clash y sus personajes icónicos? ¿Te gustan los juegos de estrategia que desafían tu mente y ponen a prueba tus habilidades? Si es así, quizás quieras echar un vistazo a <strong>Clash Mini</strong>, un nuevo juego de Supercell, los creadores de <strong>Clash of Clans</strong> y <strong>Clash Royale</strong>. </p>
- <p>Clash Mini es un juego de mesa de estrategia que te permite recoger, convocar y actualizar tu ejército de Minis, que son versiones en miniatura de los personajes familiares del universo Clash. Puedes llevar a tu adorable ejército a la batalla junto a héroes legendarios como el Rey Bárbaro, la Doncella de Escudo, la Reina Arquera y más. También puedes liberar poderosas unidades como Pekka, magos y arqueros mágicos para cambiar la marea de la batalla. </p>
-<h2>choque mini descarga pc</h2><br /><p><b><b>DOWNLOAD</b> ::: <a href="https://bltlly.com/2v6Kw5">https://bltlly.com/2v6Kw5</a></b></p><br /><br />
- <p>En este artículo, le diremos todo lo que necesita saber sobre Clash Mini, incluyendo lo que es, cómo jugarlo en PC, cuándo se lanzará, y cómo registrarse para la versión beta. ¡Vamos a empezar! </p>
- <h2>¿Qué es Clash Mini? </h2>
- <p>Clash Mini es un juego de elecciones, duelo y retumbar, miniaturas, héroes y habilidades, y combinaciones dinámicas y un sinfín de posibilidades. Echemos un vistazo más de cerca a cada aspecto. </p>
- <h3>Un juego de elecciones, duelo y retumbar</h3>
- <p>En Clash Mini, puedes jugar en modo 1v1 o rumble contra otros 7 jugadores. En cada modo, tienes que predecir los movimientos de tu oponente y luego armar tu estrategia ganadora y formación. Puedes colocar tus Minis en un tablero al mismo tiempo que tu oponente, y luego verlos chocar automáticamente en tiempo real. </p>
- <p>Cada juego está lleno de acción y dura menos de 5 minutos. Puedes jugar casualmente por diversión o en partidos clasificados para aumentar tu posición en la liga. También puedes completar misiones para recoger minis y desbloquear nuevas habilidades. </p>
- <h3>Un juego de miniaturas, héroes y habilidades</h3>
-
- <p>También puedes elegir entre 8 héroes que pueden liderar tu ejército. Cada héroe tiene su propia habilidad especial que puede cambiar las tornas a tu favor. Por ejemplo, el Rey Bárbaro puede cargar hacia adelante y aturdir a los enemigos con su martillo, la Doncella de Escudo puede proteger a tus Minis con su muro de escudo, y la Reina Arquera puede disparar flechas que atraviesan múltiples objetivos. </p>
- <p>Puedes personalizar a tus héroes y Minis con pieles únicas que muestran tu individualidad y estilo en el campo de batalla. </p>
-<p></p>
- <h3>Un juego de combinaciones dinámicas y un sinfín de posibilidades</h3>
- <p>Uno de los aspectos más emocionantes de Clash Mini es la variedad de estrategias y combinaciones que puedes crear con tus Minis y héroes. Puedes experimentar con diferentes formaciones, sinergias, contadores y tácticas para encontrar la mejor manera de ganar. </p>
- <p>También puedes ajustar tu estrategia en el juego con tanques, cuerpo a cuerpo y Minis a distancia dependiendo de la situación. Puedes actualizar Minis durante la batalla para activar habilidades más fuertes o intercambiarlas entre rondas para adaptarlas a los movimientos de tu oponente. </p>
- <p>Con tantas opciones y variables, cada batalla en Clash Mini es diferente e impredecible. Tienes que ser creativo y flexible para superar a tus rivales y reclamar la victoria. </p>
- <h2>¿Cómo se juega Clash Mini en PC? </h2>
- <p>Clash Mini está diseñado para ser jugado en dispositivos móviles, pero es posible que se pregunte si se puede jugar en el PC, así. La respuesta es sí, se puede! Jugar Clash Mini en PC tiene varias ventajas, como una pantalla más grande, mejores gráficos, un rendimiento más rápido y controles más cómodos. Así es como puedes hacerlo. </p>
- <h3>¿Por qué jugar Clash Mini en PC? </h3>
- <p>Jugar Clash Mini en PC puede mejorar su experiencia de juego de muchas maneras. Aquí están algunos de los beneficios de jugar Clash Mini en PC:</p>
- <ul>
-<li>Puedes disfrutar de una vista más amplia y clara del tablero y los Minis, lo que puede ayudarte a planificar mejor tus movimientos y ver los detalles de las animaciones y efectos. </li>
-
-<li>Puedes usar el teclado y el ratón para controlar el juego, lo que puede ser más preciso y conveniente que usar los dedos en una pantalla táctil. </li>
-<li>Puede acceder a otras características y aplicaciones en su PC mientras juega Clash Mini, como chatear con sus amigos, navegar por la web o transmitir su juego. </li>
-</ul>
- <h3>¿Cómo descargar e instalar Clash Mini en el PC usando un emulador? </h3>
- <p>La forma más fácil de jugar Clash Mini en PC es usar un emulador. Un emulador es un software que le permite ejecutar aplicaciones Android o iOS en su PC. Hay muchos emuladores disponibles en línea, pero recomendamos usar <strong>BlueStacks</strong>, que es uno de los emuladores más populares y confiables para juegos. </p>
- <p>Aquí están los pasos para descargar e instalar Clash Mini en el PC usando BlueStacks:</p>
- <ol>
-<li>Descargar e instalar BlueStacks desde su sitio web oficial: <a href="">https://www.bluestacks.com/</a></li>
-<li>Inicie BlueStacks e inicie sesión con su cuenta de Google. </li>
-<li>Ir a la Google Play Store o la App Store en BlueStacks y buscar Clash Mini.</li>
-<li>Haga clic en el botón Instalar y espere a que el juego se descargue e instale. </li>
-<li>Una vez instalado el juego, haga clic en el botón Abrir o encuentre el icono del juego en la pantalla de inicio de BlueStacks. </li>
-<li>Disfruta jugando Clash Mini en PC! </li>
-</ol>
- <h3>¿Cómo se juega Clash Mini en el PC con el teclado y el ratón? </h3>
- <p>Una de las ventajas de jugar Clash Mini en PC es que puedes usar tu teclado y ratón para controlar el juego. Esto puede darle más precisión y comodidad que usar los dedos en una pantalla táctil. Sin embargo, es posible que necesite ajustar algunos ajustes y asignaciones de claves para optimizar su juego. </p>
- <p>Aquí hay algunos consejos para jugar Clash Mini en el PC con el teclado y el ratón:</p>
- <ul>
-<li> Puede utilizar el ratón para arrastrar y soltar sus Minis en el tablero, así como para seleccionar su héroe y habilidades. </li>
-
-<li> Puede utilizar el teclado para girar el tablero pulsando las teclas de flecha izquierda y derecha. </li>
-<li>Puedes usar el teclado para acceder al menú, chat, configuración, tienda, perfil, misiones, liga, clan y amigos presionando las teclas correspondientes. Puede comprobar las asignaciones de teclas haciendo clic en el icono del teclado en la esquina inferior derecha de BlueStacks.</li>
-<li>Puede personalizar las asignaciones de teclas haciendo clic en el icono del teclado y luego haciendo clic en Editar. Puede arrastrar y soltar diferentes teclas en diferentes funciones o crear nuevas según sus preferencias. </li>
-</ul>
- <h2>¿Cuándo es la fecha de lanzamiento de Clash Mini? </h2>
- <p>Si te emociona jugar a Clash Mini, es posible que te estés preguntando cuándo se lanzará. La respuesta no es tan simple, ya que hay diferentes fechas de lanzamiento para diferentes regiones y plataformas. Esto es lo que sabemos hasta ahora. </p>
- <h3>La versión beta de Clash Mini</h3>
- <p>La versión beta de Clash Mini es una versión de prueba del juego que permite a los jugadores probarlo antes de su lanzamiento oficial. La versión beta de Clash Mini está disponible actualmente en países seleccionados solo para dispositivos Android. Estos países son Finlandia, Suecia, Noruega, Dinamarca, Islandia, Nueva Zelanda, Australia, Canadá, Singapur, Filipinas, Malasia, Indonesia, India, Hong Kong SAR China.</p>
- <p>La versión beta de Clash Mini no es un producto final y puede contener errores, fallas o errores. La versión beta de Clash Mini también puede sufrir cambios o actualizaciones basadas en los comentarios de los jugadores. La versión beta de Clash Mini no representa la calidad ni las características del juego final. </p>
- <h3>El global para dispositivos Android e iOS. Sin embargo, el juego podría lanzarse en diferentes regiones en diferentes momentos, dependiendo de la retroalimentación y el rendimiento de la versión beta. </p>
- <h4>¿Cómo se juega Clash Mini en PC? </h4>
-
- <h4>¿Cómo registrarse para la versión beta de Clash Mini? </h4>
-<p>Puede registrarse para la versión beta de Clash Mini visitando el sitio web de Supercell e ingresando su dirección de correo electrónico. La versión beta de Clash Mini está abierta para cualquier persona que tenga un dispositivo Android y viva en uno de los siguientes países: Finlandia, Suecia, Noruega, Dinamarca, Islandia, Nueva Zelanda, Australia, Canadá, Singapur, Filipinas, Malasia, Indonesia, India, Hong Kong SAR China. Si cumple con estos criterios, recibirá un correo electrónico de Supercell con un enlace para descargar el juego desde la Google Play Store o la App Store.</p> 64aa2da5cf<br />
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-<br />
-<h1>Descargar Clash Royale para Windows 10: Una guía completa</h1>
-<p>Si eres un fan de los juegos de estrategia en tiempo real, es posible que hayas oído hablar de <strong>Clash Royale</strong>, uno de los juegos más populares y adictivos del género. Clash Royale es un juego desarrollado por Supercell, la misma compañía detrás del exitoso juego <strong>Clash of Clans</strong>. En este juego, puedes recoger y actualizar docenas de cartas con tus personajes y hechizos favoritos de Clash, y usarlos para luchar contra otros jugadores en línea en partidas trepidantes y emocionantes. También puedes unirte o crear un clan, chatear con otros jugadores y participar en guerras de clanes para ganar recompensas y gloria. </p>
-<h2>descargar fonte clash royale</h2><br /><p><b><b>Download Zip</b> &#10001; &#10001; &#10001; <a href="https://bltlly.com/2v6JO9">https://bltlly.com/2v6JO9</a></b></p><br /><br />
-<p>Clash Royale está disponible para dispositivos Android e iOS, pero ¿qué pasa si desea reproducirlo en su PC con Windows 10? Bueno, hay dos maneras de hacer eso, y te mostraremos cómo en este artículo. Pero primero, echemos un vistazo a algunas de las características de Clash Royale que lo hacen tan divertido y atractivo. </p>
- <h2>¿Qué es Clash Royale? </h2>
-<p>Clash Royale es un juego multijugador en tiempo real que combina elementos de juegos de cartas, torre de defensa y MOBA (campo de batalla multijugador en línea). El juego se desarrolla en el mismo universo que Clash of Clans, pero con un estilo de juego diferente. El juego consta de dos modos: modo escalera y modo torneo. En el modo escalera, puedes jugar contra otros jugadores de nivel de habilidad similar y ganar trofeos, que determinan tu rango en la clasificación global. En el modo torneo, puedes unirte o crear torneos personalizados con diferentes reglas y premios. </p>
-
- <h3>Características de Clash Royale</h3>
-<p>Algunas de las características que hacen de Clash Royale un juego emocionante y adictivo son:</p>
-<ul>
-<li><strong>Duelistas de todo el mundo</strong>: Puedes desafiar a cualquiera en línea en tiempo real y mostrar tus habilidades y estrategias. También puedes ver las repeticiones de batallas de otros jugadores y aprender de sus movimientos. </li>
-<li><strong>Gana cofres para desbloquear recompensas</strong>: Cada vez que ganes una partida, recibirás un cofre que contenga cartas, oro, gemas u otros objetos. Puede utilizar estos recursos para actualizar sus tarjetas o comprar nuevas en la tienda. Hay diferentes tipos de cofres, como el de plata, oro, gigante, mágico, épico, legendario y de clan. </li>
-<li><strong>Recoge y actualiza docenas de cartas</strong>: Puedes recoger cartas de diferentes arenas, cada una con su propio tema y personajes. Hay cuatro rarezas de cartas: comunes, raras, épicas y legendarias. Puede actualizar sus tarjetas mediante el uso de oro y tarjetas duplicadas para aumentar su nivel y poder. </li>
-<li><strong>Crear o unirse a un clan</strong>: Puedes <p>Crear o unirse a un clan</strong>: Puedes unir fuerzas con otros jugadores y formar un clan, donde puedes chatear, donar cartas, solicitar cartas y participar en guerras de clanes. Las guerras de clanes son un modo especial donde puedes competir con otros clanes por la gloria y las recompensas. También puedes crear tu propio clan e invitar a tus amigos a unirse. </li>
-<li><strong>Progresa a través de múltiples arenas</strong>: A medida que ganes partidos y ganes trofeos, desbloquearás nuevas arenas, cada una con su propio tema y grupo de cartas. Hay 13 arenas en total, además de una arena legendaria especial para los mejores jugadores. Cada arena tiene sus propias recompensas y desafíos. </li>
-
-</ul>
- <h3>Cómo jugar Clash Royale en Windows 10</h3>
-<p>Ahora que sabe lo que es Clash Royale y lo que ofrece, es posible que se pregunte cómo jugarlo en su PC con Windows 10. Bueno, hay dos métodos que puedes usar para hacer eso: usar un emulador o usar un sitio web. Veamos cómo funciona cada método y cuáles son los pros y los contras de cada uno. </p>
- <h2>Cómo descargar Clash Royale para Windows 10</h2>
- <h3>Método 1: Usando el emulador de Bluestacks</h3>
-<p>El primer método es utilizar un emulador, que es un software que le permite ejecutar aplicaciones Android en su PC. Hay muchos emuladores disponibles en línea, pero uno de los más populares y confiables es <strong>Bluestacks</strong>. Bluestacks es un emulador gratuito que tiene una interfaz fácil de usar y es compatible con muchos juegos y aplicaciones de Android, incluyendo Clash Royale. Estos son los pasos que debe seguir para descargar Clash Royale para Windows 10 usando Bluestacks:</p>
- <h4>Paso 1: Descargar e instalar Bluestacks</h4>
-<p>Lo primero que tienes que hacer es descargar Bluestacks desde su sitio web oficial: <a href="">https://www.bluestacks.com/</a>. Verá un botón de descarga en la página de inicio que detectará automáticamente su sistema operativo y descargará la versión adecuada para usted. Una vez finalizada la descarga, ejecute el instalador y siga las instrucciones para instalar Bluestacks en su PC.</p>
- <h4>Paso 2: Inicie Bluestacks e inicie sesión con la cuenta de Google</h4>
-<p>Después de instalar Bluestacks, inicie desde su escritorio o menú de inicio. Verás una pantalla de bienvenida que te pedirá que inicies sesión con tu cuenta de Google. Esto es necesario porque usted necesita para acceder a la Google Play Store para descargar Clash Royale. Si no tienes una cuenta de Google, puedes crear una gratis. Una vez que inicie sesión, verá la pantalla de inicio de Bluestacks, que parece una tableta Android. </p>
- <h4>Paso 3: Buscar Clash Royale en la Play Store e instalarlo</h4>
-
- <h4>Paso 4: Disfruta jugando Clash Royale en tu PC</h4>
-<p>Una vez realizada la instalación, verá un botón "Abrir" en la página Clash Royale. Haga clic en él para iniciar Clash Royale en su PC. Verás la pantalla de carga del juego y el menú principal. Ahora puedes jugar a Clash Royale en tu PC con el ratón y el teclado. También puede ajustar la configuración, como sonido, gráficos, idioma, etc., haciendo clic en el icono de engranaje en la esquina superior derecha de la pantalla. </p>
-<p></p>
- <h3>Método 2: Usando Filehippo.com</h3>
-<p>El segundo método es utilizar un sitio web que ofrece descargas gratuitas de aplicaciones de Android para PC. Uno de estos sitios web es <strong>Filehippo.com</strong>, que tiene una gran colección de juegos y aplicaciones para Android que puedes descargar e instalar en tu PC sin usar un emulador. Estos son los pasos que debe seguir para descargar Clash Royale para Windows 10 usando Filehippo.com:</p>
- <h4>Paso 1: Vaya a Filehippo.com y busque Clash Royale</h4>
-<p>Lo primero que debe hacer es ir a Filehippo.com desde su navegador web: < a href=">https://filehippo.com/</a>. Verá una barra de búsqueda en la parte superior de la página principal. Escriba "Clash Royale" en la barra de búsqueda y pulse enter. Verás la aplicación Clash Royale entre los resultados de búsqueda. Haz clic en ella para abrir su página. </p>
- <h4>Paso 2: Haga clic en el botón de descarga y guarde el archivo</h4>
-<p>En la página Clash Royale, verá un botón verde "Descargar la última versión" en el lado derecho de la pantalla. Haga clic en él para comenzar a descargar el archivo Clash Royale. Verá una ventana emergente que le pedirá que guarde el archivo. Elija una ubicación en su PC donde desea guardar el archivo y haga clic en "Guardar". El tamaño del archivo es de aproximadamente 110 MB, por lo que podría tomar algún tiempo dependiendo de su velocidad de Internet. </p>
- <h4>Paso 3: Ejecute el archivo y siga las instrucciones para instalar Clash Royale</h4>
-
- <h4>Paso 4: Inicie Clash Royale y comience a jugar</h4>
-<p>Después de la instalación, verá un icono de acceso directo de Clash Royale en su escritorio o menú de inicio. Haga clic en él para lanzar Clash Royale en su PC. Verá la pantalla de carga del juego y luego el menú principal. Ahora puede jugar Clash Royale en su PC con el ratón y el teclado. También puede ajustar la configuración, como sonido, gráficos, idioma, etc., haciendo clic en el icono de engranaje en la esquina superior derecha de la pantalla. </p>
- <h2>Conclusión</h2>
-<p>Clash Royale es uno de los juegos de estrategia en tiempo real más populares y adictivos que puedes jugar en tu dispositivo Android o iOS. Pero si quieres disfrutarlo en una pantalla más grande y con mejores controles, también puedes reproducirlo en tu PC con Windows 10 usando uno de los dos métodos que te mostramos en este artículo: usando el emulador de Bluestacks o usando Filehippo.com. Ambos métodos son fáciles y gratuitos, y te permitirán descargar e instalar Clash Royale para Windows 10 en poco tiempo. Entonces, ¿qué estás esperando? Descargar Clash Royale para Windows 10 hoy y unirse a millones de jugadores de todo el mundo en batallas épicas y torneos! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Clash Royale para Windows 10:</p>
-<ul>
-<li><strong>Clash Royale es libre de jugar? </strong></li>
-<p>Sí, Clash Royale es gratis para jugar, pero también ofrece compras en la aplicación que pueden mejorar su experiencia de juego. Puedes comprar gemas, oro, cofres, tarjetas u otros artículos con dinero real. Sin embargo, estas compras son opcionales y no se requieren para jugar o progresar en el juego. </p>
-<li><strong>¿Es seguro descargar Clash Royale? </strong></li>
-
-<li><strong>¿Puedo jugar a Clash Royale sin conexión? </strong></li>
-<p>No, Clash Royale requiere una conexión a Internet para jugar, ya que es un juego multijugador que te conecta con otros jugadores en línea. Necesitas tener una conexión a Internet estable y rápida para jugar a Clash Royale sin ningún retraso o interrupción. </p>
-<li><strong>¿Puedo sincronizar mi progreso entre mi dispositivo y PC? </strong></li>
-<p>Sí, puedes sincronizar tu progreso entre tu dispositivo y PC usando tu cuenta de Google. Es necesario iniciar sesión con la misma cuenta de Google en su dispositivo y PC al jugar Clash Royale. De esta forma, puedes acceder a tus datos de juego, como tus cartas, oro, gemas, trofeos, clan, etc., en ambas plataformas. </p>
-<li><strong>¿Puedo jugar a Clash Royale con mis amigos? </strong></li>
-<p>Sí, puedes jugar a Clash Royale con tus amigos uniéndote o creando un clan. Un clan es un grupo de jugadores que pueden chatear, donar cartas, solicitar cartas y participar en guerras de clanes juntos. Puedes invitar a tus amigos a unirse a tu clan o unirse a su clan usando su nombre o etiqueta de clan. También puedes retar a tus amigos a batallas amistosas o ver sus partidos tocando su nombre en el chat del clan. </p>
-</ul>
- <p></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/__init__.py
deleted file mode 100644
index 962173c8d0a6906b59f2910c9cae759010534786..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/__init__.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2012-2022 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-import logging
-
-__version__ = '0.3.6'
-
-class DistlibException(Exception):
-    pass
-
-try:
-    from logging import NullHandler
-except ImportError: # pragma: no cover
-    class NullHandler(logging.Handler):
-        def handle(self, record): pass
-        def emit(self, record): pass
-        def createLock(self): self.lock = None
-
-logger = logging.getLogger(__name__)
-logger.addHandler(NullHandler())
diff --git a/spaces/CVPR/LIVE/pybind11/tools/check-style.sh b/spaces/CVPR/LIVE/pybind11/tools/check-style.sh
deleted file mode 100644
index f7af2a4169744334af0b9c28823e98a502b813be..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/tools/check-style.sh
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/bin/bash
-#
-# Script to check include/test code for common pybind11 code style errors.
-#
-# This script currently checks for
-#
-# 1. missing space between keyword and parenthesis, e.g.: for(, if(, while(
-# 2. Missing space between right parenthesis and brace, e.g. 'for (...){'
-# 3. opening brace on its own line. It should always be on the same line as the
-#    if/while/for/do statement.
-#
-# Invoke as: tools/check-style.sh <filenames>
-#
-
-check_style_errors=0
-IFS=$'\n'
-
-
-found="$(grep '\<\(if\|for\|while\|catch\)(\|){' $@ -rn --color=always)"
-if [ -n "$found" ]; then
-    echo -e '\033[31;01mError: found the following coding style problems:\033[0m'
-    check_style_errors=1
-    echo "$found" | sed -e 's/^/    /'
-fi
-
-found="$(awk '
-function prefix(filename, lineno) {
-    return "    \033[35m" filename "\033[36m:\033[32m" lineno "\033[36m:\033[0m"
-}
-function mark(pattern, string) { sub(pattern, "\033[01;31m&\033[0m", string); return string }
-last && /^\s*{/ {
-    print prefix(FILENAME, FNR-1) mark("\\)\\s*$", last)
-    print prefix(FILENAME, FNR)   mark("^\\s*{", $0)
-    last=""
-}
-{ last = /(if|for|while|catch|switch)\s*\(.*\)\s*$/ ? $0 : "" }
-' $(find include -type f) $@)"
-if [ -n "$found" ]; then
-    check_style_errors=1
-    echo -e '\033[31;01mError: braces should occur on the same line as the if/while/.. statement. Found issues in the following files:\033[0m'
-    echo "$found"
-fi
-
-exit $check_style_errors
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/transform_reduce.h b/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/transform_reduce.h
deleted file mode 100644
index c6ae90664ad9538e73febfde86c334011de417c8..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/transform_reduce.h
+++ /dev/null
@@ -1,22 +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 has no special version of this algorithm 
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/unique.h b/spaces/CVPR/LIVE/thrust/thrust/unique.h
deleted file mode 100644
index b4b2118d321374e2dac04592914d33b2003fad8a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/unique.h
+++ /dev/null
@@ -1,968 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file unique.h
- *  \brief Move unique elements to the front of a range
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/pair.h>
-
-namespace thrust
-{
-
-
-/*! \addtogroup stream_compaction
- *  \{
- */
-
-
-/*! For each group of consecutive elements in the range <tt>[first, last)</tt>
- *  with the same value, \p unique removes all but the first element of 
- *  the group. The return value is an iterator \c new_last such that 
- *  no two consecutive elements in the range <tt>[first, new_last)</tt> are
- *  equal. The iterators in the range <tt>[new_last, last)</tt> are all still
- *  dereferenceable, but the elements that they point to are unspecified.
- *  \p unique is stable, meaning that the relative order of elements that are
- *  not removed is unchanged.
- *
- *  This version of \p unique uses \c operator== to test for equality.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the input range.
- *  \param last  The end of the input range.
- *  \return The end of the unique range <tt>[first, new_last)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator is mutable,
- *          and \p ForwardIterator's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *
- *  The following code snippet demonstrates how to use \p unique to
- *  compact a sequence of numbers to remove consecutive duplicates using the \p thrust::host execution policy
- *  for parallelization:
- *
- *  \code
- *  #include <thrust/unique.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1};
- *  int *new_end = thrust::unique(thrust::host, A, A + N);
- *  // The first four values of A are now {1, 3, 2, 1}
- *  // Values beyond new_end are unspecified.
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/unique.html
- *  \see unique_copy
- */
-template<typename DerivedPolicy,
-         typename ForwardIterator>
-__host__ __device__
-ForwardIterator unique(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                       ForwardIterator first,
-                       ForwardIterator last);
-
-
-/*! For each group of consecutive elements in the range <tt>[first, last)</tt>
- *  with the same value, \p unique removes all but the first element of 
- *  the group. The return value is an iterator \c new_last such that 
- *  no two consecutive elements in the range <tt>[first, new_last)</tt> are
- *  equal. The iterators in the range <tt>[new_last, last)</tt> are all still
- *  dereferenceable, but the elements that they point to are unspecified.
- *  \p unique is stable, meaning that the relative order of elements that are
- *  not removed is unchanged.
- *
- *  This version of \p unique uses \c operator== to test for equality.
- *
- *  \param first The beginning of the input range.
- *  \param last  The end of the input range.
- *  \return The end of the unique range <tt>[first, new_last)</tt>.
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator is mutable,
- *          and \p ForwardIterator's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *
- *  The following code snippet demonstrates how to use \p unique to
- *  compact a sequence of numbers to remove consecutive duplicates.
- *
- *  \code
- *  #include <thrust/unique.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1};
- *  int *new_end = thrust::unique(A, A + N);
- *  // The first four values of A are now {1, 3, 2, 1}
- *  // Values beyond new_end are unspecified.
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/unique.html
- *  \see unique_copy
- */
-template<typename ForwardIterator>
-ForwardIterator unique(ForwardIterator first,
-                       ForwardIterator last);
-
-
-/*! For each group of consecutive elements in the range <tt>[first, last)</tt>
- *  with the same value, \p unique removes all but the first element of 
- *  the group. The return value is an iterator \c new_last such that 
- *  no two consecutive elements in the range <tt>[first, new_last)</tt> are
- *  equal. The iterators in the range <tt>[new_last, last)</tt> are all still
- *  dereferenceable, but the elements that they point to are unspecified.
- *  \p unique is stable, meaning that the relative order of elements that are
- *  not removed is unchanged.
- *
- *  This version of \p unique uses the function object \p binary_pred to test
- *  for equality.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the input range.
- *  \param last  The end of the input range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \return The end of the unique range <tt>[first, new_last)</tt>
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator is mutable,
- *          and \p ForwardIterator's \c value_type is convertible to \p BinaryPredicate's \c first_argument_type and to \p BinaryPredicate's \c second_argument_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p unique to
- *  compact a sequence of numbers to remove consecutive duplicates using the \p thrust::host execution policy
- *  for parallelization:
- *
- *  \code
- *  #include <thrust/unique.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1};
- *  int *new_end = thrust::unique(thrust::host, A, A + N, thrust::equal_to<int>());
- *  // The first four values of A are now {1, 3, 2, 1}
- *  // Values beyond new_end are unspecified.
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/unique.html
- *  \see unique_copy
- */
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename BinaryPredicate>
-__host__ __device__
-ForwardIterator unique(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                       ForwardIterator first,
-                       ForwardIterator last,
-                       BinaryPredicate binary_pred);
-
-
-/*! For each group of consecutive elements in the range <tt>[first, last)</tt>
- *  with the same value, \p unique removes all but the first element of 
- *  the group. The return value is an iterator \c new_last such that 
- *  no two consecutive elements in the range <tt>[first, new_last)</tt> are
- *  equal. The iterators in the range <tt>[new_last, last)</tt> are all still
- *  dereferenceable, but the elements that they point to are unspecified.
- *  \p unique is stable, meaning that the relative order of elements that are
- *  not removed is unchanged.
- *
- *  This version of \p unique uses the function object \p binary_pred to test
- *  for equality.
- *
- *  \param first The beginning of the input range.
- *  \param last  The end of the input range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \return The end of the unique range <tt>[first, new_last)</tt>
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator is mutable,
- *          and \p ForwardIterator's \c value_type is convertible to \p BinaryPredicate's \c first_argument_type and to \p BinaryPredicate's \c second_argument_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p unique to
- *  compact a sequence of numbers to remove consecutive duplicates.
- *
- *  \code
- *  #include <thrust/unique.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1};
- *  int *new_end = thrust::unique(A, A + N, thrust::equal_to<int>());
- *  // The first four values of A are now {1, 3, 2, 1}
- *  // Values beyond new_end are unspecified.
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/unique.html
- *  \see unique_copy
- */
-template<typename ForwardIterator,
-         typename BinaryPredicate>
-ForwardIterator unique(ForwardIterator first,
-                       ForwardIterator last,
-                       BinaryPredicate binary_pred);
-
-
-/*! \p unique_copy copies elements from the range <tt>[first, last)</tt>
- * to a range beginning with \p result, except that in a consecutive group
- * of duplicate elements only the first one is copied. The return value
- * is the end of the range to which the elements are copied. 
- *
- * The reason there are two different versions of unique_copy is that there
- * are two different definitions of what it means for a consecutive group of
- * elements to be duplicates. In the first version, the test is simple
- * equality: the elements in a range <tt>[f, l)</tt> are duplicates if,
- * for every iterator \p i in the range, either <tt>i == f</tt> or else 
- * <tt>*i == *(i-1)</tt>. In the second, the test is an arbitrary 
- * \p BinaryPredicate \p binary_pred: the elements in <tt>[f, l)</tt> are
- * duplicates if, for every iterator \p i in the range, either <tt>i == f</tt>
- * or else <tt>binary_pred(*i, *(i-1))</tt> is \p true.
- *
- * This version of \p unique_copy uses \c operator== to test for equality.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the input range.
- *  \param last  The end of the input range.
- *  \param result The beginning of the output range.
- *  \return The end of the unique range <tt>[result, result_end)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator's \c value_type is convertible to \c OutputIterator's \c value_type.
- *
- *  \pre The range <tt>[first,last)</tt> and the range <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p unique_copy to
- *  compact a sequence of numbers to remove consecutive duplicates using the \p thrust::host execution
- *  policy for parallelization:
- *
- *  \code
- *  #include <thrust/unique.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1};
- *  int B[N];
- *  int *result_end = thrust::unique_copy(thrust::host, A, A + N, B);
- *  // The first four values of B are now {1, 3, 2, 1} and (result_end - B) is 4
- *  // Values beyond result_end are unspecified
- *  \endcode
- *
- *  \see unique
- *  \see http://www.sgi.com/tech/stl/unique_copy.html
- */
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename OutputIterator>
-__host__ __device__
-OutputIterator unique_copy(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                           InputIterator first,
-                           InputIterator last,
-                           OutputIterator result);
-
-
-/*! \p unique_copy copies elements from the range <tt>[first, last)</tt>
- * to a range beginning with \p result, except that in a consecutive group
- * of duplicate elements only the first one is copied. The return value
- * is the end of the range to which the elements are copied. 
- *
- * The reason there are two different versions of unique_copy is that there
- * are two different definitions of what it means for a consecutive group of
- * elements to be duplicates. In the first version, the test is simple
- * equality: the elements in a range <tt>[f, l)</tt> are duplicates if,
- * for every iterator \p i in the range, either <tt>i == f</tt> or else 
- * <tt>*i == *(i-1)</tt>. In the second, the test is an arbitrary 
- * \p BinaryPredicate \p binary_pred: the elements in <tt>[f, l)</tt> are
- * duplicates if, for every iterator \p i in the range, either <tt>i == f</tt>
- * or else <tt>binary_pred(*i, *(i-1))</tt> is \p true.
- *
- * This version of \p unique_copy uses \c operator== to test for equality.
- *
- *  \param first The beginning of the input range.
- *  \param last  The end of the input range.
- *  \param result The beginning of the output range.
- *  \return The end of the unique range <tt>[result, result_end)</tt>.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator's \c value_type is convertible to \c OutputIterator's \c value_type.
- *
- *  \pre The range <tt>[first,last)</tt> and the range <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p unique_copy to
- *  compact a sequence of numbers to remove consecutive duplicates.
- *
- *  \code
- *  #include <thrust/unique.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1};
- *  int B[N];
- *  int *result_end = thrust::unique_copy(A, A + N, B);
- *  // The first four values of B are now {1, 3, 2, 1} and (result_end - B) is 4
- *  // Values beyond result_end are unspecified
- *  \endcode
- *
- *  \see unique
- *  \see http://www.sgi.com/tech/stl/unique_copy.html
- */
-template<typename InputIterator,
-         typename OutputIterator>
-OutputIterator unique_copy(InputIterator first,
-                           InputIterator last,
-                           OutputIterator result);
-
-
-/*! \p unique_copy copies elements from the range <tt>[first, last)</tt>
- * to a range beginning with \p result, except that in a consecutive group
- * of duplicate elements only the first one is copied. The return value
- * is the end of the range to which the elements are copied. 
- *
- * This version of \p unique_copy uses the function object \c binary_pred 
- * to test for equality.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the input range.
- *  \param last  The end of the input range.
- *  \param result The beginning of the output range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \return The end of the unique range <tt>[result, result_end)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator's \c value_type is convertible to \c OutputIterator's \c value_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  \pre The range <tt>[first,last)</tt> and the range <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p unique_copy to
- *  compact a sequence of numbers to remove consecutive duplicates using the \p thrust::host execution
- *  policy for parallelization:
- *
- *  \code
- *  #include <thrust/unique.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1};
- *  int B[N];
- *  int *result_end = thrust::unique_copy(thrust::host, A, A + N, B, thrust::equal_to<int>());
- *  // The first four values of B are now {1, 3, 2, 1} and (result_end - B) is 4
- *  // Values beyond result_end are unspecified.
- *  \endcode
- *
- *  \see unique
- *  \see http://www.sgi.com/tech/stl/unique_copy.html
- */
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename OutputIterator,
-         typename BinaryPredicate>
-__host__ __device__
-OutputIterator unique_copy(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                           InputIterator first,
-                           InputIterator last,
-                           OutputIterator result,
-                           BinaryPredicate binary_pred);
-                       
-
-/*! \p unique_copy copies elements from the range <tt>[first, last)</tt>
- * to a range beginning with \p result, except that in a consecutive group
- * of duplicate elements only the first one is copied. The return value
- * is the end of the range to which the elements are copied. 
- *
- * This version of \p unique_copy uses the function object \c binary_pred 
- * to test for equality.
- *
- *  \param first The beginning of the input range.
- *  \param last  The end of the input range.
- *  \param result The beginning of the output range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \return The end of the unique range <tt>[result, result_end)</tt>.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator's \c value_type is convertible to \c OutputIterator's \c value_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  \pre The range <tt>[first,last)</tt> and the range <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p unique_copy to
- *  compact a sequence of numbers to remove consecutive duplicates.
- *
- *  \code
- *  #include <thrust/unique.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1};
- *  int B[N];
- *  int *result_end = thrust::unique_copy(A, A + N, B, thrust::equal_to<int>());
- *  // The first four values of B are now {1, 3, 2, 1} and (result_end - B) is 4
- *  // Values beyond result_end are unspecified.
- *  \endcode
- *
- *  \see unique
- *  \see http://www.sgi.com/tech/stl/unique_copy.html
- */
-template<typename InputIterator,
-         typename OutputIterator,
-         typename BinaryPredicate>
-OutputIterator unique_copy(InputIterator first,
-                           InputIterator last,
-                           OutputIterator result,
-                           BinaryPredicate binary_pred);
-
-
-/*! \p unique_by_key is a generalization of \p unique to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p unique_by_key removes all but the first element of 
- *  the group.  Similarly, the corresponding values in the range
- *  <tt>[values_first, values_first + (keys_last - keys_first))</tt> 
- *  are also removed.
- *
- *  The return value is a \p pair of iterators <tt>(new_keys_last,new_values_last)</tt>
- *  such that no two consecutive elements in the range <tt>[keys_first, new_keys_last)</tt>
- *  are equal.
- *
- *  This version of \p unique_by_key uses \c operator== to test for equality and 
- *  \c project1st to reduce values with equal keys.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param keys_first The beginning of the key range.
- *  \param keys_last  The end of the key range.
- *  \param values_first The beginning of the value range.
- *  \return A pair of iterators at end of the ranges <tt>[key_first, keys_new_last)</tt> and <tt>[values_first, values_new_last)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator1 is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator1 is mutable,
- *          and \p ForwardIterator's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam ForwardIterator2 is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator2 is mutable.
- *
- *  \pre The range <tt>[keys_first, keys_last)</tt> and the range <tt>[values_first, values_first + (keys_last - keys_first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p unique_by_key to
- *  compact a sequence of key/value pairs to remove consecutive duplicates using the \p thrust::host
- *  execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/unique.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // values
- *
- *  thrust::pair<int*,int*> new_end;
- *  new_end = thrust::unique_by_key(thrust::host, A, A + N, B);
- *
- *  // The first four keys in A are now {1, 3, 2, 1} and new_end.first - A is 4.
- *  // The first four values in B are now {9, 8, 5, 3} and new_end.second - B is 4.
- *  \endcode
- *
- *  \see unique
- *  \see unique_by_key_copy
- *  \see reduce_by_key
- */
-template<typename DerivedPolicy,
-         typename ForwardIterator1,
-         typename ForwardIterator2>
-__host__ __device__
-  thrust::pair<ForwardIterator1,ForwardIterator2>
-  unique_by_key(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                ForwardIterator1 keys_first, 
-                ForwardIterator1 keys_last,
-                ForwardIterator2 values_first);
-
-
-/*! \p unique_by_key is a generalization of \p unique to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p unique_by_key removes all but the first element of 
- *  the group.  Similarly, the corresponding values in the range
- *  <tt>[values_first, values_first + (keys_last - keys_first))</tt> 
- *  are also removed.
- *
- *  The return value is a \p pair of iterators <tt>(new_keys_last,new_values_last)</tt>
- *  such that no two consecutive elements in the range <tt>[keys_first, new_keys_last)</tt>
- *  are equal.
- *
- *  This version of \p unique_by_key uses \c operator== to test for equality and 
- *  \c project1st to reduce values with equal keys.
- *
- *  \param keys_first The beginning of the key range.
- *  \param keys_last  The end of the key range.
- *  \param values_first The beginning of the value range.
- *  \return A pair of iterators at end of the ranges <tt>[key_first, keys_new_last)</tt> and <tt>[values_first, values_new_last)</tt>.
- *
- *  \tparam ForwardIterator1 is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator1 is mutable,
- *          and \p ForwardIterator's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam ForwardIterator2 is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator2 is mutable.
- *
- *  \pre The range <tt>[keys_first, keys_last)</tt> and the range <tt>[values_first, values_first + (keys_last - keys_first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p unique_by_key to
- *  compact a sequence of key/value pairs to remove consecutive duplicates.
- *
- *  \code
- *  #include <thrust/unique.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // values
- *
- *  thrust::pair<int*,int*> new_end;
- *  new_end = thrust::unique_by_key(A, A + N, B);
- *
- *  // The first four keys in A are now {1, 3, 2, 1} and new_end.first - A is 4.
- *  // The first four values in B are now {9, 8, 5, 3} and new_end.second - B is 4.
- *  \endcode
- *
- *  \see unique
- *  \see unique_by_key_copy
- *  \see reduce_by_key
- */
-template<typename ForwardIterator1,
-         typename ForwardIterator2>
-  thrust::pair<ForwardIterator1,ForwardIterator2>
-  unique_by_key(ForwardIterator1 keys_first, 
-                ForwardIterator1 keys_last,
-                ForwardIterator2 values_first);
-
-
-/*! \p unique_by_key is a generalization of \p unique to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p unique_by_key removes all but the first element of 
- *  the group.  Similarly, the corresponding values in the range
- *  <tt>[values_first, values_first + (keys_last - keys_first))</tt> 
- *  are also removed.
- *
- *  This version of \p unique_by_key uses the function object \c binary_pred
- *  to test for equality and \c project1st to reduce values with equal keys.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param keys_first The beginning of the key range.
- *  \param keys_last  The end of the key range.
- *  \param values_first The beginning of the value range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \return The end of the unique range <tt>[first, new_last)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator1 is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator1 is mutable,
- *          and \p ForwardIterator's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam ForwardIterator2 is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator2 is mutable.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  \pre The range <tt>[keys_first, keys_last)</tt> and the range <tt>[values_first, values_first + (keys_last - keys_first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p unique_by_key to
- *  compact a sequence of key/value pairs to remove consecutive duplicates using the \p thrust::host
- *  execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/unique.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // values
- *
- *  thrust::pair<int*,int*> new_end;
- *  thrust::equal_to<int> binary_pred;
- *  new_end = thrust::unique_by_key(thrust::host, keys, keys + N, values, binary_pred);
- *
- *  // The first four keys in A are now {1, 3, 2, 1} and new_end.first - A is 4.
- *  // The first four values in B are now {9, 8, 5, 3} and new_end.second - B is 4.
- *  \endcode
- *
- *  \see unique
- *  \see unique_by_key_copy
- *  \see reduce_by_key
- */
-template<typename DerivedPolicy,
-         typename ForwardIterator1,
-         typename ForwardIterator2,
-         typename BinaryPredicate>
-__host__ __device__
-  thrust::pair<ForwardIterator1,ForwardIterator2>
-    unique_by_key(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                  ForwardIterator1 keys_first, 
-                  ForwardIterator1 keys_last,
-                  ForwardIterator2 values_first,
-                  BinaryPredicate binary_pred);
-
-
-/*! \p unique_by_key is a generalization of \p unique to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p unique_by_key removes all but the first element of 
- *  the group.  Similarly, the corresponding values in the range
- *  <tt>[values_first, values_first + (keys_last - keys_first))</tt> 
- *  are also removed.
- *
- *  This version of \p unique_by_key uses the function object \c binary_pred
- *  to test for equality and \c project1st to reduce values with equal keys.
- *
- *  \param keys_first The beginning of the key range.
- *  \param keys_last  The end of the key range.
- *  \param values_first The beginning of the value range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \return The end of the unique range <tt>[first, new_last)</tt>.
- *
- *  \tparam ForwardIterator1 is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator1 is mutable,
- *          and \p ForwardIterator's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam ForwardIterator2 is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator2 is mutable.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  \pre The range <tt>[keys_first, keys_last)</tt> and the range <tt>[values_first, values_first + (keys_last - keys_first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p unique_by_key to
- *  compact a sequence of key/value pairs to remove consecutive duplicates.
- *
- *  \code
- *  #include <thrust/unique.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // values
- *
- *  thrust::pair<int*,int*> new_end;
- *  thrust::equal_to<int> binary_pred;
- *  new_end = thrust::unique_by_key(keys, keys + N, values, binary_pred);
- *
- *  // The first four keys in A are now {1, 3, 2, 1} and new_end.first - A is 4.
- *  // The first four values in B are now {9, 8, 5, 3} and new_end.second - B is 4.
- *  \endcode
- *
- *  \see unique
- *  \see unique_by_key_copy
- *  \see reduce_by_key
- */
-template<typename ForwardIterator1,
-         typename ForwardIterator2,
-         typename BinaryPredicate>
-  thrust::pair<ForwardIterator1,ForwardIterator2>
-  unique_by_key(ForwardIterator1 keys_first, 
-                ForwardIterator1 keys_last,
-                ForwardIterator2 values_first,
-                BinaryPredicate binary_pred);
-
-
-/*! \p unique_by_key_copy is a generalization of \p unique_copy to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p unique_by_key_copy copies the first element of the group to
- *  a range beginning with \c keys_result and the corresponding values from the range
- *  <tt>[values_first, values_first + (keys_last - keys_first))</tt> are copied to a range
- *  beginning with \c values_result.
- *
- *  This version of \p unique_by_key_copy uses \c operator== to test for equality and
- *  \c project1st to reduce values with equal keys.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param keys_first The beginning of the input key range.
- *  \param keys_last  The end of the input key range.
- *  \param values_first The beginning of the input value range.
- *  \param keys_result The beginning of the output key range.
- *  \param values_result The beginning of the output value range.
- *  \return A pair of iterators at end of the ranges <tt>[keys_result, keys_result_last)</tt> and <tt>[values_result, values_result_last)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam OutputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator1's \c value_type is convertible to \c OutputIterator1's \c value_type.
- *  \tparam OutputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator2's \c value_type is convertible to \c OutputIterator2's \c value_type.
- *
- *  \pre The input ranges shall not overlap either output range.
- *
- *  The following code snippet demonstrates how to use \p unique_by_key_copy to
- *  compact a sequence of key/value pairs and with equal keys using the \p thrust::host execution policy
- *  for parallelization:
- *
- *  \code
- *  #include <thrust/unique.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // input keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // input values
- *  int C[N];                         // output keys
- *  int D[N];                         // output values
- *
- *  thrust::pair<int*,int*> new_end;
- *  new_end = thrust::unique_by_key_copy(thrust::host, A, A + N, B, C, D);
- *
- *  // The first four keys in C are now {1, 3, 2, 1} and new_end.first - C is 4.
- *  // The first four values in D are now {9, 8, 5, 3} and new_end.second - D is 4.
- *  \endcode
- *
- *  \see unique_copy
- *  \see unique_by_key
- *  \see reduce_by_key
- */
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2>
-__host__ __device__
-  thrust::pair<OutputIterator1,OutputIterator2>
-    unique_by_key_copy(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                       InputIterator1 keys_first, 
-                       InputIterator1 keys_last,
-                       InputIterator2 values_first,
-                       OutputIterator1 keys_result,
-                       OutputIterator2 values_result);
-
-
-/*! \p unique_by_key_copy is a generalization of \p unique_copy to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p unique_by_key_copy copies the first element of the group to
- *  a range beginning with \c keys_result and the corresponding values from the range
- *  <tt>[values_first, values_first + (keys_last - keys_first))</tt> are copied to a range
- *  beginning with \c values_result.
- *
- *  This version of \p unique_by_key_copy uses \c operator== to test for equality and
- *  \c project1st to reduce values with equal keys.
- *
- *  \param keys_first The beginning of the input key range.
- *  \param keys_last  The end of the input key range.
- *  \param values_first The beginning of the input value range.
- *  \param keys_result The beginning of the output key range.
- *  \param values_result The beginning of the output value range.
- *  \return A pair of iterators at end of the ranges <tt>[keys_result, keys_result_last)</tt> and <tt>[values_result, values_result_last)</tt>.
- *
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam OutputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator1's \c value_type is convertible to \c OutputIterator1's \c value_type.
- *  \tparam OutputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator2's \c value_type is convertible to \c OutputIterator2's \c value_type.
- *
- *  \pre The input ranges shall not overlap either output range.
- *
- *  The following code snippet demonstrates how to use \p unique_by_key_copy to
- *  compact a sequence of key/value pairs and with equal keys.
- *
- *  \code
- *  #include <thrust/unique.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // input keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // input values
- *  int C[N];                         // output keys
- *  int D[N];                         // output values
- *
- *  thrust::pair<int*,int*> new_end;
- *  new_end = thrust::unique_by_key_copy(A, A + N, B, C, D);
- *
- *  // The first four keys in C are now {1, 3, 2, 1} and new_end.first - C is 4.
- *  // The first four values in D are now {9, 8, 5, 3} and new_end.second - D is 4.
- *  \endcode
- *
- *  \see unique_copy
- *  \see unique_by_key
- *  \see reduce_by_key
- */
-template<typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2>
-  thrust::pair<OutputIterator1,OutputIterator2>
-  unique_by_key_copy(InputIterator1 keys_first, 
-                     InputIterator1 keys_last,
-                     InputIterator2 values_first,
-                     OutputIterator1 keys_result,
-                     OutputIterator2 values_result);
-
-
-/*! \p unique_by_key_copy is a generalization of \p unique_copy to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p unique_by_key_copy copies the first element of the group to
- *  a range beginning with \c keys_result and the corresponding values from the range
- *  <tt>[values_first, values_first + (keys_last - keys_first))</tt> are copied to a range
- *  beginning with \c values_result.
- *
- *  This version of \p unique_by_key_copy uses the function object \c binary_pred
- *  to test for equality and \c project1st to reduce values with equal keys.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param keys_first The beginning of the input key range.
- *  \param keys_last  The end of the input key range.
- *  \param values_first The beginning of the input value range.
- *  \param keys_result The beginning of the output key range.
- *  \param values_result The beginning of the output value range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \return A pair of iterators at end of the ranges <tt>[keys_result, keys_result_last)</tt> and <tt>[values_result, values_result_last)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam OutputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator1's \c value_type is convertible to \c OutputIterator1's \c value_type.
- *  \tparam OutputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator2's \c value_type is convertible to \c OutputIterator2's \c value_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  \pre The input ranges shall not overlap either output range.
- *
- *  The following code snippet demonstrates how to use \p unique_by_key_copy to
- *  compact a sequence of key/value pairs and with equal keys using the \p thrust::host execution policy for
- *  parallelization:
- *
- *  \code
- *  #include <thrust/unique.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // input keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // input values
- *  int C[N];                         // output keys
- *  int D[N];                         // output values
- *
- *  thrust::pair<int*,int*> new_end;
- *  thrust::equal_to<int> binary_pred;
- *  new_end = thrust::unique_by_key_copy(thrust::host, A, A + N, B, C, D, binary_pred);
- *
- *  // The first four keys in C are now {1, 3, 2, 1} and new_end.first - C is 4.
- *  // The first four values in D are now {9, 8, 5, 3} and new_end.second - D is 4.
- *  \endcode
- *
- *  \see unique_copy
- *  \see unique_by_key
- *  \see reduce_by_key
- */
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename BinaryPredicate>
-__host__ __device__
-  thrust::pair<OutputIterator1,OutputIterator2>
-    unique_by_key_copy(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                       InputIterator1 keys_first, 
-                       InputIterator1 keys_last,
-                       InputIterator2 values_first,
-                       OutputIterator1 keys_result,
-                       OutputIterator2 values_result,
-                       BinaryPredicate binary_pred);
-
-
-/*! \p unique_by_key_copy is a generalization of \p unique_copy to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p unique_by_key_copy copies the first element of the group to
- *  a range beginning with \c keys_result and the corresponding values from the range
- *  <tt>[values_first, values_first + (keys_last - keys_first))</tt> are copied to a range
- *  beginning with \c values_result.
- *
- *  This version of \p unique_by_key_copy uses the function object \c binary_pred
- *  to test for equality and \c project1st to reduce values with equal keys.
- *
- *  \param keys_first The beginning of the input key range.
- *  \param keys_last  The end of the input key range.
- *  \param values_first The beginning of the input value range.
- *  \param keys_result The beginning of the output key range.
- *  \param values_result The beginning of the output value range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \return A pair of iterators at end of the ranges <tt>[keys_result, keys_result_last)</tt> and <tt>[values_result, values_result_last)</tt>.
- *
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam OutputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator1's \c value_type is convertible to \c OutputIterator1's \c value_type.
- *  \tparam OutputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator2's \c value_type is convertible to \c OutputIterator2's \c value_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  \pre The input ranges shall not overlap either output range.
- *
- *  The following code snippet demonstrates how to use \p unique_by_key_copy to
- *  compact a sequence of key/value pairs and with equal keys.
- *
- *  \code
- *  #include <thrust/unique.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // input keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // input values
- *  int C[N];                         // output keys
- *  int D[N];                         // output values
- *
- *  thrust::pair<int*,int*> new_end;
- *  thrust::equal_to<int> binary_pred;
- *  new_end = thrust::unique_by_key_copy(A, A + N, B, C, D, binary_pred);
- *
- *  // The first four keys in C are now {1, 3, 2, 1} and new_end.first - C is 4.
- *  // The first four values in D are now {9, 8, 5, 3} and new_end.second - D is 4.
- *  \endcode
- *
- *  \see unique_copy
- *  \see unique_by_key
- *  \see reduce_by_key
- */
-template<typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename BinaryPredicate>
-  thrust::pair<OutputIterator1,OutputIterator2>
-  unique_by_key_copy(InputIterator1 keys_first, 
-                     InputIterator1 keys_last,
-                     InputIterator2 values_first,
-                     OutputIterator1 keys_result,
-                     OutputIterator2 values_result,
-                     BinaryPredicate binary_pred);
-
-
-/*! \} // end stream_compaction
- */
-
-
-} // end namespace thrust
-
-#include <thrust/detail/unique.inl>
-
diff --git a/spaces/Cat125/text-generator-v2/utils.py b/spaces/Cat125/text-generator-v2/utils.py
deleted file mode 100644
index b71c6d736a53d0d3bed081b87931edc1e485dadb..0000000000000000000000000000000000000000
--- a/spaces/Cat125/text-generator-v2/utils.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from termcolor import colored
-
-def log(text):
-    '''The function logs a given text to a file named 'runtime.log'.
-    
-    Parameters
-    ----------
-    text
-        The text that will be written to the log file.
-    
-    '''
-    print(text, file=open('runtime.log', 'a+'))
-
-# Print iterations progress
-
-
-def progressbar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill=colored('█', 'green'), print_end="\r"):
-    """
-    Call in a loop to create terminal progress bar
-    @params:
-        iteration   - Required  : current iteration (Int)
-        total       - Required  : total iterations (Int)
-        prefix      - Optional  : prefix string (Str)
-        suffix      - Optional  : suffix string (Str)
-        decimals    - Optional  : positive number of decimals in percent complete (Int)
-        length      - Optional  : character length of bar (Int)
-        fill        - Optional  : bar fill character (Str)
-        printEnd    - Optional  : end character (e.g. "\r", "\r\n") (Str)
-    """
-    percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
-    filled_length = int(length * iteration // total)
-    bar = fill * filled_length + colored('-', 'red') * (length - filled_length)
-    print(f'\r{prefix} [{bar}] {percent}% ({iteration}/{total}) {suffix}', end = print_end)
-    # Print New Line on Complete
-    if iteration == total: 
-        print()
\ No newline at end of file
diff --git a/spaces/Celestinian/Topic-Detection/app.py b/spaces/Celestinian/Topic-Detection/app.py
deleted file mode 100644
index 9a0d67fe57bd941c185e1f9e39bc06f579fe8c24..0000000000000000000000000000000000000000
--- a/spaces/Celestinian/Topic-Detection/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import gradio as gr
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-tokenizer = AutoTokenizer.from_pretrained("Celestinian/TopicGPT")
-model = AutoModelForCausalLM.from_pretrained("Celestinian/TopicGPT")
-
-def generate_text(prompt, temperature, max_size):
-    input_ids = tokenizer.encode("#CONTEXT# " + prompt + " #TOPIC#", return_tensors='pt')
-    input_ids = input_ids.to(device)
-    model.eval()
-    model.to(device)
-
-    output_tokens = []
-    eos_token_id = tokenizer.encode('#')[0]
-
-    for _ in range(max_size):
-        with torch.no_grad():
-            outputs = model(input_ids)
-        logits = outputs.logits[:, -1, :] / temperature
-        next_token = torch.multinomial(torch.softmax(logits, dim=-1), num_samples=1)
-        if next_token.item() == eos_token_id:
-            break
-        input_ids = torch.cat((input_ids, next_token), dim=-1)
-        output_tokens.append(next_token.item())
-
-    output = tokenizer.decode(output_tokens)
-    clean_output = output.replace('\n', '\n')
-    print(prompt + clean_output)
-    return clean_output
-
-input_text = gr.inputs.Textbox(lines=5, label="Input Text")
-temperature_input = gr.inputs.Slider(minimum=0.01, maximum=2, step=0.01, default=0.01, label="Temperature")
-max_size_input = gr.inputs.Slider(minimum=1, maximum=250, step=1, default=30, label="Max Size")
-output_text = gr.outputs.Textbox(label="Generated Text")
-
-gr.Interface(generate_text, inputs=[input_text, temperature_input, max_size_input], outputs=output_text).launch()
\ No newline at end of file
diff --git a/spaces/ChandraMohanNayal/AutoGPT/autogpt/speech/base.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/speech/base.py
deleted file mode 100644
index d74fa51be75b5078134c510b393a06deb0267b2a..0000000000000000000000000000000000000000
--- a/spaces/ChandraMohanNayal/AutoGPT/autogpt/speech/base.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Base class for all voice classes."""
-import abc
-from threading import Lock
-
-from autogpt.config import AbstractSingleton
-
-
-class VoiceBase(AbstractSingleton):
-    """
-    Base class for all voice classes.
-    """
-
-    def __init__(self):
-        """
-        Initialize the voice class.
-        """
-        self._url = None
-        self._headers = None
-        self._api_key = None
-        self._voices = []
-        self._mutex = Lock()
-        self._setup()
-
-    def say(self, text: str, voice_index: int = 0) -> bool:
-        """
-        Say the given text.
-
-        Args:
-            text (str): The text to say.
-            voice_index (int): The index of the voice to use.
-        """
-        with self._mutex:
-            return self._speech(text, voice_index)
-
-    @abc.abstractmethod
-    def _setup(self) -> None:
-        """
-        Setup the voices, API key, etc.
-        """
-        pass
-
-    @abc.abstractmethod
-    def _speech(self, text: str, voice_index: int = 0) -> bool:
-        """
-        Play the given text.
-
-        Args:
-            text (str): The text to play.
-        """
-        pass
diff --git a/spaces/ChongCJ/fish/README.md b/spaces/ChongCJ/fish/README.md
deleted file mode 100644
index 13495f81ed3c597071872098d12467459e55f2b4..0000000000000000000000000000000000000000
--- a/spaces/ChongCJ/fish/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Fish
-emoji: 👁
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.15.0
-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/Clebersla/RVC_V2_Huggingface_Version/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py b/spaces/Clebersla/RVC_V2_Huggingface_Version/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
deleted file mode 100644
index b2c592527a5966e6f8e79e8c52dc5b414246dcc6..0000000000000000000000000000000000000000
--- a/spaces/Clebersla/RVC_V2_Huggingface_Version/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import parselmouth
-import numpy as np
-
-
-class PMF0Predictor(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 compute_f0(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.f0_max,
-            )
-            .selected_array["frequency"]
-        )
-
-        pad_size = (p_len - len(f0) + 1) // 2
-        if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-            f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0, uv = self.interpolate_f0(f0)
-        return f0
-
-    def compute_f0_uv(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.f0_max,
-            )
-            .selected_array["frequency"]
-        )
-
-        pad_size = (p_len - len(f0) + 1) // 2
-        if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-            f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0, uv = self.interpolate_f0(f0)
-        return f0, uv
diff --git a/spaces/CuriousDolphin/MobileSAM/app.py b/spaces/CuriousDolphin/MobileSAM/app.py
deleted file mode 100644
index abc8bbcbcf62db8ecdbd65d182ecb0860ea1e8e6..0000000000000000000000000000000000000000
--- a/spaces/CuriousDolphin/MobileSAM/app.py
+++ /dev/null
@@ -1,319 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-import os
-from mobile_sam import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
-from PIL import ImageDraw
-from utils.tools import box_prompt, format_results, point_prompt
-from utils.tools_gradio import fast_process
-
-# Most of our demo code is from [FastSAM Demo](https://huggingface.co/spaces/An-619/FastSAM). Huge thanks for AN-619.
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load the pre-trained model
-sam_checkpoint = "./mobile_sam.pt"
-model_type = "vit_t"
-
-mobile_sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
-mobile_sam = mobile_sam.to(device=device)
-mobile_sam.eval()
-
-mask_generator = SamAutomaticMaskGenerator(mobile_sam)
-predictor = SamPredictor(mobile_sam)
-
-# Description
-title = "<center><strong><font size='8'>Faster Segment Anything(MobileSAM)<font></strong></center>"
-
-description_e = """This is a demo of [Faster Segment Anything(MobileSAM) Model](https://github.com/ChaoningZhang/MobileSAM).
-
-                   We will provide box mode soon. 
-
-                   Enjoy!
-                
-              """
-
-description_p = """ # Instructions for point mode
-
-                0. Restart by click the Restart button
-                1. Select a point with Add Mask for the foreground (Must)
-                2. Select a point with Remove Area for the background (Optional)
-                3. Click the Start Segmenting.
-
-              """
-
-examples = [
-    ["assets/picture3.jpg"],
-    ["assets/picture4.jpg"],
-    ["assets/picture5.jpg"],
-    ["assets/picture6.jpg"],
-    ["assets/picture1.jpg"],
-    ["assets/picture2.jpg"],
-]
-
-default_example = examples[0]
-
-css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
-
-
-@torch.no_grad()
-def segment_everything(
-    image,
-    input_size=1024,
-    better_quality=False,
-    withContours=True,
-    use_retina=True,
-    mask_random_color=True,
-):
-    global mask_generator
-
-    input_size = int(input_size)
-    w, h = image.size
-    scale = input_size / max(w, h)
-    new_w = int(w * scale)
-    new_h = int(h * scale)
-    image = image.resize((new_w, new_h))
-
-    nd_image = np.array(image)
-    annotations = mask_generator.generate(nd_image)
-
-    fig = fast_process(
-        annotations=annotations,
-        image=image,
-        device=device,
-        scale=(1024 // input_size),
-        better_quality=better_quality,
-        mask_random_color=mask_random_color,
-        bbox=None,
-        use_retina=use_retina,
-        withContours=withContours,
-    )
-    return fig
-
-
-def segment_with_points(
-    image,
-    input_size=1024,
-    better_quality=False,
-    withContours=True,
-    use_retina=True,
-    mask_random_color=True,
-):
-    global global_points
-    global global_point_label
-
-    input_size = int(input_size)
-    w, h = image.size
-    scale = input_size / max(w, h)
-    new_w = int(w * scale)
-    new_h = int(h * scale)
-    image = image.resize((new_w, new_h))
-
-    scaled_points = np.array([[int(x * scale) for x in point] for point in global_points])
-    scaled_point_label = np.array(global_point_label)
-
-    nd_image = np.array(image)
-    predictor.set_image(nd_image)
-    masks, scores, logits = predictor.predict(
-        point_coords=scaled_points,
-        point_labels=scaled_point_label,
-        multimask_output=True,
-    )
-
-    results = format_results(masks, scores, logits, 0)
-
-    annotations, _ = point_prompt(
-        results, scaled_points, scaled_point_label, new_h, new_w
-    )
-    annotations = np.array([annotations])
-
-    fig = fast_process(
-        annotations=annotations,
-        image=image,
-        device=device,
-        scale=(1024 // input_size),
-        better_quality=better_quality,
-        mask_random_color=mask_random_color,
-        bbox=None,
-        use_retina=use_retina,
-        withContours=withContours,
-    )
-
-    global_points = []
-    global_point_label = []
-    # return fig, None
-    return fig, image
-
-
-def get_points_with_draw(image, label, evt: gr.SelectData):
-    global global_points
-    global global_point_label
-
-    x, y = evt.index[0], evt.index[1]
-    point_radius, point_color = 15, (255, 255, 0) if label == "Add Mask" else (
-        255,
-        0,
-        255,
-    )
-    global_points.append([x, y])
-    global_point_label.append(1 if label == "Add Mask" else 0)
-
-    print(x, y, label == "Add Mask")
-
-    # 创建一个可以在图像上绘图的对象
-    draw = ImageDraw.Draw(image)
-    draw.ellipse(
-        [(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)],
-        fill=point_color,
-    )
-    return image
-
-
-cond_img_e = gr.Image(label="Input", value=default_example[0], type="pil")
-cond_img_p = gr.Image(label="Input with points", value=default_example[0], type="pil")
-
-segm_img_e = gr.Image(label="Segmented Image", interactive=False, type="pil")
-segm_img_p = gr.Image(
-    label="Segmented Image with points", interactive=False, type="pil"
-)
-
-global_points = []
-global_point_label = []
-
-input_size_slider = gr.components.Slider(
-    minimum=512,
-    maximum=1024,
-    value=1024,
-    step=64,
-    label="Input_size",
-    info="Our model was trained on a size of 1024",
-)
-
-with gr.Blocks(css=css, title="Faster Segment Anything(MobileSAM)") as demo:
-    with gr.Row():
-        with gr.Column(scale=1):
-            # Title
-            gr.Markdown(title)
-
-    # with gr.Tab("Everything mode"):
-    #     # Images
-    #     with gr.Row(variant="panel"):
-    #         with gr.Column(scale=1):
-    #             cond_img_e.render()
-    #
-    #         with gr.Column(scale=1):
-    #             segm_img_e.render()
-    #
-    #     # Submit & Clear
-    #     with gr.Row():
-    #         with gr.Column():
-    #             input_size_slider.render()
-    #
-    #             with gr.Row():
-    #                 contour_check = gr.Checkbox(
-    #                     value=True,
-    #                     label="withContours",
-    #                     info="draw the edges of the masks",
-    #                 )
-    #
-    #                 with gr.Column():
-    #                     segment_btn_e = gr.Button(
-    #                         "Segment Everything", variant="primary"
-    #                     )
-    #                     clear_btn_e = gr.Button("Clear", variant="secondary")
-    #
-    #             gr.Markdown("Try some of the examples below ⬇️")
-    #             gr.Examples(
-    #                 examples=examples,
-    #                 inputs=[cond_img_e],
-    #                 outputs=segm_img_e,
-    #                 fn=segment_everything,
-    #                 cache_examples=True,
-    #                 examples_per_page=4,
-    #             )
-    #
-    #         with gr.Column():
-    #             with gr.Accordion("Advanced options", open=False):
-    #                 # text_box = gr.Textbox(label="text prompt")
-    #                 with gr.Row():
-    #                     mor_check = gr.Checkbox(
-    #                         value=False,
-    #                         label="better_visual_quality",
-    #                         info="better quality using morphologyEx",
-    #                     )
-    #                     with gr.Column():
-    #                         retina_check = gr.Checkbox(
-    #                             value=True,
-    #                             label="use_retina",
-    #                             info="draw high-resolution segmentation masks",
-    #                         )
-    #             # Description
-    #             gr.Markdown(description_e)
-    #
-    with gr.Tab("Point mode"):
-        # Images
-        with gr.Row(variant="panel"):
-            with gr.Column(scale=1):
-                cond_img_p.render()
-
-            with gr.Column(scale=1):
-                segm_img_p.render()
-
-        # Submit & Clear
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    add_or_remove = gr.Radio(
-                        ["Add Mask", "Remove Area"],
-                        value="Add Mask",
-                    )
-
-                    with gr.Column():
-                        segment_btn_p = gr.Button(
-                            "Start segmenting!", variant="primary"
-                        )
-                        clear_btn_p = gr.Button("Restart", variant="secondary")
-
-                gr.Markdown("Try some of the examples below ⬇️")
-                gr.Examples(
-                    examples=examples,
-                    inputs=[cond_img_p],
-                    # outputs=segm_img_p,
-                    # fn=segment_with_points,
-                    # cache_examples=True,
-                    examples_per_page=4,
-                )
-
-            with gr.Column():
-                # Description
-                gr.Markdown(description_p)
-
-    cond_img_p.select(get_points_with_draw, [cond_img_p, add_or_remove], cond_img_p)
-
-    # segment_btn_e.click(
-    #     segment_everything,
-    #     inputs=[
-    #         cond_img_e,
-    #         input_size_slider,
-    #         mor_check,
-    #         contour_check,
-    #         retina_check,
-    #     ],
-    #     outputs=segm_img_e,
-    # )
-
-    segment_btn_p.click(
-        segment_with_points, inputs=[cond_img_p], outputs=[segm_img_p, cond_img_p]
-    )
-
-    def clear():
-        return None, None
-
-    def clear_text():
-        return None, None, None
-
-    # clear_btn_e.click(clear, outputs=[cond_img_e, segm_img_e])
-    clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p])
-
-demo.queue()
-demo.launch()
diff --git a/spaces/Cybsechuman/Consistency_analysis/README.md b/spaces/Cybsechuman/Consistency_analysis/README.md
deleted file mode 100644
index e71df6f03de85112c83e913b0f1ed39da1be79da..0000000000000000000000000000000000000000
--- a/spaces/Cybsechuman/Consistency_analysis/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Consistency Analysis
-emoji: 🏢
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.29.0
-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/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/keypoint.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/keypoint.py
deleted file mode 100644
index a6881f72f4f757855105638f2f7a9fca81760bb7..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/keypoint.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import torch
-
-
-# transpose
-FLIP_LEFT_RIGHT = 0
-FLIP_TOP_BOTTOM = 1
-
-class Keypoints(object):
-    def __init__(self, keypoints, size, mode=None):
-        # FIXME remove check once we have better integration with device
-        # in my version this would consistently return a CPU tensor
-        device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device('cpu')
-        keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device)
-        num_keypoints = keypoints.shape[0]
-        if num_keypoints:
-            keypoints = keypoints.view(num_keypoints, -1, 3)
-        
-        # TODO should I split them?
-        # self.visibility = keypoints[..., 2]
-        self.keypoints = keypoints# [..., :2]
-
-        self.size = size
-        self.mode = mode
-        self.extra_fields = {}
-
-    def crop(self, box):
-        raise NotImplementedError()
-
-    def resize(self, size, *args, **kwargs):
-        ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size))
-        ratio_w, ratio_h = ratios
-        resized_data = self.keypoints.clone()
-        resized_data[..., 0] *= ratio_w
-        resized_data[..., 1] *= ratio_h
-        keypoints = type(self)(resized_data, size, self.mode)
-        for k, v in self.extra_fields.items():
-            keypoints.add_field(k, v)
-        return keypoints
-
-    def transpose(self, method):
-        if method not in (FLIP_LEFT_RIGHT,):
-            raise NotImplementedError(
-                    "Only FLIP_LEFT_RIGHT implemented")
-
-        flip_inds = type(self).FLIP_INDS
-        flipped_data = self.keypoints[:, flip_inds]
-        width = self.size[0]
-        TO_REMOVE = 1
-        # Flip x coordinates
-        flipped_data[..., 0] = width - flipped_data[..., 0] - TO_REMOVE
-
-        # Maintain COCO convention that if visibility == 0, then x, y = 0
-        inds = flipped_data[..., 2] == 0
-        flipped_data[inds] = 0
-
-        keypoints = type(self)(flipped_data, self.size, self.mode)
-        for k, v in self.extra_fields.items():
-            keypoints.add_field(k, v)
-        return keypoints
-
-    def to(self, *args, **kwargs):
-        keypoints = type(self)(self.keypoints.to(*args, **kwargs), self.size, self.mode)
-        for k, v in self.extra_fields.items():
-            if hasattr(v, "to"):
-                v = v.to(*args, **kwargs)
-            keypoints.add_field(k, v)
-        return keypoints
-
-    def __getitem__(self, item):
-        keypoints = type(self)(self.keypoints[item], self.size, self.mode)
-        for k, v in self.extra_fields.items():
-            keypoints.add_field(k, v[item])
-        return keypoints
-
-    def add_field(self, field, field_data):
-        self.extra_fields[field] = field_data
-
-    def get_field(self, field):
-        return self.extra_fields[field]
-
-    def __repr__(self):
-        s = self.__class__.__name__ + '('
-        s += 'num_instances={}, '.format(len(self.keypoints))
-        s += 'image_width={}, '.format(self.size[0])
-        s += 'image_height={})'.format(self.size[1])
-        return s
-
-
-def _create_flip_indices(names, flip_map):
-    full_flip_map = flip_map.copy()
-    full_flip_map.update({v: k for k, v in flip_map.items()})
-    flipped_names = [i if i not in full_flip_map else full_flip_map[i] for i in names]
-    flip_indices = [names.index(i) for i in flipped_names]
-    return torch.tensor(flip_indices)
-
-
-class PersonKeypoints(Keypoints):
-    NAMES = [
-        'nose',
-        'left_eye',
-        'right_eye',
-        'left_ear',
-        'right_ear',
-        'left_shoulder',
-        'right_shoulder',
-        'left_elbow',
-        'right_elbow',
-        'left_wrist',
-        'right_wrist',
-        'left_hip',
-        'right_hip',
-        'left_knee',
-        'right_knee',
-        'left_ankle',
-        'right_ankle'
-    ]
-    FLIP_MAP = {
-        'left_eye': 'right_eye',
-        'left_ear': 'right_ear',
-        'left_shoulder': 'right_shoulder',
-        'left_elbow': 'right_elbow',
-        'left_wrist': 'right_wrist',
-        'left_hip': 'right_hip',
-        'left_knee': 'right_knee',
-        'left_ankle': 'right_ankle'
-    }
-
-
-# TODO this doesn't look great
-PersonKeypoints.FLIP_INDS = _create_flip_indices(PersonKeypoints.NAMES, PersonKeypoints.FLIP_MAP)
-def kp_connections(keypoints):
-    kp_lines = [
-        [keypoints.index('left_eye'), keypoints.index('right_eye')],
-        [keypoints.index('left_eye'), keypoints.index('nose')],
-        [keypoints.index('right_eye'), keypoints.index('nose')],
-        [keypoints.index('right_eye'), keypoints.index('right_ear')],
-        [keypoints.index('left_eye'), keypoints.index('left_ear')],
-        [keypoints.index('right_shoulder'), keypoints.index('right_elbow')],
-        [keypoints.index('right_elbow'), keypoints.index('right_wrist')],
-        [keypoints.index('left_shoulder'), keypoints.index('left_elbow')],
-        [keypoints.index('left_elbow'), keypoints.index('left_wrist')],
-        [keypoints.index('right_hip'), keypoints.index('right_knee')],
-        [keypoints.index('right_knee'), keypoints.index('right_ankle')],
-        [keypoints.index('left_hip'), keypoints.index('left_knee')],
-        [keypoints.index('left_knee'), keypoints.index('left_ankle')],
-        [keypoints.index('right_shoulder'), keypoints.index('left_shoulder')],
-        [keypoints.index('right_hip'), keypoints.index('left_hip')],
-    ]
-    return kp_lines
-PersonKeypoints.CONNECTIONS = kp_connections(PersonKeypoints.NAMES)
-
-
-# TODO make this nicer, this is a direct translation from C2 (but removing the inner loop)
-def keypoints_to_heat_map(keypoints, rois, heatmap_size):
-    if rois.numel() == 0:
-        return rois.new().long(), rois.new().long()
-    offset_x = rois[:, 0]
-    offset_y = rois[:, 1]
-    scale_x = heatmap_size / (rois[:, 2] - rois[:, 0])
-    scale_y = heatmap_size / (rois[:, 3] - rois[:, 1])
-
-    offset_x = offset_x[:, None]
-    offset_y = offset_y[:, None]
-    scale_x = scale_x[:, None]
-    scale_y = scale_y[:, None]
-
-    x = keypoints[..., 0]
-    y = keypoints[..., 1]
-
-    x_boundary_inds = x == rois[:, 2][:, None]
-    y_boundary_inds = y == rois[:, 3][:, None]
-
-    x = (x - offset_x) * scale_x
-    x = x.floor().long()
-    y = (y - offset_y) * scale_y
-    y = y.floor().long()
-    
-    x[x_boundary_inds] = heatmap_size - 1
-    y[y_boundary_inds] = heatmap_size - 1
-
-    valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size)
-    vis = keypoints[..., 2] > 0
-    valid = (valid_loc & vis).long()
-
-    lin_ind = y * heatmap_size + x
-    heatmaps = lin_ind * valid
-
-    return heatmaps, valid
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/vegalite/v5/schema/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/vegalite/v5/schema/__init__.py
deleted file mode 100644
index 123a3fb5f048408f59a80cc0fa80097b652ceebb..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/vegalite/v5/schema/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# ruff: noqa
-from .core import *
-from .channels import *
-SCHEMA_VERSION = 'v5.8.0'
-SCHEMA_URL = 'https://vega.github.io/schema/vega-lite/v5.8.0.json'
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/streams/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/streams/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/utils.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/utils.py
deleted file mode 100644
index dd2d245a0bebcd5fc37ac20526aabbd5358dab0e..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/utils.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module offers general convenience and utility functions for dealing with
-datetimes.
-
-.. versionadded:: 2.7.0
-"""
-from __future__ import unicode_literals
-
-from datetime import datetime, time
-
-
-def today(tzinfo=None):
-    """
-    Returns a :py:class:`datetime` representing the current day at midnight
-
-    :param tzinfo:
-        The time zone to attach (also used to determine the current day).
-
-    :return:
-        A :py:class:`datetime.datetime` object representing the current day
-        at midnight.
-    """
-
-    dt = datetime.now(tzinfo)
-    return datetime.combine(dt.date(), time(0, tzinfo=tzinfo))
-
-
-def default_tzinfo(dt, tzinfo):
-    """
-    Sets the ``tzinfo`` parameter on naive datetimes only
-
-    This is useful for example when you are provided a datetime that may have
-    either an implicit or explicit time zone, such as when parsing a time zone
-    string.
-
-    .. doctest::
-
-        >>> from dateutil.tz import tzoffset
-        >>> from dateutil.parser import parse
-        >>> from dateutil.utils import default_tzinfo
-        >>> dflt_tz = tzoffset("EST", -18000)
-        >>> print(default_tzinfo(parse('2014-01-01 12:30 UTC'), dflt_tz))
-        2014-01-01 12:30:00+00:00
-        >>> print(default_tzinfo(parse('2014-01-01 12:30'), dflt_tz))
-        2014-01-01 12:30:00-05:00
-
-    :param dt:
-        The datetime on which to replace the time zone
-
-    :param tzinfo:
-        The :py:class:`datetime.tzinfo` subclass instance to assign to
-        ``dt`` if (and only if) it is naive.
-
-    :return:
-        Returns an aware :py:class:`datetime.datetime`.
-    """
-    if dt.tzinfo is not None:
-        return dt
-    else:
-        return dt.replace(tzinfo=tzinfo)
-
-
-def within_delta(dt1, dt2, delta):
-    """
-    Useful for comparing two datetimes that may have a negligible difference
-    to be considered equal.
-    """
-    delta = abs(delta)
-    difference = dt1 - dt2
-    return -delta <= difference <= delta
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/__init__.py
deleted file mode 100644
index f4cba26bf6ecaf18e96a62db69f70078498451e3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/__init__.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# DON'T EDIT! This file is generated by MetaTools/buildTableList.py.
-def _moduleFinderHint():
-    """Dummy function to let modulefinder know what tables may be
-    dynamically imported. Generated by MetaTools/buildTableList.py.
-
-            >>> _moduleFinderHint()
-    """
-    from . import B_A_S_E_
-    from . import C_B_D_T_
-    from . import C_B_L_C_
-    from . import C_F_F_
-    from . import C_F_F__2
-    from . import C_O_L_R_
-    from . import C_P_A_L_
-    from . import D_S_I_G_
-    from . import D__e_b_g
-    from . import E_B_D_T_
-    from . import E_B_L_C_
-    from . import F_F_T_M_
-    from . import F__e_a_t
-    from . import G_D_E_F_
-    from . import G_M_A_P_
-    from . import G_P_K_G_
-    from . import G_P_O_S_
-    from . import G_S_U_B_
-    from . import G__l_a_t
-    from . import G__l_o_c
-    from . import H_V_A_R_
-    from . import J_S_T_F_
-    from . import L_T_S_H_
-    from . import M_A_T_H_
-    from . import M_E_T_A_
-    from . import M_V_A_R_
-    from . import O_S_2f_2
-    from . import S_I_N_G_
-    from . import S_T_A_T_
-    from . import S_V_G_
-    from . import S__i_l_f
-    from . import S__i_l_l
-    from . import T_S_I_B_
-    from . import T_S_I_C_
-    from . import T_S_I_D_
-    from . import T_S_I_J_
-    from . import T_S_I_P_
-    from . import T_S_I_S_
-    from . import T_S_I_V_
-    from . import T_S_I__0
-    from . import T_S_I__1
-    from . import T_S_I__2
-    from . import T_S_I__3
-    from . import T_S_I__5
-    from . import T_T_F_A_
-    from . import V_D_M_X_
-    from . import V_O_R_G_
-    from . import V_V_A_R_
-    from . import _a_n_k_r
-    from . import _a_v_a_r
-    from . import _b_s_l_n
-    from . import _c_i_d_g
-    from . import _c_m_a_p
-    from . import _c_v_a_r
-    from . import _c_v_t
-    from . import _f_e_a_t
-    from . import _f_p_g_m
-    from . import _f_v_a_r
-    from . import _g_a_s_p
-    from . import _g_c_i_d
-    from . import _g_l_y_f
-    from . import _g_v_a_r
-    from . import _h_d_m_x
-    from . import _h_e_a_d
-    from . import _h_h_e_a
-    from . import _h_m_t_x
-    from . import _k_e_r_n
-    from . import _l_c_a_r
-    from . import _l_o_c_a
-    from . import _l_t_a_g
-    from . import _m_a_x_p
-    from . import _m_e_t_a
-    from . import _m_o_r_t
-    from . import _m_o_r_x
-    from . import _n_a_m_e
-    from . import _o_p_b_d
-    from . import _p_o_s_t
-    from . import _p_r_e_p
-    from . import _p_r_o_p
-    from . import _s_b_i_x
-    from . import _t_r_a_k
-    from . import _v_h_e_a
-    from . import _v_m_t_x
-
-
-if __name__ == "__main__":
-    import doctest, sys
-
-    sys.exit(doctest.testmod().failed)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/dockerfile-d67bbd50.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/dockerfile-d67bbd50.js
deleted file mode 100644
index 5405cd3af19be5d8cb56dbb55aefa442653e888a..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/dockerfile-d67bbd50.js
+++ /dev/null
@@ -1,2 +0,0 @@
-function c(n){a(n,"start");var t={},e=n.languageData||{},s=!1;for(var l in n)if(l!=e&&n.hasOwnProperty(l))for(var u=t[l]=[],o=n[l],r=0;r<o.length;r++){var i=o[r];u.push(new w(i,n)),(i.indent||i.dedent)&&(s=!0)}return{name:e.name,startState:function(){return{state:"start",pending:null,indent:s?[]:null}},copyState:function(g){var x={state:g.state,pending:g.pending,indent:g.indent&&g.indent.slice(0)};return g.stack&&(x.stack=g.stack.slice(0)),x},token:v(t),indent:R(t,e),languageData:e}}function a(n,t){if(!n.hasOwnProperty(t))throw new Error("Undefined state "+t+" in simple mode")}function h(n,t){if(!n)return/(?:)/;var e="";return n instanceof RegExp?(n.ignoreCase&&(e="i"),n=n.source):n=String(n),new RegExp((t===!1?"":"^")+"(?:"+n+")",e)}function y(n){if(!n)return null;if(n.apply)return n;if(typeof n=="string")return n.replace(/\./g," ");for(var t=[],e=0;e<n.length;e++)t.push(n[e]&&n[e].replace(/\./g," "));return t}function w(n,t){(n.next||n.push)&&a(t,n.next||n.push),this.regex=h(n.regex),this.token=y(n.token),this.data=n}function v(n){return function(t,e){if(e.pending){var s=e.pending.shift();return e.pending.length==0&&(e.pending=null),t.pos+=s.text.length,s.token}for(var l=n[e.state],u=0;u<l.length;u++){var o=l[u],r=(!o.data.sol||t.sol())&&t.match(o.regex);if(r){o.data.next?e.state=o.data.next:o.data.push?((e.stack||(e.stack=[])).push(e.state),e.state=o.data.push):o.data.pop&&e.stack&&e.stack.length&&(e.state=e.stack.pop()),o.data.indent&&e.indent.push(t.indentation()+t.indentUnit),o.data.dedent&&e.indent.pop();var i=o.token;if(i&&i.apply&&(i=i(r)),r.length>2&&o.token&&typeof o.token!="string"){e.pending=[];for(var g=2;g<r.length;g++)r[g]&&e.pending.push({text:r[g],token:o.token[g-1]});return t.backUp(r[0].length-(r[1]?r[1].length:0)),i[0]}else return i&&i.join?i[0]:i}}return t.next(),null}}function R(n,t){return function(e,s){if(e.indent==null||t.dontIndentStates&&t.doneIndentState.indexOf(e.state)>-1)return null;var l=e.indent.length-1,u=n[e.state];n:for(;;){for(var o=0;o<u.length;o++){var r=u[o];if(r.data.dedent&&r.data.dedentIfLineStart!==!1){var i=r.regex.exec(s);if(i&&i[0]){l--,(r.next||r.push)&&(u=n[r.next||r.push]),s=s.slice(i[0].length);continue n}}}break}return l<0?0:e.indent[l]}}var p="from",$=new RegExp("^(\\s*)\\b("+p+")\\b","i"),d=["run","cmd","entrypoint","shell"],b=new RegExp("^(\\s*)("+d.join("|")+")(\\s+\\[)","i"),f="expose",m=new RegExp("^(\\s*)("+f+")(\\s+)","i"),S=["arg","from","maintainer","label","env","add","copy","volume","user","workdir","onbuild","stopsignal","healthcheck","shell"],E=[p,f].concat(d).concat(S),k="("+E.join("|")+")",I=new RegExp("^(\\s*)"+k+"(\\s*)(#.*)?$","i"),O=new RegExp("^(\\s*)"+k+"(\\s+)","i");const D=c({start:[{regex:/^\s*#.*$/,sol:!0,token:"comment"},{regex:$,token:[null,"keyword"],sol:!0,next:"from"},{regex:I,token:[null,"keyword",null,"error"],sol:!0},{regex:b,token:[null,"keyword",null],sol:!0,next:"array"},{regex:m,token:[null,"keyword",null],sol:!0,next:"expose"},{regex:O,token:[null,"keyword",null],sol:!0,next:"arguments"},{regex:/./,token:null}],from:[{regex:/\s*$/,token:null,next:"start"},{regex:/(\s*)(#.*)$/,token:[null,"error"],next:"start"},{regex:/(\s*\S+\s+)(as)/i,token:[null,"keyword"],next:"start"},{token:null,next:"start"}],single:[{regex:/(?:[^\\']|\\.)/,token:"string"},{regex:/'/,token:"string",pop:!0}],double:[{regex:/(?:[^\\"]|\\.)/,token:"string"},{regex:/"/,token:"string",pop:!0}],array:[{regex:/\]/,token:null,next:"start"},{regex:/"(?:[^\\"]|\\.)*"?/,token:"string"}],expose:[{regex:/\d+$/,token:"number",next:"start"},{regex:/[^\d]+$/,token:null,next:"start"},{regex:/\d+/,token:"number"},{regex:/[^\d]+/,token:null},{token:null,next:"start"}],arguments:[{regex:/^\s*#.*$/,sol:!0,token:"comment"},{regex:/"(?:[^\\"]|\\.)*"?$/,token:"string",next:"start"},{regex:/"/,token:"string",push:"double"},{regex:/'(?:[^\\']|\\.)*'?$/,token:"string",next:"start"},{regex:/'/,token:"string",push:"single"},{regex:/[^#"']+[\\`]$/,token:null},{regex:/[^#"']+$/,token:null,next:"start"},{regex:/[^#"']+/,token:null},{token:null,next:"start"}],languageData:{commentTokens:{line:"#"}}});export{D as dockerFile};
-//# sourceMappingURL=dockerfile-d67bbd50.js.map
diff --git a/spaces/DaFujaTyping/hf-Chat-ui/src/routes/r/[id]/message/[messageId]/prompt/+server.ts b/spaces/DaFujaTyping/hf-Chat-ui/src/routes/r/[id]/message/[messageId]/prompt/+server.ts
deleted file mode 100644
index 636eeece68127c09c46d4acf3c01d81b845ddd65..0000000000000000000000000000000000000000
--- a/spaces/DaFujaTyping/hf-Chat-ui/src/routes/r/[id]/message/[messageId]/prompt/+server.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-import { buildPrompt } from "$lib/buildPrompt.js";
-import { collections } from "$lib/server/database";
-import { models } from "$lib/server/models.js";
-import { error } from "@sveltejs/kit";
-
-export async function GET({ params }) {
-	const conv = await collections.sharedConversations.findOne({
-		_id: params.id,
-	});
-
-	if (!conv) {
-		throw error(404, "Conversation not found");
-	}
-
-	const messageId = params.messageId;
-
-	const messageIndex = conv.messages.findIndex((msg) => msg.id === messageId);
-
-	if (messageIndex === -1) {
-		throw error(404, "Message not found");
-	}
-
-	const model = models.find((m) => m.id === conv.model);
-
-	if (!model) {
-		throw error(404, "Conversation model not found");
-	}
-
-	const prompt = buildPrompt(conv.messages.slice(0, messageIndex + 1), model);
-
-	return new Response(
-		JSON.stringify(
-			{
-				note: "This is a preview of the prompt that will be sent to the model when retrying the message. It may differ from what was sent in the past if the parameters have been updated since",
-				prompt,
-				model: model.name,
-				parameters: {
-					...model.parameters,
-					return_full_text: false,
-				},
-			},
-			null,
-			2
-		),
-		{ headers: { "Content-Type": "application/json" } }
-	);
-}
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/utils/misc.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/utils/misc.py
deleted file mode 100644
index d262d86e2c9a12e22bad7266dba429ce09c9a036..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/utils/misc.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""
-@date: 2021/8/4
-@description:
-"""
-import numpy as np
-import torch
-
-
-def tensor2np(t: torch.Tensor) -> np.array:
-    if isinstance(t, torch.Tensor):
-        if t.device == 'cpu':
-            return t.detach().numpy()
-        else:
-            return t.detach().cpu().numpy()
-    else:
-        return t
-
-
-def tensor2np_d(d: dict) -> dict:
-    output = {}
-    for k in d.keys():
-        output[k] = tensor2np(d[k])
-    return output
diff --git a/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/mapper/latent_mappers.py b/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/mapper/latent_mappers.py
deleted file mode 100644
index 63637adc9646986a3546edd19f4555a2f75a379f..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/mapper/latent_mappers.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import Module
-
-from models.StyleCLIP.models.stylegan2.model import EqualLinear, PixelNorm
-
-
-class Mapper(Module):
-
-    def __init__(self, opts):
-        super(Mapper, self).__init__()
-
-        self.opts = opts
-        layers = [PixelNorm()]
-
-        for i in range(4):
-            layers.append(
-                EqualLinear(
-                    512, 512, lr_mul=0.01, activation='fused_lrelu'
-                )
-            )
-
-        self.mapping = nn.Sequential(*layers)
-
-
-    def forward(self, x):
-        x = self.mapping(x)
-        return x
-
-
-class SingleMapper(Module):
-
-    def __init__(self, opts):
-        super(SingleMapper, self).__init__()
-
-        self.opts = opts
-
-        self.mapping = Mapper(opts)
-
-    def forward(self, x):
-        out = self.mapping(x)
-        return out
-
-
-class LevelsMapper(Module):
-
-    def __init__(self, opts):
-        super(LevelsMapper, self).__init__()
-
-        self.opts = opts
-
-        if not opts.no_coarse_mapper:
-            self.course_mapping = Mapper(opts)
-        if not opts.no_medium_mapper:
-            self.medium_mapping = Mapper(opts)
-        if not opts.no_fine_mapper:
-            self.fine_mapping = Mapper(opts)
-
-    def forward(self, x):
-        x_coarse = x[:, :4, :]
-        x_medium = x[:, 4:8, :]
-        x_fine = x[:, 8:, :]
-
-        if not self.opts.no_coarse_mapper:
-            x_coarse = self.course_mapping(x_coarse)
-        else:
-            x_coarse = torch.zeros_like(x_coarse)
-        if not self.opts.no_medium_mapper:
-            x_medium = self.medium_mapping(x_medium)
-        else:
-            x_medium = torch.zeros_like(x_medium)
-        if not self.opts.no_fine_mapper:
-            x_fine = self.fine_mapping(x_fine)
-        else:
-            x_fine = torch.zeros_like(x_fine)
-
-
-        out = torch.cat([x_coarse, x_medium, x_fine], dim=1)
-
-        return out
-
diff --git a/spaces/DragGan/DragGan/legacy.py b/spaces/DragGan/DragGan/legacy.py
deleted file mode 100644
index 8cf53cb9396a639261bbcadb4e264e39415c1a56..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/legacy.py
+++ /dev/null
@@ -1,323 +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.
-
-"""Converting legacy network pickle into the new format."""
-
-import click
-import pickle
-import re
-import copy
-import numpy as np
-import torch
-import dnnlib
-from torch_utils import misc
-
-#----------------------------------------------------------------------------
-
-def load_network_pkl(f, force_fp16=False):
-    data = _LegacyUnpickler(f).load()
-
-    # Legacy TensorFlow pickle => convert.
-    if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
-        tf_G, tf_D, tf_Gs = data
-        G = convert_tf_generator(tf_G)
-        D = convert_tf_discriminator(tf_D)
-        G_ema = convert_tf_generator(tf_Gs)
-        data = dict(G=G, D=D, G_ema=G_ema)
-
-    # Add missing fields.
-    if 'training_set_kwargs' not in data:
-        data['training_set_kwargs'] = None
-    if 'augment_pipe' not in data:
-        data['augment_pipe'] = None
-
-    # Validate contents.
-    assert isinstance(data['G'], torch.nn.Module)
-    assert isinstance(data['D'], torch.nn.Module)
-    assert isinstance(data['G_ema'], torch.nn.Module)
-    assert isinstance(data['training_set_kwargs'], (dict, type(None)))
-    assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))
-
-    # Force FP16.
-    if force_fp16:
-        for key in ['G', 'D', 'G_ema']:
-            old = data[key]
-            kwargs = copy.deepcopy(old.init_kwargs)
-            fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs)
-            fp16_kwargs.num_fp16_res = 4
-            fp16_kwargs.conv_clamp = 256
-            if kwargs != old.init_kwargs:
-                new = type(old)(**kwargs).eval().requires_grad_(False)
-                misc.copy_params_and_buffers(old, new, require_all=True)
-                data[key] = new
-    return data
-
-#----------------------------------------------------------------------------
-
-class _TFNetworkStub(dnnlib.EasyDict):
-    pass
-
-class _LegacyUnpickler(pickle.Unpickler):
-    def find_class(self, module, name):
-        if module == 'dnnlib.tflib.network' and name == 'Network':
-            return _TFNetworkStub
-        return super().find_class(module, name)
-
-#----------------------------------------------------------------------------
-
-def _collect_tf_params(tf_net):
-    # pylint: disable=protected-access
-    tf_params = dict()
-    def recurse(prefix, tf_net):
-        for name, value in tf_net.variables:
-            tf_params[prefix + name] = value
-        for name, comp in tf_net.components.items():
-            recurse(prefix + name + '/', comp)
-    recurse('', tf_net)
-    return tf_params
-
-#----------------------------------------------------------------------------
-
-def _populate_module_params(module, *patterns):
-    for name, tensor in misc.named_params_and_buffers(module):
-        found = False
-        value = None
-        for pattern, value_fn in zip(patterns[0::2], patterns[1::2]):
-            match = re.fullmatch(pattern, name)
-            if match:
-                found = True
-                if value_fn is not None:
-                    value = value_fn(*match.groups())
-                break
-        try:
-            assert found
-            if value is not None:
-                tensor.copy_(torch.from_numpy(np.array(value)))
-        except:
-            print(name, list(tensor.shape))
-            raise
-
-#----------------------------------------------------------------------------
-
-def convert_tf_generator(tf_G):
-    if tf_G.version < 4:
-        raise ValueError('TensorFlow pickle version too low')
-
-    # Collect kwargs.
-    tf_kwargs = tf_G.static_kwargs
-    known_kwargs = set()
-    def kwarg(tf_name, default=None, none=None):
-        known_kwargs.add(tf_name)
-        val = tf_kwargs.get(tf_name, default)
-        return val if val is not None else none
-
-    # Convert kwargs.
-    from training import networks_stylegan2
-    network_class = networks_stylegan2.Generator
-    kwargs = dnnlib.EasyDict(
-        z_dim               = kwarg('latent_size',          512),
-        c_dim               = kwarg('label_size',           0),
-        w_dim               = kwarg('dlatent_size',         512),
-        img_resolution      = kwarg('resolution',           1024),
-        img_channels        = kwarg('num_channels',         3),
-        channel_base        = kwarg('fmap_base',            16384) * 2,
-        channel_max         = kwarg('fmap_max',             512),
-        num_fp16_res        = kwarg('num_fp16_res',         0),
-        conv_clamp          = kwarg('conv_clamp',           None),
-        architecture        = kwarg('architecture',         'skip'),
-        resample_filter     = kwarg('resample_kernel',      [1,3,3,1]),
-        use_noise           = kwarg('use_noise',            True),
-        activation          = kwarg('nonlinearity',         'lrelu'),
-        mapping_kwargs      = dnnlib.EasyDict(
-            num_layers      = kwarg('mapping_layers',       8),
-            embed_features  = kwarg('label_fmaps',          None),
-            layer_features  = kwarg('mapping_fmaps',        None),
-            activation      = kwarg('mapping_nonlinearity', 'lrelu'),
-            lr_multiplier   = kwarg('mapping_lrmul',        0.01),
-            w_avg_beta      = kwarg('w_avg_beta',           0.995,  none=1),
-        ),
-    )
-
-    # Check for unknown kwargs.
-    kwarg('truncation_psi')
-    kwarg('truncation_cutoff')
-    kwarg('style_mixing_prob')
-    kwarg('structure')
-    kwarg('conditioning')
-    kwarg('fused_modconv')
-    unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
-    if len(unknown_kwargs) > 0:
-        raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
-
-    # Collect params.
-    tf_params = _collect_tf_params(tf_G)
-    for name, value in list(tf_params.items()):
-        match = re.fullmatch(r'ToRGB_lod(\d+)/(.*)', name)
-        if match:
-            r = kwargs.img_resolution // (2 ** int(match.group(1)))
-            tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value
-            kwargs.synthesis.kwargs.architecture = 'orig'
-    #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
-
-    # Convert params.
-    G = network_class(**kwargs).eval().requires_grad_(False)
-    # pylint: disable=unnecessary-lambda
-    # pylint: disable=f-string-without-interpolation
-    _populate_module_params(G,
-        r'mapping\.w_avg',                                  lambda:     tf_params[f'dlatent_avg'],
-        r'mapping\.embed\.weight',                          lambda:     tf_params[f'mapping/LabelEmbed/weight'].transpose(),
-        r'mapping\.embed\.bias',                            lambda:     tf_params[f'mapping/LabelEmbed/bias'],
-        r'mapping\.fc(\d+)\.weight',                        lambda i:   tf_params[f'mapping/Dense{i}/weight'].transpose(),
-        r'mapping\.fc(\d+)\.bias',                          lambda i:   tf_params[f'mapping/Dense{i}/bias'],
-        r'synthesis\.b4\.const',                            lambda:     tf_params[f'synthesis/4x4/Const/const'][0],
-        r'synthesis\.b4\.conv1\.weight',                    lambda:     tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1),
-        r'synthesis\.b4\.conv1\.bias',                      lambda:     tf_params[f'synthesis/4x4/Conv/bias'],
-        r'synthesis\.b4\.conv1\.noise_const',               lambda:     tf_params[f'synthesis/noise0'][0, 0],
-        r'synthesis\.b4\.conv1\.noise_strength',            lambda:     tf_params[f'synthesis/4x4/Conv/noise_strength'],
-        r'synthesis\.b4\.conv1\.affine\.weight',            lambda:     tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(),
-        r'synthesis\.b4\.conv1\.affine\.bias',              lambda:     tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1,
-        r'synthesis\.b(\d+)\.conv0\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
-        r'synthesis\.b(\d+)\.conv0\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'],
-        r'synthesis\.b(\d+)\.conv0\.noise_const',           lambda r:   tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0],
-        r'synthesis\.b(\d+)\.conv0\.noise_strength',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'],
-        r'synthesis\.b(\d+)\.conv0\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(),
-        r'synthesis\.b(\d+)\.conv0\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1,
-        r'synthesis\.b(\d+)\.conv1\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1),
-        r'synthesis\.b(\d+)\.conv1\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/bias'],
-        r'synthesis\.b(\d+)\.conv1\.noise_const',           lambda r:   tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0],
-        r'synthesis\.b(\d+)\.conv1\.noise_strength',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'],
-        r'synthesis\.b(\d+)\.conv1\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(),
-        r'synthesis\.b(\d+)\.conv1\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1,
-        r'synthesis\.b(\d+)\.torgb\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1),
-        r'synthesis\.b(\d+)\.torgb\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/bias'],
-        r'synthesis\.b(\d+)\.torgb\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(),
-        r'synthesis\.b(\d+)\.torgb\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1,
-        r'synthesis\.b(\d+)\.skip\.weight',                 lambda r:   tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
-        r'.*\.resample_filter',                             None,
-        r'.*\.act_filter',                                  None,
-    )
-    return G
-
-#----------------------------------------------------------------------------
-
-def convert_tf_discriminator(tf_D):
-    if tf_D.version < 4:
-        raise ValueError('TensorFlow pickle version too low')
-
-    # Collect kwargs.
-    tf_kwargs = tf_D.static_kwargs
-    known_kwargs = set()
-    def kwarg(tf_name, default=None):
-        known_kwargs.add(tf_name)
-        return tf_kwargs.get(tf_name, default)
-
-    # Convert kwargs.
-    kwargs = dnnlib.EasyDict(
-        c_dim                   = kwarg('label_size',           0),
-        img_resolution          = kwarg('resolution',           1024),
-        img_channels            = kwarg('num_channels',         3),
-        architecture            = kwarg('architecture',         'resnet'),
-        channel_base            = kwarg('fmap_base',            16384) * 2,
-        channel_max             = kwarg('fmap_max',             512),
-        num_fp16_res            = kwarg('num_fp16_res',         0),
-        conv_clamp              = kwarg('conv_clamp',           None),
-        cmap_dim                = kwarg('mapping_fmaps',        None),
-        block_kwargs = dnnlib.EasyDict(
-            activation          = kwarg('nonlinearity',         'lrelu'),
-            resample_filter     = kwarg('resample_kernel',      [1,3,3,1]),
-            freeze_layers       = kwarg('freeze_layers',        0),
-        ),
-        mapping_kwargs = dnnlib.EasyDict(
-            num_layers          = kwarg('mapping_layers',       0),
-            embed_features      = kwarg('mapping_fmaps',        None),
-            layer_features      = kwarg('mapping_fmaps',        None),
-            activation          = kwarg('nonlinearity',         'lrelu'),
-            lr_multiplier       = kwarg('mapping_lrmul',        0.1),
-        ),
-        epilogue_kwargs = dnnlib.EasyDict(
-            mbstd_group_size    = kwarg('mbstd_group_size',     None),
-            mbstd_num_channels  = kwarg('mbstd_num_features',   1),
-            activation          = kwarg('nonlinearity',         'lrelu'),
-        ),
-    )
-
-    # Check for unknown kwargs.
-    kwarg('structure')
-    kwarg('conditioning')
-    unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
-    if len(unknown_kwargs) > 0:
-        raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
-
-    # Collect params.
-    tf_params = _collect_tf_params(tf_D)
-    for name, value in list(tf_params.items()):
-        match = re.fullmatch(r'FromRGB_lod(\d+)/(.*)', name)
-        if match:
-            r = kwargs.img_resolution // (2 ** int(match.group(1)))
-            tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value
-            kwargs.architecture = 'orig'
-    #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
-
-    # Convert params.
-    from training import networks_stylegan2
-    D = networks_stylegan2.Discriminator(**kwargs).eval().requires_grad_(False)
-    # pylint: disable=unnecessary-lambda
-    # pylint: disable=f-string-without-interpolation
-    _populate_module_params(D,
-        r'b(\d+)\.fromrgb\.weight',     lambda r:       tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1),
-        r'b(\d+)\.fromrgb\.bias',       lambda r:       tf_params[f'{r}x{r}/FromRGB/bias'],
-        r'b(\d+)\.conv(\d+)\.weight',   lambda r, i:    tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose(3, 2, 0, 1),
-        r'b(\d+)\.conv(\d+)\.bias',     lambda r, i:    tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'],
-        r'b(\d+)\.skip\.weight',        lambda r:       tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1),
-        r'mapping\.embed\.weight',      lambda:         tf_params[f'LabelEmbed/weight'].transpose(),
-        r'mapping\.embed\.bias',        lambda:         tf_params[f'LabelEmbed/bias'],
-        r'mapping\.fc(\d+)\.weight',    lambda i:       tf_params[f'Mapping{i}/weight'].transpose(),
-        r'mapping\.fc(\d+)\.bias',      lambda i:       tf_params[f'Mapping{i}/bias'],
-        r'b4\.conv\.weight',            lambda:         tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1),
-        r'b4\.conv\.bias',              lambda:         tf_params[f'4x4/Conv/bias'],
-        r'b4\.fc\.weight',              lambda:         tf_params[f'4x4/Dense0/weight'].transpose(),
-        r'b4\.fc\.bias',                lambda:         tf_params[f'4x4/Dense0/bias'],
-        r'b4\.out\.weight',             lambda:         tf_params[f'Output/weight'].transpose(),
-        r'b4\.out\.bias',               lambda:         tf_params[f'Output/bias'],
-        r'.*\.resample_filter',         None,
-    )
-    return D
-
-#----------------------------------------------------------------------------
-
-@click.command()
-@click.option('--source', help='Input pickle', required=True, metavar='PATH')
-@click.option('--dest', help='Output pickle', required=True, metavar='PATH')
-@click.option('--force-fp16', help='Force the networks to use FP16', type=bool, default=False, metavar='BOOL', show_default=True)
-def convert_network_pickle(source, dest, force_fp16):
-    """Convert legacy network pickle into the native PyTorch format.
-
-    The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA.
-    It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks.
-
-    Example:
-
-    \b
-    python legacy.py \\
-        --source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\
-        --dest=stylegan2-cat-config-f.pkl
-    """
-    print(f'Loading "{source}"...')
-    with dnnlib.util.open_url(source) as f:
-        data = load_network_pkl(f, force_fp16=force_fp16)
-    print(f'Saving "{dest}"...')
-    with open(dest, 'wb') as f:
-        pickle.dump(data, f)
-    print('Done.')
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    convert_network_pickle() # pylint: disable=no-value-for-parameter
-
-#----------------------------------------------------------------------------
diff --git a/spaces/DragGan/DragGan/stylegan_human/training/augment.py b/spaces/DragGan/DragGan/stylegan_human/training/augment.py
deleted file mode 100644
index d68e35c96ef9fa9c18bbb6668f03b9463098710e..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/training/augment.py
+++ /dev/null
@@ -1,436 +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.
-
-"""Augmentation pipeline from the paper
-"Training Generative Adversarial Networks with Limited Data".
-Matches the original implementation by Karras et al. at
-https://github.com/NVlabs/stylegan2-ada/blob/main/training/augment.py"""
-
-import numpy as np
-import scipy.signal
-import torch
-from torch_utils import persistence
-from torch_utils import misc
-from torch_utils.ops import upfirdn2d
-from torch_utils.ops import grid_sample_gradfix
-from torch_utils.ops import conv2d_gradfix
-
-#----------------------------------------------------------------------------
-# Coefficients of various wavelet decomposition low-pass filters.
-
-wavelets = {
-    'haar': [0.7071067811865476, 0.7071067811865476],
-    'db1':  [0.7071067811865476, 0.7071067811865476],
-    'db2':  [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
-    'db3':  [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
-    'db4':  [-0.010597401784997278, 0.032883011666982945, 0.030841381835986965, -0.18703481171888114, -0.02798376941698385, 0.6308807679295904, 0.7148465705525415, 0.23037781330885523],
-    'db5':  [0.003335725285001549, -0.012580751999015526, -0.006241490213011705, 0.07757149384006515, -0.03224486958502952, -0.24229488706619015, 0.13842814590110342, 0.7243085284385744, 0.6038292697974729, 0.160102397974125],
-    'db6':  [-0.00107730108499558, 0.004777257511010651, 0.0005538422009938016, -0.031582039318031156, 0.02752286553001629, 0.09750160558707936, -0.12976686756709563, -0.22626469396516913, 0.3152503517092432, 0.7511339080215775, 0.4946238903983854, 0.11154074335008017],
-    'db7':  [0.0003537138000010399, -0.0018016407039998328, 0.00042957797300470274, 0.012550998556013784, -0.01657454163101562, -0.03802993693503463, 0.0806126091510659, 0.07130921926705004, -0.22403618499416572, -0.14390600392910627, 0.4697822874053586, 0.7291320908465551, 0.39653931948230575, 0.07785205408506236],
-    'db8':  [-0.00011747678400228192, 0.0006754494059985568, -0.0003917403729959771, -0.00487035299301066, 0.008746094047015655, 0.013981027917015516, -0.04408825393106472, -0.01736930100202211, 0.128747426620186, 0.00047248457399797254, -0.2840155429624281, -0.015829105256023893, 0.5853546836548691, 0.6756307362980128, 0.3128715909144659, 0.05441584224308161],
-    'sym2': [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
-    'sym3': [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
-    'sym4': [-0.07576571478927333, -0.02963552764599851, 0.49761866763201545, 0.8037387518059161, 0.29785779560527736, -0.09921954357684722, -0.012603967262037833, 0.0322231006040427],
-    'sym5': [0.027333068345077982, 0.029519490925774643, -0.039134249302383094, 0.1993975339773936, 0.7234076904024206, 0.6339789634582119, 0.01660210576452232, -0.17532808990845047, -0.021101834024758855, 0.019538882735286728],
-    'sym6': [0.015404109327027373, 0.0034907120842174702, -0.11799011114819057, -0.048311742585633, 0.4910559419267466, 0.787641141030194, 0.3379294217276218, -0.07263752278646252, -0.021060292512300564, 0.04472490177066578, 0.0017677118642428036, -0.007800708325034148],
-    'sym7': [0.002681814568257878, -0.0010473848886829163, -0.01263630340325193, 0.03051551316596357, 0.0678926935013727, -0.049552834937127255, 0.017441255086855827, 0.5361019170917628, 0.767764317003164, 0.2886296317515146, -0.14004724044296152, -0.10780823770381774, 0.004010244871533663, 0.010268176708511255],
-    'sym8': [-0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298, 0.007607487324917605, -0.1432942383508097, -0.061273359067658524, 0.4813596512583722, 0.7771857517005235, 0.3644418948353314, -0.05194583810770904, -0.027219029917056003, 0.049137179673607506, 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668, 0.0018899503327594609],
-}
-
-#----------------------------------------------------------------------------
-# Helpers for constructing transformation matrices.
-
-def matrix(*rows, device=None):
-    assert all(len(row) == len(rows[0]) for row in rows)
-    elems = [x for row in rows for x in row]
-    ref = [x for x in elems if isinstance(x, torch.Tensor)]
-    if len(ref) == 0:
-        return misc.constant(np.asarray(rows), device=device)
-    assert device is None or device == ref[0].device
-    elems = [x if isinstance(x, torch.Tensor) else misc.constant(x, shape=ref[0].shape, device=ref[0].device) for x in elems]
-    return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1))
-
-def translate2d(tx, ty, **kwargs):
-    return matrix(
-        [1, 0, tx],
-        [0, 1, ty],
-        [0, 0, 1],
-        **kwargs)
-
-def translate3d(tx, ty, tz, **kwargs):
-    return matrix(
-        [1, 0, 0, tx],
-        [0, 1, 0, ty],
-        [0, 0, 1, tz],
-        [0, 0, 0, 1],
-        **kwargs)
-
-def scale2d(sx, sy, **kwargs):
-    return matrix(
-        [sx, 0,  0],
-        [0,  sy, 0],
-        [0,  0,  1],
-        **kwargs)
-
-def scale3d(sx, sy, sz, **kwargs):
-    return matrix(
-        [sx, 0,  0,  0],
-        [0,  sy, 0,  0],
-        [0,  0,  sz, 0],
-        [0,  0,  0,  1],
-        **kwargs)
-
-def rotate2d(theta, **kwargs):
-    return matrix(
-        [torch.cos(theta), torch.sin(-theta), 0],
-        [torch.sin(theta), torch.cos(theta),  0],
-        [0,                0,                 1],
-        **kwargs)
-
-def rotate3d(v, theta, **kwargs):
-    vx = v[..., 0]; vy = v[..., 1]; vz = v[..., 2]
-    s = torch.sin(theta); c = torch.cos(theta); cc = 1 - c
-    return matrix(
-        [vx*vx*cc+c,    vx*vy*cc-vz*s, vx*vz*cc+vy*s, 0],
-        [vy*vx*cc+vz*s, vy*vy*cc+c,    vy*vz*cc-vx*s, 0],
-        [vz*vx*cc-vy*s, vz*vy*cc+vx*s, vz*vz*cc+c,    0],
-        [0,             0,             0,             1],
-        **kwargs)
-
-def translate2d_inv(tx, ty, **kwargs):
-    return translate2d(-tx, -ty, **kwargs)
-
-def scale2d_inv(sx, sy, **kwargs):
-    return scale2d(1 / sx, 1 / sy, **kwargs)
-
-def rotate2d_inv(theta, **kwargs):
-    return rotate2d(-theta, **kwargs)
-
-#----------------------------------------------------------------------------
-# Versatile image augmentation pipeline from the paper
-# "Training Generative Adversarial Networks with Limited Data".
-#
-# All augmentations are disabled by default; individual augmentations can
-# be enabled by setting their probability multipliers to 1.
-
-@persistence.persistent_class
-class AugmentPipe(torch.nn.Module):
-    def __init__(self,
-        xflip=0, rotate90=0, xint=0, xint_max=0.125,
-        scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125,
-        brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1,
-        imgfilter=0, imgfilter_bands=[1,1,1,1], imgfilter_std=1,
-        noise=0, cutout=0, noise_std=0.1, cutout_size=0.5,
-    ):
-        super().__init__()
-        self.register_buffer('p', torch.ones([]))       # Overall multiplier for augmentation probability.
-
-        # Pixel blitting.
-        self.xflip            = float(xflip)            # Probability multiplier for x-flip.
-        self.rotate90         = float(rotate90)         # Probability multiplier for 90 degree rotations.
-        self.xint             = float(xint)             # Probability multiplier for integer translation.
-        self.xint_max         = float(xint_max)         # Range of integer translation, relative to image dimensions.
-
-        # General geometric transformations.
-        self.scale            = float(scale)            # Probability multiplier for isotropic scaling.
-        self.rotate           = float(rotate)           # Probability multiplier for arbitrary rotation.
-        self.aniso            = float(aniso)            # Probability multiplier for anisotropic scaling.
-        self.xfrac            = float(xfrac)            # Probability multiplier for fractional translation.
-        self.scale_std        = float(scale_std)        # Log2 standard deviation of isotropic scaling.
-        self.rotate_max       = float(rotate_max)       # Range of arbitrary rotation, 1 = full circle.
-        self.aniso_std        = float(aniso_std)        # Log2 standard deviation of anisotropic scaling.
-        self.xfrac_std        = float(xfrac_std)        # Standard deviation of frational translation, relative to image dimensions.
-
-        # Color transformations.
-        self.brightness       = float(brightness)       # Probability multiplier for brightness.
-        self.contrast         = float(contrast)         # Probability multiplier for contrast.
-        self.lumaflip         = float(lumaflip)         # Probability multiplier for luma flip.
-        self.hue              = float(hue)              # Probability multiplier for hue rotation.
-        self.saturation       = float(saturation)       # Probability multiplier for saturation.
-        self.brightness_std   = float(brightness_std)   # Standard deviation of brightness.
-        self.contrast_std     = float(contrast_std)     # Log2 standard deviation of contrast.
-        self.hue_max          = float(hue_max)          # Range of hue rotation, 1 = full circle.
-        self.saturation_std   = float(saturation_std)   # Log2 standard deviation of saturation.
-
-        # Image-space filtering.
-        self.imgfilter        = float(imgfilter)        # Probability multiplier for image-space filtering.
-        self.imgfilter_bands  = list(imgfilter_bands)   # Probability multipliers for individual frequency bands.
-        self.imgfilter_std    = float(imgfilter_std)    # Log2 standard deviation of image-space filter amplification.
-
-        # Image-space corruptions.
-        self.noise            = float(noise)            # Probability multiplier for additive RGB noise.
-        self.cutout           = float(cutout)           # Probability multiplier for cutout.
-        self.noise_std        = float(noise_std)        # Standard deviation of additive RGB noise.
-        self.cutout_size      = float(cutout_size)      # Size of the cutout rectangle, relative to image dimensions.
-
-        # Setup orthogonal lowpass filter for geometric augmentations.
-        self.register_buffer('Hz_geom', upfirdn2d.setup_filter(wavelets['sym6']))
-
-        # Construct filter bank for image-space filtering.
-        Hz_lo = np.asarray(wavelets['sym2'])            # H(z)
-        Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z)
-        Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2    # H(z) * H(z^-1) / 2
-        Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2    # H(-z) * H(-z^-1) / 2
-        Hz_fbank = np.eye(4, 1)                         # Bandpass(H(z), b_i)
-        for i in range(1, Hz_fbank.shape[0]):
-            Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape(Hz_fbank.shape[0], -1)[:, :-1]
-            Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2])
-            Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // 2 : (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2
-        self.register_buffer('Hz_fbank', torch.as_tensor(Hz_fbank, dtype=torch.float32))
-
-    def forward(self, images, debug_percentile=None):
-        assert isinstance(images, torch.Tensor) and images.ndim == 4
-        batch_size, num_channels, height, width = images.shape
-        device = images.device
-        if debug_percentile is not None:
-            debug_percentile = torch.as_tensor(debug_percentile, dtype=torch.float32, device=device)
-
-        # -------------------------------------
-        # Select parameters for pixel blitting.
-        # -------------------------------------
-
-        # Initialize inverse homogeneous 2D transform: G_inv @ pixel_out ==> pixel_in
-        I_3 = torch.eye(3, device=device)
-        G_inv = I_3
-
-        # Apply x-flip with probability (xflip * strength).
-        if self.xflip > 0:
-            i = torch.floor(torch.rand([batch_size], device=device) * 2)
-            i = torch.where(torch.rand([batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i))
-            if debug_percentile is not None:
-                i = torch.full_like(i, torch.floor(debug_percentile * 2))
-            G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1)
-
-        # Apply 90 degree rotations with probability (rotate90 * strength).
-        if self.rotate90 > 0:
-            i = torch.floor(torch.rand([batch_size], device=device) * 4)
-            i = torch.where(torch.rand([batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i))
-            if debug_percentile is not None:
-                i = torch.full_like(i, torch.floor(debug_percentile * 4))
-            G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i)
-
-        # Apply integer translation with probability (xint * strength).
-        if self.xint > 0:
-            t = (torch.rand([batch_size, 2], device=device) * 2 - 1) * self.xint_max
-            t = torch.where(torch.rand([batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t))
-            if debug_percentile is not None:
-                t = torch.full_like(t, (debug_percentile * 2 - 1) * self.xint_max)
-            G_inv = G_inv @ translate2d_inv(torch.round(t[:,0] * width), torch.round(t[:,1] * height))
-
-        # --------------------------------------------------------
-        # Select parameters for general geometric transformations.
-        # --------------------------------------------------------
-
-        # Apply isotropic scaling with probability (scale * strength).
-        if self.scale > 0:
-            s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std)
-            s = torch.where(torch.rand([batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s))
-            if debug_percentile is not None:
-                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.scale_std))
-            G_inv = G_inv @ scale2d_inv(s, s)
-
-        # Apply pre-rotation with probability p_rot.
-        p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) # P(pre OR post) = p
-        if self.rotate > 0:
-            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
-            theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
-            if debug_percentile is not None:
-                theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max)
-            G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling.
-
-        # Apply anisotropic scaling with probability (aniso * strength).
-        if self.aniso > 0:
-            s = torch.exp2(torch.randn([batch_size], device=device) * self.aniso_std)
-            s = torch.where(torch.rand([batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s))
-            if debug_percentile is not None:
-                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.aniso_std))
-            G_inv = G_inv @ scale2d_inv(s, 1 / s)
-
-        # Apply post-rotation with probability p_rot.
-        if self.rotate > 0:
-            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
-            theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
-            if debug_percentile is not None:
-                theta = torch.zeros_like(theta)
-            G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling.
-
-        # Apply fractional translation with probability (xfrac * strength).
-        if self.xfrac > 0:
-            t = torch.randn([batch_size, 2], device=device) * self.xfrac_std
-            t = torch.where(torch.rand([batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t))
-            if debug_percentile is not None:
-                t = torch.full_like(t, torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std)
-            G_inv = G_inv @ translate2d_inv(t[:,0] * width, t[:,1] * height)
-
-        # ----------------------------------
-        # Execute geometric transformations.
-        # ----------------------------------
-
-        # Execute if the transform is not identity.
-        if G_inv is not I_3:
-
-            # Calculate padding.
-            cx = (width - 1) / 2
-            cy = (height - 1) / 2
-            cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1], device=device) # [idx, xyz]
-            cp = G_inv @ cp.t() # [batch, xyz, idx]
-            Hz_pad = self.Hz_geom.shape[0] // 4
-            margin = cp[:, :2, :].permute(1, 0, 2).flatten(1) # [xy, batch * idx]
-            margin = torch.cat([-margin, margin]).max(dim=1).values # [x0, y0, x1, y1]
-            margin = margin + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device)
-            margin = margin.max(misc.constant([0, 0] * 2, device=device))
-            margin = margin.min(misc.constant([width-1, height-1] * 2, device=device))
-            mx0, my0, mx1, my1 = margin.ceil().to(torch.int32)
-
-            # Pad image and adjust origin.
-            images = torch.nn.functional.pad(input=images, pad=[mx0,mx1,my0,my1], mode='reflect')
-            G_inv = translate2d((mx0 - mx1) / 2, (my0 - my1) / 2) @ G_inv
-
-            # Upsample.
-            images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2)
-            G_inv = scale2d(2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device)
-            G_inv = translate2d(-0.5, -0.5, device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device)
-
-            # Execute transformation.
-            shape = [batch_size, num_channels, (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2]
-            G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv(2 / shape[3], 2 / shape[2], device=device)
-            grid = torch.nn.functional.affine_grid(theta=G_inv[:,:2,:], size=shape, align_corners=False)
-            images = grid_sample_gradfix.grid_sample(images, grid)
-
-            # Downsample and crop.
-            images = upfirdn2d.downsample2d(x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True)
-
-        # --------------------------------------------
-        # Select parameters for color transformations.
-        # --------------------------------------------
-
-        # Initialize homogeneous 3D transformation matrix: C @ color_in ==> color_out
-        I_4 = torch.eye(4, device=device)
-        C = I_4
-
-        # Apply brightness with probability (brightness * strength).
-        if self.brightness > 0:
-            b = torch.randn([batch_size], device=device) * self.brightness_std
-            b = torch.where(torch.rand([batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b))
-            if debug_percentile is not None:
-                b = torch.full_like(b, torch.erfinv(debug_percentile * 2 - 1) * self.brightness_std)
-            C = translate3d(b, b, b) @ C
-
-        # Apply contrast with probability (contrast * strength).
-        if self.contrast > 0:
-            c = torch.exp2(torch.randn([batch_size], device=device) * self.contrast_std)
-            c = torch.where(torch.rand([batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c))
-            if debug_percentile is not None:
-                c = torch.full_like(c, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.contrast_std))
-            C = scale3d(c, c, c) @ C
-
-        # Apply luma flip with probability (lumaflip * strength).
-        v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) # Luma axis.
-        if self.lumaflip > 0:
-            i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2)
-            i = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i))
-            if debug_percentile is not None:
-                i = torch.full_like(i, torch.floor(debug_percentile * 2))
-            C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection.
-
-        # Apply hue rotation with probability (hue * strength).
-        if self.hue > 0 and num_channels > 1:
-            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.hue_max
-            theta = torch.where(torch.rand([batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta))
-            if debug_percentile is not None:
-                theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max)
-            C = rotate3d(v, theta) @ C # Rotate around v.
-
-        # Apply saturation with probability (saturation * strength).
-        if self.saturation > 0 and num_channels > 1:
-            s = torch.exp2(torch.randn([batch_size, 1, 1], device=device) * self.saturation_std)
-            s = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s))
-            if debug_percentile is not None:
-                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.saturation_std))
-            C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C
-
-        # ------------------------------
-        # Execute color transformations.
-        # ------------------------------
-
-        # Execute if the transform is not identity.
-        if C is not I_4:
-            images = images.reshape([batch_size, num_channels, height * width])
-            if num_channels == 3:
-                images = C[:, :3, :3] @ images + C[:, :3, 3:]
-            elif num_channels == 1:
-                C = C[:, :3, :].mean(dim=1, keepdims=True)
-                images = images * C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:]
-            else:
-                raise ValueError('Image must be RGB (3 channels) or L (1 channel)')
-            images = images.reshape([batch_size, num_channels, height, width])
-
-        # ----------------------
-        # Image-space filtering.
-        # ----------------------
-
-        if self.imgfilter > 0:
-            num_bands = self.Hz_fbank.shape[0]
-            assert len(self.imgfilter_bands) == num_bands
-            expected_power = misc.constant(np.array([10, 1, 1, 1]) / 13, device=device) # Expected power spectrum (1/f).
-
-            # Apply amplification for each band with probability (imgfilter * strength * band_strength).
-            g = torch.ones([batch_size, num_bands], device=device) # Global gain vector (identity).
-            for i, band_strength in enumerate(self.imgfilter_bands):
-                t_i = torch.exp2(torch.randn([batch_size], device=device) * self.imgfilter_std)
-                t_i = torch.where(torch.rand([batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i))
-                if debug_percentile is not None:
-                    t_i = torch.full_like(t_i, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i)
-                t = torch.ones([batch_size, num_bands], device=device)                  # Temporary gain vector.
-                t[:, i] = t_i                                                           # Replace i'th element.
-                t = t / (expected_power * t.square()).sum(dim=-1, keepdims=True).sqrt() # Normalize power.
-                g = g * t                                                               # Accumulate into global gain.
-
-            # Construct combined amplification filter.
-            Hz_prime = g @ self.Hz_fbank                                    # [batch, tap]
-            Hz_prime = Hz_prime.unsqueeze(1).repeat([1, num_channels, 1])   # [batch, channels, tap]
-            Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # [batch * channels, 1, tap]
-
-            # Apply filter.
-            p = self.Hz_fbank.shape[1] // 2
-            images = images.reshape([1, batch_size * num_channels, height, width])
-            images = torch.nn.functional.pad(input=images, pad=[p,p,p,p], mode='reflect')
-            images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels)
-            images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels)
-            images = images.reshape([batch_size, num_channels, height, width])
-
-        # ------------------------
-        # Image-space corruptions.
-        # ------------------------
-
-        # Apply additive RGB noise with probability (noise * strength).
-        if self.noise > 0:
-            sigma = torch.randn([batch_size, 1, 1, 1], device=device).abs() * self.noise_std
-            sigma = torch.where(torch.rand([batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma))
-            if debug_percentile is not None:
-                sigma = torch.full_like(sigma, torch.erfinv(debug_percentile) * self.noise_std)
-            images = images + torch.randn([batch_size, num_channels, height, width], device=device) * sigma
-
-        # Apply cutout with probability (cutout * strength).
-        if self.cutout > 0:
-            size = torch.full([batch_size, 2, 1, 1, 1], self.cutout_size, device=device)
-            size = torch.where(torch.rand([batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size))
-            center = torch.rand([batch_size, 2, 1, 1, 1], device=device)
-            if debug_percentile is not None:
-                size = torch.full_like(size, self.cutout_size)
-                center = torch.full_like(center, debug_percentile)
-            coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1])
-            coord_y = torch.arange(height, device=device).reshape([1, 1, -1, 1])
-            mask_x = (((coord_x + 0.5) / width - center[:, 0]).abs() >= size[:, 0] / 2)
-            mask_y = (((coord_y + 0.5) / height - center[:, 1]).abs() >= size[:, 1] / 2)
-            mask = torch.logical_or(mask_x, mask_y).to(torch.float32)
-            images = images * mask
-
-        return images
-
-#----------------------------------------------------------------------------
diff --git a/spaces/ECCV2022/bytetrack/tutorials/ctracker/test_byte.py b/spaces/ECCV2022/bytetrack/tutorials/ctracker/test_byte.py
deleted file mode 100644
index bbb8a53b7a98de5e1c4c5fcffa1a546cc36f0e4b..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/tutorials/ctracker/test_byte.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import numpy as np
-import torchvision
-import time
-import math
-import os
-import copy
-import pdb
-import argparse
-import sys
-import cv2
-import skimage.io
-import skimage.transform
-import skimage.color
-import skimage
-import torch
-import model
-
-from torch.utils.data import Dataset, DataLoader
-from torchvision import datasets, models, transforms
-from dataloader import CSVDataset, collater, Resizer, AspectRatioBasedSampler, Augmenter, UnNormalizer, Normalizer, RGB_MEAN, RGB_STD
-from scipy.optimize import linear_sum_assignment
-from tracker import BYTETracker
-
-
-def write_results(filename, results):
-    save_format = '{frame},{id},{x1},{y1},{w},{h},{s},-1,-1,-1\n'
-    with open(filename, 'w') as f:
-        for frame_id, tlwhs, track_ids, scores in results:
-            for tlwh, track_id, score in zip(tlwhs, track_ids, scores):
-                if track_id < 0:
-                    continue
-                x1, y1, w, h = tlwh
-                line = save_format.format(frame=frame_id, id=track_id, x1=round(x1, 1), y1=round(y1, 1), w=round(w, 1), h=round(h, 1), s=round(score, 2))
-                f.write(line)
-
-def write_results_no_score(filename, results):
-    save_format = '{frame},{id},{x1},{y1},{w},{h},-1,-1,-1,-1\n'
-    with open(filename, 'w') as f:
-        for frame_id, tlwhs, track_ids in results:
-            for tlwh, track_id in zip(tlwhs, track_ids):
-                if track_id < 0:
-                    continue
-                x1, y1, w, h = tlwh
-                line = save_format.format(frame=frame_id, id=track_id, x1=round(x1, 1), y1=round(y1, 1), w=round(w, 1), h=round(h, 1))
-                f.write(line)
-                
-def run_each_dataset(model_dir, retinanet, dataset_path, subset, cur_dataset):
-    print(cur_dataset)
-
-    img_list = os.listdir(os.path.join(dataset_path, subset, cur_dataset, 'img1'))
-    img_list = [os.path.join(dataset_path, subset, cur_dataset, 'img1', _) for _ in img_list if ('jpg' in _) or ('png' in _)]
-    img_list = sorted(img_list)
-
-    img_len = len(img_list)
-    last_feat = None
-
-    confidence_threshold = 0.6
-    IOU_threshold = 0.5
-    retention_threshold = 10
-
-    det_list_all = []
-    tracklet_all = []
-    results = []
-    max_id = 0
-    max_draw_len = 100
-    draw_interval = 5
-    img_width = 1920
-    img_height = 1080
-    fps = 30
-
-    tracker = BYTETracker()
-
-    for idx in range((int(img_len / 2)), img_len + 1):
-        i = idx - 1
-        print('tracking: ', i)
-        with torch.no_grad():
-            data_path1 = img_list[min(idx, img_len - 1)]
-            img_origin1 = skimage.io.imread(data_path1)
-            img_h, img_w, _ = img_origin1.shape
-            img_height, img_width = img_h, img_w
-            resize_h, resize_w = math.ceil(img_h / 32) * 32, math.ceil(img_w / 32) * 32
-            img1 = np.zeros((resize_h, resize_w, 3), dtype=img_origin1.dtype)
-            img1[:img_h, :img_w, :] = img_origin1
-            img1 = (img1.astype(np.float32) / 255.0 - np.array([[RGB_MEAN]])) / np.array([[RGB_STD]])
-            img1 = torch.from_numpy(img1).permute(2, 0, 1).view(1, 3, resize_h, resize_w)
-            scores, transformed_anchors, last_feat = retinanet(img1.cuda().float(), last_feat=last_feat)
-            
-            if idx > (int(img_len / 2)):
-                idxs = np.where(scores > 0.1)
-                # run tracking
-                online_targets = tracker.update(transformed_anchors[idxs[0], :4], scores[idxs[0]])
-                online_tlwhs = []
-                online_ids = []
-                online_scores = []
-                for t in online_targets:
-                    tlwh = t.tlwh
-                    tid = t.track_id
-                    online_tlwhs.append(tlwh)
-                    online_ids.append(tid)
-                    online_scores.append(t.score)
-                results.append((idx, online_tlwhs, online_ids, online_scores))
-            
-    fout_tracking = os.path.join(model_dir, 'results', cur_dataset + '.txt')
-    write_results(fout_tracking, results)           
-
-    
-
-def main(args=None):
-    parser = argparse.ArgumentParser(description='Simple script for testing a CTracker network.')
-    parser.add_argument('--dataset_path', default='/dockerdata/home/jeromepeng/data/MOT/MOT17/', type=str,
-                        help='Dataset path, location of the images sequence.')
-    parser.add_argument('--model_dir', default='./trained_model/', help='Path to model (.pt) file.')
-    parser.add_argument('--model_path', default='./trained_model/model_final.pth', help='Path to model (.pt) file.')
-    parser.add_argument('--seq_nums', default=0, type=int)
-
-    parser = parser.parse_args(args)
-
-    if not os.path.exists(os.path.join(parser.model_dir, 'results')):
-        os.makedirs(os.path.join(parser.model_dir, 'results'))
-
-    retinanet = model.resnet50(num_classes=1, pretrained=True)
-    # 	retinanet_save = torch.load(os.path.join(parser.model_dir, 'model_final.pth'))
-    retinanet_save = torch.load(os.path.join(parser.model_path))
-
-    # rename moco pre-trained keys
-    state_dict = retinanet_save.state_dict()
-    for k in list(state_dict.keys()):
-        # retain only encoder up to before the embedding layer
-        if k.startswith('module.'):
-            # remove prefix
-            state_dict[k[len("module."):]] = state_dict[k]
-        # delete renamed or unused k
-        del state_dict[k]
-
-    retinanet.load_state_dict(state_dict)
-
-    use_gpu = True
-
-    if use_gpu: retinanet = retinanet.cuda()
-
-    retinanet.eval()
-    seq_nums = []
-    if parser.seq_nums > 0:
-        seq_nums.append(parser.seq_nums)
-    else:
-        seq_nums = [2, 4, 5, 9, 10, 11, 13]
-
-    for seq_num in seq_nums:
-        run_each_dataset(parser.model_dir, retinanet, parser.dataset_path, 'train', 'MOT17-{:02d}'.format(seq_num))
-
-
-# 	for seq_num in [1, 3, 6, 7, 8, 12, 14]:
-# 		run_each_dataset(parser.model_dir, retinanet, parser.dataset_path, 'test', 'MOT17-{:02d}'.format(seq_num))
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/ECCV2022/bytetrack/yolox/__init__.py b/spaces/ECCV2022/bytetrack/yolox/__init__.py
deleted file mode 100644
index 1cbc411d419c55098e7d4e24ff0f21caaaf10a1f..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/yolox/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-
-from .utils import configure_module
-
-configure_module()
-
-__version__ = "0.1.0"
diff --git a/spaces/Faridmaruf/RVCV2MODEL/lib/infer_pack/models.py b/spaces/Faridmaruf/RVCV2MODEL/lib/infer_pack/models.py
deleted file mode 100644
index 44c08d361bcb13b84b38dc29beff5cdaddad4ea2..0000000000000000000000000000000000000000
--- a/spaces/Faridmaruf/RVCV2MODEL/lib/infer_pack/models.py
+++ /dev/null
@@ -1,1124 +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 SynthesizerTrnMs256NSFsid(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,
-        **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
-        self.enc_p = TextEncoder256(
-            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)
-        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 forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * 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, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(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,
-        **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
-        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)
-        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 forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * 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, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(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=None,
-        **kwargs
-    ):
-        super().__init__()
-        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
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        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, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        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 forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(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=None,
-        **kwargs
-    ):
-        super().__init__()
-        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
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        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, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        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 forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-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/FelixLuoX/codeformer/CodeFormer/facelib/detection/yolov5face/utils/general.py b/spaces/FelixLuoX/codeformer/CodeFormer/facelib/detection/yolov5face/utils/general.py
deleted file mode 100644
index 1c8e14f56a107ec3a4269c382cfc5168ad780ffc..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/codeformer/CodeFormer/facelib/detection/yolov5face/utils/general.py
+++ /dev/null
@@ -1,271 +0,0 @@
-import math
-import time
-
-import numpy as np
-import torch
-import torchvision
-
-
-def check_img_size(img_size, s=32):
-    # Verify img_size is a multiple of stride s
-    new_size = make_divisible(img_size, int(s))  # ceil gs-multiple
-    # if new_size != img_size:
-    #     print(f"WARNING: --img-size {img_size:g} must be multiple of max stride {s:g}, updating to {new_size:g}")
-    return new_size
-
-
-def make_divisible(x, divisor):
-    # Returns x evenly divisible by divisor
-    return math.ceil(x / divisor) * divisor
-
-
-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 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]
-
-    coords[:, [0, 2]] -= pad[0]  # x padding
-    coords[:, [1, 3]] -= pad[1]  # y padding
-    coords[:, :4] /= gain
-    clip_coords(coords, img0_shape)
-    return coords
-
-
-def clip_coords(boxes, img_shape):
-    # Clip bounding xyxy bounding boxes to image shape (height, width)
-    boxes[:, 0].clamp_(0, img_shape[1])  # x1
-    boxes[:, 1].clamp_(0, img_shape[0])  # y1
-    boxes[:, 2].clamp_(0, img_shape[1])  # x2
-    boxes[:, 3].clamp_(0, img_shape[0])  # y2
-
-
-def box_iou(box1, box2):
-    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
-    """
-    Return intersection-over-union (Jaccard index) of boxes.
-    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-    Arguments:
-        box1 (Tensor[N, 4])
-        box2 (Tensor[M, 4])
-    Returns:
-        iou (Tensor[N, M]): the NxM matrix containing the pairwise
-            IoU values for every element in boxes1 and boxes2
-    """
-
-    def box_area(box):
-        return (box[2] - box[0]) * (box[3] - box[1])
-
-    area1 = box_area(box1.T)
-    area2 = box_area(box2.T)
-
-    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
-    return inter / (area1[:, None] + area2 - inter)
-
-
-def non_max_suppression_face(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
-    """Performs Non-Maximum Suppression (NMS) on inference results
-    Returns:
-         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
-    """
-
-    nc = prediction.shape[2] - 15  # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Settings
-    # (pixels) maximum box width and height
-    max_wh = 4096
-    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, 16), device=prediction.device)] * prediction.shape[0]
-    for xi, x in enumerate(prediction):  # image index, image inference
-        # Apply constraints
-        x = x[xc[xi]]  # confidence
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            label = labels[xi]
-            v = torch.zeros((len(label), nc + 15), device=x.device)
-            v[:, :4] = label[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(label)), label[:, 0].long() + 15] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        x[:, 15:] *= 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, landmarks, cls)
-        if multi_label:
-            i, j = (x[:, 15:] > conf_thres).nonzero(as_tuple=False).T
-            x = torch.cat((box[i], x[i, j + 15, None], x[:, 5:15], j[:, None].float()), 1)
-        else:  # best class only
-            conf, j = x[:, 15:].max(1, keepdim=True)
-            x = torch.cat((box, conf, x[:, 5:15], 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)]
-
-        # If none remain process next image
-        n = x.shape[0]  # number of boxes
-        if not n:
-            continue
-
-        # Batched NMS
-        c = x[:, 15:16] * (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 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:
-            break  # time limit exceeded
-
-    return output
-
-
-def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
-    """Performs Non-Maximum Suppression (NMS) on inference results
-
-    Returns:
-         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
-    """
-
-    nc = prediction.shape[2] - 5  # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Settings
-    # (pixels) maximum box width and height
-    max_wh = 4096
-    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
-        x = x[xc[xi]]  # confidence
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            label_id = labels[xi]
-            v = torch.zeros((len(label_id), nc + 5), device=x.device)
-            v[:, :4] = label_id[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(label_id)), label_id[:, 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)]
-
-        # Check shape
-        n = x.shape[0]  # number of boxes
-        if not n:  # no boxes
-            continue
-
-        x = x[x[:, 4].argsort(descending=True)]  # 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 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 scale_coords_landmarks(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]
-
-    coords[:, [0, 2, 4, 6, 8]] -= pad[0]  # x padding
-    coords[:, [1, 3, 5, 7, 9]] -= pad[1]  # y padding
-    coords[:, :10] /= gain
-    coords[:, 0].clamp_(0, img0_shape[1])  # x1
-    coords[:, 1].clamp_(0, img0_shape[0])  # y1
-    coords[:, 2].clamp_(0, img0_shape[1])  # x2
-    coords[:, 3].clamp_(0, img0_shape[0])  # y2
-    coords[:, 4].clamp_(0, img0_shape[1])  # x3
-    coords[:, 5].clamp_(0, img0_shape[0])  # y3
-    coords[:, 6].clamp_(0, img0_shape[1])  # x4
-    coords[:, 7].clamp_(0, img0_shape[0])  # y4
-    coords[:, 8].clamp_(0, img0_shape[1])  # x5
-    coords[:, 9].clamp_(0, img0_shape[0])  # y5
-    return coords
diff --git a/spaces/Fengbinbin/gpt-academic/.github/ISSUE_TEMPLATE/bug_report.md b/spaces/Fengbinbin/gpt-academic/.github/ISSUE_TEMPLATE/bug_report.md
deleted file mode 100644
index ac668766a39892be5bc9e03f3ea626f8b3bf4b57..0000000000000000000000000000000000000000
--- a/spaces/Fengbinbin/gpt-academic/.github/ISSUE_TEMPLATE/bug_report.md
+++ /dev/null
@@ -1,25 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-- **(1) Describe the bug 简述**
-
-
-- **(2) Screen Shot 截图**
-
-
-- **(3) Terminal Traceback 终端traceback（如有）**
-
-
-- **(4) Material to Help Reproduce Bugs 帮助我们复现的测试材料样本（如有）**
-
-
-
-Before submitting an issue 提交issue之前：
-- Please try to upgrade your code. 如果您的代码不是最新的，建议您先尝试更新代码
-- Please check project wiki for common problem solutions.项目[wiki](https://github.com/binary-husky/chatgpt_academic/wiki)有一些常见问题的解决方法
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/vdecoder/hifigan/utils.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/vdecoder/hifigan/utils.py
deleted file mode 100644
index 9c93c996d3cc73c30d71c1fc47056e4230f35c0f..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/vdecoder/hifigan/utils.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-# matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-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 apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern) # get checkpoint paths
-    cp_list = sorted(cp_list)# sort by iter
-    if len(cp_list) > n_models: # if more than n_models models are found
-        for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
-            open(cp, 'w').close()# empty file contents
-            os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
-
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/align_rope_along_line.py b/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/align_rope_along_line.py
deleted file mode 100644
index bab4c7a326cc97c490e5b4cfba2531cd2823e967..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/align_rope_along_line.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import numpy as np
-from cliport.tasks.task import Task
-from cliport.utils import utils
-from cliport.tasks import primitives
-from cliport.tasks.grippers import Spatula
-
-class AlignRopeAlongLine(Task):
-    """Align a deformable rope along a straight line marked on the tabletop."""
-
-    def __init__(self):
-        super().__init__()
-        self.max_steps = 20
-        self.lang_template = "align the rope along the line"
-        self.task_completed_desc = "done aligning."
-        self.additional_reset()
-
-    def reset(self, env):
-        super().reset(env)
-
-        # Add line.
-        length = np.random.uniform(0.18, 0.25)
-        line_size = (length, 0.01, 0.01)
-        line_pose = self.get_random_pose(env, line_size)
-        line_template = 'line/line-template.urdf'
-        replace = {'DIM': line_size, 'HALF': (line_size[0] / 2, line_size[1] / 2, line_size[2] / 2)}
-        line_urdf = self.fill_template(line_template, replace)
-        env.add_object(line_urdf, line_pose, 'fixed')
-
-        # Add rope.
-        rope_size  = (length, 0.01, 0.01)
-        rope_pose = self.get_random_pose(env, rope_size)
-        corner1_pose = utils.apply(line_pose, (length / 2, 0.01, 0.01))
-        corner2_pose = utils.apply(line_pose, (-length / 2, 0.01, 0.01))
-        rope_id, targets, matches = self.make_rope(env, (corner1_pose, corner2_pose), n_parts=15)
-
-        # Goal: rope is aligned with the line.
-        self.add_goal(objs=rope_id, matches=matches, targ_poses=targets, replace=False,
-                rotations=False, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template)
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/clip_unet_lat.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/clip_unet_lat.py
deleted file mode 100644
index 811414569b33609353ed4eae5708aab1c6251025..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/models/clip_unet_lat.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import cliport.utils.utils as utils
-from cliport.models.resnet import IdentityBlock, ConvBlock
-from cliport.models.core.unet import Up
-from cliport.models.core.fusion import FusionConvLat
-from cliport.models.clip_lingunet_lat import CLIPLingUNetLat
-
-
-class CLIPUNetLat(CLIPLingUNetLat):
-    """ CLIP RN50 with U-Net skip connections and lateral connections without language """
-
-    def __init__(self, input_shape, output_dim, cfg, device, preprocess):
-        super().__init__(input_shape, output_dim, cfg, device, preprocess)
-
-    def _build_decoder(self):
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False),
-            nn.ReLU(True)
-        )
-
-        self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear)
-        self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512)
-
-        self.up2 = Up(1024, 512 // self.up_factor, self.bilinear)
-        self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256)
-
-        self.up3 = Up(512, 256 // self.up_factor, self.bilinear)
-        self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128)
-
-        self.layer1 = nn.Sequential(
-            ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-        self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64)
-
-        self.layer2 = nn.Sequential(
-            ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-        self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32)
-
-        self.layer3 = nn.Sequential(
-            ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-        self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16)
-
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(16, self.output_dim, kernel_size=1)
-        )
-
-    def forward(self, x, lat):
-        x = self.preprocess(x, dist='clip')
-
-        in_type = x.dtype
-        in_shape = x.shape
-        x = x[:,:3]  # select RGB
-        x, im = self.encode_image(x)
-        x = x.to(in_type)
-
-        x = self.conv1(x)
-
-        x = self.up1(x, im[-2])
-        x = self.lat_fusion1(x, lat[-6])
-
-        x = self.up2(x, im[-3])
-        x = self.lat_fusion2(x, lat[-5])
-
-        x = self.up3(x, im[-4])
-        x = self.lat_fusion3(x, lat[-4])
-
-        x = self.layer1(x)
-        x = self.lat_fusion4(x, lat[-3])
-
-        x = self.layer2(x)
-        x = self.lat_fusion5(x, lat[-2])
-
-        x = self.layer3(x)
-        x = self.lat_fusion6(x, lat[-1])
-
-        x = self.conv2(x)
-
-        x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear')
-        return x
\ No newline at end of file
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/tasks/packing_google_objects.py b/spaces/Gen-Sim/Gen-Sim/cliport/tasks/packing_google_objects.py
deleted file mode 100644
index 3716d33e5dbbdff3a313642eacd3fcbb672a9295..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/tasks/packing_google_objects.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import os
-
-import numpy as np
-from cliport.tasks.task import Task
-from cliport.utils import utils
-
-import pybullet as p
-
-
-class PackingSeenGoogleObjectsSeq(Task):
-    """: Place the specified objects in the brown box following the order prescribed in the language
-instruction at each timestep."""
-
-    def __init__(self):
-        super().__init__()
-        self.max_steps = 6
-        self.lang_template = "pack the {obj} in the brown box"
-        self.task_completed_desc = "done packing objects."
-        self.object_names = self.get_object_names()
-        self.additional_reset()
-
-    def get_object_names(self):
-        return utils.google_all_shapes
-
-    def reset(self, env):
-        super().reset(env)
-
-        # object names
-        object_names = self.object_names[self.mode]
-
-        # Add container box.
-        zone_size = self.get_random_size(0.2, 0.35, 0.2, 0.35, 0.05, 0.05)
-        zone_pose = self.get_random_pose(env, zone_size)
-        container_template = 'container/container-template_DIM_HALF.urdf'
-        replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)}
-        container_urdf = self.fill_template(container_template, replace)
-        env.add_object(container_urdf, zone_pose, 'fixed')
-
-        margin = 0.01
-        min_object_dim = 0.08
-        bboxes = []
-
-        # Split container space with KD trees.
-        stack_size = np.array(zone_size)
-        stack_size[0] -= 0.01
-        stack_size[1] -= 0.01
-        root_size = (0.01, 0.01, 0) + tuple(stack_size)
-        root = utils.TreeNode(None, [], bbox=np.array(root_size))
-        utils.KDTree(root, min_object_dim, margin, bboxes)
-
-        # Add Google Scanned Objects to scene.
-        object_ids = []
-        bboxes = np.array(bboxes)
-        scale_factor = 5
-        object_template = 'google/object-template_FNAME_COLOR_SCALE.urdf'
-        chosen_objs, repeat_category = self.choose_objects(object_names, len(bboxes))
-        object_descs = []
-        for i, bbox in enumerate(bboxes):
-            size = bbox[3:] - bbox[:3]
-            max_size = size.max()
-            position = size / 2. + bbox[:3]
-            position[0] += -zone_size[0] / 2
-            position[1] += -zone_size[1] / 2
-            shape_size = max_size * scale_factor
-            pose = self.get_random_pose(env, size)
-
-            # Add object only if valid pose found.
-            if pose[0] is not None:
-                # Initialize with a slightly tilted pose so that the objects aren't always erect.
-                slight_tilt = utils.q_mult(pose[1], (-0.1736482, 0, 0, 0.9848078))
-                ps = ((pose[0][0], pose[0][1], pose[0][2]+0.05), slight_tilt)
-
-                object_name = chosen_objs[i]
-                object_name_with_underscore = object_name.replace(" ", "_")
-                mesh_file = os.path.join(self.assets_root,
-                                         'google',
-                                         'meshes_fixed',
-                                         f'{object_name_with_underscore}.obj')
-                texture_file = os.path.join(self.assets_root,
-                                            'google',
-                                            'textures',
-                                            f'{object_name_with_underscore}.png')
-
-                try:
-                    replace = {'FNAME': (mesh_file,),
-                               'SCALE': [shape_size, shape_size, shape_size],
-                               'COLOR': (0.2, 0.2, 0.2)}
-                    urdf = self.fill_template(object_template, replace)
-                    box_id = env.add_object(urdf, ps)
-                    object_ids.append((box_id, (0, None)))
-
-                    texture_id = p.loadTexture(texture_file)
-                    p.changeVisualShape(box_id, -1, textureUniqueId=texture_id)
-                    p.changeVisualShape(box_id, -1, rgbaColor=[1, 1, 1, 1])
-
-                    object_descs.append(object_name)
-
-                except Exception as e:
-                    print("Failed to load Google Scanned Object in PyBullet")
-                    print(object_name_with_underscore, mesh_file, texture_file)
-                    print(f"Exception: {e}")
-
-        self.set_goals(object_descs, object_ids, repeat_category, zone_pose, zone_size)
-
-        for i in range(480):
-            p.stepSimulation()
-
-    def choose_objects(self, object_names, k):
-        repeat_category = None
-        return np.random.choice(object_names, k, replace=False), repeat_category
-
-    def set_goals(self, object_descs, object_ids,  repeat_category, zone_pose, zone_size):
-        # Random picking sequence.
-        num_pack_objs = np.random.randint(1, len(object_ids))
-
-        object_ids = object_ids[:num_pack_objs]
-        true_poses = []
-        for obj_idx, (object_id, _) in enumerate(object_ids):
-            true_poses.append(zone_pose)
-            language_goal = self.lang_template.format(obj=object_descs[obj_idx])
-            self.add_goal(objs=[object_id], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False,
-                rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / len(object_ids),
-                language_goal=language_goal)
-
-        # Only mistake allowed.
-        self.max_steps = len(object_ids)+1
-
diff --git "a/spaces/Gmq-x/gpt-academic/crazy_functions/\344\273\243\347\240\201\351\207\215\345\206\231\344\270\272\345\205\250\350\213\261\346\226\207_\345\244\232\347\272\277\347\250\213.py" "b/spaces/Gmq-x/gpt-academic/crazy_functions/\344\273\243\347\240\201\351\207\215\345\206\231\344\270\272\345\205\250\350\213\261\346\226\207_\345\244\232\347\272\277\347\250\213.py"
deleted file mode 100644
index e57f80f1d45bd3ec23837253848f7b32a5ccd751..0000000000000000000000000000000000000000
--- "a/spaces/Gmq-x/gpt-academic/crazy_functions/\344\273\243\347\240\201\351\207\215\345\206\231\344\270\272\345\205\250\350\213\261\346\226\207_\345\244\232\347\272\277\347\250\213.py"
+++ /dev/null
@@ -1,138 +0,0 @@
-import threading
-from request_llm.bridge_all import predict_no_ui_long_connection
-from toolbox import update_ui
-from toolbox import CatchException, write_results_to_file, report_execption
-from .crazy_utils import breakdown_txt_to_satisfy_token_limit
-
-def extract_code_block_carefully(txt):
-    splitted = txt.split('```')
-    n_code_block_seg = len(splitted) - 1
-    if n_code_block_seg <= 1: return txt
-    # 剩下的情况都开头除去 ``` 结尾除去一次 ```
-    txt_out = '```'.join(splitted[1:-1])
-    return txt_out
-
-
-
-def break_txt_into_half_at_some_linebreak(txt):
-    lines = txt.split('\n')
-    n_lines = len(lines)
-    pre = lines[:(n_lines//2)]
-    post = lines[(n_lines//2):]
-    return "\n".join(pre), "\n".join(post)
-
-
-@CatchException
-def 全项目切换英文(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
-    # 第1步：清空历史，以免输入溢出
-    history = []
-
-    # 第2步：尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    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 tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 第3步：集合文件
-    import time, glob, os, shutil, re
-    os.makedirs('gpt_log/generated_english_version', exist_ok=True)
-    os.makedirs('gpt_log/generated_english_version/crazy_functions', exist_ok=True)
-    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
-                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
-    # file_manifest = ['./toolbox.py']
-    i_say_show_user_buffer = []
-
-    # 第4步：随便显示点什么防止卡顿的感觉
-    for index, fp in enumerate(file_manifest):
-        # if 'test_project' in fp: continue
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-        i_say_show_user =f'[{index}/{len(file_manifest)}] 接下来请将以下代码中包含的所有中文转化为英文，只输出转化后的英文代码，请用代码块输出代码: {os.path.abspath(fp)}'
-        i_say_show_user_buffer.append(i_say_show_user)
-        chatbot.append((i_say_show_user, "[Local Message] 等待多线程操作，中间过程不予显示."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-    # 第5步：Token限制下的截断与处理
-    MAX_TOKEN = 3000
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
-
-
-    # 第6步：任务函数
-    mutable_return = [None for _ in file_manifest]
-    observe_window = [[""] for _ in file_manifest]
-    def thread_worker(fp,index):
-        if index > 10: 
-            time.sleep(60)
-            print('Openai 限制免费用户每分钟20次请求，降低请求频率中。')
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-        i_say_template = lambda fp, file_content: f'接下来请将以下代码中包含的所有中文转化为英文，只输出代码，文件名是{fp}，文件代码是 ```{file_content}```'
-        try:
-            gpt_say = ""
-            # 分解代码文件
-            file_content_breakdown = breakdown_txt_to_satisfy_token_limit(file_content, get_token_fn, MAX_TOKEN)
-            for file_content_partial in file_content_breakdown:
-                i_say = i_say_template(fp, file_content_partial)
-                # # ** gpt request **
-                gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=observe_window[index])
-                gpt_say_partial = extract_code_block_carefully(gpt_say_partial)
-                gpt_say += gpt_say_partial
-            mutable_return[index] = gpt_say
-        except ConnectionAbortedError as token_exceed_err:
-            print('至少一个线程任务Token溢出而失败', e)
-        except Exception as e:
-            print('至少一个线程任务意外失败', e)
-
-    # 第7步：所有线程同时开始执行任务函数
-    handles = [threading.Thread(target=thread_worker, args=(fp,index)) for index, fp in enumerate(file_manifest)]
-    for h in handles:
-        h.daemon = True
-        h.start()
-    chatbot.append(('开始了吗？', f'多线程操作已经开始'))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 第8步：循环轮询各个线程是否执行完毕
-    cnt = 0
-    while True:
-        cnt += 1
-        time.sleep(0.2)
-        th_alive = [h.is_alive() for h in handles]
-        if not any(th_alive): break
-        # 更好的UI视觉效果
-        observe_win = []
-        for thread_index, alive in enumerate(th_alive): 
-            observe_win.append("[ ..."+observe_window[thread_index][0][-60:].replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"... ]")
-        stat = [f'执行中: {obs}\n\n' if alive else '已完成\n\n' for alive, obs in zip(th_alive, observe_win)]
-        stat_str = ''.join(stat)
-        chatbot[-1] = (chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt%10+1)))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 第9步：把结果写入文件
-    for index, h in enumerate(handles):
-        h.join() # 这里其实不需要join了，肯定已经都结束了
-        fp = file_manifest[index]
-        gpt_say = mutable_return[index]
-        i_say_show_user = i_say_show_user_buffer[index]
-
-        where_to_relocate = f'gpt_log/generated_english_version/{fp}'
-        if gpt_say is not None:
-            with open(where_to_relocate, 'w+', encoding='utf-8') as f:  
-                f.write(gpt_say)
-        else:  # 失败
-            shutil.copyfile(file_manifest[index], where_to_relocate)
-        chatbot.append((i_say_show_user, f'[Local Message] 已完成{os.path.abspath(fp)}的转化，\n\n存入{os.path.abspath(where_to_relocate)}'))
-        history.append(i_say_show_user); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        time.sleep(1)
-
-    # 第10步：备份一个文件
-    res = write_results_to_file(history)
-    chatbot.append(("生成一份任务执行报告", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
deleted file mode 100644
index 86584573a3d1afac73041b85516112ac21f1f17c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/pspnet_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/HarryLee/eCommerceImageCaptioning/fairseq/scripts/split_train_valid_docs.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/scripts/split_train_valid_docs.py
deleted file mode 100644
index ff159785284a13b44626b207d84430c592acaf8f..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/scripts/split_train_valid_docs.py
+++ /dev/null
@@ -1,86 +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.
-"""
-Split a large file into a train and valid set while respecting document
-boundaries. Documents should be separated by a single empty line.
-"""
-
-import argparse
-import random
-import sys
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("input")
-    parser.add_argument("sample_output", help="train output file")
-    parser.add_argument("remainder_output", help="valid output file")
-    parser.add_argument("-k", type=int, help="remainder size")
-    parser.add_argument(
-        "--lines", action="store_true", help="split lines instead of docs"
-    )
-    args = parser.parse_args()
-
-    assert args.k is not None
-
-    sample = []
-    remainder = []
-    num_docs = [0]
-
-    def update_sample(doc):
-        if len(sample) < args.k:
-            sample.append(doc.copy())
-        else:
-            i = num_docs[0]
-            j = random.randrange(i + 1)
-            if j < args.k:
-                remainder.append(sample[j])
-                sample[j] = doc.copy()
-            else:
-                remainder.append(doc.copy())
-        num_docs[0] += 1
-        doc.clear()
-
-    with open(args.input, "r", encoding="utf-8") as h:
-        doc = []
-        for i, line in enumerate(h):
-            if line.strip() == "":  # empty line indicates new document
-                update_sample(doc)
-            else:
-                doc.append(line)
-            if args.lines:
-                update_sample(doc)
-            if i % 1000000 == 0:
-                print(i, file=sys.stderr, end="", flush=True)
-            elif i % 100000 == 0:
-                print(".", file=sys.stderr, end="", flush=True)
-        if len(doc) > 0:
-            update_sample(doc)
-    print(file=sys.stderr, flush=True)
-
-    assert len(sample) == args.k
-
-    with open(args.sample_output, "w", encoding="utf-8") as out:
-        first = True
-        for doc in sample:
-            if not first and not args.lines:
-                out.write("\n")
-            first = False
-            for line in doc:
-                out.write(line)
-
-    with open(args.remainder_output, "w", encoding="utf-8") as out:
-        first = True
-        for doc in remainder:
-            if not first and not args.lines:
-                out.write("\n")
-            first = False
-            for line in doc:
-                out.write(line)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/HighCWu/GPEN/face_model/op/__init__.py b/spaces/HighCWu/GPEN/face_model/op/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/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/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates.py b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates.py
deleted file mode 100644
index 561a07d38de21f362ea8871549ad8a80926dc375..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates.py
+++ /dev/null
@@ -1,563 +0,0 @@
-from __future__ import annotations
-
-import typing
-from typing import Any, Callable, Tuple
-
-import numpy as np
-from PIL.Image import Image
-
-from gradio import components
-
-
-class TextArea(components.Textbox):
-    """
-    Sets: lines=7
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: str | Callable | None = "",
-        *,
-        lines: int = 7,
-        max_lines: int = 20,
-        placeholder: str | None = None,
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            lines=lines,
-            max_lines=max_lines,
-            placeholder=placeholder,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            **kwargs,
-        )
-
-
-class Webcam(components.Image):
-    """
-    Sets: source="webcam", interactive=True
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: str | Image | np.ndarray | None = None,
-        *,
-        shape: Tuple[int, int] | None = None,
-        image_mode: str = "RGB",
-        invert_colors: bool = False,
-        source: str = "webcam",
-        tool: str | None = None,
-        type: str = "numpy",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = True,
-        visible: bool = True,
-        streaming: bool = False,
-        elem_id: str | None = None,
-        mirror_webcam: bool = True,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            shape=shape,
-            image_mode=image_mode,
-            invert_colors=invert_colors,
-            source=source,
-            tool=tool,
-            type=type,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            streaming=streaming,
-            elem_id=elem_id,
-            mirror_webcam=mirror_webcam,
-            **kwargs,
-        )
-
-
-class Sketchpad(components.Image):
-    """
-    Sets: image_mode="L", source="canvas", shape=(28, 28), invert_colors=True, interactive=True
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: str | Image | np.ndarray | None = None,
-        *,
-        shape: Tuple[int, int] = (28, 28),
-        image_mode: str = "L",
-        invert_colors: bool = True,
-        source: str = "canvas",
-        tool: str | None = None,
-        type: str = "numpy",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = True,
-        visible: bool = True,
-        streaming: bool = False,
-        elem_id: str | None = None,
-        mirror_webcam: bool = True,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            shape=shape,
-            image_mode=image_mode,
-            invert_colors=invert_colors,
-            source=source,
-            tool=tool,
-            type=type,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            streaming=streaming,
-            elem_id=elem_id,
-            mirror_webcam=mirror_webcam,
-            **kwargs,
-        )
-
-
-class Paint(components.Image):
-    """
-    Sets: source="canvas", tool="color-sketch", interactive=True
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: str | Image | np.ndarray | None = None,
-        *,
-        shape: Tuple[int, int] | None = None,
-        image_mode: str = "RGB",
-        invert_colors: bool = False,
-        source: str = "canvas",
-        tool: str = "color-sketch",
-        type: str = "numpy",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = True,
-        visible: bool = True,
-        streaming: bool = False,
-        elem_id: str | None = None,
-        mirror_webcam: bool = True,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            shape=shape,
-            image_mode=image_mode,
-            invert_colors=invert_colors,
-            source=source,
-            tool=tool,
-            type=type,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            streaming=streaming,
-            elem_id=elem_id,
-            mirror_webcam=mirror_webcam,
-            **kwargs,
-        )
-
-
-class ImageMask(components.Image):
-    """
-    Sets: source="upload", tool="sketch", interactive=True
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: str | Image | np.ndarray | None = None,
-        *,
-        shape: Tuple[int, int] | None = None,
-        image_mode: str = "RGB",
-        invert_colors: bool = False,
-        source: str = "upload",
-        tool: str = "sketch",
-        type: str = "numpy",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = True,
-        visible: bool = True,
-        streaming: bool = False,
-        elem_id: str | None = None,
-        mirror_webcam: bool = True,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            shape=shape,
-            image_mode=image_mode,
-            invert_colors=invert_colors,
-            source=source,
-            tool=tool,
-            type=type,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            streaming=streaming,
-            elem_id=elem_id,
-            mirror_webcam=mirror_webcam,
-            **kwargs,
-        )
-
-
-class ImagePaint(components.Image):
-    """
-    Sets: source="upload", tool="color-sketch", interactive=True
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: str | Image | np.ndarray | None = None,
-        *,
-        shape: Tuple[int, int] | None = None,
-        image_mode: str = "RGB",
-        invert_colors: bool = False,
-        source: str = "upload",
-        tool: str = "color-sketch",
-        type: str = "numpy",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = True,
-        visible: bool = True,
-        streaming: bool = False,
-        elem_id: str | None = None,
-        mirror_webcam: bool = True,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            shape=shape,
-            image_mode=image_mode,
-            invert_colors=invert_colors,
-            source=source,
-            tool=tool,
-            type=type,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            streaming=streaming,
-            elem_id=elem_id,
-            mirror_webcam=mirror_webcam,
-            **kwargs,
-        )
-
-
-class Pil(components.Image):
-    """
-    Sets: type="pil"
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: str | Image | np.ndarray | None = None,
-        *,
-        shape: Tuple[int, int] | None = None,
-        image_mode: str = "RGB",
-        invert_colors: bool = False,
-        source: str = "upload",
-        tool: str | None = None,
-        type: str = "pil",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = None,
-        visible: bool = True,
-        streaming: bool = False,
-        elem_id: str | None = None,
-        mirror_webcam: bool = True,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            shape=shape,
-            image_mode=image_mode,
-            invert_colors=invert_colors,
-            source=source,
-            tool=tool,
-            type=type,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            streaming=streaming,
-            elem_id=elem_id,
-            mirror_webcam=mirror_webcam,
-            **kwargs,
-        )
-
-
-class PlayableVideo(components.Video):
-    """
-    Sets: format="mp4"
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: str | Callable | None = None,
-        *,
-        format: str | None = "mp4",
-        source: str = "upload",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        mirror_webcam: bool = True,
-        include_audio: bool | None = None,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            format=format,
-            source=source,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            mirror_webcam=mirror_webcam,
-            include_audio=include_audio,
-            **kwargs,
-        )
-
-
-class Microphone(components.Audio):
-    """
-    Sets: source="microphone"
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: str | Tuple[int, np.ndarray] | Callable | None = None,
-        *,
-        source: str = "microphone",
-        type: str = "numpy",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = None,
-        visible: bool = True,
-        streaming: bool = False,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            source=source,
-            type=type,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            streaming=streaming,
-            elem_id=elem_id,
-            **kwargs,
-        )
-
-
-class Files(components.File):
-    """
-    Sets: file_count="multiple"
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: str | typing.List[str] | Callable | None = None,
-        *,
-        file_count: str = "multiple",
-        type: str = "file",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            file_count=file_count,
-            type=type,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            **kwargs,
-        )
-
-
-class Numpy(components.Dataframe):
-    """
-    Sets: type="numpy"
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: typing.List[typing.List[Any]] | Callable | None = None,
-        *,
-        headers: typing.List[str] | None = None,
-        row_count: int | Tuple[int, str] = (1, "dynamic"),
-        col_count: int | Tuple[int, str] | None = None,
-        datatype: str | typing.List[str] = "str",
-        type: str = "numpy",
-        max_rows: int | None = 20,
-        max_cols: int | None = None,
-        overflow_row_behaviour: str = "paginate",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        wrap: bool = False,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            headers=headers,
-            row_count=row_count,
-            col_count=col_count,
-            datatype=datatype,
-            type=type,
-            max_rows=max_rows,
-            max_cols=max_cols,
-            overflow_row_behaviour=overflow_row_behaviour,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            wrap=wrap,
-            **kwargs,
-        )
-
-
-class Matrix(components.Dataframe):
-    """
-    Sets: type="array"
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: typing.List[typing.List[Any]] | Callable | None = None,
-        *,
-        headers: typing.List[str] | None = None,
-        row_count: int | Tuple[int, str] = (1, "dynamic"),
-        col_count: int | Tuple[int, str] | None = None,
-        datatype: str | typing.List[str] = "str",
-        type: str = "array",
-        max_rows: int | None = 20,
-        max_cols: int | None = None,
-        overflow_row_behaviour: str = "paginate",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        wrap: bool = False,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            headers=headers,
-            row_count=row_count,
-            col_count=col_count,
-            datatype=datatype,
-            type=type,
-            max_rows=max_rows,
-            max_cols=max_cols,
-            overflow_row_behaviour=overflow_row_behaviour,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            wrap=wrap,
-            **kwargs,
-        )
-
-
-class List(components.Dataframe):
-    """
-    Sets: type="array", col_count=1
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        value: typing.List[typing.List[Any]] | Callable | None = None,
-        *,
-        headers: typing.List[str] | None = None,
-        row_count: int | Tuple[int, str] = (1, "dynamic"),
-        col_count: int | Tuple[int, str] = 1,
-        datatype: str | typing.List[str] = "str",
-        type: str = "array",
-        max_rows: int | None = 20,
-        max_cols: int | None = None,
-        overflow_row_behaviour: str = "paginate",
-        label: str | None = None,
-        show_label: bool = True,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        wrap: bool = False,
-        **kwargs,
-    ):
-        super().__init__(
-            value=value,
-            headers=headers,
-            row_count=row_count,
-            col_count=col_count,
-            datatype=datatype,
-            type=type,
-            max_rows=max_rows,
-            max_cols=max_cols,
-            overflow_row_behaviour=overflow_row_behaviour,
-            label=label,
-            show_label=show_label,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            wrap=wrap,
-            **kwargs,
-        )
-
-
-Mic = Microphone
diff --git a/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/utils.py b/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/utils.py
deleted file mode 100644
index 2c7b03733d2290d3834d2c68a16034198daa1e69..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/utils.py
+++ /dev/null
@@ -1,101 +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 numpy as np
-import torch
-from scipy.interpolate import interp1d
-import torchaudio
-
-from fairseq.tasks.text_to_speech import (
-    batch_compute_distortion, compute_rms_dist
-)
-
-
-def batch_mel_spectral_distortion(
-        y1, y2, sr, normalize_type="path", mel_fn=None
-):
-    """
-    https://arxiv.org/pdf/2011.03568.pdf
-
-    Same as Mel Cepstral Distortion, but computed on log-mel spectrograms.
-    """
-    if mel_fn is None or mel_fn.sample_rate != sr:
-        mel_fn = torchaudio.transforms.MelSpectrogram(
-            sr, n_fft=int(0.05 * sr), win_length=int(0.05 * sr),
-            hop_length=int(0.0125 * sr), f_min=20, n_mels=80,
-            window_fn=torch.hann_window
-        ).to(y1[0].device)
-    offset = 1e-6
-    return batch_compute_distortion(
-        y1, y2, sr, lambda y: torch.log(mel_fn(y) + offset).transpose(-1, -2),
-        compute_rms_dist, normalize_type
-    )
-
-
-# This code is based on
-# "https://github.com/bastibe/MAPS-Scripts/blob/master/helper.py"
-def _same_t_in_true_and_est(func):
-    def new_func(true_t, true_f, est_t, est_f):
-        assert type(true_t) is np.ndarray
-        assert type(true_f) is np.ndarray
-        assert type(est_t) is np.ndarray
-        assert type(est_f) is np.ndarray
-
-        interpolated_f = interp1d(
-            est_t, est_f, bounds_error=False, kind='nearest', fill_value=0
-        )(true_t)
-        return func(true_t, true_f, true_t, interpolated_f)
-
-    return new_func
-
-
-@_same_t_in_true_and_est
-def gross_pitch_error(true_t, true_f, est_t, est_f):
-    """The relative frequency in percent of pitch estimates that are
-    outside a threshold around the true pitch. Only frames that are
-    considered pitched by both the ground truth and the estimator (if
-    applicable) are considered.
-    """
-
-    correct_frames = _true_voiced_frames(true_t, true_f, est_t, est_f)
-    gross_pitch_error_frames = _gross_pitch_error_frames(
-        true_t, true_f, est_t, est_f
-    )
-    return np.sum(gross_pitch_error_frames) / np.sum(correct_frames)
-
-
-def _gross_pitch_error_frames(true_t, true_f, est_t, est_f, eps=1e-8):
-    voiced_frames = _true_voiced_frames(true_t, true_f, est_t, est_f)
-    true_f_p_eps = [x + eps for x in true_f]
-    pitch_error_frames = np.abs(est_f / true_f_p_eps - 1) > 0.2
-    return voiced_frames & pitch_error_frames
-
-
-def _true_voiced_frames(true_t, true_f, est_t, est_f):
-    return (est_f != 0) & (true_f != 0)
-
-
-def _voicing_decision_error_frames(true_t, true_f, est_t, est_f):
-    return (est_f != 0) != (true_f != 0)
-
-
-@_same_t_in_true_and_est
-def f0_frame_error(true_t, true_f, est_t, est_f):
-    gross_pitch_error_frames = _gross_pitch_error_frames(
-        true_t, true_f, est_t, est_f
-    )
-    voicing_decision_error_frames = _voicing_decision_error_frames(
-        true_t, true_f, est_t, est_f
-    )
-    return (np.sum(gross_pitch_error_frames) +
-            np.sum(voicing_decision_error_frames)) / (len(true_t))
-
-
-@_same_t_in_true_and_est
-def voicing_decision_error(true_t, true_f, est_t, est_f):
-    voicing_decision_error_frames = _voicing_decision_error_frames(
-        true_t, true_f, est_t, est_f
-    )
-    return np.sum(voicing_decision_error_frames) / (len(true_t))
diff --git a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py b/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py
deleted file mode 100644
index 2c87445d810cd790f887d1a135287a334cbdf223..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py
+++ /dev/null
@@ -1,125 +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 argparse
-import logging
-import os
-
-import numpy as np
-
-import joblib
-from examples.textless_nlp.gslm.speech2unit.clustering.utils import (
-    get_audio_files,
-)
-from examples.textless_nlp.gslm.speech2unit.pretrained.utils import (
-    get_features,
-)
-
-
-def get_logger():
-    log_format = "[%(asctime)s] [%(levelname)s]: %(message)s"
-    logging.basicConfig(format=log_format, level=logging.INFO)
-    logger = logging.getLogger(__name__)
-    return logger
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        description="Quantize using K-means clustering over acoustic features."
-    )
-    parser.add_argument(
-        "--feature_type",
-        type=str,
-        choices=["logmel", "hubert", "w2v2", "cpc"],
-        default=None,
-        required=True,
-        help="Acoustic feature type",
-    )
-    parser.add_argument(
-        "--acoustic_model_path",
-        type=str,
-        help="Pretrained acoustic model checkpoint"
-    )
-    parser.add_argument(
-        "--layer",
-        type=int,
-        help="The layer of the pretrained model to extract features from",
-        default=-1,
-    )
-    parser.add_argument(
-        "--kmeans_model_path",
-        type=str,
-        required=True,
-        help="K-means model file path to use for inference",
-    )
-    parser.add_argument(
-        "--features_path",
-        type=str,
-        default=None,
-        help="Features file path. You don't need to enter acoustic model details if you have dumped features",
-    )
-    parser.add_argument(
-        "--manifest_path",
-        type=str,
-        default=None,
-        help="Manifest file containing the root dir and file names",
-    )
-    parser.add_argument(
-        "--out_quantized_file_path",
-        required=True,
-        type=str,
-        help="File path of quantized output.",
-    )
-    parser.add_argument(
-        "--extension", type=str, default=".flac", help="Features file path"
-    )
-    return parser
-
-
-def main(args, logger):
-    # Feature extraction
-    if args.features_path is not None:
-        logger.info(f"Loading acoustic features from {args.features_path}...")
-        features_batch = np.load(args.features_path)
-    else:
-        logger.info(f"Extracting {args.feature_type} acoustic features...")
-        features_batch = get_features(
-            feature_type=args.feature_type,
-            checkpoint_path=args.acoustic_model_path,
-            layer=args.layer,
-            manifest_path=args.manifest_path,
-            sample_pct=1.0,
-            flatten=False,
-        )
-        logger.info(
-            f"Features extracted for {len(features_batch)} utterances.\n"
-        )
-        logger.info(
-            f"Dimensionality of representation = {features_batch[0].shape[1]}"
-        )
-
-    # K-means model
-    logger.info(f"Loading K-means model from {args.kmeans_model_path} ...")
-    kmeans_model = joblib.load(open(args.kmeans_model_path, "rb"))
-    kmeans_model.verbose = False
-
-    _, fnames, _ = get_audio_files(args.manifest_path)
-
-    os.makedirs(os.path.dirname(args.out_quantized_file_path), exist_ok=True)
-    print(f"Writing quantized predictions to {args.out_quantized_file_path}")
-    with open(args.out_quantized_file_path, "w") as fout:
-        for i, feats in enumerate(features_batch):
-            pred = kmeans_model.predict(feats)
-            pred_str = " ".join(str(p) for p in pred)
-            base_fname = os.path.basename(fnames[i]).rstrip(args.extension)
-            fout.write(f"{base_fname}|{pred_str}\n")
-
-
-if __name__ == "__main__":
-    parser = get_parser()
-    args = parser.parse_args()
-    logger = get_logger()
-    logger.info(args)
-    main(args, logger)
diff --git a/spaces/IDEA-CCNL/Erlangshen-UniMC-Zero-Shot/modeling_albert.py b/spaces/IDEA-CCNL/Erlangshen-UniMC-Zero-Shot/modeling_albert.py
deleted file mode 100644
index 7c5298825fb471e0575dabaefb2b8514e5bedcd8..0000000000000000000000000000000000000000
--- a/spaces/IDEA-CCNL/Erlangshen-UniMC-Zero-Shot/modeling_albert.py
+++ /dev/null
@@ -1,1363 +0,0 @@
-# coding=utf-8
-# Copyright 2018 Google AI, Google Brain 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.
-"""PyTorch ALBERT model. """
-
-import math
-import os
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import torch
-from packaging import version
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
-    ModelOutput,
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutput,
-    BaseModelOutputWithPooling,
-    MaskedLMOutput,
-    MultipleChoiceModelOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import (
-    PreTrainedModel,
-    apply_chunking_to_forward,
-    find_pruneable_heads_and_indices,
-    prune_linear_layer,
-)
-from transformers.utils import logging
-from transformers import AlbertConfig
-
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "albert-base-v2"
-_CONFIG_FOR_DOC = "AlbertConfig"
-_TOKENIZER_FOR_DOC = "AlbertTokenizer"
-
-
-ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "albert-base-v1",
-    "albert-large-v1",
-    "albert-xlarge-v1",
-    "albert-xxlarge-v1",
-    "albert-base-v2",
-    "albert-large-v2",
-    "albert-xlarge-v2",
-    "albert-xxlarge-v2",
-    # See all ALBERT models at https://huggingface.co/models?filter=albert
-]
-
-
-def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
-    """Load tf checkpoints in a pytorch model."""
-    try:
-        import re
-
-        import numpy as np
-        import tensorflow as tf
-    except ImportError:
-        logger.error(
-            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
-            "https://www.tensorflow.org/install/ for installation instructions."
-        )
-        raise
-    tf_path = os.path.abspath(tf_checkpoint_path)
-    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
-    # Load weights from TF model
-    init_vars = tf.train.list_variables(tf_path)
-    names = []
-    arrays = []
-    for name, shape in init_vars:
-        logger.info(f"Loading TF weight {name} with shape {shape}")
-        array = tf.train.load_variable(tf_path, name)
-        names.append(name)
-        arrays.append(array)
-
-    for name, array in zip(names, arrays):
-        print(name)
-
-    for name, array in zip(names, arrays):
-        original_name = name
-
-        # If saved from the TF HUB module
-        name = name.replace("module/", "")
-
-        # Renaming and simplifying
-        name = name.replace("ffn_1", "ffn")
-        name = name.replace("bert/", "albert/")
-        name = name.replace("attention_1", "attention")
-        name = name.replace("transform/", "")
-        name = name.replace("LayerNorm_1", "full_layer_layer_norm")
-        name = name.replace("LayerNorm", "attention/LayerNorm")
-        name = name.replace("transformer/", "")
-
-        # The feed forward layer had an 'intermediate' step which has been abstracted away
-        name = name.replace("intermediate/dense/", "")
-        name = name.replace("ffn/intermediate/output/dense/", "ffn_output/")
-
-        # ALBERT attention was split between self and output which have been abstracted away
-        name = name.replace("/output/", "/")
-        name = name.replace("/self/", "/")
-
-        # The pooler is a linear layer
-        name = name.replace("pooler/dense", "pooler")
-
-        # The classifier was simplified to predictions from cls/predictions
-        name = name.replace("cls/predictions", "predictions")
-        name = name.replace("predictions/attention", "predictions")
-
-        # Naming was changed to be more explicit
-        name = name.replace("embeddings/attention", "embeddings")
-        name = name.replace("inner_group_", "albert_layers/")
-        name = name.replace("group_", "albert_layer_groups/")
-
-        # Classifier
-        if len(name.split("/")) == 1 and ("output_bias" in name or "output_weights" in name):
-            name = "classifier/" + name
-
-        # No ALBERT model currently handles the next sentence prediction task
-        if "seq_relationship" in name:
-            name = name.replace("seq_relationship/output_", "sop_classifier/classifier/")
-            name = name.replace("weights", "weight")
-
-        name = name.split("/")
-
-        # Ignore the gradients applied by the LAMB/ADAM optimizers.
-        if (
-            "adam_m" in name
-            or "adam_v" in name
-            or "AdamWeightDecayOptimizer" in name
-            or "AdamWeightDecayOptimizer_1" in name
-            or "global_step" in name
-        ):
-            logger.info(f"Skipping {'/'.join(name)}")
-            continue
-
-        pointer = model
-        for m_name in name:
-            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
-                scope_names = re.split(r"_(\d+)", m_name)
-            else:
-                scope_names = [m_name]
-
-            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
-                pointer = getattr(pointer, "weight")
-            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
-                pointer = getattr(pointer, "bias")
-            elif scope_names[0] == "output_weights":
-                pointer = getattr(pointer, "weight")
-            elif scope_names[0] == "squad":
-                pointer = getattr(pointer, "classifier")
-            else:
-                try:
-                    pointer = getattr(pointer, scope_names[0])
-                except AttributeError:
-                    logger.info(f"Skipping {'/'.join(name)}")
-                    continue
-            if len(scope_names) >= 2:
-                num = int(scope_names[1])
-                pointer = pointer[num]
-
-        if m_name[-11:] == "_embeddings":
-            pointer = getattr(pointer, "weight")
-        elif m_name == "kernel":
-            array = np.transpose(array)
-        try:
-            if pointer.shape != array.shape:
-                raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
-        except AssertionError as e:
-            e.args += (pointer.shape, array.shape)
-            raise
-        print(f"Initialize PyTorch weight {name} from {original_name}")
-        pointer.data = torch.from_numpy(array)
-
-    return model
-
-
-class AlbertEmbeddings(nn.Module):
-    """
-    Construct the embeddings from word, position and token_type embeddings.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        if version.parse(torch.__version__) > version.parse("1.6.0"):
-            self.register_buffer(
-                "token_type_ids",
-                torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
-                persistent=False,
-            )
-
-    # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.forward
-    def forward(
-        self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
-
-        # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
-        # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
-        # issue #5664
-        if token_type_ids is None:
-            if hasattr(self, "token_type_ids"):
-                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
-                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
-                token_type_ids = buffered_token_type_ids_expanded
-            else:
-                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = inputs_embeds + token_type_embeddings
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class AlbertAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
-                f"heads ({config.num_attention_heads}"
-            )
-
-        self.num_attention_heads = config.num_attention_heads
-        self.hidden_size = config.hidden_size
-        self.attention_head_size = config.hidden_size // config.num_attention_heads
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.attention_dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.output_dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.pruned_heads = set()
-
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
-
-    # Copied from transformers.models.bert.modeling_bert.BertSelfAttention.transpose_for_scores
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads, self.num_attention_heads, self.attention_head_size, self.pruned_heads
-        )
-
-        # Prune linear layers
-        self.query = prune_linear_layer(self.query, index)
-        self.key = prune_linear_layer(self.key, index)
-        self.value = prune_linear_layer(self.value, index)
-        self.dense = prune_linear_layer(self.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.num_attention_heads = self.num_attention_heads - len(heads)
-        self.all_head_size = self.attention_head_size * self.num_attention_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
-        mixed_query_layer = self.query(hidden_states)
-        mixed_key_layer = self.key(hidden_states)
-        mixed_value_layer = self.value(hidden_states)
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        key_layer = self.transpose_for_scores(mixed_key_layer)
-        value_layer = self.transpose_for_scores(mixed_value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.attention_dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs = attention_probs * head_mask
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-        context_layer = context_layer.transpose(2, 1).flatten(2)
-
-        projected_context_layer = self.dense(context_layer)
-        projected_context_layer_dropout = self.output_dropout(projected_context_layer)
-        layernormed_context_layer = self.LayerNorm(hidden_states + projected_context_layer_dropout)
-        return (layernormed_context_layer, attention_probs) if output_attentions else (layernormed_context_layer,)
-
-
-class AlbertLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        self.config = config
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.attention = AlbertAttention(config)
-        self.ffn = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.activation = ACT2FN[config.hidden_act]
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(
-        self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False
-    ):
-        attention_output = self.attention(hidden_states, attention_mask, head_mask, output_attentions)
-
-        ffn_output = apply_chunking_to_forward(
-            self.ff_chunk,
-            self.chunk_size_feed_forward,
-            self.seq_len_dim,
-            attention_output[0],
-        )
-        hidden_states = self.full_layer_layer_norm(ffn_output + attention_output[0])
-
-        return (hidden_states,) + attention_output[1:]  # add attentions if we output them
-
-    def ff_chunk(self, attention_output):
-        ffn_output = self.ffn(attention_output)
-        ffn_output = self.activation(ffn_output)
-        ffn_output = self.ffn_output(ffn_output)
-        return ffn_output
-
-
-class AlbertLayerGroup(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)])
-
-    def forward(
-        self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False
-    ):
-        layer_hidden_states = ()
-        layer_attentions = ()
-
-        for layer_index, albert_layer in enumerate(self.albert_layers):
-            layer_output = albert_layer(hidden_states, attention_mask, head_mask[layer_index], output_attentions)
-            hidden_states = layer_output[0]
-
-            if output_attentions:
-                layer_attentions = layer_attentions + (layer_output[1],)
-
-            if output_hidden_states:
-                layer_hidden_states = layer_hidden_states + (hidden_states,)
-
-        outputs = (hidden_states,)
-        if output_hidden_states:
-            outputs = outputs + (layer_hidden_states,)
-        if output_attentions:
-            outputs = outputs + (layer_attentions,)
-        return outputs  # last-layer hidden state, (layer hidden states), (layer attentions)
-
-
-class AlbertTransformer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        self.config = config
-        self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
-        self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)])
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
-    ):
-        hidden_states = self.embedding_hidden_mapping_in(hidden_states)
-
-        all_hidden_states = (hidden_states,) if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        head_mask = [None] * self.config.num_hidden_layers if head_mask is None else head_mask
-
-        for i in range(self.config.num_hidden_layers):
-            # Number of layers in a hidden group
-            layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups)
-
-            # Index of the hidden group
-            group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
-
-            layer_group_output = self.albert_layer_groups[group_idx](
-                hidden_states,
-                attention_mask,
-                head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group],
-                output_attentions,
-                output_hidden_states,
-            )
-            hidden_states = layer_group_output[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + layer_group_output[-1]
-
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
-        )
-
-
-class AlbertPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = AlbertConfig
-    load_tf_weights = load_tf_weights_in_albert
-    base_model_prefix = "albert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """Initialize the weights."""
-        if isinstance(module, nn.Linear):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-
-@dataclass
-class AlbertForPreTrainingOutput(ModelOutput):
-    """
-    Output type of :class:`~transformers.AlbertForPreTraining`.
-
-    Args:
-        loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
-            Total loss as the sum of the masked language modeling loss and the next sequence prediction
-            (classification) loss.
-        prediction_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        sop_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
-            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
-            before SoftMax).
-        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
-            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
-            sequence_length, sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    prediction_logits: torch.FloatTensor = None
-    sop_logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-ALBERT_START_DOCSTRING = r"""
-
-    This model inherits from :class:`~transformers.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.
-
-    Args:
-        config (:class:`~transformers.AlbertConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
-            weights.
-"""
-
-ALBERT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using :class:`~transformers.AlbertTokenizer`. See
-            :meth:`transformers.PreTrainedTokenizer.__call__` and :meth:`transformers.PreTrainedTokenizer.encode` for
-            details.
-
-            `What are input IDs? <../glossary.html#input-ids>`__
-        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `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.html#attention-mask>`__
-        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
-            1]``:
-
-            - 0 corresponds to a `sentence A` token,
-            - 1 corresponds to a `sentence B` token.
-
-            `What are token type IDs? <../glossary.html#token-type-ids>`_
-        position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
-            config.max_position_embeddings - 1]``.
-
-            `What are position IDs? <../glossary.html#position-ids>`_
-        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
-            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
-            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
-            vectors than the model's internal embedding lookup matrix.
-        output_attentions (:obj:`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 (:obj:`bool`, `optional`):
-            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
-            more detail.
-        return_dict (:obj:`bool`, `optional`):
-            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare ALBERT Model transformer outputting raw hidden-states without any specific head on top.",
-    ALBERT_START_DOCSTRING,
-)
-class AlbertModel(AlbertPreTrainedModel):
-
-    config_class = AlbertConfig
-    base_model_prefix = "albert"
-
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-
-        self.config = config
-        self.embeddings = AlbertEmbeddings(config)
-        self.encoder = AlbertTransformer(config)
-        if add_pooling_layer:
-            self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
-            self.pooler_activation = nn.Tanh()
-        else:
-            self.pooler = None
-            self.pooler_activation = None
-
-        self.init_weights()
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} ALBERT has
-        a different architecture in that its layers are shared across groups, which then has inner groups. If an ALBERT
-        model has 12 hidden layers and 2 hidden groups, with two inner groups, there is a total of 4 different layers.
-
-        These layers are flattened: the indices [0,1] correspond to the two inner groups of the first hidden layer,
-        while [2,3] correspond to the two inner groups of the second hidden layer.
-
-        Any layer with in index other than [0,1,2,3] will result in an error. See base class PreTrainedModel for more
-        information about head pruning
-        """
-        for layer, heads in heads_to_prune.items():
-            group_idx = int(layer / self.config.inner_group_num)
-            inner_group_idx = int(layer - group_idx * self.config.inner_group_num)
-            self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)
-
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPooling,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        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
-
-        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:
-            input_shape = input_ids.size()
-        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")
-
-        batch_size, seq_length = input_shape
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if attention_mask is None:
-            attention_mask = torch.ones(input_shape, device=device)
-        if token_type_ids is None:
-            if hasattr(self.embeddings, "token_type_ids"):
-                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
-                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
-                token_type_ids = buffered_token_type_ids_expanded
-            else:
-                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        # extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) # 
-        extended_attention_mask = attention_mask[:, None, :, :]
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        embedding_output = self.embeddings(
-            input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
-        )
-        encoder_outputs = self.encoder(
-            embedding_output,
-            extended_attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = encoder_outputs[0]
-
-        pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPooling(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Albert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
-    `sentence order prediction (classification)` head.
-    """,
-    ALBERT_START_DOCSTRING,
-)
-class AlbertForPreTraining(AlbertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.albert = AlbertModel(config)
-        self.predictions = AlbertMLMHead(config)
-        self.sop_classifier = AlbertSOPHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.predictions.decoder = new_embeddings
-
-    def get_input_embeddings(self):
-        return self.albert.embeddings.word_embeddings
-
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=AlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-        sentence_order_label=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`):
-            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
-            config.vocab_size]`` (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]``
-        sentence_order_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`):
-            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
-            (see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``. ``0`` indicates original order (sequence
-            A, then sequence B), ``1`` indicates switched order (sequence B, then sequence A).
-
-        Returns:
-
-        Example::
-
-            >>> from transformers import AlbertTokenizer, AlbertForPreTraining
-            >>> import torch
-
-            >>> tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
-            >>> model = AlbertForPreTraining.from_pretrained('albert-base-v2')
-
-            >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-            >>> outputs = model(input_ids)
-
-            >>> prediction_logits = outputs.prediction_logits
-            >>> sop_logits = outputs.sop_logits
-
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.albert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output, pooled_output = outputs[:2]
-
-        prediction_scores = self.predictions(sequence_output)
-        sop_scores = self.sop_classifier(pooled_output)
-
-        total_loss = None
-        if labels is not None and sentence_order_label is not None:
-            loss_fct = CrossEntropyLoss()
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-            sentence_order_loss = loss_fct(sop_scores.view(-1, 2), sentence_order_label.view(-1))
-            total_loss = masked_lm_loss + sentence_order_loss
-
-        if not return_dict:
-            output = (prediction_scores, sop_scores) + outputs[2:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return AlbertForPreTrainingOutput(
-            loss=total_loss,
-            prediction_logits=prediction_scores,
-            sop_logits=sop_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-class AlbertMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        self.LayerNorm = nn.LayerNorm(config.embedding_size)
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-        self.dense = nn.Linear(config.hidden_size, config.embedding_size)
-        self.decoder = nn.Linear(config.embedding_size, config.vocab_size)
-        self.activation = ACT2FN[config.hidden_act]
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.activation(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-
-        prediction_scores = hidden_states
-
-        return prediction_scores
-
-    def _tie_weights(self):
-        # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
-        self.bias = self.decoder.bias
-
-
-class AlbertSOPHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        self.dropout = nn.Dropout(config.classifier_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-    def forward(self, pooled_output):
-        dropout_pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(dropout_pooled_output)
-        return logits
-
-
-@add_start_docstrings(
-    "Albert Model with a `language modeling` head on top.",
-    ALBERT_START_DOCSTRING,
-)
-class AlbertForMaskedLM(AlbertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.albert = AlbertModel(config, add_pooling_layer=False)
-        self.predictions = AlbertMLMHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.predictions.decoder = new_embeddings
-
-    def get_input_embeddings(self):
-        return self.albert.embeddings.word_embeddings
-
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MaskedLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
-            config.vocab_size]`` (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]``
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.albert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_outputs = outputs[0]
-
-        prediction_scores = self.predictions(sequence_outputs)
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return MaskedLMOutput(
-            loss=masked_lm_loss,
-            logits=prediction_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
-    output) e.g. for GLUE tasks.
-    """,
-    ALBERT_START_DOCSTRING,
-)
-class AlbertForSequenceClassification(AlbertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.config = config
-
-        self.albert = AlbertModel(config)
-        self.dropout = nn.Dropout(config.classifier_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for computing the sequence classification/regression loss. Indices should be in ``[0, ...,
-            config.num_labels - 1]``. If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
-            If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.albert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Albert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    ALBERT_START_DOCSTRING,
-)
-class AlbertForTokenClassification(AlbertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.albert = AlbertModel(config, add_pooling_layer=False)
-        classifier_dropout_prob = (
-            config.classifier_dropout_prob
-            if config.classifier_dropout_prob is not None
-            else config.hidden_dropout_prob
-        )
-        self.dropout = nn.Dropout(classifier_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
-            1]``.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.albert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        sequence_output = self.dropout(sequence_output)
-        logits = self.classifier(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            # Only keep active parts of the loss
-            if attention_mask is not None:
-                active_loss = attention_mask.view(-1) == 1
-                active_logits = logits.view(-1, self.num_labels)
-                active_labels = torch.where(
-                    active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
-                )
-                loss = loss_fct(active_logits, active_labels)
-            else:
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Albert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
-    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    ALBERT_START_DOCSTRING,
-)
-class AlbertForQuestionAnswering(AlbertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.albert = AlbertModel(config, add_pooling_layer=False)
-        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=QuestionAnsweringModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        start_positions=None,
-        end_positions=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
-            sequence are not taken into account for computing the loss.
-        end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
-            sequence are not taken into account for computing the loss.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.albert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1).contiguous()
-
-        total_loss = None
-        if start_positions is not None and end_positions is not None:
-            # If we are on multi-GPU, split add a dimension
-            if len(start_positions.size()) > 1:
-                start_positions = start_positions.squeeze(-1)
-            if len(end_positions.size()) > 1:
-                end_positions = end_positions.squeeze(-1)
-            # sometimes the start/end positions are outside our model inputs, we ignore these terms
-            ignored_index = start_logits.size(1)
-            start_positions = start_positions.clamp(0, ignored_index)
-            end_positions = end_positions.clamp(0, ignored_index)
-
-            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
-            total_loss = (start_loss + end_loss) / 2
-
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[2:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return QuestionAnsweringModelOutput(
-            loss=total_loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Albert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
-    softmax) e.g. for RocStories/SWAG tasks.
-    """,
-    ALBERT_START_DOCSTRING,
-)
-class AlbertForMultipleChoice(AlbertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.albert = AlbertModel(config)
-        self.dropout = nn.Dropout(config.classifier_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, 1)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
-    @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MultipleChoiceModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
-            num_choices-1]`` where `num_choices` is the size of the second dimension of the input tensors. (see
-            `input_ids` above)
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
-
-        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
-        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
-        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
-        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
-        inputs_embeds = (
-            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
-            if inputs_embeds is not None
-            else None
-        )
-        outputs = self.albert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-        reshaped_logits = logits.view(-1, num_choices)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(reshaped_logits, labels)
-
-        if not return_dict:
-            output = (reshaped_logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return MultipleChoiceModelOutput(
-            loss=loss,
-            logits=reshaped_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
diff --git a/spaces/Iceclear/StableSR/StableSR/taming/models/cond_transformer.py b/spaces/Iceclear/StableSR/StableSR/taming/models/cond_transformer.py
deleted file mode 100644
index e4c63730fa86ac1b92b37af14c14fb696595b1ab..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/taming/models/cond_transformer.py
+++ /dev/null
@@ -1,352 +0,0 @@
-import os, math
-import torch
-import torch.nn.functional as F
-import pytorch_lightning as pl
-
-from main import instantiate_from_config
-from taming.modules.util import SOSProvider
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class Net2NetTransformer(pl.LightningModule):
-    def __init__(self,
-                 transformer_config,
-                 first_stage_config,
-                 cond_stage_config,
-                 permuter_config=None,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 first_stage_key="image",
-                 cond_stage_key="depth",
-                 downsample_cond_size=-1,
-                 pkeep=1.0,
-                 sos_token=0,
-                 unconditional=False,
-                 ):
-        super().__init__()
-        self.be_unconditional = unconditional
-        self.sos_token = sos_token
-        self.first_stage_key = first_stage_key
-        self.cond_stage_key = cond_stage_key
-        self.init_first_stage_from_ckpt(first_stage_config)
-        self.init_cond_stage_from_ckpt(cond_stage_config)
-        if permuter_config is None:
-            permuter_config = {"target": "taming.modules.transformer.permuter.Identity"}
-        self.permuter = instantiate_from_config(config=permuter_config)
-        self.transformer = instantiate_from_config(config=transformer_config)
-
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-        self.downsample_cond_size = downsample_cond_size
-        self.pkeep = pkeep
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        for k in sd.keys():
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    self.print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path}")
-
-    def init_first_stage_from_ckpt(self, config):
-        model = instantiate_from_config(config)
-        model = model.eval()
-        model.train = disabled_train
-        self.first_stage_model = model
-
-    def init_cond_stage_from_ckpt(self, config):
-        if config == "__is_first_stage__":
-            print("Using first stage also as cond stage.")
-            self.cond_stage_model = self.first_stage_model
-        elif config == "__is_unconditional__" or self.be_unconditional:
-            print(f"Using no cond stage. Assuming the training is intended to be unconditional. "
-                  f"Prepending {self.sos_token} as a sos token.")
-            self.be_unconditional = True
-            self.cond_stage_key = self.first_stage_key
-            self.cond_stage_model = SOSProvider(self.sos_token)
-        else:
-            model = instantiate_from_config(config)
-            model = model.eval()
-            model.train = disabled_train
-            self.cond_stage_model = model
-
-    def forward(self, x, c):
-        # one step to produce the logits
-        _, z_indices = self.encode_to_z(x)
-        _, c_indices = self.encode_to_c(c)
-
-        if self.training and self.pkeep < 1.0:
-            mask = torch.bernoulli(self.pkeep*torch.ones(z_indices.shape,
-                                                         device=z_indices.device))
-            mask = mask.round().to(dtype=torch.int64)
-            r_indices = torch.randint_like(z_indices, self.transformer.config.vocab_size)
-            a_indices = mask*z_indices+(1-mask)*r_indices
-        else:
-            a_indices = z_indices
-
-        cz_indices = torch.cat((c_indices, a_indices), dim=1)
-
-        # target includes all sequence elements (no need to handle first one
-        # differently because we are conditioning)
-        target = z_indices
-        # make the prediction
-        logits, _ = self.transformer(cz_indices[:, :-1])
-        # cut off conditioning outputs - output i corresponds to p(z_i | z_{<i}, c)
-        logits = logits[:, c_indices.shape[1]-1:]
-
-        return logits, target
-
-    def top_k_logits(self, logits, k):
-        v, ix = torch.topk(logits, k)
-        out = logits.clone()
-        out[out < v[..., [-1]]] = -float('Inf')
-        return out
-
-    @torch.no_grad()
-    def sample(self, x, c, steps, temperature=1.0, sample=False, top_k=None,
-               callback=lambda k: None):
-        x = torch.cat((c,x),dim=1)
-        block_size = self.transformer.get_block_size()
-        assert not self.transformer.training
-        if self.pkeep <= 0.0:
-            # one pass suffices since input is pure noise anyway
-            assert len(x.shape)==2
-            noise_shape = (x.shape[0], steps-1)
-            #noise = torch.randint(self.transformer.config.vocab_size, noise_shape).to(x)
-            noise = c.clone()[:,x.shape[1]-c.shape[1]:-1]
-            x = torch.cat((x,noise),dim=1)
-            logits, _ = self.transformer(x)
-            # take all logits for now and scale by temp
-            logits = logits / temperature
-            # optionally crop probabilities to only the top k options
-            if top_k is not None:
-                logits = self.top_k_logits(logits, top_k)
-            # apply softmax to convert to probabilities
-            probs = F.softmax(logits, dim=-1)
-            # sample from the distribution or take the most likely
-            if sample:
-                shape = probs.shape
-                probs = probs.reshape(shape[0]*shape[1],shape[2])
-                ix = torch.multinomial(probs, num_samples=1)
-                probs = probs.reshape(shape[0],shape[1],shape[2])
-                ix = ix.reshape(shape[0],shape[1])
-            else:
-                _, ix = torch.topk(probs, k=1, dim=-1)
-            # cut off conditioning
-            x = ix[:, c.shape[1]-1:]
-        else:
-            for k in range(steps):
-                callback(k)
-                assert x.size(1) <= block_size # make sure model can see conditioning
-                x_cond = x if x.size(1) <= block_size else x[:, -block_size:]  # crop context if needed
-                logits, _ = self.transformer(x_cond)
-                # pluck the logits at the final step and scale by temperature
-                logits = logits[:, -1, :] / temperature
-                # optionally crop probabilities to only the top k options
-                if top_k is not None:
-                    logits = self.top_k_logits(logits, top_k)
-                # apply softmax to convert to probabilities
-                probs = F.softmax(logits, dim=-1)
-                # sample from the distribution or take the most likely
-                if sample:
-                    ix = torch.multinomial(probs, num_samples=1)
-                else:
-                    _, ix = torch.topk(probs, k=1, dim=-1)
-                # append to the sequence and continue
-                x = torch.cat((x, ix), dim=1)
-            # cut off conditioning
-            x = x[:, c.shape[1]:]
-        return x
-
-    @torch.no_grad()
-    def encode_to_z(self, x):
-        quant_z, _, info = self.first_stage_model.encode(x)
-        indices = info[2].view(quant_z.shape[0], -1)
-        indices = self.permuter(indices)
-        return quant_z, indices
-
-    @torch.no_grad()
-    def encode_to_c(self, c):
-        if self.downsample_cond_size > -1:
-            c = F.interpolate(c, size=(self.downsample_cond_size, self.downsample_cond_size))
-        quant_c, _, [_,_,indices] = self.cond_stage_model.encode(c)
-        if len(indices.shape) > 2:
-            indices = indices.view(c.shape[0], -1)
-        return quant_c, indices
-
-    @torch.no_grad()
-    def decode_to_img(self, index, zshape):
-        index = self.permuter(index, reverse=True)
-        bhwc = (zshape[0],zshape[2],zshape[3],zshape[1])
-        quant_z = self.first_stage_model.quantize.get_codebook_entry(
-            index.reshape(-1), shape=bhwc)
-        x = self.first_stage_model.decode(quant_z)
-        return x
-
-    @torch.no_grad()
-    def log_images(self, batch, temperature=None, top_k=None, callback=None, lr_interface=False, **kwargs):
-        log = dict()
-
-        N = 4
-        if lr_interface:
-            x, c = self.get_xc(batch, N, diffuse=False, upsample_factor=8)
-        else:
-            x, c = self.get_xc(batch, N)
-        x = x.to(device=self.device)
-        c = c.to(device=self.device)
-
-        quant_z, z_indices = self.encode_to_z(x)
-        quant_c, c_indices = self.encode_to_c(c)
-
-        # create a "half"" sample
-        z_start_indices = z_indices[:,:z_indices.shape[1]//2]
-        index_sample = self.sample(z_start_indices, c_indices,
-                                   steps=z_indices.shape[1]-z_start_indices.shape[1],
-                                   temperature=temperature if temperature is not None else 1.0,
-                                   sample=True,
-                                   top_k=top_k if top_k is not None else 100,
-                                   callback=callback if callback is not None else lambda k: None)
-        x_sample = self.decode_to_img(index_sample, quant_z.shape)
-
-        # sample
-        z_start_indices = z_indices[:, :0]
-        index_sample = self.sample(z_start_indices, c_indices,
-                                   steps=z_indices.shape[1],
-                                   temperature=temperature if temperature is not None else 1.0,
-                                   sample=True,
-                                   top_k=top_k if top_k is not None else 100,
-                                   callback=callback if callback is not None else lambda k: None)
-        x_sample_nopix = self.decode_to_img(index_sample, quant_z.shape)
-
-        # det sample
-        z_start_indices = z_indices[:, :0]
-        index_sample = self.sample(z_start_indices, c_indices,
-                                   steps=z_indices.shape[1],
-                                   sample=False,
-                                   callback=callback if callback is not None else lambda k: None)
-        x_sample_det = self.decode_to_img(index_sample, quant_z.shape)
-
-        # reconstruction
-        x_rec = self.decode_to_img(z_indices, quant_z.shape)
-
-        log["inputs"] = x
-        log["reconstructions"] = x_rec
-
-        if self.cond_stage_key in ["objects_bbox", "objects_center_points"]:
-            figure_size = (x_rec.shape[2], x_rec.shape[3])
-            dataset = kwargs["pl_module"].trainer.datamodule.datasets["validation"]
-            label_for_category_no = dataset.get_textual_label_for_category_no
-            plotter = dataset.conditional_builders[self.cond_stage_key].plot
-            log["conditioning"] = torch.zeros_like(log["reconstructions"])
-            for i in range(quant_c.shape[0]):
-                log["conditioning"][i] = plotter(quant_c[i], label_for_category_no, figure_size)
-            log["conditioning_rec"] = log["conditioning"]
-        elif self.cond_stage_key != "image":
-            cond_rec = self.cond_stage_model.decode(quant_c)
-            if self.cond_stage_key == "segmentation":
-                # get image from segmentation mask
-                num_classes = cond_rec.shape[1]
-
-                c = torch.argmax(c, dim=1, keepdim=True)
-                c = F.one_hot(c, num_classes=num_classes)
-                c = c.squeeze(1).permute(0, 3, 1, 2).float()
-                c = self.cond_stage_model.to_rgb(c)
-
-                cond_rec = torch.argmax(cond_rec, dim=1, keepdim=True)
-                cond_rec = F.one_hot(cond_rec, num_classes=num_classes)
-                cond_rec = cond_rec.squeeze(1).permute(0, 3, 1, 2).float()
-                cond_rec = self.cond_stage_model.to_rgb(cond_rec)
-            log["conditioning_rec"] = cond_rec
-            log["conditioning"] = c
-
-        log["samples_half"] = x_sample
-        log["samples_nopix"] = x_sample_nopix
-        log["samples_det"] = x_sample_det
-        return log
-
-    def get_input(self, key, batch):
-        x = batch[key]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        if len(x.shape) == 4:
-            x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
-        if x.dtype == torch.double:
-            x = x.float()
-        return x
-
-    def get_xc(self, batch, N=None):
-        x = self.get_input(self.first_stage_key, batch)
-        c = self.get_input(self.cond_stage_key, batch)
-        if N is not None:
-            x = x[:N]
-            c = c[:N]
-        return x, c
-
-    def shared_step(self, batch, batch_idx):
-        x, c = self.get_xc(batch)
-        logits, target = self(x, c)
-        loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), target.reshape(-1))
-        return loss
-
-    def training_step(self, batch, batch_idx):
-        loss = self.shared_step(batch, batch_idx)
-        self.log("train/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        loss = self.shared_step(batch, batch_idx)
-        self.log("val/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        return loss
-
-    def configure_optimizers(self):
-        """
-        Following minGPT:
-        This long function is unfortunately doing something very simple and is being very defensive:
-        We are separating out all parameters of the model into two buckets: those that will experience
-        weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
-        We are then returning the PyTorch optimizer object.
-        """
-        # separate out all parameters to those that will and won't experience regularizing weight decay
-        decay = set()
-        no_decay = set()
-        whitelist_weight_modules = (torch.nn.Linear, )
-        blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
-        for mn, m in self.transformer.named_modules():
-            for pn, p in m.named_parameters():
-                fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
-
-                if pn.endswith('bias'):
-                    # all biases will not be decayed
-                    no_decay.add(fpn)
-                elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
-                    # weights of whitelist modules will be weight decayed
-                    decay.add(fpn)
-                elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
-                    # weights of blacklist modules will NOT be weight decayed
-                    no_decay.add(fpn)
-
-        # special case the position embedding parameter in the root GPT module as not decayed
-        no_decay.add('pos_emb')
-
-        # validate that we considered every parameter
-        param_dict = {pn: p for pn, p in self.transformer.named_parameters()}
-        inter_params = decay & no_decay
-        union_params = decay | no_decay
-        assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), )
-        assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
-                                                    % (str(param_dict.keys() - union_params), )
-
-        # create the pytorch optimizer object
-        optim_groups = [
-            {"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": 0.01},
-            {"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
-        ]
-        optimizer = torch.optim.AdamW(optim_groups, lr=self.learning_rate, betas=(0.9, 0.95))
-        return optimizer
diff --git a/spaces/Illumotion/Koboldcpp/README.md b/spaces/Illumotion/Koboldcpp/README.md
deleted file mode 100644
index 2707d9b4cbe6ab9d89e858bab8f215a57e244ba0..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-title: Koboldcpp
-sdk: docker
-emoji: 💻
-colorFrom: blue
-colorTo: blue
----
\ No newline at end of file
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/configs/webpack/prod.js b/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/configs/webpack/prod.js
deleted file mode 100644
index b598f486b642bda9df05d0fa51b0ba7eaf3a8974..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/configs/webpack/prod.js
+++ /dev/null
@@ -1,22 +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.
-
-// production config
-const { merge } = require("webpack-merge");
-const { resolve } = require("path");
-const Dotenv = require("dotenv-webpack");
-const commonConfig = require("./common");
-
-module.exports = merge(commonConfig, {
-  mode: "production",
-  output: {
-    filename: "js/bundle.[contenthash].min.js",
-    path: resolve(__dirname, "../../dist"),
-    publicPath: "/",
-  },
-  devtool: "source-map",
-  plugins: [new Dotenv()],
-});
diff --git a/spaces/Jamkonams/AutoGPT/scripts/check_requirements.py b/spaces/Jamkonams/AutoGPT/scripts/check_requirements.py
deleted file mode 100644
index e4eab024a6280c0d54110c69b2e03de639325fa6..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/scripts/check_requirements.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import sys
-
-import pkg_resources
-
-
-def main():
-    requirements_file = sys.argv[1]
-    with open(requirements_file, "r") as f:
-        required_packages = [
-            line.strip().split("#")[0].strip() for line in f.readlines()
-        ]
-
-    installed_packages = [package.key for package in pkg_resources.working_set]
-
-    missing_packages = []
-    for package in required_packages:
-        if not package:  # Skip empty lines
-            continue
-        package_name = package.strip().split("==")[0]
-        if package_name.lower() not in installed_packages:
-            missing_packages.append(package_name)
-
-    if missing_packages:
-        print("Missing packages:")
-        print(", ".join(missing_packages))
-        sys.exit(1)
-    else:
-        print("All packages are installed.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/JerynC/catloaf/README.md b/spaces/JerynC/catloaf/README.md
deleted file mode 100644
index 0dac7daf6fde3a9845e5a7a5c684ff586708a490..0000000000000000000000000000000000000000
--- a/spaces/JerynC/catloaf/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Catloaf
-emoji: 🐱
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Joeythemonster/Text-To-image-AllModels/README.md b/spaces/Joeythemonster/Text-To-image-AllModels/README.md
deleted file mode 100644
index fd88f124f1e902b8d98b1cad1a16b7150fffc935..0000000000000000000000000000000000000000
--- a/spaces/Joeythemonster/Text-To-image-AllModels/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Text To Image AllModels
-emoji: 🐠
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: openrail
-duplicated_from: BilalSardar/Text-To-image-AllModels
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kevin676/AutoGPT/autogpt/speech/brian.py b/spaces/Kevin676/AutoGPT/autogpt/speech/brian.py
deleted file mode 100644
index 821fdf2f482a9cfa928e5c9680152ad6766d8326..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/AutoGPT/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/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/__init__.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/__init__.py
deleted file mode 100644
index 6905fa0da4ea5b5b30797d5dae08dd2a199318ad..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-
-from .core import Opyrator
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/vocoder/wavernn/train.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/vocoder/wavernn/train.py
deleted file mode 100644
index 44e0929ac67d778b5cc78b669b42fb89e17acf9e..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/vocoder/wavernn/train.py
+++ /dev/null
@@ -1,127 +0,0 @@
-from vocoder.wavernn.models.fatchord_version import WaveRNN
-from vocoder.vocoder_dataset import VocoderDataset, collate_vocoder
-from vocoder.distribution import discretized_mix_logistic_loss
-from vocoder.display import stream, simple_table
-from vocoder.wavernn.gen_wavernn import gen_testset
-from torch.utils.data import DataLoader
-from pathlib import Path
-from torch import optim
-import torch.nn.functional as F
-import vocoder.wavernn.hparams as hp
-import numpy as np
-import time
-import torch
-
-
-def train(run_id: str, syn_dir: Path, voc_dir: Path, models_dir: Path, ground_truth: bool,
-          save_every: int, backup_every: int, force_restart: bool):
-    # Check to make sure the hop length is correctly factorised
-    assert np.cumprod(hp.voc_upsample_factors)[-1] == hp.hop_length
-    
-    # Instantiate the model
-    print("Initializing the model...")
-    model = WaveRNN(
-        rnn_dims=hp.voc_rnn_dims,
-        fc_dims=hp.voc_fc_dims,
-        bits=hp.bits,
-        pad=hp.voc_pad,
-        upsample_factors=hp.voc_upsample_factors,
-        feat_dims=hp.num_mels,
-        compute_dims=hp.voc_compute_dims,
-        res_out_dims=hp.voc_res_out_dims,
-        res_blocks=hp.voc_res_blocks,
-        hop_length=hp.hop_length,
-        sample_rate=hp.sample_rate,
-        mode=hp.voc_mode
-    )
-
-    if torch.cuda.is_available():
-        model = model.cuda()
-        device = torch.device('cuda')
-    else:
-        device = torch.device('cpu')   
-
-    # Initialize the optimizer
-    optimizer = optim.Adam(model.parameters())
-    for p in optimizer.param_groups: 
-        p["lr"] = hp.voc_lr
-    loss_func = F.cross_entropy if model.mode == "RAW" else discretized_mix_logistic_loss
-
-    # Load the weights
-    model_dir = models_dir.joinpath(run_id)
-    model_dir.mkdir(exist_ok=True)
-    weights_fpath = model_dir.joinpath(run_id + ".pt")
-    if force_restart or not weights_fpath.exists():
-        print("\nStarting the training of WaveRNN from scratch\n")
-        model.save(weights_fpath, optimizer)
-    else:
-        print("\nLoading weights at %s" % weights_fpath)
-        model.load(weights_fpath, optimizer)
-        print("WaveRNN weights loaded from step %d" % model.step)
-    
-    # Initialize the dataset
-    metadata_fpath = syn_dir.joinpath("train.txt") if ground_truth else \
-        voc_dir.joinpath("synthesized.txt")
-    mel_dir = syn_dir.joinpath("mels") if ground_truth else voc_dir.joinpath("mels_gta")
-    wav_dir = syn_dir.joinpath("audio")
-    dataset = VocoderDataset(metadata_fpath, mel_dir, wav_dir)
-    test_loader = DataLoader(dataset,
-                             batch_size=1,
-                             shuffle=True,
-                             pin_memory=True)
-
-    # Begin the training
-    simple_table([('Batch size', hp.voc_batch_size),
-                  ('LR', hp.voc_lr),
-                  ('Sequence Len', hp.voc_seq_len)])
-    
-    for epoch in range(1, 350):
-        data_loader = DataLoader(dataset,
-                                 collate_fn=collate_vocoder,
-                                 batch_size=hp.voc_batch_size,
-                                 num_workers=2,
-                                 shuffle=True,
-                                 pin_memory=True)
-        start = time.time()
-        running_loss = 0.
-
-        for i, (x, y, m) in enumerate(data_loader, 1):
-            if torch.cuda.is_available():
-                x, m, y = x.cuda(), m.cuda(), y.cuda()
-            
-            # Forward pass
-            y_hat = model(x, m)
-            if model.mode == 'RAW':
-                y_hat = y_hat.transpose(1, 2).unsqueeze(-1)
-            elif model.mode == 'MOL':
-                y = y.float()
-            y = y.unsqueeze(-1)
-            
-            # Backward pass
-            loss = loss_func(y_hat, y)
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-            running_loss += loss.item()
-            speed = i / (time.time() - start)
-            avg_loss = running_loss / i
-
-            step = model.get_step()
-            k = step // 1000
-
-            if backup_every != 0 and step % backup_every == 0 :
-                model.checkpoint(model_dir, optimizer)
-                
-            if save_every != 0 and step % save_every == 0 :
-                model.save(weights_fpath, optimizer)
-
-            msg = f"| Epoch: {epoch} ({i}/{len(data_loader)}) | " \
-                f"Loss: {avg_loss:.4f} | {speed:.1f} " \
-                f"steps/s | Step: {k}k | "
-            stream(msg)
-
-
-        gen_testset(model, test_loader, hp.voc_gen_at_checkpoint, hp.voc_gen_batched,
-                    hp.voc_target, hp.voc_overlap, model_dir)
-        print("")
diff --git a/spaces/KoboldAI/Koboldcpp-Tiefighter/Dockerfile b/spaces/KoboldAI/Koboldcpp-Tiefighter/Dockerfile
deleted file mode 100644
index 6095b01d001f40f55c9454bacfa7e91f29c75680..0000000000000000000000000000000000000000
--- a/spaces/KoboldAI/Koboldcpp-Tiefighter/Dockerfile
+++ /dev/null
@@ -1,12 +0,0 @@
-FROM nvidia/cuda:11.8.0-devel-ubuntu22.04
-ARG MODEL
-ARG MODEL_NAME
-ARG ADDITIONAL
-RUN mkdir /opt/koboldcpp
-RUN apt update && apt install git build-essential libopenblas-dev wget python3-pip -y
-RUN git clone https://github.com/lostruins/koboldcpp /opt/koboldcpp
-WORKDIR /opt/koboldcpp
-RUN make LLAMA_OPENBLAS=1 LLAMA_CUBLAS=1 LLAMA_PORTABLE=1
-RUN wget -O model.ggml $MODEL
-CMD /bin/python3 ./koboldcpp.py --model model.ggml $ADDITIONAL --port 7860 --hordeconfig $MODEL_NAME 1 1
-
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/backbones/resnext.py b/spaces/KyanChen/RSPrompter/mmdet/models/backbones/resnext.py
deleted file mode 100644
index df3d79e046c3ab9b289bcfeb6f937c87f6c09bfa..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/backbones/resnext.py
+++ /dev/null
@@ -1,154 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-from mmcv.cnn import build_conv_layer, build_norm_layer
-
-from mmdet.registry import MODELS
-from ..layers import ResLayer
-from .resnet import Bottleneck as _Bottleneck
-from .resnet import ResNet
-
-
-class Bottleneck(_Bottleneck):
-    expansion = 4
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 groups=1,
-                 base_width=4,
-                 base_channels=64,
-                 **kwargs):
-        """Bottleneck block for ResNeXt.
-
-        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
-        it is "caffe", the stride-two layer is the first 1x1 conv layer.
-        """
-        super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
-
-        if groups == 1:
-            width = self.planes
-        else:
-            width = math.floor(self.planes *
-                               (base_width / base_channels)) * groups
-
-        self.norm1_name, norm1 = build_norm_layer(
-            self.norm_cfg, width, postfix=1)
-        self.norm2_name, norm2 = build_norm_layer(
-            self.norm_cfg, width, postfix=2)
-        self.norm3_name, norm3 = build_norm_layer(
-            self.norm_cfg, self.planes * self.expansion, postfix=3)
-
-        self.conv1 = build_conv_layer(
-            self.conv_cfg,
-            self.inplanes,
-            width,
-            kernel_size=1,
-            stride=self.conv1_stride,
-            bias=False)
-        self.add_module(self.norm1_name, norm1)
-        fallback_on_stride = False
-        self.with_modulated_dcn = False
-        if self.with_dcn:
-            fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
-        if not self.with_dcn or fallback_on_stride:
-            self.conv2 = build_conv_layer(
-                self.conv_cfg,
-                width,
-                width,
-                kernel_size=3,
-                stride=self.conv2_stride,
-                padding=self.dilation,
-                dilation=self.dilation,
-                groups=groups,
-                bias=False)
-        else:
-            assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
-            self.conv2 = build_conv_layer(
-                self.dcn,
-                width,
-                width,
-                kernel_size=3,
-                stride=self.conv2_stride,
-                padding=self.dilation,
-                dilation=self.dilation,
-                groups=groups,
-                bias=False)
-
-        self.add_module(self.norm2_name, norm2)
-        self.conv3 = build_conv_layer(
-            self.conv_cfg,
-            width,
-            self.planes * self.expansion,
-            kernel_size=1,
-            bias=False)
-        self.add_module(self.norm3_name, norm3)
-
-        if self.with_plugins:
-            self._del_block_plugins(self.after_conv1_plugin_names +
-                                    self.after_conv2_plugin_names +
-                                    self.after_conv3_plugin_names)
-            self.after_conv1_plugin_names = self.make_block_plugins(
-                width, self.after_conv1_plugins)
-            self.after_conv2_plugin_names = self.make_block_plugins(
-                width, self.after_conv2_plugins)
-            self.after_conv3_plugin_names = self.make_block_plugins(
-                self.planes * self.expansion, self.after_conv3_plugins)
-
-    def _del_block_plugins(self, plugin_names):
-        """delete plugins for block if exist.
-
-        Args:
-            plugin_names (list[str]): List of plugins name to delete.
-        """
-        assert isinstance(plugin_names, list)
-        for plugin_name in plugin_names:
-            del self._modules[plugin_name]
-
-
-@MODELS.register_module()
-class ResNeXt(ResNet):
-    """ResNeXt backbone.
-
-    Args:
-        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
-        in_channels (int): Number of input image channels. Default: 3.
-        num_stages (int): Resnet stages. Default: 4.
-        groups (int): Group of resnext.
-        base_width (int): Base width of resnext.
-        strides (Sequence[int]): Strides of the first block of each stage.
-        dilations (Sequence[int]): Dilation of each stage.
-        out_indices (Sequence[int]): Output from which stages.
-        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
-            layer is the 3x3 conv layer, otherwise the stride-two layer is
-            the first 1x1 conv layer.
-        frozen_stages (int): Stages to be frozen (all param fixed). -1 means
-            not freezing any parameters.
-        norm_cfg (dict): dictionary to construct and config norm layer.
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed.
-        zero_init_residual (bool): whether to use zero init for last norm layer
-            in resblocks to let them behave as identity.
-    """
-
-    arch_settings = {
-        50: (Bottleneck, (3, 4, 6, 3)),
-        101: (Bottleneck, (3, 4, 23, 3)),
-        152: (Bottleneck, (3, 8, 36, 3))
-    }
-
-    def __init__(self, groups=1, base_width=4, **kwargs):
-        self.groups = groups
-        self.base_width = base_width
-        super(ResNeXt, self).__init__(**kwargs)
-
-    def make_res_layer(self, **kwargs):
-        """Pack all blocks in a stage into a ``ResLayer``"""
-        return ResLayer(
-            groups=self.groups,
-            base_width=self.base_width,
-            base_channels=self.base_channels,
-            **kwargs)
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/losses/seesaw_loss.py b/spaces/KyanChen/RSPrompter/mmdet/models/losses/seesaw_loss.py
deleted file mode 100644
index 4dec62b0afdc01e848e0c7f53ba0b6b10b899ea4..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/losses/seesaw_loss.py
+++ /dev/null
@@ -1,278 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from .accuracy import accuracy
-from .cross_entropy_loss import cross_entropy
-from .utils import weight_reduce_loss
-
-
-def seesaw_ce_loss(cls_score: Tensor,
-                   labels: Tensor,
-                   label_weights: Tensor,
-                   cum_samples: Tensor,
-                   num_classes: int,
-                   p: float,
-                   q: float,
-                   eps: float,
-                   reduction: str = 'mean',
-                   avg_factor: Optional[int] = None) -> Tensor:
-    """Calculate the Seesaw CrossEntropy loss.
-
-    Args:
-        cls_score (Tensor): The prediction with shape (N, C),
-             C is the number of classes.
-        labels (Tensor): The learning label of the prediction.
-        label_weights (Tensor): Sample-wise loss weight.
-        cum_samples (Tensor): Cumulative samples for each category.
-        num_classes (int): The number of classes.
-        p (float): The ``p`` in the mitigation factor.
-        q (float): The ``q`` in the compenstation factor.
-        eps (float): The minimal value of divisor to smooth
-             the computation of compensation factor
-        reduction (str, optional): The method used to reduce the loss.
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-
-    Returns:
-        Tensor: The calculated loss
-    """
-    assert cls_score.size(-1) == num_classes
-    assert len(cum_samples) == num_classes
-
-    onehot_labels = F.one_hot(labels, num_classes)
-    seesaw_weights = cls_score.new_ones(onehot_labels.size())
-
-    # mitigation factor
-    if p > 0:
-        sample_ratio_matrix = cum_samples[None, :].clamp(
-            min=1) / cum_samples[:, None].clamp(min=1)
-        index = (sample_ratio_matrix < 1.0).float()
-        sample_weights = sample_ratio_matrix.pow(p) * index + (1 - index)
-        mitigation_factor = sample_weights[labels.long(), :]
-        seesaw_weights = seesaw_weights * mitigation_factor
-
-    # compensation factor
-    if q > 0:
-        scores = F.softmax(cls_score.detach(), dim=1)
-        self_scores = scores[
-            torch.arange(0, len(scores)).to(scores.device).long(),
-            labels.long()]
-        score_matrix = scores / self_scores[:, None].clamp(min=eps)
-        index = (score_matrix > 1.0).float()
-        compensation_factor = score_matrix.pow(q) * index + (1 - index)
-        seesaw_weights = seesaw_weights * compensation_factor
-
-    cls_score = cls_score + (seesaw_weights.log() * (1 - onehot_labels))
-
-    loss = F.cross_entropy(cls_score, labels, weight=None, reduction='none')
-
-    if label_weights is not None:
-        label_weights = label_weights.float()
-    loss = weight_reduce_loss(
-        loss, weight=label_weights, reduction=reduction, avg_factor=avg_factor)
-    return loss
-
-
-@MODELS.register_module()
-class SeesawLoss(nn.Module):
-    """
-    Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021)
-    arXiv: https://arxiv.org/abs/2008.10032
-
-    Args:
-        use_sigmoid (bool, optional): Whether the prediction uses sigmoid
-             of softmax. Only False is supported.
-        p (float, optional): The ``p`` in the mitigation factor.
-             Defaults to 0.8.
-        q (float, optional): The ``q`` in the compenstation factor.
-             Defaults to 2.0.
-        num_classes (int, optional): The number of classes.
-             Default to 1203 for LVIS v1 dataset.
-        eps (float, optional): The minimal value of divisor to smooth
-             the computation of compensation factor
-        reduction (str, optional): The method that reduces the loss to a
-             scalar. Options are "none", "mean" and "sum".
-        loss_weight (float, optional): The weight of the loss. Defaults to 1.0
-        return_dict (bool, optional): Whether return the losses as a dict.
-             Default to True.
-    """
-
-    def __init__(self,
-                 use_sigmoid: bool = False,
-                 p: float = 0.8,
-                 q: float = 2.0,
-                 num_classes: int = 1203,
-                 eps: float = 1e-2,
-                 reduction: str = 'mean',
-                 loss_weight: float = 1.0,
-                 return_dict: bool = True) -> None:
-        super().__init__()
-        assert not use_sigmoid
-        self.use_sigmoid = False
-        self.p = p
-        self.q = q
-        self.num_classes = num_classes
-        self.eps = eps
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-        self.return_dict = return_dict
-
-        # 0 for pos, 1 for neg
-        self.cls_criterion = seesaw_ce_loss
-
-        # cumulative samples for each category
-        self.register_buffer(
-            'cum_samples',
-            torch.zeros(self.num_classes + 1, dtype=torch.float))
-
-        # custom output channels of the classifier
-        self.custom_cls_channels = True
-        # custom activation of cls_score
-        self.custom_activation = True
-        # custom accuracy of the classsifier
-        self.custom_accuracy = True
-
-    def _split_cls_score(self, cls_score: Tensor) -> Tuple[Tensor, Tensor]:
-        """split cls_score.
-
-        Args:
-            cls_score (Tensor): The prediction with shape (N, C + 2).
-
-        Returns:
-            Tuple[Tensor, Tensor]: The score for classes and objectness,
-                 respectively
-        """
-        # split cls_score to cls_score_classes and cls_score_objectness
-        assert cls_score.size(-1) == self.num_classes + 2
-        cls_score_classes = cls_score[..., :-2]
-        cls_score_objectness = cls_score[..., -2:]
-        return cls_score_classes, cls_score_objectness
-
-    def get_cls_channels(self, num_classes: int) -> int:
-        """Get custom classification channels.
-
-        Args:
-            num_classes (int): The number of classes.
-
-        Returns:
-            int: The custom classification channels.
-        """
-        assert num_classes == self.num_classes
-        return num_classes + 2
-
-    def get_activation(self, cls_score: Tensor) -> Tensor:
-        """Get custom activation of cls_score.
-
-        Args:
-            cls_score (Tensor): The prediction with shape (N, C + 2).
-
-        Returns:
-            Tensor: The custom activation of cls_score with shape
-                 (N, C + 1).
-        """
-        cls_score_classes, cls_score_objectness = self._split_cls_score(
-            cls_score)
-        score_classes = F.softmax(cls_score_classes, dim=-1)
-        score_objectness = F.softmax(cls_score_objectness, dim=-1)
-        score_pos = score_objectness[..., [0]]
-        score_neg = score_objectness[..., [1]]
-        score_classes = score_classes * score_pos
-        scores = torch.cat([score_classes, score_neg], dim=-1)
-        return scores
-
-    def get_accuracy(self, cls_score: Tensor,
-                     labels: Tensor) -> Dict[str, Tensor]:
-        """Get custom accuracy w.r.t. cls_score and labels.
-
-        Args:
-            cls_score (Tensor): The prediction with shape (N, C + 2).
-            labels (Tensor): The learning label of the prediction.
-
-        Returns:
-            Dict [str, Tensor]: The accuracy for objectness and classes,
-                 respectively.
-        """
-        pos_inds = labels < self.num_classes
-        obj_labels = (labels == self.num_classes).long()
-        cls_score_classes, cls_score_objectness = self._split_cls_score(
-            cls_score)
-        acc_objectness = accuracy(cls_score_objectness, obj_labels)
-        acc_classes = accuracy(cls_score_classes[pos_inds], labels[pos_inds])
-        acc = dict()
-        acc['acc_objectness'] = acc_objectness
-        acc['acc_classes'] = acc_classes
-        return acc
-
-    def forward(
-        self,
-        cls_score: Tensor,
-        labels: Tensor,
-        label_weights: Optional[Tensor] = None,
-        avg_factor: Optional[int] = None,
-        reduction_override: Optional[str] = None
-    ) -> Union[Tensor, Dict[str, Tensor]]:
-        """Forward function.
-
-        Args:
-            cls_score (Tensor): The prediction with shape (N, C + 2).
-            labels (Tensor): The learning label of the prediction.
-            label_weights (Tensor, optional): Sample-wise loss weight.
-            avg_factor (int, optional): Average factor that is used to average
-                 the loss. Defaults to None.
-            reduction (str, optional): The method used to reduce the loss.
-                 Options are "none", "mean" and "sum".
-
-        Returns:
-            Tensor | Dict [str, Tensor]:
-                 if return_dict == False: The calculated loss |
-                 if return_dict == True: The dict of calculated losses
-                 for objectness and classes, respectively.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        assert cls_score.size(-1) == self.num_classes + 2
-        pos_inds = labels < self.num_classes
-        # 0 for pos, 1 for neg
-        obj_labels = (labels == self.num_classes).long()
-
-        # accumulate the samples for each category
-        unique_labels = labels.unique()
-        for u_l in unique_labels:
-            inds_ = labels == u_l.item()
-            self.cum_samples[u_l] += inds_.sum()
-
-        if label_weights is not None:
-            label_weights = label_weights.float()
-        else:
-            label_weights = labels.new_ones(labels.size(), dtype=torch.float)
-
-        cls_score_classes, cls_score_objectness = self._split_cls_score(
-            cls_score)
-        # calculate loss_cls_classes (only need pos samples)
-        if pos_inds.sum() > 0:
-            loss_cls_classes = self.loss_weight * self.cls_criterion(
-                cls_score_classes[pos_inds], labels[pos_inds],
-                label_weights[pos_inds], self.cum_samples[:self.num_classes],
-                self.num_classes, self.p, self.q, self.eps, reduction,
-                avg_factor)
-        else:
-            loss_cls_classes = cls_score_classes[pos_inds].sum()
-        # calculate loss_cls_objectness
-        loss_cls_objectness = self.loss_weight * cross_entropy(
-            cls_score_objectness, obj_labels, label_weights, reduction,
-            avg_factor)
-
-        if self.return_dict:
-            loss_cls = dict()
-            loss_cls['loss_cls_objectness'] = loss_cls_objectness
-            loss_cls['loss_cls_classes'] = loss_cls_classes
-        else:
-            loss_cls = loss_cls_classes + loss_cls_objectness
-        return loss_cls
diff --git a/spaces/KyanChen/RSPrompter/mmpl/utils/collect_env.py b/spaces/KyanChen/RSPrompter/mmpl/utils/collect_env.py
deleted file mode 100644
index 94c675c841d74af49964c17ab360a6d3d754b4e2..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpl/utils/collect_env.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import mmcv
-from mmengine.utils import get_git_hash
-from mmengine.utils.dl_utils import collect_env as collect_base_env
-
-
-def collect_env() -> dict:
-    """Collect the information of the running environments."""
-    env_info = collect_base_env()
-    env_info['MMCV'] = mmcv.__version__
-    env_info['MMDetection'] = mmdet.__version__
-    env_info['MMYOLO'] = mmyolo.__version__ + '+' + get_git_hash()[:7]
-    return env_info
-
-
-if __name__ == '__main__':
-    for name, val in collect_env().items():
-        print(f'{name}: {val}')
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/attentions.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/attentions.py
deleted file mode 100644
index 693966841d9b371ce3b4497d74d040db3e6aaa46..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_pack/attentions.py
+++ /dev/null
@@ -1,414 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from lib.infer.infer_pack import commons
-from lib.infer.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **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
-
-        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):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            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):
-        max_relative_position = 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()
-        # padd 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/Lehele/bingai/Dockerfile b/spaces/Lehele/bingai/Dockerfile
deleted file mode 100644
index f544a833408dceebddb5637c78e572ef72f3cdbf..0000000000000000000000000000000000000000
--- a/spaces/Lehele/bingai/Dockerfile
+++ /dev/null
@@ -1,4 +0,0 @@
-FROM zklcdc/go-proxy-bingai
-# ENV USER_MUID=""
-EXPOSE 8080
-CMD ["/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/MGLDZM/chgpt/static/js/tabsHandler.js b/spaces/MGLDZM/chgpt/static/js/tabsHandler.js
deleted file mode 100644
index 32aa5cb888857c597f310af3888c51da6752a414..0000000000000000000000000000000000000000
--- a/spaces/MGLDZM/chgpt/static/js/tabsHandler.js
+++ /dev/null
@@ -1,26 +0,0 @@
-$(document).ready(function() {
-    $(document).on("chat:creado",function(event){
-        $(".tab-switch").off("change")
-        $(".tab-switch").on("change", radioChanged)
-        radioChanged()
-    });
-
-    $("#nuevoChat").on("click", (event) => {
-        $(document).trigger("chat:crear");
-    })
-
-})
-
-function radioChanged(){
-    let tab = $(".tab");
-    let tabActive = $(tab[$(".tab-label input:checked").val()]);
-    let chat = tabActive.find(".chat")
-    
-    
-    tab.removeClass("active")
-    tabActive.addClass("active")
-    tabActive.find("textarea").focus()
-    if(chat.length>0){
-        chat.scrollTop(chat[0].scrollHeight);
-    }
-}
\ No newline at end of file
diff --git a/spaces/MKFMIKU/Bi-Noising.Diffusion/header.html b/spaces/MKFMIKU/Bi-Noising.Diffusion/header.html
deleted file mode 100644
index 78097967981e9fd501bb9eebdd112c96fc109912..0000000000000000000000000000000000000000
--- a/spaces/MKFMIKU/Bi-Noising.Diffusion/header.html
+++ /dev/null
@@ -1,18 +0,0 @@
-<div style="text-align: center; max-width: 650px; margin: 0 auto;">
-    <div style="
-          display: inline-flex;
-          gap: 0.8rem;
-          font-size: 1.75rem;
-          justify-content: center;
-          margin-bottom: 10px;
-        ">
-      <h1 style="font-weight: 900; align-items: center; margin-bottom: 7px; margin-top: 20px;">
-        💊 Bi-Noising for Image-to-Image (I2I) Diffusion
-      </h1>
-    </div>
-    <div>
-      <p style="align-items: center; margin-bottom: 7px;">
-        <a href="https://arxiv.org/abs/2212.07352">https://arxiv.org/abs/2212.07352</a>. A new plug-and-play prior for diffusion guidance, which can fix biased noise during sampling. <br> It is shown to be effective on all existing diffusion restoration models including, ILVR, SR3, Guided-Diffusion, etc.
-      </p>
-    </div>
-  </div>
\ No newline at end of file
diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/modeling/meta_arch/d2_deformable_detr.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/modeling/meta_arch/d2_deformable_detr.py
deleted file mode 100644
index 47ff220fc3946d1bf68fad87076589e46b274ef3..0000000000000000000000000000000000000000
--- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/modeling/meta_arch/d2_deformable_detr.py
+++ /dev/null
@@ -1,308 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. 
-import torch
-import torch.nn.functional as F
-from torch import nn
-import math
-
-from detectron2.modeling import META_ARCH_REGISTRY, build_backbone
-from detectron2.structures import Boxes, Instances
-from ..utils import load_class_freq, get_fed_loss_inds
-
-from models.backbone import Joiner
-from models.deformable_detr import DeformableDETR, SetCriterion, MLP
-from models.deformable_detr import _get_clones
-from models.matcher import HungarianMatcher
-from models.position_encoding import PositionEmbeddingSine
-from models.deformable_transformer import DeformableTransformer
-from models.segmentation import sigmoid_focal_loss
-from util.box_ops import box_cxcywh_to_xyxy, box_xyxy_to_cxcywh
-from util.misc import NestedTensor, accuracy
-
-
-__all__ = ["DeformableDetr"]
-
-class CustomSetCriterion(SetCriterion):
-    def __init__(self, num_classes, matcher, weight_dict, losses, \
-        focal_alpha=0.25, use_fed_loss=False):
-        super().__init__(num_classes, matcher, weight_dict, losses, focal_alpha)
-        self.use_fed_loss = use_fed_loss
-        if self.use_fed_loss:
-            self.register_buffer(
-                'fed_loss_weight', load_class_freq(freq_weight=0.5))
-
-    def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
-        """Classification loss (NLL)
-        targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
-        """
-        assert 'pred_logits' in outputs
-        src_logits = outputs['pred_logits']
-
-        idx = self._get_src_permutation_idx(indices)
-        target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
-        target_classes = torch.full(src_logits.shape[:2], self.num_classes,
-                                    dtype=torch.int64, device=src_logits.device)
-        target_classes[idx] = target_classes_o
-
-        target_classes_onehot = torch.zeros(
-            [src_logits.shape[0], src_logits.shape[1], src_logits.shape[2] + 1],
-            dtype=src_logits.dtype, layout=src_logits.layout, 
-            device=src_logits.device)
-        target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1)
-
-        target_classes_onehot = target_classes_onehot[:,:,:-1] # B x N x C
-        if self.use_fed_loss:
-            inds = get_fed_loss_inds(
-                gt_classes=target_classes_o,
-                num_sample_cats=50,
-                weight=self.fed_loss_weight,
-                C=target_classes_onehot.shape[2])
-            loss_ce = sigmoid_focal_loss(
-                src_logits[:, :, inds], 
-                target_classes_onehot[:, :, inds], 
-                num_boxes, 
-                alpha=self.focal_alpha, 
-                gamma=2) * src_logits.shape[1]
-        else:
-            loss_ce = sigmoid_focal_loss(
-                src_logits, target_classes_onehot, num_boxes, 
-                alpha=self.focal_alpha, 
-                gamma=2) * src_logits.shape[1]
-        losses = {'loss_ce': loss_ce}
-
-        if log:
-            # TODO this should probably be a separate loss, not hacked in this one here
-            losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0]
-        return losses
-
-
-class MaskedBackbone(nn.Module):
-    """ This is a thin wrapper around D2's backbone to provide padding masking"""
-
-    def __init__(self, cfg):
-        super().__init__()
-        self.backbone = build_backbone(cfg)
-        backbone_shape = self.backbone.output_shape()
-        self.feature_strides = [backbone_shape[f].stride for f in backbone_shape.keys()]
-        self.strides = [backbone_shape[f].stride for f in backbone_shape.keys()]
-        self.num_channels = [backbone_shape[x].channels for x in backbone_shape.keys()]
-
-    def forward(self, tensor_list: NestedTensor):
-        xs = self.backbone(tensor_list.tensors)
-        out = {}
-        for name, x in xs.items():
-            m = tensor_list.mask
-            assert m is not None
-            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
-            out[name] = NestedTensor(x, mask)
-        return out
-
-@META_ARCH_REGISTRY.register()
-class DeformableDetr(nn.Module):
-    """
-    Implement Deformable Detr
-    """
-
-    def __init__(self, cfg):
-        super().__init__()
-        self.with_image_labels = cfg.WITH_IMAGE_LABELS
-        self.weak_weight = cfg.MODEL.DETR.WEAK_WEIGHT
-
-        self.device = torch.device(cfg.MODEL.DEVICE)
-        self.test_topk = cfg.TEST.DETECTIONS_PER_IMAGE
-        self.num_classes = cfg.MODEL.DETR.NUM_CLASSES
-        self.mask_on = cfg.MODEL.MASK_ON
-        hidden_dim = cfg.MODEL.DETR.HIDDEN_DIM
-        num_queries = cfg.MODEL.DETR.NUM_OBJECT_QUERIES
-
-        # Transformer parameters:
-        nheads = cfg.MODEL.DETR.NHEADS
-        dropout = cfg.MODEL.DETR.DROPOUT
-        dim_feedforward = cfg.MODEL.DETR.DIM_FEEDFORWARD
-        enc_layers = cfg.MODEL.DETR.ENC_LAYERS
-        dec_layers = cfg.MODEL.DETR.DEC_LAYERS
-        num_feature_levels = cfg.MODEL.DETR.NUM_FEATURE_LEVELS
-        two_stage = cfg.MODEL.DETR.TWO_STAGE
-        with_box_refine = cfg.MODEL.DETR.WITH_BOX_REFINE
-
-        # Loss parameters:
-        giou_weight = cfg.MODEL.DETR.GIOU_WEIGHT
-        l1_weight = cfg.MODEL.DETR.L1_WEIGHT
-        deep_supervision = cfg.MODEL.DETR.DEEP_SUPERVISION
-        cls_weight = cfg.MODEL.DETR.CLS_WEIGHT
-        focal_alpha = cfg.MODEL.DETR.FOCAL_ALPHA
-
-        N_steps = hidden_dim // 2
-        d2_backbone = MaskedBackbone(cfg)
-        backbone = Joiner(d2_backbone, PositionEmbeddingSine(N_steps, normalize=True))
-
-        transformer = DeformableTransformer(
-            d_model=hidden_dim,
-            nhead=nheads,
-            num_encoder_layers=enc_layers,
-            num_decoder_layers=dec_layers,
-            dim_feedforward=dim_feedforward,
-            dropout=dropout,
-            activation="relu",
-            return_intermediate_dec=True,
-            num_feature_levels=num_feature_levels,
-            dec_n_points=4,
-            enc_n_points=4,
-            two_stage=two_stage,
-            two_stage_num_proposals=num_queries)
-
-        self.detr = DeformableDETR(
-            backbone, transformer, num_classes=self.num_classes, 
-            num_queries=num_queries,
-            num_feature_levels=num_feature_levels,
-            aux_loss=deep_supervision,
-            with_box_refine=with_box_refine,
-            two_stage=two_stage,
-        )
-
-        if self.mask_on:
-            assert 0, 'Mask is not supported yet :('
-
-        matcher = HungarianMatcher(
-            cost_class=cls_weight, cost_bbox=l1_weight, cost_giou=giou_weight)
-        weight_dict = {"loss_ce": cls_weight, "loss_bbox": l1_weight}
-        weight_dict["loss_giou"] = giou_weight
-        if deep_supervision:
-            aux_weight_dict = {}
-            for i in range(dec_layers - 1):
-                aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
-            weight_dict.update(aux_weight_dict)
-        print('weight_dict', weight_dict)
-        losses = ["labels", "boxes", "cardinality"]
-        if self.mask_on:
-            losses += ["masks"]
-        self.criterion = CustomSetCriterion(
-            self.num_classes, matcher=matcher, weight_dict=weight_dict, 
-            focal_alpha=focal_alpha, 
-            losses=losses,
-            use_fed_loss=cfg.MODEL.DETR.USE_FED_LOSS
-        )
-        pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(3, 1, 1)
-        pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(3, 1, 1)
-        self.normalizer = lambda x: (x - pixel_mean) / pixel_std
-
-
-    def forward(self, batched_inputs):
-        """
-        Args:
-        Returns:
-            dict[str: Tensor]:
-                mapping from a named loss to a tensor storing the loss. Used during training only.
-        """
-        images = self.preprocess_image(batched_inputs)
-        output = self.detr(images)
-        if self.training:
-            gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
-            targets = self.prepare_targets(gt_instances)
-            loss_dict = self.criterion(output, targets)
-            weight_dict = self.criterion.weight_dict
-            for k in loss_dict.keys():
-                if k in weight_dict:
-                    loss_dict[k] *= weight_dict[k]
-            if self.with_image_labels:
-                if batched_inputs[0]['ann_type'] in ['image', 'captiontag']:
-                    loss_dict['loss_image'] = self.weak_weight * self._weak_loss(
-                        output, batched_inputs)
-                else:
-                    loss_dict['loss_image'] = images[0].new_zeros(
-                        [1], dtype=torch.float32)[0]
-                # import pdb; pdb.set_trace()
-            return loss_dict
-        else:
-            image_sizes = output["pred_boxes"].new_tensor(
-                [(t["height"], t["width"]) for t in batched_inputs])
-            results = self.post_process(output, image_sizes)
-            return results
-
-
-    def prepare_targets(self, targets):
-        new_targets = []
-        for targets_per_image in targets:
-            h, w = targets_per_image.image_size
-            image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float, device=self.device)
-            gt_classes = targets_per_image.gt_classes
-            gt_boxes = targets_per_image.gt_boxes.tensor / image_size_xyxy
-            gt_boxes = box_xyxy_to_cxcywh(gt_boxes)
-            new_targets.append({"labels": gt_classes, "boxes": gt_boxes})
-            if self.mask_on and hasattr(targets_per_image, 'gt_masks'):
-                assert 0, 'Mask is not supported yet :('
-                gt_masks = targets_per_image.gt_masks
-                gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)
-                new_targets[-1].update({'masks': gt_masks})
-        return new_targets
-
-
-    def post_process(self, outputs, target_sizes):
-        """
-        """
-        out_logits, out_bbox = outputs['pred_logits'], outputs['pred_boxes']
-        assert len(out_logits) == len(target_sizes)
-        assert target_sizes.shape[1] == 2
-
-        prob = out_logits.sigmoid()
-        topk_values, topk_indexes = torch.topk(
-            prob.view(out_logits.shape[0], -1), self.test_topk, dim=1)
-        scores = topk_values
-        topk_boxes = topk_indexes // out_logits.shape[2]
-        labels = topk_indexes % out_logits.shape[2]
-        boxes = box_cxcywh_to_xyxy(out_bbox)
-        boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1,1,4))
-
-        # and from relative [0, 1] to absolute [0, height] coordinates
-        img_h, img_w = target_sizes.unbind(1)
-        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
-        boxes = boxes * scale_fct[:, None, :]
-
-        results = []
-        for s, l, b, size in zip(scores, labels, boxes, target_sizes):
-            r = Instances((size[0], size[1]))
-            r.pred_boxes = Boxes(b)
-            r.scores = s
-            r.pred_classes = l
-            results.append({'instances': r})
-        return results
-
-
-    def preprocess_image(self, batched_inputs):
-        """
-        Normalize, pad and batch the input images.
-        """
-        images = [self.normalizer(x["image"].to(self.device)) for x in batched_inputs]
-        return images
-
-
-    def _weak_loss(self, outputs, batched_inputs):
-        loss = 0
-        for b, x in enumerate(batched_inputs):
-            labels = x['pos_category_ids']
-            pred_logits = [outputs['pred_logits'][b]]
-            pred_boxes = [outputs['pred_boxes'][b]]
-            for xx in outputs['aux_outputs']:
-                pred_logits.append(xx['pred_logits'][b])
-                pred_boxes.append(xx['pred_boxes'][b])
-            pred_logits = torch.stack(pred_logits, dim=0) # L x N x C
-            pred_boxes = torch.stack(pred_boxes, dim=0) # L x N x 4
-            for label in labels:
-                loss += self._max_size_loss(
-                    pred_logits, pred_boxes, label) / len(labels)
-        loss = loss / len(batched_inputs)
-        return loss
-
-
-    def _max_size_loss(self, logits, boxes, label):
-        '''
-        Inputs:
-          logits: L x N x C
-          boxes: L x N x 4
-        '''
-        target = logits.new_zeros((logits.shape[0], logits.shape[2]))
-        target[:, label] = 1.
-        sizes = boxes[..., 2] * boxes[..., 3] # L x N
-        ind = sizes.argmax(dim=1) # L
-        loss = F.binary_cross_entropy_with_logits(
-            logits[range(len(ind)), ind], target, reduction='sum')
-        return loss
\ No newline at end of file
diff --git a/spaces/MercurialAi/OncoMedleyMini/OncoMedley/GMIC/gmic3d.py b/spaces/MercurialAi/OncoMedleyMini/OncoMedley/GMIC/gmic3d.py
deleted file mode 100644
index 6aceca1480c78b79599561f45f02fd6cb4172aae..0000000000000000000000000000000000000000
--- a/spaces/MercurialAi/OncoMedleyMini/OncoMedley/GMIC/gmic3d.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-import OncoMedley.GMIC.tools as tools
-import OncoMedley.GMIC.modules as m
-
-class GMIC3D(nn.Module):
-    def __init__(self, parameters):
-        super(GMIC3D, self).__init__()
-
-        # save parameters
-        self.experiment_parameters = parameters
-
-        # construct networks
-        # global network
-        self.global_network = m.GlobalNetwork(self.experiment_parameters, self)
-        self.global_network.add_layers()
-
-        # aggregation function
-        self.aggregation_function = m.TopTPercentAggregationFunctionFlattened(self.experiment_parameters, self)
-
-        # detection module
-        self.retrieve_roi_crops = m.RetrieveROIModule3D(self.experiment_parameters, self)
-
-        # detection network
-        self.local_network = m.LocalNetwork(self.experiment_parameters, self)
-        self.local_network.add_layers()
-
-        # MIL module
-        self.attention_module = m.AttentionModule(self.experiment_parameters, self)
-        self.attention_module.add_layers()
-
-    def _convert_crop_position(self, crops_x_small, cam_size, x_original):
-        """
-        Function that converts the crop locations from cam_size to x_original
-        :param crops_x_small: N, k*c, 2 numpy matrix
-        :param cam_size: (h,w)
-        :param x_original: N, C, H, W pytorch variable
-        :return: N, k*c, 2 numpy matrix
-        """
-        # retrieve the dimension of both the original image and the small version
-        h, w = cam_size
-        _, _, H, W = x_original.size()
-
-        # interpolate the 2d index in h_small to index in x_original
-        top_k_prop_x = crops_x_small[:, :, 0] / h
-        top_k_prop_y = crops_x_small[:, :, 1] / w
-        # sanity check
-        assert np.max(top_k_prop_x) <= 1.0, "top_k_prop_x >= 1.0"
-        assert np.min(top_k_prop_x) >= 0.0, "top_k_prop_x <= 0.0"
-        assert np.max(top_k_prop_y) <= 1.0, "top_k_prop_y >= 1.0"
-        assert np.min(top_k_prop_y) >= 0.0, "top_k_prop_y <= 0.0"
-        # interpolate the crop position from cam_size to x_original
-        top_k_interpolate_x = np.expand_dims(np.around(top_k_prop_x * H), -1)
-        top_k_interpolate_y = np.expand_dims(np.around(top_k_prop_y * W), -1)
-        top_k_interpolate_2d = np.concatenate([top_k_interpolate_x, top_k_interpolate_y], axis=-1)
-        return top_k_interpolate_2d
-
-    def _retrieve_crop_3d(self, x_original_pytorch, crop_positions, crop_method, max_slice_numbers):
-        """
-        Function that takes in the original image and cropping position and returns the crops
-        
-        crop_positions contains all potential crop locations for all slices at each step.
-        However, only the maximum crop among all slices is used at each step, indicated by max_slice_numbers.
-        Therefore, for each step j, select only the true globally-maximum crop and ignore the rest.
-        
-        Assumes batch size of 1
-        
-        :param x_original_pytorch: PyTorch Tensor array (N,C,H,W)
-        :param crop_positions:
-        :return:
-        """
-        batch_size = 1
-        num_slices, num_crops, _ = crop_positions.shape
-        crop_h, crop_w = self.experiment_parameters["crop_shape"]
-
-        output = torch.ones(
-            (batch_size, num_crops, 1, crop_h, crop_w))  
-        if self.experiment_parameters["half"]:
-            output = output.half()
-        if self.experiment_parameters["device_type"] == "gpu":
-            output = output.cuda()
-        for i in range(batch_size):
-            for j in range(num_crops):
-                tools.crop_pytorch_3d(x_original_pytorch[max_slice_numbers[j].item(), :, :, :],
-                                   self.experiment_parameters["crop_shape"],
-                                   crop_positions[max_slice_numbers[j].item(), j, :],
-                                   output[i, j, :, :, :],
-                                   method=crop_method)
-        return output
-
-
-    def forward(self, x_original):
-        """
-        :param x_original: N,C,D,H,W torch tensor 
-        """
-        device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-        final_preds = torch.Tensor([]).to(device)
-        for p in x_original:
-            p = torch.unsqueeze(p, 0).to(device)
-            N, C, num_slices, image_height, image_width = p.shape
-            
-            assert N == 1, "3D-GMIC is designed to work with batch size of 1 per GPU"
-            assert C == 1, "Input is expected to be 1-channel image"
-            
-            # reshape the tensor so that the slice dimension is now batch dimension
-            p = p.reshape(num_slices, 1, image_height, image_width)
-            
-            # global network
-            h_g, self.saliency_map = self.global_network.forward(p)
-            
-            num_slices, num_classes, H, W = self.saliency_map.shape
-            cam_size = (H, W)
-
-            # calculate y_global
-            saliency_map_flattened = self.saliency_map.permute(1,0,2,3).reshape(1, 1, -1)
-            self.y_global = self.aggregation_function.forward(saliency_map_flattened, num_slices=num_slices) 
-
-            # region proposal network
-            self.intended_max_slice_numbers, self.max_slice_numbers, small_x_locations = self.retrieve_roi_crops.forward(p, cam_size, self.saliency_map)
-
-            # convert crop locations that is on cam_size to p
-            self.patch_locations = self._convert_crop_position(small_x_locations, cam_size, p) 
-
-            # patch retriever
-            crops_variable = self._retrieve_crop_3d(p, self.patch_locations, self.retrieve_roi_crops.crop_method, self.max_slice_numbers)
-
-            # detection network
-            batch_size, num_crops, num_slices_per_patch, I, J = crops_variable.size()
-            assert batch_size == 1
-            self.patches = crops_variable
-            crops_variable_reshaped = crops_variable.view(batch_size * num_crops, num_slices_per_patch, I, J).to(device)
-            h_crops = self.local_network.forward(crops_variable_reshaped).view(batch_size, num_crops, -1).to(device)
-
-            # MIL module
-            z, self.patch_attns, self.y_local = self.attention_module.forward(h_crops)
-
-            # final output without using fusion branch
-            self.final_prediction = 0.5 * self.y_global + 0.5 * self.y_local
-            
-            final_preds = torch.cat([final_preds, self.final_prediction.to(device)], 0)
-
-        return final_preds
\ No newline at end of file
diff --git a/spaces/MirageML/sjc/sd1/ldm/modules/embedding_manager.py b/spaces/MirageML/sjc/sd1/ldm/modules/embedding_manager.py
deleted file mode 100644
index cbabc4174da38a3cc0f2f5480e0d268172627c3a..0000000000000000000000000000000000000000
--- a/spaces/MirageML/sjc/sd1/ldm/modules/embedding_manager.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import torch
-from torch import nn
-
-from ldm.data.personalized import per_img_token_list
-from transformers import CLIPTokenizer
-from functools import partial
-
-DEFAULT_PLACEHOLDER_TOKEN = ["*"]
-
-PROGRESSIVE_SCALE = 2000
-
-def get_clip_token_for_string(tokenizer, string):
-    batch_encoding = tokenizer(string, truncation=True, max_length=77, return_length=True,
-                               return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-    tokens = batch_encoding["input_ids"]
-    assert torch.count_nonzero(tokens - 49407) == 2, f"String '{string}' maps to more than a single token. Please use another string"
-
-    return tokens[0, 1]
-
-def get_bert_token_for_string(tokenizer, string):
-    token = tokenizer(string)
-    assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"
-
-    token = token[0, 1]
-
-    return token
-
-def get_embedding_for_clip_token(embedder, token):
-    return embedder(token.unsqueeze(0))[0, 0]
-
-
-class EmbeddingManager(nn.Module):
-    def __init__(
-            self,
-            embedder,
-            placeholder_strings=None,
-            initializer_words=None,
-            per_image_tokens=False,
-            num_vectors_per_token=1,
-            progressive_words=False,
-            **kwargs
-    ):
-        super().__init__()
-
-        self.string_to_token_dict = {}
-        
-        self.string_to_param_dict = nn.ParameterDict()
-
-        self.initial_embeddings = nn.ParameterDict() # These should not be optimized
-
-        self.progressive_words = progressive_words
-        self.progressive_counter = 0
-
-        self.max_vectors_per_token = num_vectors_per_token
-
-        if hasattr(embedder, 'tokenizer'): # using Stable Diffusion's CLIP encoder
-            self.is_clip = True
-            get_token_for_string = partial(get_clip_token_for_string, embedder.tokenizer)
-            get_embedding_for_tkn = partial(get_embedding_for_clip_token, embedder.transformer.text_model.embeddings)
-            token_dim = 768
-        else: # using LDM's BERT encoder
-            self.is_clip = False
-            get_token_for_string = partial(get_bert_token_for_string, embedder.tknz_fn)
-            get_embedding_for_tkn = embedder.transformer.token_emb
-            token_dim = 1280
-
-        if per_image_tokens:
-            placeholder_strings.extend(per_img_token_list)
-
-        for idx, placeholder_string in enumerate(placeholder_strings):
-            
-            token = get_token_for_string(placeholder_string)
-
-            if initializer_words and idx < len(initializer_words):
-                init_word_token = get_token_for_string(initializer_words[idx])
-
-                with torch.no_grad():
-                    init_word_embedding = get_embedding_for_tkn(init_word_token.cpu())
-
-                token_params = torch.nn.Parameter(init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1), requires_grad=True)
-                self.initial_embeddings[placeholder_string] = torch.nn.Parameter(init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1), requires_grad=False)
-            else:
-                token_params = torch.nn.Parameter(torch.rand(size=(num_vectors_per_token, token_dim), requires_grad=True))
-            
-            self.string_to_token_dict[placeholder_string] = token
-            self.string_to_param_dict[placeholder_string] = token_params
-
-    def forward(
-            self,
-            tokenized_text,
-            embedded_text,
-    ):
-        b, n, device = *tokenized_text.shape, tokenized_text.device
-
-        for placeholder_string, placeholder_token in self.string_to_token_dict.items():
-
-            placeholder_embedding = self.string_to_param_dict[placeholder_string].to(device)
-
-            if self.max_vectors_per_token == 1: # If there's only one vector per token, we can do a simple replacement
-                placeholder_idx = torch.where(tokenized_text == placeholder_token.to(device))
-                embedded_text[placeholder_idx] = placeholder_embedding
-            else: # otherwise, need to insert and keep track of changing indices
-                if self.progressive_words:
-                    self.progressive_counter += 1
-                    max_step_tokens = 1 + self.progressive_counter // PROGRESSIVE_SCALE
-                else:
-                    max_step_tokens = self.max_vectors_per_token
-
-                num_vectors_for_token = min(placeholder_embedding.shape[0], max_step_tokens)
-
-                placeholder_rows, placeholder_cols = torch.where(tokenized_text == placeholder_token.to(device))
-
-                if placeholder_rows.nelement() == 0:
-                    continue
-
-                sorted_cols, sort_idx = torch.sort(placeholder_cols, descending=True)
-                sorted_rows = placeholder_rows[sort_idx]
-
-                for idx in range(len(sorted_rows)):
-                    row = sorted_rows[idx]
-                    col = sorted_cols[idx]
-
-                    new_token_row = torch.cat([tokenized_text[row][:col], placeholder_token.repeat(num_vectors_for_token).to(device), tokenized_text[row][col + 1:]], axis=0)[:n]
-                    new_embed_row = torch.cat([embedded_text[row][:col], placeholder_embedding[:num_vectors_for_token], embedded_text[row][col + 1:]], axis=0)[:n]
-
-                    embedded_text[row]  = new_embed_row
-                    tokenized_text[row] = new_token_row
-
-        return embedded_text
-
-    def save(self, ckpt_path):
-        torch.save({"string_to_token": self.string_to_token_dict,
-                    "string_to_param": self.string_to_param_dict}, ckpt_path)
-
-    def load(self, ckpt_path):
-        ckpt = torch.load(ckpt_path, map_location='cpu')
-
-        self.string_to_token_dict = ckpt["string_to_token"]
-        self.string_to_param_dict = ckpt["string_to_param"]
-
-    def get_embedding_norms_squared(self):
-        all_params = torch.cat(list(self.string_to_param_dict.values()), axis=0) # num_placeholders x embedding_dim
-        param_norm_squared = (all_params * all_params).sum(axis=-1)              # num_placeholders
-
-        return param_norm_squared
-
-    def embedding_parameters(self):
-        return self.string_to_param_dict.parameters()
-
-    def embedding_to_coarse_loss(self):
-        
-        loss = 0.
-        num_embeddings = len(self.initial_embeddings)
-
-        for key in self.initial_embeddings:
-            optimized = self.string_to_param_dict[key]
-            coarse = self.initial_embeddings[key].clone().to(optimized.device)
-
-            loss = loss + (optimized - coarse) @ (optimized - coarse).T / num_embeddings
-
-        return loss
\ No newline at end of file
diff --git a/spaces/Missinginaction/stablediffusionwithnofilter/app.py b/spaces/Missinginaction/stablediffusionwithnofilter/app.py
deleted file mode 100644
index 4eab1984c438dcee135fc7f5404191798893a5d8..0000000000000000000000000000000000000000
--- a/spaces/Missinginaction/stablediffusionwithnofilter/app.py
+++ /dev/null
@@ -1,72 +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 -b v1.5 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://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"python launch.py --force-enable-xformers --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/Mountchicken/MAERec-Gradio/mmocr/models/textdet/postprocessors/pan_postprocessor.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/postprocessors/pan_postprocessor.py
deleted file mode 100644
index 63676856bebd78dfc97156739a2745e51cb272da..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/postprocessors/pan_postprocessor.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Sequence
-
-import cv2
-import numpy as np
-import torch
-from mmcv.ops import pixel_group
-from mmengine.structures import InstanceData
-
-from mmocr.registry import MODELS
-from mmocr.structures import TextDetDataSample
-from .base import BaseTextDetPostProcessor
-
-
-@MODELS.register_module()
-class PANPostprocessor(BaseTextDetPostProcessor):
-    """Convert scores to quadrangles via post processing in PANet. This is
-    partially adapted from https://github.com/WenmuZhou/PAN.pytorch.
-
-    Args:
-        text_repr_type (str): The boundary encoding type 'poly' or 'quad'.
-            Defaults to 'poly'.
-        score_threshold (float): The minimal text score.
-            Defaults to 0.3.
-        rescale_fields (list[str]): The bbox/polygon field names to
-            be rescaled. If None, no rescaling will be performed. Defaults to
-            ['polygons'].
-        min_text_confidence (float): The minimal text confidence.
-            Defaults to 0.5.
-        min_kernel_confidence (float): The minimal kernel confidence.
-            Defaults to 0.5.
-        distance_threshold (float): The minimal distance between the point to
-            mean of text kernel. Defaults to 3.0.
-        min_text_area (int): The minimal text instance region area.
-            Defaults to 16.
-        downsample_ratio (float): Downsample ratio. Defaults to 0.25.
-    """
-
-    def __init__(self,
-                 text_repr_type: str = 'poly',
-                 score_threshold: float = 0.3,
-                 rescale_fields: Sequence[str] = ['polygons'],
-                 min_text_confidence: float = 0.5,
-                 min_kernel_confidence: float = 0.5,
-                 distance_threshold: float = 3.0,
-                 min_text_area: int = 16,
-                 downsample_ratio: float = 0.25) -> None:
-        super().__init__(text_repr_type, rescale_fields)
-
-        self.min_text_confidence = min_text_confidence
-        self.min_kernel_confidence = min_kernel_confidence
-        self.score_threshold = score_threshold
-        self.min_text_area = min_text_area
-        self.distance_threshold = distance_threshold
-        self.downsample_ratio = downsample_ratio
-
-    def get_text_instances(self, pred_results: torch.Tensor,
-                           data_sample: TextDetDataSample,
-                           **kwargs) -> TextDetDataSample:
-        """Get text instance predictions of one image.
-
-        Args:
-            pred_result (torch.Tensor): Prediction results of an image which
-                is a tensor of shape :math:`(N, H, W)`.
-            data_sample (TextDetDataSample): Datasample of an image.
-
-        Returns:
-            TextDetDataSample: A new DataSample with predictions filled in.
-            Polygons and results are saved in
-            ``TextDetDataSample.pred_instances.polygons``. The confidence
-            scores are saved in ``TextDetDataSample.pred_instances.scores``.
-        """
-        assert pred_results.dim() == 3
-
-        pred_results[:2, :, :] = torch.sigmoid(pred_results[:2, :, :])
-        pred_results = pred_results.detach().cpu().numpy()
-
-        text_score = pred_results[0].astype(np.float32)
-        text = pred_results[0] > self.min_text_confidence
-        kernel = (pred_results[1] > self.min_kernel_confidence) * text
-        embeddings = pred_results[2:] * text.astype(np.float32)
-        embeddings = embeddings.transpose((1, 2, 0))  # (h, w, 4)
-
-        region_num, labels = cv2.connectedComponents(
-            kernel.astype(np.uint8), connectivity=4)
-        contours, _ = cv2.findContours((kernel * 255).astype(np.uint8),
-                                       cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
-        kernel_contours = np.zeros(text.shape, dtype='uint8')
-        cv2.drawContours(kernel_contours, contours, -1, 255)
-        text_points = pixel_group(text_score, text, embeddings, labels,
-                                  kernel_contours, region_num,
-                                  self.distance_threshold)
-
-        polygons = []
-        scores = []
-        for text_point in text_points:
-            text_confidence = text_point[0]
-            text_point = text_point[2:]
-            text_point = np.array(text_point, dtype=int).reshape(-1, 2)
-            area = text_point.shape[0]
-            if (area < self.min_text_area
-                    or text_confidence <= self.score_threshold):
-                continue
-
-            polygon = self._points2boundary(text_point)
-            if len(polygon) > 0:
-                polygons.append(polygon)
-                scores.append(text_confidence)
-        pred_instances = InstanceData()
-        pred_instances.polygons = polygons
-        pred_instances.scores = torch.FloatTensor(scores)
-        data_sample.pred_instances = pred_instances
-        scale_factor = data_sample.scale_factor
-        scale_factor = tuple(factor * self.downsample_ratio
-                             for factor in scale_factor)
-        data_sample.set_metainfo(dict(scale_factor=scale_factor))
-        return data_sample
-
-    def _points2boundary(self,
-                         points: np.ndarray,
-                         min_width: int = 0) -> List[float]:
-        """Convert a text mask represented by point coordinates sequence into a
-        text boundary.
-
-        Args:
-            points (ndarray): Mask index of size (n, 2).
-            min_width (int): Minimum bounding box width to be converted. Only
-                applicable to 'quad' type. Defaults to 0.
-
-        Returns:
-            list[float]: The text boundary point coordinates (x, y) list.
-            Return [] if no text boundary found.
-        """
-        assert isinstance(points, np.ndarray)
-        assert points.shape[1] == 2
-        assert self.text_repr_type in ['quad', 'poly']
-
-        if self.text_repr_type == 'quad':
-            rect = cv2.minAreaRect(points)
-            vertices = cv2.boxPoints(rect)
-            boundary = []
-            if min(rect[1]) >= min_width:
-                boundary = [p for p in vertices.flatten().tolist()]
-        elif self.text_repr_type == 'poly':
-
-            height = np.max(points[:, 1]) + 10
-            width = np.max(points[:, 0]) + 10
-
-            mask = np.zeros((height, width), np.uint8)
-            mask[points[:, 1], points[:, 0]] = 255
-
-            contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL,
-                                           cv2.CHAIN_APPROX_SIMPLE)
-            boundary = list(contours[0].flatten().tolist())
-
-        if len(boundary) < 8:
-            return []
-
-        return boundary
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/recognizers/satrn.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/recognizers/satrn.py
deleted file mode 100644
index 9182d8bea829b5453dc8228d842b91c6d9915a9e..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/recognizers/satrn.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.registry import MODELS
-from .encoder_decoder_recognizer import EncoderDecoderRecognizer
-
-
-@MODELS.register_module()
-class SATRN(EncoderDecoderRecognizer):
-    """Implementation of `SATRN <https://arxiv.org/abs/1910.04396>`_"""
diff --git a/spaces/NAACL2022/GlobEnc/src/model_attentions.py b/spaces/NAACL2022/GlobEnc/src/model_attentions.py
deleted file mode 100644
index ed79a05d37733bf21f84365df4eb80922e474fb9..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/GlobEnc/src/model_attentions.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import torch
-from tqdm.auto import tqdm
-import numpy as np
-
-
-def extract_attentions(model, encoder_func, dataset_len, device="cpu", delete_sep=False):
-    """
-    Extract raw attentions and norms
-    :param model:
-    :param encoder_func:
-    :param dataset_len:
-    :param device:
-    :return:
-    """
-    raw_attentions = []
-    norms_list = [[] for i in range(9)]
-    # head_attn_n, attn_n, attnres_n, attnresln_n, (+attn_enc),
-    # attn_n_ratio, attnres_n_ratio, attnresln_n_ratio, (+attn_enc_ratio)
-    model.to(device)
-    model.eval()
-    for id in tqdm(range(dataset_len)):
-        encoded = encoder_func(id).to(device)
-        # encoded = tokenizer.encode_plus(data["text"], return_tensors="pt").to(device)        
-        with torch.no_grad():
-            logits, attentions, norms = model(**encoded, output_attentions=True, output_norms=True, return_dict=False)
-            # logits:     [1, 2],
-            # attentions: 12(layer) * [1, 12(heads), 24(sentence_len), 24(sentence_len)],
-            # norms:      12(layer) * 7+2(type)
-
-        last_token = attentions[0].shape[-1]
-        if delete_sep:
-            last_token -= 1
-
-        num_layers = len(attentions)
-        for attention_type in range(9):
-            norm = torch.stack([norms[i][attention_type] for i in range(num_layers)]).squeeze().cpu().numpy()
-            if 0 < attention_type < 5:  # N: 1, N-Res: 2, N-ResLN: 3, N-Enc: 4
-                norm = norm[:, :last_token, :last_token]
-            elif attention_type >= 5:
-                norm = norm[:, :last_token]
-            norms_list[attention_type].append(norm)
-
-        raw_attention = torch.mean(torch.stack(attentions).squeeze(), axis=1).cpu().numpy()  # Mean of heads
-        raw_attentions.append(raw_attention[:, :last_token, :last_token])  # (12, sentence_len, sentence_len)
-    return raw_attentions, norms_list
-
-
-def build_ratio_residual_attentions(raw_attentions_list, norms_list):
-    r_ratio_attentions = {
-        "W-FixedRes": [],
-        "W-Res": [],
-        "N-FixedRes": [],
-        # "Uniform-Res": []
-    }
-    for idx in tqdm(range(len(raw_attentions_list))):
-        raw_attention = raw_attentions_list[idx]
-        r_half_matrix = np.ones(norms_list[5][idx].shape) * 0.5
-
-        r_ratio_attentions["W-FixedRes"].append(__build_ratio_residual_attention(raw_attention, r_half_matrix))
-
-        # normalized_attn_n = norms_list[1][idx] / np.max(norms_list[1][idx], axis=(1, 2), keepdims=True)
-        normalized_attn_n = norms_list[1][idx] / np.sum(norms_list[1][idx], axis=2, keepdims=True)
-        r_ratio_attentions["N-FixedRes"].append(__build_ratio_residual_attention(normalized_attn_n, r_half_matrix))
-
-        # norms_list[8]: N-Enc_ratio
-        r_ratio_attentions["W-Res"].append(__build_ratio_residual_attention(raw_attention, norms_list[8][idx], wres=True))
-
-        # r_ratio_attentions["Uniform-Res"].append(
-        #     __build_ratio_residual_attention(np.ones_like(raw_attention) / len(raw_attention), norms_list[8][idx]))
-
-    return r_ratio_attentions
-
-
-def __build_ratio_residual_attention(raw_attention, ratio_matrix, wres=False):
-    """
-    :param raw_attention: (layers, sentence_len, sentence_len)
-    :param ratio_matrix: (layers, sentence_len)
-    :return:
-    """
-    result_attention = np.zeros(raw_attention.shape)
-    for layer in range(raw_attention.shape[0]):
-        result_attention[layer] = __add_residual(raw_attention[layer], ratio_matrix[layer], wres)
-    return result_attention
-
-
-def __add_residual(att_mat, ratios, wres=False):
-    """
-    :param att_mat: (sentence_len, sentence_len)
-    :param ratios: (sentence_len)
-    :return:
-    """
-    att_mat_cp = np.copy(att_mat)
-    for token_idx in range(att_mat_cp.shape[0]):
-        r = ratios[token_idx]
-        if wres:
-            att_mat_cp[token_idx][token_idx] = 0
-            att_mat_cp[token_idx] /= np.sum(att_mat_cp[token_idx])
-        att_mat_cp[token_idx] *= r
-        att_mat_cp[token_idx][token_idx] += (1 - r)
-    return att_mat_cp
diff --git a/spaces/NATSpeech/DiffSpeech/modules/vocoder/hifigan/hifigan.py b/spaces/NATSpeech/DiffSpeech/modules/vocoder/hifigan/hifigan.py
deleted file mode 100644
index fddd5278760427d5d93b9b38240319ba5bdb0bdf..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/modules/vocoder/hifigan/hifigan.py
+++ /dev/null
@@ -1,338 +0,0 @@
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-import numpy as np
-
-LRELU_SLOPE = 0.1
-
-
-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 apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.h = h
-        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):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        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, h, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.h = h
-        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):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Conv1d1x1(Conv1d):
-    """1x1 Conv1d with customized initialization."""
-
-    def __init__(self, in_channels, out_channels, bias):
-        """Initialize 1x1 Conv1d module."""
-        super(Conv1d1x1, self).__init__(in_channels, out_channels,
-                                        kernel_size=1, padding=0,
-                                        dilation=1, bias=bias)
-
-
-class HifiGanGenerator(torch.nn.Module):
-    def __init__(self, h, c_out=1):
-        super(HifiGanGenerator, self).__init__()
-        self.h = h
-        self.num_kernels = len(h['resblock_kernel_sizes'])
-        self.num_upsamples = len(h['upsample_rates'])
-
-        self.conv_pre = weight_norm(Conv1d(80, h['upsample_initial_channel'], 7, 1, padding=3))
-        resblock = ResBlock1 if h['resblock'] == '1' else ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h['upsample_rates'], h['upsample_kernel_sizes'])):
-            c_cur = h['upsample_initial_channel'] // (2 ** (i + 1))
-            self.ups.append(weight_norm(
-                ConvTranspose1d(c_cur * 2, c_cur, k, u, padding=(k - u) // 2)))
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h['upsample_initial_channel'] // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(h['resblock_kernel_sizes'], h['resblock_dilation_sizes'])):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, c_out, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-
-    def forward(self, x, f0=None):
-        x = self.conv_pre(x)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, 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()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, use_cond=False, c_in=1):
-        super(DiscriminatorP, self).__init__()
-        self.use_cond = use_cond
-        if use_cond:
-            from utils.commons.hparams import hparams
-            t = hparams['hop_size']
-            self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
-            c_in = 2
-
-        self.period = period
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(c_in, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x, mel):
-        fmap = []
-        if self.use_cond:
-            x_mel = self.cond_net(mel)
-            x = torch.cat([x_mel, x], 1)
-        # 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, 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_cond=False, c_in=1):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorP(2, use_cond=use_cond, c_in=c_in),
-            DiscriminatorP(3, use_cond=use_cond, c_in=c_in),
-            DiscriminatorP(5, use_cond=use_cond, c_in=c_in),
-            DiscriminatorP(7, use_cond=use_cond, c_in=c_in),
-            DiscriminatorP(11, use_cond=use_cond, c_in=c_in),
-        ])
-
-    def forward(self, y, y_hat, mel=None):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y, mel)
-            y_d_g, fmap_g = d(y_hat, mel)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            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, use_cond=False, upsample_rates=None, c_in=1):
-        super(DiscriminatorS, self).__init__()
-        self.use_cond = use_cond
-        if use_cond:
-            t = np.prod(upsample_rates)
-            self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
-            c_in = 2
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(c_in, 128, 15, 1, padding=7)),
-            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, 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, mel):
-        if self.use_cond:
-            x_mel = self.cond_net(mel)
-            x = torch.cat([x_mel, x], 1)
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    def __init__(self, use_cond=False, c_in=1):
-        super(MultiScaleDiscriminator, self).__init__()
-        from utils.commons.hparams import hparams
-        self.discriminators = nn.ModuleList([
-            DiscriminatorS(use_spectral_norm=True, use_cond=use_cond,
-                           upsample_rates=[4, 4, hparams['hop_size'] // 16],
-                           c_in=c_in),
-            DiscriminatorS(use_cond=use_cond,
-                           upsample_rates=[4, 4, hparams['hop_size'] // 32],
-                           c_in=c_in),
-            DiscriminatorS(use_cond=use_cond,
-                           upsample_rates=[4, 4, hparams['hop_size'] // 64],
-                           c_in=c_in),
-        ])
-        self.meanpools = nn.ModuleList([
-            AvgPool1d(4, 2, padding=1),
-            AvgPool1d(4, 2, padding=1)
-        ])
-
-    def forward(self, y, y_hat, mel=None):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                y = self.meanpools[i - 1](y)
-                y_hat = self.meanpools[i - 1](y_hat)
-            y_d_r, fmap_r = d(y, mel)
-            y_d_g, fmap_g = d(y_hat, mel)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    r_losses = 0
-    g_losses = 0
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg ** 2)
-        r_losses += r_loss
-        g_losses += g_loss
-    r_losses = r_losses / len(disc_real_outputs)
-    g_losses = g_losses / len(disc_real_outputs)
-    return r_losses, g_losses
-
-
-def cond_discriminator_loss(outputs):
-    loss = 0
-    for dg in outputs:
-        g_loss = torch.mean(dg ** 2)
-        loss += g_loss
-    loss = loss / len(outputs)
-    return loss
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    for dg in disc_outputs:
-        l = torch.mean((1 - dg) ** 2)
-        loss += l
-    loss = loss / len(disc_outputs)
-    return loss
diff --git a/spaces/NATSpeech/PortaSpeech/modules/vocoder/hifigan/mel_utils.py b/spaces/NATSpeech/PortaSpeech/modules/vocoder/hifigan/mel_utils.py
deleted file mode 100644
index a75fce72db54812320bc60aedfdd378ccecb3374..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/modules/vocoder/hifigan/mel_utils.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import numpy as np
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-
-MAX_WAV_VALUE = 32768.0
-
-
-def load_wav(full_path):
-    sampling_rate, data = read(full_path)
-    return data, sampling_rate
-
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-
-def dynamic_range_decompression(x, C=1):
-    return np.exp(x) / C
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    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 mel_spectrogram(y, hparams, center=False, complex=False):
-    # hop_size: 512  # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate)
-    # win_size: 2048  # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate)
-    # fmin: 55  # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
-    # fmax: 10000  # To be increased/reduced depending on data.
-    # fft_size: 2048  # Extra window size is filled with 0 paddings to match this parameter
-    # n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax,
-    n_fft = hparams['fft_size']
-    num_mels = hparams['audio_num_mel_bins']
-    sampling_rate = hparams['audio_sample_rate']
-    hop_size = hparams['hop_size']
-    win_size = hparams['win_size']
-    fmin = hparams['fmin']
-    fmax = hparams['fmax']
-    y = y.clamp(min=-1., max=1.)
-    global mel_basis, hann_window
-    if fmax not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[str(fmax) + '_' + str(y.device)] = torch.from_numpy(mel).float().to(y.device)
-        hann_window[str(y.device)] = torch.hann_window(win_size).to(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[str(y.device)],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    if not complex:
-        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
-        spec = torch.matmul(mel_basis[str(fmax) + '_' + str(y.device)], spec)
-        spec = spectral_normalize_torch(spec)
-    else:
-        B, C, T, _ = spec.shape
-        spec = spec.transpose(1, 2)  # [B, T, n_fft, 2]
-    return spec
diff --git a/spaces/NATSpeech/PortaSpeech/tasks/tts/ps_flow.py b/spaces/NATSpeech/PortaSpeech/tasks/tts/ps_flow.py
deleted file mode 100644
index 37a2469ed08d382b58bcb6b8b1750986bb3dd345..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/tasks/tts/ps_flow.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import torch
-from modules.tts.portaspeech.portaspeech_flow import PortaSpeechFlow
-from tasks.tts.fs import FastSpeechTask
-from tasks.tts.ps import PortaSpeechTask
-from utils.audio.pitch.utils import denorm_f0
-from utils.commons.hparams import hparams
-
-
-class PortaSpeechFlowTask(PortaSpeechTask):
-    def __init__(self):
-        super().__init__()
-        self.training_post_glow = False
-
-    def build_tts_model(self):
-        ph_dict_size = len(self.token_encoder)
-        word_dict_size = len(self.word_encoder)
-        self.model = PortaSpeechFlow(ph_dict_size, word_dict_size, hparams)
-
-    def _training_step(self, sample, batch_idx, opt_idx):
-        self.training_post_glow = self.global_step >= hparams['post_glow_training_start'] \
-                                  and hparams['use_post_flow']
-        if hparams['two_stage'] and \
-                ((opt_idx == 0 and self.training_post_glow) or (opt_idx == 1 and not self.training_post_glow)):
-            return None
-        loss_output, _ = self.run_model(sample)
-        total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad])
-        loss_output['batch_size'] = sample['txt_tokens'].size()[0]
-        if 'postflow' in loss_output and loss_output['postflow'] is None:
-            return None
-        return total_loss, loss_output
-
-    def run_model(self, sample, infer=False, *args, **kwargs):
-        if not infer:
-            training_post_glow = self.training_post_glow
-            spk_embed = sample.get('spk_embed')
-            spk_id = sample.get('spk_ids')
-            output = self.model(sample['txt_tokens'],
-                                sample['word_tokens'],
-                                ph2word=sample['ph2word'],
-                                mel2word=sample['mel2word'],
-                                mel2ph=sample['mel2ph'],
-                                word_len=sample['word_lengths'].max(),
-                                tgt_mels=sample['mels'],
-                                pitch=sample.get('pitch'),
-                                spk_embed=spk_embed,
-                                spk_id=spk_id,
-                                infer=False,
-                                forward_post_glow=training_post_glow,
-                                two_stage=hparams['two_stage'],
-                                global_step=self.global_step)
-            losses = {}
-            self.add_mel_loss(output['mel_out'], sample['mels'], losses)
-            if (training_post_glow or not hparams['two_stage']) and hparams['use_post_flow']:
-                losses['postflow'] = output['postflow']
-                losses['l1'] = losses['l1'].detach()
-                losses['ssim'] = losses['ssim'].detach()
-            if not training_post_glow or not hparams['two_stage'] or not self.training:
-                losses['kl'] = output['kl']
-                if self.global_step < hparams['kl_start_steps']:
-                    losses['kl'] = losses['kl'].detach()
-                else:
-                    losses['kl'] = torch.clamp(losses['kl'], min=hparams['kl_min'])
-                losses['kl'] = losses['kl'] * hparams['lambda_kl']
-                if hparams['dur_level'] == 'word':
-                    self.add_dur_loss(
-                        output['dur'], sample['mel2word'], sample['word_lengths'], sample['txt_tokens'], losses)
-                    self.get_attn_stats(output['attn'], sample, losses)
-                else:
-                    super().add_dur_loss(output['dur'], sample['mel2ph'], sample['txt_tokens'], losses)
-            return losses, output
-        else:
-            use_gt_dur = kwargs.get('infer_use_gt_dur', hparams['use_gt_dur'])
-            forward_post_glow = self.global_step >= hparams['post_glow_training_start'] + 1000 \
-                                and hparams['use_post_flow']
-            spk_embed = sample.get('spk_embed')
-            spk_id = sample.get('spk_ids')
-            output = self.model(
-                sample['txt_tokens'],
-                sample['word_tokens'],
-                ph2word=sample['ph2word'],
-                word_len=sample['word_lengths'].max(),
-                pitch=sample.get('pitch'),
-                mel2ph=sample['mel2ph'] if use_gt_dur else None,
-                mel2word=sample['mel2word'] if hparams['profile_infer'] or hparams['use_gt_dur'] else None,
-                infer=True,
-                forward_post_glow=forward_post_glow,
-                spk_embed=spk_embed,
-                spk_id=spk_id,
-                two_stage=hparams['two_stage']
-            )
-            return output
-
-    def validation_step(self, sample, batch_idx):
-        self.training_post_glow = self.global_step >= hparams['post_glow_training_start'] \
-                                  and hparams['use_post_flow']
-        return super().validation_step(sample, batch_idx)
-
-    def save_valid_result(self, sample, batch_idx, model_out):
-        super(PortaSpeechFlowTask, self).save_valid_result(sample, batch_idx, model_out)
-        sr = hparams['audio_sample_rate']
-        f0_gt = None
-        if sample.get('f0') is not None:
-            f0_gt = denorm_f0(sample['f0'][0].cpu(), sample['uv'][0].cpu())
-        if self.global_step > 0:
-            # save FVAE result
-            if hparams['use_post_flow']:
-                wav_pred = self.vocoder.spec2wav(model_out['mel_out_fvae'][0].cpu(), f0=f0_gt)
-                self.logger.add_audio(f'wav_fvae_{batch_idx}', wav_pred, self.global_step, sr)
-                self.plot_mel(batch_idx, sample['mels'], model_out['mel_out_fvae'][0],
-                              f'mel_fvae_{batch_idx}', f0s=f0_gt)
-
-    def build_optimizer(self, model):
-        if hparams['two_stage'] and hparams['use_post_flow']:
-            self.optimizer = torch.optim.AdamW(
-                [p for name, p in self.model.named_parameters() if 'post_flow' not in name],
-                lr=hparams['lr'],
-                betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-                weight_decay=hparams['weight_decay'])
-            self.post_flow_optimizer = torch.optim.AdamW(
-                self.model.post_flow.parameters(),
-                lr=hparams['post_flow_lr'],
-                betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-                weight_decay=hparams['weight_decay'])
-            return [self.optimizer, self.post_flow_optimizer]
-        else:
-            self.optimizer = torch.optim.AdamW(
-                self.model.parameters(),
-                lr=hparams['lr'],
-                betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-                weight_decay=hparams['weight_decay'])
-            return [self.optimizer]
-
-    def build_scheduler(self, optimizer):
-        return FastSpeechTask.build_scheduler(self, optimizer[0])
\ No newline at end of file
diff --git a/spaces/NN520/AI/src/components/chat.tsx b/spaces/NN520/AI/src/components/chat.tsx
deleted file mode 100644
index a37ab1cc96ca2e6bfd9acbe313a8d946bfd5c3d4..0000000000000000000000000000000000000000
--- a/spaces/NN520/AI/src/components/chat.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-'use client'
-
-import { useCallback, useEffect, useMemo, useState } from 'react'
-import { useAtom } from 'jotai'
-import Image from 'next/image'
-import { cn } from '@/lib/utils'
-import { ChatList } from '@/components/chat-list'
-import { ChatPanel } from '@/components/chat-panel'
-import { WelcomeScreen } from '@/components/welcome-screen'
-import { ChatScrollAnchor } from '@/components/chat-scroll-anchor'
-import { ToneSelector } from './tone-selector'
-import { ChatHeader } from './chat-header'
-import { ChatSuggestions } from './chat-suggestions'
-import { bingConversationStyleAtom } from '@/state'
-import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom'
-import StopIcon from '@/assets/images/stop.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { ChatNotification } from './chat-notification'
-import { Settings } from './settings'
-import { ChatHistory } from './chat-history'
-
-export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] }
-
-export default function Chat({ className }: ChatProps) {
-
-  const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom)
-  const {
-    messages,
-    sendMessage,
-    resetConversation,
-    stopGenerating,
-    setInput,
-    bot,
-    input,
-    generating,
-    isSpeaking,
-    uploadImage,
-    attachmentList,
-    setAttachmentList,
-  } = useBing()
-
-  useEffect(() => {
-    window.scrollTo({
-      top: document.body.offsetHeight,
-      behavior: 'smooth'
-    })
-  }, [])
-
-  return (
-    <div className="flex flex-1 flex-col">
-      <Settings />
-      <div className={cn('flex-1 pb-16', className)}>
-        <ChatHeader />
-        <WelcomeScreen setInput={setInput} />
-        <ToneSelector type={bingStyle} onChange={setBingStyle} />
-        {messages.length ? (
-          <>
-            <ChatList messages={messages} />
-            <ChatScrollAnchor trackVisibility={generating} />
-            <ChatNotification message={messages.at(-1)} bot={bot} />
-            <ChatSuggestions setInput={setInput} suggestions={messages.at(-1)?.suggestedResponses} />
-
-            {generating ? (
-              <div className="flex h-10 items-center justify-center my-4">
-                <button
-                  onClick={stopGenerating}
-                  className="typing-control-item stop"
-                >
-                  <Image alt="stop" src={StopIcon} width={24} className="mr-1" />
-                  <span>停止响应</span>
-                </button>
-              </div>
-            ) : null}
-          </>
-        ) : null}
-      </div>
-      <ChatPanel
-        className="pt-24 z-10"
-        isSpeaking={isSpeaking}
-        generating={generating}
-        sendMessage={sendMessage}
-        input={input}
-        setInput={setInput}
-        resetConversation={resetConversation}
-        uploadImage={uploadImage}
-        attachmentList={attachmentList}
-        setAttachmentList={setAttachmentList}
-      />
-      <ButtonScrollToBottom />
-    </div>
-  )
-}
diff --git a/spaces/NSect/VALL-E-X/utils/download.py b/spaces/NSect/VALL-E-X/utils/download.py
deleted file mode 100644
index 9ab5cd9394e8d9f2793e2e2eb8d3740c2cc2ee0b..0000000000000000000000000000000000000000
--- a/spaces/NSect/VALL-E-X/utils/download.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import sys
-import requests
-
-
-def download_file_from_google_drive(id, destination):
-    URL = "https://docs.google.com/uc?export=download&confirm=1"
-
-    session = requests.Session()
-
-    response = session.get(URL, params={"id": id}, stream=True)
-    token = get_confirm_token(response)
-
-    if token:
-        params = {"id": id, "confirm": token}
-        response = session.get(URL, params=params, stream=True)
-
-    save_response_content(response, destination)
-
-
-def get_confirm_token(response):
-    for key, value in response.cookies.items():
-        if key.startswith("download_warning"):
-            return value
-
-    return None
-
-
-def save_response_content(response, destination):
-    CHUNK_SIZE = 32768
-
-    with open(destination, "wb") as f:
-        for chunk in response.iter_content(CHUNK_SIZE):
-            if chunk:  # filter out keep-alive new chunks
-                f.write(chunk)
-
-
-def main():
-    if len(sys.argv) >= 3:
-        file_id = sys.argv[1]
-        destination = sys.argv[2]
-    else:
-        file_id = "TAKE_ID_FROM_SHAREABLE_LINK"
-        destination = "DESTINATION_FILE_ON_YOUR_DISK"
-    print(f"dowload {file_id} to {destination}")
-    download_file_from_google_drive(file_id, destination)
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/Nee001/bing0/src/state/index.ts b/spaces/Nee001/bing0/src/state/index.ts
deleted file mode 100644
index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000
--- a/spaces/Nee001/bing0/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/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/scripts/wer.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/scripts/wer.py
deleted file mode 100644
index 613ab50d39019f6edf67c56c2353646be2a2f17d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/scripts/wer.py
+++ /dev/null
@@ -1,82 +0,0 @@
-#!/usr/bin/env python3 -u
-# 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.
-
-"""
-Implement unsupervised metric for decoding hyperparameter selection:
-    $$ alpha * LM_PPL + ViterbitUER(%) * 100 $$
-"""
-import argparse
-import logging
-import sys
-
-import editdistance
-
-logging.root.setLevel(logging.INFO)
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-def get_parser():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-s", "--hypo", help="hypo transcription", required=True)
-    parser.add_argument(
-        "-r", "--reference", help="reference transcription", required=True
-    )
-    return parser
-
-
-def compute_wer(ref_uid_to_tra, hyp_uid_to_tra, g2p):
-    d_cnt = 0
-    w_cnt = 0
-    w_cnt_h = 0
-    for uid in hyp_uid_to_tra:
-        ref = ref_uid_to_tra[uid].split()
-        if g2p is not None:
-            hyp = g2p(hyp_uid_to_tra[uid])
-            hyp = [p for p in hyp if p != "'" and p != " "]
-            hyp = [p[:-1] if p[-1].isnumeric() else p for p in hyp]
-        else:
-            hyp = hyp_uid_to_tra[uid].split()
-        d_cnt += editdistance.eval(ref, hyp)
-        w_cnt += len(ref)
-        w_cnt_h += len(hyp)
-    wer = float(d_cnt) / w_cnt
-    logger.debug(
-        (
-            f"wer = {wer * 100:.2f}%; num. of ref words = {w_cnt}; "
-            f"num. of hyp words = {w_cnt_h}; num. of sentences = {len(ref_uid_to_tra)}"
-        )
-    )
-    return wer
-
-
-def main():
-    args = get_parser().parse_args()
-
-    errs = 0
-    count = 0
-    with open(args.hypo, "r") as hf, open(args.reference, "r") as rf:
-        for h, r in zip(hf, rf):
-            h = h.rstrip().split()
-            r = r.rstrip().split()
-            errs += editdistance.eval(r, h)
-            count += len(r)
-
-    logger.info(f"UER: {errs / count * 100:.2f}%")
-
-
-if __name__ == "__main__":
-    main()
-
-
-def load_tra(tra_path):
-    with open(tra_path, "r") as f:
-        uid_to_tra = {}
-        for line in f:
-            uid, tra = line.split(None, 1)
-            uid_to_tra[uid] = tra
-    logger.debug(f"loaded {len(uid_to_tra)} utterances from {tra_path}")
-    return uid_to_tra
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/mask_tokens_dataset.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/mask_tokens_dataset.py
deleted file mode 100644
index 9123235594c3977994a3ae8a03ab4c9e395cc5de..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/mask_tokens_dataset.py
+++ /dev/null
@@ -1,220 +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 functools import lru_cache
-
-import numpy as np
-import torch
-from fairseq.data import Dictionary, data_utils
-
-from . import BaseWrapperDataset, LRUCacheDataset
-
-
-class MaskTokensDataset(BaseWrapperDataset):
-    """
-    A wrapper Dataset for masked language modeling.
-
-    Input items are masked according to the specified masking probability.
-
-    Args:
-        dataset: Dataset to wrap.
-        sizes: Sentence lengths
-        vocab: Dictionary with the vocabulary and special tokens.
-        pad_idx: Id of pad token in vocab
-        mask_idx: Id of mask token in vocab
-        return_masked_tokens: controls whether to return the non-masked tokens
-            (the default) or to return a tensor with the original masked token
-            IDs (and *pad_idx* elsewhere). The latter is useful as targets for
-            masked LM training.
-        seed: Seed for random number generator for reproducibility.
-        mask_prob: probability of replacing a token with *mask_idx*.
-        leave_unmasked_prob: probability that a masked token is unmasked.
-        random_token_prob: probability of replacing a masked token with a
-            random token from the vocabulary.
-        freq_weighted_replacement: sample random replacement words based on
-            word frequencies in the vocab.
-        mask_whole_words: only mask whole words. This should be a byte mask
-            over vocab indices, indicating whether it is the beginning of a
-            word. We will extend any mask to encompass the whole word.
-        bpe: BPE to use for whole-word masking.
-        mask_multiple_length : repeat each mask index multiple times. Default
-            value is 1.
-        mask_stdev : standard deviation of masks distribution in case of
-            multiple masking. Default value is 0.
-    """
-
-    @classmethod
-    def apply_mask(cls, dataset: torch.utils.data.Dataset, *args, **kwargs):
-        """Return the source and target datasets for masked LM training."""
-        dataset = LRUCacheDataset(dataset)
-        return (
-            LRUCacheDataset(cls(dataset, *args, **kwargs, return_masked_tokens=False)),
-            LRUCacheDataset(cls(dataset, *args, **kwargs, return_masked_tokens=True)),
-        )
-
-    def __init__(
-        self,
-        dataset: torch.utils.data.Dataset,
-        vocab: Dictionary,
-        pad_idx: int,
-        mask_idx: int,
-        return_masked_tokens: bool = False,
-        seed: int = 1,
-        mask_prob: float = 0.15,
-        leave_unmasked_prob: float = 0.1,
-        random_token_prob: float = 0.1,
-        freq_weighted_replacement: bool = False,
-        mask_whole_words: torch.Tensor = None,
-        mask_multiple_length: int = 1,
-        mask_stdev: float = 0.0,
-    ):
-        assert 0.0 < mask_prob < 1.0
-        assert 0.0 <= random_token_prob <= 1.0
-        assert 0.0 <= leave_unmasked_prob <= 1.0
-        assert random_token_prob + leave_unmasked_prob <= 1.0
-        assert mask_multiple_length >= 1
-        assert mask_stdev >= 0.0
-
-        self.dataset = dataset
-        self.vocab = vocab
-        self.pad_idx = pad_idx
-        self.mask_idx = mask_idx
-        self.return_masked_tokens = return_masked_tokens
-        self.seed = seed
-        self.mask_prob = mask_prob
-        self.leave_unmasked_prob = leave_unmasked_prob
-        self.random_token_prob = random_token_prob
-        self.mask_whole_words = mask_whole_words
-        self.mask_multiple_length = mask_multiple_length
-        self.mask_stdev = mask_stdev
-
-        if random_token_prob > 0.0:
-            if freq_weighted_replacement:
-                weights = np.array(self.vocab.count)
-            else:
-                weights = np.ones(len(self.vocab))
-            weights[: self.vocab.nspecial] = 0
-            self.weights = weights / weights.sum()
-
-        self.epoch = 0
-
-    @property
-    def can_reuse_epoch_itr_across_epochs(self):
-        return True  # only the noise changes, not item sizes
-
-    def set_epoch(self, epoch, **unused):
-        super().set_epoch(epoch)
-        self.epoch = epoch
-
-    def __getitem__(self, index: int):
-        return self.__getitem_cached__(self.seed, self.epoch, index)
-
-    @lru_cache(maxsize=8)
-    def __getitem_cached__(self, seed: int, epoch: int, index: int):
-        with data_utils.numpy_seed(self.seed, self.epoch, index):
-            item = self.dataset[index]
-            sz = len(item)
-
-            assert (
-                self.mask_idx not in item
-            ), "Dataset contains mask_idx (={}), this is not expected!".format(
-                self.mask_idx,
-            )
-
-            if self.mask_whole_words is not None:
-                word_begins_mask = self.mask_whole_words.gather(0, item)
-                word_begins_idx = word_begins_mask.nonzero().view(-1)
-                sz = len(word_begins_idx)
-                words = np.split(word_begins_mask, word_begins_idx)[1:]
-                assert len(words) == sz
-                word_lens = list(map(len, words))
-
-            # decide elements to mask
-            mask = np.full(sz, False)
-            num_mask = int(
-                # add a random number for probabilistic rounding
-                self.mask_prob * sz / float(self.mask_multiple_length)
-                + np.random.rand()
-            )
-
-            # multiple masking as described in the vq-wav2vec paper (https://arxiv.org/abs/1910.05453)
-            mask_idc = np.random.choice(sz, num_mask, replace=False)
-            if self.mask_stdev > 0.0:
-                lengths = np.random.normal(
-                    self.mask_multiple_length, self.mask_stdev, size=num_mask
-                )
-                lengths = [max(0, int(round(x))) for x in lengths]
-                mask_idc = np.asarray(
-                    [
-                        mask_idc[j] + offset
-                        for j in range(len(mask_idc))
-                        for offset in range(lengths[j])
-                    ],
-                    dtype=np.int64,
-                )
-            else:
-                mask_idc = np.concatenate(
-                    [mask_idc + i for i in range(self.mask_multiple_length)]
-                )
-            mask_idc = mask_idc[mask_idc < len(mask)]
-            try:
-                mask[mask_idc] = True
-            except:  # something wrong
-                print(
-                    "Assigning mask indexes {} to mask {} failed!".format(
-                        mask_idc, mask
-                    )
-                )
-                raise
-
-            if self.return_masked_tokens:
-                # exit early if we're just returning the masked tokens
-                # (i.e., the targets for masked LM training)
-                if self.mask_whole_words is not None:
-                    mask = np.repeat(mask, word_lens)
-                new_item = np.full(len(mask), self.pad_idx)
-                new_item[mask] = item[torch.from_numpy(mask.astype(np.uint8)) == 1]
-                return torch.from_numpy(new_item)
-
-            # decide unmasking and random replacement
-            rand_or_unmask_prob = self.random_token_prob + self.leave_unmasked_prob
-            if rand_or_unmask_prob > 0.0:
-                rand_or_unmask = mask & (np.random.rand(sz) < rand_or_unmask_prob)
-                if self.random_token_prob == 0.0:
-                    unmask = rand_or_unmask
-                    rand_mask = None
-                elif self.leave_unmasked_prob == 0.0:
-                    unmask = None
-                    rand_mask = rand_or_unmask
-                else:
-                    unmask_prob = self.leave_unmasked_prob / rand_or_unmask_prob
-                    decision = np.random.rand(sz) < unmask_prob
-                    unmask = rand_or_unmask & decision
-                    rand_mask = rand_or_unmask & (~decision)
-            else:
-                unmask = rand_mask = None
-
-            if unmask is not None:
-                mask = mask ^ unmask
-
-            if self.mask_whole_words is not None:
-                mask = np.repeat(mask, word_lens)
-
-            new_item = np.copy(item)
-            new_item[mask] = self.mask_idx
-            if rand_mask is not None:
-                num_rand = rand_mask.sum()
-                if num_rand > 0:
-                    if self.mask_whole_words is not None:
-                        rand_mask = np.repeat(rand_mask, word_lens)
-                        num_rand = rand_mask.sum()
-
-                    new_item[rand_mask] = np.random.choice(
-                        len(self.vocab),
-                        num_rand,
-                        p=self.weights,
-                    )
-
-            return torch.from_numpy(new_item)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/lightconv_layer/lightconv_layer.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/lightconv_layer/lightconv_layer.py
deleted file mode 100644
index e7e597f4749c591b057d776aacec39b44d99c037..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/lightconv_layer/lightconv_layer.py
+++ /dev/null
@@ -1,137 +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 lightconv_cuda
-import torch
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.incremental_decoding_utils import with_incremental_state
-from fairseq.modules.fairseq_dropout import FairseqDropout
-from torch import nn
-from torch.autograd import Function
-
-
-class lightconvFunction(Function):
-    @staticmethod
-    def forward(ctx, x, weights, padding_l):
-        ctx.padding_l = padding_l
-        outputs = lightconv_cuda.forward(x, weights, padding_l)
-        variables = [x, weights]
-        ctx.save_for_backward(*variables)
-        return outputs[0]
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        outputs = lightconv_cuda.backward(
-            grad_output.contiguous(), ctx.padding_l, *ctx.saved_tensors
-        )
-        grad_input, grad_weights = outputs
-        return grad_input, grad_weights, None
-
-
-@with_incremental_state
-class LightconvLayer(nn.Module):
-    def __init__(
-        self,
-        input_size,
-        kernel_size=1,
-        padding_l=None,
-        weight_softmax=False,
-        num_heads=1,
-        weight_dropout=0.0,
-        bias=False,
-    ):
-        super(LightconvLayer, self).__init__()
-        self.input_size = input_size
-        self.kernel_size = kernel_size
-        self.padding_l = padding_l
-        self.num_heads = num_heads
-        self.weight_softmax = weight_softmax
-        self.weight_dropout_module = FairseqDropout(
-            weight_dropout, module_name=self.__class__.__name__
-        )
-
-        self.weight = nn.Parameter(torch.Tensor(num_heads, kernel_size))
-        if bias:
-            self.bias = nn.Parameter(torch.Tensor(input_size))
-        else:
-            self.bias = None
-        self.reset_parameters()
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        prefix = name + "." if name != "" else ""
-        for k, v in state_dict.items():
-            if k.endswith(prefix + "weight"):
-                if v.dim() == 3 and v.size(1) == 1:
-                    state_dict[k] = v.squeeze(1)
-
-    def reset_parameters(self):
-        nn.init.xavier_uniform_(self.weight)
-        if self.bias is not None:
-            nn.init.constant_(self.bias, 0.0)
-
-    def forward(self, x, incremental_state=None):
-
-        # during inference time, incremental BMM is faster
-        if incremental_state is not None:
-            T, B, C = x.size()
-            K, H = self.kernel_size, self.num_heads
-            R = C // H
-            input_buffer = self._get_input_buffer(incremental_state)
-            if input_buffer is None:
-                input_buffer = x.new()
-            x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
-            if self.kernel_size > 1:
-                self._set_input_buffer(
-                    incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :]
-                )
-            x_unfold = x_unfold.view(T * B * H, R, -1)
-
-            weight = self.weight
-            if self.weight_softmax:
-                weight = F.softmax(weight.float(), dim=1).type_as(weight)
-
-            weight = weight[:, -x_unfold.size(2) :]
-
-            K = weight.size(1)
-
-            weight = (
-                weight.view(1, H, K)
-                .expand(T * B, H, K)
-                .contiguous()
-                .view(T * B * H, K, 1)
-            )
-
-            weight = self.weight_dropout_module(weight)
-            output = torch.bmm(x_unfold, weight)  # T*B*H x R x 1
-            output = output.view(T, B, C)
-            return output
-
-        # during training time, use CUDA kernel
-        else:
-            x = x.permute(1, 2, 0).contiguous()
-            weight = self.weight
-            if self.weight_softmax:
-                weight = F.softmax(self.weight, -1)
-            if self.weight_dropout_module.p:
-                weight = self.weight_dropout_module(weight)
-            return lightconvFunction.apply(x, weight, self.padding_l).permute(2, 0, 1)
-
-    def reorder_incremental_state(self, incremental_state, new_order):
-        input_buffer = self._get_input_buffer(incremental_state)
-        if input_buffer is not None:
-            input_buffer = input_buffer.index_select(1, new_order)
-            self._set_input_buffer(incremental_state, input_buffer)
-
-    def _get_input_buffer(self, incremental_state):
-        return utils.get_incremental_state(self, incremental_state, "input_buffer")
-
-    def _set_input_buffer(self, incremental_state, new_buffer):
-        return utils.set_incremental_state(
-            self, incremental_state, "input_buffer", new_buffer
-        )
-
-    def half(self):
-        return self._apply(lambda t: t.half() if t.is_floating_point() else t)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/gpu/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/gpu/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/speech_recognition/test_collaters.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/speech_recognition/test_collaters.py
deleted file mode 100644
index 6a5029a48faea2426d7a0277655a2c7c08c1d16c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/speech_recognition/test_collaters.py
+++ /dev/null
@@ -1,58 +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 unittest
-
-import numpy as np
-import torch
-from examples.speech_recognition.data.collaters import Seq2SeqCollater
-
-
-class TestSeq2SeqCollator(unittest.TestCase):
-    def test_collate(self):
-
-        eos_idx = 1
-        pad_idx = 0
-        collater = Seq2SeqCollater(
-            feature_index=0, label_index=1, pad_index=pad_idx, eos_index=eos_idx
-        )
-
-        # 2 frames in the first sample and 3 frames in the second one
-        frames1 = np.array([[7, 8], [9, 10]])
-        frames2 = np.array([[1, 2], [3, 4], [5, 6]])
-        target1 = np.array([4, 2, 3, eos_idx])
-        target2 = np.array([3, 2, eos_idx])
-        sample1 = {"id": 0, "data": [frames1, target1]}
-        sample2 = {"id": 1, "data": [frames2, target2]}
-        batch = collater.collate([sample1, sample2])
-
-        # collate sort inputs by frame's length before creating the batch
-        self.assertTensorEqual(batch["id"], torch.tensor([1, 0]))
-        self.assertEqual(batch["ntokens"], 7)
-        self.assertTensorEqual(
-            batch["net_input"]["src_tokens"],
-            torch.tensor(
-                [[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [pad_idx, pad_idx]]]
-            ),
-        )
-        self.assertTensorEqual(
-            batch["net_input"]["prev_output_tokens"],
-            torch.tensor([[eos_idx, 3, 2, pad_idx], [eos_idx, 4, 2, 3]]),
-        )
-        self.assertTensorEqual(batch["net_input"]["src_lengths"], torch.tensor([3, 2]))
-        self.assertTensorEqual(
-            batch["target"],
-            torch.tensor([[3, 2, eos_idx, pad_idx], [4, 2, 3, eos_idx]]),
-        )
-        self.assertEqual(batch["nsentences"], 2)
-
-    def assertTensorEqual(self, t1, t2):
-        self.assertEqual(t1.size(), t2.size(), "size mismatch")
-        self.assertEqual(t1.ne(t2).long().sum(), 0)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/mean_pool.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/mean_pool.py
deleted file mode 100644
index 4eea048ef3455cb3c897e74c18778c78fdc9fcbf..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/mean_pool.py
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/usr/bin/env python3 -u
-# 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 argparse
-import os
-import os.path as osp
-import math
-import numpy as np
-import tqdm
-import torch
-import torch.nn.functional as F
-from shutil import copyfile
-
-from npy_append_array import NpyAppendArray
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        description="mean pools representations by compressing uniform splits of the data"
-    )
-    # fmt: off
-    parser.add_argument('source', help='directory with features')
-    parser.add_argument('--split', help='which split to read', required=True)
-    parser.add_argument('--save-dir', help='where to save the output', required=True)
-    parser.add_argument('--subsample-rate', type=float, default=0.5, help='size to subsample data to')
-
-    parser.add_argument('--remove-extra', action='store_true', help='if true, removes extra states that cant be pooled, otherwise pads with 0s')
-    # fmt: on
-
-    return parser
-
-
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    source_path = osp.join(args.source, args.split)
-
-    print(f"data path: {source_path}")
-
-    features = np.load(source_path + ".npy", mmap_mode="r")
-
-    os.makedirs(args.save_dir, exist_ok=True)
-    save_path = osp.join(args.save_dir, args.split)
-
-    copyfile(source_path + ".tsv", save_path + ".tsv")
-
-    if os.path.exists(source_path + ".phn"):
-        copyfile(source_path + ".phn", save_path + ".phn")
-    if os.path.exists(source_path + ".wrd"):
-        copyfile(source_path + ".wrd", save_path + ".wrd")
-
-    if os.path.exists(osp.join(args.source, "dict.phn.txt")):
-        copyfile(
-            osp.join(args.source, "dict.phn.txt"),
-            osp.join(args.save_dir, "dict.phn.txt"),
-        )
-
-    if osp.exists(save_path + ".npy"):
-        os.remove(save_path + ".npy")
-    npaa = NpyAppendArray(save_path + ".npy")
-
-    with open(source_path + ".lengths", "r") as lf:
-        lengths = lf.readlines()
-
-    fsz = features.shape[-1]
-    start = 0
-    with torch.no_grad():
-        with open(save_path + ".lengths", "w") as lengths_out:
-            for length in tqdm.tqdm(lengths):
-                length = int(length)
-                end = start + length
-                feats = features[start:end]
-                start += length
-                x = torch.from_numpy(feats).cuda()
-                target_num = math.ceil(length * args.subsample_rate)
-                rem = length % target_num
-
-                if rem > 0:
-                    if args.remove_extra:
-                        to_rem = target_num - rem
-                        target_num -= 1
-                        x = x[:-to_rem]
-                    else:
-                        to_add = target_num - rem
-                        x = F.pad(x, [0, 0, 0, to_add])
-                        x[-to_add:] = x[-to_add - 1]
-
-                x = x.view(target_num, -1, fsz)
-                x = x.mean(dim=-2)
-                print(target_num, file=lengths_out)
-                npaa.append(x.cpu().numpy())
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/quantization/pq/modules/qconv.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/quantization/pq/modules/qconv.py
deleted file mode 100644
index d15ec192e8cda6265a198e583a9bf7fb194dd129..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/quantization/pq/modules/qconv.py
+++ /dev/null
@@ -1,115 +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 numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.modules.utils import _pair
-
-
-class PQConv2d(nn.Module):
-    """
-    Quantized counterpart of nn.Conv2d module. Stores the centroid, the assignments
-    and the non-quantized biases. The full weight is re-instantiated at each forward
-    pass and autograd automatically computes the gradients with respect to the
-    centroids.
-
-    Args:
-        - centroids: centroids of size n_centroids x block_size
-        - assignments: assignments of the centroids to the subvectors
-          of size self.out_channels x n_blocks
-        - bias: the non-quantized bias, must be either torch.Tensor or None
-
-    Remarks:
-        - We refer the reader to the official documentation of the nn.Conv2d module
-          for the other arguments and the behavior of the module.
-        - Performance tests on GPU show that this implementation is 10% slower than
-          the non-quantized nn.Conv2d module for a standard training loop.
-        - During the backward, the gradients are averaged by cluster and not summed.
-          This explains the hook registered to the centroids.
-    """
-
-    def __init__(
-        self,
-        centroids,
-        assignments,
-        bias,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        dilation=1,
-        groups=1,
-        padding_mode="zeros",
-    ):
-        super(PQConv2d, self).__init__()
-        self.block_size = centroids.size(1)
-        self.n_centroids = centroids.size(0)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = _pair(kernel_size)
-        self.stride = _pair(stride)
-        self.padding = _pair(padding)
-        self.dilation = _pair(dilation)
-        self.groups = groups
-        self.padding_mode = padding_mode
-        # check compatibility
-        if in_channels // groups * np.prod(self.kernel_size) % self.block_size != 0:
-            raise ValueError("Wrong PQ sizes")
-        if len(assignments) % out_channels != 0:
-            raise ValueError("Wrong PQ sizes")
-        if in_channels % groups != 0:
-            raise ValueError("in_channels must be divisible by groups")
-        if out_channels % groups != 0:
-            raise ValueError("out_channels must be divisible by groups")
-        # define parameters
-        self.centroids = nn.Parameter(centroids, requires_grad=True)
-        self.register_buffer("assignments", assignments)
-        self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
-        if bias is not None:
-            self.bias = nn.Parameter(bias)
-        else:
-            self.register_parameter("bias", None)
-        # register hook for averaging gradients per centroids instead of summing
-        self.centroids.register_hook(lambda x: x / self.counts[:, None])
-
-    @property
-    def weight(self):
-        return (
-            self.centroids[self.assignments]
-            .reshape(-1, self.out_channels, self.block_size)
-            .permute(1, 0, 2)
-            .reshape(
-                self.out_channels, self.in_channels // self.groups, *self.kernel_size
-            )
-        )
-
-    def forward(self, x):
-        return F.conv2d(
-            x,
-            self.weight,
-            self.bias,
-            self.stride,
-            self.padding,
-            self.dilation,
-            self.groups,
-        )
-
-    def extra_repr(self):
-        s = "{in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}"
-        if self.padding != (0,) * len(self.padding):
-            s += ", padding={padding}"
-        if self.dilation != (1,) * len(self.dilation):
-            s += ", dilation={dilation}"
-        if self.groups != 1:
-            s += ", groups={groups}"
-        if self.bias is None:
-            s += ", bias=False"
-        if self.padding_mode != "zeros":
-            s += ", padding_mode={padding_mode}"
-        s += ", n_centroids={n_centroids}, block_size={block_size}"
-        return s.format(**self.__dict__)
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/bart/README.summarization.md b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/bart/README.summarization.md
deleted file mode 100644
index 8727584f2b2bdd880c6cd3abbf39b75dfbf4a67c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/bart/README.summarization.md
+++ /dev/null
@@ -1,102 +0,0 @@
-# Fine-tuning BART on CNN-Dailymail summarization task
-
-### 1) Download the CNN and Daily Mail data and preprocess it into data files with non-tokenized cased samples.
-
-Follow the instructions [here](https://github.com/abisee/cnn-dailymail) to download the original CNN and Daily Mail datasets. To preprocess the data, refer to the pointers in [this issue](https://github.com/pytorch/fairseq/issues/1391) or check out the code [here](https://github.com/artmatsak/cnn-dailymail).
-
-Follow the instructions [here](https://github.com/EdinburghNLP/XSum) to download the original Extreme Summarization datasets, or check out the code [here](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset), Please keep the raw dataset and make sure no tokenization nor BPE on the dataset.
-
-### 2) BPE preprocess:
-
-```bash
-wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json'
-wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe'
-wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt'
-
-TASK=cnn_dm
-for SPLIT in train val
-do
-  for LANG in source target
-  do
-    python -m examples.roberta.multiprocessing_bpe_encoder \
-    --encoder-json encoder.json \
-    --vocab-bpe vocab.bpe \
-    --inputs "$TASK/$SPLIT.$LANG" \
-    --outputs "$TASK/$SPLIT.bpe.$LANG" \
-    --workers 60 \
-    --keep-empty;
-  done
-done
-```
-
-### 3) Binarize dataset:
-```bash
-fairseq-preprocess \
-  --source-lang "source" \
-  --target-lang "target" \
-  --trainpref "${TASK}/train.bpe" \
-  --validpref "${TASK}/val.bpe" \
-  --destdir "${TASK}-bin/" \
-  --workers 60 \
-  --srcdict dict.txt \
-  --tgtdict dict.txt;
-```
-
-### 4) Fine-tuning on CNN-DM summarization task:
-Example fine-tuning CNN-DM
-```bash
-TOTAL_NUM_UPDATES=20000  
-WARMUP_UPDATES=500      
-LR=3e-05
-MAX_TOKENS=2048
-UPDATE_FREQ=4
-BART_PATH=/path/to/bart/model.pt
-
-CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 fairseq-train cnn_dm-bin \
-    --restore-file $BART_PATH \
-    --max-tokens $MAX_TOKENS \
-    --task translation \
-    --source-lang source --target-lang target \
-    --truncate-source \
-    --layernorm-embedding \
-    --share-all-embeddings \
-    --share-decoder-input-output-embed \
-    --reset-optimizer --reset-dataloader --reset-meters \
-    --required-batch-size-multiple 1 \
-    --arch bart_large \
-    --criterion label_smoothed_cross_entropy \
-    --label-smoothing 0.1 \
-    --dropout 0.1 --attention-dropout 0.1 \
-    --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-08 \
-    --clip-norm 0.1 \
-    --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \
-    --fp16 --update-freq $UPDATE_FREQ \
-    --skip-invalid-size-inputs-valid-test \
-    --find-unused-parameters;
-```
-Above is expected to run on `1` node with `8 32gb-V100`.
-Expected training time is about `5 hours`. Training time can be reduced with distributed training on `4` nodes and `--update-freq 1`.
-
-Use TOTAL_NUM_UPDATES=15000 UPDATE_FREQ=2 for Xsum task
-
-### Inference for CNN-DM test data using above trained checkpoint.
-After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using `eval_cnn.py`, for example
-
-```bash
-cp data-bin/cnn_dm/dict.source.txt  checkpoints/
-python examples/bart/summarize.py \
-  --model-dir checkpoints \
-  --model-file checkpoint_best.pt \
-  --src cnn_dm/test.source \
-  --out cnn_dm/test.hypo
-```
-For XSUM, which uses beam=6, lenpen=1.0, max_len_b=60, min_len=10:
-```bash
-cp data-bin/cnn_dm/dict.source.txt  checkpoints/
-python examples/bart/summarize.py \
-  --model-dir checkpoints \
-  --model-file checkpoint_best.pt \
-  --src cnn_dm/test.source \
-  --out cnn_dm/test.hypo \
-  --xsum-kwargs
-```
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/encoders/subword_nmt_bpe.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/encoders/subword_nmt_bpe.py
deleted file mode 100644
index 5d724d2730a5895ca55af2998c2ced471625b516..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/encoders/subword_nmt_bpe.py
+++ /dev/null
@@ -1,54 +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 dataclasses import dataclass, field
-
-from fairseq import file_utils
-from fairseq.data.encoders import register_bpe
-from fairseq.dataclass import FairseqDataclass
-
-
-@dataclass
-class SubwordNMTBPEConfig(FairseqDataclass):
-    bpe_codes: str = field(default="???", metadata={"help": "path to subword NMT BPE"})
-    bpe_separator: str = field(default="@@", metadata={"help": "BPE separator"})
-
-
-@register_bpe("subword_nmt", dataclass=SubwordNMTBPEConfig)
-class SubwordNMTBPE(object):
-    def __init__(self, cfg):
-        if cfg.bpe_codes is None:
-            raise ValueError("--bpe-codes is required for --bpe=subword_nmt")
-        codes = file_utils.cached_path(cfg.bpe_codes)
-        try:
-            from subword_nmt import apply_bpe
-
-            bpe_parser = apply_bpe.create_parser()
-            bpe_args = bpe_parser.parse_args(
-                [
-                    "--codes",
-                    codes,
-                    "--separator",
-                    cfg.bpe_separator,
-                ]
-            )
-            self.bpe = apply_bpe.BPE(
-                bpe_args.codes,
-                bpe_args.merges,
-                bpe_args.separator,
-                None,
-                bpe_args.glossaries,
-            )
-            self.bpe_symbol = bpe_args.separator + " "
-        except ImportError:
-            raise ImportError(
-                "Please install subword_nmt with: pip install subword-nmt"
-            )
-
-    def encode(self, x: str) -> str:
-        return self.bpe.process_line(x)
-
-    def decode(self, x: str) -> str:
-        return (x + " ").replace(self.bpe_symbol, "").rstrip()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/text_to_speech/hifigan.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/text_to_speech/hifigan.py
deleted file mode 100644
index edc7db6015ebea18f40c8949ae78c0b5b61e1297..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/text_to_speech/hifigan.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn import Conv1d, ConvTranspose1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-LRELU_SLOPE = 0.1
-
-
-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 (kernel_size * dilation - dilation) // 2
-
-
-class ResBlock(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock, 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):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for layer in self.convs1:
-            remove_weight_norm(layer)
-        for layer in self.convs2:
-            remove_weight_norm(layer)
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, cfg):
-        super(Generator, self).__init__()
-        self.num_kernels = len(cfg["resblock_kernel_sizes"])
-        self.num_upsamples = len(cfg["upsample_rates"])
-        self.conv_pre = weight_norm(
-            Conv1d(80, cfg["upsample_initial_channel"], 7, 1, padding=3)
-        )
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(
-                zip(cfg["upsample_rates"], cfg["upsample_kernel_sizes"])
-        ):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        cfg["upsample_initial_channel"] // (2 ** i),
-                        cfg["upsample_initial_channel"] // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = cfg["upsample_initial_channel"] // (2 ** (i + 1))
-            for k, d in zip(
-                    cfg["resblock_kernel_sizes"], cfg["resblock_dilation_sizes"]
-            ):
-                self.resblocks.append(ResBlock(ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-
-    def forward(self, x):
-        x = self.conv_pre(x)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, 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 layer in self.ups:
-            remove_weight_norm(layer)
-        for layer in self.resblocks:
-            layer.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
diff --git a/spaces/Omnibus/MusicGen/tests/utils/__init__.py b/spaces/Omnibus/MusicGen/tests/utils/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/utils/__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/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md
deleted file mode 100644
index 9765b24a730b77556104187ac3ef5439ab0859fd..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Utility functions
-
-This folder contain utility functions that are not used in the
-core library, but are useful for building models or training
-code using the config system.
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/training/modules/fake_fakes.py b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/training/modules/fake_fakes.py
deleted file mode 100644
index 45c4ad559cef2730b771a709197e00ae1c87683c..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/training/modules/fake_fakes.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch
-from kornia import SamplePadding
-from kornia.augmentation import RandomAffine, CenterCrop
-
-
-class FakeFakesGenerator:
-    def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2):
-        self.grad_aug = RandomAffine(degrees=360,
-                                     translate=0.2,
-                                     padding_mode=SamplePadding.REFLECTION,
-                                     keepdim=False,
-                                     p=1)
-        self.img_aug = RandomAffine(degrees=img_aug_degree,
-                                    translate=img_aug_translate,
-                                    padding_mode=SamplePadding.REFLECTION,
-                                    keepdim=True,
-                                    p=1)
-        self.aug_proba = aug_proba
-
-    def __call__(self, input_images, masks):
-        blend_masks = self._fill_masks_with_gradient(masks)
-        blend_target = self._make_blend_target(input_images)
-        result = input_images * (1 - blend_masks) + blend_target * blend_masks
-        return result, blend_masks
-
-    def _make_blend_target(self, input_images):
-        batch_size = input_images.shape[0]
-        permuted = input_images[torch.randperm(batch_size)]
-        augmented = self.img_aug(input_images)
-        is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float()
-        result = augmented * is_aug + permuted * (1 - is_aug)
-        return result
-
-    def _fill_masks_with_gradient(self, masks):
-        batch_size, _, height, width = masks.shape
-        grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \
-            .view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2)
-        grad = self.grad_aug(grad)
-        grad = CenterCrop((height, width))(grad)
-        grad *= masks
-
-        grad_for_min = grad + (1 - masks) * 10
-        grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None]
-        grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6
-        grad.clamp_(min=0, max=1)
-
-        return grad
diff --git a/spaces/Oppenheimer57/claude-proxy/README.md b/spaces/Oppenheimer57/claude-proxy/README.md
deleted file mode 100644
index 829c8701d26b01fa12a6f88d0dc52f57d9fb27cd..0000000000000000000000000000000000000000
--- a/spaces/Oppenheimer57/claude-proxy/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Claude
-emoji: 🇷🇺
-colorFrom: gray
-colorTo: pink
-sdk: docker
-pinned: false
-duplicated_from: Xtrip111/Z
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/utils/misc.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/utils/misc.py
deleted file mode 100644
index f2bca7733278c3a4b1f145bd7e5da23683b74961..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/utils/misc.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py
-"""
-Misc functions, including distributed helpers.
-
-Mostly copy-paste from torchvision references.
-"""
-from typing import List, Optional
-
-import torch
-import torch.distributed as dist
-import torchvision
-from torch import Tensor
-import warnings
-import torch.nn.functional as F
-import math
-
-def inverse_sigmoid(x, eps=1e-3):
-    x = x.clamp(min=0, max=1)
-    x1 = x.clamp(min=eps)
-    x2 = (1 - x).clamp(min=eps)
-    return torch.log(x1/x2)
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
-    # Cut & paste from PyTorch official master until it's in a few official releases - RW
-    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1. + math.erf(x / math.sqrt(2.))) / 2.
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
-                      "The distribution of values may be incorrect.",
-                      stacklevel=2)
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        l = norm_cdf((a - mean) / std)
-        u = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [l, u], then translate to
-        # [2l-1, 2u-1].
-        tensor.uniform_(2 * l - 1, 2 * u - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
-    # type: (Tensor, float, float, float, float) -> Tensor
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-    Args:
-        tensor: an n-dimensional `torch.Tensor`
-        mean: the mean of the normal distribution
-        std: the standard deviation of the normal distribution
-        a: the minimum cutoff value
-        b: the maximum cutoff value
-    Examples:
-        >>> w = torch.empty(3, 5)
-        >>> nn.init.trunc_normal_(w)
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-def resize(input,
-           size=None,
-           scale_factor=None,
-           mode='nearest',
-           align_corners=None,
-           warning=True):
-    if warning:
-        if size is not None and align_corners:
-            input_h, input_w = tuple(int(x) for x in input.shape[2:])
-            output_h, output_w = tuple(int(x) for x in size)
-            if output_h > input_h or output_w > output_h:
-                if ((output_h > 1 and output_w > 1 and input_h > 1
-                     and input_w > 1) and (output_h - 1) % (input_h - 1)
-                        and (output_w - 1) % (input_w - 1)):
-                    warnings.warn(
-                        f'When align_corners={align_corners}, '
-                        'the output would more aligned if '
-                        f'input size {(input_h, input_w)} is `x+1` and '
-                        f'out size {(output_h, output_w)} is `nx+1`')
-    if isinstance(size, torch.Size):
-        size = tuple(int(x) for x in size)
-    return F.interpolate(input, size, scale_factor, mode, align_corners)
-
-def _max_by_axis(the_list):
-    # type: (List[List[int]]) -> List[int]
-    maxes = the_list[0]
-    for sublist in the_list[1:]:
-        for index, item in enumerate(sublist):
-            maxes[index] = max(maxes[index], item)
-    return maxes
-
-
-class NestedTensor(object):
-    def __init__(self, tensors, mask: Optional[Tensor]):
-        self.tensors = tensors
-        self.mask = mask
-
-    def to(self, device):
-        # type: (Device) -> NestedTensor # noqa
-        cast_tensor = self.tensors.to(device)
-        mask = self.mask
-        if mask is not None:
-            assert mask is not None
-            cast_mask = mask.to(device)
-        else:
-            cast_mask = None
-        return NestedTensor(cast_tensor, cast_mask)
-
-    def decompose(self):
-        return self.tensors, self.mask
-
-    def __repr__(self):
-        return str(self.tensors)
-
-
-def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-    # TODO make this more general
-    if tensor_list[0].ndim == 3:
-        if torchvision._is_tracing():
-            # nested_tensor_from_tensor_list() does not export well to ONNX
-            # call _onnx_nested_tensor_from_tensor_list() instead
-            return _onnx_nested_tensor_from_tensor_list(tensor_list)
-
-        # TODO make it support different-sized images
-        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
-        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
-        batch_shape = [len(tensor_list)] + max_size
-        b, c, h, w = batch_shape
-        dtype = tensor_list[0].dtype
-        device = tensor_list[0].device
-        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
-        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
-        for img, pad_img, m in zip(tensor_list, tensor, mask):
-            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-            m[: img.shape[1], : img.shape[2]] = False
-    else:
-        raise ValueError("not supported")
-    return NestedTensor(tensor, mask)
-
-
-# _onnx_nested_tensor_from_tensor_list() is an implementation of
-# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
-@torch.jit.unused
-def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
-    max_size = []
-    for i in range(tensor_list[0].dim()):
-        max_size_i = torch.max(
-            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
-        ).to(torch.int64)
-        max_size.append(max_size_i)
-    max_size = tuple(max_size)
-
-    # work around for
-    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-    # m[: img.shape[1], :img.shape[2]] = False
-    # which is not yet supported in onnx
-    padded_imgs = []
-    padded_masks = []
-    for img in tensor_list:
-        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
-        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
-        padded_imgs.append(padded_img)
-
-        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
-        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
-        padded_masks.append(padded_mask.to(torch.bool))
-
-    tensor = torch.stack(padded_imgs)
-    mask = torch.stack(padded_masks)
-
-    return NestedTensor(tensor, mask=mask)
-
-
-def is_dist_avail_and_initialized():
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/utils/testing.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/utils/testing.py
deleted file mode 100644
index a27f936da8ec14bac18562ede0a79d476d82f797..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/utils/testing.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) Open-MMLab.
-import sys
-from collections.abc import Iterable
-from runpy import run_path
-from shlex import split
-from typing import Any, Dict, List
-from unittest.mock import patch
-
-
-def check_python_script(cmd):
-    """Run the python cmd script with `__main__`. The difference between
-    `os.system` is that, this function exectues code in the current process, so
-    that it can be tracked by coverage tools. Currently it supports two forms:
-
-    - ./tests/data/scripts/hello.py zz
-    - python tests/data/scripts/hello.py zz
-    """
-    args = split(cmd)
-    if args[0] == 'python':
-        args = args[1:]
-    with patch.object(sys, 'argv', args):
-        run_path(args[0], run_name='__main__')
-
-
-def _any(judge_result):
-    """Since built-in ``any`` works only when the element of iterable is not
-    iterable, implement the function."""
-    if not isinstance(judge_result, Iterable):
-        return judge_result
-
-    try:
-        for element in judge_result:
-            if _any(element):
-                return True
-    except TypeError:
-        # Maybe encounter the case: torch.tensor(True) | torch.tensor(False)
-        if judge_result:
-            return True
-    return False
-
-
-def assert_dict_contains_subset(dict_obj: Dict[Any, Any],
-                                expected_subset: Dict[Any, Any]) -> bool:
-    """Check if the dict_obj contains the expected_subset.
-
-    Args:
-        dict_obj (Dict[Any, Any]): Dict object to be checked.
-        expected_subset (Dict[Any, Any]): Subset expected to be contained in
-            dict_obj.
-
-    Returns:
-        bool: Whether the dict_obj contains the expected_subset.
-    """
-
-    for key, value in expected_subset.items():
-        if key not in dict_obj.keys() or _any(dict_obj[key] != value):
-            return False
-    return True
-
-
-def assert_attrs_equal(obj: Any, expected_attrs: Dict[str, Any]) -> bool:
-    """Check if attribute of class object is correct.
-
-    Args:
-        obj (object): Class object to be checked.
-        expected_attrs (Dict[str, Any]): Dict of the expected attrs.
-
-    Returns:
-        bool: Whether the attribute of class object is correct.
-    """
-    for attr, value in expected_attrs.items():
-        if not hasattr(obj, attr) or _any(getattr(obj, attr) != value):
-            return False
-    return True
-
-
-def assert_dict_has_keys(obj: Dict[str, Any],
-                         expected_keys: List[str]) -> bool:
-    """Check if the obj has all the expected_keys.
-
-    Args:
-        obj (Dict[str, Any]): Object to be checked.
-        expected_keys (List[str]): Keys expected to contained in the keys of
-            the obj.
-
-    Returns:
-        bool: Whether the obj has the expected keys.
-    """
-    return set(expected_keys).issubset(set(obj.keys()))
-
-
-def assert_keys_equal(result_keys: List[str], target_keys: List[str]) -> bool:
-    """Check if target_keys is equal to result_keys.
-
-    Args:
-        result_keys (List[str]): Result keys to be checked.
-        target_keys (List[str]): Target keys to be checked.
-
-    Returns:
-        bool: Whether target_keys is equal to result_keys.
-    """
-    return set(result_keys) == set(target_keys)
-
-
-def assert_is_norm_layer(module) -> bool:
-    """Check if the module is a norm layer.
-
-    Args:
-        module (nn.Module): The module to be checked.
-
-    Returns:
-        bool: Whether the module is a norm layer.
-    """
-    from .parrots_wrapper import _BatchNorm, _InstanceNorm
-    from torch.nn import GroupNorm, LayerNorm
-    norm_layer_candidates = (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm)
-    return isinstance(module, norm_layer_candidates)
-
-
-def assert_params_all_zeros(module) -> bool:
-    """Check if the parameters of the module is all zeros.
-
-    Args:
-        module (nn.Module): The module to be checked.
-
-    Returns:
-        bool: Whether the parameters of the module is all zeros.
-    """
-    weight_data = module.weight.data
-    is_weight_zero = weight_data.allclose(
-        weight_data.new_zeros(weight_data.size()))
-
-    if hasattr(module, 'bias') and module.bias is not None:
-        bias_data = module.bias.data
-        is_bias_zero = bias_data.allclose(
-            bias_data.new_zeros(bias_data.size()))
-    else:
-        is_bias_zero = True
-
-    return is_weight_zero and is_bias_zero
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/scripts/autofrisk.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/scripts/autofrisk.go
deleted file mode 100644
index d694571d0c5c78d538e8988ec6795d1ab9a3c5c4..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/scripts/autofrisk.go and /dev/null differ
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py b/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py
deleted file mode 100644
index 615aa3ff703942b6c22b2d6e9642504dd3e41ebd..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='CascadeEncoderDecoder',
-    num_stages=2,
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=[
-        dict(
-            type='FCNHead',
-            in_channels=1024,
-            in_index=2,
-            channels=256,
-            num_convs=1,
-            concat_input=False,
-            dropout_ratio=0.1,
-            num_classes=19,
-            norm_cfg=norm_cfg,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-        dict(
-            type='OCRHead',
-            in_channels=2048,
-            in_index=3,
-            channels=512,
-            ocr_channels=256,
-            dropout_ratio=0.1,
-            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/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/utils/mdetr_dist.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/utils/mdetr_dist.py
deleted file mode 100644
index d1d4d14d496ed1c1326026840d6775c6dd3c1814..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/utils/mdetr_dist.py
+++ /dev/null
@@ -1,229 +0,0 @@
-# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Utilities related to distributed mode.
-
-By default, the reduce of metrics and such are done on GPU, since it's more straightforward (we reuse the NCCL backend)
-If you want to reduce on CPU instead (required for big datasets like GQA), use the env variable MDETR_CPU_REDUCE=1
-"""
-import functools
-import io
-import os
-import datetime
-
-import torch
-import torch.distributed as dist
-
-_LOCAL_PROCESS_GROUP = None
-
-
-@functools.lru_cache()
-def _get_global_gloo_group():
-    """
-    Return a process group based on gloo backend, containing all the ranks
-    The result is cached.
-    """
-
-    if dist.get_backend() == "nccl":
-        return dist.new_group(backend="gloo")
-
-    return dist.group.WORLD
-
-def all_gather(data):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors)
-    Args:
-        data: any picklable object
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-
-    world_size = get_world_size()
-    if world_size == 1:
-        return [data]
-
-    cpu_group = None
-    if os.getenv("MDETR_CPU_REDUCE") == "1":
-        cpu_group = _get_global_gloo_group()
-
-    buffer = io.BytesIO()
-    torch.save(data, buffer)
-    data_view = buffer.getbuffer()
-    device = "cuda" if cpu_group is None else "cpu"
-    tensor = torch.ByteTensor(data_view).to(device)
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
-    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
-    if cpu_group is None:
-        dist.all_gather(size_list, local_size)
-    else:
-        print("gathering on cpu")
-        dist.all_gather(size_list, local_size, group=cpu_group)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-    assert isinstance(local_size.item(), int)
-    local_size = int(local_size.item())
-
-    # receiving Tensor from all ranks
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
-        tensor = torch.cat((tensor, padding), dim=0)
-    if cpu_group is None:
-        dist.all_gather(tensor_list, tensor)
-    else:
-        dist.all_gather(tensor_list, tensor, group=cpu_group)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
-        buffer = io.BytesIO(tensor.cpu().numpy())
-        obj = torch.load(buffer)
-        data_list.append(obj)
-
-    return data_list
-
-
-def reduce_dict(input_dict, average=True):
-    """
-    Args:
-        input_dict (dict): all the values will be reduced
-        average (bool): whether to do average or sum
-    Reduce the values in the dictionary from all processes so that all processes
-    have the averaged results. Returns a dict with the same fields as
-    input_dict, after reduction.
-    """
-    world_size = get_world_size()
-    if world_size < 2:
-        return input_dict
-    with torch.no_grad():
-        names = []
-        values = []
-        # sort the keys so that they are consistent across processes
-        for k in sorted(input_dict.keys()):
-            names.append(k)
-            values.append(input_dict[k])
-        values = torch.stack(values, dim=0)
-        dist.all_reduce(values)
-        if average:
-            values /= world_size
-        reduced_dict = {k: v for k, v in zip(names, values)}
-    return reduced_dict
-
-
-def setup_for_distributed(is_master):
-    """
-    This function disables printing when not in master process
-    """
-    import builtins as __builtin__
-
-    builtin_print = __builtin__.print
-
-    def print(*args, **kwargs):
-        force = kwargs.pop("force", False)
-        if is_master or force:
-            builtin_print(*args, **kwargs)
-
-    __builtin__.print = print
-
-
-def is_dist_avail_and_initialized():
-    """
-    Returns:
-        True if distributed training is enabled
-    """
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
-
-
-def get_world_size():
-    """
-    Returns:
-        The number of processes in the process group
-    """
-    if not is_dist_avail_and_initialized():
-        return 1
-    return dist.get_world_size()
-
-
-def get_rank():
-    """
-    Returns:
-        The rank of the current process within the global process group.
-    """
-    if not is_dist_avail_and_initialized():
-        return 0
-    return dist.get_rank()
-
-
-def get_local_rank() -> int:
-    """
-    Returns:
-        The rank of the current process within the local (per-machine) process group.
-    """
-    if not dist.is_available():
-        return 0
-    if not dist.is_initialized():
-        return 0
-    assert _LOCAL_PROCESS_GROUP is not None
-    return dist.get_rank(group=_LOCAL_PROCESS_GROUP)
-
-
-def get_local_size() -> int:
-    """
-    Returns:
-        The size of the per-machine process group,
-        i.e. the number of processes per machine.
-    """
-    if not dist.is_available():
-        return 1
-    if not dist.is_initialized():
-        return 1
-    return dist.get_world_size(group=_LOCAL_PROCESS_GROUP)
-
-
-def is_main_process():
-    """Return true if the current process is the main one"""
-    return get_rank() == 0
-
-
-def save_on_master(*args, **kwargs):
-    """Utility function to save only from the main process"""
-    if is_main_process():
-        torch.save(*args, **kwargs)
-
-
-def init_distributed_mode(args):
-    """Initialize distributed training, if appropriate"""
-    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
-        args.rank = int(os.environ["RANK"])
-        args.world_size = int(os.environ["WORLD_SIZE"])
-        args.gpu = int(os.environ["LOCAL_RANK"])
-    elif "SLURM_PROCID" in os.environ:
-        args.rank = int(os.environ["SLURM_PROCID"])
-        args.gpu = args.rank % torch.cuda.device_count()
-    else:
-        print("Not using distributed mode")
-        args.distributed = False
-        return
-
-    args.distributed = True
-
-    torch.cuda.set_device(args.gpu)
-    args.dist_backend = "nccl"
-    print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
-
-    dist.init_process_group(
-        backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank,
-        timeout=datetime.timedelta(0, 7200)
-    )
-    dist.barrier()
-    setup_for_distributed(args.debug or args.rank == 0)
diff --git a/spaces/Plachta/VALL-E-X/modules/activation.py b/spaces/Plachta/VALL-E-X/modules/activation.py
deleted file mode 100644
index e51c8eb49a378b039ed819820444f784fdc52dba..0000000000000000000000000000000000000000
--- a/spaces/Plachta/VALL-E-X/modules/activation.py
+++ /dev/null
@@ -1,612 +0,0 @@
-from typing import Optional, Tuple, List
-import math
-
-import torch
-from torch import Tensor
-from torch.nn import Linear, Module
-from torch.nn import functional as F
-from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
-from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
-from torch.nn.parameter import Parameter
-
-def _in_projection_packed(
-    q: Tensor,
-    k: Tensor,
-    v: Tensor,
-    w: Tensor,
-    b: Optional[Tensor] = None,
-) -> List[Tensor]:
-    r"""
-    Performs the in-projection step of the attention operation, using packed weights.
-    Output is a triple containing projection tensors for query, key and value.
-
-    Args:
-        q, k, v: query, key and value tensors to be projected. For self-attention,
-            these are typically the same tensor; for encoder-decoder attention,
-            k and v are typically the same tensor. (We take advantage of these
-            identities for performance if they are present.) Regardless, q, k and v
-            must share a common embedding dimension; otherwise their shapes may vary.
-        w: projection weights for q, k and v, packed into a single tensor. Weights
-            are packed along dimension 0, in q, k, v order.
-        b: optional projection biases for q, k and v, packed into a single tensor
-            in q, k, v order.
-
-    Shape:
-        Inputs:
-        - q: :math:`(..., E)` where E is the embedding dimension
-        - k: :math:`(..., E)` where E is the embedding dimension
-        - v: :math:`(..., E)` where E is the embedding dimension
-        - w: :math:`(E * 3, E)` where E is the embedding dimension
-        - b: :math:`E * 3` where E is the embedding dimension
-
-        Output:
-        - in output list :math:`[q', k', v']`, each output tensor will have the
-            same shape as the corresponding input tensor.
-    """
-    E = q.size(-1)
-    if k is v:
-        if q is k:
-            # self-attention
-            return F.linear(q, w, b).chunk(3, dim=-1)
-        else:
-            # encoder-decoder attention
-            w_q, w_kv = w.split([E, E * 2])
-            if b is None:
-                b_q = b_kv = None
-            else:
-                b_q, b_kv = b.split([E, E * 2])
-            return (F.linear(q, w_q, b_q),) + F.linear(k, w_kv, b_kv).chunk(2, dim=-1)
-    else:
-        w_q, w_k, w_v = w.chunk(3)
-        if b is None:
-            b_q = b_k = b_v = None
-        else:
-            b_q, b_k, b_v = b.chunk(3)
-        return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v)
-
-def _scaled_dot_product_attention(
-    q: Tensor,
-    k: Tensor,
-    v: Tensor,
-    attn_mask: Optional[Tensor] = None,
-    dropout_p: float = 0.0,
-) -> Tuple[Tensor, Tensor]:
-    r"""
-    Computes scaled dot product attention on query, key and value tensors, using
-    an optional attention mask if passed, and applying dropout if a probability
-    greater than 0.0 is specified.
-    Returns a tensor pair containing attended values and attention weights.
-
-    Args:
-        q, k, v: query, key and value tensors. See Shape section for shape details.
-        attn_mask: optional tensor containing mask values to be added to calculated
-            attention. May be 2D or 3D; see Shape section for details.
-        dropout_p: dropout probability. If greater than 0.0, dropout is applied.
-
-    Shape:
-        - q: :math:`(B, Nt, E)` where B is batch size, Nt is the target sequence length,
-            and E is embedding dimension.
-        - key: :math:`(B, Ns, E)` where B is batch size, Ns is the source sequence length,
-            and E is embedding dimension.
-        - value: :math:`(B, Ns, E)` where B is batch size, Ns is the source sequence length,
-            and E is embedding dimension.
-        - attn_mask: either a 3D tensor of shape :math:`(B, Nt, Ns)` or a 2D tensor of
-            shape :math:`(Nt, Ns)`.
-
-        - Output: attention values have shape :math:`(B, Nt, E)`; attention weights
-            have shape :math:`(B, Nt, Ns)`
-    """
-    B, Nt, E = q.shape
-    q = q / math.sqrt(E)
-    # (B, Nt, E) x (B, E, Ns) -> (B, Nt, Ns)
-    if attn_mask is not None:
-        attn = torch.baddbmm(attn_mask, q, k.transpose(-2, -1))
-    else:
-        attn = torch.bmm(q, k.transpose(-2, -1))
-
-    attn = F.softmax(attn, dim=-1)
-    if dropout_p > 0.0:
-        attn = F.dropout(attn, p=dropout_p)
-    # (B, Nt, Ns) x (B, Ns, E) -> (B, Nt, E)
-    output = torch.bmm(attn, v)
-    return output, attn
-
-def multi_head_attention_forward(
-        x,
-        ipw,
-        ipb,
-        opw,
-        opb,
-        n_head,
-        attn_mask,
-        past_kv=None,
-        use_cache=False,
-):
-    # x = x.transpose(1, 0)
-    # tgt_len, bsz, embed_dim = x.shape
-    # head_dim = embed_dim // n_head
-    # q, k, v = _in_projection_packed(x, x, x, ipw, ipb)
-    # q = q.contiguous().view(tgt_len, bsz * n_head, head_dim).transpose(0, 1)
-    # k = k.contiguous().view(k.shape[0], bsz * n_head, head_dim).transpose(0, 1)
-    # v = v.contiguous().view(v.shape[0], bsz * n_head, head_dim).transpose(0, 1)
-
-    # new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
-    # new_attn_mask.masked_fill_(attn_mask, float("-inf"))
-    # attn_mask = new_attn_mask
-    #
-    # attn_output, attn_output_weights = _scaled_dot_product_attention(q, k, v, attn_mask, 0.0)
-    # attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
-    # attn_output = torch._C._nn.linear(attn_output, opw, opb)
-    # attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
-
-    B, T, C = x.size()
-
-    q, k, v = torch._C._nn.linear(x, ipw, ipb).chunk(3, dim=-1)
-    k = k.view(B, T, n_head, C // n_head).transpose(1, 2)  # (B, nh, T, hs)
-    q = q.view(B, T, n_head, C // n_head).transpose(1, 2)  # (B, nh, T, hs)
-    v = v.view(B, T, n_head, C // n_head).transpose(1, 2)  # (B, nh, T, hs)
-    if past_kv is not None:
-        past_key = past_kv[0]
-        past_value = past_kv[1]
-        k = torch.cat((past_key, k), dim=-2)
-        v = torch.cat((past_value, v), dim=-2)
-
-    FULL_T = k.shape[-2]
-
-    if use_cache is True:
-        present = (k, v)
-    else:
-        present = None
-
-    att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-    att = att.masked_fill(attn_mask[FULL_T - T:FULL_T, :FULL_T], float('-inf'))
-    att = F.softmax(att, dim=-1)
-    y = att @ v  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
-    y = y.transpose(1, 2).contiguous().view(B, T, C)  # re-assemble all head outputs side by side
-    y = torch._C._nn.linear(y, opw, opb)
-    return (y, present)
-
-
-class MultiheadAttention(Module):
-    r"""Allows the model to jointly attend to information
-    from different representation subspaces as described in the paper:
-    `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.
-
-    Multi-Head Attention is defined as:
-
-    .. math::
-        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
-
-    where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`.
-
-    ``forward()`` will use a special optimized implementation if all of the following
-    conditions are met:
-
-    - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This
-      restriction will be loosened in the future.)
-    - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad``
-    - training is disabled (using ``.eval()``)
-    - dropout is 0
-    - ``add_bias_kv`` is ``False``
-    - ``add_zero_attn`` is ``False``
-    - ``batch_first`` is ``True`` and the input is batched
-    - ``kdim`` and ``vdim`` are equal to ``embed_dim``
-    - at most one of ``key_padding_mask`` or ``attn_mask`` is passed
-    - if a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ is passed, neither ``key_padding_mask``
-      nor ``attn_mask`` is passed
-
-    If the optimized implementation is in use, a
-    `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ can be passed for
-    ``query``/``key``/``value`` to represent padding more efficiently than using a
-    padding mask. In this case, a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_
-    will be returned, and an additional speedup proportional to the fraction of the input
-    that is padding can be expected.
-
-    Args:
-        embed_dim: Total dimension of the model.
-        num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split
-            across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``).
-        dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout).
-        bias: If specified, adds bias to input / output projection layers. Default: ``True``.
-        add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``.
-        add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1.
-            Default: ``False``.
-        kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``).
-        vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``).
-        batch_first: If ``True``, then the input and output tensors are provided
-            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
-
-    Examples::
-
-        >>> # xdoctest: +SKIP
-        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
-        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
-
-    """
-    __constants__ = ["batch_first"]
-    bias_k: Optional[torch.Tensor]
-    bias_v: Optional[torch.Tensor]
-
-    def __init__(
-            self,
-            embed_dim,
-            num_heads,
-            dropout=0.0,
-            bias=True,
-            add_bias_kv=False,
-            add_zero_attn=False,
-            kdim=None,
-            vdim=None,
-            batch_first=False,
-            linear1_cls=Linear,
-            linear2_cls=Linear,
-            device=None,
-            dtype=None,
-    ) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super(MultiheadAttention, self).__init__()
-        self.embed_dim = embed_dim
-        self.kdim = kdim if kdim is not None else embed_dim
-        self.vdim = vdim if vdim is not None else embed_dim
-        self._qkv_same_embed_dim = (
-                self.kdim == embed_dim and self.vdim == embed_dim
-        )
-
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.batch_first = batch_first
-        self.head_dim = embed_dim // num_heads
-        assert (
-                self.head_dim * num_heads == self.embed_dim
-        ), "embed_dim must be divisible by num_heads"
-
-        if add_bias_kv:
-            self.bias_k = Parameter(
-                torch.empty((1, 1, embed_dim), **factory_kwargs)
-            )
-            self.bias_v = Parameter(
-                torch.empty((1, 1, embed_dim), **factory_kwargs)
-            )
-        else:
-            self.bias_k = self.bias_v = None
-
-        if linear1_cls == Linear:
-            if not self._qkv_same_embed_dim:
-                self.q_proj_weight = Parameter(
-                    torch.empty((embed_dim, embed_dim), **factory_kwargs)
-                )
-                self.k_proj_weight = Parameter(
-                    torch.empty((embed_dim, self.kdim), **factory_kwargs)
-                )
-                self.v_proj_weight = Parameter(
-                    torch.empty((embed_dim, self.vdim), **factory_kwargs)
-                )
-                self.register_parameter("in_proj_weight", None)
-            else:
-                self.in_proj_weight = Parameter(
-                    torch.empty((3 * embed_dim, embed_dim), **factory_kwargs)
-                )
-                self.register_parameter("q_proj_weight", None)
-                self.register_parameter("k_proj_weight", None)
-                self.register_parameter("v_proj_weight", None)
-
-            if bias:
-                self.in_proj_bias = Parameter(
-                    torch.empty(3 * embed_dim, **factory_kwargs)
-                )
-            else:
-                self.register_parameter("in_proj_bias", None)
-            self.out_proj = NonDynamicallyQuantizableLinear(
-                embed_dim, embed_dim, bias=bias, **factory_kwargs
-            )
-
-            self._reset_parameters()
-        else:
-            if not self._qkv_same_embed_dim:
-                raise NotImplementedError
-            else:
-                self.in_proj_linear = linear1_cls(
-                    embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs
-                )
-                self.in_proj_weight = self.in_proj_linear.weight
-
-                self.register_parameter("q_proj_weight", None)
-                self.register_parameter("k_proj_weight", None)
-                self.register_parameter("v_proj_weight", None)
-
-                if bias:
-                    self.in_proj_bias = self.in_proj_linear.bias
-                else:
-                    self.register_parameter("in_proj_bias", None)
-
-            self.out_proj = linear2_cls(
-                embed_dim, embed_dim, bias=bias, **factory_kwargs
-            )
-
-            if self.bias_k is not None:
-                xavier_normal_(self.bias_k)
-            if self.bias_v is not None:
-                xavier_normal_(self.bias_v)
-
-        self.add_zero_attn = add_zero_attn
-
-    def _reset_parameters(self):
-        if self._qkv_same_embed_dim:
-            xavier_uniform_(self.in_proj_weight)
-        else:
-            xavier_uniform_(self.q_proj_weight)
-            xavier_uniform_(self.k_proj_weight)
-            xavier_uniform_(self.v_proj_weight)
-
-        if self.in_proj_bias is not None:
-            constant_(self.in_proj_bias, 0.0)
-            constant_(self.out_proj.bias, 0.0)
-
-        if self.bias_k is not None:
-            xavier_normal_(self.bias_k)
-        if self.bias_v is not None:
-            xavier_normal_(self.bias_v)
-
-    def __setstate__(self, state):
-        # Support loading old MultiheadAttention checkpoints generated by v1.1.0
-        if "_qkv_same_embed_dim" not in state:
-            state["_qkv_same_embed_dim"] = True
-
-        super(MultiheadAttention, self).__setstate__(state)
-
-    def forward(
-            self,
-            query: Tensor,
-            key: Tensor,
-            value: Tensor,
-            key_padding_mask: Optional[Tensor] = None,
-            need_weights: bool = True,
-            attn_mask: Optional[Tensor] = None,
-            average_attn_weights: bool = True,
-    ) -> Tuple[Tensor, Optional[Tensor]]:
-        r"""
-        Args:
-            query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False``
-                or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length,
-                :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``.
-                Queries are compared against key-value pairs to produce the output.
-                See "Attention Is All You Need" for more details.
-            key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False``
-                or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length,
-                :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``.
-                See "Attention Is All You Need" for more details.
-            value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when
-                ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source
-                sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``.
-                See "Attention Is All You Need" for more details.
-            key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
-                to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`.
-                Binary and byte masks are supported.
-                For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
-                the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value.
-            need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
-                Default: ``True``.
-            attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
-                :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
-                :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
-                broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
-                Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the
-                corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the
-                corresponding position is not allowed to attend. For a float mask, the mask values will be added to
-                the attention weight.
-            average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
-                heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
-                effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)
-
-        Outputs:
-            - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched,
-              :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``,
-              where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the
-              embedding dimension ``embed_dim``.
-            - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``,
-              returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
-              :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
-              :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
-              head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.
-
-            .. note::
-                `batch_first` argument is ignored for unbatched inputs.
-        """
-        is_batched = query.dim() == 3
-        if key_padding_mask is not None:
-            _kpm_dtype = key_padding_mask.dtype
-            if _kpm_dtype != torch.bool and not torch.is_floating_point(
-                    key_padding_mask
-            ):
-                raise AssertionError(
-                    "only bool and floating types of key_padding_mask are supported"
-                )
-        why_not_fast_path = ""
-        if not is_batched:
-            why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}"
-        elif query is not key or key is not value:
-            # When lifting this restriction, don't forget to either
-            # enforce that the dtypes all match or test cases where
-            # they don't!
-            why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
-        elif (
-                self.in_proj_bias is not None
-                and query.dtype != self.in_proj_bias.dtype
-        ):
-            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
-        elif (
-                self.in_proj_weight is not None
-                and query.dtype != self.in_proj_weight.dtype
-        ):
-            # this case will fail anyway, but at least they'll get a useful error message.
-            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
-        elif self.training:
-            why_not_fast_path = "training is enabled"
-        elif not self.batch_first:
-            why_not_fast_path = "batch_first was not True"
-        elif self.bias_k is not None:
-            why_not_fast_path = "self.bias_k was not None"
-        elif self.bias_v is not None:
-            why_not_fast_path = "self.bias_v was not None"
-        elif self.dropout:
-            why_not_fast_path = f"dropout was {self.dropout}, required zero"
-        elif self.add_zero_attn:
-            why_not_fast_path = "add_zero_attn was enabled"
-        elif not self._qkv_same_embed_dim:
-            why_not_fast_path = "_qkv_same_embed_dim was not True"
-        elif attn_mask is not None:
-            why_not_fast_path = "attn_mask was not None"
-        elif query.is_nested and key_padding_mask is not None:
-            why_not_fast_path = (
-                "key_padding_mask is not supported with NestedTensor input"
-            )
-        elif self.num_heads % 2 == 1:
-            why_not_fast_path = "num_heads is odd"
-        elif torch.is_autocast_enabled():
-            why_not_fast_path = "autocast is enabled"
-
-        if not why_not_fast_path:
-            tensor_args = (
-                query,
-                key,
-                value,
-                self.in_proj_weight,
-                self.in_proj_bias,
-                self.out_proj.weight,
-                self.out_proj.bias,
-            )
-            # We have to use list comprehensions below because TorchScript does not support
-            # generator expressions.
-            if torch.overrides.has_torch_function(tensor_args):
-                why_not_fast_path = "some Tensor argument has_torch_function"
-            elif not all(
-                    [
-                        (x is None or x.is_cuda or "cpu" in str(x.device))
-                        for x in tensor_args
-                    ]
-            ):
-                why_not_fast_path = (
-                    "some Tensor argument is neither CUDA nor CPU"
-                )
-            elif torch.is_grad_enabled() and any(
-                    [x is not None and x.requires_grad for x in tensor_args]
-            ):
-                why_not_fast_path = (
-                    "grad is enabled and at least one of query or the "
-                    "input/output projection weights or biases requires_grad"
-                )
-            if not why_not_fast_path:
-                return torch._native_multi_head_attention(
-                    query,
-                    key,
-                    value,
-                    self.embed_dim,
-                    self.num_heads,
-                    self.in_proj_weight,
-                    self.in_proj_bias,
-                    self.out_proj.weight,
-                    self.out_proj.bias,
-                    key_padding_mask
-                    if key_padding_mask is not None
-                    else attn_mask,
-                    need_weights,
-                    average_attn_weights,
-                    1
-                    if key_padding_mask is not None
-                    else 0
-                    if attn_mask is not None
-                    else None,
-                )
-
-        any_nested = query.is_nested or key.is_nested or value.is_nested
-        assert not any_nested, (
-                "MultiheadAttention does not support NestedTensor outside of its fast path. "
-                + f"The fast path was not hit because {why_not_fast_path}"
-        )
-
-        if self.batch_first and is_batched:
-            # make sure that the transpose op does not affect the "is" property
-            if key is value:
-                if query is key:
-                    query = key = value = query.transpose(1, 0)
-                else:
-                    query, key = [x.transpose(1, 0) for x in (query, key)]
-                    value = key
-            else:
-                query, key, value = [
-                    x.transpose(1, 0) for x in (query, key, value)
-                ]
-
-        if not self._qkv_same_embed_dim:
-            attn_output, attn_output_weights = F.multi_head_attention_forward(
-                query,
-                key,
-                value,
-                self.embed_dim,
-                self.num_heads,
-                self.in_proj_weight,
-                self.in_proj_bias,
-                self.bias_k,
-                self.bias_v,
-                self.add_zero_attn,
-                self.dropout,
-                self.out_proj.weight,
-                self.out_proj.bias,
-                training=self.training,
-                key_padding_mask=key_padding_mask,
-                need_weights=need_weights,
-                attn_mask=attn_mask,
-                use_separate_proj_weight=True,
-                q_proj_weight=self.q_proj_weight,
-                k_proj_weight=self.k_proj_weight,
-                v_proj_weight=self.v_proj_weight,
-                average_attn_weights=average_attn_weights,
-            )
-        else:
-            attn_output, attn_output_weights = F.multi_head_attention_forward(
-                query,
-                key,
-                value,
-                self.embed_dim,
-                self.num_heads,
-                self.in_proj_weight,
-                self.in_proj_bias,
-                self.bias_k,
-                self.bias_v,
-                self.add_zero_attn,
-                self.dropout,
-                self.out_proj.weight,
-                self.out_proj.bias,
-                training=self.training,
-                key_padding_mask=key_padding_mask,
-                need_weights=need_weights,
-                attn_mask=attn_mask,
-                average_attn_weights=average_attn_weights,
-            )
-        if self.batch_first and is_batched:
-            return attn_output.transpose(1, 0), attn_output_weights
-        else:
-            return attn_output, attn_output_weights
-
-    def infer(self,
-              x: Tensor,
-              key_padding_mask: Optional[Tensor] = None,
-              need_weights: bool = True,
-              attn_mask: Optional[Tensor] = None,
-              average_attn_weights: bool = True,
-              past_kv = None,
-              use_cache = False
-              ):
-        # x = x.transpose(1, 0)
-        y, kv = multi_head_attention_forward(
-                x=x,
-                ipw=self.in_proj_weight,
-                ipb=self.in_proj_bias,
-                opw=self.out_proj.weight,
-                opb=self.out_proj.bias,
-                n_head=self.num_heads,
-                attn_mask=attn_mask,
-                past_kv=past_kv,
-                use_cache=use_cache,
-        )
-        return (y, kv)
diff --git a/spaces/Podtekatel/ArcaneSVK2/inference/__init__.py b/spaces/Podtekatel/ArcaneSVK2/inference/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Pranjal12345/Text_to_Speech/app.py b/spaces/Pranjal12345/Text_to_Speech/app.py
deleted file mode 100644
index d72d95153dce2462204a65d6204bdbb442ed8543..0000000000000000000000000000000000000000
--- a/spaces/Pranjal12345/Text_to_Speech/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import torch
-import gradio as gr
-from tortoise.api import TextToSpeech
-from tortoise.utils.audio import load_voice, load_voices
-
-tts = TextToSpeech(kv_cache=True, use_deepspeed=True, half=True)
-
-languages = ['Male', 'Female']
-voices = {'Male': ['deniro', 'freeman'], 'Female': ['emma', 'angie']}
-
-def inference(text, Gender, voice, Emotion, Preset):
-    texts = [text]
-
-    Angry_tone = "[I am so angry]"
-    Sad_tone = "[I am so sad]"
-    Happy_tone = "[I am so happy]"
-    Scared_tone = "[I am so scared]"
-
-    if Emotion == "Angry":
-        text = Angry_tone + text
-    if Emotion == "Sad":
-        text = Sad_tone + text
-    if Emotion == "Happy":
-        text = Happy_tone + text
-    if Emotion == "Scared":
-        text = Scared_tone + text
-
-    voice_samples, conditioning_latents = load_voice(voice)
-
-    audio_frames = []
-
-    for j, text in enumerate(texts):
-        for audio_frame in tts.tts_with_preset(
-            text,
-            voice_samples=voice_samples,
-            conditioning_latents=conditioning_latents,
-            preset=Preset,
-            k=1
-        ):
-            audio_frames.append(torch.from_numpy(audio_frame.cpu().detach().numpy()))
-
-    complete_audio = torch.cat(audio_frames, dim=0)
-    yield (24000, complete_audio.numpy())
-
-def rs_change(rs):
-    new_choices = voices[rs]
-    return gr.update(choices=new_choices, value=new_choices[0] if new_choices else None)
-
-title = "Tortoise TTS"
-
-with gr.Blocks() as app:
-    text = gr.Textbox(lines=4, label="Text:")
-    rs = gr.Dropdown(choices=languages, value='Male', label="Gender")
-    rs_hw = gr.Dropdown(choices=voices['Male'], interactive=True, label="Voice")
-    rs.change(fn=rs_change, inputs=[rs], outputs=[rs_hw])
-    Emotion = gr.Radio(["Angry", "Sad", "Happy", "Scared"], type="value", label="Emotion")
-    Preset = gr.Radio(["ultra_fast", "fast", "standard", "high_quality"], type="value", value="ultra_fast", label="Preset")
-    output_audio = gr.Audio(label="Streaming audio:", streaming=True, autoplay=True)
-    btn = gr.Button("Generate")
-    btn.click(inference, inputs=[text, rs, rs_hw, Emotion, Preset], outputs=[output_audio])
-
-app.launch()
\ No newline at end of file
diff --git a/spaces/RMXK/RVC_HFF/infer/lib/uvr5_pack/lib_v5/nets_33966KB.py b/spaces/RMXK/RVC_HFF/infer/lib/uvr5_pack/lib_v5/nets_33966KB.py
deleted file mode 100644
index 73a5b836177b706c306e27875f8391c1aed4b948..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/infer/lib/uvr5_pack/lib_v5/nets_33966KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_33966KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16, 32)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 16)
-        self.stg1_high_band_net = BaseASPPNet(2, 16)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(8, 16)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(16, 32)
-
-        self.out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/Rakot2223/faster-whisper-webui/src/vadParallel.py b/spaces/Rakot2223/faster-whisper-webui/src/vadParallel.py
deleted file mode 100644
index c2323c0b632c34014ac1fe7ac79141b5bd9c5731..0000000000000000000000000000000000000000
--- a/spaces/Rakot2223/faster-whisper-webui/src/vadParallel.py
+++ /dev/null
@@ -1,298 +0,0 @@
-import multiprocessing
-from queue import Empty
-import threading
-import time
-from src.hooks.progressListener import ProgressListener
-from src.vad import AbstractTranscription, TranscriptionConfig, get_audio_duration
-
-from multiprocessing import Pool, Queue
-
-from typing import Any, Dict, List, Union
-import os
-
-from src.whisper.abstractWhisperContainer import AbstractWhisperCallback
-
-class _ProgressListenerToQueue(ProgressListener):
-    def __init__(self, progress_queue: Queue):
-        self.progress_queue = progress_queue
-        self.progress_total = 0
-        self.prev_progress = 0
-
-    def on_progress(self, current: Union[int, float], total: Union[int, float]):
-        delta = current - self.prev_progress
-        self.prev_progress = current
-        self.progress_total = total
-        self.progress_queue.put(delta)
-
-    def on_finished(self):
-        if self.progress_total > self.prev_progress:
-            delta = self.progress_total - self.prev_progress
-            self.progress_queue.put(delta)
-            self.prev_progress = self.progress_total
-
-class ParallelContext:
-    def __init__(self, num_processes: int = None, auto_cleanup_timeout_seconds: float = None):
-        self.num_processes = num_processes
-        self.auto_cleanup_timeout_seconds = auto_cleanup_timeout_seconds
-        self.lock = threading.Lock()
-
-        self.ref_count = 0
-        self.pool = None
-        self.cleanup_timer = None
-
-    def get_pool(self):
-        # Initialize pool lazily
-        if (self.pool is None):
-            context = multiprocessing.get_context('spawn')
-            self.pool = context.Pool(self.num_processes)
-
-        self.ref_count = self.ref_count + 1
-
-        if (self.auto_cleanup_timeout_seconds is not None):
-            self._stop_auto_cleanup()
-
-        return self.pool
-
-    def return_pool(self, pool):
-        if (self.pool == pool and self.ref_count > 0):
-            self.ref_count = self.ref_count - 1
-
-            if (self.ref_count == 0):
-                if (self.auto_cleanup_timeout_seconds is not None):
-                    self._start_auto_cleanup()
-
-    def _start_auto_cleanup(self):
-        if (self.cleanup_timer is not None):
-            self.cleanup_timer.cancel()
-        self.cleanup_timer = threading.Timer(self.auto_cleanup_timeout_seconds, self._execute_cleanup)
-        self.cleanup_timer.start()
-
-        print("Started auto cleanup of pool in " + str(self.auto_cleanup_timeout_seconds) + " seconds")
-
-    def _stop_auto_cleanup(self):
-        if (self.cleanup_timer is not None):
-            self.cleanup_timer.cancel()
-            self.cleanup_timer = None
-
-            print("Stopped auto cleanup of pool")
-
-    def _execute_cleanup(self):
-        print("Executing cleanup of pool")
-
-        if (self.ref_count == 0):
-            self.close()
-
-    def close(self):
-        self._stop_auto_cleanup()
-
-        if (self.pool is not None):
-            print("Closing pool of " + str(self.num_processes) + " processes")
-            self.pool.close()
-            self.pool.join()
-        self.pool = None
-
-class ParallelTranscriptionConfig(TranscriptionConfig):
-    def __init__(self, device_id: str, override_timestamps, initial_segment_index, copy: TranscriptionConfig = None):
-        super().__init__(copy.non_speech_strategy, copy.segment_padding_left, copy.segment_padding_right, copy.max_silent_period, copy.max_merge_size, copy.max_prompt_window, initial_segment_index)
-        self.device_id = device_id
-        self.override_timestamps = override_timestamps
-
-class ParallelTranscription(AbstractTranscription):
-    # Silero VAD typically takes about 3 seconds per minute, so there's no need to split the chunks 
-    # into smaller segments than 2 minute (min 6 seconds per CPU core)
-    MIN_CPU_CHUNK_SIZE_SECONDS = 2 * 60
-
-    def __init__(self, sampling_rate: int = 16000):
-        super().__init__(sampling_rate=sampling_rate)
-
-    def transcribe_parallel(self, transcription: AbstractTranscription, audio: str, whisperCallable: AbstractWhisperCallback, config: TranscriptionConfig, 
-                            cpu_device_count: int, gpu_devices: List[str], cpu_parallel_context: ParallelContext = None, gpu_parallel_context: ParallelContext = None, 
-                            progress_listener: ProgressListener = None):
-        total_duration = get_audio_duration(audio)
-
-        # First, get the timestamps for the original audio
-        if (cpu_device_count > 1 and not transcription.is_transcribe_timestamps_fast()):
-            merged = self._get_merged_timestamps_parallel(transcription, audio, config, total_duration, cpu_device_count, cpu_parallel_context)
-        else:
-            timestamp_segments = transcription.get_transcribe_timestamps(audio, config, 0, total_duration)
-            merged = transcription.get_merged_timestamps(timestamp_segments, config, total_duration)
-
-        # We must make sure the whisper model is downloaded
-        if (len(gpu_devices) > 1):
-            whisperCallable.model_container.ensure_downloaded()
-
-        # Split into a list for each device
-        # TODO: Split by time instead of by number of chunks
-        merged_split = list(self._split(merged, len(gpu_devices)))
-
-        # Parameters that will be passed to the transcribe function
-        parameters = []
-        segment_index = config.initial_segment_index
-
-        processing_manager = multiprocessing.Manager()
-        progress_queue = processing_manager.Queue()
-
-        for i in range(len(gpu_devices)):
-            # Note that device_segment_list can be empty. But we will still create a process for it,
-            # as otherwise we run the risk of assigning the same device to multiple processes.
-            device_segment_list = list(merged_split[i]) if i < len(merged_split) else []
-            device_id = gpu_devices[i]
-
-            print("Device " + str(device_id) + " (index " + str(i) + ") has " + str(len(device_segment_list)) + " segments")
-
-            # Create a new config with the given device ID
-            device_config = ParallelTranscriptionConfig(device_id, device_segment_list, segment_index, config)
-            segment_index += len(device_segment_list)
-
-            progress_listener_to_queue = _ProgressListenerToQueue(progress_queue)
-            parameters.append([audio, whisperCallable, device_config, progress_listener_to_queue]);
-
-        merged = {
-            'text': '',
-            'segments': [],
-            'language': None
-        }
-
-        created_context = False
-
-        perf_start_gpu = time.perf_counter()
-
-        # Spawn a separate process for each device
-        try:
-            if (gpu_parallel_context is None):
-                gpu_parallel_context = ParallelContext(len(gpu_devices))
-                created_context = True
-
-            # Get a pool of processes
-            pool = gpu_parallel_context.get_pool()
-
-            # Run the transcription in parallel
-            results_async = pool.starmap_async(self.transcribe, parameters)
-            total_progress = 0
-
-            while not results_async.ready():
-                try:
-                    delta = progress_queue.get(timeout=5)  # Set a timeout of 5 seconds
-                except Empty:
-                    continue
-                
-                total_progress += delta
-                if progress_listener is not None:
-                    progress_listener.on_progress(total_progress, total_duration)
-
-            results = results_async.get()
-
-            # Call the finished callback
-            if progress_listener is not None:
-                progress_listener.on_finished()
-
-            for result in results:
-                # Merge the results
-                if (result['text'] is not None):
-                    merged['text'] += result['text']
-                if (result['segments'] is not None):
-                    merged['segments'].extend(result['segments'])
-                if (result['language'] is not None):
-                    merged['language'] = result['language']
-
-        finally:
-            # Return the pool to the context
-            if (gpu_parallel_context is not None):
-                gpu_parallel_context.return_pool(pool)
-            # Always close the context if we created it
-            if (created_context):
-                gpu_parallel_context.close()
-
-        perf_end_gpu = time.perf_counter()
-        print("Parallel transcription took " + str(perf_end_gpu - perf_start_gpu) + " seconds")
-
-        return merged
-
-    def _get_merged_timestamps_parallel(self, transcription: AbstractTranscription, audio: str, config: TranscriptionConfig, total_duration: float, 
-                                       cpu_device_count: int, cpu_parallel_context: ParallelContext = None):
-        parameters = []
-
-        chunk_size = max(total_duration / cpu_device_count, self.MIN_CPU_CHUNK_SIZE_SECONDS)
-        chunk_start = 0
-        cpu_device_id = 0
-
-        perf_start_time = time.perf_counter()
-
-        # Create chunks that will be processed on the CPU
-        while (chunk_start < total_duration):
-            chunk_end = min(chunk_start + chunk_size, total_duration)
-
-            if (chunk_end - chunk_start < 1):
-                # No need to process chunks that are less than 1 second
-                break
-
-            print("Parallel VAD: Executing chunk from " + str(chunk_start) + " to " + 
-                    str(chunk_end) + " on CPU device " + str(cpu_device_id))
-            parameters.append([audio, config, chunk_start, chunk_end]);
-
-            cpu_device_id += 1
-            chunk_start = chunk_end
-
-        created_context = False
-
-        # Spawn a separate process for each device
-        try:
-            if (cpu_parallel_context is None):
-                cpu_parallel_context = ParallelContext(cpu_device_count)
-                created_context = True
-
-            # Get a pool of processes
-            pool = cpu_parallel_context.get_pool()
-
-            # Run the transcription in parallel. Note that transcription must be picklable.
-            results = pool.starmap(transcription.get_transcribe_timestamps, parameters)
-
-            timestamps = []
-
-            # Flatten the results
-            for result in results:
-                timestamps.extend(result)
-
-            merged = transcription.get_merged_timestamps(timestamps, config, total_duration)
-
-            perf_end_time = time.perf_counter()
-            print("Parallel VAD processing took {} seconds".format(perf_end_time - perf_start_time))
-            return merged
-
-        finally:
-            # Return the pool to the context
-            if (cpu_parallel_context is not None):
-                cpu_parallel_context.return_pool(pool)
-            # Always close the context if we created it
-            if (created_context):
-                cpu_parallel_context.close()
-
-    def get_transcribe_timestamps(self, audio: str, config: ParallelTranscriptionConfig, start_time: float, duration: float):
-        return []
-
-    def get_merged_timestamps(self,  timestamps: List[Dict[str, Any]], config: ParallelTranscriptionConfig, total_duration: float):
-        # Override timestamps that will be processed
-        if (config.override_timestamps is not None):
-            print("(get_merged_timestamps) Using override timestamps of size " + str(len(config.override_timestamps)))
-            return config.override_timestamps
-        return super().get_merged_timestamps(timestamps, config, total_duration)
-
-    def transcribe(self, audio: str, whisperCallable: AbstractWhisperCallback, config: ParallelTranscriptionConfig, 
-                   progressListener: ProgressListener = None):
-        # Override device ID the first time
-        if (os.environ.get("INITIALIZED", None) is None):
-            os.environ["INITIALIZED"] = "1"
-
-            # Note that this may be None if the user didn't specify a device. In that case, Whisper will
-            # just use the default GPU device.
-            if (config.device_id is not None):
-                print("Using device " + config.device_id)
-                os.environ["CUDA_VISIBLE_DEVICES"] = config.device_id
-        
-        return super().transcribe(audio, whisperCallable, config, progressListener)
-
-    def _split(self, a, n):
-        """Split a list into n approximately equal parts."""
-        k, m = divmod(len(a), n)
-        return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n))
-
diff --git a/spaces/RamAnanth1/conformer-asr/app.py b/spaces/RamAnanth1/conformer-asr/app.py
deleted file mode 100644
index 7903cfcc6f894baa95b45d5fec71f3d0b44d9519..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/conformer-asr/app.py
+++ /dev/null
@@ -1,239 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-import time
-import pandas as pd
-import io 
-from scipy.io.wavfile import write
- 
-# AssemblyAI transcript endpoint (where we submit the file)
-transcript_endpoint = "https://api.assemblyai.com/v2/transcript"
-
-upload_endpoint = "https://api.assemblyai.com/v2/upload"
-
-headers={
-"Authorization": os.environ["ASSEMBLYAI_KEY"],
-"Content-Type": "application/json"
-}
-
-# Helper function to upload data
-def _read_file(filename, chunk_size=5242880):
-    with open(filename, "rb") as f:
-        while True:
-            data = f.read(chunk_size)
-            if not data:
-                break
-            yield data
-
-def _read_array(audio, chunk_size=5242880):
-    """Like _read_file but for array - creates temporary unsaved "file" from sample rate and audio np.array"""
-    sr, aud = audio
-
-    # Create temporary "file" and write data to it
-    bytes_wav = bytes()
-    temp_file = io.BytesIO(bytes_wav)
-    write(temp_file, sr, aud)
-
-    while True:
-        data = temp_file.read(chunk_size)
-        if not data:
-            break
-        yield data
-
-
-
-def get_audio_from_upload(audio):
-    upload_response = requests.post(
-    upload_endpoint,
-    headers=headers, 
-    data=_read_array(audio))
-    return upload_response.json()['upload_url']
-
-def get_transcript_url(audio):    
-    url = get_audio_from_upload(audio)
-    # JSON that tells the API which file to trancsribe
-    json={
-    # URL of the audio file to process
-    "audio_url": url,
-
-    # Turn on speaker labels
-    "speaker_labels": True,
-
-    # Turn on cusom vocabulary
-    "word_boost": ["assembly ai"],
-
-    # Turn on custom spelling
-    "custom_spelling": [
-        {"from": ["assembly AI"], "to": "AssemblyAI"},
-        {"from": ["assembly AI's"], "to": "AssemblyAI's"}
-        ],
-
-    # Turn on PII Redaction and specify policies
-    "redact_pii": True,
-    "redact_pii_policies": ["drug", "injury", "person_name"],
-    "redact_pii_audio": True,
-
-    # Turn on Auto Highlights
-    "auto_highlights": True,
-
-    # Turn on Content Moderation
-    "content_safety": True,
-
-    # Turn on Topic Detection
-    "iab_categories": True,
-
-    # Turn on Sentiment Analysis
-    "sentiment_analysis": True,
-
-    # Turn on Summarization and specify configuration
-    "summarization": True,
-    "summary_model": "informative",
-    "summary_type": "bullets",
-
-    # Turn on Entity Detection
-    "entity_detection": True,}
-
-    response = requests.post(
-        transcript_endpoint,
-        json=json,
-        headers=headers  # Authorization to link this transcription with your account
-      )
-
-    polling_endpoint = f"https://api.assemblyai.com/v2/transcript/{response.json()['id']}"
-    while True:
-      transcription_result = requests.get(polling_endpoint, headers=headers).json()
-      if transcription_result['status'] == 'completed':
-        break
-      elif transcription_result['status'] == 'error':
-        raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
-      else:
-        time.sleep(3)
-
-    res = transcription_result['sentiment_analysis_results']
-    sentiment_analysis_result = ''
-
-    df = pd.DataFrame(res)
-    df = df.loc[:, ["text", "sentiment", "confidence"]]
-
-    topic = transcription_result['iab_categories_result']['summary']
-
-    topics = []
-
-    for k in topic:
-        topic_dict = {}
-        topic_dict["Topic"] = " > ".join(k.split(">"))
-        topic_dict["Relevance"] = topic[k]
-        topics.append(topic_dict)
-
-    df_topic = pd.DataFrame(topics)
-    
-    
-    return transcription_result['text'], transcription_result['summary'], df, df_topic.head()
-
-# def get_transcript_file(filename):
-
-#     upload_response = requests.post(
-#     upload_endpoint,
-#     headers=headers, 
-#     data=_read_file(filename))
-    
-#     # JSON that tells the API which file to trancsribe
-#     json = {
-#     # URL of the audio file to process
-#     "audio_url": upload_response.json()['upload_url'],
-
-#     # Turn on speaker labels
-#     "speaker_labels": True,
-
-#     # Turn on custom vocabulary
-#     "word_boost": ["assembly ai"],
-
-#     # Turn on custom spelling
-#     "custom_spelling": [
-#         {"from": ["assembly AI"], "to": "AssemblyAI"},
-#         {"from": ["assembly AI's"], "to": "AssemblyAI's"}
-#         ],
-
-#     # Turn on PII Redaction and specify policies
-#     "redact_pii": True,
-#     "redact_pii_policies": ["drug", "injury", "person_name"],
-#     "redact_pii_audio": True,
-
-#     # Turn on Auto Highlights
-#     "auto_highlights": True,
-
-#     # Turn on Content Moderation
-#     "content_safety": True,
-
-#     # Turn on Topic Detection
-#     "iab_categories": True,
-
-#     # Turn on Sentiment Analysis
-#     "sentiment_analysis": True,
-
-#     # Turn on Summarization and specify configuration
-#     "summarization": True,
-#     "summary_model": "informative",
-#     "summary_type": "bullets",
-
-#     # Turn on Entity Detection
-#     "entity_detection": True,
-#     }
-
-#     response = requests.post(
-#         transcript_endpoint,
-#         json=json,
-#         headers=headers  # Authorization to link this transcription with your account
-#       )
-
-#     polling_endpoint = f"https://api.assemblyai.com/v2/transcript/{response.json()['id']}"
-#     while True:
-#       transcription_result = requests.get(polling_endpoint, headers=headers).json()
-#       if transcription_result['status'] == 'completed':
-#         break
-#       elif transcription_result['status'] == 'error':
-#         raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
-#       else:
-#         time.sleep(3)
-#     return transcription_result['text']
-
-audio_intelligence_list = [
-    "Summarization",
-    "Sentiment Analysis"
-]
-
-title = """<h1 align="center">🔥Conformer-1 API </h1>"""
-description = """
-### In this demo, you can explore the outputs of a Conformer-1 Speech Recognition Model from AssemblyAI.
-"""
-                
-with gr.Blocks() as demo:
-    gr.HTML(title)
-    gr.Markdown(description)
-
-    with gr.Column(elem_id = "col_container"):
-        
-        #audio_intelligence_options = gr.CheckboxGroup(audio_intelligence_list, label="Audio Intelligence Options")
-        inputs = gr.Audio(source = "upload",label = "Upload the input Audio file")
-        b1 = gr.Button('Process Audio')
-        
-
-    with gr.Tabs():
-        with gr.TabItem('Transcript') as transcript_tab:
-            transcript = gr.Textbox(label = "Transcript Result" )
-        with gr.TabItem('Summary', visible = False) as summary_tab: 
-           summary = gr.Textbox(label = "Summary Result")
-        with gr.TabItem('Sentiment Analysis', visible = False) as sentiment_tab:
-            sentiment_analysis = gr.Dataframe(label = "Sentiment Analysis Result" )
-        with gr.TabItem('Topic Detection', visible = False) as topic_detection_tab:
-            topic_detection = gr.Dataframe(label = "Topic Detection Result" )
-        
-    
-    b1.click(get_transcript_url, [inputs], [transcript, summary, sentiment_analysis,topic_detection])
-    
-    examples = gr.Examples(examples = [["audio.mp3"]], inputs = inputs, outputs=[transcript, summary, sentiment_analysis, topic_detection], cache_examples = True, fn = get_transcript_url)
-                    
-    
-    demo.queue().launch(debug=True)
-
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/extern/__init__.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/extern/__init__.py
deleted file mode 100644
index d3a6dc99fe175507a94e3440da1f637f318add2f..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/extern/__init__.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import importlib.util
-import sys
-
-
-class VendorImporter:
-    """
-    A PEP 302 meta path importer for finding optionally-vendored
-    or otherwise naturally-installed packages from root_name.
-    """
-
-    def __init__(self, root_name, vendored_names=(), vendor_pkg=None):
-        self.root_name = root_name
-        self.vendored_names = set(vendored_names)
-        self.vendor_pkg = vendor_pkg or root_name.replace('extern', '_vendor')
-
-    @property
-    def search_path(self):
-        """
-        Search first the vendor package then as a natural package.
-        """
-        yield self.vendor_pkg + '.'
-        yield ''
-
-    def _module_matches_namespace(self, fullname):
-        """Figure out if the target module is vendored."""
-        root, base, target = fullname.partition(self.root_name + '.')
-        return not root and any(map(target.startswith, self.vendored_names))
-
-    def load_module(self, fullname):
-        """
-        Iterate over the search path to locate and load fullname.
-        """
-        root, base, target = fullname.partition(self.root_name + '.')
-        for prefix in self.search_path:
-            try:
-                extant = prefix + target
-                __import__(extant)
-                mod = sys.modules[extant]
-                sys.modules[fullname] = mod
-                return mod
-            except ImportError:
-                pass
-        else:
-            raise ImportError(
-                "The '{target}' package is required; "
-                "normally this is bundled with this package so if you get "
-                "this warning, consult the packager of your "
-                "distribution.".format(**locals())
-            )
-
-    def create_module(self, spec):
-        return self.load_module(spec.name)
-
-    def exec_module(self, module):
-        pass
-
-    def find_spec(self, fullname, path=None, target=None):
-        """Return a module spec for vendored names."""
-        return (
-            importlib.util.spec_from_loader(fullname, self)
-            if self._module_matches_namespace(fullname) else None
-        )
-
-    def install(self):
-        """
-        Install this importer into sys.meta_path if not already present.
-        """
-        if self not in sys.meta_path:
-            sys.meta_path.append(self)
-
-
-names = (
-    'packaging', 'pyparsing', 'ordered_set', 'more_itertools', 'importlib_metadata',
-    'zipp', 'importlib_resources', 'jaraco', 'typing_extensions', 'tomli',
-)
-VendorImporter(__name__, names, 'setuptools._vendor').install()
diff --git a/spaces/Ricecake123/RVC-demo/config.py b/spaces/Ricecake123/RVC-demo/config.py
deleted file mode 100644
index 959b2b73b86b33942ba1e581eb858655ea9149fa..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/config.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import argparse
-import torch
-from multiprocessing import cpu_count
-
-
-def use_fp32_config():
-    for config_file in ["32k.json", "40k.json", "48k.json"]:
-        with open(f"configs/{config_file}", "r") as f:
-            strr = f.read().replace("true", "false")
-        with open(f"configs/{config_file}", "w") as f:
-            f.write(strr)
-    with open("trainset_preprocess_pipeline_print.py", "r") as f:
-        strr = f.read().replace("3.7", "3.0")
-    with open("trainset_preprocess_pipeline_print.py", "w") as f:
-        f.write(strr)
-
-
-class Config:
-    def __init__(self):
-        self.device = "cuda:0"
-        self.is_half = True
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        (
-            self.python_cmd,
-            self.listen_port,
-            self.iscolab,
-            self.noparallel,
-            self.noautoopen,
-        ) = self.arg_parse()
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    @staticmethod
-    def arg_parse() -> tuple:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--port", type=int, default=7865, help="Listen port")
-        parser.add_argument(
-            "--pycmd", type=str, default="python", help="Python command"
-        )
-        parser.add_argument("--colab", action="store_true", help="Launch in colab")
-        parser.add_argument(
-            "--noparallel", action="store_true", help="Disable parallel processing"
-        )
-        parser.add_argument(
-            "--noautoopen",
-            action="store_true",
-            help="Do not open in browser automatically",
-        )
-        cmd_opts = parser.parse_args()
-
-        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
-
-        return (
-            cmd_opts.pycmd,
-            cmd_opts.port,
-            cmd_opts.colab,
-            cmd_opts.noparallel,
-            cmd_opts.noautoopen,
-        )
-
-    # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
-    # check `getattr` and try it for compatibility
-    @staticmethod
-    def has_mps() -> bool:
-        if not torch.backends.mps.is_available():
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                print("Found GPU", self.gpu_name, ", force to fp32")
-                self.is_half = False
-                use_fp32_config()
-            else:
-                print("Found GPU", self.gpu_name)
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-            if self.gpu_mem <= 4:
-                with open("trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-        elif self.has_mps():
-            print("No supported Nvidia GPU found, use MPS instead")
-            self.device = "mps"
-            self.is_half = False
-            use_fp32_config()
-        else:
-            print("No supported Nvidia GPU found, use CPU instead")
-            self.device = "cpu"
-            self.is_half = False
-            use_fp32_config()
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max
diff --git a/spaces/SalmanHabeeb/Blatt/README.md b/spaces/SalmanHabeeb/Blatt/README.md
deleted file mode 100644
index b9f2c3b79c84631cbdb22cbac2363c2b78db22e5..0000000000000000000000000000000000000000
--- a/spaces/SalmanHabeeb/Blatt/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Blatt
-emoji: 🌿
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.7
-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/Samarth991/LLAMA-QA-AudioFiles/whisper_app.py b/spaces/Samarth991/LLAMA-QA-AudioFiles/whisper_app.py
deleted file mode 100644
index 737e7cbf04bdf0eb63250cca5beb7e3849f80726..0000000000000000000000000000000000000000
--- a/spaces/Samarth991/LLAMA-QA-AudioFiles/whisper_app.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-import torch as th
-import whisper
-from whisper.audio import SAMPLE_RATE
-from tenacity import retry, wait_random
-import openai
-import requests
-# os.environ['OPENAI_API_KEY'] = "sk-<API KEY>"
-
-class WHISPERModel:
-    def __init__(self, model_name='small', device='cuda',openai_flag=False):
-        self.device = device
-        self.openai_flag = openai_flag
-        self.model = whisper.load_model(model_name, device=self.device)
-
-    def get_info(self, audio_data, conv_duration=30):
-        clip_audio = whisper.pad_or_trim(audio_data, length=SAMPLE_RATE * conv_duration)
-        result = self.model.transcribe(clip_audio)
-        return result['language']
-    
-    def read_audio(self,audio_path,duration=None):
-        audio = None
-        try:
-            audio = whisper.load_audio(audio_path)
-            if duration :
-                audio = whisper.pad_or_trim(audio, length=SAMPLE_RATE * duration*60)
-        except IOError as err:
-            raise err
-        return audio 
-    
-    def speech_to_text(self, audio_path,duration=None):
-        text_data = dict()
-        audio_duration = 0
-        conv_language = ""
-        if audio_path.startswith('http'):
-            r = requests.get(audio_path)
-            if r.status_code == 200:
-                audio = self.read_audio(audio_path)
-            else:
-                raise("Unable to reach for URL {}".format(audio_path))
-        else:
-            audio = self.read_audio(audio_path)
-            conv_language = self.get_info(audio)
-            if conv_language !='en':
-                res = self.model.transcribe(audio,task='translate')
-                if self.openai_flag:
-                    res['text'] = self.translate_text(res['text'], orginal_text=conv_language, convert_to='English')
-            else:
-                res = self.model.transcribe(audio)
-            audio_duration = audio.shape[0] / SAMPLE_RATE
-            text_data['text'] = res['text']
-        
-        text_data['duration'] = audio_duration
-        text_data['language'] = conv_language
-        return text_data
-
-
-    @retry(wait=wait_random(min=5, max=10))
-    def translate_text(self, text, orginal_text='ar', convert_to='english'):
-        prompt = f'Translate the following {orginal_text} text to {convert_to}:\n\n{orginal_text}: ' + text + '\n{convert_to}:'
-        # Generate response using ChatGPT
-        response = openai.Completion.create(
-            engine='text-davinci-003',
-            prompt=prompt,
-            max_tokens=100,
-            n=1,
-            stop=None,
-            temperature=0.7
-        )
-        # Extract the translated English text from the response
-        translation = response.choices[0].text.strip()
-        return translation
-
-if __name__ == '__main__':
-    url = "https://prypto-api.aswat.co/surveillance/recordings/5f53c28b-3504-4b8b-9db5-0c8b69a96233.mp3"
-    audio2text = WHISPERModel()
-    text = audio2text.speech_to_text(url)
diff --git a/spaces/Sanathkumar1603/hackathon/app/templates/similarity.html b/spaces/Sanathkumar1603/hackathon/app/templates/similarity.html
deleted file mode 100644
index 7294ec54c539e5debfe83af97a15edb7c59711bd..0000000000000000000000000000000000000000
--- a/spaces/Sanathkumar1603/hackathon/app/templates/similarity.html
+++ /dev/null
@@ -1,35 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <title>Index</title>
-</head>
-<body>
-    <div>
-        <h1 style="background-color:LightGray;">
-            <center>Face Similarity</center>
-        </h1>
-    </div>
-    <div>
-        <fieldset>
-        <ul>
-            <!li>
-            <br>
-            <form action="/predict_similarity/" enctype="multipart/form-data" method="post">
-                <span style="font-weight:bold;font-family:sans-serif">Upload First Image:</span> <br><br>
-                <input name="file1" type="file" onchange="readURL(this);" />
-                <br><br><br>
-                <span style="font-weight:bold;font-family:sans-serif">Upload Second Image:</span> <br><br>
-                <input name="file2" type="file" onchange="readURL(this);" />
-                <br><br><br><br>
-                <button type="submit">Check Similarity</button>
-            </form>
-            <!/li>
-            <br><br>
-            <form action="/" method="get">
-                <button type="submit">Home</button>
-            </form>
-        </ul>
-        </fieldset>
-    </div>
-</body>
-</html>
diff --git a/spaces/ServerX/PorcoDiaz/demucs/__init__.py b/spaces/ServerX/PorcoDiaz/demucs/__init__.py
deleted file mode 100644
index d4182e356427e1b05a79f8da641c70bb732514fa..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/demucs/__init__.py
+++ /dev/null
@@ -1,7 +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.
-
-__version__ = "2.0.3"
diff --git a/spaces/SeyedAli/Food-Image-Classification/app.py b/spaces/SeyedAli/Food-Image-Classification/app.py
deleted file mode 100644
index 8e4ab82cb2daecb5547179ca5401ed22f6c21138..0000000000000000000000000000000000000000
--- a/spaces/SeyedAli/Food-Image-Classification/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import gradio as gr
-from  PIL import Image
-import tempfile
-import torch
-from torchvision.io import read_image
-from transformers import ViTForImageClassification, ViTFeatureExtractor,ViTImageProcessor
-
-# With ViTImageProcessor we have error so i comment it
-# model = ViTImageProcessor.from_pretrained('SeyedAli/Food-Image-Classification-VIT')
-
-model = ViTForImageClassification.from_pretrained('SeyedAli/Food-Image-Classification-VIT')
-feature_extractor = ViTFeatureExtractor.from_pretrained('SeyedAli/Food-Image-Classification-VIT')
-
-def FoodClassification(image):
-    with tempfile.NamedTemporaryFile(suffix=".png") as temp_image_file:
-        # Copy the contents of the uploaded image file to the temporary file
-        Image.fromarray(image).save(temp_image_file.name)
-        # Load the image file using torchvision
-        image = read_image(temp_image_file.name)
-        # Preprocess the image using the ViT feature extractor
-        inputs = feature_extractor(images=image, return_tensors="pt")
-        # Use the ViT model for image classification
-        outputs = model(**inputs)
-        predicted_class_idx = torch.argmax(outputs.logits)
-        predicted_class = model.config.id2label[predicted_class_idx.item()]
-        return predicted_class
-
-iface = gr.Interface(fn=FoodClassification, inputs="image", outputs="text")
-iface.launch(share=False)
\ No newline at end of file
diff --git a/spaces/Shreeraj/SEO_APP/app.py b/spaces/Shreeraj/SEO_APP/app.py
deleted file mode 100644
index bed56abd4e0695598ff90ab56431c90ded8d12e0..0000000000000000000000000000000000000000
--- a/spaces/Shreeraj/SEO_APP/app.py
+++ /dev/null
@@ -1,336 +0,0 @@
-import requests
-from bs4 import BeautifulSoup
-import nltk
-import time
-from nltk.tokenize import word_tokenize
-from nltk.probability import FreqDist
-from googlesearch import search
-import streamlit as st
-import spacy
-from collections import Counter
-from textstat.textstat import textstat
-import urllib3
-from urllib.error import HTTPError
-from requests.exceptions import HTTPError
-import matplotlib.pyplot as plt
-import pandas as pd
-import altair as alt
-import plotly.graph_objects as go
-import plotly.express as px
-import plotly.io as pio
-
-urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
-
-nltk.download('stopwords')
-nltk.download('punkt')
-from nltk.corpus import stopwords
-stop_words = set(stopwords.words("english"))
-
-nlp = spacy.load("en_core_web_sm")
-
-# Keyword Analysis
-def suggest_keywords(content):
-    doc = nlp(content)
-    keywords = [token.text.lower() for token in doc if token.is_alpha and not token.is_stop]
-    freq_dist = Counter(keywords)
-    return freq_dist.most_common(5)
-
-
-# On-Page Optimization
-def optimize_on_page_elements(content):
-    recommendations = []
-    doc = nlp(content)
-
-    # Recommendation for meta tags
-    if len(doc.text) > 160:
-        recommendations.append('Your meta description is too long.')
-    elif len(doc.text) < 70:
-        recommendations.append('Your meta description is too short.')
-
-    # Recommendation for headings
-    num_headings = content.count('<h1>') + content.count('<h2>') + content.count('<h3>') + content.count(
-        '<h4>') + content.count('<h5>') + content.count('<h6>')
-    if num_headings == 0:
-        recommendations.append('You have no headings. Consider adding some for better SEO.')
-
-
-    recommendations.append('Check your URL structure to ensure it is SEO friendly.')
-    recommendations.append('Ensure your images have appropriate alt tags.')
-    recommendations.append('Check your internal linking structure to ensure a good user experience.')
-
-    return recommendations
-
-
-# Content Analysis
-def analyze_content(content, target_keywords):
-    word_count = len(content.split())
-    readability_score = textstat.flesch_reading_ease(content)
-    keyword_density = calculate_keyword_density(content, target_keywords)
-    return word_count, readability_score, keyword_density
-
-
-# Calculate Keyword Density
-def calculate_keyword_density(content, target_keywords):
-    tokens = nltk.word_tokenize(content)
-    stop_words = set(nltk.corpus.stopwords.words("english"))
-    filtered_tokens = [token.lower() for token in tokens if token.lower() not in stop_words and token.isalpha()]
-    keyword_count = sum(filtered_tokens.count(keyword.lower()) for keyword in target_keywords)
-    total_words = len(filtered_tokens)
-
-    if total_words > 0:
-        return keyword_count / total_words
-    else:
-        return 0.0
-
-
-# SEO Score Calculation
-def calculate_seo_score(content, target_keywords):
-    keyword_density = calculate_keyword_density(content, target_keywords)
-    readability_score = textstat.flesch_reading_ease(content)
-    seo_score = (keyword_density + (206 - readability_score) / 10) / 2
-    return seo_score
-
-
-def fetch_webpage_content(url):
-    try:
-        response = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'})
-        soup = BeautifulSoup(response.content, 'html.parser')
-        return soup.text
-    except Exception as e:
-        print(f"Error while fetching webpage content: {e}")
-        return None
-
-
-def extract_keywords(webpage_content):
-    tokens = word_tokenize(webpage_content)
-    filtered_tokens = [word.lower() for word in tokens if word.lower() not in stop_words and word.isalpha()]
-    freq_dist = FreqDist(filtered_tokens)
-    most_common_keywords = freq_dist.most_common(5)
-    return most_common_keywords
-
-
-# # Competitor content dictionary in case http request are failing
-# competitor_contents = {
-#     'Competitor1': 'Predefined content for Competitor1',
-#     'Competitor2': 'Predefined content for Competitor2',
-#     'Competitor3': 'Predefined content for Competitor3',
-# }
-
-
-def analyze_competitors(niche, target_keywords):
-    competitors = {
-        'Competitor1': '',
-        'Competitor2': '',
-        'Competitor3': ''
-    }
-    insights = []
-    all_keywords = []  # collect all keywords here
-
-    for competitor in competitors.keys():
-        competitor_insight = f"<span style='font-weight: bold; color: #00008B; font-size: 20px;'>Competitor: {competitor}</span>\n"
-
-        # Perform a search query for the competitor and niche
-        query = f"{competitor} {niche}"
-        search_results = list(search(query, num_results=1))
-        if not search_results:
-            competitor_insight += "No search results found for the competitor.\n"
-            insights.append(competitor_insight)
-            continue
-
-        search_result = search_results[0]
-        competitor_url = search_result
-
-        # Fetch the webpage content for the search result
-        webpage_content = fetch_webpage_content(competitor_url)
-
-        if webpage_content is None:
-            competitor_insight += "Unable to fetch the content. Check the URL or the website's accessibility."
-            insights.append(competitor_insight)
-            continue
-
-        # Extract the top 5 keywords from the webpage content
-        blog_keywords = extract_keywords(webpage_content)[:5]
-
-        all_keywords.extend(blog_keywords)  # add the keywords to our list
-
-        competitor_insight += f"<span style='font-weight: bold;'>Keywords:</span>\n"
-        for keyword, count in blog_keywords:
-            competitor_insight += f"- {keyword} ({count} occurrences)\n"
-
-        competitor_insight += f"<span style='font-weight: bold;'>Link:</span> {competitor_url}\n\n"
-        insights.append(competitor_insight)
-
-    # Get the 5 most common keywords from all_keywords
-    most_common_keywords = Counter(all_keywords).most_common(5)
-
-    return insights, most_common_keywords
-
-
-def generate_serp_preview(title, description, content):
-    MAX_LINES = 4  # Maximum number of lines to display
-
-    # Truncate content if it exceeds the maximum number of lines
-    lines = content.split('\n')
-    if len(lines) > MAX_LINES:
-        lines = lines[:MAX_LINES]
-        content = '\n'.join(lines) + '...'
-
-    serp_preview = f"<div style='background-color: #f8f9fa; padding: 10px;'>"
-    serp_preview += f"<h3 style='margin: 0;'><a href='#'>{title}</a></h3>"
-    serp_preview += f"<cite style='color: #006621; font-size: 14px;'>www.yourblog.com</cite>"
-    serp_preview += f"<p style='margin: 0; color: #545454;'>{description}</p>"
-    serp_preview += f"<p>{content}</p>"
-    serp_preview += f"</div>"
-
-    # Real-time search results preview (based on your title and description)
-    query = f"{title} {description}"  # Use title and description as the search query
-    search_results = list(search(query, num_results=3))
-    if search_results:
-        serp_preview += "<h4>Real-time Search Preview:</h4>"
-        for index, result in enumerate(search_results):
-            serp_preview += f"<p>Result {index+1}: <a href='{result}'>{result}</a></p>"
-    
-    return serp_preview
-
-# Function to generate bar chart
-def generate_bar_chart(labels, values, title, x_label, y_label):
-    fig, ax = plt.subplots()
-    ax.bar(labels, values)
-    ax.set_title(title)
-    ax.set_xlabel(x_label)
-    ax.set_ylabel(y_label)
-    st.pyplot(fig)
-    
-
-# Function to generate speedometer chart
-
-def generate_speedometer_chart(value, min_value, max_value, title):
-    fig = go.Figure(go.Indicator(
-        mode="gauge+number",
-        value=value,
-        title={'text': title},
-        gauge={'axis': {'range': [min_value, max_value]},
-               'bar': {'color': "darkblue"},
-               'bgcolor': "white",
-               'borderwidth': 2,
-               'bordercolor': "gray",
-               'steps': [
-                   {'range': [min_value, max_value], 'color': 'lightgray'}],
-               'threshold': {'line': {'color': "red", 'width': 4},
-                             'thickness': 0.75,
-                             'value': value}}))
-
-    return fig
-    
-def main():
-    st.title("SEO Blog Post Optimizer")
-    option = st.radio("Select an option", ("Enter blog post content", "Paste web page link"), key="option_selection")
-
-    word_count = 0
-    readability_score = 0
-    keyword_density = 0.0
-    seo_score = 0.0
-    content = ""
-
-    if option == "Enter blog post content":
-        content = st.text_area("Enter your blog post content")
-        niche = st.text_input("Enter your niche")
-        title = st.text_input("Enter the title of your blog post")
-        description = st.text_input("Enter the meta description of your blog post")
-        target_keywords = st.text_input("Enter the target keywords (separated by commas)").split(",")
-        target_keywords = [keyword.strip().lower() for keyword in target_keywords]
-    elif option == "Paste web page link":
-        target_keywords = st.text_input("Enter the target keywords (separated by commas)").split(",")
-        target_keywords = [keyword.strip().lower() for keyword in target_keywords]
-        web_link = st.text_input("Paste the web page link")
-        if st.button("Scrape Content"):
-            content = fetch_webpage_content(web_link)
-            if content is None:
-                st.error("Failed to scrape the content from the provided link. Please check the link and try again.")
-                return
-        else:
-            return
-    else:
-        return
-
-    suggested_keywords = suggest_keywords(content)
-    recommendations = optimize_on_page_elements(content)
-
-    if content:
-        word_count, readability_score, keyword_density = analyze_content(content, target_keywords)
-        seo_score = calculate_seo_score(content, target_keywords)
-
-    if option == "Enter blog post content":
-        competitor_insights, most_common_keywords = analyze_competitors(niche, target_keywords)
-
-    if option == "Enter blog post content":
-        serp_preview = generate_serp_preview(title, description, content)
-
-    st.sidebar.markdown("<h2 style='color: #00008B; font-size: 20px;'>Blog Analysis</h2>", unsafe_allow_html=True)
-    st.sidebar.markdown("---")
-    st.sidebar.subheader("Keyword Analysis")
-    keyword_labels = [keyword for keyword, _ in suggested_keywords]
-    keyword_counts = [count for _, count in suggested_keywords]
-    keyword_df = pd.DataFrame({"Keywords": keyword_labels, "Counts": keyword_counts})
-    st.sidebar.bar_chart(keyword_df.set_index("Keywords"))
-    st.sidebar.write(keyword_df)
-
-    # Content Analysis
-    st.sidebar.markdown("---")
-    st.sidebar.subheader("Content Analysis")
-    st.sidebar.markdown("#### Word Count")
-    if word_count >= 300:
-        st.sidebar.markdown(f"Value: {word_count} (<span style='color: green; font-weight: bold;'>Optimal</span>)", unsafe_allow_html=True)
-    elif word_count >= 200:
-        st.sidebar.markdown(f"Value: {word_count} (<span style='color: orange; font-weight: bold;'>Good</span>)", unsafe_allow_html=True)
-    else:
-        st.sidebar.markdown(f"Value: {word_count} (<span style='color: red; font-weight: bold;'>Low</span>)", unsafe_allow_html=True)
-
-    st.sidebar.markdown("#### Readability Score")
-    if readability_score >= 70:
-        st.sidebar.markdown(f"Value: {readability_score} (<span style='color: green; font-weight: bold;'>Optimal</span>)", unsafe_allow_html=True)
-    elif readability_score >= 50:
-        st.sidebar.markdown(f"Value: {readability_score} (<span style='color: orange; font-weight: bold;'>Good</span>)", unsafe_allow_html=True)
-    else:
-        st.sidebar.markdown(f"Value: {readability_score} (<span style='color: red; font-weight: bold;'>Low</span>)", unsafe_allow_html=True)
-
-    st.sidebar.markdown("#### Keyword Density")
-    if 0.03 <= keyword_density <= 0.04:
-        st.sidebar.markdown(f"Value: {keyword_density} (<span style='color: green; font-weight: bold;'>Optimal</span>)", unsafe_allow_html=True)
-    elif 0.02 <= keyword_density < 0.03:
-        st.sidebar.markdown(f"Value: {keyword_density} (<span style='color: orange; font-weight: bold;'>Good</span>)", unsafe_allow_html=True)
-    else:
-        st.sidebar.markdown(f"Value: {keyword_density} (<span style='color: red; font-weight: bold;'>Low</span>)", unsafe_allow_html=True)
-
-    st.subheader("SEO Score")
-    st.plotly_chart(generate_speedometer_chart(seo_score, 0, 10, "SEO Score"))
-
-    st.markdown("---")
-
-    # On-Page Optimization
-    st.subheader("On-Page Optimization")
-    for rec in recommendations:
-        st.write(rec)
-
-    if option == "Enter blog post content":
-        # Recommended Keywords
-        st.markdown("### Recommended Keywords")
-        st.markdown("Try to add the below keywords to your content to increase the search engine appearance score against given niche. The values beside indicates the frequencies in your competitors blogs")
-        for keyword in most_common_keywords[:5]:
-            st.markdown(f"- {keyword[0]}")
-
-        st.subheader("Competitor Analysis")
-        for insight in competitor_insights:
-            st.markdown(insight, unsafe_allow_html=True)
-
-        st.subheader("SERP Preview")
-        st.markdown(serp_preview, unsafe_allow_html=True)
-
-    st.write("---")
-    st.write("Thank you for using SEO Blog Post Optimizer!")
-
-
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/ShubhamVermaDS/text_to_image/README.md b/spaces/ShubhamVermaDS/text_to_image/README.md
deleted file mode 100644
index 6d5896c88a797af3260dfb3c6dd7e5a309a85c5b..0000000000000000000000000000000000000000
--- a/spaces/ShubhamVermaDS/text_to_image/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Text To Image
-emoji: 🔥
-colorFrom: red
-colorTo: gray
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Shypanties22/FantasyMe/train_dreambooth.py b/spaces/Shypanties22/FantasyMe/train_dreambooth.py
deleted file mode 100644
index 35b568425de57d117a1ad001ad7beba79378e638..0000000000000000000000000000000000000000
--- a/spaces/Shypanties22/FantasyMe/train_dreambooth.py
+++ /dev/null
@@ -1,890 +0,0 @@
-import argparse
-import itertools
-import math
-import os
-from pathlib import Path
-from typing import Optional
-import subprocess
-import sys
-import gc
-import random
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.utils.import_utils import is_xformers_available
-from huggingface_hub import HfFolder, Repository, whoami
-from PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-
-logger = get_logger(__name__)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        #required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--instance_data_dir",
-        type=str,
-        default=None,
-        #required=True,
-        help="A folder containing the training data of instance images.",
-    )
-    parser.add_argument(
-        "--class_data_dir",
-        type=str,
-        default=None,
-        #required=False,
-        help="A folder containing the training data of class images.",
-    )
-    parser.add_argument(
-        "--instance_prompt",
-        type=str,
-        default=None,
-        help="The prompt with identifier specifying the instance",
-    )
-    parser.add_argument(
-        "--class_prompt",
-        type=str,
-        default="",
-        help="The prompt to specify images in the same class as provided instance images.",
-    )
-    parser.add_argument(
-        "--with_prior_preservation",
-        default=False,
-        action="store_true",
-        help="Flag to add prior preservation loss.",
-    )
-    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
-    parser.add_argument(
-        "--num_class_images",
-        type=int,
-        default=100,
-        help=(
-            "Minimal class images for prior preservation loss. If not have enough images, additional images will be"
-            " sampled with class_prompt."
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
-    )
-    parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
-    parser.add_argument(
-        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument(
-        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=1)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=5e-6,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."
-        ),
-    )
-
-    parser.add_argument(
-        "--save_n_steps",
-        type=int,
-        default=1,
-        help=("Save the model every n global_steps"),
-    )
-
-
-    parser.add_argument(
-        "--save_starting_step",
-        type=int,
-        default=1,
-        help=("The step from which it starts saving intermediary checkpoints"),
-    )
-
-    parser.add_argument(
-        "--stop_text_encoder_training",
-        type=int,
-        default=1000000,
-        help=("The step at which the text_encoder is no longer trained"),
-    )
-
-
-    parser.add_argument(
-        "--image_captions_filename",
-        action="store_true",
-        help="Get captions from filename",
-    )
-
-
-    parser.add_argument(
-        "--dump_only_text_encoder",
-        action="store_true",
-        default=False,
-        help="Dump only text encoder",
-    )
-
-    parser.add_argument(
-        "--train_only_unet",
-        action="store_true",
-        default=False,
-        help="Train only the unet",
-    )
-
-    parser.add_argument(
-        "--cache_latents",
-        action="store_true",
-        default=False,
-        help="Train only the unet",
-    )
-
-    parser.add_argument(
-        "--Session_dir",
-        type=str,
-        default="",
-        help="Current session directory",
-    )
-
-
-
-
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    #if args.instance_data_dir is None:
-    #    raise ValueError("You must specify a train data directory.")
-
-    #if args.with_prior_preservation:
-    #    if args.class_data_dir is None:
-    #        raise ValueError("You must specify a data directory for class images.")
-    #    if args.class_prompt is None:
-    #        raise ValueError("You must specify prompt for class images.")
-
-    return args
-
-
-class DreamBoothDataset(Dataset):
-    """
-    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
-    It pre-processes the images and the tokenizes prompts.
-    """
-
-    def __init__(
-        self,
-        instance_data_root,
-        instance_prompt,
-        tokenizer,
-        args,
-        class_data_root=None,
-        class_prompt=None,
-        size=512,
-        center_crop=False,
-    ):
-        self.size = size
-        self.center_crop = center_crop
-        self.tokenizer = tokenizer
-        self.image_captions_filename = None
-
-        self.instance_data_root = Path(instance_data_root)
-        if not self.instance_data_root.exists():
-            raise ValueError("Instance images root doesn't exists.")
-
-        self.instance_images_path = list(Path(instance_data_root).iterdir())
-        self.num_instance_images = len(self.instance_images_path)
-        self.instance_prompt = instance_prompt
-        self._length = self.num_instance_images
-
-        if args.image_captions_filename:
-            self.image_captions_filename = True
-
-        if class_data_root is not None:
-            self.class_data_root = Path(class_data_root)
-            self.class_data_root.mkdir(parents=True, exist_ok=True)
-            self.class_images_path = list(self.class_data_root.iterdir())
-            random.shuffle(self.class_images_path)
-            self.num_class_images = len(self.class_images_path)
-            self._length = max(self.num_class_images, self.num_instance_images)
-            self.class_prompt = class_prompt
-        else:
-            self.class_data_root = None
-
-        self.image_transforms = transforms.Compose(
-            [
-                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
-                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-        )
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, index):
-        example = {}
-        path = self.instance_images_path[index % self.num_instance_images]
-        instance_image = Image.open(path)
-        if not instance_image.mode == "RGB":
-            instance_image = instance_image.convert("RGB")
-
-        instance_prompt = self.instance_prompt
-
-        if self.image_captions_filename:
-            filename = Path(path).stem
-            pt=''.join([i for i in filename if not i.isdigit()])
-            pt=pt.replace("_"," ")
-            pt=pt.replace("(","")
-            pt=pt.replace(")","")
-            pt=pt.replace("-","")
-            instance_prompt = pt
-            sys.stdout.write(" " +instance_prompt+" ")
-            sys.stdout.flush()
-
-
-        example["instance_images"] = self.image_transforms(instance_image)
-        example["instance_prompt_ids"] = self.tokenizer(
-            instance_prompt,
-            padding="do_not_pad",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-        ).input_ids
-
-        if self.class_data_root:
-            class_image = Image.open(self.class_images_path[index % self.num_class_images])
-            if not class_image.mode == "RGB":
-                class_image = class_image.convert("RGB")
-            example["class_images"] = self.image_transforms(class_image)
-            example["class_prompt_ids"] = self.tokenizer(
-                self.class_prompt,
-                padding="do_not_pad",
-                truncation=True,
-                max_length=self.tokenizer.model_max_length,
-            ).input_ids
-
-        return example
-
-
-
-class PromptDataset(Dataset):
-    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
-    def __init__(self, prompt, num_samples):
-        self.prompt = prompt
-        self.num_samples = num_samples
-
-    def __len__(self):
-        return self.num_samples
-
-    def __getitem__(self, index):
-        example = {}
-        example["prompt"] = self.prompt
-        example["index"] = index
-        return example
-
-class LatentsDataset(Dataset):
-    def __init__(self, latents_cache, text_encoder_cache):
-        self.latents_cache = latents_cache
-        self.text_encoder_cache = text_encoder_cache
-
-    def __len__(self):
-        return len(self.latents_cache)
-
-    def __getitem__(self, index):
-        return self.latents_cache[index], self.text_encoder_cache[index]
-
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
-    if token is None:
-        token = HfFolder.get_token()
-    if organization is None:
-        username = whoami(token)["name"]
-        return f"{username}/{model_id}"
-    else:
-        return f"{organization}/{model_id}"
-
-def merge_two_dicts(starting_dict: dict, updater_dict: dict) -> dict:
-    """
-    Starts from base starting dict and then adds the remaining key values from updater replacing the values from
-    the first starting/base dict with the second updater dict.
-
-    For later: how does d = {**d1, **d2} replace collision?
-
-    :param starting_dict:
-    :param updater_dict:
-    :return:
-    """
-    new_dict: dict = starting_dict.copy()   # start with keys and values of starting_dict
-    new_dict.update(updater_dict)    # modifies starting_dict with keys and values of updater_dict
-    return new_dict
-
-def merge_args(args1: argparse.Namespace, args2: argparse.Namespace) -> argparse.Namespace:
-    """
-
-    ref: https://stackoverflow.com/questions/56136549/how-can-i-merge-two-argparse-namespaces-in-python-2-x
-    :param args1:
-    :param args2:
-    :return:
-    """
-    # - the merged args
-    # The vars() function returns the __dict__ attribute to values of the given object e.g {field:value}.
-    merged_key_values_for_namespace: dict = merge_two_dicts(vars(args1), vars(args2))
-    args = argparse.Namespace(**merged_key_values_for_namespace)
-    return args
-
-def run_training(args_imported):
-    args_default = parse_args()
-    args = merge_args(args_default, args_imported)
-    print(args)
-    logging_dir = Path(args.output_dir, args.logging_dir)
-    i=args.save_starting_step
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with="tensorboard",
-        logging_dir=logging_dir,
-    )
-
-    # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
-    # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
-    # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
-    if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
-        raise ValueError(
-            "Gradient accumulation is not supported when training the text encoder in distributed training. "
-            "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
-        )
-
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    if args.with_prior_preservation:
-        class_images_dir = Path(args.class_data_dir)
-        if not class_images_dir.exists():
-            class_images_dir.mkdir(parents=True)
-        cur_class_images = len(list(class_images_dir.iterdir()))
-
-        if cur_class_images < args.num_class_images:
-            torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
-            pipeline = StableDiffusionPipeline.from_pretrained(
-                args.pretrained_model_name_or_path, torch_dtype=torch_dtype
-            )
-            pipeline.set_progress_bar_config(disable=True)
-
-            num_new_images = args.num_class_images - cur_class_images
-            logger.info(f"Number of class images to sample: {num_new_images}.")
-
-            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
-            sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
-            sample_dataloader = accelerator.prepare(sample_dataloader)
-            pipeline.to(accelerator.device)
-
-            for example in tqdm(
-                sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
-            ):
-                with torch.autocast("cuda"):
-                    images = pipeline(example["prompt"]).images
-
-                for i, image in enumerate(images):
-                    image.save(class_images_dir / f"{example['index'][i] + cur_class_images}.jpg")
-
-            del pipeline
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
-        elif args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-    # Load the tokenizer
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
-
-    # Load models and create wrapper for stable diffusion
-    if args.train_only_unet:
-      if os.path.exists(str(args.output_dir+"/text_encoder_trained")):
-        text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder_trained")
-      elif os.path.exists(str(args.output_dir+"/text_encoder")):
-        text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder")
-      else:
-        text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
-    else:
-      text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
-    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
-    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
-    if is_xformers_available():
-        try:
-            print("Enabling memory efficient attention with xformers...")
-            unet.enable_xformers_memory_efficient_attention()
-        except Exception as e:
-            logger.warning(
-                f"Could not enable memory efficient attention. Make sure xformers is installed correctly and a GPU is available: {e}"
-            )
-
-    vae.requires_grad_(False)
-    if not args.train_text_encoder:
-        text_encoder.requires_grad_(False)
-
-    if args.gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-        if args.train_text_encoder:
-            text_encoder.gradient_checkpointing_enable()
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
-            )
-
-        optimizer_class = bnb.optim.AdamW8bit
-    else:
-        optimizer_class = torch.optim.AdamW
-
-    params_to_optimize = (
-        itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
-    )
-    optimizer = optimizer_class(
-        params_to_optimize,
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
-
-    train_dataset = DreamBoothDataset(
-        instance_data_root=args.instance_data_dir,
-        instance_prompt=args.instance_prompt,
-        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
-        class_prompt=args.class_prompt,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        center_crop=args.center_crop,
-        args=args,
-    )
-
-    def collate_fn(examples):
-        input_ids = [example["instance_prompt_ids"] for example in examples]
-        pixel_values = [example["instance_images"] for example in examples]
-
-        # Concat class and instance examples for prior preservation.
-        # We do this to avoid doing two forward passes.
-        if args.with_prior_preservation:
-            input_ids += [example["class_prompt_ids"] for example in examples]
-            pixel_values += [example["class_images"] for example in examples]
-
-        pixel_values = torch.stack(pixel_values)
-        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-        input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids
-
-        batch = {
-            "input_ids": input_ids,
-            "pixel_values": pixel_values,
-        }
-        return batch
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
-        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
-    )
-
-    if args.train_text_encoder:
-        unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            unet, text_encoder, optimizer, train_dataloader, lr_scheduler
-        )
-    else:
-        unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            unet, optimizer, train_dataloader, lr_scheduler
-        )
-
-    weight_dtype = torch.float32
-    if args.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif args.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move text_encode and vae to gpu.
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    vae.to(accelerator.device, dtype=weight_dtype)
-    if not args.train_text_encoder:
-        text_encoder.to(accelerator.device, dtype=weight_dtype)
-
-
-    if args.cache_latents:
-        latents_cache = []
-        text_encoder_cache = []
-        for batch in tqdm(train_dataloader, desc="Caching latents"):
-            with torch.no_grad():
-                batch["pixel_values"] = batch["pixel_values"].to(accelerator.device, non_blocking=True, dtype=weight_dtype)
-                batch["input_ids"] = batch["input_ids"].to(accelerator.device, non_blocking=True)
-                latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist)
-                if args.train_text_encoder:
-                    text_encoder_cache.append(batch["input_ids"])
-                else:
-                    text_encoder_cache.append(text_encoder(batch["input_ids"])[0])
-        train_dataset = LatentsDataset(latents_cache, text_encoder_cache)
-        train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, collate_fn=lambda x: x, shuffle=True)
-
-        del vae
-        #if not args.train_text_encoder:
-        #    del text_encoder
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("dreambooth", config=vars(args))
-
-    def bar(prg):
-       br='|'+'█' * prg + ' ' * (25-prg)+'|'
-       return br
-
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
-    global_step = 0
-
-    for epoch in range(args.num_train_epochs):
-        unet.train()
-        if args.train_text_encoder:
-            text_encoder.train()
-        for step, batch in enumerate(train_dataloader):
-            with accelerator.accumulate(unet):
-                # Convert images to latent space
-                with torch.no_grad():
-                    if args.cache_latents:
-                        latents_dist = batch[0][0]
-                    else:
-                        latents_dist = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist
-                    latents = latents_dist.sample() * 0.18215
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                # Get the text embedding for conditioning
-                if(args.cache_latents):
-                    if args.train_text_encoder:
-                        encoder_hidden_states = text_encoder(batch[0][1])[0]
-                    else:
-                        encoder_hidden_states = batch[0][1]
-                else:
-                    encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
-                # Predict the noise residual
-                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-                if args.with_prior_preservation:
-                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
-                    model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
-                    target, target_prior = torch.chunk(target, 2, dim=0)
-
-                    # Compute instance loss
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
-
-                    # Compute prior loss
-                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
-
-                    # Add the prior loss to the instance loss.
-                    loss = loss + args.prior_loss_weight * prior_loss
-                else:
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    params_to_clip = (
-                        itertools.chain(unet.parameters(), text_encoder.parameters())
-                        if args.train_text_encoder
-                        else unet.parameters()
-                    )
-                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-            fll=round((global_step*100)/args.max_train_steps)
-            fll=round(fll/4)
-            pr=bar(fll)
-
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            progress_bar.set_description_str("Progress:"+pr)
-            accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-            if args.train_text_encoder and global_step == args.stop_text_encoder_training and global_step >= 30:
-              if accelerator.is_main_process:
-                print(" " +" Freezing the text_encoder ..."+" ")
-                frz_dir=args.output_dir + "/text_encoder_frozen"
-                if os.path.exists(frz_dir):
-                  subprocess.call('rm -r '+ frz_dir, shell=True)
-                os.mkdir(frz_dir)
-                pipeline = StableDiffusionPipeline.from_pretrained(
-                    args.pretrained_model_name_or_path,
-                    unet=accelerator.unwrap_model(unet),
-                    text_encoder=accelerator.unwrap_model(text_encoder),
-                )
-                pipeline.text_encoder.save_pretrained(frz_dir)
-
-            if args.save_n_steps >= 200:
-               if global_step < args.max_train_steps and global_step+1==i:
-                  ckpt_name = "_step_" + str(global_step+1)
-                  save_dir = Path(args.output_dir+ckpt_name)
-                  save_dir=str(save_dir)
-                  save_dir=save_dir.replace(" ", "_")
-                  if not os.path.exists(save_dir):
-                     os.mkdir(save_dir)
-                  inst=save_dir[16:]
-                  inst=inst.replace(" ", "_")
-                  print(" SAVING CHECKPOINT: "+args.Session_dir+"/"+inst+".ckpt")
-                  # Create the pipeline using the trained modules and save it.
-                  if accelerator.is_main_process:
-                     pipeline = StableDiffusionPipeline.from_pretrained(
-                           args.pretrained_model_name_or_path,
-                           unet=accelerator.unwrap_model(unet),
-                           text_encoder=accelerator.unwrap_model(text_encoder),
-                     )
-                     pipeline.save_pretrained(save_dir)
-                     frz_dir=args.output_dir + "/text_encoder_frozen"
-                     if args.train_text_encoder and os.path.exists(frz_dir):
-                        subprocess.call('rm -r '+save_dir+'/text_encoder/*.*', shell=True)
-                        subprocess.call('cp -f '+frz_dir +'/*.* '+ save_dir+'/text_encoder', shell=True)
-                     chkpth=args.Session_dir+"/"+inst+".ckpt"
-                     subprocess.call('python /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py --model_path ' + save_dir + ' --checkpoint_path ' + chkpth + ' --half', shell=True)
-                     subprocess.call('rm -r '+ save_dir, shell=True)
-                     i=i+args.save_n_steps
-
-        accelerator.wait_for_everyone()
-
-    # Create the pipeline using using the trained modules and save it.
-    if accelerator.is_main_process:
-      if args.dump_only_text_encoder:
-         txt_dir=args.output_dir + "/text_encoder_trained"
-         if not os.path.exists(txt_dir):
-           os.mkdir(txt_dir)
-         pipeline = StableDiffusionPipeline.from_pretrained(
-             args.pretrained_model_name_or_path,
-             unet=accelerator.unwrap_model(unet),
-             text_encoder=accelerator.unwrap_model(text_encoder),
-         )
-         pipeline.text_encoder.save_pretrained(txt_dir)
-
-      elif args.train_only_unet:
-        pipeline = StableDiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            unet=accelerator.unwrap_model(unet),
-            text_encoder=accelerator.unwrap_model(text_encoder),
-        )
-        pipeline.save_pretrained(args.output_dir)
-        txt_dir=args.output_dir + "/text_encoder_trained"
-        subprocess.call('rm -r '+txt_dir, shell=True)
-
-      else:
-        pipeline = StableDiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            unet=accelerator.unwrap_model(unet),
-            text_encoder=accelerator.unwrap_model(text_encoder),
-        )
-        frz_dir=args.output_dir + "/text_encoder_frozen"
-        pipeline.save_pretrained(args.output_dir)
-        if args.train_text_encoder and os.path.exists(frz_dir):
-           subprocess.call('mv -f '+frz_dir +'/*.* '+ args.output_dir+'/text_encoder', shell=True)
-           subprocess.call('rm -r '+ frz_dir, shell=True)
-
-        if args.push_to_hub:
-            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
-
-    accelerator.end_training()
-    del pipeline
-    torch.cuda.empty_cache()
-    gc.collect()
-if __name__ == "__main__":
-    pass
-    #main()
-
diff --git a/spaces/SoUmNerd/FlowiseAI/README.md b/spaces/SoUmNerd/FlowiseAI/README.md
deleted file mode 100644
index 4ff23ec34f32333283368c0fb27f40fa9ba49343..0000000000000000000000000000000000000000
--- a/spaces/SoUmNerd/FlowiseAI/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: FlowiseAI
-emoji: 🐠
-colorFrom: blue
-colorTo: gray
-sdk: docker
-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/completer.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/completer.py
deleted file mode 100644
index 01af0bc88227da4af1ec1b6593e5abaee050d85f..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/completer.py
+++ /dev/null
@@ -1,3346 +0,0 @@
-"""Completion for IPython.
-
-This module started as fork of the rlcompleter module in the Python standard
-library.  The original enhancements made to rlcompleter have been sent
-upstream and were accepted as of Python 2.3,
-
-This module now support a wide variety of completion mechanism both available
-for normal classic Python code, as well as completer for IPython specific
-Syntax like magics.
-
-Latex and Unicode completion
-============================
-
-IPython and compatible frontends not only can complete your code, but can help
-you to input a wide range of characters. In particular we allow you to insert
-a unicode character using the tab completion mechanism.
-
-Forward latex/unicode completion
---------------------------------
-
-Forward completion allows you to easily type a unicode character using its latex
-name, or unicode long description. To do so type a backslash follow by the
-relevant name and press tab:
-
-
-Using latex completion:
-
-.. code::
-
-    \\alpha<tab>
-    α
-
-or using unicode completion:
-
-
-.. code::
-
-    \\GREEK SMALL LETTER ALPHA<tab>
-    α
-
-
-Only valid Python identifiers will complete. Combining characters (like arrow or
-dots) are also available, unlike latex they need to be put after the their
-counterpart that is to say, ``F\\\\vec<tab>`` is correct, not ``\\\\vec<tab>F``.
-
-Some browsers are known to display combining characters incorrectly.
-
-Backward latex completion
--------------------------
-
-It is sometime challenging to know how to type a character, if you are using
-IPython, or any compatible frontend you can prepend backslash to the character
-and press :kbd:`Tab` to expand it to its latex form.
-
-.. code::
-
-    \\α<tab>
-    \\alpha
-
-
-Both forward and backward completions can be deactivated by setting the
-:std:configtrait:`Completer.backslash_combining_completions` option to
-``False``.
-
-
-Experimental
-============
-
-Starting with IPython 6.0, this module can make use of the Jedi library to
-generate completions both using static analysis of the code, and dynamically
-inspecting multiple namespaces. Jedi is an autocompletion and static analysis
-for Python. The APIs attached to this new mechanism is unstable and will
-raise unless use in an :any:`provisionalcompleter` context manager.
-
-You will find that the following are experimental:
-
-    - :any:`provisionalcompleter`
-    - :any:`IPCompleter.completions`
-    - :any:`Completion`
-    - :any:`rectify_completions`
-
-.. note::
-
-    better name for :any:`rectify_completions` ?
-
-We welcome any feedback on these new API, and we also encourage you to try this
-module in debug mode (start IPython with ``--Completer.debug=True``) in order
-to have extra logging information if :any:`jedi` is crashing, or if current
-IPython completer pending deprecations are returning results not yet handled
-by :any:`jedi`
-
-Using Jedi for tab completion allow snippets like the following to work without
-having to execute any code:
-
-   >>> myvar = ['hello', 42]
-   ... myvar[1].bi<tab>
-
-Tab completion will be able to infer that ``myvar[1]`` is a real number without
-executing almost any code unlike the deprecated :any:`IPCompleter.greedy`
-option.
-
-Be sure to update :any:`jedi` to the latest stable version or to try the
-current development version to get better completions.
-
-Matchers
-========
-
-All completions routines are implemented using unified *Matchers* API.
-The matchers API is provisional and subject to change without notice.
-
-The built-in matchers include:
-
-- :any:`IPCompleter.dict_key_matcher`:  dictionary key completions,
-- :any:`IPCompleter.magic_matcher`: completions for magics,
-- :any:`IPCompleter.unicode_name_matcher`,
-  :any:`IPCompleter.fwd_unicode_matcher`
-  and :any:`IPCompleter.latex_name_matcher`: see `Forward latex/unicode completion`_,
-- :any:`back_unicode_name_matcher` and :any:`back_latex_name_matcher`: see `Backward latex completion`_,
-- :any:`IPCompleter.file_matcher`: paths to files and directories,
-- :any:`IPCompleter.python_func_kw_matcher` - function keywords,
-- :any:`IPCompleter.python_matches` - globals and attributes (v1 API),
-- ``IPCompleter.jedi_matcher`` - static analysis with Jedi,
-- :any:`IPCompleter.custom_completer_matcher` - pluggable completer with a default
-  implementation in :any:`InteractiveShell` which uses IPython hooks system
-  (`complete_command`) with string dispatch (including regular expressions).
-  Differently to other matchers, ``custom_completer_matcher`` will not suppress
-  Jedi results to match behaviour in earlier IPython versions.
-
-Custom matchers can be added by appending to ``IPCompleter.custom_matchers`` list.
-
-Matcher API
------------
-
-Simplifying some details, the ``Matcher`` interface can described as
-
-.. code-block::
-
-    MatcherAPIv1 = Callable[[str], list[str]]
-    MatcherAPIv2 = Callable[[CompletionContext], SimpleMatcherResult]
-
-    Matcher = MatcherAPIv1 | MatcherAPIv2
-
-The ``MatcherAPIv1`` reflects the matcher API as available prior to IPython 8.6.0
-and remains supported as a simplest way for generating completions. This is also
-currently the only API supported by the IPython hooks system `complete_command`.
-
-To distinguish between matcher versions ``matcher_api_version`` attribute is used.
-More precisely, the API allows to omit ``matcher_api_version`` for v1 Matchers,
-and requires a literal ``2`` for v2 Matchers.
-
-Once the API stabilises future versions may relax the requirement for specifying
-``matcher_api_version`` by switching to :any:`functools.singledispatch`, therefore
-please do not rely on the presence of ``matcher_api_version`` for any purposes.
-
-Suppression of competing matchers
----------------------------------
-
-By default results from all matchers are combined, in the order determined by
-their priority. Matchers can request to suppress results from subsequent
-matchers by setting ``suppress`` to ``True`` in the ``MatcherResult``.
-
-When multiple matchers simultaneously request surpression, the results from of
-the matcher with higher priority will be returned.
-
-Sometimes it is desirable to suppress most but not all other matchers;
-this can be achieved by adding a set of identifiers of matchers which
-should not be suppressed to ``MatcherResult`` under ``do_not_suppress`` key.
-
-The suppression behaviour can is user-configurable via
-:std:configtrait:`IPCompleter.suppress_competing_matchers`.
-"""
-
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-#
-# Some of this code originated from rlcompleter in the Python standard library
-# Copyright (C) 2001 Python Software Foundation, www.python.org
-
-from __future__ import annotations
-import builtins as builtin_mod
-import enum
-import glob
-import inspect
-import itertools
-import keyword
-import os
-import re
-import string
-import sys
-import tokenize
-import time
-import unicodedata
-import uuid
-import warnings
-from ast import literal_eval
-from collections import defaultdict
-from contextlib import contextmanager
-from dataclasses import dataclass
-from functools import cached_property, partial
-from types import SimpleNamespace
-from typing import (
-    Iterable,
-    Iterator,
-    List,
-    Tuple,
-    Union,
-    Any,
-    Sequence,
-    Dict,
-    Optional,
-    TYPE_CHECKING,
-    Set,
-    Sized,
-    TypeVar,
-    Literal,
-)
-
-from IPython.core.guarded_eval import guarded_eval, EvaluationContext
-from IPython.core.error import TryNext
-from IPython.core.inputtransformer2 import ESC_MAGIC
-from IPython.core.latex_symbols import latex_symbols, reverse_latex_symbol
-from IPython.core.oinspect import InspectColors
-from IPython.testing.skipdoctest import skip_doctest
-from IPython.utils import generics
-from IPython.utils.decorators import sphinx_options
-from IPython.utils.dir2 import dir2, get_real_method
-from IPython.utils.docs import GENERATING_DOCUMENTATION
-from IPython.utils.path import ensure_dir_exists
-from IPython.utils.process import arg_split
-from traitlets import (
-    Bool,
-    Enum,
-    Int,
-    List as ListTrait,
-    Unicode,
-    Dict as DictTrait,
-    Union as UnionTrait,
-    observe,
-)
-from traitlets.config.configurable import Configurable
-
-import __main__
-
-# skip module docstests
-__skip_doctest__ = True
-
-
-try:
-    import jedi
-    jedi.settings.case_insensitive_completion = False
-    import jedi.api.helpers
-    import jedi.api.classes
-    JEDI_INSTALLED = True
-except ImportError:
-    JEDI_INSTALLED = False
-
-
-if TYPE_CHECKING or GENERATING_DOCUMENTATION and sys.version_info >= (3, 11):
-    from typing import cast
-    from typing_extensions import TypedDict, NotRequired, Protocol, TypeAlias, TypeGuard
-else:
-    from typing import Generic
-
-    def cast(type_, obj):
-        """Workaround for `TypeError: MatcherAPIv2() takes no arguments`"""
-        return obj
-
-    # do not require on runtime
-    NotRequired = Tuple  # requires Python >=3.11
-    TypedDict = Dict  # by extension of `NotRequired` requires 3.11 too
-    Protocol = object  # requires Python >=3.8
-    TypeAlias = Any  # requires Python >=3.10
-    TypeGuard = Generic  # requires Python >=3.10
-if GENERATING_DOCUMENTATION:
-    from typing import TypedDict
-
-# -----------------------------------------------------------------------------
-# Globals
-#-----------------------------------------------------------------------------
-
-# ranges where we have most of the valid unicode names. We could be more finer
-# grained but is it worth it for performance  While unicode have character in the
-# range 0, 0x110000, we seem to have name for about 10% of those. (131808 as I
-# write this). With below range we cover them all, with a density of ~67%
-# biggest next gap we consider only adds up about 1% density and there are 600
-# gaps that would need hard coding.
-_UNICODE_RANGES = [(32, 0x323B0), (0xE0001, 0xE01F0)]
-
-# Public API
-__all__ = ["Completer", "IPCompleter"]
-
-if sys.platform == 'win32':
-    PROTECTABLES = ' '
-else:
-    PROTECTABLES = ' ()[]{}?=\\|;:\'#*"^&'
-
-# Protect against returning an enormous number of completions which the frontend
-# may have trouble processing.
-MATCHES_LIMIT = 500
-
-# Completion type reported when no type can be inferred.
-_UNKNOWN_TYPE = "<unknown>"
-
-# sentinel value to signal lack of a match
-not_found = object()
-
-class ProvisionalCompleterWarning(FutureWarning):
-    """
-    Exception raise by an experimental feature in this module.
-
-    Wrap code in :any:`provisionalcompleter` context manager if you
-    are certain you want to use an unstable feature.
-    """
-    pass
-
-warnings.filterwarnings('error', category=ProvisionalCompleterWarning)
-
-
-@skip_doctest
-@contextmanager
-def provisionalcompleter(action='ignore'):
-    """
-    This context manager has to be used in any place where unstable completer
-    behavior and API may be called.
-
-    >>> with provisionalcompleter():
-    ...     completer.do_experimental_things() # works
-
-    >>> completer.do_experimental_things() # raises.
-
-    .. note::
-
-        Unstable
-
-        By using this context manager you agree that the API in use may change
-        without warning, and that you won't complain if they do so.
-
-        You also understand that, if the API is not to your liking, you should report
-        a bug to explain your use case upstream.
-
-        We'll be happy to get your feedback, feature requests, and improvements on
-        any of the unstable APIs!
-    """
-    with warnings.catch_warnings():
-        warnings.filterwarnings(action, category=ProvisionalCompleterWarning)
-        yield
-
-
-def has_open_quotes(s):
-    """Return whether a string has open quotes.
-
-    This simply counts whether the number of quote characters of either type in
-    the string is odd.
-
-    Returns
-    -------
-    If there is an open quote, the quote character is returned.  Else, return
-    False.
-    """
-    # We check " first, then ', so complex cases with nested quotes will get
-    # the " to take precedence.
-    if s.count('"') % 2:
-        return '"'
-    elif s.count("'") % 2:
-        return "'"
-    else:
-        return False
-
-
-def protect_filename(s, protectables=PROTECTABLES):
-    """Escape a string to protect certain characters."""
-    if set(s) & set(protectables):
-        if sys.platform == "win32":
-            return '"' + s + '"'
-        else:
-            return "".join(("\\" + c if c in protectables else c) for c in s)
-    else:
-        return s
-
-
-def expand_user(path:str) -> Tuple[str, bool, str]:
-    """Expand ``~``-style usernames in strings.
-
-    This is similar to :func:`os.path.expanduser`, but it computes and returns
-    extra information that will be useful if the input was being used in
-    computing completions, and you wish to return the completions with the
-    original '~' instead of its expanded value.
-
-    Parameters
-    ----------
-    path : str
-        String to be expanded.  If no ~ is present, the output is the same as the
-        input.
-
-    Returns
-    -------
-    newpath : str
-        Result of ~ expansion in the input path.
-    tilde_expand : bool
-        Whether any expansion was performed or not.
-    tilde_val : str
-        The value that ~ was replaced with.
-    """
-    # Default values
-    tilde_expand = False
-    tilde_val = ''
-    newpath = path
-
-    if path.startswith('~'):
-        tilde_expand = True
-        rest = len(path)-1
-        newpath = os.path.expanduser(path)
-        if rest:
-            tilde_val = newpath[:-rest]
-        else:
-            tilde_val = newpath
-
-    return newpath, tilde_expand, tilde_val
-
-
-def compress_user(path:str, tilde_expand:bool, tilde_val:str) -> str:
-    """Does the opposite of expand_user, with its outputs.
-    """
-    if tilde_expand:
-        return path.replace(tilde_val, '~')
-    else:
-        return path
-
-
-def completions_sorting_key(word):
-    """key for sorting completions
-
-    This does several things:
-
-    - Demote any completions starting with underscores to the end
-    - Insert any %magic and %%cellmagic completions in the alphabetical order
-      by their name
-    """
-    prio1, prio2 = 0, 0
-
-    if word.startswith('__'):
-        prio1 = 2
-    elif word.startswith('_'):
-        prio1 = 1
-
-    if word.endswith('='):
-        prio1 = -1
-
-    if word.startswith('%%'):
-        # If there's another % in there, this is something else, so leave it alone
-        if not "%" in word[2:]:
-            word = word[2:]
-            prio2 = 2
-    elif word.startswith('%'):
-        if not "%" in word[1:]:
-            word = word[1:]
-            prio2 = 1
-
-    return prio1, word, prio2
-
-
-class _FakeJediCompletion:
-    """
-    This is a workaround to communicate to the UI that Jedi has crashed and to
-    report a bug. Will be used only id :any:`IPCompleter.debug` is set to true.
-
-    Added in IPython 6.0 so should likely be removed for 7.0
-
-    """
-
-    def __init__(self, name):
-
-        self.name = name
-        self.complete = name
-        self.type = 'crashed'
-        self.name_with_symbols = name
-        self.signature = ""
-        self._origin = "fake"
-        self.text = "crashed"
-
-    def __repr__(self):
-        return '<Fake completion object jedi has crashed>'
-
-
-_JediCompletionLike = Union["jedi.api.Completion", _FakeJediCompletion]
-
-
-class Completion:
-    """
-    Completion object used and returned by IPython completers.
-
-    .. warning::
-
-        Unstable
-
-        This function is unstable, API may change without warning.
-        It will also raise unless use in proper context manager.
-
-    This act as a middle ground :any:`Completion` object between the
-    :any:`jedi.api.classes.Completion` object and the Prompt Toolkit completion
-    object. While Jedi need a lot of information about evaluator and how the
-    code should be ran/inspected, PromptToolkit (and other frontend) mostly
-    need user facing information.
-
-    - Which range should be replaced replaced by what.
-    - Some metadata (like completion type), or meta information to displayed to
-      the use user.
-
-    For debugging purpose we can also store the origin of the completion (``jedi``,
-    ``IPython.python_matches``, ``IPython.magics_matches``...).
-    """
-
-    __slots__ = ['start', 'end', 'text', 'type', 'signature', '_origin']
-
-    def __init__(
-        self,
-        start: int,
-        end: int,
-        text: str,
-        *,
-        type: Optional[str] = None,
-        _origin="",
-        signature="",
-    ) -> None:
-        warnings.warn(
-            "``Completion`` is a provisional API (as of IPython 6.0). "
-            "It may change without warnings. "
-            "Use in corresponding context manager.",
-            category=ProvisionalCompleterWarning,
-            stacklevel=2,
-        )
-
-        self.start = start
-        self.end = end
-        self.text = text
-        self.type = type
-        self.signature = signature
-        self._origin = _origin
-
-    def __repr__(self):
-        return '<Completion start=%s end=%s text=%r type=%r, signature=%r,>' % \
-                (self.start, self.end, self.text, self.type or '?', self.signature or '?')
-
-    def __eq__(self, other) -> bool:
-        """
-        Equality and hash do not hash the type (as some completer may not be
-        able to infer the type), but are use to (partially) de-duplicate
-        completion.
-
-        Completely de-duplicating completion is a bit tricker that just
-        comparing as it depends on surrounding text, which Completions are not
-        aware of.
-        """
-        return self.start == other.start and \
-            self.end == other.end and \
-            self.text == other.text
-
-    def __hash__(self):
-        return hash((self.start, self.end, self.text))
-
-
-class SimpleCompletion:
-    """Completion item to be included in the dictionary returned by new-style Matcher (API v2).
-
-    .. warning::
-
-        Provisional
-
-        This class is used to describe the currently supported attributes of
-        simple completion items, and any additional implementation details
-        should not be relied on. Additional attributes may be included in
-        future versions, and meaning of text disambiguated from the current
-        dual meaning of "text to insert" and "text to used as a label".
-    """
-
-    __slots__ = ["text", "type"]
-
-    def __init__(self, text: str, *, type: Optional[str] = None):
-        self.text = text
-        self.type = type
-
-    def __repr__(self):
-        return f"<SimpleCompletion text={self.text!r} type={self.type!r}>"
-
-
-class _MatcherResultBase(TypedDict):
-    """Definition of dictionary to be returned by new-style Matcher (API v2)."""
-
-    #: Suffix of the provided ``CompletionContext.token``, if not given defaults to full token.
-    matched_fragment: NotRequired[str]
-
-    #: Whether to suppress results from all other matchers (True), some
-    #: matchers (set of identifiers) or none (False); default is False.
-    suppress: NotRequired[Union[bool, Set[str]]]
-
-    #: Identifiers of matchers which should NOT be suppressed when this matcher
-    #: requests to suppress all other matchers; defaults to an empty set.
-    do_not_suppress: NotRequired[Set[str]]
-
-    #: Are completions already ordered and should be left as-is? default is False.
-    ordered: NotRequired[bool]
-
-
-@sphinx_options(show_inherited_members=True, exclude_inherited_from=["dict"])
-class SimpleMatcherResult(_MatcherResultBase, TypedDict):
-    """Result of new-style completion matcher."""
-
-    # note: TypedDict is added again to the inheritance chain
-    # in order to get __orig_bases__ for documentation
-
-    #: List of candidate completions
-    completions: Sequence[SimpleCompletion] | Iterator[SimpleCompletion]
-
-
-class _JediMatcherResult(_MatcherResultBase):
-    """Matching result returned by Jedi (will be processed differently)"""
-
-    #: list of candidate completions
-    completions: Iterator[_JediCompletionLike]
-
-
-AnyMatcherCompletion = Union[_JediCompletionLike, SimpleCompletion]
-AnyCompletion = TypeVar("AnyCompletion", AnyMatcherCompletion, Completion)
-
-
-@dataclass
-class CompletionContext:
-    """Completion context provided as an argument to matchers in the Matcher API v2."""
-
-    # rationale: many legacy matchers relied on completer state (`self.text_until_cursor`)
-    # which was not explicitly visible as an argument of the matcher, making any refactor
-    # prone to errors; by explicitly passing `cursor_position` we can decouple the matchers
-    # from the completer, and make substituting them in sub-classes easier.
-
-    #: Relevant fragment of code directly preceding the cursor.
-    #: The extraction of token is implemented via splitter heuristic
-    #: (following readline behaviour for legacy reasons), which is user configurable
-    #: (by switching the greedy mode).
-    token: str
-
-    #: The full available content of the editor or buffer
-    full_text: str
-
-    #: Cursor position in the line (the same for ``full_text`` and ``text``).
-    cursor_position: int
-
-    #: Cursor line in ``full_text``.
-    cursor_line: int
-
-    #: The maximum number of completions that will be used downstream.
-    #: Matchers can use this information to abort early.
-    #: The built-in Jedi matcher is currently excepted from this limit.
-    # If not given, return all possible completions.
-    limit: Optional[int]
-
-    @cached_property
-    def text_until_cursor(self) -> str:
-        return self.line_with_cursor[: self.cursor_position]
-
-    @cached_property
-    def line_with_cursor(self) -> str:
-        return self.full_text.split("\n")[self.cursor_line]
-
-
-#: Matcher results for API v2.
-MatcherResult = Union[SimpleMatcherResult, _JediMatcherResult]
-
-
-class _MatcherAPIv1Base(Protocol):
-    def __call__(self, text: str) -> List[str]:
-        """Call signature."""
-        ...
-
-    #: Used to construct the default matcher identifier
-    __qualname__: str
-
-
-class _MatcherAPIv1Total(_MatcherAPIv1Base, Protocol):
-    #: API version
-    matcher_api_version: Optional[Literal[1]]
-
-    def __call__(self, text: str) -> List[str]:
-        """Call signature."""
-        ...
-
-
-#: Protocol describing Matcher API v1.
-MatcherAPIv1: TypeAlias = Union[_MatcherAPIv1Base, _MatcherAPIv1Total]
-
-
-class MatcherAPIv2(Protocol):
-    """Protocol describing Matcher API v2."""
-
-    #: API version
-    matcher_api_version: Literal[2] = 2
-
-    def __call__(self, context: CompletionContext) -> MatcherResult:
-        """Call signature."""
-        ...
-
-    #: Used to construct the default matcher identifier
-    __qualname__: str
-
-
-Matcher: TypeAlias = Union[MatcherAPIv1, MatcherAPIv2]
-
-
-def _is_matcher_v1(matcher: Matcher) -> TypeGuard[MatcherAPIv1]:
-    api_version = _get_matcher_api_version(matcher)
-    return api_version == 1
-
-
-def _is_matcher_v2(matcher: Matcher) -> TypeGuard[MatcherAPIv2]:
-    api_version = _get_matcher_api_version(matcher)
-    return api_version == 2
-
-
-def _is_sizable(value: Any) -> TypeGuard[Sized]:
-    """Determines whether objects is sizable"""
-    return hasattr(value, "__len__")
-
-
-def _is_iterator(value: Any) -> TypeGuard[Iterator]:
-    """Determines whether objects is sizable"""
-    return hasattr(value, "__next__")
-
-
-def has_any_completions(result: MatcherResult) -> bool:
-    """Check if any result includes any completions."""
-    completions = result["completions"]
-    if _is_sizable(completions):
-        return len(completions) != 0
-    if _is_iterator(completions):
-        try:
-            old_iterator = completions
-            first = next(old_iterator)
-            result["completions"] = cast(
-                Iterator[SimpleCompletion],
-                itertools.chain([first], old_iterator),
-            )
-            return True
-        except StopIteration:
-            return False
-    raise ValueError(
-        "Completions returned by matcher need to be an Iterator or a Sizable"
-    )
-
-
-def completion_matcher(
-    *,
-    priority: Optional[float] = None,
-    identifier: Optional[str] = None,
-    api_version: int = 1,
-):
-    """Adds attributes describing the matcher.
-
-    Parameters
-    ----------
-    priority : Optional[float]
-        The priority of the matcher, determines the order of execution of matchers.
-        Higher priority means that the matcher will be executed first. Defaults to 0.
-    identifier : Optional[str]
-        identifier of the matcher allowing users to modify the behaviour via traitlets,
-        and also used to for debugging (will be passed as ``origin`` with the completions).
-
-        Defaults to matcher function's ``__qualname__`` (for example,
-        ``IPCompleter.file_matcher`` for the built-in matched defined
-        as a ``file_matcher`` method of the ``IPCompleter`` class).
-    api_version: Optional[int]
-        version of the Matcher API used by this matcher.
-        Currently supported values are 1 and 2.
-        Defaults to 1.
-    """
-
-    def wrapper(func: Matcher):
-        func.matcher_priority = priority or 0  # type: ignore
-        func.matcher_identifier = identifier or func.__qualname__  # type: ignore
-        func.matcher_api_version = api_version  # type: ignore
-        if TYPE_CHECKING:
-            if api_version == 1:
-                func = cast(MatcherAPIv1, func)
-            elif api_version == 2:
-                func = cast(MatcherAPIv2, func)
-        return func
-
-    return wrapper
-
-
-def _get_matcher_priority(matcher: Matcher):
-    return getattr(matcher, "matcher_priority", 0)
-
-
-def _get_matcher_id(matcher: Matcher):
-    return getattr(matcher, "matcher_identifier", matcher.__qualname__)
-
-
-def _get_matcher_api_version(matcher):
-    return getattr(matcher, "matcher_api_version", 1)
-
-
-context_matcher = partial(completion_matcher, api_version=2)
-
-
-_IC = Iterable[Completion]
-
-
-def _deduplicate_completions(text: str, completions: _IC)-> _IC:
-    """
-    Deduplicate a set of completions.
-
-    .. warning::
-
-        Unstable
-
-        This function is unstable, API may change without warning.
-
-    Parameters
-    ----------
-    text : str
-        text that should be completed.
-    completions : Iterator[Completion]
-        iterator over the completions to deduplicate
-
-    Yields
-    ------
-    `Completions` objects
-    Completions coming from multiple sources, may be different but end up having
-    the same effect when applied to ``text``. If this is the case, this will
-    consider completions as equal and only emit the first encountered.
-    Not folded in `completions()` yet for debugging purpose, and to detect when
-    the IPython completer does return things that Jedi does not, but should be
-    at some point.
-    """
-    completions = list(completions)
-    if not completions:
-        return
-
-    new_start = min(c.start for c in completions)
-    new_end = max(c.end for c in completions)
-
-    seen = set()
-    for c in completions:
-        new_text = text[new_start:c.start] + c.text + text[c.end:new_end]
-        if new_text not in seen:
-            yield c
-            seen.add(new_text)
-
-
-def rectify_completions(text: str, completions: _IC, *, _debug: bool = False) -> _IC:
-    """
-    Rectify a set of completions to all have the same ``start`` and ``end``
-
-    .. warning::
-
-        Unstable
-
-        This function is unstable, API may change without warning.
-        It will also raise unless use in proper context manager.
-
-    Parameters
-    ----------
-    text : str
-        text that should be completed.
-    completions : Iterator[Completion]
-        iterator over the completions to rectify
-    _debug : bool
-        Log failed completion
-
-    Notes
-    -----
-    :any:`jedi.api.classes.Completion` s returned by Jedi may not have the same start and end, though
-    the Jupyter Protocol requires them to behave like so. This will readjust
-    the completion to have the same ``start`` and ``end`` by padding both
-    extremities with surrounding text.
-
-    During stabilisation should support a ``_debug`` option to log which
-    completion are return by the IPython completer and not found in Jedi in
-    order to make upstream bug report.
-    """
-    warnings.warn("`rectify_completions` is a provisional API (as of IPython 6.0). "
-                 "It may change without warnings. "
-                 "Use in corresponding context manager.",
-                  category=ProvisionalCompleterWarning, stacklevel=2)
-
-    completions = list(completions)
-    if not completions:
-        return
-    starts = (c.start for c in completions)
-    ends = (c.end for c in completions)
-
-    new_start = min(starts)
-    new_end = max(ends)
-
-    seen_jedi = set()
-    seen_python_matches = set()
-    for c in completions:
-        new_text = text[new_start:c.start] + c.text + text[c.end:new_end]
-        if c._origin == 'jedi':
-            seen_jedi.add(new_text)
-        elif c._origin == 'IPCompleter.python_matches':
-            seen_python_matches.add(new_text)
-        yield Completion(new_start, new_end, new_text, type=c.type, _origin=c._origin, signature=c.signature)
-    diff = seen_python_matches.difference(seen_jedi)
-    if diff and _debug:
-        print('IPython.python matches have extras:', diff)
-
-
-if sys.platform == 'win32':
-    DELIMS = ' \t\n`!@#$^&*()=+[{]}|;\'",<>?'
-else:
-    DELIMS = ' \t\n`!@#$^&*()=+[{]}\\|;:\'",<>?'
-
-GREEDY_DELIMS = ' =\r\n'
-
-
-class CompletionSplitter(object):
-    """An object to split an input line in a manner similar to readline.
-
-    By having our own implementation, we can expose readline-like completion in
-    a uniform manner to all frontends.  This object only needs to be given the
-    line of text to be split and the cursor position on said line, and it
-    returns the 'word' to be completed on at the cursor after splitting the
-    entire line.
-
-    What characters are used as splitting delimiters can be controlled by
-    setting the ``delims`` attribute (this is a property that internally
-    automatically builds the necessary regular expression)"""
-
-    # Private interface
-
-    # A string of delimiter characters.  The default value makes sense for
-    # IPython's most typical usage patterns.
-    _delims = DELIMS
-
-    # The expression (a normal string) to be compiled into a regular expression
-    # for actual splitting.  We store it as an attribute mostly for ease of
-    # debugging, since this type of code can be so tricky to debug.
-    _delim_expr = None
-
-    # The regular expression that does the actual splitting
-    _delim_re = None
-
-    def __init__(self, delims=None):
-        delims = CompletionSplitter._delims if delims is None else delims
-        self.delims = delims
-
-    @property
-    def delims(self):
-        """Return the string of delimiter characters."""
-        return self._delims
-
-    @delims.setter
-    def delims(self, delims):
-        """Set the delimiters for line splitting."""
-        expr = '[' + ''.join('\\'+ c for c in delims) + ']'
-        self._delim_re = re.compile(expr)
-        self._delims = delims
-        self._delim_expr = expr
-
-    def split_line(self, line, cursor_pos=None):
-        """Split a line of text with a cursor at the given position.
-        """
-        l = line if cursor_pos is None else line[:cursor_pos]
-        return self._delim_re.split(l)[-1]
-
-
-
-class Completer(Configurable):
-
-    greedy = Bool(
-        False,
-        help="""Activate greedy completion.
-
-        .. deprecated:: 8.8
-            Use :std:configtrait:`Completer.evaluation` and :std:configtrait:`Completer.auto_close_dict_keys` instead.
-
-        When enabled in IPython 8.8 or newer, changes configuration as follows:
-
-        - ``Completer.evaluation = 'unsafe'``
-        - ``Completer.auto_close_dict_keys = True``
-        """,
-    ).tag(config=True)
-
-    evaluation = Enum(
-        ("forbidden", "minimal", "limited", "unsafe", "dangerous"),
-        default_value="limited",
-        help="""Policy for code evaluation under completion.
-
-        Successive options allow to enable more eager evaluation for better
-        completion suggestions, including for nested dictionaries, nested lists,
-        or even results of function calls.
-        Setting ``unsafe`` or higher can lead to evaluation of arbitrary user
-        code on :kbd:`Tab` with potentially unwanted or dangerous side effects.
-
-        Allowed values are:
-
-        - ``forbidden``: no evaluation of code is permitted,
-        - ``minimal``: evaluation of literals and access to built-in namespace;
-          no item/attribute evaluationm no access to locals/globals,
-          no evaluation of any operations or comparisons.
-        - ``limited``: access to all namespaces, evaluation of hard-coded methods
-          (for example: :any:`dict.keys`, :any:`object.__getattr__`,
-          :any:`object.__getitem__`) on allow-listed objects (for example:
-          :any:`dict`, :any:`list`, :any:`tuple`, ``pandas.Series``),
-        - ``unsafe``: evaluation of all methods and function calls but not of
-          syntax with side-effects like `del x`,
-        - ``dangerous``: completely arbitrary evaluation.
-        """,
-    ).tag(config=True)
-
-    use_jedi = Bool(default_value=JEDI_INSTALLED,
-                    help="Experimental: Use Jedi to generate autocompletions. "
-                    "Default to True if jedi is installed.").tag(config=True)
-
-    jedi_compute_type_timeout = Int(default_value=400,
-        help="""Experimental: restrict time (in milliseconds) during which Jedi can compute types.
-        Set to 0 to stop computing types. Non-zero value lower than 100ms may hurt
-        performance by preventing jedi to build its cache.
-        """).tag(config=True)
-
-    debug = Bool(default_value=False,
-                 help='Enable debug for the Completer. Mostly print extra '
-                      'information for experimental jedi integration.')\
-                      .tag(config=True)
-
-    backslash_combining_completions = Bool(True,
-        help="Enable unicode completions, e.g. \\alpha<tab> . "
-             "Includes completion of latex commands, unicode names, and expanding "
-             "unicode characters back to latex commands.").tag(config=True)
-
-    auto_close_dict_keys = Bool(
-        False,
-        help="""
-        Enable auto-closing dictionary keys.
-
-        When enabled string keys will be suffixed with a final quote
-        (matching the opening quote), tuple keys will also receive a
-        separating comma if needed, and keys which are final will
-        receive a closing bracket (``]``).
-        """,
-    ).tag(config=True)
-
-    def __init__(self, namespace=None, global_namespace=None, **kwargs):
-        """Create a new completer for the command line.
-
-        Completer(namespace=ns, global_namespace=ns2) -> completer instance.
-
-        If unspecified, the default namespace where completions are performed
-        is __main__ (technically, __main__.__dict__). Namespaces should be
-        given as dictionaries.
-
-        An optional second namespace can be given.  This allows the completer
-        to handle cases where both the local and global scopes need to be
-        distinguished.
-        """
-
-        # Don't bind to namespace quite yet, but flag whether the user wants a
-        # specific namespace or to use __main__.__dict__. This will allow us
-        # to bind to __main__.__dict__ at completion time, not now.
-        if namespace is None:
-            self.use_main_ns = True
-        else:
-            self.use_main_ns = False
-            self.namespace = namespace
-
-        # The global namespace, if given, can be bound directly
-        if global_namespace is None:
-            self.global_namespace = {}
-        else:
-            self.global_namespace = global_namespace
-
-        self.custom_matchers = []
-
-        super(Completer, self).__init__(**kwargs)
-
-    def complete(self, text, state):
-        """Return the next possible completion for 'text'.
-
-        This is called successively with state == 0, 1, 2, ... until it
-        returns None.  The completion should begin with 'text'.
-
-        """
-        if self.use_main_ns:
-            self.namespace = __main__.__dict__
-
-        if state == 0:
-            if "." in text:
-                self.matches = self.attr_matches(text)
-            else:
-                self.matches = self.global_matches(text)
-        try:
-            return self.matches[state]
-        except IndexError:
-            return None
-
-    def global_matches(self, text):
-        """Compute matches when text is a simple name.
-
-        Return a list of all keywords, built-in functions and names currently
-        defined in self.namespace or self.global_namespace that match.
-
-        """
-        matches = []
-        match_append = matches.append
-        n = len(text)
-        for lst in [
-            keyword.kwlist,
-            builtin_mod.__dict__.keys(),
-            list(self.namespace.keys()),
-            list(self.global_namespace.keys()),
-        ]:
-            for word in lst:
-                if word[:n] == text and word != "__builtins__":
-                    match_append(word)
-
-        snake_case_re = re.compile(r"[^_]+(_[^_]+)+?\Z")
-        for lst in [list(self.namespace.keys()), list(self.global_namespace.keys())]:
-            shortened = {
-                "_".join([sub[0] for sub in word.split("_")]): word
-                for word in lst
-                if snake_case_re.match(word)
-            }
-            for word in shortened.keys():
-                if word[:n] == text and word != "__builtins__":
-                    match_append(shortened[word])
-        return matches
-
-    def attr_matches(self, text):
-        """Compute matches when text contains a dot.
-
-        Assuming the text is of the form NAME.NAME....[NAME], and is
-        evaluatable in self.namespace or self.global_namespace, it will be
-        evaluated and its attributes (as revealed by dir()) are used as
-        possible completions.  (For class instances, class members are
-        also considered.)
-
-        WARNING: this can still invoke arbitrary C code, if an object
-        with a __getattr__ hook is evaluated.
-
-        """
-        m2 = re.match(r"(.+)\.(\w*)$", self.line_buffer)
-        if not m2:
-            return []
-        expr, attr = m2.group(1, 2)
-
-        obj = self._evaluate_expr(expr)
-
-        if obj is not_found:
-            return []
-
-        if self.limit_to__all__ and hasattr(obj, '__all__'):
-            words = get__all__entries(obj)
-        else:
-            words = dir2(obj)
-
-        try:
-            words = generics.complete_object(obj, words)
-        except TryNext:
-            pass
-        except AssertionError:
-            raise
-        except Exception:
-            # Silence errors from completion function
-            pass
-        # Build match list to return
-        n = len(attr)
-
-        # Note: ideally we would just return words here and the prefix
-        # reconciliator would know that we intend to append to rather than
-        # replace the input text; this requires refactoring to return range
-        # which ought to be replaced (as does jedi).
-        tokens = _parse_tokens(expr)
-        rev_tokens = reversed(tokens)
-        skip_over = {tokenize.ENDMARKER, tokenize.NEWLINE}
-        name_turn = True
-
-        parts = []
-        for token in rev_tokens:
-            if token.type in skip_over:
-                continue
-            if token.type == tokenize.NAME and name_turn:
-                parts.append(token.string)
-                name_turn = False
-            elif token.type == tokenize.OP and token.string == "." and not name_turn:
-                parts.append(token.string)
-                name_turn = True
-            else:
-                # short-circuit if not empty nor name token
-                break
-
-        prefix_after_space = "".join(reversed(parts))
-
-        return ["%s.%s" % (prefix_after_space, w) for w in words if w[:n] == attr]
-
-    def _evaluate_expr(self, expr):
-        obj = not_found
-        done = False
-        while not done and expr:
-            try:
-                obj = guarded_eval(
-                    expr,
-                    EvaluationContext(
-                        globals=self.global_namespace,
-                        locals=self.namespace,
-                        evaluation=self.evaluation,
-                    ),
-                )
-                done = True
-            except Exception as e:
-                if self.debug:
-                    print("Evaluation exception", e)
-                # trim the expression to remove any invalid prefix
-                # e.g. user starts `(d[`, so we get `expr = '(d'`,
-                # where parenthesis is not closed.
-                # TODO: make this faster by reusing parts of the computation?
-                expr = expr[1:]
-        return obj
-
-def get__all__entries(obj):
-    """returns the strings in the __all__ attribute"""
-    try:
-        words = getattr(obj, '__all__')
-    except:
-        return []
-
-    return [w for w in words if isinstance(w, str)]
-
-
-class _DictKeyState(enum.Flag):
-    """Represent state of the key match in context of other possible matches.
-
-    - given `d1 = {'a': 1}` completion on `d1['<tab>` will yield `{'a': END_OF_ITEM}` as there is no tuple.
-    - given `d2 = {('a', 'b'): 1}`: `d2['a', '<tab>` will yield `{'b': END_OF_TUPLE}` as there is no tuple members to add beyond `'b'`.
-    - given `d3 = {('a', 'b'): 1}`: `d3['<tab>` will yield `{'a': IN_TUPLE}` as `'a'` can be added.
-    - given `d4 = {'a': 1, ('a', 'b'): 2}`: `d4['<tab>` will yield `{'a': END_OF_ITEM & END_OF_TUPLE}`
-    """
-
-    BASELINE = 0
-    END_OF_ITEM = enum.auto()
-    END_OF_TUPLE = enum.auto()
-    IN_TUPLE = enum.auto()
-
-
-def _parse_tokens(c):
-    """Parse tokens even if there is an error."""
-    tokens = []
-    token_generator = tokenize.generate_tokens(iter(c.splitlines()).__next__)
-    while True:
-        try:
-            tokens.append(next(token_generator))
-        except tokenize.TokenError:
-            return tokens
-        except StopIteration:
-            return tokens
-
-
-def _match_number_in_dict_key_prefix(prefix: str) -> Union[str, None]:
-    """Match any valid Python numeric literal in a prefix of dictionary keys.
-
-    References:
-    - https://docs.python.org/3/reference/lexical_analysis.html#numeric-literals
-    - https://docs.python.org/3/library/tokenize.html
-    """
-    if prefix[-1].isspace():
-        # if user typed a space we do not have anything to complete
-        # even if there was a valid number token before
-        return None
-    tokens = _parse_tokens(prefix)
-    rev_tokens = reversed(tokens)
-    skip_over = {tokenize.ENDMARKER, tokenize.NEWLINE}
-    number = None
-    for token in rev_tokens:
-        if token.type in skip_over:
-            continue
-        if number is None:
-            if token.type == tokenize.NUMBER:
-                number = token.string
-                continue
-            else:
-                # we did not match a number
-                return None
-        if token.type == tokenize.OP:
-            if token.string == ",":
-                break
-            if token.string in {"+", "-"}:
-                number = token.string + number
-        else:
-            return None
-    return number
-
-
-_INT_FORMATS = {
-    "0b": bin,
-    "0o": oct,
-    "0x": hex,
-}
-
-
-def match_dict_keys(
-    keys: List[Union[str, bytes, Tuple[Union[str, bytes], ...]]],
-    prefix: str,
-    delims: str,
-    extra_prefix: Optional[Tuple[Union[str, bytes], ...]] = None,
-) -> Tuple[str, int, Dict[str, _DictKeyState]]:
-    """Used by dict_key_matches, matching the prefix to a list of keys
-
-    Parameters
-    ----------
-    keys
-        list of keys in dictionary currently being completed.
-    prefix
-        Part of the text already typed by the user. E.g. `mydict[b'fo`
-    delims
-        String of delimiters to consider when finding the current key.
-    extra_prefix : optional
-        Part of the text already typed in multi-key index cases. E.g. for
-        `mydict['foo', "bar", 'b`, this would be `('foo', 'bar')`.
-
-    Returns
-    -------
-    A tuple of three elements: ``quote``, ``token_start``, ``matched``, with
-    ``quote`` being the quote that need to be used to close current string.
-    ``token_start`` the position where the replacement should start occurring,
-    ``matches`` a dictionary of replacement/completion keys on keys and values
-        indicating whether the state.
-    """
-    prefix_tuple = extra_prefix if extra_prefix else ()
-
-    prefix_tuple_size = sum(
-        [
-            # for pandas, do not count slices as taking space
-            not isinstance(k, slice)
-            for k in prefix_tuple
-        ]
-    )
-    text_serializable_types = (str, bytes, int, float, slice)
-
-    def filter_prefix_tuple(key):
-        # Reject too short keys
-        if len(key) <= prefix_tuple_size:
-            return False
-        # Reject keys which cannot be serialised to text
-        for k in key:
-            if not isinstance(k, text_serializable_types):
-                return False
-        # Reject keys that do not match the prefix
-        for k, pt in zip(key, prefix_tuple):
-            if k != pt and not isinstance(pt, slice):
-                return False
-        # All checks passed!
-        return True
-
-    filtered_key_is_final: Dict[
-        Union[str, bytes, int, float], _DictKeyState
-    ] = defaultdict(lambda: _DictKeyState.BASELINE)
-
-    for k in keys:
-        # If at least one of the matches is not final, mark as undetermined.
-        # This can happen with `d = {111: 'b', (111, 222): 'a'}` where
-        # `111` appears final on first match but is not final on the second.
-
-        if isinstance(k, tuple):
-            if filter_prefix_tuple(k):
-                key_fragment = k[prefix_tuple_size]
-                filtered_key_is_final[key_fragment] |= (
-                    _DictKeyState.END_OF_TUPLE
-                    if len(k) == prefix_tuple_size + 1
-                    else _DictKeyState.IN_TUPLE
-                )
-        elif prefix_tuple_size > 0:
-            # we are completing a tuple but this key is not a tuple,
-            # so we should ignore it
-            pass
-        else:
-            if isinstance(k, text_serializable_types):
-                filtered_key_is_final[k] |= _DictKeyState.END_OF_ITEM
-
-    filtered_keys = filtered_key_is_final.keys()
-
-    if not prefix:
-        return "", 0, {repr(k): v for k, v in filtered_key_is_final.items()}
-
-    quote_match = re.search("(?:\"|')", prefix)
-    is_user_prefix_numeric = False
-
-    if quote_match:
-        quote = quote_match.group()
-        valid_prefix = prefix + quote
-        try:
-            prefix_str = literal_eval(valid_prefix)
-        except Exception:
-            return "", 0, {}
-    else:
-        # If it does not look like a string, let's assume
-        # we are dealing with a number or variable.
-        number_match = _match_number_in_dict_key_prefix(prefix)
-
-        # We do not want the key matcher to suggest variable names so we yield:
-        if number_match is None:
-            # The alternative would be to assume that user forgort the quote
-            # and if the substring matches, suggest adding it at the start.
-            return "", 0, {}
-
-        prefix_str = number_match
-        is_user_prefix_numeric = True
-        quote = ""
-
-    pattern = '[^' + ''.join('\\' + c for c in delims) + ']*$'
-    token_match = re.search(pattern, prefix, re.UNICODE)
-    assert token_match is not None # silence mypy
-    token_start = token_match.start()
-    token_prefix = token_match.group()
-
-    matched: Dict[str, _DictKeyState] = {}
-
-    str_key: Union[str, bytes]
-
-    for key in filtered_keys:
-        if isinstance(key, (int, float)):
-            # User typed a number but this key is not a number.
-            if not is_user_prefix_numeric:
-                continue
-            str_key = str(key)
-            if isinstance(key, int):
-                int_base = prefix_str[:2].lower()
-                # if user typed integer using binary/oct/hex notation:
-                if int_base in _INT_FORMATS:
-                    int_format = _INT_FORMATS[int_base]
-                    str_key = int_format(key)
-        else:
-            # User typed a string but this key is a number.
-            if is_user_prefix_numeric:
-                continue
-            str_key = key
-        try:
-            if not str_key.startswith(prefix_str):
-                continue
-        except (AttributeError, TypeError, UnicodeError) as e:
-            # Python 3+ TypeError on b'a'.startswith('a') or vice-versa
-            continue
-
-        # reformat remainder of key to begin with prefix
-        rem = str_key[len(prefix_str) :]
-        # force repr wrapped in '
-        rem_repr = repr(rem + '"') if isinstance(rem, str) else repr(rem + b'"')
-        rem_repr = rem_repr[1 + rem_repr.index("'"):-2]
-        if quote == '"':
-            # The entered prefix is quoted with ",
-            # but the match is quoted with '.
-            # A contained " hence needs escaping for comparison:
-            rem_repr = rem_repr.replace('"', '\\"')
-
-        # then reinsert prefix from start of token
-        match = "%s%s" % (token_prefix, rem_repr)
-
-        matched[match] = filtered_key_is_final[key]
-    return quote, token_start, matched
-
-
-def cursor_to_position(text:str, line:int, column:int)->int:
-    """
-    Convert the (line,column) position of the cursor in text to an offset in a
-    string.
-
-    Parameters
-    ----------
-    text : str
-        The text in which to calculate the cursor offset
-    line : int
-        Line of the cursor; 0-indexed
-    column : int
-        Column of the cursor 0-indexed
-
-    Returns
-    -------
-    Position of the cursor in ``text``, 0-indexed.
-
-    See Also
-    --------
-    position_to_cursor : reciprocal of this function
-
-    """
-    lines = text.split('\n')
-    assert line <= len(lines), '{} <= {}'.format(str(line), str(len(lines)))
-
-    return sum(len(l) + 1 for l in lines[:line]) + column
-
-def position_to_cursor(text:str, offset:int)->Tuple[int, int]:
-    """
-    Convert the position of the cursor in text (0 indexed) to a line
-    number(0-indexed) and a column number (0-indexed) pair
-
-    Position should be a valid position in ``text``.
-
-    Parameters
-    ----------
-    text : str
-        The text in which to calculate the cursor offset
-    offset : int
-        Position of the cursor in ``text``, 0-indexed.
-
-    Returns
-    -------
-    (line, column) : (int, int)
-        Line of the cursor; 0-indexed, column of the cursor 0-indexed
-
-    See Also
-    --------
-    cursor_to_position : reciprocal of this function
-
-    """
-
-    assert 0 <= offset <= len(text) , "0 <= %s <= %s" % (offset , len(text))
-
-    before = text[:offset]
-    blines = before.split('\n')  # ! splitnes trim trailing \n
-    line = before.count('\n')
-    col = len(blines[-1])
-    return line, col
-
-
-def _safe_isinstance(obj, module, class_name, *attrs):
-    """Checks if obj is an instance of module.class_name if loaded
-    """
-    if module in sys.modules:
-        m = sys.modules[module]
-        for attr in [class_name, *attrs]:
-            m = getattr(m, attr)
-        return isinstance(obj, m)
-
-
-@context_matcher()
-def back_unicode_name_matcher(context: CompletionContext):
-    """Match Unicode characters back to Unicode name
-
-    Same as :any:`back_unicode_name_matches`, but adopted to new Matcher API.
-    """
-    fragment, matches = back_unicode_name_matches(context.text_until_cursor)
-    return _convert_matcher_v1_result_to_v2(
-        matches, type="unicode", fragment=fragment, suppress_if_matches=True
-    )
-
-
-def back_unicode_name_matches(text: str) -> Tuple[str, Sequence[str]]:
-    """Match Unicode characters back to Unicode name
-
-    This does  ``☃`` -> ``\\snowman``
-
-    Note that snowman is not a valid python3 combining character but will be expanded.
-    Though it will not recombine back to the snowman character by the completion machinery.
-
-    This will not either back-complete standard sequences like \\n, \\b ...
-
-    .. deprecated:: 8.6
-        You can use :meth:`back_unicode_name_matcher` instead.
-
-    Returns
-    =======
-
-    Return a tuple with two elements:
-
-    - The Unicode character that was matched (preceded with a backslash), or
-        empty string,
-    - a sequence (of 1), name for the match Unicode character, preceded by
-        backslash, or empty if no match.
-    """
-    if len(text)<2:
-        return '', ()
-    maybe_slash = text[-2]
-    if maybe_slash != '\\':
-        return '', ()
-
-    char = text[-1]
-    # no expand on quote for completion in strings.
-    # nor backcomplete standard ascii keys
-    if char in string.ascii_letters or char in ('"',"'"):
-        return '', ()
-    try :
-        unic = unicodedata.name(char)
-        return '\\'+char,('\\'+unic,)
-    except KeyError:
-        pass
-    return '', ()
-
-
-@context_matcher()
-def back_latex_name_matcher(context: CompletionContext):
-    """Match latex characters back to unicode name
-
-    Same as :any:`back_latex_name_matches`, but adopted to new Matcher API.
-    """
-    fragment, matches = back_latex_name_matches(context.text_until_cursor)
-    return _convert_matcher_v1_result_to_v2(
-        matches, type="latex", fragment=fragment, suppress_if_matches=True
-    )
-
-
-def back_latex_name_matches(text: str) -> Tuple[str, Sequence[str]]:
-    """Match latex characters back to unicode name
-
-    This does ``\\ℵ`` -> ``\\aleph``
-
-    .. deprecated:: 8.6
-        You can use :meth:`back_latex_name_matcher` instead.
-    """
-    if len(text)<2:
-        return '', ()
-    maybe_slash = text[-2]
-    if maybe_slash != '\\':
-        return '', ()
-
-
-    char = text[-1]
-    # no expand on quote for completion in strings.
-    # nor backcomplete standard ascii keys
-    if char in string.ascii_letters or char in ('"',"'"):
-        return '', ()
-    try :
-        latex = reverse_latex_symbol[char]
-        # '\\' replace the \ as well
-        return '\\'+char,[latex]
-    except KeyError:
-        pass
-    return '', ()
-
-
-def _formatparamchildren(parameter) -> str:
-    """
-    Get parameter name and value from Jedi Private API
-
-    Jedi does not expose a simple way to get `param=value` from its API.
-
-    Parameters
-    ----------
-    parameter
-        Jedi's function `Param`
-
-    Returns
-    -------
-    A string like 'a', 'b=1', '*args', '**kwargs'
-
-    """
-    description = parameter.description
-    if not description.startswith('param '):
-        raise ValueError('Jedi function parameter description have change format.'
-                         'Expected "param ...", found %r".' % description)
-    return description[6:]
-
-def _make_signature(completion)-> str:
-    """
-    Make the signature from a jedi completion
-
-    Parameters
-    ----------
-    completion : jedi.Completion
-        object does not complete a function type
-
-    Returns
-    -------
-    a string consisting of the function signature, with the parenthesis but
-    without the function name. example:
-    `(a, *args, b=1, **kwargs)`
-
-    """
-
-    # it looks like this might work on jedi 0.17
-    if hasattr(completion, 'get_signatures'):
-        signatures = completion.get_signatures()
-        if not signatures:
-            return  '(?)'
-
-        c0 = completion.get_signatures()[0]
-        return '('+c0.to_string().split('(', maxsplit=1)[1]
-
-    return '(%s)'% ', '.join([f for f in (_formatparamchildren(p) for signature in completion.get_signatures()
-                                          for p in signature.defined_names()) if f])
-
-
-_CompleteResult = Dict[str, MatcherResult]
-
-
-DICT_MATCHER_REGEX = re.compile(
-    r"""(?x)
-(  # match dict-referring - or any get item object - expression
-    .+
-)
-\[   # open bracket
-\s*  # and optional whitespace
-# Capture any number of serializable objects (e.g. "a", "b", 'c')
-# and slices
-((?:(?:
-    (?: # closed string
-        [uUbB]?  # string prefix (r not handled)
-        (?:
-            '(?:[^']|(?<!\\)\\')*'
-        |
-            "(?:[^"]|(?<!\\)\\")*"
-        )
-    )
-    |
-        # capture integers and slices
-        (?:[-+]?\d+)?(?::(?:[-+]?\d+)?){0,2}
-    |
-        # integer in bin/hex/oct notation
-        0[bBxXoO]_?(?:\w|\d)+
-    )
-    \s*,\s*
-)*)
-((?:
-    (?: # unclosed string
-        [uUbB]?  # string prefix (r not handled)
-        (?:
-            '(?:[^']|(?<!\\)\\')*
-            |
-            "(?:[^"]|(?<!\\)\\")*
-        )
-    )
-    |
-        # unfinished integer
-        (?:[-+]?\d+)
-    |
-        # integer in bin/hex/oct notation
-        0[bBxXoO]_?(?:\w|\d)+
-    )
-)?
-$
-"""
-)
-
-
-def _convert_matcher_v1_result_to_v2(
-    matches: Sequence[str],
-    type: str,
-    fragment: Optional[str] = None,
-    suppress_if_matches: bool = False,
-) -> SimpleMatcherResult:
-    """Utility to help with transition"""
-    result = {
-        "completions": [SimpleCompletion(text=match, type=type) for match in matches],
-        "suppress": (True if matches else False) if suppress_if_matches else False,
-    }
-    if fragment is not None:
-        result["matched_fragment"] = fragment
-    return cast(SimpleMatcherResult, result)
-
-
-class IPCompleter(Completer):
-    """Extension of the completer class with IPython-specific features"""
-
-    @observe('greedy')
-    def _greedy_changed(self, change):
-        """update the splitter and readline delims when greedy is changed"""
-        if change["new"]:
-            self.evaluation = "unsafe"
-            self.auto_close_dict_keys = True
-            self.splitter.delims = GREEDY_DELIMS
-        else:
-            self.evaluation = "limited"
-            self.auto_close_dict_keys = False
-            self.splitter.delims = DELIMS
-
-    dict_keys_only = Bool(
-        False,
-        help="""
-        Whether to show dict key matches only.
-
-        (disables all matchers except for `IPCompleter.dict_key_matcher`).
-        """,
-    )
-
-    suppress_competing_matchers = UnionTrait(
-        [Bool(allow_none=True), DictTrait(Bool(None, allow_none=True))],
-        default_value=None,
-        help="""
-        Whether to suppress completions from other *Matchers*.
-
-        When set to ``None`` (default) the matchers will attempt to auto-detect
-        whether suppression of other matchers is desirable. For example, at
-        the beginning of a line followed by `%` we expect a magic completion
-        to be the only applicable option, and after ``my_dict['`` we usually
-        expect a completion with an existing dictionary key.
-
-        If you want to disable this heuristic and see completions from all matchers,
-        set ``IPCompleter.suppress_competing_matchers = False``.
-        To disable the heuristic for specific matchers provide a dictionary mapping:
-        ``IPCompleter.suppress_competing_matchers = {'IPCompleter.dict_key_matcher': False}``.
-
-        Set ``IPCompleter.suppress_competing_matchers = True`` to limit
-        completions to the set of matchers with the highest priority;
-        this is equivalent to ``IPCompleter.merge_completions`` and
-        can be beneficial for performance, but will sometimes omit relevant
-        candidates from matchers further down the priority list.
-        """,
-    ).tag(config=True)
-
-    merge_completions = Bool(
-        True,
-        help="""Whether to merge completion results into a single list
-
-        If False, only the completion results from the first non-empty
-        completer will be returned.
-
-        As of version 8.6.0, setting the value to ``False`` is an alias for:
-        ``IPCompleter.suppress_competing_matchers = True.``.
-        """,
-    ).tag(config=True)
-
-    disable_matchers = ListTrait(
-        Unicode(),
-        help="""List of matchers to disable.
-
-        The list should contain matcher identifiers (see :any:`completion_matcher`).
-        """,
-    ).tag(config=True)
-
-    omit__names = Enum(
-        (0, 1, 2),
-        default_value=2,
-        help="""Instruct the completer to omit private method names
-
-        Specifically, when completing on ``object.<tab>``.
-
-        When 2 [default]: all names that start with '_' will be excluded.
-
-        When 1: all 'magic' names (``__foo__``) will be excluded.
-
-        When 0: nothing will be excluded.
-        """
-    ).tag(config=True)
-    limit_to__all__ = Bool(False,
-        help="""
-        DEPRECATED as of version 5.0.
-
-        Instruct the completer to use __all__ for the completion
-
-        Specifically, when completing on ``object.<tab>``.
-
-        When True: only those names in obj.__all__ will be included.
-
-        When False [default]: the __all__ attribute is ignored
-        """,
-    ).tag(config=True)
-
-    profile_completions = Bool(
-        default_value=False,
-        help="If True, emit profiling data for completion subsystem using cProfile."
-    ).tag(config=True)
-
-    profiler_output_dir = Unicode(
-        default_value=".completion_profiles",
-        help="Template for path at which to output profile data for completions."
-    ).tag(config=True)
-
-    @observe('limit_to__all__')
-    def _limit_to_all_changed(self, change):
-        warnings.warn('`IPython.core.IPCompleter.limit_to__all__` configuration '
-            'value has been deprecated since IPython 5.0, will be made to have '
-            'no effects and then removed in future version of IPython.',
-            UserWarning)
-
-    def __init__(
-        self, shell=None, namespace=None, global_namespace=None, config=None, **kwargs
-    ):
-        """IPCompleter() -> completer
-
-        Return a completer object.
-
-        Parameters
-        ----------
-        shell
-            a pointer to the ipython shell itself.  This is needed
-            because this completer knows about magic functions, and those can
-            only be accessed via the ipython instance.
-        namespace : dict, optional
-            an optional dict where completions are performed.
-        global_namespace : dict, optional
-            secondary optional dict for completions, to
-            handle cases (such as IPython embedded inside functions) where
-            both Python scopes are visible.
-        config : Config
-            traitlet's config object
-        **kwargs
-            passed to super class unmodified.
-        """
-
-        self.magic_escape = ESC_MAGIC
-        self.splitter = CompletionSplitter()
-
-        # _greedy_changed() depends on splitter and readline being defined:
-        super().__init__(
-            namespace=namespace,
-            global_namespace=global_namespace,
-            config=config,
-            **kwargs,
-        )
-
-        # List where completion matches will be stored
-        self.matches = []
-        self.shell = shell
-        # Regexp to split filenames with spaces in them
-        self.space_name_re = re.compile(r'([^\\] )')
-        # Hold a local ref. to glob.glob for speed
-        self.glob = glob.glob
-
-        # Determine if we are running on 'dumb' terminals, like (X)Emacs
-        # buffers, to avoid completion problems.
-        term = os.environ.get('TERM','xterm')
-        self.dumb_terminal = term in ['dumb','emacs']
-
-        # Special handling of backslashes needed in win32 platforms
-        if sys.platform == "win32":
-            self.clean_glob = self._clean_glob_win32
-        else:
-            self.clean_glob = self._clean_glob
-
-        #regexp to parse docstring for function signature
-        self.docstring_sig_re = re.compile(r'^[\w|\s.]+\(([^)]*)\).*')
-        self.docstring_kwd_re = re.compile(r'[\s|\[]*(\w+)(?:\s*=\s*.*)')
-        #use this if positional argument name is also needed
-        #= re.compile(r'[\s|\[]*(\w+)(?:\s*=?\s*.*)')
-
-        self.magic_arg_matchers = [
-            self.magic_config_matcher,
-            self.magic_color_matcher,
-        ]
-
-        # This is set externally by InteractiveShell
-        self.custom_completers = None
-
-        # This is a list of names of unicode characters that can be completed
-        # into their corresponding unicode value. The list is large, so we
-        # lazily initialize it on first use. Consuming code should access this
-        # attribute through the `@unicode_names` property.
-        self._unicode_names = None
-
-        self._backslash_combining_matchers = [
-            self.latex_name_matcher,
-            self.unicode_name_matcher,
-            back_latex_name_matcher,
-            back_unicode_name_matcher,
-            self.fwd_unicode_matcher,
-        ]
-
-        if not self.backslash_combining_completions:
-            for matcher in self._backslash_combining_matchers:
-                self.disable_matchers.append(_get_matcher_id(matcher))
-
-        if not self.merge_completions:
-            self.suppress_competing_matchers = True
-
-    @property
-    def matchers(self) -> List[Matcher]:
-        """All active matcher routines for completion"""
-        if self.dict_keys_only:
-            return [self.dict_key_matcher]
-
-        if self.use_jedi:
-            return [
-                *self.custom_matchers,
-                *self._backslash_combining_matchers,
-                *self.magic_arg_matchers,
-                self.custom_completer_matcher,
-                self.magic_matcher,
-                self._jedi_matcher,
-                self.dict_key_matcher,
-                self.file_matcher,
-            ]
-        else:
-            return [
-                *self.custom_matchers,
-                *self._backslash_combining_matchers,
-                *self.magic_arg_matchers,
-                self.custom_completer_matcher,
-                self.dict_key_matcher,
-                # TODO: convert python_matches to v2 API
-                self.magic_matcher,
-                self.python_matches,
-                self.file_matcher,
-                self.python_func_kw_matcher,
-            ]
-
-    def all_completions(self, text:str) -> List[str]:
-        """
-        Wrapper around the completion methods for the benefit of emacs.
-        """
-        prefix = text.rpartition('.')[0]
-        with provisionalcompleter():
-            return ['.'.join([prefix, c.text]) if prefix and self.use_jedi else c.text
-                    for c in self.completions(text, len(text))]
-
-        return self.complete(text)[1]
-
-    def _clean_glob(self, text:str):
-        return self.glob("%s*" % text)
-
-    def _clean_glob_win32(self, text:str):
-        return [f.replace("\\","/")
-                for f in self.glob("%s*" % text)]
-
-    @context_matcher()
-    def file_matcher(self, context: CompletionContext) -> SimpleMatcherResult:
-        """Same as :any:`file_matches`, but adopted to new Matcher API."""
-        matches = self.file_matches(context.token)
-        # TODO: add a heuristic for suppressing (e.g. if it has OS-specific delimiter,
-        #  starts with `/home/`, `C:\`, etc)
-        return _convert_matcher_v1_result_to_v2(matches, type="path")
-
-    def file_matches(self, text: str) -> List[str]:
-        """Match filenames, expanding ~USER type strings.
-
-        Most of the seemingly convoluted logic in this completer is an
-        attempt to handle filenames with spaces in them.  And yet it's not
-        quite perfect, because Python's readline doesn't expose all of the
-        GNU readline details needed for this to be done correctly.
-
-        For a filename with a space in it, the printed completions will be
-        only the parts after what's already been typed (instead of the
-        full completions, as is normally done).  I don't think with the
-        current (as of Python 2.3) Python readline it's possible to do
-        better.
-
-        .. deprecated:: 8.6
-            You can use :meth:`file_matcher` instead.
-        """
-
-        # chars that require escaping with backslash - i.e. chars
-        # that readline treats incorrectly as delimiters, but we
-        # don't want to treat as delimiters in filename matching
-        # when escaped with backslash
-        if text.startswith('!'):
-            text = text[1:]
-            text_prefix = u'!'
-        else:
-            text_prefix = u''
-
-        text_until_cursor = self.text_until_cursor
-        # track strings with open quotes
-        open_quotes = has_open_quotes(text_until_cursor)
-
-        if '(' in text_until_cursor or '[' in text_until_cursor:
-            lsplit = text
-        else:
-            try:
-                # arg_split ~ shlex.split, but with unicode bugs fixed by us
-                lsplit = arg_split(text_until_cursor)[-1]
-            except ValueError:
-                # typically an unmatched ", or backslash without escaped char.
-                if open_quotes:
-                    lsplit = text_until_cursor.split(open_quotes)[-1]
-                else:
-                    return []
-            except IndexError:
-                # tab pressed on empty line
-                lsplit = ""
-
-        if not open_quotes and lsplit != protect_filename(lsplit):
-            # if protectables are found, do matching on the whole escaped name
-            has_protectables = True
-            text0,text = text,lsplit
-        else:
-            has_protectables = False
-            text = os.path.expanduser(text)
-
-        if text == "":
-            return [text_prefix + protect_filename(f) for f in self.glob("*")]
-
-        # Compute the matches from the filesystem
-        if sys.platform == 'win32':
-            m0 = self.clean_glob(text)
-        else:
-            m0 = self.clean_glob(text.replace('\\', ''))
-
-        if has_protectables:
-            # If we had protectables, we need to revert our changes to the
-            # beginning of filename so that we don't double-write the part
-            # of the filename we have so far
-            len_lsplit = len(lsplit)
-            matches = [text_prefix + text0 +
-                       protect_filename(f[len_lsplit:]) for f in m0]
-        else:
-            if open_quotes:
-                # if we have a string with an open quote, we don't need to
-                # protect the names beyond the quote (and we _shouldn't_, as
-                # it would cause bugs when the filesystem call is made).
-                matches = m0 if sys.platform == "win32" else\
-                    [protect_filename(f, open_quotes) for f in m0]
-            else:
-                matches = [text_prefix +
-                           protect_filename(f) for f in m0]
-
-        # Mark directories in input list by appending '/' to their names.
-        return [x+'/' if os.path.isdir(x) else x for x in matches]
-
-    @context_matcher()
-    def magic_matcher(self, context: CompletionContext) -> SimpleMatcherResult:
-        """Match magics."""
-        text = context.token
-        matches = self.magic_matches(text)
-        result = _convert_matcher_v1_result_to_v2(matches, type="magic")
-        is_magic_prefix = len(text) > 0 and text[0] == "%"
-        result["suppress"] = is_magic_prefix and bool(result["completions"])
-        return result
-
-    def magic_matches(self, text: str):
-        """Match magics.
-
-        .. deprecated:: 8.6
-            You can use :meth:`magic_matcher` instead.
-        """
-        # Get all shell magics now rather than statically, so magics loaded at
-        # runtime show up too.
-        lsm = self.shell.magics_manager.lsmagic()
-        line_magics = lsm['line']
-        cell_magics = lsm['cell']
-        pre = self.magic_escape
-        pre2 = pre+pre
-
-        explicit_magic = text.startswith(pre)
-
-        # Completion logic:
-        # - user gives %%: only do cell magics
-        # - user gives %: do both line and cell magics
-        # - no prefix: do both
-        # In other words, line magics are skipped if the user gives %% explicitly
-        #
-        # We also exclude magics that match any currently visible names:
-        # https://github.com/ipython/ipython/issues/4877, unless the user has
-        # typed a %:
-        # https://github.com/ipython/ipython/issues/10754
-        bare_text = text.lstrip(pre)
-        global_matches = self.global_matches(bare_text)
-        if not explicit_magic:
-            def matches(magic):
-                """
-                Filter magics, in particular remove magics that match
-                a name present in global namespace.
-                """
-                return ( magic.startswith(bare_text) and
-                         magic not in global_matches )
-        else:
-            def matches(magic):
-                return magic.startswith(bare_text)
-
-        comp = [ pre2+m for m in cell_magics if matches(m)]
-        if not text.startswith(pre2):
-            comp += [ pre+m for m in line_magics if matches(m)]
-
-        return comp
-
-    @context_matcher()
-    def magic_config_matcher(self, context: CompletionContext) -> SimpleMatcherResult:
-        """Match class names and attributes for %config magic."""
-        # NOTE: uses `line_buffer` equivalent for compatibility
-        matches = self.magic_config_matches(context.line_with_cursor)
-        return _convert_matcher_v1_result_to_v2(matches, type="param")
-
-    def magic_config_matches(self, text: str) -> List[str]:
-        """Match class names and attributes for %config magic.
-
-        .. deprecated:: 8.6
-            You can use :meth:`magic_config_matcher` instead.
-        """
-        texts = text.strip().split()
-
-        if len(texts) > 0 and (texts[0] == 'config' or texts[0] == '%config'):
-            # get all configuration classes
-            classes = sorted(set([ c for c in self.shell.configurables
-                                   if c.__class__.class_traits(config=True)
-                                   ]), key=lambda x: x.__class__.__name__)
-            classnames = [ c.__class__.__name__ for c in classes ]
-
-            # return all classnames if config or %config is given
-            if len(texts) == 1:
-                return classnames
-
-            # match classname
-            classname_texts = texts[1].split('.')
-            classname = classname_texts[0]
-            classname_matches = [ c for c in classnames
-                                  if c.startswith(classname) ]
-
-            # return matched classes or the matched class with attributes
-            if texts[1].find('.') < 0:
-                return classname_matches
-            elif len(classname_matches) == 1 and \
-                            classname_matches[0] == classname:
-                cls = classes[classnames.index(classname)].__class__
-                help = cls.class_get_help()
-                # strip leading '--' from cl-args:
-                help = re.sub(re.compile(r'^--', re.MULTILINE), '', help)
-                return [ attr.split('=')[0]
-                         for attr in help.strip().splitlines()
-                         if attr.startswith(texts[1]) ]
-        return []
-
-    @context_matcher()
-    def magic_color_matcher(self, context: CompletionContext) -> SimpleMatcherResult:
-        """Match color schemes for %colors magic."""
-        # NOTE: uses `line_buffer` equivalent for compatibility
-        matches = self.magic_color_matches(context.line_with_cursor)
-        return _convert_matcher_v1_result_to_v2(matches, type="param")
-
-    def magic_color_matches(self, text: str) -> List[str]:
-        """Match color schemes for %colors magic.
-
-        .. deprecated:: 8.6
-            You can use :meth:`magic_color_matcher` instead.
-        """
-        texts = text.split()
-        if text.endswith(' '):
-            # .split() strips off the trailing whitespace. Add '' back
-            # so that: '%colors ' -> ['%colors', '']
-            texts.append('')
-
-        if len(texts) == 2 and (texts[0] == 'colors' or texts[0] == '%colors'):
-            prefix = texts[1]
-            return [ color for color in InspectColors.keys()
-                     if color.startswith(prefix) ]
-        return []
-
-    @context_matcher(identifier="IPCompleter.jedi_matcher")
-    def _jedi_matcher(self, context: CompletionContext) -> _JediMatcherResult:
-        matches = self._jedi_matches(
-            cursor_column=context.cursor_position,
-            cursor_line=context.cursor_line,
-            text=context.full_text,
-        )
-        return {
-            "completions": matches,
-            # static analysis should not suppress other matchers
-            "suppress": False,
-        }
-
-    def _jedi_matches(
-        self, cursor_column: int, cursor_line: int, text: str
-    ) -> Iterator[_JediCompletionLike]:
-        """
-        Return a list of :any:`jedi.api.Completion`s object from a ``text`` and
-        cursor position.
-
-        Parameters
-        ----------
-        cursor_column : int
-            column position of the cursor in ``text``, 0-indexed.
-        cursor_line : int
-            line position of the cursor in ``text``, 0-indexed
-        text : str
-            text to complete
-
-        Notes
-        -----
-        If ``IPCompleter.debug`` is ``True`` may return a :any:`_FakeJediCompletion`
-        object containing a string with the Jedi debug information attached.
-
-        .. deprecated:: 8.6
-            You can use :meth:`_jedi_matcher` instead.
-        """
-        namespaces = [self.namespace]
-        if self.global_namespace is not None:
-            namespaces.append(self.global_namespace)
-
-        completion_filter = lambda x:x
-        offset = cursor_to_position(text, cursor_line, cursor_column)
-        # filter output if we are completing for object members
-        if offset:
-            pre = text[offset-1]
-            if pre == '.':
-                if self.omit__names == 2:
-                    completion_filter = lambda c:not c.name.startswith('_')
-                elif self.omit__names == 1:
-                    completion_filter = lambda c:not (c.name.startswith('__') and c.name.endswith('__'))
-                elif self.omit__names == 0:
-                    completion_filter = lambda x:x
-                else:
-                    raise ValueError("Don't understand self.omit__names == {}".format(self.omit__names))
-
-        interpreter = jedi.Interpreter(text[:offset], namespaces)
-        try_jedi = True
-
-        try:
-            # find the first token in the current tree -- if it is a ' or " then we are in a string
-            completing_string = False
-            try:
-                first_child = next(c for c in interpreter._get_module().tree_node.children if hasattr(c, 'value'))
-            except StopIteration:
-                pass
-            else:
-                # note the value may be ', ", or it may also be ''' or """, or
-                # in some cases, """what/you/typed..., but all of these are
-                # strings.
-                completing_string = len(first_child.value) > 0 and first_child.value[0] in {"'", '"'}
-
-            # if we are in a string jedi is likely not the right candidate for
-            # now. Skip it.
-            try_jedi = not completing_string
-        except Exception as e:
-            # many of things can go wrong, we are using private API just don't crash.
-            if self.debug:
-                print("Error detecting if completing a non-finished string :", e, '|')
-
-        if not try_jedi:
-            return iter([])
-        try:
-            return filter(completion_filter, interpreter.complete(column=cursor_column, line=cursor_line + 1))
-        except Exception as e:
-            if self.debug:
-                return iter(
-                    [
-                        _FakeJediCompletion(
-                            'Oops Jedi has crashed, please report a bug with the following:\n"""\n%s\ns"""'
-                            % (e)
-                        )
-                    ]
-                )
-            else:
-                return iter([])
-
-    @completion_matcher(api_version=1)
-    def python_matches(self, text: str) -> Iterable[str]:
-        """Match attributes or global python names"""
-        if "." in text:
-            try:
-                matches = self.attr_matches(text)
-                if text.endswith('.') and self.omit__names:
-                    if self.omit__names == 1:
-                        # true if txt is _not_ a __ name, false otherwise:
-                        no__name = (lambda txt:
-                                    re.match(r'.*\.__.*?__',txt) is None)
-                    else:
-                        # true if txt is _not_ a _ name, false otherwise:
-                        no__name = (lambda txt:
-                                    re.match(r'\._.*?',txt[txt.rindex('.'):]) is None)
-                    matches = filter(no__name, matches)
-            except NameError:
-                # catches <undefined attributes>.<tab>
-                matches = []
-        else:
-            matches = self.global_matches(text)
-        return matches
-
-    def _default_arguments_from_docstring(self, doc):
-        """Parse the first line of docstring for call signature.
-
-        Docstring should be of the form 'min(iterable[, key=func])\n'.
-        It can also parse cython docstring of the form
-        'Minuit.migrad(self, int ncall=10000, resume=True, int nsplit=1)'.
-        """
-        if doc is None:
-            return []
-
-        #care only the firstline
-        line = doc.lstrip().splitlines()[0]
-
-        #p = re.compile(r'^[\w|\s.]+\(([^)]*)\).*')
-        #'min(iterable[, key=func])\n' -> 'iterable[, key=func]'
-        sig = self.docstring_sig_re.search(line)
-        if sig is None:
-            return []
-        # iterable[, key=func]' -> ['iterable[' ,' key=func]']
-        sig = sig.groups()[0].split(',')
-        ret = []
-        for s in sig:
-            #re.compile(r'[\s|\[]*(\w+)(?:\s*=\s*.*)')
-            ret += self.docstring_kwd_re.findall(s)
-        return ret
-
-    def _default_arguments(self, obj):
-        """Return the list of default arguments of obj if it is callable,
-        or empty list otherwise."""
-        call_obj = obj
-        ret = []
-        if inspect.isbuiltin(obj):
-            pass
-        elif not (inspect.isfunction(obj) or inspect.ismethod(obj)):
-            if inspect.isclass(obj):
-                #for cython embedsignature=True the constructor docstring
-                #belongs to the object itself not __init__
-                ret += self._default_arguments_from_docstring(
-                            getattr(obj, '__doc__', ''))
-                # for classes, check for __init__,__new__
-                call_obj = (getattr(obj, '__init__', None) or
-                       getattr(obj, '__new__', None))
-            # for all others, check if they are __call__able
-            elif hasattr(obj, '__call__'):
-                call_obj = obj.__call__
-        ret += self._default_arguments_from_docstring(
-                 getattr(call_obj, '__doc__', ''))
-
-        _keeps = (inspect.Parameter.KEYWORD_ONLY,
-                  inspect.Parameter.POSITIONAL_OR_KEYWORD)
-
-        try:
-            sig = inspect.signature(obj)
-            ret.extend(k for k, v in sig.parameters.items() if
-                       v.kind in _keeps)
-        except ValueError:
-            pass
-
-        return list(set(ret))
-
-    @context_matcher()
-    def python_func_kw_matcher(self, context: CompletionContext) -> SimpleMatcherResult:
-        """Match named parameters (kwargs) of the last open function."""
-        matches = self.python_func_kw_matches(context.token)
-        return _convert_matcher_v1_result_to_v2(matches, type="param")
-
-    def python_func_kw_matches(self, text):
-        """Match named parameters (kwargs) of the last open function.
-
-        .. deprecated:: 8.6
-            You can use :meth:`python_func_kw_matcher` instead.
-        """
-
-        if "." in text: # a parameter cannot be dotted
-            return []
-        try: regexp = self.__funcParamsRegex
-        except AttributeError:
-            regexp = self.__funcParamsRegex = re.compile(r'''
-                '.*?(?<!\\)' |    # single quoted strings or
-                ".*?(?<!\\)" |    # double quoted strings or
-                \w+          |    # identifier
-                \S                # other characters
-                ''', re.VERBOSE | re.DOTALL)
-        # 1. find the nearest identifier that comes before an unclosed
-        # parenthesis before the cursor
-        # e.g. for "foo (1+bar(x), pa<cursor>,a=1)", the candidate is "foo"
-        tokens = regexp.findall(self.text_until_cursor)
-        iterTokens = reversed(tokens); openPar = 0
-
-        for token in iterTokens:
-            if token == ')':
-                openPar -= 1
-            elif token == '(':
-                openPar += 1
-                if openPar > 0:
-                    # found the last unclosed parenthesis
-                    break
-        else:
-            return []
-        # 2. Concatenate dotted names ("foo.bar" for "foo.bar(x, pa" )
-        ids = []
-        isId = re.compile(r'\w+$').match
-
-        while True:
-            try:
-                ids.append(next(iterTokens))
-                if not isId(ids[-1]):
-                    ids.pop(); break
-                if not next(iterTokens) == '.':
-                    break
-            except StopIteration:
-                break
-
-        # Find all named arguments already assigned to, as to avoid suggesting
-        # them again
-        usedNamedArgs = set()
-        par_level = -1
-        for token, next_token in zip(tokens, tokens[1:]):
-            if token == '(':
-                par_level += 1
-            elif token == ')':
-                par_level -= 1
-
-            if par_level != 0:
-                continue
-
-            if next_token != '=':
-                continue
-
-            usedNamedArgs.add(token)
-
-        argMatches = []
-        try:
-            callableObj = '.'.join(ids[::-1])
-            namedArgs = self._default_arguments(eval(callableObj,
-                                                    self.namespace))
-
-            # Remove used named arguments from the list, no need to show twice
-            for namedArg in set(namedArgs) - usedNamedArgs:
-                if namedArg.startswith(text):
-                    argMatches.append("%s=" %namedArg)
-        except:
-            pass
-
-        return argMatches
-
-    @staticmethod
-    def _get_keys(obj: Any) -> List[Any]:
-        # Objects can define their own completions by defining an
-        # _ipy_key_completions_() method.
-        method = get_real_method(obj, '_ipython_key_completions_')
-        if method is not None:
-            return method()
-
-        # Special case some common in-memory dict-like types
-        if isinstance(obj, dict) or _safe_isinstance(obj, "pandas", "DataFrame"):
-            try:
-                return list(obj.keys())
-            except Exception:
-                return []
-        elif _safe_isinstance(obj, "pandas", "core", "indexing", "_LocIndexer"):
-            try:
-                return list(obj.obj.keys())
-            except Exception:
-                return []
-        elif _safe_isinstance(obj, 'numpy', 'ndarray') or\
-             _safe_isinstance(obj, 'numpy', 'void'):
-            return obj.dtype.names or []
-        return []
-
-    @context_matcher()
-    def dict_key_matcher(self, context: CompletionContext) -> SimpleMatcherResult:
-        """Match string keys in a dictionary, after e.g. ``foo[``."""
-        matches = self.dict_key_matches(context.token)
-        return _convert_matcher_v1_result_to_v2(
-            matches, type="dict key", suppress_if_matches=True
-        )
-
-    def dict_key_matches(self, text: str) -> List[str]:
-        """Match string keys in a dictionary, after e.g. ``foo[``.
-
-        .. deprecated:: 8.6
-            You can use :meth:`dict_key_matcher` instead.
-        """
-
-        # Short-circuit on closed dictionary (regular expression would
-        # not match anyway, but would take quite a while).
-        if self.text_until_cursor.strip().endswith("]"):
-            return []
-
-        match = DICT_MATCHER_REGEX.search(self.text_until_cursor)
-
-        if match is None:
-            return []
-
-        expr, prior_tuple_keys, key_prefix = match.groups()
-
-        obj = self._evaluate_expr(expr)
-
-        if obj is not_found:
-            return []
-
-        keys = self._get_keys(obj)
-        if not keys:
-            return keys
-
-        tuple_prefix = guarded_eval(
-            prior_tuple_keys,
-            EvaluationContext(
-                globals=self.global_namespace,
-                locals=self.namespace,
-                evaluation=self.evaluation,
-                in_subscript=True,
-            ),
-        )
-
-        closing_quote, token_offset, matches = match_dict_keys(
-            keys, key_prefix, self.splitter.delims, extra_prefix=tuple_prefix
-        )
-        if not matches:
-            return []
-
-        # get the cursor position of
-        # - the text being completed
-        # - the start of the key text
-        # - the start of the completion
-        text_start = len(self.text_until_cursor) - len(text)
-        if key_prefix:
-            key_start = match.start(3)
-            completion_start = key_start + token_offset
-        else:
-            key_start = completion_start = match.end()
-
-        # grab the leading prefix, to make sure all completions start with `text`
-        if text_start > key_start:
-            leading = ''
-        else:
-            leading = text[text_start:completion_start]
-
-        # append closing quote and bracket as appropriate
-        # this is *not* appropriate if the opening quote or bracket is outside
-        # the text given to this method, e.g. `d["""a\nt
-        can_close_quote = False
-        can_close_bracket = False
-
-        continuation = self.line_buffer[len(self.text_until_cursor) :].strip()
-
-        if continuation.startswith(closing_quote):
-            # do not close if already closed, e.g. `d['a<tab>'`
-            continuation = continuation[len(closing_quote) :]
-        else:
-            can_close_quote = True
-
-        continuation = continuation.strip()
-
-        # e.g. `pandas.DataFrame` has different tuple indexer behaviour,
-        # handling it is out of scope, so let's avoid appending suffixes.
-        has_known_tuple_handling = isinstance(obj, dict)
-
-        can_close_bracket = (
-            not continuation.startswith("]") and self.auto_close_dict_keys
-        )
-        can_close_tuple_item = (
-            not continuation.startswith(",")
-            and has_known_tuple_handling
-            and self.auto_close_dict_keys
-        )
-        can_close_quote = can_close_quote and self.auto_close_dict_keys
-
-        # fast path if closing qoute should be appended but not suffix is allowed
-        if not can_close_quote and not can_close_bracket and closing_quote:
-            return [leading + k for k in matches]
-
-        results = []
-
-        end_of_tuple_or_item = _DictKeyState.END_OF_TUPLE | _DictKeyState.END_OF_ITEM
-
-        for k, state_flag in matches.items():
-            result = leading + k
-            if can_close_quote and closing_quote:
-                result += closing_quote
-
-            if state_flag == end_of_tuple_or_item:
-                # We do not know which suffix to add,
-                # e.g. both tuple item and string
-                # match this item.
-                pass
-
-            if state_flag in end_of_tuple_or_item and can_close_bracket:
-                result += "]"
-            if state_flag == _DictKeyState.IN_TUPLE and can_close_tuple_item:
-                result += ", "
-            results.append(result)
-        return results
-
-    @context_matcher()
-    def unicode_name_matcher(self, context: CompletionContext):
-        """Same as :any:`unicode_name_matches`, but adopted to new Matcher API."""
-        fragment, matches = self.unicode_name_matches(context.text_until_cursor)
-        return _convert_matcher_v1_result_to_v2(
-            matches, type="unicode", fragment=fragment, suppress_if_matches=True
-        )
-
-    @staticmethod
-    def unicode_name_matches(text: str) -> Tuple[str, List[str]]:
-        """Match Latex-like syntax for unicode characters base
-        on the name of the character.
-
-        This does  ``\\GREEK SMALL LETTER ETA`` -> ``η``
-
-        Works only on valid python 3 identifier, or on combining characters that
-        will combine to form a valid identifier.
-        """
-        slashpos = text.rfind('\\')
-        if slashpos > -1:
-            s = text[slashpos+1:]
-            try :
-                unic = unicodedata.lookup(s)
-                # allow combining chars
-                if ('a'+unic).isidentifier():
-                    return '\\'+s,[unic]
-            except KeyError:
-                pass
-        return '', []
-
-    @context_matcher()
-    def latex_name_matcher(self, context: CompletionContext):
-        """Match Latex syntax for unicode characters.
-
-        This does both ``\\alp`` -> ``\\alpha`` and ``\\alpha`` -> ``α``
-        """
-        fragment, matches = self.latex_matches(context.text_until_cursor)
-        return _convert_matcher_v1_result_to_v2(
-            matches, type="latex", fragment=fragment, suppress_if_matches=True
-        )
-
-    def latex_matches(self, text: str) -> Tuple[str, Sequence[str]]:
-        """Match Latex syntax for unicode characters.
-
-        This does both ``\\alp`` -> ``\\alpha`` and ``\\alpha`` -> ``α``
-
-        .. deprecated:: 8.6
-            You can use :meth:`latex_name_matcher` instead.
-        """
-        slashpos = text.rfind('\\')
-        if slashpos > -1:
-            s = text[slashpos:]
-            if s in latex_symbols:
-                # Try to complete a full latex symbol to unicode
-                # \\alpha -> α
-                return s, [latex_symbols[s]]
-            else:
-                # If a user has partially typed a latex symbol, give them
-                # a full list of options \al -> [\aleph, \alpha]
-                matches = [k for k in latex_symbols if k.startswith(s)]
-                if matches:
-                    return s, matches
-        return '', ()
-
-    @context_matcher()
-    def custom_completer_matcher(self, context):
-        """Dispatch custom completer.
-
-        If a match is found, suppresses all other matchers except for Jedi.
-        """
-        matches = self.dispatch_custom_completer(context.token) or []
-        result = _convert_matcher_v1_result_to_v2(
-            matches, type=_UNKNOWN_TYPE, suppress_if_matches=True
-        )
-        result["ordered"] = True
-        result["do_not_suppress"] = {_get_matcher_id(self._jedi_matcher)}
-        return result
-
-    def dispatch_custom_completer(self, text):
-        """
-        .. deprecated:: 8.6
-            You can use :meth:`custom_completer_matcher` instead.
-        """
-        if not self.custom_completers:
-            return
-
-        line = self.line_buffer
-        if not line.strip():
-            return None
-
-        # Create a little structure to pass all the relevant information about
-        # the current completion to any custom completer.
-        event = SimpleNamespace()
-        event.line = line
-        event.symbol = text
-        cmd = line.split(None,1)[0]
-        event.command = cmd
-        event.text_until_cursor = self.text_until_cursor
-
-        # for foo etc, try also to find completer for %foo
-        if not cmd.startswith(self.magic_escape):
-            try_magic = self.custom_completers.s_matches(
-                self.magic_escape + cmd)
-        else:
-            try_magic = []
-
-        for c in itertools.chain(self.custom_completers.s_matches(cmd),
-                 try_magic,
-                 self.custom_completers.flat_matches(self.text_until_cursor)):
-            try:
-                res = c(event)
-                if res:
-                    # first, try case sensitive match
-                    withcase = [r for r in res if r.startswith(text)]
-                    if withcase:
-                        return withcase
-                    # if none, then case insensitive ones are ok too
-                    text_low = text.lower()
-                    return [r for r in res if r.lower().startswith(text_low)]
-            except TryNext:
-                pass
-            except KeyboardInterrupt:
-                """
-                If custom completer take too long,
-                let keyboard interrupt abort and return nothing.
-                """
-                break
-
-        return None
-
-    def completions(self, text: str, offset: int)->Iterator[Completion]:
-        """
-        Returns an iterator over the possible completions
-
-        .. warning::
-
-            Unstable
-
-            This function is unstable, API may change without warning.
-            It will also raise unless use in proper context manager.
-
-        Parameters
-        ----------
-        text : str
-            Full text of the current input, multi line string.
-        offset : int
-            Integer representing the position of the cursor in ``text``. Offset
-            is 0-based indexed.
-
-        Yields
-        ------
-        Completion
-
-        Notes
-        -----
-        The cursor on a text can either be seen as being "in between"
-        characters or "On" a character depending on the interface visible to
-        the user. For consistency the cursor being on "in between" characters X
-        and Y is equivalent to the cursor being "on" character Y, that is to say
-        the character the cursor is on is considered as being after the cursor.
-
-        Combining characters may span more that one position in the
-        text.
-
-        .. note::
-
-            If ``IPCompleter.debug`` is :any:`True` will yield a ``--jedi/ipython--``
-            fake Completion token to distinguish completion returned by Jedi
-            and usual IPython completion.
-
-        .. note::
-
-            Completions are not completely deduplicated yet. If identical
-            completions are coming from different sources this function does not
-            ensure that each completion object will only be present once.
-        """
-        warnings.warn("_complete is a provisional API (as of IPython 6.0). "
-                      "It may change without warnings. "
-                      "Use in corresponding context manager.",
-                      category=ProvisionalCompleterWarning, stacklevel=2)
-
-        seen = set()
-        profiler:Optional[cProfile.Profile]
-        try:
-            if self.profile_completions:
-                import cProfile
-                profiler = cProfile.Profile()
-                profiler.enable()
-            else:
-                profiler = None
-
-            for c in self._completions(text, offset, _timeout=self.jedi_compute_type_timeout/1000):
-                if c and (c in seen):
-                    continue
-                yield c
-                seen.add(c)
-        except KeyboardInterrupt:
-            """if completions take too long and users send keyboard interrupt,
-            do not crash and return ASAP. """
-            pass
-        finally:
-            if profiler is not None:
-                profiler.disable()
-                ensure_dir_exists(self.profiler_output_dir)
-                output_path = os.path.join(self.profiler_output_dir, str(uuid.uuid4()))
-                print("Writing profiler output to", output_path)
-                profiler.dump_stats(output_path)
-
-    def _completions(self, full_text: str, offset: int, *, _timeout) -> Iterator[Completion]:
-        """
-        Core completion module.Same signature as :any:`completions`, with the
-        extra `timeout` parameter (in seconds).
-
-        Computing jedi's completion ``.type`` can be quite expensive (it is a
-        lazy property) and can require some warm-up, more warm up than just
-        computing the ``name`` of a completion. The warm-up can be :
-
-            - Long warm-up the first time a module is encountered after
-            install/update: actually build parse/inference tree.
-
-            - first time the module is encountered in a session: load tree from
-            disk.
-
-        We don't want to block completions for tens of seconds so we give the
-        completer a "budget" of ``_timeout`` seconds per invocation to compute
-        completions types, the completions that have not yet been computed will
-        be marked as "unknown" an will have a chance to be computed next round
-        are things get cached.
-
-        Keep in mind that Jedi is not the only thing treating the completion so
-        keep the timeout short-ish as if we take more than 0.3 second we still
-        have lots of processing to do.
-
-        """
-        deadline = time.monotonic() + _timeout
-
-        before = full_text[:offset]
-        cursor_line, cursor_column = position_to_cursor(full_text, offset)
-
-        jedi_matcher_id = _get_matcher_id(self._jedi_matcher)
-
-        def is_non_jedi_result(
-            result: MatcherResult, identifier: str
-        ) -> TypeGuard[SimpleMatcherResult]:
-            return identifier != jedi_matcher_id
-
-        results = self._complete(
-            full_text=full_text, cursor_line=cursor_line, cursor_pos=cursor_column
-        )
-
-        non_jedi_results: Dict[str, SimpleMatcherResult] = {
-            identifier: result
-            for identifier, result in results.items()
-            if is_non_jedi_result(result, identifier)
-        }
-
-        jedi_matches = (
-            cast(_JediMatcherResult, results[jedi_matcher_id])["completions"]
-            if jedi_matcher_id in results
-            else ()
-        )
-
-        iter_jm = iter(jedi_matches)
-        if _timeout:
-            for jm in iter_jm:
-                try:
-                    type_ = jm.type
-                except Exception:
-                    if self.debug:
-                        print("Error in Jedi getting type of ", jm)
-                    type_ = None
-                delta = len(jm.name_with_symbols) - len(jm.complete)
-                if type_ == 'function':
-                    signature = _make_signature(jm)
-                else:
-                    signature = ''
-                yield Completion(start=offset - delta,
-                                 end=offset,
-                                 text=jm.name_with_symbols,
-                                 type=type_,
-                                 signature=signature,
-                                 _origin='jedi')
-
-                if time.monotonic() > deadline:
-                    break
-
-        for jm in iter_jm:
-            delta = len(jm.name_with_symbols) - len(jm.complete)
-            yield Completion(
-                start=offset - delta,
-                end=offset,
-                text=jm.name_with_symbols,
-                type=_UNKNOWN_TYPE,  # don't compute type for speed
-                _origin="jedi",
-                signature="",
-            )
-
-        # TODO:
-        # Suppress this, right now just for debug.
-        if jedi_matches and non_jedi_results and self.debug:
-            some_start_offset = before.rfind(
-                next(iter(non_jedi_results.values()))["matched_fragment"]
-            )
-            yield Completion(
-                start=some_start_offset,
-                end=offset,
-                text="--jedi/ipython--",
-                _origin="debug",
-                type="none",
-                signature="",
-            )
-
-        ordered: List[Completion] = []
-        sortable: List[Completion] = []
-
-        for origin, result in non_jedi_results.items():
-            matched_text = result["matched_fragment"]
-            start_offset = before.rfind(matched_text)
-            is_ordered = result.get("ordered", False)
-            container = ordered if is_ordered else sortable
-
-            # I'm unsure if this is always true, so let's assert and see if it
-            # crash
-            assert before.endswith(matched_text)
-
-            for simple_completion in result["completions"]:
-                completion = Completion(
-                    start=start_offset,
-                    end=offset,
-                    text=simple_completion.text,
-                    _origin=origin,
-                    signature="",
-                    type=simple_completion.type or _UNKNOWN_TYPE,
-                )
-                container.append(completion)
-
-        yield from list(self._deduplicate(ordered + self._sort(sortable)))[
-            :MATCHES_LIMIT
-        ]
-
-    def complete(self, text=None, line_buffer=None, cursor_pos=None) -> Tuple[str, Sequence[str]]:
-        """Find completions for the given text and line context.
-
-        Note that both the text and the line_buffer are optional, but at least
-        one of them must be given.
-
-        Parameters
-        ----------
-        text : string, optional
-            Text to perform the completion on.  If not given, the line buffer
-            is split using the instance's CompletionSplitter object.
-        line_buffer : string, optional
-            If not given, the completer attempts to obtain the current line
-            buffer via readline.  This keyword allows clients which are
-            requesting for text completions in non-readline contexts to inform
-            the completer of the entire text.
-        cursor_pos : int, optional
-            Index of the cursor in the full line buffer.  Should be provided by
-            remote frontends where kernel has no access to frontend state.
-
-        Returns
-        -------
-        Tuple of two items:
-        text : str
-            Text that was actually used in the completion.
-        matches : list
-            A list of completion matches.
-
-        Notes
-        -----
-            This API is likely to be deprecated and replaced by
-            :any:`IPCompleter.completions` in the future.
-
-        """
-        warnings.warn('`Completer.complete` is pending deprecation since '
-                'IPython 6.0 and will be replaced by `Completer.completions`.',
-                      PendingDeprecationWarning)
-        # potential todo, FOLD the 3rd throw away argument of _complete
-        # into the first 2 one.
-        # TODO: Q: does the above refer to jedi completions (i.e. 0-indexed?)
-        # TODO: should we deprecate now, or does it stay?
-
-        results = self._complete(
-            line_buffer=line_buffer, cursor_pos=cursor_pos, text=text, cursor_line=0
-        )
-
-        jedi_matcher_id = _get_matcher_id(self._jedi_matcher)
-
-        return self._arrange_and_extract(
-            results,
-            # TODO: can we confirm that excluding Jedi here was a deliberate choice in previous version?
-            skip_matchers={jedi_matcher_id},
-            # this API does not support different start/end positions (fragments of token).
-            abort_if_offset_changes=True,
-        )
-
-    def _arrange_and_extract(
-        self,
-        results: Dict[str, MatcherResult],
-        skip_matchers: Set[str],
-        abort_if_offset_changes: bool,
-    ):
-        sortable: List[AnyMatcherCompletion] = []
-        ordered: List[AnyMatcherCompletion] = []
-        most_recent_fragment = None
-        for identifier, result in results.items():
-            if identifier in skip_matchers:
-                continue
-            if not result["completions"]:
-                continue
-            if not most_recent_fragment:
-                most_recent_fragment = result["matched_fragment"]
-            if (
-                abort_if_offset_changes
-                and result["matched_fragment"] != most_recent_fragment
-            ):
-                break
-            if result.get("ordered", False):
-                ordered.extend(result["completions"])
-            else:
-                sortable.extend(result["completions"])
-
-        if not most_recent_fragment:
-            most_recent_fragment = ""  # to satisfy typechecker (and just in case)
-
-        return most_recent_fragment, [
-            m.text for m in self._deduplicate(ordered + self._sort(sortable))
-        ]
-
-    def _complete(self, *, cursor_line, cursor_pos, line_buffer=None, text=None,
-                  full_text=None) -> _CompleteResult:
-        """
-        Like complete but can also returns raw jedi completions as well as the
-        origin of the completion text. This could (and should) be made much
-        cleaner but that will be simpler once we drop the old (and stateful)
-        :any:`complete` API.
-
-        With current provisional API, cursor_pos act both (depending on the
-        caller) as the offset in the ``text`` or ``line_buffer``, or as the
-        ``column`` when passing multiline strings this could/should be renamed
-        but would add extra noise.
-
-        Parameters
-        ----------
-        cursor_line
-            Index of the line the cursor is on. 0 indexed.
-        cursor_pos
-            Position of the cursor in the current line/line_buffer/text. 0
-            indexed.
-        line_buffer : optional, str
-            The current line the cursor is in, this is mostly due to legacy
-            reason that readline could only give a us the single current line.
-            Prefer `full_text`.
-        text : str
-            The current "token" the cursor is in, mostly also for historical
-            reasons. as the completer would trigger only after the current line
-            was parsed.
-        full_text : str
-            Full text of the current cell.
-
-        Returns
-        -------
-        An ordered dictionary where keys are identifiers of completion
-        matchers and values are ``MatcherResult``s.
-        """
-
-        # if the cursor position isn't given, the only sane assumption we can
-        # make is that it's at the end of the line (the common case)
-        if cursor_pos is None:
-            cursor_pos = len(line_buffer) if text is None else len(text)
-
-        if self.use_main_ns:
-            self.namespace = __main__.__dict__
-
-        # if text is either None or an empty string, rely on the line buffer
-        if (not line_buffer) and full_text:
-            line_buffer = full_text.split('\n')[cursor_line]
-        if not text:  # issue #11508: check line_buffer before calling split_line
-            text = (
-                self.splitter.split_line(line_buffer, cursor_pos) if line_buffer else ""
-            )
-
-        # If no line buffer is given, assume the input text is all there was
-        if line_buffer is None:
-            line_buffer = text
-
-        # deprecated - do not use `line_buffer` in new code.
-        self.line_buffer = line_buffer
-        self.text_until_cursor = self.line_buffer[:cursor_pos]
-
-        if not full_text:
-            full_text = line_buffer
-
-        context = CompletionContext(
-            full_text=full_text,
-            cursor_position=cursor_pos,
-            cursor_line=cursor_line,
-            token=text,
-            limit=MATCHES_LIMIT,
-        )
-
-        # Start with a clean slate of completions
-        results: Dict[str, MatcherResult] = {}
-
-        jedi_matcher_id = _get_matcher_id(self._jedi_matcher)
-
-        suppressed_matchers: Set[str] = set()
-
-        matchers = {
-            _get_matcher_id(matcher): matcher
-            for matcher in sorted(
-                self.matchers, key=_get_matcher_priority, reverse=True
-            )
-        }
-
-        for matcher_id, matcher in matchers.items():
-            matcher_id = _get_matcher_id(matcher)
-
-            if matcher_id in self.disable_matchers:
-                continue
-
-            if matcher_id in results:
-                warnings.warn(f"Duplicate matcher ID: {matcher_id}.")
-
-            if matcher_id in suppressed_matchers:
-                continue
-
-            result: MatcherResult
-            try:
-                if _is_matcher_v1(matcher):
-                    result = _convert_matcher_v1_result_to_v2(
-                        matcher(text), type=_UNKNOWN_TYPE
-                    )
-                elif _is_matcher_v2(matcher):
-                    result = matcher(context)
-                else:
-                    api_version = _get_matcher_api_version(matcher)
-                    raise ValueError(f"Unsupported API version {api_version}")
-            except:
-                # Show the ugly traceback if the matcher causes an
-                # exception, but do NOT crash the kernel!
-                sys.excepthook(*sys.exc_info())
-                continue
-
-            # set default value for matched fragment if suffix was not selected.
-            result["matched_fragment"] = result.get("matched_fragment", context.token)
-
-            if not suppressed_matchers:
-                suppression_recommended: Union[bool, Set[str]] = result.get(
-                    "suppress", False
-                )
-
-                suppression_config = (
-                    self.suppress_competing_matchers.get(matcher_id, None)
-                    if isinstance(self.suppress_competing_matchers, dict)
-                    else self.suppress_competing_matchers
-                )
-                should_suppress = (
-                    (suppression_config is True)
-                    or (suppression_recommended and (suppression_config is not False))
-                ) and has_any_completions(result)
-
-                if should_suppress:
-                    suppression_exceptions: Set[str] = result.get(
-                        "do_not_suppress", set()
-                    )
-                    if isinstance(suppression_recommended, Iterable):
-                        to_suppress = set(suppression_recommended)
-                    else:
-                        to_suppress = set(matchers)
-                    suppressed_matchers = to_suppress - suppression_exceptions
-
-                    new_results = {}
-                    for previous_matcher_id, previous_result in results.items():
-                        if previous_matcher_id not in suppressed_matchers:
-                            new_results[previous_matcher_id] = previous_result
-                    results = new_results
-
-            results[matcher_id] = result
-
-        _, matches = self._arrange_and_extract(
-            results,
-            # TODO Jedi completions non included in legacy stateful API; was this deliberate or omission?
-            #  if it was omission, we can remove the filtering step, otherwise remove this comment.
-            skip_matchers={jedi_matcher_id},
-            abort_if_offset_changes=False,
-        )
-
-        # populate legacy stateful API
-        self.matches = matches
-
-        return results
-
-    @staticmethod
-    def _deduplicate(
-        matches: Sequence[AnyCompletion],
-    ) -> Iterable[AnyCompletion]:
-        filtered_matches: Dict[str, AnyCompletion] = {}
-        for match in matches:
-            text = match.text
-            if (
-                text not in filtered_matches
-                or filtered_matches[text].type == _UNKNOWN_TYPE
-            ):
-                filtered_matches[text] = match
-
-        return filtered_matches.values()
-
-    @staticmethod
-    def _sort(matches: Sequence[AnyCompletion]):
-        return sorted(matches, key=lambda x: completions_sorting_key(x.text))
-
-    @context_matcher()
-    def fwd_unicode_matcher(self, context: CompletionContext):
-        """Same as :any:`fwd_unicode_match`, but adopted to new Matcher API."""
-        # TODO: use `context.limit` to terminate early once we matched the maximum
-        #  number that will be used downstream; can be added as an optional to
-        #  `fwd_unicode_match(text: str, limit: int = None)` or we could re-implement here.
-        fragment, matches = self.fwd_unicode_match(context.text_until_cursor)
-        return _convert_matcher_v1_result_to_v2(
-            matches, type="unicode", fragment=fragment, suppress_if_matches=True
-        )
-
-    def fwd_unicode_match(self, text: str) -> Tuple[str, Sequence[str]]:
-        """
-        Forward match a string starting with a backslash with a list of
-        potential Unicode completions.
-
-        Will compute list of Unicode character names on first call and cache it.
-
-        .. deprecated:: 8.6
-            You can use :meth:`fwd_unicode_matcher` instead.
-
-        Returns
-        -------
-        At tuple with:
-            - matched text (empty if no matches)
-            - list of potential completions, empty tuple  otherwise)
-        """
-        # TODO: self.unicode_names is here a list we traverse each time with ~100k elements.
-        # We could do a faster match using a Trie.
-
-        # Using pygtrie the following seem to work:
-
-        #     s = PrefixSet()
-
-        #     for c in range(0,0x10FFFF + 1):
-        #         try:
-        #             s.add(unicodedata.name(chr(c)))
-        #         except ValueError:
-        #             pass
-        #     [''.join(k) for k in s.iter(prefix)]
-
-        # But need to be timed and adds an extra dependency.
-
-        slashpos = text.rfind('\\')
-        # if text starts with slash
-        if slashpos > -1:
-            # PERF: It's important that we don't access self._unicode_names
-            # until we're inside this if-block. _unicode_names is lazily
-            # initialized, and it takes a user-noticeable amount of time to
-            # initialize it, so we don't want to initialize it unless we're
-            # actually going to use it.
-            s = text[slashpos + 1 :]
-            sup = s.upper()
-            candidates = [x for x in self.unicode_names if x.startswith(sup)]
-            if candidates:
-                return s, candidates
-            candidates = [x for x in self.unicode_names if sup in x]
-            if candidates:
-                return s, candidates
-            splitsup = sup.split(" ")
-            candidates = [
-                x for x in self.unicode_names if all(u in x for u in splitsup)
-            ]
-            if candidates:
-                return s, candidates
-
-            return "", ()
-
-        # if text does not start with slash
-        else:
-            return '', ()
-
-    @property
-    def unicode_names(self) -> List[str]:
-        """List of names of unicode code points that can be completed.
-
-        The list is lazily initialized on first access.
-        """
-        if self._unicode_names is None:
-            names = []
-            for c in range(0,0x10FFFF + 1):
-                try:
-                    names.append(unicodedata.name(chr(c)))
-                except ValueError:
-                    pass
-            self._unicode_names = _unicode_name_compute(_UNICODE_RANGES)
-
-        return self._unicode_names
-
-def _unicode_name_compute(ranges:List[Tuple[int,int]]) -> List[str]:
-    names = []
-    for start,stop in ranges:
-        for c in range(start, stop) :
-            try:
-                names.append(unicodedata.name(chr(c)))
-            except ValueError:
-                pass
-    return names
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/magics/display.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/magics/display.py
deleted file mode 100644
index 6c0eff6884f7666548f2c701c3a901b59f4c5abc..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/magics/display.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""Simple magics for display formats"""
-#-----------------------------------------------------------------------------
-#  Copyright (c) 2012 The IPython Development Team.
-#
-#  Distributed under the terms of the Modified BSD License.
-#
-#  The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
-
-# Our own packages
-from IPython.display import display, Javascript, Latex, SVG, HTML, Markdown
-from IPython.core.magic import  (
-    Magics, magics_class, cell_magic
-)
-from IPython.core import magic_arguments
-
-#-----------------------------------------------------------------------------
-# Magic implementation classes
-#-----------------------------------------------------------------------------
-
-
-@magics_class
-class DisplayMagics(Magics):
-    """Magics for displaying various output types with literals
-
-    Defines javascript/latex/svg/html cell magics for writing
-    blocks in those languages, to be rendered in the frontend.
-    """
-
-    @cell_magic
-    def js(self, line, cell):
-        """Run the cell block of Javascript code
-
-        Alias of `%%javascript`
-
-        Starting with IPython 8.0 %%javascript is pending deprecation to be replaced
-        by a more flexible system
-
-        Please See https://github.com/ipython/ipython/issues/13376
-        """
-        self.javascript(line, cell)
-
-    @cell_magic
-    def javascript(self, line, cell):
-        """Run the cell block of Javascript code
-
-        Starting with IPython 8.0 %%javascript is pending deprecation to be replaced
-        by a more flexible system
-
-        Please See https://github.com/ipython/ipython/issues/13376
-        """
-        display(Javascript(cell))
-
-
-    @cell_magic
-    def latex(self, line, cell):
-        """Render the cell as a block of LaTeX
-
-        The subset of LaTeX which is supported depends on the implementation in
-        the client.  In the Jupyter Notebook, this magic only renders the subset
-        of LaTeX defined by MathJax
-        [here](https://docs.mathjax.org/en/v2.5-latest/tex.html)."""
-        display(Latex(cell))
-
-    @cell_magic
-    def svg(self, line, cell):
-        """Render the cell as an SVG literal"""
-        display(SVG(cell))
-
-    @magic_arguments.magic_arguments()
-    @magic_arguments.argument(
-        '--isolated', action='store_true', default=False,
-        help="""Annotate the cell as 'isolated'.
-Isolated cells are rendered inside their own <iframe> tag"""
-    )
-    @cell_magic
-    def html(self, line, cell):
-        """Render the cell as a block of HTML"""
-        args = magic_arguments.parse_argstring(self.html, line)
-        html = HTML(cell)
-        if args.isolated:
-            display(html, metadata={'text/html':{'isolated':True}})
-        else:
-            display(html)
-
-    @cell_magic
-    def markdown(self, line, cell):
-        """Render the cell as Markdown text block"""
-        display(Markdown(cell))
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/tz.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/tz.py
deleted file mode 100644
index dbe1e8e7325e6abf781e363d03831633fd893990..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/tz.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# encoding: utf-8
-"""
-Timezone utilities
-
-Just UTC-awareness right now
-"""
-
-#-----------------------------------------------------------------------------
-#  Copyright (C) 2013 The IPython Development Team
-#
-#  Distributed under the terms of the BSD License.  The full license is in
-#  the file COPYING, distributed as part of this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
-
-from datetime import tzinfo, timedelta, datetime
-
-#-----------------------------------------------------------------------------
-# Code
-#-----------------------------------------------------------------------------
-# constant for zero offset
-ZERO = timedelta(0)
-
-class tzUTC(tzinfo):
-    """tzinfo object for UTC (zero offset)"""
-
-    def utcoffset(self, d):
-        return ZERO
-
-    def dst(self, d):
-        return ZERO
-
-
-UTC = tzUTC()  # type: ignore[abstract]
-
-
-def utc_aware(unaware):
-    """decorator for adding UTC tzinfo to datetime's utcfoo methods"""
-    def utc_method(*args, **kwargs):
-        dt = unaware(*args, **kwargs)
-        return dt.replace(tzinfo=UTC)
-    return utc_method
-
-utcfromtimestamp = utc_aware(datetime.utcfromtimestamp)
-utcnow = utc_aware(datetime.utcnow)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/video/video_tensorflow_tensor.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/video/video_tensorflow_tensor.py
deleted file mode 100644
index d98794f8aa30e9508b5e02e37a778cea8f4fbde5..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/video/video_tensorflow_tensor.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
-
-import numpy as np
-
-from docarray.typing.proto_register import _register_proto
-from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
-from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
-
-T = TypeVar('T', bound='VideoTensorFlowTensor')
-
-if TYPE_CHECKING:
-    from pydantic import BaseConfig
-    from pydantic.fields import ModelField
-
-
-@_register_proto(proto_type_name='video_tensorflow_tensor')
-class VideoTensorFlowTensor(
-    TensorFlowTensor, VideoTensorMixin, metaclass=metaTensorFlow
-):
-    """
-    Subclass of [`TensorFlowTensor`][docarray.typing.TensorFlowTensor],
-    to represent a video tensor. Adds video-specific features to the tensor.
-
-    ---
-
-    ```python
-    from typing import Optional
-
-    import tensorflow as tf
-
-    from docarray import BaseDoc
-    from docarray.typing import VideoTensorFlowTensor, VideoUrl
-
-
-    class MyVideoDoc(BaseDoc):
-        title: str
-        url: Optional[VideoUrl]
-        video_tensor: Optional[VideoTensorFlowTensor]
-
-
-    doc_1 = MyVideoDoc(
-        title='my_first_video_doc',
-        video_tensor=tf.random.normal((100, 224, 224, 3)),
-    )
-    # doc_1.video_tensor.save(file_path='file_1.mp4')
-
-    doc_2 = MyVideoDoc(
-        title='my_second_video_doc',
-        url='https://github.com/docarray/docarray/blob/main/tests/toydata/mov_bbb.mp4?raw=true',
-    )
-
-    doc_2.video_tensor = doc_2.url.load().video
-    # doc_2.video_tensor.save(file_path='file_2.wav')
-    ```
-
-    ---
-    """
-
-    @classmethod
-    def validate(
-        cls: Type[T],
-        value: Union[T, np.ndarray, List[Any], Tuple[Any], Any],
-        field: 'ModelField',
-        config: 'BaseConfig',
-    ) -> T:
-        tensor = super().validate(value=value, field=field, config=config)
-        return cls.validate_shape(value=tensor)
diff --git a/spaces/Surn/UnlimitedMusicGen/README.md b/spaces/Surn/UnlimitedMusicGen/README.md
deleted file mode 100644
index cd0430c03946cc5984ec69e5e8b3107535fc502b..0000000000000000000000000000000000000000
--- a/spaces/Surn/UnlimitedMusicGen/README.md
+++ /dev/null
@@ -1,209 +0,0 @@
----
-title: UnlimitedMusicGen
-emoji: 🎼
-colorFrom: white
-colorTo: red
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
-tags:
-- musicgen
-- unlimited
----
-
-[arxiv]: https://arxiv.org/abs/2306.05284
-[musicgen_samples]: https://ai.honu.io/papers/musicgen/
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-# UnlimitedMusicGen
-This is my modification of the Audiocraft project to enable unlimited Audio generation. I have added a few features to the original project to enable this. I have also added a few features to the gradio interface to make it easier to use.
-
-# Audiocraft
-![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg)
-![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg)
-![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg)
-
-Audiocraft is a PyTorch library for deep learning research on audio generation. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model.
-
-## MusicGen
-
-Audiocraft provides the code and models for MusicGen, [a simple and controllable model for music generation][arxiv]. MusicGen is a single stage auto-regressive
-Transformer model trained over a 32kHz <a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> with 4 codebooks sampled at 50 Hz. Unlike existing methods like [MusicLM](https://arxiv.org/abs/2301.11325), MusicGen doesn't require a self-supervised semantic representation, and it generates
-all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict
-them in parallel, thus having only 50 auto-regressive steps per second of audio.
-Check out our [sample page][musicgen_samples] or test the available demo!
-
-<a target="_blank" href="https://colab.research.google.com/drive/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?usp=sharing">
-  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
-</a>
-<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
-  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/>
-</a>
-<br>
-
-We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data.
-
-## Installation
-Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following:
-
-```shell
-# Best to make sure you have torch installed first, in particular before installing xformers.
-# Don't run this if you already have PyTorch installed.
-pip install 'torch>=2.0'
-# Then proceed to one of the following
-pip install -U audiocraft  # stable release
-pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft  # bleeding edge
-pip install -e .  # or if you cloned the repo locally
-```
-
-## Usage
-We offer a number of way to interact with MusicGen:
-1. A demo is also available on the [`facebook/MusicGen`  HuggingFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support).
-2. You can run the Gradio demo in Colab: [colab notebook](https://colab.research.google.com/drive/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?usp=sharing).
-3. You can use the gradio demo locally by running `python app.py`.
-4. You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally (if you have a GPU).
-5. Checkout [@camenduru Colab page](https://github.com/camenduru/MusicGen-colab) which is regularly
-  updated with contributions from @camenduru and the community.
-6. Finally, MusicGen is available in 🤗 Transformers from v4.31.0 onwards, see section [🤗 Transformers Usage](#-transformers-usage) below.
-
-### More info about Top-k, Top-p, Temperature and Classifier Free Guidance from ChatGPT
-6. Finally, MusicGen is available in 🤗 Transformers from v4.31.0 onwards, see section [🤗 Transformers Usage](#-transformers-usage) below.
-
-
-Top-k: Top-k is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
-Top-p (or nucleus sampling): Top-p, also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
-Temperature: Temperature is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
-Classifier-Free Guidance: Classifier-Free Guidance refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
-
-These parameters, such as top-k, top-p, temperature, and classifier-free guidance, provide different ways to influence the output of a music generation model and strike a balance between creativity, diversity, coherence, and control. The specific values for these parameters can be tuned based on the desired outcome and user preferences.
-
-## API
-
-We provide a simple API and 4 pre-trained models. The pre trained models are:
-- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small)
-- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium)
-- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody)
-- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large)
-
-We observe the best trade-off between quality and compute with the `medium` or `melody` model.
-In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller
-GPUs will be able to generate short sequences, or longer sequences with the `small` model.
-
-**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`.
-You can install it with:
-```
-apt-get install ffmpeg
-```
-
-See after a quick example for using the API.
-
-```python
-import torchaudio
-from audiocraft.models import MusicGen
-from audiocraft.data.audio import audio_write
-
-model = MusicGen.get_pretrained('melody')
-model.set_generation_params(duration=8)  # generate 8 seconds.
-wav = model.generate_unconditional(4)    # generates 4 unconditional audio samples
-descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
-wav = model.generate(descriptions)  # generates 3 samples.
-
-melody, sr = torchaudio.load('./assets/bach.mp3')
-# generates using the melody from the given audio and the provided descriptions.
-wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr)
-
-for idx, one_wav in enumerate(wav):
-    # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
-    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
-```
-## 🤗 Transformers Usage
-
-MusicGen is available in the 🤗 Transformers library from version 4.31.0 onwards, requiring minimal dependencies 
-and additional packages. Steps to get started:
-
-1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) from main:
-
-```
-pip install git+https://github.com/huggingface/transformers.git
-```
-
-2. Run the following Python code to generate text-conditional audio samples:
-
-```py
-from transformers import AutoProcessor, MusicgenForConditionalGeneration
-
-
-processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
-model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
-
-inputs = processor(
-    text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
-    padding=True,
-    return_tensors="pt",
-)
-
-audio_values = model.generate(**inputs, max_new_tokens=256)
-```
-
-3. Listen to the audio samples either in an ipynb notebook:
-
-```py
-from IPython.display import Audio
-
-sampling_rate = model.config.audio_encoder.sampling_rate
-Audio(audio_values[0].numpy(), rate=sampling_rate)
-```
-
-Or save them as a `.wav` file using a third-party library, e.g. `scipy`:
-
-```py
-import scipy
-
-sampling_rate = model.config.audio_encoder.sampling_rate
-scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy())
-```
-
-For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the 
-[MusicGen docs](https://huggingface.co/docs/transformers/main/en/model_doc/musicgen) or the hands-on 
-[Google Colab](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb).
-
-
-## Model Card
-
-See [the model card page](./MODEL_CARD.md).
-
-## FAQ
-
-#### Will the training code be released?
-
-Yes. We will soon release the training code for MusicGen and EnCodec.
-
-
-#### I need help on Windows
-
-@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4)
-
-#### I need help for running the demo on Colab
-
-Check [@camenduru tutorial on Youtube](https://www.youtube.com/watch?v=EGfxuTy9Eeo).
-
-## Citation
-```
-@article{copet2023simple,
-      title={Simple and Controllable Music Generation},
-      author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
-      year={2023},
-      journal={arXiv preprint arXiv:2306.05284},
-}
-```
-
-## License
-* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
-* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights).
-[arxiv]: https://arxiv.org/abs/2306.05284
-[musicgen_samples]: https://ai.honu.io/papers/musicgen/
\ No newline at end of file
diff --git a/spaces/TH5314/newbing/src/components/ui/tooltip.tsx b/spaces/TH5314/newbing/src/components/ui/tooltip.tsx
deleted file mode 100644
index af1d48beb90dd5ae311796539843700871052cae..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/components/ui/tooltip.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as TooltipPrimitive from '@radix-ui/react-tooltip'
-
-import { cn } from '@/lib/utils'
-
-const TooltipProvider = TooltipPrimitive.Provider
-
-const Tooltip = TooltipPrimitive.Root
-
-const TooltipTrigger = TooltipPrimitive.Trigger
-
-const TooltipContent = React.forwardRef<
-  React.ElementRef<typeof TooltipPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof TooltipPrimitive.Content>
->(({ className, sideOffset = 4, ...props }, ref) => (
-  <TooltipPrimitive.Content
-    ref={ref}
-    sideOffset={sideOffset}
-    className={cn(
-      'z-50 overflow-hidden rounded-md border bg-popover px-3 py-1.5 text-xs font-medium text-popover-foreground shadow-md animate-in fade-in-50 data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1',
-      className
-    )}
-    {...props}
-  />
-))
-TooltipContent.displayName = TooltipPrimitive.Content.displayName
-
-export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider }
diff --git a/spaces/TandCAcceptMe/face-swap-docker/roop/core.py b/spaces/TandCAcceptMe/face-swap-docker/roop/core.py
deleted file mode 100644
index 68d1808117837f1d903f7ba90f954dfd3c0f9554..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/roop/core.py
+++ /dev/null
@@ -1,323 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import sys
-import shutil
-# single thread doubles cuda performance - needs to be set before torch import
-if any(arg.startswith('--execution-provider') for arg in sys.argv):
-    os.environ['OMP_NUM_THREADS'] = '1'
-
-import warnings
-from typing import List
-import platform
-import signal
-import argparse
-import torch
-import onnxruntime
-
-import roop.globals
-import roop.metadata
-import roop.utilities as util
-import roop.ui as ui
-from settings import Settings
-from roop.face_helper import extract_face_images
-from chain_img_processor import ChainImgProcessor, ChainVideoProcessor, ChainBatchImageProcessor
-
-clip_text = None
-
-
-if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-    del torch
-
-warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
-
-
-def parse_args() -> None:
-    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
-    program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100))
-    program.add_argument('-s', '--source', help='select a source image', dest='source_path')
-    program.add_argument('-t', '--target', help='select a target image or video', dest='target_path')
-    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
-    program.add_argument('-f', '--folder', help='select a target folder with images or videos to batch process', dest='target_folder_path')
-    program.add_argument('--frame-processor', help='frame processors (choices: face_swapper, face_enhancer, ...)', dest='frame_processor', default=['face_swapper'], nargs='+')
-    program.add_argument('--keep-fps', help='keep target fps', dest='keep_fps', action='store_true')
-    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true')
-    program.add_argument('--skip-audio', help='skip target audio', dest='skip_audio', action='store_true')
-    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true')
-    program.add_argument('--source-face_index', help='index position of source face in image', dest='source_face_index', type=int, default=0)
-    program.add_argument('--target-face_index', help='index position of target face in image', dest='target_face_index', type=int, default=0)
-    program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
-    program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
-    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
-    program.add_argument('--execution-provider', help='available execution provider (choices: cpu, ...)', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+')
-    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
-    program.add_argument('-v', '--version', action='version', version=f'{roop.metadata.name} {roop.metadata.version}')
-
-    args = program.parse_args()
-
-    roop.globals.source_path = args.source_path
-    roop.globals.target_path = args.target_path
-    roop.globals.output_path = util.normalize_output_path(roop.globals.source_path, roop.globals.target_path, args.output_path)
-    roop.globals.target_folder_path = args.target_folder_path
-    roop.globals.headless = args.source_path or args.target_path or args.output_path
-    # Always enable all processors when using GUI
-    if not roop.globals.headless:
-        roop.globals.frame_processors = ['face_swapper', 'face_enhancer']
-    else:
-        roop.globals.frame_processors = args.frame_processor
-
-    roop.globals.keep_fps = args.keep_fps
-    roop.globals.keep_frames = args.keep_frames
-    roop.globals.skip_audio = args.skip_audio
-    roop.globals.many_faces = args.many_faces
-    roop.globals.source_face_index = args.source_face_index
-    roop.globals.target_face_index = args.target_face_index
-    roop.globals.video_encoder = args.video_encoder
-    roop.globals.video_quality = args.video_quality
-    roop.globals.max_memory = args.max_memory
-    roop.globals.execution_providers = decode_execution_providers(args.execution_provider)
-    roop.globals.execution_threads = args.execution_threads
-
-
-def encode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
-
-
-def decode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
-            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
-
-
-def suggest_max_memory() -> int:
-    if platform.system().lower() == 'darwin':
-        return 4
-    return 16
-
-
-def suggest_execution_providers() -> List[str]:
-    return encode_execution_providers(onnxruntime.get_available_providers())
-
-
-def suggest_execution_threads() -> int:
-    if 'DmlExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    return 8
-
-
-def limit_resources() -> None:
-    # prevent tensorflow memory leak
-    # gpus = tensorflow.config.experimental.list_physical_devices('GPU')
-    # for gpu in gpus:
-        # tensorflow.config.experimental.set_virtual_device_configuration(gpu, [
-            # tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)
-        # ])
-    # limit memory usage
-    if roop.globals.max_memory:
-        memory = roop.globals.max_memory * 1024 ** 3
-        if platform.system().lower() == 'darwin':
-            memory = roop.globals.max_memory * 1024 ** 6
-        if platform.system().lower() == 'windows':
-            import ctypes
-            kernel32 = ctypes.windll.kernel32
-            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
-        else:
-            import resource
-            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
-
-
-def release_resources() -> None:
-    if 'CUDAExecutionProvider' in roop.globals.execution_providers:
-        torch.cuda.empty_cache()
-
-
-def pre_check() -> bool:
-    if sys.version_info < (3, 9):
-        update_status('Python version is not supported - please upgrade to 3.9 or higher.')
-        return False
-    
-    download_directory_path = util.resolve_relative_path('../models')
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/inswapper_128.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/GFPGANv1.4.pth'])
-    util.conditional_download(download_directory_path, ['https://github.com/csxmli2016/DMDNet/releases/download/v1/DMDNet.pth'])
-    download_directory_path = util.resolve_relative_path('../models/CLIP')
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/rd64-uni-refined.pth'])
-    download_directory_path = util.resolve_relative_path('../models/CodeFormer')
-    util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'])
-    download_directory_path = util.resolve_relative_path('../models/CodeFormer/facelib')
-    util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth'])
-    util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth'])
-    download_directory_path = util.resolve_relative_path('../models/CodeFormer/realesrgan')
-    util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/RealESRGAN_x2plus.pth'])
-
-    if not shutil.which('ffmpeg'):
-       update_status('ffmpeg is not installed.')
-    return True
-
-
-def update_status(message: str, scope: str = 'ROOP.CORE') -> None:
-    print(f'[{scope}] {message}')
-    # if not roop.globals.headless:
-        # ui.update_status(message)
-
-
-
-def start() -> None:
-    if roop.globals.headless:
-        faces = extract_face_images(roop.globals.source_path,  (False, 0))
-        roop.globals.SELECTED_FACE_DATA_INPUT = faces[roop.globals.source_face_index]
-        faces = extract_face_images(roop.globals.target_path,  (False, util.has_image_extension(roop.globals.target_path)))
-        roop.globals.SELECTED_FACE_DATA_OUTPUT = faces[roop.globals.target_face_index]
-        if 'face_enhancer' in roop.globals.frame_processors:
-            roop.globals.selected_enhancer = 'GFPGAN'
-       
-    batch_process(None, False, None)
-
-
-def InitPlugins():
-    if not roop.globals.IMAGE_CHAIN_PROCESSOR:
-        roop.globals.IMAGE_CHAIN_PROCESSOR = ChainImgProcessor()
-        roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR = ChainBatchImageProcessor()
-        roop.globals.VIDEO_CHAIN_PROCESSOR = ChainVideoProcessor()
-        roop.globals.IMAGE_CHAIN_PROCESSOR.init_with_plugins()
-        roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR.init_with_plugins()
-        roop.globals.VIDEO_CHAIN_PROCESSOR.init_with_plugins()
-
-
-def get_processing_plugins(use_clip):
-    processors = "faceswap"
-    if use_clip:
-        processors += ",txt2clip"
-    
-    if roop.globals.selected_enhancer == 'GFPGAN':
-        processors += ",gfpgan"
-    elif roop.globals.selected_enhancer == 'Codeformer':
-        processors += ",codeformer"
-    elif roop.globals.selected_enhancer == 'DMDNet':
-        processors += ",dmdnet"
-    
-    return processors
-
-
-def live_swap(frame, swap_mode, use_clip, clip_text):
-    if frame is None:
-        return frame
-
-    InitPlugins()
-    processors = get_processing_plugins(use_clip)
-
-
-    temp_frame, _ = roop.globals.IMAGE_CHAIN_PROCESSOR.run_chain(frame,  
-                                                    {"swap_mode": swap_mode,
-                                                        "original_frame": frame,
-                                                        "blend_ratio": roop.globals.blend_ratio,
-                                                        "face_distance_threshold": roop.globals.distance_threshold,
-                                                        "input_face_datas": [roop.globals.SELECTED_FACE_DATA_INPUT], "target_face_datas": [roop.globals.SELECTED_FACE_DATA_OUTPUT],
-                                                        "clip_prompt": clip_text},
-                                                        processors)
-    return temp_frame
-
-
-
-def params_gen_func(proc, frame):
-    global clip_text
-
-    return {"original_frame": frame, "blend_ratio": roop.globals.blend_ratio,
-             "swap_mode": roop.globals.face_swap_mode, "face_distance_threshold": roop.globals.distance_threshold, 
-             "input_face_datas": [roop.globals.SELECTED_FACE_DATA_INPUT], "target_face_datas": [roop.globals.SELECTED_FACE_DATA_OUTPUT],
-             "clip_prompt": clip_text}
-
-def batch_process(files, use_clip, new_clip_text) -> None:
-    global clip_text
-
-    InitPlugins()
-    processors = get_processing_plugins(use_clip)
-
-    clip_text = new_clip_text
-
-    imagefiles = []
-    imagefinalnames = []
-    videofiles = []
-    videofinalnames = []
-    need_join = False
-
-    if files is None:
-        need_join = True
-        if roop.globals.target_folder_path is None:
-            roop.globals.target_folder_path = os.path.dirname(roop.globals.target_path)
-            files = [os.path.basename(roop.globals.target_path)]
-            roop.globals.output_path = os.path.dirname(roop.globals.output_path)
-        else:
-            files = [f for f in os.listdir(roop.globals.target_folder_path) if os.path.isfile(os.path.join(roop.globals.target_folder_path, f))]
-            
-        update_status('Sorting videos/images')
-
-
-    for f in files:
-        if need_join:
-            fullname = os.path.join(roop.globals.target_folder_path, f)
-        else:
-            fullname = f
-        if util.has_image_extension(fullname):
-            imagefiles.append(fullname)
-            imagefinalnames.append(util.get_destfilename_from_path(fullname, roop.globals.output_path, f'_fake.{roop.globals.CFG.output_image_format}'))
-        elif util.is_video(fullname) or util.has_extension(fullname, ['gif']):
-            videofiles.append(fullname)
-            videofinalnames.append(util.get_destfilename_from_path(fullname, roop.globals.output_path, f'_fake.{roop.globals.CFG.output_video_format}'))
-
-
-    if(len(imagefiles) > 0):
-        update_status('Processing image(s)')
-        roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR.run_batch_chain(imagefiles, imagefinalnames, roop.globals.execution_threads, processors, params_gen_func)
-    if(len(videofiles) > 0):
-        for index,v in enumerate(videofiles):
-            update_status(f'Processing video {v}')
-            fps = util.detect_fps(v)
-            if roop.globals.keep_frames:
-                update_status('Creating temp resources...')
-                util.create_temp(v)
-                update_status('Extracting frames...')
-                util.extract_frames(v)
-                temp_frame_paths = util.get_temp_frame_paths(v)
-                roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR.run_batch_chain(temp_frame_paths, temp_frame_paths, roop.globals.execution_threads, processors, params_gen_func)
-                update_status(f'Creating video with {fps} FPS...')
-                util.create_video(v, videofinalnames[index], fps)
-            else:
-                update_status(f'Creating video with {fps} FPS...')
-                roop.globals.VIDEO_CHAIN_PROCESSOR.run_video_chain(v,videofinalnames[index], fps, roop.globals.execution_threads, processors, params_gen_func, roop.globals.target_path)
-            if os.path.isfile(videofinalnames[index]):
-                if util.has_extension(v, ['gif']):
-                    gifname = roop.utilities.get_destfilename_from_path(v, './output', '_fake.gif')
-                    update_status('Creating final GIF')
-                    util.create_gif_from_video(videofinalnames[index], gifname)
-                elif not roop.globals.skip_audio:
-                    finalname = roop.utilities.get_destfilename_from_path(videofinalnames[index], roop.globals.output_path, f'_final.{roop.globals.CFG.output_video_format}')
-                    util.restore_audio(videofinalnames[index], v, finalname)
-                    if os.path.isfile(videofinalnames[index]):
-                        os.remove(videofinalnames[index])
-            else:
-                update_status('Failed!')
-
-            
-    update_status('Finished')
-    roop.globals.target_folder_path = None
-
-
-def destroy() -> None:
-    if roop.globals.target_path:
-        util.clean_temp(roop.globals.target_path)
-    sys.exit()
-
-
-def run() -> None:
-    parse_args()
-    if not pre_check():
-        return
-    limit_resources()
-    roop.globals.CFG = Settings('config.yaml')
-    if roop.globals.headless:
-        start()
-    else:
-        ui.run()
diff --git a/spaces/Tej3/ECG_Classification/README.md b/spaces/Tej3/ECG_Classification/README.md
deleted file mode 100644
index 68687dfd68fc23addd3242983e372d75946a6a3f..0000000000000000000000000000000000000000
--- a/spaces/Tej3/ECG_Classification/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ECG Classification
-emoji: 💻
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript_patch.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript_patch.py
deleted file mode 100644
index da9b324f1582e31d1a16d2fe462ac2989bea56ea..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript_patch.py
+++ /dev/null
@@ -1,406 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import os
-import sys
-import tempfile
-from contextlib import ExitStack, contextmanager
-from copy import deepcopy
-from unittest import mock
-import torch
-from torch import nn
-
-# need some explicit imports due to https://github.com/pytorch/pytorch/issues/38964
-import detectron2  # noqa F401
-from detectron2.structures import Boxes, Instances
-from detectron2.utils.env import _import_file
-
-_counter = 0
-
-
-def _clear_jit_cache():
-    from torch.jit._recursive import concrete_type_store
-    from torch.jit._state import _jit_caching_layer
-
-    concrete_type_store.type_store.clear()  # for modules
-    _jit_caching_layer.clear()  # for free functions
-
-
-def _add_instances_conversion_methods(newInstances):
-    """
-    Add from_instances methods to the scripted Instances class.
-    """
-    cls_name = newInstances.__name__
-
-    @torch.jit.unused
-    def from_instances(instances: Instances):
-        """
-        Create scripted Instances from original Instances
-        """
-        fields = instances.get_fields()
-        image_size = instances.image_size
-        ret = newInstances(image_size)
-        for name, val in fields.items():
-            assert hasattr(ret, f"_{name}"), f"No attribute named {name} in {cls_name}"
-            setattr(ret, name, deepcopy(val))
-        return ret
-
-    newInstances.from_instances = from_instances
-
-
-@contextmanager
-def patch_instances(fields):
-    """
-    A contextmanager, under which the Instances class in detectron2 is replaced
-    by a statically-typed scriptable class, defined by `fields`.
-    See more in `scripting_with_instances`.
-    """
-
-    with tempfile.TemporaryDirectory(prefix="detectron2") as dir, tempfile.NamedTemporaryFile(
-        mode="w", encoding="utf-8", suffix=".py", dir=dir, delete=False
-    ) as f:
-        try:
-            # Objects that use Instances should not reuse previously-compiled
-            # results in cache, because `Instances` could be a new class each time.
-            _clear_jit_cache()
-
-            cls_name, s = _gen_instance_module(fields)
-            f.write(s)
-            f.flush()
-            f.close()
-
-            module = _import(f.name)
-            new_instances = getattr(module, cls_name)
-            _ = torch.jit.script(new_instances)
-            # let torchscript think Instances was scripted already
-            Instances.__torch_script_class__ = True
-            # let torchscript find new_instances when looking for the jit type of Instances
-            Instances._jit_override_qualname = torch._jit_internal._qualified_name(new_instances)
-
-            _add_instances_conversion_methods(new_instances)
-            yield new_instances
-        finally:
-            try:
-                del Instances.__torch_script_class__
-                del Instances._jit_override_qualname
-            except AttributeError:
-                pass
-            sys.modules.pop(module.__name__)
-
-
-def _gen_instance_class(fields):
-    """
-    Args:
-        fields (dict[name: type])
-    """
-
-    class _FieldType:
-        def __init__(self, name, type_):
-            assert isinstance(name, str), f"Field name must be str, got {name}"
-            self.name = name
-            self.type_ = type_
-            self.annotation = f"{type_.__module__}.{type_.__name__}"
-
-    fields = [_FieldType(k, v) for k, v in fields.items()]
-
-    def indent(level, s):
-        return " " * 4 * level + s
-
-    lines = []
-
-    global _counter
-    _counter += 1
-
-    cls_name = "ScriptedInstances{}".format(_counter)
-
-    field_names = tuple(x.name for x in fields)
-    extra_args = ", ".join([f"{f.name}: Optional[{f.annotation}] = None" for f in fields])
-    lines.append(
-        f"""
-class {cls_name}:
-    def __init__(self, image_size: Tuple[int, int], {extra_args}):
-        self.image_size = image_size
-        self._field_names = {field_names}
-"""
-    )
-
-    for f in fields:
-        lines.append(
-            indent(2, f"self._{f.name} = torch.jit.annotate(Optional[{f.annotation}], {f.name})")
-        )
-
-    for f in fields:
-        lines.append(
-            f"""
-    @property
-    def {f.name}(self) -> {f.annotation}:
-        # has to use a local for type refinement
-        # https://pytorch.org/docs/stable/jit_language_reference.html#optional-type-refinement
-        t = self._{f.name}
-        assert t is not None, "{f.name} is None and cannot be accessed!"
-        return t
-
-    @{f.name}.setter
-    def {f.name}(self, value: {f.annotation}) -> None:
-        self._{f.name} = value
-"""
-        )
-
-    # support method `__len__`
-    lines.append(
-        """
-    def __len__(self) -> int:
-"""
-    )
-    for f in fields:
-        lines.append(
-            f"""
-        t = self._{f.name}
-        if t is not None:
-            return len(t)
-"""
-        )
-    lines.append(
-        """
-        raise NotImplementedError("Empty Instances does not support __len__!")
-"""
-    )
-
-    # support method `has`
-    lines.append(
-        """
-    def has(self, name: str) -> bool:
-"""
-    )
-    for f in fields:
-        lines.append(
-            f"""
-        if name == "{f.name}":
-            return self._{f.name} is not None
-"""
-        )
-    lines.append(
-        """
-        return False
-"""
-    )
-
-    # support method `to`
-    none_args = ", None" * len(fields)
-    lines.append(
-        f"""
-    def to(self, device: torch.device) -> "{cls_name}":
-        ret = {cls_name}(self.image_size{none_args})
-"""
-    )
-    for f in fields:
-        if hasattr(f.type_, "to"):
-            lines.append(
-                f"""
-        t = self._{f.name}
-        if t is not None:
-            ret._{f.name} = t.to(device)
-"""
-            )
-        else:
-            # For now, ignore fields that cannot be moved to devices.
-            # Maybe can support other tensor-like classes (e.g. __torch_function__)
-            pass
-    lines.append(
-        """
-        return ret
-"""
-    )
-
-    # support method `getitem`
-    none_args = ", None" * len(fields)
-    lines.append(
-        f"""
-    def __getitem__(self, item) -> "{cls_name}":
-        ret = {cls_name}(self.image_size{none_args})
-"""
-    )
-    for f in fields:
-        lines.append(
-            f"""
-        t = self._{f.name}
-        if t is not None:
-            ret._{f.name} = t[item]
-"""
-        )
-    lines.append(
-        """
-        return ret
-"""
-    )
-
-    # support method `cat`
-    # this version does not contain checks that all instances have same size and fields
-    none_args = ", None" * len(fields)
-    lines.append(
-        f"""
-    def cat(self, instances: List["{cls_name}"]) -> "{cls_name}":
-        ret = {cls_name}(self.image_size{none_args})
-"""
-    )
-    for f in fields:
-        lines.append(
-            f"""
-        t = self._{f.name}
-        if t is not None:
-            values: List[{f.annotation}] = [x.{f.name} for x in instances]
-            if torch.jit.isinstance(t, torch.Tensor):
-                ret._{f.name} = torch.cat(values, dim=0)
-            else:
-                ret._{f.name} = t.cat(values)
-"""
-        )
-    lines.append(
-        """
-        return ret"""
-    )
-
-    # support method `get_fields()`
-    lines.append(
-        """
-    def get_fields(self) -> Dict[str, Tensor]:
-        ret = {}
-    """
-    )
-    for f in fields:
-        if f.type_ == Boxes:
-            stmt = "t.tensor"
-        elif f.type_ == torch.Tensor:
-            stmt = "t"
-        else:
-            stmt = f'assert False, "unsupported type {str(f.type_)}"'
-        lines.append(
-            f"""
-        t = self._{f.name}
-        if t is not None:
-            ret["{f.name}"] = {stmt}
-        """
-        )
-    lines.append(
-        """
-        return ret"""
-    )
-    return cls_name, os.linesep.join(lines)
-
-
-def _gen_instance_module(fields):
-    # TODO: find a more automatic way to enable import of other classes
-    s = """
-from copy import deepcopy
-import torch
-from torch import Tensor
-import typing
-from typing import *
-
-import detectron2
-from detectron2.structures import Boxes, Instances
-
-"""
-
-    cls_name, cls_def = _gen_instance_class(fields)
-    s += cls_def
-    return cls_name, s
-
-
-def _import(path):
-    return _import_file(
-        "{}{}".format(sys.modules[__name__].__name__, _counter), path, make_importable=True
-    )
-
-
-@contextmanager
-def patch_builtin_len(modules=()):
-    """
-    Patch the builtin len() function of a few detectron2 modules
-    to use __len__ instead, because __len__ does not convert values to
-    integers and therefore is friendly to tracing.
-
-    Args:
-        modules (list[stsr]): names of extra modules to patch len(), in
-            addition to those in detectron2.
-    """
-
-    def _new_len(obj):
-        return obj.__len__()
-
-    with ExitStack() as stack:
-        MODULES = [
-            "detectron2.modeling.roi_heads.fast_rcnn",
-            "detectron2.modeling.roi_heads.mask_head",
-            "detectron2.modeling.roi_heads.keypoint_head",
-        ] + list(modules)
-        ctxs = [stack.enter_context(mock.patch(mod + ".len")) for mod in MODULES]
-        for m in ctxs:
-            m.side_effect = _new_len
-        yield
-
-
-def patch_nonscriptable_classes():
-    """
-    Apply patches on a few nonscriptable detectron2 classes.
-    Should not have side-effects on eager usage.
-    """
-    # __prepare_scriptable__ can also be added to models for easier maintenance.
-    # But it complicates the clean model code.
-
-    from detectron2.modeling.backbone import ResNet, FPN
-
-    # Due to https://github.com/pytorch/pytorch/issues/36061,
-    # we change backbone to use ModuleList for scripting.
-    # (note: this changes param names in state_dict)
-
-    def prepare_resnet(self):
-        ret = deepcopy(self)
-        ret.stages = nn.ModuleList(ret.stages)
-        for k in self.stage_names:
-            delattr(ret, k)
-        return ret
-
-    ResNet.__prepare_scriptable__ = prepare_resnet
-
-    def prepare_fpn(self):
-        ret = deepcopy(self)
-        ret.lateral_convs = nn.ModuleList(ret.lateral_convs)
-        ret.output_convs = nn.ModuleList(ret.output_convs)
-        for name, _ in self.named_children():
-            if name.startswith("fpn_"):
-                delattr(ret, name)
-        return ret
-
-    FPN.__prepare_scriptable__ = prepare_fpn
-
-    # Annotate some attributes to be constants for the purpose of scripting,
-    # even though they are not constants in eager mode.
-    from detectron2.modeling.roi_heads import StandardROIHeads
-
-    if hasattr(StandardROIHeads, "__annotations__"):
-        # copy first to avoid editing annotations of base class
-        StandardROIHeads.__annotations__ = deepcopy(StandardROIHeads.__annotations__)
-        StandardROIHeads.__annotations__["mask_on"] = torch.jit.Final[bool]
-        StandardROIHeads.__annotations__["keypoint_on"] = torch.jit.Final[bool]
-
-
-# These patches are not supposed to have side-effects.
-patch_nonscriptable_classes()
-
-
-@contextmanager
-def freeze_training_mode(model):
-    """
-    A context manager that annotates the "training" attribute of every submodule
-    to constant, so that the training codepath in these modules can be
-    meta-compiled away. Upon exiting, the annotations are reverted.
-    """
-    classes = {type(x) for x in model.modules()}
-    # __constants__ is the old way to annotate constants and not compatible
-    # with __annotations__ .
-    classes = {x for x in classes if not hasattr(x, "__constants__")}
-    for cls in classes:
-        cls.__annotations__["training"] = torch.jit.Final[bool]
-    yield
-    for cls in classes:
-        cls.__annotations__["training"] = bool
diff --git a/spaces/Th3BossC/TranscriptApi/app.py b/spaces/Th3BossC/TranscriptApi/app.py
deleted file mode 100644
index 208e07112fff0dcaea2d63d3281b4b5ccead18f3..0000000000000000000000000000000000000000
--- a/spaces/Th3BossC/TranscriptApi/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from TranscriptApi import create_app
-from threading import Thread
-
-app = create_app()
-
-
-if __name__ == '__main__':
-    app.run(debug = False, host="0.0.0.0", port=7860)
-
-
-# def run():
-#     app.run(host = "0.0.0.0", port = 8080)  
-
-# def keep_alive():
-#     t = Thread(target = run)
-#     t.start()
-
-# keep_alive()
\ No newline at end of file
diff --git a/spaces/UserXTheUnknown/stablediffusion-infinity/js/mode.js b/spaces/UserXTheUnknown/stablediffusion-infinity/js/mode.js
deleted file mode 100644
index c11264029f5a72c44988ff7318345f3d62a3e28e..0000000000000000000000000000000000000000
--- a/spaces/UserXTheUnknown/stablediffusion-infinity/js/mode.js
+++ /dev/null
@@ -1,6 +0,0 @@
-function(mode){
-    let app=document.querySelector("gradio-app").shadowRoot;
-    let frame=app.querySelector("#sdinfframe").contentWindow.document;
-    frame.querySelector("#mode").value=mode;
-    return mode;
-}
\ No newline at end of file
diff --git a/spaces/Varun6579/mygenAiAvatarSpeech/README.md b/spaces/Varun6579/mygenAiAvatarSpeech/README.md
deleted file mode 100644
index 9af62524b946534853a5318a8f78555c19a63e9e..0000000000000000000000000000000000000000
--- a/spaces/Varun6579/mygenAiAvatarSpeech/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MygenAiAvatarSpeech
-emoji: 🦀
-colorFrom: gray
-colorTo: red
-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/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_depth.py b/spaces/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_depth.py
deleted file mode 100644
index 75f173659f81b686e9b638a897222dcee43a2427..0000000000000000000000000000000000000000
--- a/spaces/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_depth.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from diffusers import ControlNetModel
-from PIL import Image
-from transformers import pipeline
-
-from diffusion_webui.diffusion_models.controlnet.controlnet_inpaint.pipeline_stable_diffusion_controlnet_inpaint import (
-    StableDiffusionControlNetInpaintPipeline,
-)
-from diffusion_webui.utils.model_list import (
-    controlnet_depth_model_list,
-    stable_inpiant_model_list,
-)
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_LIST,
-    get_scheduler_list,
-)
-
-# https://github.com/mikonvergence/ControlNetInpaint
-
-
-class StableDiffusionControlInpaintNetDepthGenerator:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(self, stable_model_path, controlnet_model_path, scheduler):
-        if self.pipe is None:
-            controlnet = ControlNetModel.from_pretrained(
-                controlnet_model_path, torch_dtype=torch.float16
-            )
-            self.pipe = (
-                StableDiffusionControlNetInpaintPipeline.from_pretrained(
-                    pretrained_model_name_or_path=stable_model_path,
-                    controlnet=controlnet,
-                    safety_checker=None,
-                    torch_dtype=torch.float16,
-                )
-            )
-
-        self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
-        self.pipe.to("cuda")
-        self.pipe.enable_xformers_memory_efficient_attention()
-
-        return self.pipe
-
-    def load_image(self, image_path):
-        image = np.array(image_path)
-        image = Image.fromarray(image)
-        return image
-
-    def controlnet_inpaint_depth(self, image_path: str):
-        depth_estimator = pipeline("depth-estimation")
-        image = image_path["image"].convert("RGB").resize((512, 512))
-        image = depth_estimator(image)["depth"]
-        image = np.array(image)
-        image = image[:, :, None]
-        image = np.concatenate([image, image, image], axis=2)
-        image = Image.fromarray(image)
-
-        return image
-
-    def generate_image(
-        self,
-        image_path: str,
-        stable_model_path: str,
-        controlnet_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        guidance_scale: int,
-        num_inference_step: int,
-        controlnet_conditioning_scale: int,
-        scheduler: str,
-        seed_generator: int,
-    ):
-        normal_image = image_path["image"].convert("RGB").resize((512, 512))
-        mask_image = image_path["mask"].convert("RGB").resize((512, 512))
-
-        normal_image = self.load_image(image_path=normal_image)
-        mask_image = self.load_image(image_path=mask_image)
-
-        control_image = self.controlnet_inpaint_depth(image_path=image_path)
-
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            controlnet_model_path=controlnet_model_path,
-            scheduler=scheduler,
-        )
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        output = pipe(
-            prompt=prompt,
-            image=normal_image,
-            mask_image=mask_image,
-            control_image=control_image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            controlnet_conditioning_scale=controlnet_conditioning_scale,
-            generator=generator,
-        ).images
-
-        return output
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    controlnet_depth_inpaint_image_file = gr.Image(
-                        source="upload",
-                        tool="sketch",
-                        elem_id="image_upload",
-                        type="pil",
-                        label="Upload",
-                    )
-
-                    controlnet_depth_inpaint_prompt = gr.Textbox(
-                        lines=1, placeholder="Prompt", show_label=False
-                    )
-
-                    controlnet_depth_inpaint_negative_prompt = gr.Textbox(
-                        lines=1,
-                        show_label=False,
-                        placeholder="Negative Prompt",
-                    )
-                    with gr.Row():
-                        with gr.Column():
-                            controlnet_depth_inpaint_stable_model_id = (
-                                gr.Dropdown(
-                                    choices=stable_inpiant_model_list,
-                                    value=stable_inpiant_model_list[0],
-                                    label="Stable Model Id",
-                                )
-                            )
-
-                            controlnet_depth_inpaint_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-
-                            controlnet_depth_inpaint_num_inference_step = (
-                                gr.Slider(
-                                    minimum=1,
-                                    maximum=100,
-                                    step=1,
-                                    value=50,
-                                    label="Num Inference Step",
-                                )
-                            )
-                            controlnet_depth_inpaint_num_images_per_prompt = (
-                                gr.Slider(
-                                    minimum=1,
-                                    maximum=10,
-                                    step=1,
-                                    value=1,
-                                    label="Number Of Images",
-                                )
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                controlnet_depth_inpaint_model_id = gr.Dropdown(
-                                    choices=controlnet_depth_model_list,
-                                    value=controlnet_depth_model_list[0],
-                                    label="Controlnet Model Id",
-                                )
-                                controlnet_depth_inpaint_scheduler = (
-                                    gr.Dropdown(
-                                        choices=SCHEDULER_LIST,
-                                        value=SCHEDULER_LIST[0],
-                                        label="Scheduler",
-                                    )
-                                )
-                                controlnet_depth_inpaint_controlnet_conditioning_scale = gr.Slider(
-                                    minimum=0.1,
-                                    maximum=1.0,
-                                    step=0.1,
-                                    value=0.5,
-                                    label="Controlnet Conditioning Scale",
-                                )
-
-                                controlnet_depth_inpaint_seed_generator = (
-                                    gr.Slider(
-                                        minimum=0,
-                                        maximum=1000000,
-                                        step=1,
-                                        value=0,
-                                        label="Seed Generator",
-                                    )
-                                )
-
-                    controlnet_depth_inpaint_predict = gr.Button(
-                        value="Generator"
-                    )
-
-                with gr.Column():
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-            controlnet_depth_inpaint_predict.click(
-                fn=StableDiffusionControlInpaintNetDepthGenerator().generate_image,
-                inputs=[
-                    controlnet_depth_inpaint_image_file,
-                    controlnet_depth_inpaint_stable_model_id,
-                    controlnet_depth_inpaint_model_id,
-                    controlnet_depth_inpaint_prompt,
-                    controlnet_depth_inpaint_negative_prompt,
-                    controlnet_depth_inpaint_num_images_per_prompt,
-                    controlnet_depth_inpaint_guidance_scale,
-                    controlnet_depth_inpaint_num_inference_step,
-                    controlnet_depth_inpaint_controlnet_conditioning_scale,
-                    controlnet_depth_inpaint_scheduler,
-                    controlnet_depth_inpaint_seed_generator,
-                ],
-                outputs=[output_image],
-            )
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/datasets/builders/base_dataset_builder.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/datasets/builders/base_dataset_builder.py
deleted file mode 100644
index 4b607e3c0a8abaa6b1ccbc711e27ff3755f5ec11..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/datasets/builders/base_dataset_builder.py
+++ /dev/null
@@ -1,236 +0,0 @@
-"""
- This file is from
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import logging
-import os
-import shutil
-import warnings
-
-from omegaconf import OmegaConf
-import torch.distributed as dist
-from torchvision.datasets.utils import download_url
-
-import minigpt4.common.utils as utils
-from minigpt4.common.dist_utils import is_dist_avail_and_initialized, is_main_process
-from minigpt4.common.registry import registry
-from minigpt4.processors.base_processor import BaseProcessor
-
-
-
-class BaseDatasetBuilder:
-    train_dataset_cls, eval_dataset_cls = None, None
-
-    def __init__(self, cfg=None):
-        super().__init__()
-
-        if cfg is None:
-            # help to create datasets from default config.
-            self.config = load_dataset_config(self.default_config_path())
-        elif isinstance(cfg, str):
-            self.config = load_dataset_config(cfg)
-        else:
-            # when called from task.build_dataset()
-            self.config = cfg
-
-        self.data_type = self.config.data_type
-
-        self.vis_processors = {"train": BaseProcessor(), "eval": BaseProcessor()}
-        self.text_processors = {"train": BaseProcessor(), "eval": BaseProcessor()}
-
-    def build_datasets(self):
-        # download, split, etc...
-        # only called on 1 GPU/TPU in distributed
-
-        if is_main_process():
-            self._download_data()
-
-        if is_dist_avail_and_initialized():
-            dist.barrier()
-
-        # at this point, all the annotations and image/videos should be all downloaded to the specified locations.
-        logging.info("Building datasets...")
-        datasets = self.build()  # dataset['train'/'val'/'test']
-
-        return datasets
-
-    def build_processors(self):
-        vis_proc_cfg = self.config.get("vis_processor")
-        txt_proc_cfg = self.config.get("text_processor")
-
-        if vis_proc_cfg is not None:
-            vis_train_cfg = vis_proc_cfg.get("train")
-            vis_eval_cfg = vis_proc_cfg.get("eval")
-
-            self.vis_processors["train"] = self._build_proc_from_cfg(vis_train_cfg)
-            self.vis_processors["eval"] = self._build_proc_from_cfg(vis_eval_cfg)
-
-        if txt_proc_cfg is not None:
-            txt_train_cfg = txt_proc_cfg.get("train")
-            txt_eval_cfg = txt_proc_cfg.get("eval")
-
-            self.text_processors["train"] = self._build_proc_from_cfg(txt_train_cfg)
-            self.text_processors["eval"] = self._build_proc_from_cfg(txt_eval_cfg)
-
-    @staticmethod
-    def _build_proc_from_cfg(cfg):
-        return (
-            registry.get_processor_class(cfg.name).from_config(cfg)
-            if cfg is not None
-            else None
-        )
-
-    @classmethod
-    def default_config_path(cls, type="default"):
-        return utils.get_abs_path(cls.DATASET_CONFIG_DICT[type])
-
-    def _download_data(self):
-        self._download_ann()
-        self._download_vis()
-
-    def _download_ann(self):
-        """
-        Download annotation files if necessary.
-        All the vision-language datasets should have annotations of unified format.
-
-        storage_path can be:
-          (1) relative/absolute: will be prefixed with env.cache_root to make full path if relative.
-          (2) basename/dirname: will be suffixed with base name of URL if dirname is provided.
-
-        Local annotation paths should be relative.
-        """
-        anns = self.config.build_info.annotations
-
-        splits = anns.keys()
-
-        cache_root = registry.get_path("cache_root")
-
-        for split in splits:
-            info = anns[split]
-
-            urls, storage_paths = info.get("url", None), info.storage
-
-            if isinstance(urls, str):
-                urls = [urls]
-            if isinstance(storage_paths, str):
-                storage_paths = [storage_paths]
-
-            assert len(urls) == len(storage_paths)
-
-            for url_or_filename, storage_path in zip(urls, storage_paths):
-                # if storage_path is relative, make it full by prefixing with cache_root.
-                if not os.path.isabs(storage_path):
-                    storage_path = os.path.join(cache_root, storage_path)
-
-                dirname = os.path.dirname(storage_path)
-                if not os.path.exists(dirname):
-                    os.makedirs(dirname)
-
-                if os.path.isfile(url_or_filename):
-                    src, dst = url_or_filename, storage_path
-                    if not os.path.exists(dst):
-                        shutil.copyfile(src=src, dst=dst)
-                    else:
-                        logging.info("Using existing file {}.".format(dst))
-                else:
-                    if os.path.isdir(storage_path):
-                        # if only dirname is provided, suffix with basename of URL.
-                        raise ValueError(
-                            "Expecting storage_path to be a file path, got directory {}".format(
-                                storage_path
-                            )
-                        )
-                    else:
-                        filename = os.path.basename(storage_path)
-
-                    download_url(url=url_or_filename, root=dirname, filename=filename)
-
-    def _download_vis(self):
-
-        storage_path = self.config.build_info.get(self.data_type).storage
-        storage_path = utils.get_cache_path(storage_path)
-
-        if not os.path.exists(storage_path):
-            warnings.warn(
-                f"""
-                The specified path {storage_path} for visual inputs does not exist.
-                Please provide a correct path to the visual inputs or
-                refer to datasets/download_scripts/README.md for downloading instructions.
-                """
-            )
-
-    def build(self):
-        """
-        Create by split datasets inheriting torch.utils.data.Datasets.
-
-        # build() can be dataset-specific. Overwrite to customize.
-        """
-        self.build_processors()
-
-        build_info = self.config.build_info
-
-        ann_info = build_info.annotations
-        vis_info = build_info.get(self.data_type)
-
-        datasets = dict()
-        for split in ann_info.keys():
-            if split not in ["train", "val", "test"]:
-                continue
-
-            is_train = split == "train"
-
-            # processors
-            vis_processor = (
-                self.vis_processors["train"]
-                if is_train
-                else self.vis_processors["eval"]
-            )
-            text_processor = (
-                self.text_processors["train"]
-                if is_train
-                else self.text_processors["eval"]
-            )
-
-            # annotation path
-            ann_paths = ann_info.get(split).storage
-            if isinstance(ann_paths, str):
-                ann_paths = [ann_paths]
-
-            abs_ann_paths = []
-            for ann_path in ann_paths:
-                if not os.path.isabs(ann_path):
-                    ann_path = utils.get_cache_path(ann_path)
-                abs_ann_paths.append(ann_path)
-            ann_paths = abs_ann_paths
-
-            # visual data storage path
-            vis_path = os.path.join(vis_info.storage, split)
-
-            if not os.path.isabs(vis_path):
-                # vis_path = os.path.join(utils.get_cache_path(), vis_path)
-                vis_path = utils.get_cache_path(vis_path)
-
-            if not os.path.exists(vis_path):
-                warnings.warn("storage path {} does not exist.".format(vis_path))
-
-            # create datasets
-            dataset_cls = self.train_dataset_cls if is_train else self.eval_dataset_cls
-            datasets[split] = dataset_cls(
-                vis_processor=vis_processor,
-                text_processor=text_processor,
-                ann_paths=ann_paths,
-                vis_root=vis_path,
-            )
-
-        return datasets
-
-
-def load_dataset_config(cfg_path):
-    cfg = OmegaConf.load(cfg_path).datasets
-    cfg = cfg[list(cfg.keys())[0]]
-
-    return cfg
diff --git a/spaces/Vision-CAIR/minigpt4/minigpt4/datasets/builders/image_text_pair_builder.py b/spaces/Vision-CAIR/minigpt4/minigpt4/datasets/builders/image_text_pair_builder.py
deleted file mode 100644
index 3752be547778b7460c4cf3739723e723f9cb50d3..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/minigpt4/minigpt4/datasets/builders/image_text_pair_builder.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import os
-
-from minigpt4.common.registry import registry
-from minigpt4.datasets.builders.base_dataset_builder import BaseDatasetBuilder
-from minigpt4.datasets.datasets.laion_dataset import LaionDataset
-from minigpt4.datasets.datasets.cc_combine_dataset import CCCombineDataset, CCAlignDataset
-
-
-@registry.register_builder("cc_combine")
-class CCCombineBuilder(BaseDatasetBuilder):
-    train_dataset_cls = CCCombineDataset
-
-    DATASET_CONFIG_DICT = {"default": "configs/datasets/cc_combine/defaults.yaml"}
-
-    def _download_ann(self):
-        pass
-
-    def _download_vis(self):
-        pass
-
-    def build(self):
-        self.build_processors()
-
-        build_info = self.config.build_info
-
-        datasets = dict()
-        split = "train"
-
-        # create datasets
-        # [NOTE] return inner_datasets (wds.DataPipeline)
-        dataset_cls = self.train_dataset_cls
-        datasets[split] = dataset_cls(
-            vis_processor=self.vis_processors[split],
-            text_processor=self.text_processors[split],
-            location=build_info.storage,
-        ).inner_dataset
-
-        return datasets
-
-
-@registry.register_builder("laion")
-class LaionBuilder(BaseDatasetBuilder):
-    train_dataset_cls = LaionDataset
-
-    DATASET_CONFIG_DICT = {"default": "configs/datasets/laion/defaults.yaml"}
-
-    def _download_ann(self):
-        pass
-
-    def _download_vis(self):
-        pass
-
-    def build(self):
-        self.build_processors()
-
-        build_info = self.config.build_info
-
-        datasets = dict()
-        split = "train"
-
-        # create datasets
-        # [NOTE] return inner_datasets (wds.DataPipeline)
-        dataset_cls = self.train_dataset_cls
-        datasets[split] = dataset_cls(
-            vis_processor=self.vis_processors[split],
-            text_processor=self.text_processors[split],
-            location=build_info.storage,
-        ).inner_dataset
-
-        return datasets
-
-
-@registry.register_builder("cc_align")
-class CCAlignBuilder(BaseDatasetBuilder):
-    train_dataset_cls = CCAlignDataset
-
-    DATASET_CONFIG_DICT = {
-        "default": "configs/datasets/cc_combine/align.yaml",
-    }
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/utils/check_perf.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/utils/check_perf.py
deleted file mode 100644
index 3d7b9c7c4994fc39c8d7262589691e5b3e159712..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/utils/check_perf.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from ..script import *
-from .collect_env import *
-
-# Temporary POC for module-based script
-call_parse(check_perf)
-
diff --git a/spaces/XzJosh/Aatrox-Bert-VITS2/app.py b/spaces/XzJosh/Aatrox-Bert-VITS2/app.py
deleted file mode 100644
index 1a0b4e139b5068f77ff536e26e63a8c3c8628825..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Aatrox-Bert-VITS2/app.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import sys, os
-
-if sys.platform == "darwin":
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-import logging
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s")
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-
-
-net_g = None
-
-
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str)
-    del word2ph
-
-    assert bert.shape[-1] == len(phone)
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-
-    return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
-    global net_g
-    bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
-    with torch.no_grad():
-        x_tst=phones.to(device).unsqueeze(0)
-        tones=tones.to(device).unsqueeze(0)
-        lang_ids=lang_ids.to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        del phones
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
-        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
-                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
-        return audio
-
-def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
-    with torch.no_grad():
-        audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
-    return "Success", (hps.data.sampling_rate, audio)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_dir", default="./logs/Aatrox/G_2000.pth", help="path of your model")
-    parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file")
-    parser.add_argument("--share", default=False, help="make link public")
-    parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")
-
-    args = parser.parse_args()
-    if args.debug:
-        logger.info("Enable DEBUG-LEVEL log")
-        logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file(args.config_dir)
-    device = "cuda:0" if torch.cuda.is_available() else "cpu"
-    '''
-    device = (
-        "cuda:0"
-        if torch.cuda.is_available()
-        else (
-            "mps"
-            if sys.platform == "darwin" and torch.backends.mps.is_available()
-            else "cpu"
-        )
-    )
-    '''
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model).to(device)
-    _ = net_g.eval()
-
-    _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True)
-
-    speaker_ids = hps.data.spk2id
-    speakers = list(speaker_ids.keys())
-    with gr.Blocks() as app:
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown(value="""
-                【AI剑魔①】在线语音合成（Bert-Vits2）\n
-                作者：Xz乔希 https://space.bilibili.com/5859321\n
-                声音归属：《英雄联盟》暗裔剑魔·亚托克斯\n
-                Bert-VITS2项目：https://github.com/Stardust-minus/Bert-VITS2\n
-                【AI剑魔②】https://huggingface.co/spaces/XzJosh/Jianmo-Bert-VITS2\n
-                【AI剑魔③】https://huggingface.co/spaces/XzJosh/JM-Bert-VITS2\n
-                使用本模型请严格遵守法律法规！\n
-                发布二创作品请标注本项目作者及链接、作品使用Bert-VITS2 AI生成！\n                
-                """)
-                text = gr.TextArea(label="Text", placeholder="Input Text Here",
-                                      value="我是亚托克斯！我是世界的终结者！")
-                speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
-                sdp_ratio = gr.Slider(minimum=0.1, maximum=1, value=0.2, step=0.01, label='SDP/DP混合比')
-                noise_scale = gr.Slider(minimum=0.1, maximum=1, value=0.5, step=0.01, label='感情调节')
-                noise_scale_w = gr.Slider(minimum=0.1, maximum=1, value=0.9, step=0.01, label='音素长度')
-                length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='生成长度')
-                btn = gr.Button("点击生成", variant="primary")
-            with gr.Column():
-                text_output = gr.Textbox(label="Message")
-                audio_output = gr.Audio(label="Output Audio")
-                gr.Markdown(value="""
-                【AI塔菲】https://huggingface.co/spaces/XzJosh/Taffy-Bert-VITS2\n
-                【AI东雪莲】https://huggingface.co/spaces/XzJosh/Azuma-Bert-VITS2\n
-                【AI奶绿】https://huggingface.co/spaces/XzJosh/LAPLACE-Bert-VITS2\n
-                【AI尼奈】https://huggingface.co/spaces/XzJosh/nine1-Bert-VITS2\n
-                【AI珈乐】https://huggingface.co/spaces/XzJosh/Carol-Bert-VITS2\n
-                【AI电棍】https://huggingface.co/spaces/XzJosh/otto-Bert-VITS2\n
-                【AI七海】https://huggingface.co/spaces/XzJosh/Nana7mi-Bert-VITS2\n
-                【AI阿梓】https://huggingface.co/spaces/XzJosh/Azusa-Bert-VITS2\n
-                【AI星瞳】https://huggingface.co/spaces/XzJosh/XingTong-Bert-VITS2\n
-                【AI向晚】https://huggingface.co/spaces/XzJosh/Ava-Bert-VITS2\n
-                【AI嘉然】https://huggingface.co/spaces/XzJosh/Diana-Bert-VITS2\n
-                 """)
-        btn.click(tts_fn,
-                inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
-                outputs=[text_output, audio_output])
-    
-#    webbrowser.open("http://127.0.0.1:6006")
-#    app.launch(server_port=6006, show_error=True)
-        
-    app.launch(show_error=True)
diff --git a/spaces/XzJosh/Wenjing-Bert-VITS2/utils.py b/spaces/XzJosh/Wenjing-Bert-VITS2/utils.py
deleted file mode 100644
index c6aa6cfc64c33e2eed33e9845239e831fc1c4a1a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Wenjing-Bert-VITS2/utils.py
+++ /dev/null
@@ -1,293 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
-    learning_rate = checkpoint_dict['learning_rate']
-    if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
-        optimizer.load_state_dict(checkpoint_dict['optimizer'])
-    elif optimizer is None and not skip_optimizer:  
-    #else: #Disable this line if Infer ,and enable the line upper
-        new_opt_dict = optimizer.state_dict()
-        new_opt_dict_params = new_opt_dict['param_groups'][0]['params']
-        new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups']
-        new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params
-        optimizer.load_state_dict(new_opt_dict)
-    saved_state_dict = checkpoint_dict['model']
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            #assert "emb_g" not in k
-            # print("load", k)
-            new_state_dict[k] = saved_state_dict[k]
-            assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
-        except:
-            print("error, %s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, 'module'):
-        model.module.load_state_dict(new_state_dict, strict=False)
-    else:
-        model.load_state_dict(new_state_dict, strict=False)
-    print("load ")
-    logger.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info("Saving model and optimizer state at iteration {} to {}".format(
-        iteration, checkpoint_path))
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save({'model': state_dict,
-                'iteration': iteration,
-                'optimizer': optimizer.state_dict(),
-                'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
-    for k, v in scalars.items():
-        writer.add_scalar(k, v, global_step)
-    for k, v in histograms.items():
-        writer.add_histogram(k, v, global_step)
-    for k, v in images.items():
-        writer.add_image(k, v, global_step, dataformats='HWC')
-    for k, v in audios.items():
-        writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-    f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-    x = f_list[-1]
-    print(x)
-    return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                   interpolation='none')
-    fig.colorbar(im, ax=ax)
-    xlabel = 'Decoder timestep'
-    if info is not None:
-        xlabel += '\n\n' + info
-    plt.xlabel(xlabel)
-    plt.ylabel('Encoder timestep')
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding='utf-8') as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                        help='JSON file for configuration')
-    parser.add_argument('-m', '--model', type=str, default="./OUTPUT_MODEL",
-                        help='Model name')
-    parser.add_argument('--cont', dest='cont', action="store_true", default=False, help="whether to continue training on the latest checkpoint")
-
-    args = parser.parse_args()
-    model_dir = os.path.join("./logs", args.model)
-
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-
-    config_path = args.config
-    config_save_path = os.path.join(model_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    hparams.cont = args.cont
-    return hparams
-
-
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
-    """Freeing up space by deleting saved ckpts
-
-  Arguments:
-  path_to_models    --  Path to the model directory
-  n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
-  sort_by_time      --  True -> chronologically delete ckpts
-                        False -> lexicographically delete ckpts
-  """
-    import re
-    ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
-    name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
-    time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
-    sort_key = time_key if sort_by_time else name_key
-    x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')],
-                                 key=sort_key)
-    to_del = [os.path.join(path_to_models, fn) for fn in
-              (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
-    del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
-    del_routine = lambda x: [os.remove(x), del_info(x)]
-    rs = [del_routine(fn) for fn in to_del]
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-            source_dir
-        ))
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-                saved_hash[:8], cur_hash[:8]))
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams():
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/YONG627/456123/yolov5-code-main/restapi.py b/spaces/YONG627/456123/yolov5-code-main/restapi.py
deleted file mode 100644
index 0afc11e9e9d2a3a3c9f30bb547b06aafb8d4bf79..0000000000000000000000000000000000000000
--- a/spaces/YONG627/456123/yolov5-code-main/restapi.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run a Flask REST API exposing one or more YOLOv5s models
-"""
-
-import argparse
-import io
-import numpy as np
-import cv2
-
-import torch
-from flask import Flask, request
-from PIL import Image
-
-app = Flask(__name__)
-models = {}
-
-DETECTION_URL = '/v1/object-detection/<model>'
-
-
-@app.route(DETECTION_URL, methods=['POST'])
-def predict(model):
-    if request.method != 'POST':
-        return
-    
-    if request.data:
-        img = cv2.imdecode(np.frombuffer(request.data, dtype=np.uint8), cv2.IMREAD_COLOR)
-        if model in models:
-            results = models[model](img)  # reduce size=320 for faster inference
-            results = results.render()[0]
-            return cv2.imencode(".jpg", results)[1].tobytes()
-
-    if request.files.get('image'):
-        # Method 1
-        # with request.files["image"] as f:
-        #     im = Image.open(io.BytesIO(f.read()))
-
-        # Method 2
-        im_file = request.files['image']
-        im_bytes = im_file.read()
-        im = Image.open(io.BytesIO(im_bytes))
-
-        if model in models:
-            results = models[model](im, 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')
-    parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s')
-    opt = parser.parse_args()
-
-    for m in opt.model:
-        models[m] = torch.hub.load('./', m, source="local")
-
-    app.run(host='0.0.0.0', port=opt.port)  # debug=True causes Restarting with stat
diff --git a/spaces/YONG627/456123/yolov5-code-main/utils/activations.py b/spaces/YONG627/456123/yolov5-code-main/utils/activations.py
deleted file mode 100644
index 084ce8c41230dcde25f0c01311a4c0abcd4584e7..0000000000000000000000000000000000000000
--- a/spaces/YONG627/456123/yolov5-code-main/utils/activations.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Activation functions
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class SiLU(nn.Module):
-    # SiLU activation https://arxiv.org/pdf/1606.08415.pdf
-    @staticmethod
-    def forward(x):
-        return x * torch.sigmoid(x)
-
-
-class Hardswish(nn.Module):
-    # Hard-SiLU activation
-    @staticmethod
-    def forward(x):
-        # return x * F.hardsigmoid(x)  # for TorchScript and CoreML
-        return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0  # for TorchScript, CoreML and ONNX
-
-
-class Mish(nn.Module):
-    # Mish activation https://github.com/digantamisra98/Mish
-    @staticmethod
-    def forward(x):
-        return x * F.softplus(x).tanh()
-
-
-class MemoryEfficientMish(nn.Module):
-    # Mish activation memory-efficient
-    class F(torch.autograd.Function):
-
-        @staticmethod
-        def forward(ctx, x):
-            ctx.save_for_backward(x)
-            return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            x = ctx.saved_tensors[0]
-            sx = torch.sigmoid(x)
-            fx = F.softplus(x).tanh()
-            return grad_output * (fx + x * sx * (1 - fx * fx))
-
-    def forward(self, x):
-        return self.F.apply(x)
-
-
-class FReLU(nn.Module):
-    # FReLU activation https://arxiv.org/abs/2007.11824
-    def __init__(self, c1, k=3):  # ch_in, kernel
-        super().__init__()
-        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
-        self.bn = nn.BatchNorm2d(c1)
-
-    def forward(self, x):
-        return torch.max(x, self.bn(self.conv(x)))
-
-
-class AconC(nn.Module):
-    r""" ACON activation (activate or not)
-    AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
-    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
-    """
-
-    def __init__(self, c1):
-        super().__init__()
-        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
-
-    def forward(self, x):
-        dpx = (self.p1 - self.p2) * x
-        return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
-
-
-class MetaAconC(nn.Module):
-    r""" ACON activation (activate or not)
-    MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
-    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
-    """
-
-    def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
-        super().__init__()
-        c2 = max(r, c1 // r)
-        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
-        self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
-        # self.bn1 = nn.BatchNorm2d(c2)
-        # self.bn2 = nn.BatchNorm2d(c1)
-
-    def forward(self, x):
-        y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
-        # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
-        # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y)))))  # bug/unstable
-        beta = torch.sigmoid(self.fc2(self.fc1(y)))  # bug patch BN layers removed
-        dpx = (self.p1 - self.p2) * x
-        return dpx * torch.sigmoid(beta * dpx) + self.p2 * x
diff --git a/spaces/YazawaSunrise/so-vits-svc-LoveLive/share.py b/spaces/YazawaSunrise/so-vits-svc-LoveLive/share.py
deleted file mode 100644
index b9c3150693c47115bd9193f5fea5e42ab830732a..0000000000000000000000000000000000000000
--- a/spaces/YazawaSunrise/so-vits-svc-LoveLive/share.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from inference.infer_tool_grad import VitsSvc
-import gradio as gr
-import os
-
-class VitsGradio:
-    def __init__(self):
-        self.so = VitsSvc()
-        self.lspk = []
-        self.modelPaths = []
-        for root,dirs,files in os.walk("checkpoints"):
-            for dir in dirs:
-                self.modelPaths.append(dir)
-        with gr.Blocks() as self.Vits:
-            with gr.Tab("VoiceConversion"):
-                with gr.Row(visible=False) as self.VoiceConversion:
-                    with gr.Column():
-                        with gr.Row():
-                            with gr.Column():
-                                self.srcaudio = gr.Audio(label = "输入音频")
-                                self.btnVC = gr.Button("说话人转换")
-                            with gr.Column():
-                                self.dsid = gr.Dropdown(label = "目标角色", choices = self.lspk)
-                                self.tran = gr.Slider(label = "升降调", maximum = 60, minimum = -60, step = 1, value = 0)
-                                self.th = gr.Slider(label = "切片阈值", maximum = 32767, minimum = -32768, step = 0.1, value = -40)
-                        with gr.Row():
-                            self.VCOutputs = gr.Audio()
-                self.btnVC.click(self.so.inference, inputs=[self.srcaudio,self.dsid,self.tran,self.th], outputs=[self.VCOutputs])
-            with gr.Tab("SelectModel"):
-                with gr.Column():
-                    modelstrs = gr.Dropdown(label = "模型", choices = self.modelPaths, value = self.modelPaths[0], type = "value")
-                    devicestrs = gr.Dropdown(label = "设备", choices = ["cpu","cuda"], value = "cpu", type = "value")
-                    btnMod = gr.Button("载入模型")
-                    btnMod.click(self.loadModel, inputs=[modelstrs,devicestrs], outputs = [self.dsid,self.VoiceConversion])
-
-    def loadModel(self, path, device):
-        self.lspk = []
-        self.so.set_device(device)
-        self.so.loadCheckpoint(path)
-        for spk, sid in self.so.hps.spk.items():
-            self.lspk.append(spk)
-        VChange = gr.update(visible = True)
-        SDChange = gr.update(choices = self.lspk, value = self.lspk[0])
-        return [SDChange,VChange]
-
-grVits = VitsGradio()
-
-grVits.Vits.launch(share = True)
\ No newline at end of file
diff --git a/spaces/Yiqin/ChatVID/model/fastchat/serve/gradio_web_server_multi.py b/spaces/Yiqin/ChatVID/model/fastchat/serve/gradio_web_server_multi.py
deleted file mode 100644
index 705f6200c96e0e2cbc29729b472dc8157b7509e7..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/fastchat/serve/gradio_web_server_multi.py
+++ /dev/null
@@ -1,503 +0,0 @@
-import argparse
-from collections import defaultdict
-import datetime
-import json
-import os
-import time
-import uuid
-
-import gradio as gr
-import numpy as np
-import requests
-
-from fastchat.conversation import get_default_conv_template, SeparatorStyle
-from fastchat.constants import LOGDIR
-from fastchat.utils import (
-    build_logger,
-    server_error_msg,
-    violates_moderation,
-    moderation_msg,
-)
-from fastchat.serve.gradio_patch import Chatbot as grChatbot
-from fastchat.serve.gradio_web_server import (
-    http_bot,
-    set_global_vars,
-    get_window_url_params,
-    get_conv_log_filename,
-    block_css,
-    build_single_model_ui,
-    no_change_btn,
-    enable_btn,
-    disable_btn,
-    get_model_list,
-    load_demo_single,
-)
-from fastchat.serve.inference import compute_skip_echo_len
-
-
-logger = build_logger("gradio_web_server_multi", "gradio_web_server_multi.log")
-
-num_models = 2
-
-
-def load_demo_side_by_side(url_params):
-    states = (None,) * num_models
-
-    model_left = models[0]
-    if len(models) > 1:
-        weights = ([8, 4, 2, 1] + [1] * 32)[:len(models) - 1]
-        weights = weights / np.sum(weights)
-        model_right = np.random.choice(models[1:], p=weights)
-    else:
-        model_right = model_left
-
-    dropdown_updates = (
-        gr.Dropdown.update(model_left, visible=True),
-        gr.Dropdown.update(model_right, visible=True),
-    )
-
-    return (
-        states
-        + dropdown_updates
-        + (gr.Chatbot.update(visible=True),) * num_models
-        + (
-            gr.Textbox.update(visible=True),
-            gr.Box.update(visible=True),
-            gr.Row.update(visible=True),
-            gr.Row.update(visible=True),
-            gr.Accordion.update(visible=True),
-        )
-    )
-
-
-def load_demo(url_params, request: gr.Request):
-    logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")
-    selected = 0
-    if "arena" in url_params or "compare" in url_params:
-        selected = 1
-    single_updates = load_demo_single(url_params)
-    side_by_side_updates = load_demo_side_by_side(url_params)
-    return (gr.Tabs.update(selected=selected),) + single_updates + side_by_side_updates
-
-
-def vote_last_response(states, vote_type, model_selectors, request: gr.Request):
-    with open(get_conv_log_filename(), "a") as fout:
-        data = {
-            "tstamp": round(time.time(), 4),
-            "type": vote_type,
-            "models": [x for x in model_selectors],
-            "states": [x.dict() for x in states],
-            "ip": request.client.host,
-        }
-        fout.write(json.dumps(data) + "\n")
-
-
-def leftvote_last_response(
-    state0, state1, model_selector0, model_selector1, request: gr.Request
-):
-    logger.info(f"leftvote. ip: {request.client.host}")
-    vote_last_response(
-        [state0, state1], "leftvote", [model_selector0, model_selector1], request
-    )
-    return ("",) + (disable_btn,) * 3
-
-
-def rightvote_last_response(
-    state0, state1, model_selector0, model_selector1, request: gr.Request
-):
-    logger.info(f"rightvote. ip: {request.client.host}")
-    vote_last_response(
-        [state0, state1], "rightvote", [model_selector0, model_selector1], request
-    )
-    return ("",) + (disable_btn,) * 3
-
-
-def tievote_last_response(
-    state0, state1, model_selector0, model_selector1, request: gr.Request
-):
-    logger.info(f"tievote. ip: {request.client.host}")
-    vote_last_response(
-        [state0, state1], "tievote", [model_selector0, model_selector1], request
-    )
-    return ("",) + (disable_btn,) * 3
-
-
-def regenerate(state0, state1, request: gr.Request):
-    logger.info(f"regenerate. ip: {request.client.host}")
-    states = [state0, state1]
-    for i in range(num_models):
-        states[i].messages[-1][-1] = None
-        states[i].skip_next = False
-    return states + [x.to_gradio_chatbot() for x in states] + [""] + [disable_btn] * 5
-
-
-def clear_history(request: gr.Request):
-    logger.info(f"clear_history. ip: {request.client.host}")
-    return [None] * num_models + [None] * num_models + [""] + [disable_btn] * 5
-
-
-def share_click(state0, state1, model_selector0, model_selector1,
-                request: gr.Request):
-    logger.info(f"share. ip: {request.client.host}")
-    if state0 is not None and state1 is not None:
-        vote_last_response(
-            [state0, state1], "share", [model_selector0, model_selector1], request
-        )
-
-
-def add_text(state0, state1, text, request: gr.Request):
-    logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}")
-    states = [state0, state1]
-
-    for i in range(num_models):
-        if states[i] is None:
-            states[i] = get_default_conv_template("vicuna").copy()
-
-    if len(text) <= 0:
-        for i in range(num_models):
-            states[i].skip_next = True
-        return (
-            states
-            + [x.to_gradio_chatbot() for x in states]
-            + [""]
-            + [
-                no_change_btn,
-            ]
-            * 5
-        )
-
-    if args.moderate:
-        flagged = violates_moderation(text)
-        if flagged:
-            logger.info(f"violate moderation. ip: {request.client.host}. text: {text}")
-            for i in range(num_models):
-                states[i].skip_next = True
-            return (
-                states
-                + [x.to_gradio_chatbot() for x in states]
-                + [moderation_msg]
-                + [
-                    no_change_btn,
-                ]
-                * 5
-            )
-
-    text = text[:1536]  # Hard cut-off
-    for i in range(num_models):
-        states[i].append_message(states[i].roles[0], text)
-        states[i].append_message(states[i].roles[1], None)
-        states[i].skip_next = False
-
-    return (
-        states
-        + [x.to_gradio_chatbot() for x in states]
-        + [""]
-        + [
-            disable_btn,
-        ]
-        * 5
-    )
-
-
-def http_bot_all(
-    state0,
-    state1,
-    model_selector0,
-    model_selector1,
-    temperature,
-    max_new_tokens,
-    request: gr.Request,
-):
-    logger.info(f"http_bot_all. ip: {request.client.host}")
-    states = [state0, state1]
-    model_selector = [model_selector0, model_selector1]
-    gen = []
-    for i in range(num_models):
-        gen.append(
-            http_bot(states[i], model_selector[i], temperature, max_new_tokens, request)
-        )
-
-    chatbots = [None] * num_models
-    while True:
-        stop = True
-        for i in range(num_models):
-            try:
-                ret = next(gen[i])
-                states[i], chatbots[i] = ret[0], ret[1]
-                buttons = ret[2:]
-                stop = False
-            except StopIteration:
-                pass
-        yield states + chatbots + list(buttons)
-        if stop:
-            break
-
-    for i in range(10):
-        if i % 2 == 0:
-            yield states + chatbots + [disable_btn] * 3 + list(buttons)[3:]
-        else:
-            yield states + chatbots + list(buttons)
-        time.sleep(0.2)
-
-
-def build_side_by_side_ui():
-    notice_markdown = """
-# ⚔️  Chatbot Arena ⚔️ 
-- Chat with state-of-the-art open models **side-by-side** and vote for which one is better!
-- [[GitHub]](https://github.com/lm-sys/FastChat) [[Twitter]](https://twitter.com/lmsysorg) [[Discord]](https://discord.gg/h6kCZb72G7)
-
-### Terms of use
-By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. **The service collects user dialogue data for future research.**
-The demo works better on desktop devices with a wide screen.
-
-### Choose two models to chat with
-| | |
-| ---- | ---- |
-| [Vicuna](https://vicuna.lmsys.org): a chat assistant fine-tuned from LLaMA on user-shared conversations by LMSYS. | [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/): a dialogue model for academic research by BAIR |
-| [OpenAssistant (oasst)](https://open-assistant.io/): a chat-based assistant for everyone by LAION. | [Dolly](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm): an instruction-tuned open large language model by Databricks. |
-| [ChatGLM](https://chatglm.cn/blog): an open bilingual dialogue language model by Tsinghua University | [StableLM](https://github.com/stability-AI/stableLM/): Stability AI language models. |
-| [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html): a model fine-tuned from LLaMA on instruction-following demonstrations by Stanford. | [LLaMA](https://arxiv.org/abs/2302.13971): open and efficient foundation language models by Meta. |
-"""
-
-    learn_more_markdown = """
-### License
-The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
-"""
-
-    states = [gr.State() for _ in range(num_models)]
-    model_selectors = [None] * num_models
-    chatbots = [None] * num_models
-
-    notice = gr.Markdown(notice_markdown, elem_id="notice_markdown")
-
-    with gr.Box(elem_id="share-region"):
-        with gr.Row():
-            for i in range(num_models):
-                with gr.Column():
-                    model_selectors[i] = gr.Dropdown(
-                        choices=models,
-                        value=models[i] if len(models) > i else "",
-                        interactive=True,
-                        show_label=False,
-                    ).style(container=False)
-
-        with gr.Row():
-            for i in range(num_models):
-                label = "Left" if i == 0 else "Right"
-                with gr.Column():
-                    chatbots[i] = grChatbot(label=label, elem_id=f"chatbot{i}",
-                        visible=False).style(height=550)
-
-        with gr.Box() as button_row:
-            with gr.Row():
-                leftvote_btn = gr.Button(value="👈 Left is better", interactive=False)
-                tie_btn = gr.Button(value="🤝 Tie", interactive=False)
-                rightvote_btn = gr.Button(value="👉 Right is better", interactive=False)
-
-    with gr.Row():
-        with gr.Column(scale=20):
-            textbox = gr.Textbox(
-                show_label=False,
-                placeholder="Enter text and press ENTER",
-                visible=False,
-            ).style(container=False)
-        with gr.Column(scale=1, min_width=50):
-            send_btn = gr.Button(value="Send", visible=False)
-
-    with gr.Row() as button_row2:
-        regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=False)
-        clear_btn = gr.Button(value="🗑️  Clear history", interactive=False)
-        share_btn = gr.Button(value="📷  Share")
-
-    with gr.Accordion("Parameters", open=False, visible=True) as parameter_row:
-        temperature = gr.Slider(
-            minimum=0.0,
-            maximum=1.0,
-            value=0.7,
-            step=0.1,
-            interactive=True,
-            label="Temperature",
-        )
-        max_output_tokens = gr.Slider(
-            minimum=0,
-            maximum=1024,
-            value=512,
-            step=64,
-            interactive=True,
-            label="Max output tokens",
-        )
-
-    gr.Markdown(learn_more_markdown)
-
-    # Register listeners
-    btn_list = [leftvote_btn, rightvote_btn, tie_btn, regenerate_btn, clear_btn]
-    leftvote_btn.click(
-        leftvote_last_response,
-        states + model_selectors,
-        [textbox, leftvote_btn, rightvote_btn, tie_btn],
-    )
-    rightvote_btn.click(
-        rightvote_last_response,
-        states + model_selectors,
-        [textbox, leftvote_btn, rightvote_btn, tie_btn],
-    )
-    tie_btn.click(
-        tievote_last_response,
-        states + model_selectors,
-        [textbox, leftvote_btn, rightvote_btn, tie_btn],
-    )
-    regenerate_btn.click(
-        regenerate, states, states + chatbots + [textbox] + btn_list
-    ).then(
-        http_bot_all,
-        states + model_selectors + [temperature, max_output_tokens],
-        states + chatbots + btn_list,
-    )
-    clear_btn.click(clear_history, None, states + chatbots + [textbox] + btn_list)
-
-    share_js="""
-function (a, b, c, d) {
-    const captureElement = document.querySelector('#share-region');
-    html2canvas(captureElement)
-        .then(canvas => {
-            canvas.style.display = 'none'
-            document.body.appendChild(canvas)
-            return canvas
-        })
-        .then(canvas => {
-            const image = canvas.toDataURL('image/png')
-            const a = document.createElement('a')
-            a.setAttribute('download', 'chatbot-arena.png')
-            a.setAttribute('href', image)
-            a.click()
-            canvas.remove()
-        });
-    return [a, b, c, d];
-}
-"""
-    share_btn.click(share_click, states + model_selectors, [], _js=share_js)
-
-    for i in range(num_models):
-        model_selectors[i].change(
-            clear_history, None, states + chatbots + [textbox] + btn_list
-        )
-
-    textbox.submit(
-        add_text, states + [textbox], states + chatbots + [textbox] + btn_list
-    ).then(
-        http_bot_all,
-        states + model_selectors + [temperature, max_output_tokens],
-        states + chatbots + btn_list,
-    )
-    send_btn.click(
-        add_text, states + [textbox], states + chatbots + [textbox] + btn_list
-    ).then(
-        http_bot_all,
-        states + model_selectors + [temperature, max_output_tokens],
-        states + chatbots + btn_list,
-    )
-
-    return (
-        states,
-        model_selectors,
-        chatbots,
-        textbox,
-        send_btn,
-        button_row,
-        button_row2,
-        parameter_row,
-    )
-
-
-def build_demo():
-    with gr.Blocks(
-        title="Chat with Open Large Language Models",
-        theme=gr.themes.Base(),
-        css=block_css,
-    ) as demo:
-        with gr.Tabs() as tabs:
-            with gr.Tab("Single Model", id=0):
-                (
-                    a_state,
-                    a_model_selector,
-                    a_chatbot,
-                    a_textbox,
-                    a_send_btn,
-                    a_button_row,
-                    a_parameter_row,
-                ) = build_single_model_ui()
-                a_list = [
-                    a_state,
-                    a_model_selector,
-                    a_chatbot,
-                    a_textbox,
-                    a_send_btn,
-                    a_button_row,
-                    a_parameter_row,
-                ]
-
-            with gr.Tab("Chatbot Arena", id=1):
-                (
-                    b_states,
-                    b_model_selectors,
-                    b_chatbots,
-                    b_textbox,
-                    b_send_btn,
-                    b_button_row,
-                    b_button_row2,
-                    b_parameter_row,
-                ) = build_side_by_side_ui()
-                b_list = (
-                    b_states
-                    + b_model_selectors
-                    + b_chatbots
-                    + [
-                        b_textbox,
-                        b_send_btn,
-                        b_button_row,
-                        b_button_row2,
-                        b_parameter_row,
-                    ]
-                )
-
-        url_params = gr.JSON(visible=False)
-
-        if args.model_list_mode == "once":
-            demo.load(
-                load_demo,
-                [url_params],
-                [tabs] + a_list + b_list,
-                _js=get_window_url_params,
-            )
-        else:
-            raise ValueError(f"Unknown model list mode: {args.model_list_mode}")
-
-    return demo
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--host", type=str, default="0.0.0.0")
-    parser.add_argument("--port", type=int)
-    parser.add_argument("--controller-url", type=str, default="http://localhost:21001")
-    parser.add_argument("--concurrency-count", type=int, default=10)
-    parser.add_argument(
-        "--model-list-mode", type=str, default="once", choices=["once", "reload"]
-    )
-    parser.add_argument("--share", action="store_true")
-    parser.add_argument(
-        "--moderate", action="store_true", help="Enable content moderation"
-    )
-    args = parser.parse_args()
-    logger.info(f"args: {args}")
-
-    models = get_model_list(args.controller_url)
-    set_global_vars(args.controller_url, args.moderate, models)
-
-    logger.info(args)
-    demo = build_demo()
-    demo.queue(
-        concurrency_count=args.concurrency_count, status_update_rate=10, api_open=False
-    ).launch(
-        server_name=args.host, server_port=args.port, share=args.share, max_threads=200
-    )
diff --git a/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/tokenizer/ptbtokenizer.py b/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/tokenizer/ptbtokenizer.py
deleted file mode 100644
index 68ca0f0869d1b078174bb03d7087627fc2e5ed86..0000000000000000000000000000000000000000
--- a/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/tokenizer/ptbtokenizer.py
+++ /dev/null
@@ -1,75 +0,0 @@
-#!/usr/bin/env python
-# 
-# File Name : ptbtokenizer.py
-#
-# Description : Do the PTB Tokenization and remove punctuations.
-#
-# Creation Date : 29-12-2014
-# Last Modified : Thu Mar 19 09:53:35 2015
-# Authors : Hao Fang <hfang@uw.edu> and Tsung-Yi Lin <tl483@cornell.edu>
-
-# =================================================================
-# This code was pulled from https://github.com/tylin/coco-caption
-# and refactored to Python 3.
-# Image-specific names and comments have been changed to be audio-specific
-# =================================================================
-
-import os
-import sys
-import subprocess
-import tempfile
-import itertools
-
-# path to the stanford corenlp jar
-STANFORD_CORENLP_3_4_1_JAR = 'stanford-corenlp-3.4.1.jar'
-
-# punctuations to be removed from the sentences
-PUNCTUATIONS = ["''", "'", "``", "`", "-LRB-", "-RRB-", "-LCB-", "-RCB-", \
-        ".", "?", "!", ",", ":", "-", "--", "...", ";"] 
-
-class PTBTokenizer:
-    """Python wrapper of Stanford PTBTokenizer"""
-
-    def tokenize(self, captions_for_audio):
-        cmd = ['java', '-cp', STANFORD_CORENLP_3_4_1_JAR, \
-                'edu.stanford.nlp.process.PTBTokenizer', \
-                '-preserveLines', '-lowerCase']
-
-        # ======================================================
-        # prepare data for PTB Tokenizer
-        # ======================================================
-        final_tokenized_captions_for_audio = {}
-        audio_id = [k for k, v in captions_for_audio.items() for _ in range(len(v))]
-        sentences = '\n'.join([c['caption'].replace('\n', ' ') for k, v in captions_for_audio.items() for c in v])
-
-        # ======================================================
-        # save sentences to temporary file
-        # ======================================================
-        path_to_jar_dirname=os.path.dirname(os.path.abspath(__file__))
-        tmp_file = tempfile.NamedTemporaryFile(delete=False, dir=path_to_jar_dirname)
-        tmp_file.write(sentences.encode())
-        tmp_file.close()
-
-        # ======================================================
-        # tokenize sentence
-        # ======================================================
-        cmd.append(os.path.basename(tmp_file.name))
-        p_tokenizer = subprocess.Popen(cmd, cwd=path_to_jar_dirname, \
-                stdout=subprocess.PIPE)
-        token_lines = p_tokenizer.communicate(input=sentences.rstrip())[0]
-        token_lines = token_lines.decode()
-        lines = token_lines.split('\n')
-        # remove temp file
-        os.remove(tmp_file.name)
-
-        # ======================================================
-        # create dictionary for tokenized captions
-        # ======================================================
-        for k, line in zip(audio_id, lines):
-            if not k in final_tokenized_captions_for_audio:
-                final_tokenized_captions_for_audio[k] = []
-            tokenized_caption = ' '.join([w for w in line.rstrip().split(' ') \
-                    if w not in PUNCTUATIONS])
-            final_tokenized_captions_for_audio[k].append(tokenized_caption)
-
-        return final_tokenized_captions_for_audio
diff --git a/spaces/YuAnthony/Voice-Recognition/app.py b/spaces/YuAnthony/Voice-Recognition/app.py
deleted file mode 100644
index a7ad63ddd74086fe23ef3fdec4be62ac4fc9d8fb..0000000000000000000000000000000000000000
--- a/spaces/YuAnthony/Voice-Recognition/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import gradio as gr
-
-import argparse
-import functools
-
-import numpy as np
-import torch
-from infer_contrast import run
-
-from utils.reader import load_audio
-from utils.utility import add_arguments, print_arguments
-
-
-STYLE = """
-<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" integrity="sha256-YvdLHPgkqJ8DVUxjjnGVlMMJtNimJ6dYkowFFvp4kKs=" crossorigin="anonymous">
-"""
-OUTPUT_OK = (
-    STYLE
-    + """
-    <div class="container">
-        <div class="row"><h1 style="text-align: center">Speaker1 和 Speaker2</h1></div>
-        <div class="row"><h1 class="display-1 text-success" style="text-align: center">是同一人</h1></div>
-        <div class="row"><h1 style="text-align: center">相似度为:</h1></div>
-        <div class="row"><h1 class="display-1 text-success" style="text-align: center">{:.1f}%</h1></div>
-        <div class="row"><small style="text-align: center">(相似度为70%以上才能被认为是同一人)</small><div class="row">
-    </div>
-"""
-)
-OUTPUT_FAIL = (
-    STYLE
-    + """
-    <div class="container">
-        <div class="row"><h1 style="text-align: center">Speaker1 和 Speaker2</h1></div>
-        <div class="row"><h1 class="display-1 text-danger" style="text-align: center">不是同一人</h1></div>
-        <div class="row"><h1 style="text-align: center">相似度为:</h1></div>
-        <div class="row"><h1 class="text-danger" style="text-align: center">{:.1f}%</h1></div>
-        <div class="row"><small style="text-align: center">(相似度为70%以上才能被认为是同一人)</small><div class="row">
-    </div>
-"""
-)
-
-THRESHOLD = 0.70
-def voiceRecognition(audio1,audio2):
-    score = run(audio1,audio2)
-    if score >= THRESHOLD:
-        output = OUTPUT_OK.format(score * 100)
-    else:
-        output = OUTPUT_FAIL.format(score * 100)
-    return output
-
-
-title = "Voice Recognition"
-description = "This voice recognition demo(Chinese Format) is a simple implementation based on ResNet. It used ArcFace Loss and an open source Chinese voice corpus - zhvoice."
-
-inputs = [gr.inputs.Audio(source="upload",type="filepath", label="Speaker1"),
-          gr.inputs.Audio(source="upload",type="filepath", label="Speaker2")]
-
-output = gr.outputs.HTML(label="")
-          
-article = (
-    "<p style='text-align: center'>"
-    "<a href='https://github.com/yeyupiaoling/VoiceprintRecognition-Pytorch' target='_blank'>💻 Code Repository</a> | "
-    "<a href='https://github.com/fighting41love/zhvoice' target='_blank'>🎙️ zhvoice Dataset</a> | "
-    "</p>"
-)
-
-examples = [
-    ["samples/李云龙1.wav", "samples/李云龙2.wav"],
-    ["samples/马保国1.wav", "samples/马保国2.wav"],
-    ["samples/周杰伦1.wav", "samples/周杰伦2.wav"],
-    ["samples/海绵宝宝1.wav", "samples/派大星.wav"],
-    ["samples/海绵宝宝1.wav", "samples/海绵宝宝2.wav"],
-    ["samples/周星驰.wav", "samples/吴孟达.wav"]]
-
-interface = gr.Interface(
-    fn=voiceRecognition,
-    inputs=inputs,
-    outputs=output,
-    title=title,
-    description=description,
-    examples=examples,
-    examples_per_page=3,
-    article=article,
-    enable_queue=True)
-interface.launch(debug=True,share=True)
\ No newline at end of file
diff --git a/spaces/Yudha515/Rvc-Models/tests/modules/test_conv.py b/spaces/Yudha515/Rvc-Models/tests/modules/test_conv.py
deleted file mode 100644
index 28fbc4f1a0ebaf41b56947b767958ae696e75eec..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/tests/modules/test_conv.py
+++ /dev/null
@@ -1,203 +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 itertools import product
-import math
-import random
-
-import pytest
-import torch
-from torch import nn
-
-from audiocraft.modules import (
-    NormConv1d,
-    NormConvTranspose1d,
-    StreamableConv1d,
-    StreamableConvTranspose1d,
-    pad1d,
-    unpad1d,
-)
-
-
-def test_get_extra_padding_for_conv1d():
-    # TODO: Implement me!
-    pass
-
-
-def test_pad1d_zeros():
-    x = torch.randn(1, 1, 20)
-
-    xp1 = pad1d(x, (0, 5), mode='constant', value=0.)
-    assert xp1.shape[-1] == 25
-    xp2 = pad1d(x, (5, 5), mode='constant', value=0.)
-    assert xp2.shape[-1] == 30
-    xp3 = pad1d(x, (0, 0), mode='constant', value=0.)
-    assert xp3.shape[-1] == 20
-    xp4 = pad1d(x, (10, 30), mode='constant', value=0.)
-    assert xp4.shape[-1] == 60
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, 0), mode='constant', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (0, -1), mode='constant', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, -1), mode='constant', value=0.)
-
-
-def test_pad1d_reflect():
-    x = torch.randn(1, 1, 20)
-
-    xp1 = pad1d(x, (0, 5), mode='reflect', value=0.)
-    assert xp1.shape[-1] == 25
-    xp2 = pad1d(x, (5, 5), mode='reflect', value=0.)
-    assert xp2.shape[-1] == 30
-    xp3 = pad1d(x, (0, 0), mode='reflect', value=0.)
-    assert xp3.shape[-1] == 20
-    xp4 = pad1d(x, (10, 30), mode='reflect', value=0.)
-    assert xp4.shape[-1] == 60
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, 0), mode='reflect', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (0, -1), mode='reflect', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, -1), mode='reflect', value=0.)
-
-
-def test_unpad1d():
-    x = torch.randn(1, 1, 20)
-
-    u1 = unpad1d(x, (5, 5))
-    assert u1.shape[-1] == 10
-    u2 = unpad1d(x, (0, 5))
-    assert u2.shape[-1] == 15
-    u3 = unpad1d(x, (5, 0))
-    assert u3.shape[-1] == 15
-    u4 = unpad1d(x, (0, 0))
-    assert u4.shape[-1] == x.shape[-1]
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (-1, 0))
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (0, -1))
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (-1, -1))
-
-
-class TestNormConv1d:
-
-    def test_norm_conv1d_modules(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out, kernel_size, stride = 1, 4, 1
-        expected_out_length = int((T - kernel_size) / stride + 1)
-        wn_conv = NormConv1d(C, 1, kernel_size=4, norm='weight_norm')
-        gn_conv = NormConv1d(C, 1, kernel_size=4, norm='time_group_norm')
-        nn_conv = NormConv1d(C, 1, kernel_size=4, norm='none')
-
-        assert isinstance(wn_conv.norm, nn.Identity)
-        assert isinstance(wn_conv.conv, nn.Conv1d)
-
-        assert isinstance(gn_conv.norm, nn.GroupNorm)
-        assert isinstance(gn_conv.conv, nn.Conv1d)
-
-        assert isinstance(nn_conv.norm, nn.Identity)
-        assert isinstance(nn_conv.conv, nn.Conv1d)
-
-        for conv_layer in [wn_conv, gn_conv, nn_conv]:
-            out = conv_layer(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestNormConvTranspose1d:
-
-    def test_normalizations(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out, kernel_size, stride = 1, 4, 1
-        expected_out_length = (T - 1) * stride + (kernel_size - 1) + 1
-
-        wn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='weight_norm')
-        gn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='time_group_norm')
-        nn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='none')
-
-        assert isinstance(wn_convtr.norm, nn.Identity)
-        assert isinstance(wn_convtr.convtr, nn.ConvTranspose1d)
-
-        assert isinstance(gn_convtr.norm, nn.GroupNorm)
-        assert isinstance(gn_convtr.convtr, nn.ConvTranspose1d)
-
-        assert isinstance(nn_convtr.norm, nn.Identity)
-        assert isinstance(nn_convtr.convtr, nn.ConvTranspose1d)
-
-        for convtr_layer in [wn_convtr, gn_convtr, nn_convtr]:
-            out = convtr_layer(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestStreamableConv1d:
-
-    def get_streamable_conv1d_output_length(self, length, kernel_size, stride, dilation):
-        # StreamableConv1d internally pads to make sure that the last window is full
-        padding_total = (kernel_size - 1) * dilation - (stride - 1)
-        n_frames = (length - kernel_size + padding_total) / stride + 1
-        ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
-        return ideal_length // stride
-
-    def test_streamable_conv1d(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-        C_out = 1
-
-        # conv params are [(kernel_size, stride, dilation)]
-        conv_params = [(4, 1, 1), (4, 2, 1), (3, 1, 3), (10, 5, 1), (3, 2, 3)]
-        for causal, (kernel_size, stride, dilation) in product([False, True], conv_params):
-            expected_out_length = self.get_streamable_conv1d_output_length(T, kernel_size, stride, dilation)
-            sconv = StreamableConv1d(C, C_out, kernel_size=kernel_size, stride=stride, dilation=dilation, causal=causal)
-            out = sconv(t0)
-            assert isinstance(out, torch.Tensor)
-            print(list(out.shape), [N, C_out, expected_out_length])
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestStreamableConvTranspose1d:
-
-    def get_streamable_convtr1d_output_length(self, length, kernel_size, stride):
-        padding_total = (kernel_size - stride)
-        return (length - 1) * stride - padding_total + (kernel_size - 1) + 1
-
-    def test_streamable_convtr1d(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out = 1
-
-        with pytest.raises(AssertionError):
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=False, trim_right_ratio=0.5)
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=-1.)
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=2)
-
-        # causal params are [(causal, trim_right)]
-        causal_params = [(False, 1.0), (True, 1.0), (True, 0.5), (True, 0.0)]
-        # conv params are [(kernel_size, stride)]
-        conv_params = [(4, 1), (4, 2), (3, 1), (10, 5)]
-        for ((causal, trim_right_ratio), (kernel_size, stride)) in product(causal_params, conv_params):
-            expected_out_length = self.get_streamable_convtr1d_output_length(T, kernel_size, stride)
-            sconvtr = StreamableConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride,
-                                                causal=causal, trim_right_ratio=trim_right_ratio)
-            out = sconvtr(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
diff --git a/spaces/Yukki-Yui/moe-tts/text/cleaners.py b/spaces/Yukki-Yui/moe-tts/text/cleaners.py
deleted file mode 100644
index 571e96865d4fbcd830700b9c55355271b1eeef57..0000000000000000000000000000000000000000
--- a/spaces/Yukki-Yui/moe-tts/text/cleaners.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import re
-
-
-def japanese_cleaners(text):
-    from text.japanese import japanese_to_romaji_with_accent
-    text = japanese_to_romaji_with_accent(text)
-    if len(text) == 0 or re.match('[A-Za-z]', text[-1]):
-        text += '.'
-    return text
-
-
-def japanese_cleaners2(text):
-    return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-
-def korean_cleaners(text):
-    '''Pipeline for Korean text'''
-    from text.korean import latin_to_hangul, number_to_hangul, divide_hangul
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text = divide_hangul(text)
-    if len(text) == 0 or re.match('[\u3131-\u3163]', text[-1]):
-        text += '.'
-    return text
-
-
-def chinese_cleaners(text):
-    '''Pipeline for Chinese text'''
-    from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    if len(text) == 0 or re.match('[ˉˊˇˋ˙]', text[-1]):
-        text += '。'
-    return text
-
-
-def zh_ja_mixture_cleaners(text):
-    from text.mandarin import chinese_to_romaji
-    from text.japanese import japanese_to_romaji_with_accent
-    chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
-    japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
-    for chinese_text in chinese_texts:
-        cleaned_text = chinese_to_romaji(chinese_text[4:-4])
-        text = text.replace(chinese_text, cleaned_text + ' ', 1)
-    for japanese_text in japanese_texts:
-        cleaned_text = japanese_to_romaji_with_accent(
-            japanese_text[4:-4]).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')
-        text = text.replace(japanese_text, cleaned_text + ' ', 1)
-    text = text[:-1]
-    if len(text) == 0 or re.match('[A-Za-zɯɹəɥ→↓↑]', text[-1]):
-        text += '.'
-    return text
-
-
-def sanskrit_cleaners(text):
-    text = text.replace('॥', '।').replace('ॐ', 'ओम्')
-    if len(text) == 0 or text[-1] != '।':
-        text += ' ।'
-    return text
-
-
-def cjks_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_lazy_ipa
-    from text.sanskrit import devanagari_to_ipa
-    chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
-    japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
-    korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
-    sanskrit_texts = re.findall(r'\[SA\].*?\[SA\]', text)
-    for chinese_text in chinese_texts:
-        cleaned_text = chinese_to_lazy_ipa(chinese_text[4:-4])
-        text = text.replace(chinese_text, cleaned_text + ' ', 1)
-    for japanese_text in japanese_texts:
-        cleaned_text = japanese_to_ipa(japanese_text[4:-4])
-        text = text.replace(japanese_text, cleaned_text + ' ', 1)
-    for korean_text in korean_texts:
-        cleaned_text = korean_to_lazy_ipa(korean_text[4:-4])
-        text = text.replace(korean_text, cleaned_text + ' ', 1)
-    for sanskrit_text in sanskrit_texts:
-        cleaned_text = devanagari_to_ipa(sanskrit_text[4:-4])
-        text = text.replace(sanskrit_text, cleaned_text + ' ', 1)
-    text = text[:-1]
-    if len(text) == 0 or re.match(r'[^\.,!\?\-…~]', text[-1]):
-        text += '.'
-    return text
diff --git a/spaces/abby711/FaceRestoration/gfpgan/archs/stylegan2_clean_arch.py b/spaces/abby711/FaceRestoration/gfpgan/archs/stylegan2_clean_arch.py
deleted file mode 100644
index 9e2ee94e50401b95e4c9997adef5581d521d725f..0000000000000000000000000000000000000000
--- a/spaces/abby711/FaceRestoration/gfpgan/archs/stylegan2_clean_arch.py
+++ /dev/null
@@ -1,368 +0,0 @@
-import math
-import random
-import torch
-from basicsr.archs.arch_util import default_init_weights
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn
-from torch.nn import functional as F
-
-
-class NormStyleCode(nn.Module):
-
-    def forward(self, x):
-        """Normalize the style codes.
-
-        Args:
-            x (Tensor): Style codes with shape (b, c).
-
-        Returns:
-            Tensor: Normalized tensor.
-        """
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
-
-
-class ModulatedConv2d(nn.Module):
-    """Modulated Conv2d used in StyleGAN2.
-
-    There is no bias in ModulatedConv2d.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether to demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
-        eps (float): A value added to the denominator for numerical stability. Default: 1e-8.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 num_style_feat,
-                 demodulate=True,
-                 sample_mode=None,
-                 eps=1e-8):
-        super(ModulatedConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.demodulate = demodulate
-        self.sample_mode = sample_mode
-        self.eps = eps
-
-        # modulation inside each modulated conv
-        self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)
-        # initialization
-        default_init_weights(self.modulation, scale=1, bias_fill=1, a=0, mode='fan_in', nonlinearity='linear')
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) /
-            math.sqrt(in_channels * kernel_size**2))
-        self.padding = kernel_size // 2
-
-    def forward(self, x, style):
-        """Forward function.
-
-        Args:
-            x (Tensor): Tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-
-        Returns:
-            Tensor: Modulated tensor after convolution.
-        """
-        b, c, h, w = x.shape  # c = c_in
-        # weight modulation
-        style = self.modulation(style).view(b, 1, c, 1, 1)
-        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
-        weight = self.weight * style  # (b, c_out, c_in, k, k)
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
-            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
-
-        weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
-
-        # upsample or downsample if necessary
-        if self.sample_mode == 'upsample':
-            x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
-        elif self.sample_mode == 'downsample':
-            x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False)
-
-        b, c, h, w = x.shape
-        x = x.view(1, b * c, h, w)
-        # weight: (b*c_out, c_in, k, k), groups=b
-        out = F.conv2d(x, weight, padding=self.padding, groups=b)
-        out = out.view(b, self.out_channels, *out.shape[2:4])
-
-        return out
-
-    def __repr__(self):
-        return (f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, '
-                f'kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})')
-
-
-class StyleConv(nn.Module):
-    """Style conv used in StyleGAN2.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
-    """
-
-    def __init__(self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None):
-        super(StyleConv, self).__init__()
-        self.modulated_conv = ModulatedConv2d(
-            in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode)
-        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
-        self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))
-        self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x, style, noise=None):
-        # modulate
-        out = self.modulated_conv(x, style) * 2**0.5  # for conversion
-        # noise injection
-        if noise is None:
-            b, _, h, w = out.shape
-            noise = out.new_empty(b, 1, h, w).normal_()
-        out = out + self.weight * noise
-        # add bias
-        out = out + self.bias
-        # activation
-        out = self.activate(out)
-        return out
-
-
-class ToRGB(nn.Module):
-    """To RGB (image space) from features.
-
-    Args:
-        in_channels (int): Channel number of input.
-        num_style_feat (int): Channel number of style features.
-        upsample (bool): Whether to upsample. Default: True.
-    """
-
-    def __init__(self, in_channels, num_style_feat, upsample=True):
-        super(ToRGB, self).__init__()
-        self.upsample = upsample
-        self.modulated_conv = ModulatedConv2d(
-            in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None)
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, x, style, skip=None):
-        """Forward function.
-
-        Args:
-            x (Tensor): Feature tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-            skip (Tensor): Base/skip tensor. Default: None.
-
-        Returns:
-            Tensor: RGB images.
-        """
-        out = self.modulated_conv(x, style)
-        out = out + self.bias
-        if skip is not None:
-            if self.upsample:
-                skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False)
-            out = out + skip
-        return out
-
-
-class ConstantInput(nn.Module):
-    """Constant input.
-
-    Args:
-        num_channel (int): Channel number of constant input.
-        size (int): Spatial size of constant input.
-    """
-
-    def __init__(self, num_channel, size):
-        super(ConstantInput, self).__init__()
-        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
-
-    def forward(self, batch):
-        out = self.weight.repeat(batch, 1, 1, 1)
-        return out
-
-
-@ARCH_REGISTRY.register()
-class StyleGAN2GeneratorClean(nn.Module):
-    """Clean version of StyleGAN2 Generator.
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        narrow (float): Narrow ratio for channels. Default: 1.0.
-    """
-
-    def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1):
-        super(StyleGAN2GeneratorClean, self).__init__()
-        # Style MLP layers
-        self.num_style_feat = num_style_feat
-        style_mlp_layers = [NormStyleCode()]
-        for i in range(num_mlp):
-            style_mlp_layers.extend(
-                [nn.Linear(num_style_feat, num_style_feat, bias=True),
-                 nn.LeakyReLU(negative_slope=0.2, inplace=True)])
-        self.style_mlp = nn.Sequential(*style_mlp_layers)
-        # initialization
-        default_init_weights(self.style_mlp, scale=1, bias_fill=0, a=0.2, mode='fan_in', nonlinearity='leaky_relu')
-
-        # channel list
-        channels = {
-            '4': int(512 * narrow),
-            '8': int(512 * narrow),
-            '16': int(512 * narrow),
-            '32': int(512 * narrow),
-            '64': int(256 * channel_multiplier * narrow),
-            '128': int(128 * channel_multiplier * narrow),
-            '256': int(64 * channel_multiplier * narrow),
-            '512': int(32 * channel_multiplier * narrow),
-            '1024': int(16 * channel_multiplier * narrow)
-        }
-        self.channels = channels
-
-        self.constant_input = ConstantInput(channels['4'], size=4)
-        self.style_conv1 = StyleConv(
-            channels['4'],
-            channels['4'],
-            kernel_size=3,
-            num_style_feat=num_style_feat,
-            demodulate=True,
-            sample_mode=None)
-        self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False)
-
-        self.log_size = int(math.log(out_size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-        self.num_latent = self.log_size * 2 - 2
-
-        self.style_convs = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channels = channels['4']
-        # noise
-        for layer_idx in range(self.num_layers):
-            resolution = 2**((layer_idx + 5) // 2)
-            shape = [1, 1, resolution, resolution]
-            self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape))
-        # style convs and to_rgbs
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            self.style_convs.append(
-                StyleConv(
-                    in_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode='upsample'))
-            self.style_convs.append(
-                StyleConv(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode=None))
-            self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))
-            in_channels = out_channels
-
-    def make_noise(self):
-        """Make noise for noise injection."""
-        device = self.constant_input.weight.device
-        noises = [torch.randn(1, 1, 4, 4, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
-
-        return noises
-
-    def get_latent(self, x):
-        return self.style_mlp(x)
-
-    def mean_latent(self, num_latent):
-        latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device)
-        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
-        return latent
-
-    def forward(self,
-                styles,
-                input_is_latent=False,
-                noise=None,
-                randomize_noise=True,
-                truncation=1,
-                truncation_latent=None,
-                inject_index=None,
-                return_latents=False):
-        """Forward function for StyleGAN2GeneratorClean.
-
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
-                                                        noise[2::2], self.to_rgbs):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
diff --git a/spaces/abhishek/StableSAM/README.md b/spaces/abhishek/StableSAM/README.md
deleted file mode 100644
index 2dc37790c1bd4ba7d7e946426439be9a76f4990b..0000000000000000000000000000000000000000
--- a/spaces/abhishek/StableSAM/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: StableSAM
-emoji: 🍀
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.25.0
-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/configs/_base_/models/ccnet_r50-d8.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/ccnet_r50-d8.py
deleted file mode 100644
index 794148f576b9e215c3c6963e73dffe98204b7717..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/ccnet_r50-d8.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='CCHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        recurrence=2,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/roiaware_pool3d.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/roiaware_pool3d.py
deleted file mode 100644
index 291b0e5a9b692492c7d7e495ea639c46042e2f18..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/roiaware_pool3d.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import nn as nn
-from torch.autograd import Function
-
-import annotator.uniformer.mmcv as mmcv
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['roiaware_pool3d_forward', 'roiaware_pool3d_backward'])
-
-
-class RoIAwarePool3d(nn.Module):
-    """Encode the geometry-specific features of each 3D proposal.
-
-    Please refer to `PartA2 <https://arxiv.org/pdf/1907.03670.pdf>`_ for more
-    details.
-
-    Args:
-        out_size (int or tuple): The size of output features. n or
-            [n1, n2, n3].
-        max_pts_per_voxel (int, optional): The maximum number of points per
-            voxel. Default: 128.
-        mode (str, optional): Pooling method of RoIAware, 'max' or 'avg'.
-            Default: 'max'.
-    """
-
-    def __init__(self, out_size, max_pts_per_voxel=128, mode='max'):
-        super().__init__()
-
-        self.out_size = out_size
-        self.max_pts_per_voxel = max_pts_per_voxel
-        assert mode in ['max', 'avg']
-        pool_mapping = {'max': 0, 'avg': 1}
-        self.mode = pool_mapping[mode]
-
-    def forward(self, rois, pts, pts_feature):
-        """
-        Args:
-            rois (torch.Tensor): [N, 7], in LiDAR coordinate,
-                (x, y, z) is the bottom center of rois.
-            pts (torch.Tensor): [npoints, 3], coordinates of input points.
-            pts_feature (torch.Tensor): [npoints, C], features of input points.
-
-        Returns:
-            pooled_features (torch.Tensor): [N, out_x, out_y, out_z, C]
-        """
-
-        return RoIAwarePool3dFunction.apply(rois, pts, pts_feature,
-                                            self.out_size,
-                                            self.max_pts_per_voxel, self.mode)
-
-
-class RoIAwarePool3dFunction(Function):
-
-    @staticmethod
-    def forward(ctx, rois, pts, pts_feature, out_size, max_pts_per_voxel,
-                mode):
-        """
-        Args:
-            rois (torch.Tensor): [N, 7], in LiDAR coordinate,
-                (x, y, z) is the bottom center of rois.
-            pts (torch.Tensor): [npoints, 3], coordinates of input points.
-            pts_feature (torch.Tensor): [npoints, C], features of input points.
-            out_size (int or tuple): The size of output features. n or
-                [n1, n2, n3].
-            max_pts_per_voxel (int): The maximum number of points per voxel.
-                Default: 128.
-            mode (int): Pooling method of RoIAware, 0 (max pool) or 1 (average
-                pool).
-
-        Returns:
-            pooled_features (torch.Tensor): [N, out_x, out_y, out_z, C], output
-                pooled features.
-        """
-
-        if isinstance(out_size, int):
-            out_x = out_y = out_z = out_size
-        else:
-            assert len(out_size) == 3
-            assert mmcv.is_tuple_of(out_size, int)
-            out_x, out_y, out_z = out_size
-
-        num_rois = rois.shape[0]
-        num_channels = pts_feature.shape[-1]
-        num_pts = pts.shape[0]
-
-        pooled_features = pts_feature.new_zeros(
-            (num_rois, out_x, out_y, out_z, num_channels))
-        argmax = pts_feature.new_zeros(
-            (num_rois, out_x, out_y, out_z, num_channels), dtype=torch.int)
-        pts_idx_of_voxels = pts_feature.new_zeros(
-            (num_rois, out_x, out_y, out_z, max_pts_per_voxel),
-            dtype=torch.int)
-
-        ext_module.roiaware_pool3d_forward(rois, pts, pts_feature, argmax,
-                                           pts_idx_of_voxels, pooled_features,
-                                           mode)
-
-        ctx.roiaware_pool3d_for_backward = (pts_idx_of_voxels, argmax, mode,
-                                            num_pts, num_channels)
-        return pooled_features
-
-    @staticmethod
-    def backward(ctx, grad_out):
-        ret = ctx.roiaware_pool3d_for_backward
-        pts_idx_of_voxels, argmax, mode, num_pts, num_channels = ret
-
-        grad_in = grad_out.new_zeros((num_pts, num_channels))
-        ext_module.roiaware_pool3d_backward(pts_idx_of_voxels, argmax,
-                                            grad_out.contiguous(), grad_in,
-                                            mode)
-
-        return None, None, grad_in, None, None, None
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/uniformer.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/uniformer.py
deleted file mode 100644
index 0c4bb88e4c928540cca9ab609988b916520f5b7a..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/uniformer.py
+++ /dev/null
@@ -1,422 +0,0 @@
-# --------------------------------------------------------
-# UniFormer
-# Copyright (c) 2022 SenseTime X-Lab
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Kunchang Li
-# --------------------------------------------------------
-
-from collections import OrderedDict
-import math
-
-from functools import partial
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-from annotator.uniformer.mmcv_custom import load_checkpoint
-from annotator.uniformer.mmseg.utils import get_root_logger
-from ..builder import BACKBONES
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class CMlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
-        self.act = act_layer()
-        self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class CBlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = nn.BatchNorm2d(dim)
-        self.conv1 = nn.Conv2d(dim, dim, 1)
-        self.conv2 = nn.Conv2d(dim, dim, 1)
-        self.attn = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = nn.BatchNorm2d(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        x = x + self.drop_path(self.conv2(self.attn(self.conv1(self.norm1(x)))))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class SABlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        B, N, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = x + self.drop_path(self.attn(self.norm1(x)))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        x = x.transpose(1, 2).reshape(B, N, H, W)
-        return x   
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class SABlock_Windows(nn.Module):
-    def __init__(self, dim, num_heads, window_size=14, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.window_size=window_size
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        x = x.permute(0, 2, 3, 1)
-        B, H, W, C = x.shape
-        shortcut = x
-        x = self.norm1(x)
-
-        pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-        
-        x_windows = window_partition(x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        # reverse cyclic shift
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        x = x.permute(0, 3, 1, 2).reshape(B, C, H, W)
-        return x 
-             
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-        self.norm = nn.LayerNorm(embed_dim)
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-
-    def forward(self, x):
-        B, _, H, W = x.shape
-        x = self.proj(x)
-        B, _, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = self.norm(x)
-        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
-        return x
-    
-
-@BACKBONES.register_module()   
-class UniFormer(nn.Module):
-    """ Vision Transformer
-    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
-        https://arxiv.org/abs/2010.11929
-    """
-    def __init__(self, layers=[3, 4, 8, 3], img_size=224, in_chans=3, num_classes=80, embed_dim=[64, 128, 320, 512],
-                 head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6),
-                 pretrained_path=None, use_checkpoint=False, checkpoint_num=[0, 0, 0, 0], 
-                 windows=False, hybrid=False, window_size=14):
-        """
-        Args:
-            layer (list): number of block in each layer
-            img_size (int, tuple): input image size
-            in_chans (int): number of input channels
-            num_classes (int): number of classes for classification head
-            embed_dim (int): embedding dimension
-            head_dim (int): dimension of attention heads
-            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
-            qkv_bias (bool): enable bias for qkv if True
-            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
-            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
-            drop_rate (float): dropout rate
-            attn_drop_rate (float): attention dropout rate
-            drop_path_rate (float): stochastic depth rate
-            norm_layer (nn.Module): normalization layer
-            pretrained_path (str): path of pretrained model
-            use_checkpoint (bool): whether use checkpoint
-            checkpoint_num (list): index for using checkpoint in every stage
-            windows (bool): whether use window MHRA
-            hybrid (bool): whether use hybrid MHRA
-            window_size (int): size of window (>14)
-        """
-        super().__init__()
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.checkpoint_num = checkpoint_num
-        self.windows = windows
-        print(f'Use Checkpoint: {self.use_checkpoint}')
-        print(f'Checkpoint Number: {self.checkpoint_num}')
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) 
-        
-        self.patch_embed1 = PatchEmbed(
-            img_size=img_size, patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0])
-        self.patch_embed2 = PatchEmbed(
-            img_size=img_size // 4, patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1])
-        self.patch_embed3 = PatchEmbed(
-            img_size=img_size // 8, patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2])
-        self.patch_embed4 = PatchEmbed(
-            img_size=img_size // 16, patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3])
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(layers))]  # stochastic depth decay rule
-        num_heads = [dim // head_dim for dim in embed_dim]
-        self.blocks1 = nn.ModuleList([
-            CBlock(
-                dim=embed_dim[0], num_heads=num_heads[0], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
-            for i in range(layers[0])])
-        self.norm1=norm_layer(embed_dim[0])
-        self.blocks2 = nn.ModuleList([
-            CBlock(
-                dim=embed_dim[1], num_heads=num_heads[1], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]], norm_layer=norm_layer)
-            for i in range(layers[1])])
-        self.norm2 = norm_layer(embed_dim[1])
-        if self.windows:
-            print('Use local window for all blocks in stage3')
-            self.blocks3 = nn.ModuleList([
-            SABlock_Windows(
-                dim=embed_dim[2], num_heads=num_heads[2], window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer)
-            for i in range(layers[2])])
-        elif hybrid:
-            print('Use hybrid window for blocks in stage3')
-            block3 = []
-            for i in range(layers[2]):
-                if (i + 1) % 4 == 0:
-                    block3.append(SABlock(
-                    dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer))
-                else:
-                    block3.append(SABlock_Windows(
-                    dim=embed_dim[2], num_heads=num_heads[2], window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer))
-            self.blocks3 = nn.ModuleList(block3)
-        else:
-            print('Use global window for all blocks in stage3')
-            self.blocks3 = nn.ModuleList([
-            SABlock(
-                dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer)
-            for i in range(layers[2])])
-        self.norm3 = norm_layer(embed_dim[2])
-        self.blocks4 = nn.ModuleList([
-            SABlock(
-                dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]+layers[2]], norm_layer=norm_layer)
-            for i in range(layers[3])])
-        self.norm4 = norm_layer(embed_dim[3])
-        
-        # Representation layer
-        if representation_size:
-            self.num_features = representation_size
-            self.pre_logits = nn.Sequential(OrderedDict([
-                ('fc', nn.Linear(embed_dim, representation_size)),
-                ('act', nn.Tanh())
-            ]))
-        else:
-            self.pre_logits = nn.Identity()
-        
-        self.apply(self._init_weights)
-        self.init_weights(pretrained=pretrained_path)
-        
-    def init_weights(self, pretrained):
-        if isinstance(pretrained, str):
-            logger = get_root_logger()
-            load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
-            print(f'Load pretrained model from {pretrained}')
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'pos_embed', 'cls_token'}
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=''):
-        self.num_classes = num_classes
-        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-    def forward_features(self, x):
-        out = []
-        x = self.patch_embed1(x)
-        x = self.pos_drop(x)
-        for i, blk in enumerate(self.blocks1):
-            if self.use_checkpoint and i < self.checkpoint_num[0]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm1(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        x = self.patch_embed2(x)
-        for i, blk in enumerate(self.blocks2):
-            if self.use_checkpoint and i < self.checkpoint_num[1]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm2(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        x = self.patch_embed3(x)
-        for i, blk in enumerate(self.blocks3):
-            if self.use_checkpoint and i < self.checkpoint_num[2]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm3(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        x = self.patch_embed4(x)
-        for i, blk in enumerate(self.blocks4):
-            if self.use_checkpoint and i < self.checkpoint_num[3]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm4(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        return tuple(out)
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        return x
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/bricks/conv2d_adaptive_padding.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/bricks/conv2d_adaptive_padding.py
deleted file mode 100644
index b45e758ac6cf8dfb0382d072fe09125bc7e9b888..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/bricks/conv2d_adaptive_padding.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-from torch import nn
-from torch.nn import functional as F
-
-from .registry import CONV_LAYERS
-
-
-@CONV_LAYERS.register_module()
-class Conv2dAdaptivePadding(nn.Conv2d):
-    """Implementation of 2D convolution in tensorflow with `padding` as "same",
-    which applies padding to input (if needed) so that input image gets fully
-    covered by filter and stride you specified. For stride 1, this will ensure
-    that output image size is same as input. For stride of 2, output dimensions
-    will be half, for example.
-
-    Args:
-        in_channels (int): Number of channels in the input image
-        out_channels (int): Number of channels produced by the convolution
-        kernel_size (int or tuple): Size of the convolving kernel
-        stride (int or tuple, optional): Stride of the convolution. Default: 1
-        padding (int or tuple, optional): Zero-padding added to both sides of
-            the input. Default: 0
-        dilation (int or tuple, optional): Spacing between kernel elements.
-            Default: 1
-        groups (int, optional): Number of blocked connections from input
-            channels to output channels. Default: 1
-        bias (bool, optional): If ``True``, adds a learnable bias to the
-            output. Default: ``True``
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 groups=1,
-                 bias=True):
-        super().__init__(in_channels, out_channels, kernel_size, stride, 0,
-                         dilation, groups, bias)
-
-    def forward(self, x):
-        img_h, img_w = x.size()[-2:]
-        kernel_h, kernel_w = self.weight.size()[-2:]
-        stride_h, stride_w = self.stride
-        output_h = math.ceil(img_h / stride_h)
-        output_w = math.ceil(img_w / stride_w)
-        pad_h = (
-            max((output_h - 1) * self.stride[0] +
-                (kernel_h - 1) * self.dilation[0] + 1 - img_h, 0))
-        pad_w = (
-            max((output_w - 1) * self.stride[1] +
-                (kernel_w - 1) * self.dilation[1] + 1 - img_w, 0))
-        if pad_h > 0 or pad_w > 0:
-            x = F.pad(x, [
-                pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
-            ])
-        return F.conv2d(x, self.weight, self.bias, self.stride, self.padding,
-                        self.dilation, self.groups)
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/model/__init__.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/model/__init__.py
deleted file mode 100644
index b2cc563b209984fc6efa7cc2770a717b4c346bb9..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/model/__init__.py
+++ /dev/null
@@ -1,334 +0,0 @@
-"""Loading of 3D models.
-
-A :py:class:`~pyglet.model.Model` is an instance of a 3D object.
-
-The following example loads a ``"teapot.obj"`` model::
-
-    import pyglet
-
-    window = pyglet.window.Window()
-
-    teapot = pyglet.model.load('teapot.obj')
-
-    @window.event
-    def on_draw():
-        teapot.draw()
-
-    pyglet.app.run()
-
-
-You can also load models with :py:meth:`~pyglet.resource.model`.
-See :py:mod:`~pyglet.resource` for more information.
-
-
-Efficient Drawing
-=================
-
-As with Sprites or Text, Models can be added to a
-:py:class:`~pyglet.graphics.Batch` for efficient drawing. This is
-preferred to calling their ``draw`` methods individually.  To do this,
-simply pass in a reference to the :py:class:`~pyglet.graphics.Batch`
-instance when loading the Model::
-
-
-    import pyglet
-
-    window = pyglet.window.Window()
-    batch = pyglet.graphics.Batch()
-
-    teapot = pyglet.model.load('teapot.obj', batch=batch)
-
-    @window.event
-    def on_draw():
-        batch.draw()
-
-    pyglet.app.run()
-
-
-.. versionadded:: 1.4
-"""
-
-import pyglet
-
-from pyglet import gl
-from pyglet import graphics
-from pyglet.gl import current_context
-from pyglet.math import Mat4, Vec3
-from pyglet.graphics import shader
-
-from .codecs import registry as _codec_registry
-from .codecs import add_default_codecs as _add_default_codecs
-
-
-def load(filename, file=None, decoder=None, batch=None, group=None):
-    """Load a 3D model from a file.
-
-    :Parameters:
-        `filename` : str
-            Used to guess the model format, and to load the file if `file` is
-            unspecified.
-        `file` : file-like object or None
-            Source of model data in any supported format.        
-        `decoder` : ModelDecoder or None
-            If unspecified, all decoders that are registered for the filename
-            extension are tried. An exception is raised if no codecs are
-            registered for the file extension, or if decoding fails.
-        `batch` : Batch or None
-            An optional Batch instance to add this model to.
-        `group` : Group or None
-            An optional top level Group.
-
-    :rtype: :py:mod:`~pyglet.model.Model`
-    """
-    if decoder:
-        return decoder.decode(filename, file, batch=batch, group=group)
-    else:
-        return _codec_registry.decode(filename, file, batch=batch, group=group)
-
-
-def get_default_shader():
-    try:
-        return pyglet.gl.current_context.model_default_plain_shader
-    except AttributeError:
-        vert_shader = shader.Shader(MaterialGroup.default_vert_src, 'vertex')
-        frag_shader = shader.Shader(MaterialGroup.default_frag_src, 'fragment')
-        default_shader_program = shader.ShaderProgram(vert_shader, frag_shader)
-        pyglet.gl.current_context.model_default_plain_shader = default_shader_program
-        return pyglet.gl.current_context.model_default_plain_shader
-
-
-def get_default_textured_shader():
-    try:
-        return pyglet.gl.current_context.model_default_textured_shader
-    except AttributeError:
-        vert_shader = shader.Shader(TexturedMaterialGroup.default_vert_src, 'vertex')
-        frag_shader = shader.Shader(TexturedMaterialGroup.default_frag_src, 'fragment')
-        default_shader_program = shader.ShaderProgram(vert_shader, frag_shader)
-        pyglet.gl.current_context.model_default_textured_shader = default_shader_program
-        return current_context.model_default_textured_shader
-
-
-class Model:
-    """Instance of a 3D object.
-
-    See the module documentation for usage.
-    """
-
-    def __init__(self, vertex_lists, groups, batch):
-        """Create a model.
-
-        :Parameters:
-            `vertex_lists` : list
-                A list of `~pyglet.graphics.VertexList` or
-                `~pyglet.graphics.IndexedVertexList`.
-            `groups` : list
-                A list of `~pyglet.model.TexturedMaterialGroup`, or
-                 `~pyglet.model.MaterialGroup`. Each group corresponds to
-                 a vertex list in `vertex_lists` of the same index.
-            `batch` : `~pyglet.graphics.Batch`
-                Optional batch to add the model to. If no batch is provided,
-                the model will maintain its own internal batch.
-        """
-        self.vertex_lists = vertex_lists
-        self.groups = groups
-        self._batch = batch
-        self._modelview_matrix = Mat4()
-
-    @property
-    def batch(self):
-        """The graphics Batch that the Model belongs to.
-
-        The Model can be migrated from one batch to another, or removed from
-        a batch (for individual drawing). If not part of any batch, the Model
-        will keep its own internal batch. Note that batch migration can be
-        an expensive operation.
-
-        :type: :py:class:`pyglet.graphics.Batch`
-        """
-        return self._batch
-
-    @batch.setter
-    def batch(self, batch):
-        if self._batch == batch:
-            return
-
-        if batch is None:
-            batch = graphics.Batch()
-
-        for group, vlist in zip(self.groups, self.vertex_lists):
-            self._batch.migrate(vlist, gl.GL_TRIANGLES, group, batch)
-
-        self._batch = batch
-
-    @property
-    def matrix(self):
-        return self._modelview_matrix
-
-    @matrix.setter
-    def matrix(self, matrix):
-        self._modelview_matrix = matrix
-        for group in self.groups:
-            group.matrix = matrix
-
-    def draw(self):
-        """Draw the model.
-
-        This is not recommended. See the module documentation
-        for information on efficient drawing of multiple models.
-        """
-        gl.current_context.window_block.bind(0)
-        self._batch.draw_subset(self.vertex_lists)
-
-
-class Material:
-    __slots__ = ("name", "diffuse", "ambient", "specular", "emission", "shininess", "texture_name")
-
-    def __init__(self, name, diffuse, ambient, specular, emission, shininess, texture_name=None):
-        self.name = name
-        self.diffuse = diffuse
-        self.ambient = ambient
-        self.specular = specular
-        self.emission = emission
-        self.shininess = shininess
-        self.texture_name = texture_name
-
-    def __eq__(self, other):
-        return (self.name == other.name and
-                self.diffuse == other.diffuse and
-                self.ambient == other.ambient and
-                self.specular == other.specular and
-                self.emission == other.emission and
-                self.shininess == other.shininess and
-                self.texture_name == other.texture_name)
-
-
-class BaseMaterialGroup(graphics.Group):
-    default_vert_src = None
-    default_frag_src = None
-    matrix = Mat4()
-
-    def __init__(self, material, program, order=0, parent=None):
-        super().__init__(order, parent)
-        self.material = material
-        self.program = program
-
-
-class TexturedMaterialGroup(BaseMaterialGroup):
-    default_vert_src = """#version 330 core
-    in vec3 vertices;
-    in vec3 normals;
-    in vec2 tex_coords;
-    in vec4 colors;
-
-    out vec4 vertex_colors;
-    out vec3 vertex_normals;
-    out vec2 texture_coords;
-    out vec3 vertex_position;
-
-    uniform WindowBlock
-    {
-        mat4 projection;
-        mat4 view;
-    } window;
-
-    uniform mat4 model;
-
-    void main()
-    {
-        vec4 pos = window.view * model * vec4(vertices, 1.0);
-        gl_Position = window.projection * pos;
-        mat3 normal_matrix = transpose(inverse(mat3(model)));
-
-        vertex_position = pos.xyz;
-        vertex_colors = colors;
-        texture_coords = tex_coords;
-        vertex_normals = normal_matrix * normals;
-    }
-    """
-    default_frag_src = """#version 330 core
-    in vec4 vertex_colors;
-    in vec3 vertex_normals;
-    in vec2 texture_coords;
-    in vec3 vertex_position;
-    out vec4 final_colors;
-
-    uniform sampler2D our_texture;
-
-    void main()
-    {
-        float l = dot(normalize(-vertex_position), normalize(vertex_normals));
-        final_colors = (texture(our_texture, texture_coords) * vertex_colors) * l * 1.2;
-    }
-    """
-
-    def __init__(self, material, program, texture, order=0, parent=None):
-        super().__init__(material, program, order, parent)
-        self.texture = texture
-
-    def set_state(self):
-        gl.glActiveTexture(gl.GL_TEXTURE0)
-        gl.glBindTexture(self.texture.target, self.texture.id)
-        self.program.use()
-        self.program['model'] = self.matrix
-
-    def __hash__(self):
-        return hash((self.texture.target, self.texture.id, self.program, self.order, self.parent))
-
-    def __eq__(self, other):
-        return (self.__class__ is other.__class__ and
-                self.material == other.material and
-                self.texture.target == other.texture.target and
-                self.texture.id == other.texture.id and
-                self.program == other.program and
-                self.order == other.order and
-                self.parent == other.parent)
-
-
-class MaterialGroup(BaseMaterialGroup):
-    default_vert_src = """#version 330 core
-    in vec3 vertices;
-    in vec3 normals;
-    in vec4 colors;
-
-    out vec4 vertex_colors;
-    out vec3 vertex_normals;
-    out vec3 vertex_position;
-
-    uniform WindowBlock
-    {
-        mat4 projection;
-        mat4 view;
-    } window;
-
-    uniform mat4 model;
-
-    void main()
-    {
-        vec4 pos = window.view * model * vec4(vertices, 1.0);
-        gl_Position = window.projection * pos;
-        mat3 normal_matrix = transpose(inverse(mat3(model)));
-
-        vertex_position = pos.xyz;
-        vertex_colors = colors;
-        vertex_normals = normal_matrix * normals;
-    }
-    """
-    default_frag_src = """#version 330 core
-    in vec4 vertex_colors;
-    in vec3 vertex_normals;
-    in vec3 vertex_position;
-    out vec4 final_colors;
-
-    void main()
-    {
-        float l = dot(normalize(-vertex_position), normalize(vertex_normals));
-        final_colors = vertex_colors * l * 1.2;
-    }
-    """
-
-    def set_state(self):
-        self.program.use()
-        self.program['model'] = self.matrix
-
-
-_add_default_codecs()
diff --git a/spaces/ahdsoft/Persian-Automatic-Speech-Recognition/README.md b/spaces/ahdsoft/Persian-Automatic-Speech-Recognition/README.md
deleted file mode 100644
index 66b2dfb72ffde19c69d4c18ec366c7ec140f25e5..0000000000000000000000000000000000000000
--- a/spaces/ahdsoft/Persian-Automatic-Speech-Recognition/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Persian Automatic Speech Recognition
-emoji: 🐢
-colorFrom: green
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ai-guru/composer/static/_app/assets/pages/index.svelte-ad990746.css b/spaces/ai-guru/composer/static/_app/assets/pages/index.svelte-ad990746.css
deleted file mode 100644
index 017c57370c219cef7635a16af2598fc7a83bfe1a..0000000000000000000000000000000000000000
--- a/spaces/ai-guru/composer/static/_app/assets/pages/index.svelte-ad990746.css
+++ /dev/null
@@ -1 +0,0 @@
-fieldset.svelte-1r9pswz.svelte-1r9pswz{position:relative;padding:0;border:none;margin-top:1rem}legend.svelte-1r9pswz.svelte-1r9pswz{text-align:center;font-size:1.25rem;font-weight:700;padding:0}.grid.svelte-1r9pswz.svelte-1r9pswz{display:grid;grid-template-columns:repeat(4,1fr);gap:1rem;width:min-content;margin:1rem auto}img.svelte-1r9pswz.svelte-1r9pswz{width:100%;height:100%;filter:invert(1);margin:auto}label.svelte-1r9pswz.svelte-1r9pswz{background-color:transparent;border-radius:.375rem;transition:background-color .25s;cursor:pointer}label.svelte-1r9pswz>div.svelte-1r9pswz{width:3rem;aspect-ratio:1 / 1}input.svelte-1r9pswz.svelte-1r9pswz{position:fixed;opacity:0;pointer-events:none}label[data-selected=true].svelte-1r9pswz.svelte-1r9pswz{background-color:#f7f7f7;border-radius:.375rem}label[data-selected=true].svelte-1r9pswz img.svelte-1r9pswz{filter:none}@media (min-width: 600px) and (max-width: 899px){.grid.svelte-1r9pswz.svelte-1r9pswz{display:flex;flex-direction:row}}@media (min-width: 900px){.grid.svelte-1r9pswz.svelte-1r9pswz{grid-template-columns:repeat(4,1fr)}}.options.svelte-1m848u0{display:flex;flex-direction:row;justify-content:center;width:100%;margin:auto}label.svelte-1m848u0{display:block;margin-bottom:1rem;padding:.5rem;border:2px solid hsl(0 0% 97%);border-right:none;text-align:center;transition:background-color .25s;cursor:pointer}label.svelte-1m848u0:nth-of-type(1){border-top-left-radius:.375rem;border-bottom-left-radius:.375rem;border-right-width:0}label.svelte-1m848u0:last-of-type{border-top-right-radius:.375rem;border-bottom-right-radius:.375rem;border-right:2px solid hsl(0 0% 97%)}label[data-selected=true].svelte-1m848u0{background-color:#fff;color:#333;font-weight:700}label.svelte-1m848u0:focus{outline:red}input.svelte-1m848u0{position:fixed;opacity:0;pointer-events:none}fieldset.svelte-1ikh8be{padding:0;border:none;margin-top:1rem}legend.svelte-1ikh8be{text-align:center;font-size:1.25rem;font-weight:700;padding:0;margin-bottom:1rem}button.svelte-18w38ow{display:block;font-size:1.2rem;font-family:Lato,sans-serif;font-weight:700;color:#f7f7f7;background:transparent;border:3px solid hsl(0 0% 97%);border-radius:.375rem;padding:.5rem 1rem;cursor:pointer;margin:1rem auto 2rem}button[disabled].svelte-18w38ow{border-color:gray;color:gray;cursor:initial}img.svelte-18w38ow{height:1.2rem;aspect-ratio:1 / 1;vertical-align:bottom}@media (min-width: 900px){button.svelte-18w38ow{margin-top:0}}section.svelte-1536b0c{display:flex;flex-direction:column;position:relative;border:2px solid hsl(0 0% 80%);border-radius:.375rem;padding:1rem}.player.svelte-1536b0c{position:absolute;left:50%;transform:translate(-50%);cursor:pointer}.notes.svelte-1536b0c{width:min(100%,512px);margin:auto;box-shadow:0 0 5px .1px #2e3338}audio.svelte-1536b0c{width:100%;margin:1rem auto}.play-button.svelte-1536b0c{position:absolute;left:50%;top:50%;width:20%;aspect-ratio:1 / 1;transform:translate(-50%,-50%);filter:drop-shadow(0 0 5px black);pointer-events:none;cursor:pointer}.handle.svelte-1536b0c{position:absolute;left:0;top:0;height:104%;width:.2rem;border-radius:.1rem;background-color:#fff;cursor:pointer;transform:translateY(-2%)}a.download.svelte-1536b0c{display:block;font-size:1.2rem;font-family:Lato,sans-serif;font-weight:700;color:#f7f7f7;background:transparent;border:3px solid hsl(0 0% 97%);border-radius:.375rem;padding:.5rem 1rem;cursor:pointer;margin:1rem auto auto}@media (min-width: 600px){section.svelte-1536b0c{padding:2rem}}section.svelte-4un5mw{border:2px solid hsl(0 0% 80%);border-radius:.375rem;padding:1rem}p.svelte-4un5mw{font-size:.75rem}main.svelte-1rfjlkw{width:100%;display:flex;flex-direction:column;gap:1rem;margin:0 auto}h1.svelte-1rfjlkw{font-size:1.5rem;border-left:.25ch solid hsl(0 0% 97%);padding-left:.5ch}.heading.svelte-1rfjlkw{font-size:2.25rem}p.svelte-1rfjlkw:not(.heading){max-width:40rem;font-size:1.2rem;line-height:1.5rem;margin:0}#options.svelte-1rfjlkw{display:flex;flex-direction:column;justify-content:space-between;margin-top:1rem}@media (min-width: 600px){main.svelte-1rfjlkw{max-width:60rem}}@media (min-width: 900px){#options.svelte-1rfjlkw{display:flex;flex-direction:row;justify-content:space-between}}
diff --git a/spaces/akhaliq/JoJoGAN/e4e/datasets/images_dataset.py b/spaces/akhaliq/JoJoGAN/e4e/datasets/images_dataset.py
deleted file mode 100644
index 00c54c7db944569a749af4c6f0c4d99fcc37f9cc..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/JoJoGAN/e4e/datasets/images_dataset.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from torch.utils.data import Dataset
-from PIL import Image
-from utils import data_utils
-
-
-class ImagesDataset(Dataset):
-
-	def __init__(self, source_root, target_root, opts, target_transform=None, source_transform=None):
-		self.source_paths = sorted(data_utils.make_dataset(source_root))
-		self.target_paths = sorted(data_utils.make_dataset(target_root))
-		self.source_transform = source_transform
-		self.target_transform = target_transform
-		self.opts = opts
-
-	def __len__(self):
-		return len(self.source_paths)
-
-	def __getitem__(self, index):
-		from_path = self.source_paths[index]
-		from_im = Image.open(from_path)
-		from_im = from_im.convert('RGB')
-
-		to_path = self.target_paths[index]
-		to_im = Image.open(to_path).convert('RGB')
-		if self.target_transform:
-			to_im = self.target_transform(to_im)
-
-		if self.source_transform:
-			from_im = self.source_transform(from_im)
-		else:
-			from_im = to_im
-
-		return from_im, to_im
diff --git a/spaces/akhaliq/Music_Source_Separation/bytesep/models/__init__.py b/spaces/akhaliq/Music_Source_Separation/bytesep/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/akhaliq/SummerTime/__init__.py b/spaces/akhaliq/SummerTime/__init__.py
deleted file mode 100644
index 8a4686f47d52f87f9561f7a9182a2e91d8cb1e0d..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import SummerTime.model
-import SummerTime.dataset.st_dataset as data
-import SummerTime.evaluation
diff --git a/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Document.pod b/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Document.pod
deleted file mode 100644
index f8e7b81c9e6c53689280b4e0976c9c832b2f02d5..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Document.pod
+++ /dev/null
@@ -1,220 +0,0 @@
-=head1 NAME
-
-XML::DOM::Document - An XML document node in XML::DOM
-
-=head1 DESCRIPTION
-
-XML::DOM::Document extends L<XML::DOM::Node>.
-
-It is the main root of the XML document structure as returned by 
-XML::DOM::Parser::parse and XML::DOM::Parser::parsefile.
-
-Since elements, text nodes, comments, processing instructions, etc.
-cannot exist outside the context of a Document, the Document interface
-also contains the factory methods needed to create these objects. The
-Node objects created have a getOwnerDocument method which associates
-them with the Document within whose context they were created.
-
-=head2 METHODS
-
-=over 4
-
-=item getDocumentElement
-
-This is a convenience method that allows direct access to
-the child node that is the root Element of the document.
-
-=item getDoctype
-
-The Document Type Declaration (see DocumentType) associated
-with this document. For HTML documents as well as XML
-documents without a document type declaration this returns
-undef. The DOM Level 1 does not support editing the Document
-Type Declaration.
-
-B<Not In DOM Spec>: This implementation allows editing the doctype. 
-See I<XML::DOM::ignoreReadOnly> for details.
-
-=item getImplementation
-
-The DOMImplementation object that handles this document. A
-DOM application may use objects from multiple implementations.
-
-=item createElement (tagName)
-
-Creates an element of the type specified. Note that the
-instance returned implements the Element interface, so
-attributes can be specified directly on the returned object.
-
-DOMExceptions:
-
-=over 4
-
-=item * INVALID_CHARACTER_ERR
-
-Raised if the tagName does not conform to the XML spec.
-
-=back
-
-=item createTextNode (data)
-
-Creates a Text node given the specified string.
-
-=item createComment (data)
-
-Creates a Comment node given the specified string.
-
-=item createCDATASection (data)
-
-Creates a CDATASection node given the specified string.
-
-=item createAttribute (name [, value [, specified ]])
-
-Creates an Attr of the given name. Note that the Attr
-instance can then be set on an Element using the setAttribute method.
-
-B<Not In DOM Spec>: The DOM Spec does not allow passing the value or the 
-specified property in this method. In this implementation they are optional.
-
-Parameters:
- I<value>     The attribute's value. See Attr::setValue for details.
-              If the value is not supplied, the specified property is set to 0.
- I<specified> Whether the attribute value was specified or whether the default
-              value was used. If not supplied, it's assumed to be 1.
-
-DOMExceptions:
-
-=over 4
-
-=item * INVALID_CHARACTER_ERR
-
-Raised if the name does not conform to the XML spec.
-
-=back
-
-=item createProcessingInstruction (target, data)
-
-Creates a ProcessingInstruction node given the specified name and data strings.
-
-Parameters:
- I<target>  The target part of the processing instruction.
- I<data>    The data for the node.
-
-DOMExceptions:
-
-=over 4
-
-=item * INVALID_CHARACTER_ERR
-
-Raised if the target does not conform to the XML spec.
-
-=back
-
-=item createDocumentFragment
-
-Creates an empty DocumentFragment object.
-
-=item createEntityReference (name)
-
-Creates an EntityReference object.
-
-=back
-
-=head2 Additional methods not in the DOM Spec
-
-=over 4
-
-=item getXMLDecl and setXMLDecl (xmlDecl)
-
-Returns the XMLDecl for this Document or undef if none was specified.
-Note that XMLDecl is not part of the list of child nodes.
-
-=item setDoctype (doctype)
-
-Sets or replaces the DocumentType. 
-B<NOTE>: Don't use appendChild or insertBefore to set the DocumentType.
-Even though doctype will be part of the list of child nodes, it is handled
-specially.
-
-=item getDefaultAttrValue (elem, attr)
-
-Returns the default attribute value as a string or undef, if none is available.
-
-Parameters:
- I<elem>    The element tagName.
- I<attr>    The attribute name.
-
-=item getEntity (name)
-
-Returns the Entity with the specified name.
-
-=item createXMLDecl (version, encoding, standalone)
-
-Creates an XMLDecl object. All parameters may be undefined.
-
-=item createDocumentType (name, sysId, pubId)
-
-Creates a DocumentType object. SysId and pubId may be undefined.
-
-=item createNotation (name, base, sysId, pubId)
-
-Creates a new Notation object. Consider using 
-XML::DOM::DocumentType::addNotation!
-
-=item createEntity (parameter, notationName, value, sysId, pubId, ndata)
-
-Creates an Entity object. Consider using XML::DOM::DocumentType::addEntity!
-
-=item createElementDecl (name, model)
-
-Creates an ElementDecl object.
-
-DOMExceptions:
-
-=over 4
-
-=item * INVALID_CHARACTER_ERR
-
-Raised if the element name (tagName) does not conform to the XML spec.
-
-=back
-
-=item createAttlistDecl (name)
-
-Creates an AttlistDecl object.
-
-DOMExceptions:
-
-=over 4
-
-=item * INVALID_CHARACTER_ERR
-
-Raised if the element name (tagName) does not conform to the XML spec.
-
-=back
-
-=item expandEntity (entity [, parameter])
-
-Expands the specified entity or parameter entity (if parameter=1) and returns
-its value as a string, or undef if the entity does not exist.
-(The entity name should not contain the '%', '&' or ';' delimiters.)
-
-=item check ( [$checker] )
-
-Uses the specified L<XML::Checker> to validate the document.
-If no XML::Checker is supplied, a new XML::Checker is created.
-See L<XML::Checker> for details.
-
-=item check_sax ( [$checker] )
-
-Similar to check() except it uses the SAX interface to XML::Checker instead of 
-the expat interface. This method may disappear or replace check() at some time.
-
-=item createChecker ()
-
-Creates an XML::Checker based on the document's DTD.
-The $checker can be reused to check any elements within the document.
-Create a new L<XML::Checker> whenever the DOCTYPE section of the document 
-is altered!
-
-=back
diff --git a/spaces/akhaliq/deeplab2/evaluation/__init__.py b/spaces/akhaliq/deeplab2/evaluation/__init__.py
deleted file mode 100644
index 35e4ce02ff422f3aa84ab644b88d65b13e0cbc03..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/evaluation/__init__.py
+++ /dev/null
@@ -1,15 +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.
-
diff --git a/spaces/alamin655/websurfx/public/static/colorschemes/tokyo-night.css b/spaces/alamin655/websurfx/public/static/colorschemes/tokyo-night.css
deleted file mode 100644
index 16c54bd39ff41da0d42aab6e57e71947d5148edd..0000000000000000000000000000000000000000
--- a/spaces/alamin655/websurfx/public/static/colorschemes/tokyo-night.css
+++ /dev/null
@@ -1,11 +0,0 @@
-:root {
-  --background-color: #1a1b26;
-  --foreground-color: #c0caf5;
-  --color-one: #32364a;
-  --color-two: #a9b1d6;
-  --color-three: #5a5bb8;
-  --color-four: #6b7089;
-  --color-five: #e2afff;
-  --color-six: #a9a1e1;
-  --color-seven: #988bc7;
-}
diff --git a/spaces/alamin655/websurfx/src/bin/websurfx.rs b/spaces/alamin655/websurfx/src/bin/websurfx.rs
deleted file mode 100644
index d80c8e07f38f86d15cacc4ff0a2f1cb1ee4d015f..0000000000000000000000000000000000000000
--- a/spaces/alamin655/websurfx/src/bin/websurfx.rs
+++ /dev/null
@@ -1,50 +0,0 @@
-//! Main module of the application
-//!
-//! This module contains the main function which handles the logging of the application to the
-//! stdout and handles the command line arguments provided and launches the `websurfx` server.
-
-use mimalloc::MiMalloc;
-use std::net::TcpListener;
-use websurfx::{cache::cacher::Cache, config::parser::Config, run};
-
-/// A dhat heap memory profiler
-#[cfg(feature = "dhat-heap")]
-#[global_allocator]
-static ALLOC: dhat::Alloc = dhat::Alloc;
-
-#[cfg(not(feature = "dhat-heap"))]
-#[global_allocator]
-static GLOBAL: MiMalloc = MiMalloc;
-
-/// The function that launches the main server and registers all the routes of the website.
-///
-/// # Error
-///
-/// Returns an error if the port is being used by something else on the system and is not
-/// available for being used for other applications.
-#[actix_web::main]
-async fn main() -> std::io::Result<()> {
-    // A dhat heap profiler initialization.
-    #[cfg(feature = "dhat-heap")]
-    let _profiler = dhat::Profiler::new_heap();
-
-    // Initialize the parsed config file.
-    let config = Config::parse(false).unwrap();
-
-    let cache = Cache::build(&config).await;
-
-    log::info!(
-        "started server on port {} and IP {}",
-        config.port,
-        config.binding_ip
-    );
-    log::info!(
-        "Open http://{}:{}/ in your browser",
-        config.binding_ip,
-        config.port,
-    );
-
-    let listener = TcpListener::bind((config.binding_ip.clone(), config.port))?;
-
-    run(listener, config, cache)?.await
-}
diff --git a/spaces/alamin655/websurfx/src/server/mod.rs b/spaces/alamin655/websurfx/src/server/mod.rs
deleted file mode 100644
index 7f4274f2f82ce51d1287732bfbcd78d40a32630b..0000000000000000000000000000000000000000
--- a/spaces/alamin655/websurfx/src/server/mod.rs
+++ /dev/null
@@ -1,7 +0,0 @@
-//! This module provides modules that handle the functionality of handling different routes/paths
-//! for the `websurfx` search engine website. Also it handles the parsing of search parameters in
-//! the search route. Also, caches the next, current and previous search results in the search
-//! routes with the help of the redis server.
-
-pub mod router;
-pub mod routes;
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/vcs/__init__.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/vcs/__init__.py
deleted file mode 100644
index b6beddbe6d24d2949dc89ed07abfebd59d8b63b9..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/vcs/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Expose a limited set of classes and functions so callers outside of
-# the vcs package don't need to import deeper than `pip._internal.vcs`.
-# (The test directory may still need to import from a vcs sub-package.)
-# Import all vcs modules to register each VCS in the VcsSupport object.
-import pip._internal.vcs.bazaar
-import pip._internal.vcs.git
-import pip._internal.vcs.mercurial
-import pip._internal.vcs.subversion  # noqa: F401
-from pip._internal.vcs.versioncontrol import (  # noqa: F401
-    RemoteNotFoundError,
-    RemoteNotValidError,
-    is_url,
-    make_vcs_requirement_url,
-    vcs,
-)
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_windows.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_windows.py
deleted file mode 100644
index ca3a680d354eac46af4ccbb529229e11d072e9ef..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_windows.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import sys
-from dataclasses import dataclass
-
-
-@dataclass
-class WindowsConsoleFeatures:
-    """Windows features available."""
-
-    vt: bool = False
-    """The console supports VT codes."""
-    truecolor: bool = False
-    """The console supports truecolor."""
-
-
-try:
-    import ctypes
-    from ctypes import LibraryLoader, wintypes
-
-    if sys.platform == "win32":
-        windll = LibraryLoader(ctypes.WinDLL)
-    else:
-        windll = None
-        raise ImportError("Not windows")
-except (AttributeError, ImportError, ValueError):
-
-    # Fallback if we can't load the Windows DLL
-    def get_windows_console_features() -> WindowsConsoleFeatures:
-        features = WindowsConsoleFeatures()
-        return features
-
-else:
-
-    STDOUT = -11
-    ENABLE_VIRTUAL_TERMINAL_PROCESSING = 4
-    _GetConsoleMode = windll.kernel32.GetConsoleMode
-    _GetConsoleMode.argtypes = [wintypes.HANDLE, wintypes.LPDWORD]
-    _GetConsoleMode.restype = wintypes.BOOL
-
-    _GetStdHandle = windll.kernel32.GetStdHandle
-    _GetStdHandle.argtypes = [
-        wintypes.DWORD,
-    ]
-    _GetStdHandle.restype = wintypes.HANDLE
-
-    def get_windows_console_features() -> WindowsConsoleFeatures:
-        """Get windows console features.
-
-        Returns:
-            WindowsConsoleFeatures: An instance of WindowsConsoleFeatures.
-        """
-        handle = _GetStdHandle(STDOUT)
-        console_mode = wintypes.DWORD()
-        result = _GetConsoleMode(handle, console_mode)
-        vt = bool(result and console_mode.value & ENABLE_VIRTUAL_TERMINAL_PROCESSING)
-        truecolor = False
-        if vt:
-            win_version = sys.getwindowsversion()
-            truecolor = win_version.major > 10 or (
-                win_version.major == 10 and win_version.build >= 15063
-            )
-        features = WindowsConsoleFeatures(vt=vt, truecolor=truecolor)
-        return features
-
-
-if __name__ == "__main__":
-    import platform
-
-    features = get_windows_console_features()
-    from pip._vendor.rich import print
-
-    print(f'platform="{platform.system()}"')
-    print(repr(features))
diff --git a/spaces/aliabid94/AutoGPT/autogpt/commands/web_requests.py b/spaces/aliabid94/AutoGPT/autogpt/commands/web_requests.py
deleted file mode 100644
index 406338f46fc7b2381e0b1634c628b123ef20b685..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/autogpt/commands/web_requests.py
+++ /dev/null
@@ -1,190 +0,0 @@
-"""Browse a webpage and summarize it using the LLM model"""
-from __future__ import annotations
-
-from urllib.parse import urljoin, urlparse
-
-import requests
-from bs4 import BeautifulSoup
-from requests import Response
-from requests.compat import urljoin
-
-from autogpt.config import Config
-from autogpt.memory import get_memory
-from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
-
-CFG = Config()
-memory = get_memory(CFG)
-
-session = requests.Session()
-session.headers.update({"User-Agent": CFG.user_agent})
-
-
-def is_valid_url(url: str) -> bool:
-    """Check if the URL is valid
-
-    Args:
-        url (str): The URL to check
-
-    Returns:
-        bool: True if the URL is valid, False otherwise
-    """
-    try:
-        result = urlparse(url)
-        return all([result.scheme, result.netloc])
-    except ValueError:
-        return False
-
-
-def sanitize_url(url: str) -> str:
-    """Sanitize the URL
-
-    Args:
-        url (str): The URL to sanitize
-
-    Returns:
-        str: The sanitized URL
-    """
-    return urljoin(url, urlparse(url).path)
-
-
-def check_local_file_access(url: str) -> bool:
-    """Check if the URL is a local file
-
-    Args:
-        url (str): The URL to check
-
-    Returns:
-        bool: True if the URL is a local file, False otherwise
-    """
-    local_prefixes = [
-        "file:///",
-        "file://localhost/",
-        "file://localhost",
-        "http://localhost",
-        "http://localhost/",
-        "https://localhost",
-        "https://localhost/",
-        "http://2130706433",
-        "http://2130706433/",
-        "https://2130706433",
-        "https://2130706433/",
-        "http://127.0.0.1/",
-        "http://127.0.0.1",
-        "https://127.0.0.1/",
-        "https://127.0.0.1",
-        "https://0.0.0.0/",
-        "https://0.0.0.0",
-        "http://0.0.0.0/",
-        "http://0.0.0.0",
-        "http://0000",
-        "http://0000/",
-        "https://0000",
-        "https://0000/",
-    ]
-    return any(url.startswith(prefix) for prefix in local_prefixes)
-
-
-def get_response(
-    url: str, timeout: int = 10
-) -> tuple[None, str] | tuple[Response, None]:
-    """Get the response from a URL
-
-    Args:
-        url (str): The URL to get the response from
-        timeout (int): The timeout for the HTTP request
-
-    Returns:
-        tuple[None, str] | tuple[Response, None]: The response and error message
-
-    Raises:
-        ValueError: If the URL is invalid
-        requests.exceptions.RequestException: If the HTTP request fails
-    """
-    try:
-        # Restrict access to local files
-        if check_local_file_access(url):
-            raise ValueError("Access to local files is restricted")
-
-        # Most basic check if the URL is valid:
-        if not url.startswith("http://") and not url.startswith("https://"):
-            raise ValueError("Invalid URL format")
-
-        sanitized_url = sanitize_url(url)
-
-        response = session.get(sanitized_url, timeout=timeout)
-
-        # Check if the response contains an HTTP error
-        if response.status_code >= 400:
-            return None, f"Error: HTTP {str(response.status_code)} error"
-
-        return response, None
-    except ValueError as ve:
-        # Handle invalid URL format
-        return None, f"Error: {str(ve)}"
-
-    except requests.exceptions.RequestException as re:
-        # Handle exceptions related to the HTTP request
-        #  (e.g., connection errors, timeouts, etc.)
-        return None, f"Error: {str(re)}"
-
-
-def scrape_text(url: str) -> str:
-    """Scrape text from a webpage
-
-    Args:
-        url (str): The URL to scrape text from
-
-    Returns:
-        str: The scraped text
-    """
-    response, error_message = get_response(url)
-    if error_message:
-        return error_message
-    if not response:
-        return "Error: Could not get response"
-
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    for script in soup(["script", "style"]):
-        script.extract()
-
-    text = soup.get_text()
-    lines = (line.strip() for line in text.splitlines())
-    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-    text = "\n".join(chunk for chunk in chunks if chunk)
-
-    return text
-
-
-def scrape_links(url: str) -> str | list[str]:
-    """Scrape links from a webpage
-
-    Args:
-        url (str): The URL to scrape links from
-
-    Returns:
-       str | list[str]: The scraped links
-    """
-    response, error_message = get_response(url)
-    if error_message:
-        return error_message
-    if not response:
-        return "Error: Could not get response"
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    for script in soup(["script", "style"]):
-        script.extract()
-
-    hyperlinks = extract_hyperlinks(soup, url)
-
-    return format_hyperlinks(hyperlinks)
-
-
-def create_message(chunk, question):
-    """Create a message for the user to summarize a chunk of text"""
-    return {
-        "role": "user",
-        "content": f'"""{chunk}""" Using the above text, answer the following'
-        f' question: "{question}" -- if the question cannot be answered using the'
-        " text, summarize the text.",
-    }
diff --git a/spaces/allknowingroger/Image-Models-Test71/app.py b/spaces/allknowingroger/Image-Models-Test71/app.py
deleted file mode 100644
index 0b7f4bbe7e80a8f61d32149777b25340cecbf83a..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test71/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "alarcon7a/lora-trained-xl-colab",
-    "GabrielLogspace/lora-trained-xl-colab",
-    "Norod78/sdxl-BrainSlug-dreambooth",
-    "iampraveenvemula/lora-trained-xl-colab",
-    "cyriac880/dog",
-    "IsraelSalgado/cosmo-mom",
-    "Norod78/sd15-bender-lora",
-    "LinoyTsaban/huggy_v14",
-    "Yntec/AnythingV3-768",
-]
-
-
-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/amarchheda/ChordDuplicate/portaudio/examples/paex_record.c b/spaces/amarchheda/ChordDuplicate/portaudio/examples/paex_record.c
deleted file mode 100644
index 53bf571d4fd0d74df5619d4cc4805522ea3c4972..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/examples/paex_record.c
+++ /dev/null
@@ -1,353 +0,0 @@
-/** @file paex_record.c
-    @ingroup examples_src
-    @brief Record input into an array; Save array to a file; Playback recorded data.
-    @author Phil Burk  http://www.softsynth.com
-*/
-/*
- * $Id$
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * 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.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-#include <stdio.h>
-#include <stdlib.h>
-#include "portaudio.h"
-
-/* #define SAMPLE_RATE  (17932) // Test failure to open with this value. */
-#define SAMPLE_RATE  (44100)
-#define FRAMES_PER_BUFFER (512)
-#define NUM_SECONDS     (5)
-#define NUM_CHANNELS    (2)
-/* #define DITHER_FLAG     (paDitherOff) */
-#define DITHER_FLAG     (0) /**/
-/** Set to 1 if you want to capture the recording to a file. */
-#define WRITE_TO_FILE   (0)
-
-/* Select sample format. */
-#if 1
-#define PA_SAMPLE_TYPE  paFloat32
-typedef float SAMPLE;
-#define SAMPLE_SILENCE  (0.0f)
-#define PRINTF_S_FORMAT "%.8f"
-#elif 1
-#define PA_SAMPLE_TYPE  paInt16
-typedef short SAMPLE;
-#define SAMPLE_SILENCE  (0)
-#define PRINTF_S_FORMAT "%d"
-#elif 0
-#define PA_SAMPLE_TYPE  paInt8
-typedef char SAMPLE;
-#define SAMPLE_SILENCE  (0)
-#define PRINTF_S_FORMAT "%d"
-#else
-#define PA_SAMPLE_TYPE  paUInt8
-typedef unsigned char SAMPLE;
-#define SAMPLE_SILENCE  (128)
-#define PRINTF_S_FORMAT "%d"
-#endif
-
-typedef struct
-{
-    int          frameIndex;  /* Index into sample array. */
-    int          maxFrameIndex;
-    SAMPLE      *recordedSamples;
-}
-paTestData;
-
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may be called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int recordCallback( const void *inputBuffer, void *outputBuffer,
-                           unsigned long framesPerBuffer,
-                           const PaStreamCallbackTimeInfo* timeInfo,
-                           PaStreamCallbackFlags statusFlags,
-                           void *userData )
-{
-    paTestData *data = (paTestData*)userData;
-    const SAMPLE *rptr = (const SAMPLE*)inputBuffer;
-    SAMPLE *wptr = &data->recordedSamples[data->frameIndex * NUM_CHANNELS];
-    long framesToCalc;
-    long i;
-    int finished;
-    unsigned long framesLeft = data->maxFrameIndex - data->frameIndex;
-
-    (void) outputBuffer; /* Prevent unused variable warnings. */
-    (void) timeInfo;
-    (void) statusFlags;
-    (void) userData;
-
-    if( framesLeft < framesPerBuffer )
-    {
-        framesToCalc = framesLeft;
-        finished = paComplete;
-    }
-    else
-    {
-        framesToCalc = framesPerBuffer;
-        finished = paContinue;
-    }
-
-    if( inputBuffer == NULL )
-    {
-        for( i=0; i<framesToCalc; i++ )
-        {
-            *wptr++ = SAMPLE_SILENCE;  /* left */
-            if( NUM_CHANNELS == 2 ) *wptr++ = SAMPLE_SILENCE;  /* right */
-        }
-    }
-    else
-    {
-        for( i=0; i<framesToCalc; i++ )
-        {
-            *wptr++ = *rptr++;  /* left */
-            if( NUM_CHANNELS == 2 ) *wptr++ = *rptr++;  /* right */
-        }
-    }
-    data->frameIndex += framesToCalc;
-    return finished;
-}
-
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may be called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int playCallback( const void *inputBuffer, void *outputBuffer,
-                         unsigned long framesPerBuffer,
-                         const PaStreamCallbackTimeInfo* timeInfo,
-                         PaStreamCallbackFlags statusFlags,
-                         void *userData )
-{
-    paTestData *data = (paTestData*)userData;
-    SAMPLE *rptr = &data->recordedSamples[data->frameIndex * NUM_CHANNELS];
-    SAMPLE *wptr = (SAMPLE*)outputBuffer;
-    unsigned int i;
-    int finished;
-    unsigned int framesLeft = data->maxFrameIndex - data->frameIndex;
-
-    (void) inputBuffer; /* Prevent unused variable warnings. */
-    (void) timeInfo;
-    (void) statusFlags;
-    (void) userData;
-
-    if( framesLeft < framesPerBuffer )
-    {
-        /* final buffer... */
-        for( i=0; i<framesLeft; i++ )
-        {
-            *wptr++ = *rptr++;  /* left */
-            if( NUM_CHANNELS == 2 ) *wptr++ = *rptr++;  /* right */
-        }
-        for( ; i<framesPerBuffer; i++ )
-        {
-            *wptr++ = 0;  /* left */
-            if( NUM_CHANNELS == 2 ) *wptr++ = 0;  /* right */
-        }
-        data->frameIndex += framesLeft;
-        finished = paComplete;
-    }
-    else
-    {
-        for( i=0; i<framesPerBuffer; i++ )
-        {
-            *wptr++ = *rptr++;  /* left */
-            if( NUM_CHANNELS == 2 ) *wptr++ = *rptr++;  /* right */
-        }
-        data->frameIndex += framesPerBuffer;
-        finished = paContinue;
-    }
-    return finished;
-}
-
-/*******************************************************************/
-int main(void);
-int main(void)
-{
-    PaStreamParameters  inputParameters,
-                        outputParameters;
-    PaStream*           stream;
-    PaError             err = paNoError;
-    paTestData          data;
-    int                 i;
-    int                 totalFrames;
-    int                 numSamples;
-    int                 numBytes;
-    SAMPLE              max, val;
-    double              average;
-
-    printf("patest_record.c\n"); fflush(stdout);
-
-    data.maxFrameIndex = totalFrames = NUM_SECONDS * SAMPLE_RATE; /* Record for a few seconds. */
-    data.frameIndex = 0;
-    numSamples = totalFrames * NUM_CHANNELS;
-    numBytes = numSamples * sizeof(SAMPLE);
-    data.recordedSamples = (SAMPLE *) malloc( numBytes ); /* From now on, recordedSamples is initialised. */
-    if( data.recordedSamples == NULL )
-    {
-        printf("Could not allocate record array.\n");
-        goto done;
-    }
-    for( i=0; i<numSamples; i++ ) data.recordedSamples[i] = 0;
-
-    err = Pa_Initialize();
-    if( err != paNoError ) goto done;
-
-    inputParameters.device = Pa_GetDefaultInputDevice(); /* default input device */
-    if (inputParameters.device == paNoDevice) {
-        fprintf(stderr,"Error: No default input device.\n");
-        goto done;
-    }
-    inputParameters.channelCount = 2;                    /* stereo input */
-    inputParameters.sampleFormat = PA_SAMPLE_TYPE;
-    inputParameters.suggestedLatency = Pa_GetDeviceInfo( inputParameters.device )->defaultLowInputLatency;
-    inputParameters.hostApiSpecificStreamInfo = NULL;
-
-    /* Record some audio. -------------------------------------------- */
-    err = Pa_OpenStream(
-              &stream,
-              &inputParameters,
-              NULL,                  /* &outputParameters, */
-              SAMPLE_RATE,
-              FRAMES_PER_BUFFER,
-              paClipOff,      /* we won't output out of range samples so don't bother clipping them */
-              recordCallback,
-              &data );
-    if( err != paNoError ) goto done;
-
-    err = Pa_StartStream( stream );
-    if( err != paNoError ) goto done;
-    printf("\n=== Now recording!! Please speak into the microphone. ===\n"); fflush(stdout);
-
-    while( ( err = Pa_IsStreamActive( stream ) ) == 1 )
-    {
-        Pa_Sleep(1000);
-        printf("index = %d\n", data.frameIndex ); fflush(stdout);
-    }
-    if( err < 0 ) goto done;
-
-    err = Pa_CloseStream( stream );
-    if( err != paNoError ) goto done;
-
-    /* Measure maximum peak amplitude. */
-    max = 0;
-    average = 0.0;
-    for( i=0; i<numSamples; i++ )
-    {
-        val = data.recordedSamples[i];
-        if( val < 0 ) val = -val; /* ABS */
-        if( val > max )
-        {
-            max = val;
-        }
-        average += val;
-    }
-
-    average = average / (double)numSamples;
-
-    printf("sample max amplitude = "PRINTF_S_FORMAT"\n", max );
-    printf("sample average = %lf\n", average );
-
-    /* Write recorded data to a file. */
-#if WRITE_TO_FILE
-    {
-        FILE  *fid;
-        fid = fopen("recorded.raw", "wb");
-        if( fid == NULL )
-        {
-            printf("Could not open file.");
-        }
-        else
-        {
-            fwrite( data.recordedSamples, NUM_CHANNELS * sizeof(SAMPLE), totalFrames, fid );
-            fclose( fid );
-            printf("Wrote data to 'recorded.raw'\n");
-        }
-    }
-#endif
-
-    /* Playback recorded data.  -------------------------------------------- */
-    data.frameIndex = 0;
-
-    outputParameters.device = Pa_GetDefaultOutputDevice(); /* default output device */
-    if (outputParameters.device == paNoDevice) {
-        fprintf(stderr,"Error: No default output device.\n");
-        goto done;
-    }
-    outputParameters.channelCount = 2;                     /* stereo output */
-    outputParameters.sampleFormat =  PA_SAMPLE_TYPE;
-    outputParameters.suggestedLatency = Pa_GetDeviceInfo( outputParameters.device )->defaultLowOutputLatency;
-    outputParameters.hostApiSpecificStreamInfo = NULL;
-
-    printf("\n=== Now playing back. ===\n"); fflush(stdout);
-    err = Pa_OpenStream(
-              &stream,
-              NULL, /* no input */
-              &outputParameters,
-              SAMPLE_RATE,
-              FRAMES_PER_BUFFER,
-              paClipOff,      /* we won't output out of range samples so don't bother clipping them */
-              playCallback,
-              &data );
-    if( err != paNoError ) goto done;
-
-    if( stream )
-    {
-        err = Pa_StartStream( stream );
-        if( err != paNoError ) goto done;
-
-        printf("Waiting for playback to finish.\n"); fflush(stdout);
-
-        while( ( err = Pa_IsStreamActive( stream ) ) == 1 ) Pa_Sleep(100);
-        if( err < 0 ) goto done;
-
-        err = Pa_CloseStream( stream );
-        if( err != paNoError ) goto done;
-
-        printf("Done.\n"); fflush(stdout);
-    }
-
-done:
-    Pa_Terminate();
-    if( data.recordedSamples )       /* Sure it is NULL or valid. */
-        free( data.recordedSamples );
-    if( err != paNoError )
-    {
-        fprintf( stderr, "An error occurred while using the portaudio stream\n" );
-        fprintf( stderr, "Error number: %d\n", err );
-        fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
-        err = 1;          /* Always return 0 or 1, but no other return codes. */
-    }
-    return err;
-}
diff --git a/spaces/annt/mrc_uit_squadv2/retro_reader/metrics.py b/spaces/annt/mrc_uit_squadv2/retro_reader/metrics.py
deleted file mode 100644
index c11e1a2cf2c0156878cdb2fc5cb9d789680e4932..0000000000000000000000000000000000000000
--- a/spaces/annt/mrc_uit_squadv2/retro_reader/metrics.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import datasets
-from transformers.trainer_utils import EvalPrediction
-
-accuracy = datasets.load_metric("accuracy").compute
-precision = datasets.load_metric("precision").compute
-recall = datasets.load_metric("recall").compute
-f1 = datasets.load_metric("f1").compute
-squad_v2 = datasets.load_metric("squad_v2").compute
-
-
-def compute_classification_metric(p: EvalPrediction):
-    """
-    'predictions': Value(dtype='int32', id=None)
-    'references': Value(dtype='int32', id=None)
-    """
-    predictions = p.predictions.argmax(axis=1)
-    references = p.label_ids
-    metric = accuracy(predictions=predictions, references=references)
-    metric.update(precision(predictions=predictions, references=references))
-    metric.update(recall(predictions=predictions, references=references))
-    metric.update(f1(predictions=predictions, references=references))
-    return metric
-
-
-def compute_squad_v2(p: EvalPrediction):
-    """
-    'predictions': {
-        'id': Value(dtype='string', id=None),
-        'prediction_text': Value(dtype='string', id=None),
-        'no_answer_probability': Value(dtype='float32', id=None)
-    }
-    'references': {
-        'id': Value(dtype='string', id=None),
-        'answers': Sequence(
-            feature={
-                'text': Value(dtype='string', id=None), 
-                'answer_start': Value(dtype='int32', id=None)
-            },
-            length=-1, id=None
-        )
-    }
-    """
-    predictions = p.predictions
-    references = p.label_ids
-    return squad_v2(predictions=predictions, references=references)
\ No newline at end of file
diff --git a/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/modules/deepspeed_parameters.py b/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/modules/deepspeed_parameters.py
deleted file mode 100644
index 3dbed437f5b5196d0b1fcbc582085319fb8d40d1..0000000000000000000000000000000000000000
--- a/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/modules/deepspeed_parameters.py
+++ /dev/null
@@ -1,75 +0,0 @@
-def generate_ds_config(ds_bf16, train_batch_size, nvme_offload_dir):
-
-    '''
-    DeepSpeed configration
-    https://huggingface.co/docs/transformers/main_classes/deepspeed
-    '''
-
-    if nvme_offload_dir:
-        ds_config = {
-            "fp16": {
-                "enabled": not ds_bf16,
-            },
-            "bf16": {
-                "enabled": ds_bf16,
-            },
-            "zero_optimization": {
-                "stage": 3,
-                "offload_param": {
-                    "device": "nvme",
-                    "nvme_path": nvme_offload_dir,
-                    "pin_memory": True,
-                    "buffer_count": 5,
-                    "buffer_size": 1e9,
-                    "max_in_cpu": 1e9
-                },
-                "overlap_comm": True,
-                "reduce_bucket_size": "auto",
-                "contiguous_gradients": True,
-                "sub_group_size": 1e8,
-                "stage3_prefetch_bucket_size": "auto",
-                "stage3_param_persistence_threshold": "auto",
-                "stage3_max_live_parameters": "auto",
-                "stage3_max_reuse_distance": "auto",
-            },
-            "aio": {
-                "block_size": 262144,
-                "queue_depth": 32,
-                "thread_count": 1,
-                "single_submit": False,
-                "overlap_events": True
-            },
-            "steps_per_print": 2000,
-            "train_batch_size": train_batch_size,
-            "train_micro_batch_size_per_gpu": 1,
-            "wall_clock_breakdown": False
-        }
-    else:
-        ds_config = {
-            "fp16": {
-                "enabled": not ds_bf16,
-            },
-            "bf16": {
-                "enabled": ds_bf16,
-            },
-            "zero_optimization": {
-                "stage": 3,
-                "offload_param": {
-                    "device": "cpu",
-                    "pin_memory": True
-                },
-                "overlap_comm": True,
-                "contiguous_gradients": True,
-                "reduce_bucket_size": "auto",
-                "stage3_prefetch_bucket_size": "auto",
-                "stage3_param_persistence_threshold": "auto",
-                "stage3_max_live_parameters": "auto",
-                "stage3_max_reuse_distance": "auto",
-            },
-            "steps_per_print": 2000,
-            "train_batch_size": train_batch_size,
-            "train_micro_batch_size_per_gpu": 1,
-            "wall_clock_breakdown": False
-        }
-
-    return ds_config
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/MemoryView.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/MemoryView.py
deleted file mode 100644
index 0406d6c716a8ae6ed04a19a663884d387913c0b5..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/MemoryView.py
+++ /dev/null
@@ -1,858 +0,0 @@
-from __future__ import absolute_import
-
-from .Errors import CompileError, error
-from . import ExprNodes
-from .ExprNodes import IntNode, NameNode, AttributeNode
-from . import Options
-from .Code import UtilityCode, TempitaUtilityCode
-from .UtilityCode import CythonUtilityCode
-from . import Buffer
-from . import PyrexTypes
-from . import ModuleNode
-
-START_ERR = "Start must not be given."
-STOP_ERR = "Axis specification only allowed in the 'step' slot."
-STEP_ERR = "Step must be omitted, 1, or a valid specifier."
-BOTH_CF_ERR = "Cannot specify an array that is both C and Fortran contiguous."
-INVALID_ERR = "Invalid axis specification."
-NOT_CIMPORTED_ERR = "Variable was not cimported from cython.view"
-EXPR_ERR = "no expressions allowed in axis spec, only names and literals."
-CF_ERR = "Invalid axis specification for a C/Fortran contiguous array."
-ERR_UNINITIALIZED = ("Cannot check if memoryview %s is initialized without the "
-                     "GIL, consider using initializedcheck(False)")
-
-
-def concat_flags(*flags):
-    return "(%s)" % "|".join(flags)
-
-
-format_flag = "PyBUF_FORMAT"
-
-memview_c_contiguous = "(PyBUF_C_CONTIGUOUS | PyBUF_FORMAT)"
-memview_f_contiguous = "(PyBUF_F_CONTIGUOUS | PyBUF_FORMAT)"
-memview_any_contiguous = "(PyBUF_ANY_CONTIGUOUS | PyBUF_FORMAT)"
-memview_full_access = "PyBUF_FULL_RO"
-#memview_strided_access = "PyBUF_STRIDED_RO"
-memview_strided_access = "PyBUF_RECORDS_RO"
-
-MEMVIEW_DIRECT = '__Pyx_MEMVIEW_DIRECT'
-MEMVIEW_PTR    = '__Pyx_MEMVIEW_PTR'
-MEMVIEW_FULL   = '__Pyx_MEMVIEW_FULL'
-MEMVIEW_CONTIG = '__Pyx_MEMVIEW_CONTIG'
-MEMVIEW_STRIDED= '__Pyx_MEMVIEW_STRIDED'
-MEMVIEW_FOLLOW = '__Pyx_MEMVIEW_FOLLOW'
-
-_spec_to_const = {
-        'direct' : MEMVIEW_DIRECT,
-        'ptr'    : MEMVIEW_PTR,
-        'full'   : MEMVIEW_FULL,
-        'contig' : MEMVIEW_CONTIG,
-        'strided': MEMVIEW_STRIDED,
-        'follow' : MEMVIEW_FOLLOW,
-        }
-
-_spec_to_abbrev = {
-    'direct'  : 'd',
-    'ptr'     : 'p',
-    'full'    : 'f',
-    'contig'  : 'c',
-    'strided' : 's',
-    'follow'  : '_',
-}
-
-memslice_entry_init = "{ 0, 0, { 0 }, { 0 }, { 0 } }"
-
-memview_name = u'memoryview'
-memview_typeptr_cname = '__pyx_memoryview_type'
-memview_objstruct_cname = '__pyx_memoryview_obj'
-memviewslice_cname = u'__Pyx_memviewslice'
-
-
-def put_init_entry(mv_cname, code):
-    code.putln("%s.data = NULL;" % mv_cname)
-    code.putln("%s.memview = NULL;" % mv_cname)
-
-
-#def axes_to_str(axes):
-#    return "".join([access[0].upper()+packing[0] for (access, packing) in axes])
-
-
-def put_acquire_memoryviewslice(lhs_cname, lhs_type, lhs_pos, rhs, code,
-                                have_gil=False, first_assignment=True):
-    "We can avoid decreffing the lhs if we know it is the first assignment"
-    assert rhs.type.is_memoryviewslice
-
-    pretty_rhs = rhs.result_in_temp() or rhs.is_simple()
-    if pretty_rhs:
-        rhstmp = rhs.result()
-    else:
-        rhstmp = code.funcstate.allocate_temp(lhs_type, manage_ref=False)
-        code.putln("%s = %s;" % (rhstmp, rhs.result_as(lhs_type)))
-
-    # Allow uninitialized assignment
-    #code.putln(code.put_error_if_unbound(lhs_pos, rhs.entry))
-    put_assign_to_memviewslice(lhs_cname, rhs, rhstmp, lhs_type, code,
-                               have_gil=have_gil, first_assignment=first_assignment)
-
-    if not pretty_rhs:
-        code.funcstate.release_temp(rhstmp)
-
-
-def put_assign_to_memviewslice(lhs_cname, rhs, rhs_cname, memviewslicetype, code,
-                               have_gil=False, first_assignment=False):
-    if not first_assignment:
-        code.put_xdecref_memoryviewslice(lhs_cname, have_gil=have_gil)
-
-    if not rhs.result_in_temp():
-        rhs.make_owned_memoryviewslice(code)
-
-    code.putln("%s = %s;" % (lhs_cname, rhs_cname))
-
-
-def get_buf_flags(specs):
-    is_c_contig, is_f_contig = is_cf_contig(specs)
-
-    if is_c_contig:
-        return memview_c_contiguous
-    elif is_f_contig:
-        return memview_f_contiguous
-
-    access, packing = zip(*specs)
-
-    if 'full' in access or 'ptr' in access:
-        return memview_full_access
-    else:
-        return memview_strided_access
-
-
-def insert_newaxes(memoryviewtype, n):
-    axes = [('direct', 'strided')] * n
-    axes.extend(memoryviewtype.axes)
-    return PyrexTypes.MemoryViewSliceType(memoryviewtype.dtype, axes)
-
-
-def broadcast_types(src, dst):
-    n = abs(src.ndim - dst.ndim)
-    if src.ndim < dst.ndim:
-        return insert_newaxes(src, n), dst
-    else:
-        return src, insert_newaxes(dst, n)
-
-
-def valid_memslice_dtype(dtype, i=0):
-    """
-    Return whether type dtype can be used as the base type of a
-    memoryview slice.
-
-    We support structs, numeric types and objects
-    """
-    if dtype.is_complex and dtype.real_type.is_int:
-        return False
-
-    if dtype is PyrexTypes.c_bint_type:
-        return False
-
-    if dtype.is_struct and dtype.kind == 'struct':
-        for member in dtype.scope.var_entries:
-            if not valid_memslice_dtype(member.type):
-                return False
-
-        return True
-
-    return (
-        dtype.is_error or
-        # Pointers are not valid (yet)
-        # (dtype.is_ptr and valid_memslice_dtype(dtype.base_type)) or
-        (dtype.is_array and i < 8 and
-         valid_memslice_dtype(dtype.base_type, i + 1)) or
-        dtype.is_numeric or
-        dtype.is_pyobject or
-        dtype.is_fused or # accept this as it will be replaced by specializations later
-        (dtype.is_typedef and valid_memslice_dtype(dtype.typedef_base_type))
-    )
-
-
-class MemoryViewSliceBufferEntry(Buffer.BufferEntry):
-    """
-    May be used during code generation time to be queried for
-    shape/strides/suboffsets attributes, or to perform indexing or slicing.
-    """
-    def __init__(self, entry):
-        self.entry = entry
-        self.type = entry.type
-        self.cname = entry.cname
-
-        self.buf_ptr = "%s.data" % self.cname
-
-        dtype = self.entry.type.dtype
-        self.buf_ptr_type = PyrexTypes.CPtrType(dtype)
-        self.init_attributes()
-
-    def get_buf_suboffsetvars(self):
-        return self._for_all_ndim("%s.suboffsets[%d]")
-
-    def get_buf_stridevars(self):
-        return self._for_all_ndim("%s.strides[%d]")
-
-    def get_buf_shapevars(self):
-        return self._for_all_ndim("%s.shape[%d]")
-
-    def generate_buffer_lookup_code(self, code, index_cnames):
-        axes = [(dim, index_cnames[dim], access, packing)
-                    for dim, (access, packing) in enumerate(self.type.axes)]
-        return self._generate_buffer_lookup_code(code, axes)
-
-    def _generate_buffer_lookup_code(self, code, axes, cast_result=True):
-        """
-        Generate a single expression that indexes the memory view slice
-        in each dimension.
-        """
-        bufp = self.buf_ptr
-        type_decl = self.type.dtype.empty_declaration_code()
-
-        for dim, index, access, packing in axes:
-            shape = "%s.shape[%d]" % (self.cname, dim)
-            stride = "%s.strides[%d]" % (self.cname, dim)
-            suboffset = "%s.suboffsets[%d]" % (self.cname, dim)
-
-            flag = get_memoryview_flag(access, packing)
-
-            if flag in ("generic", "generic_contiguous"):
-                # Note: we cannot do cast tricks to avoid stride multiplication
-                #       for generic_contiguous, as we may have to do (dtype *)
-                #       or (dtype **) arithmetic, we won't know which unless
-                #       we check suboffsets
-                code.globalstate.use_utility_code(memviewslice_index_helpers)
-                bufp = ('__pyx_memviewslice_index_full(%s, %s, %s, %s)' %
-                                            (bufp, index, stride, suboffset))
-
-            elif flag == "indirect":
-                bufp = "(%s + %s * %s)" % (bufp, index, stride)
-                bufp = ("(*((char **) %s) + %s)" % (bufp, suboffset))
-
-            elif flag == "indirect_contiguous":
-                # Note: we do char ** arithmetic
-                bufp = "(*((char **) %s + %s) + %s)" % (bufp, index, suboffset)
-
-            elif flag == "strided":
-                bufp = "(%s + %s * %s)" % (bufp, index, stride)
-
-            else:
-                assert flag == 'contiguous', flag
-                bufp = '((char *) (((%s *) %s) + %s))' % (type_decl, bufp, index)
-
-            bufp = '( /* dim=%d */ %s )' % (dim, bufp)
-
-        if cast_result:
-            return "((%s *) %s)" % (type_decl, bufp)
-
-        return bufp
-
-    def generate_buffer_slice_code(self, code, indices, dst, have_gil,
-                                   have_slices, directives):
-        """
-        Slice a memoryviewslice.
-
-        indices     - list of index nodes. If not a SliceNode, or NoneNode,
-                      then it must be coercible to Py_ssize_t
-
-        Simply call __pyx_memoryview_slice_memviewslice with the right
-        arguments, unless the dimension is omitted or a bare ':', in which
-        case we copy over the shape/strides/suboffsets attributes directly
-        for that dimension.
-        """
-        src = self.cname
-
-        code.putln("%(dst)s.data = %(src)s.data;" % locals())
-        code.putln("%(dst)s.memview = %(src)s.memview;" % locals())
-        code.put_incref_memoryviewslice(dst)
-
-        all_dimensions_direct = all(access == 'direct' for access, packing in self.type.axes)
-        suboffset_dim_temp = []
-
-        def get_suboffset_dim():
-            # create global temp variable at request
-            if not suboffset_dim_temp:
-                suboffset_dim = code.funcstate.allocate_temp(PyrexTypes.c_int_type, manage_ref=False)
-                code.putln("%s = -1;" % suboffset_dim)
-                suboffset_dim_temp.append(suboffset_dim)
-            return suboffset_dim_temp[0]
-
-        dim = -1
-        new_ndim = 0
-        for index in indices:
-            if index.is_none:
-                # newaxis
-                for attrib, value in [('shape', 1), ('strides', 0), ('suboffsets', -1)]:
-                    code.putln("%s.%s[%d] = %d;" % (dst, attrib, new_ndim, value))
-
-                new_ndim += 1
-                continue
-
-            dim += 1
-            access, packing = self.type.axes[dim]
-
-            if isinstance(index, ExprNodes.SliceNode):
-                # slice, unspecified dimension, or part of ellipsis
-                d = dict(locals())
-                for s in "start stop step".split():
-                    idx = getattr(index, s)
-                    have_idx = d['have_' + s] = not idx.is_none
-                    d[s] = idx.result() if have_idx else "0"
-
-                if not (d['have_start'] or d['have_stop'] or d['have_step']):
-                    # full slice (:), simply copy over the extent, stride
-                    # and suboffset. Also update suboffset_dim if needed
-                    d['access'] = access
-                    util_name = "SimpleSlice"
-                else:
-                    util_name = "ToughSlice"
-                    d['error_goto'] = code.error_goto(index.pos)
-
-                new_ndim += 1
-            else:
-                # normal index
-                idx = index.result()
-
-                indirect = access != 'direct'
-                if indirect:
-                    generic = access == 'full'
-                    if new_ndim != 0:
-                        return error(index.pos,
-                                     "All preceding dimensions must be "
-                                     "indexed and not sliced")
-
-                d = dict(
-                    locals(),
-                    wraparound=int(directives['wraparound']),
-                    boundscheck=int(directives['boundscheck']),
-                )
-                if d['boundscheck']:
-                    d['error_goto'] = code.error_goto(index.pos)
-                util_name = "SliceIndex"
-
-            _, impl = TempitaUtilityCode.load_as_string(util_name, "MemoryView_C.c", context=d)
-            code.put(impl)
-
-        if suboffset_dim_temp:
-            code.funcstate.release_temp(suboffset_dim_temp[0])
-
-
-def empty_slice(pos):
-    none = ExprNodes.NoneNode(pos)
-    return ExprNodes.SliceNode(pos, start=none,
-                               stop=none, step=none)
-
-
-def unellipsify(indices, ndim):
-    result = []
-    seen_ellipsis = False
-    have_slices = False
-
-    newaxes = [newaxis for newaxis in indices if newaxis.is_none]
-    n_indices = len(indices) - len(newaxes)
-
-    for index in indices:
-        if isinstance(index, ExprNodes.EllipsisNode):
-            have_slices = True
-            full_slice = empty_slice(index.pos)
-
-            if seen_ellipsis:
-                result.append(full_slice)
-            else:
-                nslices = ndim - n_indices + 1
-                result.extend([full_slice] * nslices)
-                seen_ellipsis = True
-        else:
-            have_slices = have_slices or index.is_slice or index.is_none
-            result.append(index)
-
-    result_length = len(result) - len(newaxes)
-    if result_length < ndim:
-        have_slices = True
-        nslices = ndim - result_length
-        result.extend([empty_slice(indices[-1].pos)] * nslices)
-
-    return have_slices, result, newaxes
-
-
-def get_memoryview_flag(access, packing):
-    if access == 'full' and packing in ('strided', 'follow'):
-        return 'generic'
-    elif access == 'full' and packing == 'contig':
-        return 'generic_contiguous'
-    elif access == 'ptr' and packing in ('strided', 'follow'):
-        return 'indirect'
-    elif access == 'ptr' and packing == 'contig':
-        return 'indirect_contiguous'
-    elif access == 'direct' and packing in ('strided', 'follow'):
-        return 'strided'
-    else:
-        assert (access, packing) == ('direct', 'contig'), (access, packing)
-        return 'contiguous'
-
-
-def get_is_contig_func_name(contig_type, ndim):
-    assert contig_type in ('C', 'F')
-    return "__pyx_memviewslice_is_contig_%s%d" % (contig_type, ndim)
-
-
-def get_is_contig_utility(contig_type, ndim):
-    assert contig_type in ('C', 'F')
-    C = dict(context, ndim=ndim, contig_type=contig_type)
-    utility = load_memview_c_utility("MemviewSliceCheckContig", C, requires=[is_contig_utility])
-    return utility
-
-
-def slice_iter(slice_type, slice_result, ndim, code):
-    if slice_type.is_c_contig or slice_type.is_f_contig:
-        return ContigSliceIter(slice_type, slice_result, ndim, code)
-    else:
-        return StridedSliceIter(slice_type, slice_result, ndim, code)
-
-
-class SliceIter(object):
-    def __init__(self, slice_type, slice_result, ndim, code):
-        self.slice_type = slice_type
-        self.slice_result = slice_result
-        self.code = code
-        self.ndim = ndim
-
-
-class ContigSliceIter(SliceIter):
-    def start_loops(self):
-        code = self.code
-        code.begin_block()
-
-        type_decl = self.slice_type.dtype.empty_declaration_code()
-
-        total_size = ' * '.join("%s.shape[%d]" % (self.slice_result, i)
-                                for i in range(self.ndim))
-        code.putln("Py_ssize_t __pyx_temp_extent = %s;" % total_size)
-        code.putln("Py_ssize_t __pyx_temp_idx;")
-        code.putln("%s *__pyx_temp_pointer = (%s *) %s.data;" % (
-            type_decl, type_decl, self.slice_result))
-        code.putln("for (__pyx_temp_idx = 0; "
-                        "__pyx_temp_idx < __pyx_temp_extent; "
-                        "__pyx_temp_idx++) {")
-
-        return "__pyx_temp_pointer"
-
-    def end_loops(self):
-        self.code.putln("__pyx_temp_pointer += 1;")
-        self.code.putln("}")
-        self.code.end_block()
-
-
-class StridedSliceIter(SliceIter):
-    def start_loops(self):
-        code = self.code
-        code.begin_block()
-
-        for i in range(self.ndim):
-            t = i, self.slice_result, i
-            code.putln("Py_ssize_t __pyx_temp_extent_%d = %s.shape[%d];" % t)
-            code.putln("Py_ssize_t __pyx_temp_stride_%d = %s.strides[%d];" % t)
-            code.putln("char *__pyx_temp_pointer_%d;" % i)
-            code.putln("Py_ssize_t __pyx_temp_idx_%d;" % i)
-
-        code.putln("__pyx_temp_pointer_0 = %s.data;" % self.slice_result)
-
-        for i in range(self.ndim):
-            if i > 0:
-                code.putln("__pyx_temp_pointer_%d = __pyx_temp_pointer_%d;" % (i, i - 1))
-
-            code.putln("for (__pyx_temp_idx_%d = 0; "
-                            "__pyx_temp_idx_%d < __pyx_temp_extent_%d; "
-                            "__pyx_temp_idx_%d++) {" % (i, i, i, i))
-
-        return "__pyx_temp_pointer_%d" % (self.ndim - 1)
-
-    def end_loops(self):
-        code = self.code
-        for i in range(self.ndim - 1, -1, -1):
-            code.putln("__pyx_temp_pointer_%d += __pyx_temp_stride_%d;" % (i, i))
-            code.putln("}")
-
-        code.end_block()
-
-
-def copy_c_or_fortran_cname(memview):
-    if memview.is_c_contig:
-        c_or_f = 'c'
-    else:
-        c_or_f = 'f'
-
-    return "__pyx_memoryview_copy_slice_%s_%s" % (
-            memview.specialization_suffix(), c_or_f)
-
-
-def get_copy_new_utility(pos, from_memview, to_memview):
-    if (from_memview.dtype != to_memview.dtype and
-            not (from_memview.dtype.is_const and from_memview.dtype.const_base_type == to_memview.dtype)):
-        error(pos, "dtypes must be the same!")
-        return
-    if len(from_memview.axes) != len(to_memview.axes):
-        error(pos, "number of dimensions must be same")
-        return
-    if not (to_memview.is_c_contig or to_memview.is_f_contig):
-        error(pos, "to_memview must be c or f contiguous.")
-        return
-
-    for (access, packing) in from_memview.axes:
-        if access != 'direct':
-            error(pos, "cannot handle 'full' or 'ptr' access at this time.")
-            return
-
-    if to_memview.is_c_contig:
-        mode = 'c'
-        contig_flag = memview_c_contiguous
-    elif to_memview.is_f_contig:
-        mode = 'fortran'
-        contig_flag = memview_f_contiguous
-
-    return load_memview_c_utility(
-        "CopyContentsUtility",
-        context=dict(
-            context,
-            mode=mode,
-            dtype_decl=to_memview.dtype.empty_declaration_code(),
-            contig_flag=contig_flag,
-            ndim=to_memview.ndim,
-            func_cname=copy_c_or_fortran_cname(to_memview),
-            dtype_is_object=int(to_memview.dtype.is_pyobject)),
-        requires=[copy_contents_new_utility])
-
-
-def get_axes_specs(env, axes):
-    '''
-    get_axes_specs(env, axes) -> list of (access, packing) specs for each axis.
-    access is one of 'full', 'ptr' or 'direct'
-    packing is one of 'contig', 'strided' or 'follow'
-    '''
-
-    cythonscope = env.global_scope().context.cython_scope
-    cythonscope.load_cythonscope()
-    viewscope = cythonscope.viewscope
-
-    access_specs = tuple([viewscope.lookup(name)
-                    for name in ('full', 'direct', 'ptr')])
-    packing_specs = tuple([viewscope.lookup(name)
-                    for name in ('contig', 'strided', 'follow')])
-
-    is_f_contig, is_c_contig = False, False
-    default_access, default_packing = 'direct', 'strided'
-    cf_access, cf_packing = default_access, 'follow'
-
-    axes_specs = []
-    # analyse all axes.
-    for idx, axis in enumerate(axes):
-        if not axis.start.is_none:
-            raise CompileError(axis.start.pos,  START_ERR)
-
-        if not axis.stop.is_none:
-            raise CompileError(axis.stop.pos, STOP_ERR)
-
-        if axis.step.is_none:
-            axes_specs.append((default_access, default_packing))
-
-        elif isinstance(axis.step, IntNode):
-            # the packing for the ::1 axis is contiguous,
-            # all others are cf_packing.
-            if axis.step.compile_time_value(env) != 1:
-                raise CompileError(axis.step.pos, STEP_ERR)
-
-            axes_specs.append((cf_access, 'cfcontig'))
-
-        elif isinstance(axis.step, (NameNode, AttributeNode)):
-            entry = _get_resolved_spec(env, axis.step)
-            if entry.name in view_constant_to_access_packing:
-                axes_specs.append(view_constant_to_access_packing[entry.name])
-            else:
-                raise CompileError(axis.step.pos, INVALID_ERR)
-
-        else:
-            raise CompileError(axis.step.pos, INVALID_ERR)
-
-    # First, find out if we have a ::1 somewhere
-    contig_dim = 0
-    is_contig = False
-    for idx, (access, packing) in enumerate(axes_specs):
-        if packing == 'cfcontig':
-            if is_contig:
-                raise CompileError(axis.step.pos, BOTH_CF_ERR)
-
-            contig_dim = idx
-            axes_specs[idx] = (access, 'contig')
-            is_contig = True
-
-    if is_contig:
-        # We have a ::1 somewhere, see if we're C or Fortran contiguous
-        if contig_dim == len(axes) - 1:
-            is_c_contig = True
-        else:
-            is_f_contig = True
-
-            if contig_dim and not axes_specs[contig_dim - 1][0] in ('full', 'ptr'):
-                raise CompileError(axes[contig_dim].pos,
-                                   "Fortran contiguous specifier must follow an indirect dimension")
-
-        if is_c_contig:
-            # Contiguous in the last dimension, find the last indirect dimension
-            contig_dim = -1
-            for idx, (access, packing) in enumerate(reversed(axes_specs)):
-                if access in ('ptr', 'full'):
-                    contig_dim = len(axes) - idx - 1
-
-        # Replace 'strided' with 'follow' for any dimension following the last
-        # indirect dimension, the first dimension or the dimension following
-        # the ::1.
-        #               int[::indirect, ::1, :, :]
-        #                                    ^  ^
-        #               int[::indirect, :, :, ::1]
-        #                               ^  ^
-        start = contig_dim + 1
-        stop = len(axes) - is_c_contig
-        for idx, (access, packing) in enumerate(axes_specs[start:stop]):
-            idx = contig_dim + 1 + idx
-            if access != 'direct':
-                raise CompileError(axes[idx].pos,
-                                   "Indirect dimension may not follow "
-                                   "Fortran contiguous dimension")
-            if packing == 'contig':
-                raise CompileError(axes[idx].pos,
-                                   "Dimension may not be contiguous")
-            axes_specs[idx] = (access, cf_packing)
-
-        if is_c_contig:
-            # For C contiguity, we need to fix the 'contig' dimension
-            # after the loop
-            a, p = axes_specs[-1]
-            axes_specs[-1] = a, 'contig'
-
-    validate_axes_specs([axis.start.pos for axis in axes],
-                        axes_specs,
-                        is_c_contig,
-                        is_f_contig)
-
-    return axes_specs
-
-
-def validate_axes(pos, axes):
-    if len(axes) >= Options.buffer_max_dims:
-        error(pos, "More dimensions than the maximum number"
-                   " of buffer dimensions were used.")
-        return False
-
-    return True
-
-
-def is_cf_contig(specs):
-    is_c_contig = is_f_contig = False
-
-    if len(specs) == 1 and specs == [('direct', 'contig')]:
-        is_c_contig = True
-
-    elif (specs[-1] == ('direct','contig') and
-          all(axis == ('direct','follow') for axis in specs[:-1])):
-        # c_contiguous: 'follow', 'follow', ..., 'follow', 'contig'
-        is_c_contig = True
-
-    elif (len(specs) > 1 and
-        specs[0] == ('direct','contig') and
-        all(axis == ('direct','follow') for axis in specs[1:])):
-        # f_contiguous: 'contig', 'follow', 'follow', ..., 'follow'
-        is_f_contig = True
-
-    return is_c_contig, is_f_contig
-
-
-def get_mode(specs):
-    is_c_contig, is_f_contig = is_cf_contig(specs)
-
-    if is_c_contig:
-        return 'c'
-    elif is_f_contig:
-        return 'fortran'
-
-    for access, packing in specs:
-        if access in ('ptr', 'full'):
-            return 'full'
-
-    return 'strided'
-
-view_constant_to_access_packing = {
-    'generic':              ('full',   'strided'),
-    'strided':              ('direct', 'strided'),
-    'indirect':             ('ptr',    'strided'),
-    'generic_contiguous':   ('full',   'contig'),
-    'contiguous':           ('direct', 'contig'),
-    'indirect_contiguous':  ('ptr',    'contig'),
-}
-
-def validate_axes_specs(positions, specs, is_c_contig, is_f_contig):
-
-    packing_specs = ('contig', 'strided', 'follow')
-    access_specs = ('direct', 'ptr', 'full')
-
-    # is_c_contig, is_f_contig = is_cf_contig(specs)
-
-    has_contig = has_follow = has_strided = has_generic_contig = False
-
-    last_indirect_dimension = -1
-    for idx, (access, packing) in enumerate(specs):
-        if access == 'ptr':
-            last_indirect_dimension = idx
-
-    for idx, (pos, (access, packing)) in enumerate(zip(positions, specs)):
-
-        if not (access in access_specs and
-                packing in packing_specs):
-            raise CompileError(pos, "Invalid axes specification.")
-
-        if packing == 'strided':
-            has_strided = True
-        elif packing == 'contig':
-            if has_contig:
-                raise CompileError(pos, "Only one direct contiguous "
-                                        "axis may be specified.")
-
-            valid_contig_dims = last_indirect_dimension + 1, len(specs) - 1
-            if idx not in valid_contig_dims and access != 'ptr':
-                if last_indirect_dimension + 1 != len(specs) - 1:
-                    dims = "dimensions %d and %d" % valid_contig_dims
-                else:
-                    dims = "dimension %d" % valid_contig_dims[0]
-
-                raise CompileError(pos, "Only %s may be contiguous and direct" % dims)
-
-            has_contig = access != 'ptr'
-        elif packing == 'follow':
-            if has_strided:
-                raise CompileError(pos, "A memoryview cannot have both follow and strided axis specifiers.")
-            if not (is_c_contig or is_f_contig):
-                raise CompileError(pos, "Invalid use of the follow specifier.")
-
-        if access in ('ptr', 'full'):
-            has_strided = False
-
-def _get_resolved_spec(env, spec):
-    # spec must be a NameNode or an AttributeNode
-    if isinstance(spec, NameNode):
-        return _resolve_NameNode(env, spec)
-    elif isinstance(spec, AttributeNode):
-        return _resolve_AttributeNode(env, spec)
-    else:
-        raise CompileError(spec.pos, INVALID_ERR)
-
-def _resolve_NameNode(env, node):
-    try:
-        resolved_name = env.lookup(node.name).name
-    except AttributeError:
-        raise CompileError(node.pos, INVALID_ERR)
-
-    viewscope = env.global_scope().context.cython_scope.viewscope
-    entry = viewscope.lookup(resolved_name)
-    if entry is None:
-        raise CompileError(node.pos, NOT_CIMPORTED_ERR)
-
-    return entry
-
-def _resolve_AttributeNode(env, node):
-    path = []
-    while isinstance(node, AttributeNode):
-        path.insert(0, node.attribute)
-        node = node.obj
-    if isinstance(node, NameNode):
-        path.insert(0, node.name)
-    else:
-        raise CompileError(node.pos, EXPR_ERR)
-    modnames = path[:-1]
-    # must be at least 1 module name, o/w not an AttributeNode.
-    assert modnames
-
-    scope = env
-    for modname in modnames:
-        mod = scope.lookup(modname)
-        if not mod or not mod.as_module:
-            raise CompileError(
-                    node.pos, "undeclared name not builtin: %s" % modname)
-        scope = mod.as_module
-
-    entry = scope.lookup(path[-1])
-    if not entry:
-        raise CompileError(node.pos, "No such attribute '%s'" % path[-1])
-
-    return entry
-
-#
-### Utility loading
-#
-
-def load_memview_cy_utility(util_code_name, context=None, **kwargs):
-    return CythonUtilityCode.load(util_code_name, "MemoryView.pyx",
-                                  context=context, **kwargs)
-
-def load_memview_c_utility(util_code_name, context=None, **kwargs):
-    if context is None:
-        return UtilityCode.load(util_code_name, "MemoryView_C.c", **kwargs)
-    else:
-        return TempitaUtilityCode.load(util_code_name, "MemoryView_C.c",
-                                       context=context, **kwargs)
-
-def use_cython_array_utility_code(env):
-    cython_scope = env.global_scope().context.cython_scope
-    cython_scope.load_cythonscope()
-    cython_scope.viewscope.lookup('array_cwrapper').used = True
-
-context = {
-    'memview_struct_name': memview_objstruct_cname,
-    'max_dims': Options.buffer_max_dims,
-    'memviewslice_name': memviewslice_cname,
-    'memslice_init': memslice_entry_init,
-}
-memviewslice_declare_code = load_memview_c_utility(
-        "MemviewSliceStruct",
-        context=context,
-        requires=[])
-
-atomic_utility = load_memview_c_utility("Atomics", context)
-
-memviewslice_init_code = load_memview_c_utility(
-    "MemviewSliceInit",
-    context=dict(context, BUF_MAX_NDIMS=Options.buffer_max_dims),
-    requires=[memviewslice_declare_code,
-              atomic_utility],
-)
-
-memviewslice_index_helpers = load_memview_c_utility("MemviewSliceIndex")
-
-typeinfo_to_format_code = load_memview_cy_utility(
-        "BufferFormatFromTypeInfo", requires=[Buffer._typeinfo_to_format_code])
-
-is_contig_utility = load_memview_c_utility("MemviewSliceIsContig", context)
-overlapping_utility = load_memview_c_utility("OverlappingSlices", context)
-copy_contents_new_utility = load_memview_c_utility(
-    "MemviewSliceCopyTemplate",
-    context,
-    requires=[], # require cython_array_utility_code
-)
-
-view_utility_code = load_memview_cy_utility(
-        "View.MemoryView",
-        context=context,
-        requires=[Buffer.GetAndReleaseBufferUtilityCode(),
-                  Buffer.buffer_struct_declare_code,
-                  Buffer.buffer_formats_declare_code,
-                  memviewslice_init_code,
-                  is_contig_utility,
-                  overlapping_utility,
-                  copy_contents_new_utility,
-                  ModuleNode.capsule_utility_code],
-)
-view_utility_whitelist = ('array', 'memoryview', 'array_cwrapper',
-                          'generic', 'strided', 'indirect', 'contiguous',
-                          'indirect_contiguous')
-
-memviewslice_declare_code.requires.append(view_utility_code)
-copy_contents_new_utility.requires.append(view_utility_code)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/Naming.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/Naming.py
deleted file mode 100644
index 2c9b620788a28c0d85b5731751f48009a642934d..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/Naming.py
+++ /dev/null
@@ -1,162 +0,0 @@
-#
-#   C naming conventions
-#
-#
-#   Prefixes for generating C names.
-#   Collected here to facilitate ensuring uniqueness.
-#
-
-pyrex_prefix    = "__pyx_"
-
-
-codewriter_temp_prefix = pyrex_prefix + "t_"
-
-temp_prefix       = u"__cyt_"
-
-builtin_prefix    = pyrex_prefix + "builtin_"
-arg_prefix        = pyrex_prefix + "arg_"
-funcdoc_prefix    = pyrex_prefix + "doc_"
-enum_prefix       = pyrex_prefix + "e_"
-func_prefix       = pyrex_prefix + "f_"
-func_prefix_api   = pyrex_prefix + "api_f_"
-pyfunc_prefix     = pyrex_prefix + "pf_"
-pywrap_prefix     = pyrex_prefix + "pw_"
-genbody_prefix    = pyrex_prefix + "gb_"
-gstab_prefix      = pyrex_prefix + "getsets_"
-prop_get_prefix   = pyrex_prefix + "getprop_"
-const_prefix      = pyrex_prefix + "k_"
-py_const_prefix   = pyrex_prefix + "kp_"
-label_prefix      = pyrex_prefix + "L"
-pymethdef_prefix  = pyrex_prefix + "mdef_"
-method_wrapper_prefix = pyrex_prefix + "specialmethod_"
-methtab_prefix    = pyrex_prefix + "methods_"
-memtab_prefix     = pyrex_prefix + "members_"
-objstruct_prefix  = pyrex_prefix + "obj_"
-typeptr_prefix    = pyrex_prefix + "ptype_"
-prop_set_prefix   = pyrex_prefix + "setprop_"
-type_prefix       = pyrex_prefix + "t_"
-typeobj_prefix    = pyrex_prefix + "type_"
-var_prefix        = pyrex_prefix + "v_"
-varptr_prefix     = pyrex_prefix + "vp_"
-varptr_prefix_api = pyrex_prefix + "api_vp_"
-wrapperbase_prefix= pyrex_prefix + "wrapperbase_"
-pybuffernd_prefix   = pyrex_prefix + "pybuffernd_"
-pybufferstruct_prefix  = pyrex_prefix + "pybuffer_"
-vtable_prefix     = pyrex_prefix + "vtable_"
-vtabptr_prefix    = pyrex_prefix + "vtabptr_"
-vtabstruct_prefix = pyrex_prefix + "vtabstruct_"
-opt_arg_prefix    = pyrex_prefix + "opt_args_"
-convert_func_prefix = pyrex_prefix + "convert_"
-closure_scope_prefix = pyrex_prefix + "scope_"
-closure_class_prefix = pyrex_prefix + "scope_struct_"
-lambda_func_prefix = pyrex_prefix + "lambda_"
-module_is_main   = pyrex_prefix + "module_is_main_"
-defaults_struct_prefix = pyrex_prefix + "defaults"
-dynamic_args_cname = pyrex_prefix + "dynamic_args"
-
-interned_prefixes = {
-    'str': pyrex_prefix + "n_",
-    'int': pyrex_prefix + "int_",
-    'float': pyrex_prefix + "float_",
-    'tuple': pyrex_prefix + "tuple_",
-    'codeobj': pyrex_prefix + "codeobj_",
-    'slice': pyrex_prefix + "slice_",
-    'ustring': pyrex_prefix + "ustring_",
-    'umethod': pyrex_prefix + "umethod_",
-}
-
-ctuple_type_prefix = pyrex_prefix + "ctuple_"
-args_cname       = pyrex_prefix + "args"
-generator_cname  = pyrex_prefix + "generator"
-sent_value_cname = pyrex_prefix + "sent_value"
-pykwdlist_cname  = pyrex_prefix + "pyargnames"
-obj_base_cname   = pyrex_prefix + "base"
-builtins_cname   = pyrex_prefix + "b"
-preimport_cname  = pyrex_prefix + "i"
-moddict_cname    = pyrex_prefix + "d"
-dummy_cname      = pyrex_prefix + "dummy"
-filename_cname   = pyrex_prefix + "filename"
-modulename_cname = pyrex_prefix + "modulename"
-filetable_cname  = pyrex_prefix + "f"
-intern_tab_cname = pyrex_prefix + "intern_tab"
-kwds_cname       = pyrex_prefix + "kwds"
-lineno_cname     = pyrex_prefix + "lineno"
-clineno_cname    = pyrex_prefix + "clineno"
-cfilenm_cname    = pyrex_prefix + "cfilenm"
-local_tstate_cname = pyrex_prefix + "tstate"
-module_cname     = pyrex_prefix + "m"
-moddoc_cname     = pyrex_prefix + "mdoc"
-methtable_cname  = pyrex_prefix + "methods"
-retval_cname     = pyrex_prefix + "r"
-reqd_kwds_cname  = pyrex_prefix + "reqd_kwds"
-self_cname       = pyrex_prefix + "self"
-stringtab_cname  = pyrex_prefix + "string_tab"
-vtabslot_cname   = pyrex_prefix + "vtab"
-c_api_tab_cname  = pyrex_prefix + "c_api_tab"
-gilstate_cname   = pyrex_prefix + "state"
-skip_dispatch_cname = pyrex_prefix + "skip_dispatch"
-empty_tuple      = pyrex_prefix + "empty_tuple"
-empty_bytes      = pyrex_prefix + "empty_bytes"
-empty_unicode	 = pyrex_prefix + "empty_unicode"
-print_function   = pyrex_prefix + "print"
-print_function_kwargs   = pyrex_prefix + "print_kwargs"
-cleanup_cname    = pyrex_prefix + "module_cleanup"
-pymoduledef_cname = pyrex_prefix + "moduledef"
-pymoduledef_slots_cname = pyrex_prefix + "moduledef_slots"
-pymodinit_module_arg = pyrex_prefix + "pyinit_module"
-pymodule_create_func_cname = pyrex_prefix + "pymod_create"
-pymodule_exec_func_cname = pyrex_prefix + "pymod_exec"
-optional_args_cname = pyrex_prefix + "optional_args"
-import_star      = pyrex_prefix + "import_star"
-import_star_set  = pyrex_prefix + "import_star_set"
-outer_scope_cname= pyrex_prefix + "outer_scope"
-cur_scope_cname  = pyrex_prefix + "cur_scope"
-enc_scope_cname  = pyrex_prefix + "enc_scope"
-frame_cname      = pyrex_prefix + "frame"
-frame_code_cname = pyrex_prefix + "frame_code"
-binding_cfunc    = pyrex_prefix + "binding_PyCFunctionType"
-fused_func_prefix = pyrex_prefix + 'fuse_'
-quick_temp_cname = pyrex_prefix + "temp" # temp variable for quick'n'dirty temping
-tp_dict_version_temp = pyrex_prefix + "tp_dict_version"
-obj_dict_version_temp = pyrex_prefix + "obj_dict_version"
-type_dict_guard_temp = pyrex_prefix + "type_dict_guard"
-cython_runtime_cname   = pyrex_prefix + "cython_runtime"
-
-global_code_object_cache_find = pyrex_prefix + 'find_code_object'
-global_code_object_cache_insert = pyrex_prefix + 'insert_code_object'
-
-genexpr_id_ref = 'genexpr'
-freelist_name  = 'freelist'
-freecount_name = 'freecount'
-
-line_c_macro = "__LINE__"
-
-file_c_macro = "__FILE__"
-
-extern_c_macro  = pyrex_prefix.upper() + "EXTERN_C"
-
-exc_type_name   = pyrex_prefix + "exc_type"
-exc_value_name  = pyrex_prefix + "exc_value"
-exc_tb_name     = pyrex_prefix + "exc_tb"
-exc_lineno_name = pyrex_prefix + "exc_lineno"
-
-parallel_exc_type = pyrex_prefix + "parallel_exc_type"
-parallel_exc_value = pyrex_prefix + "parallel_exc_value"
-parallel_exc_tb = pyrex_prefix + "parallel_exc_tb"
-parallel_filename = pyrex_prefix + "parallel_filename"
-parallel_lineno = pyrex_prefix + "parallel_lineno"
-parallel_clineno = pyrex_prefix + "parallel_clineno"
-parallel_why = pyrex_prefix + "parallel_why"
-
-exc_vars = (exc_type_name, exc_value_name, exc_tb_name)
-
-api_name        = pyrex_prefix + "capi__"
-
-h_guard_prefix   = "__PYX_HAVE__"
-api_guard_prefix = "__PYX_HAVE_API__"
-api_func_guard   = "__PYX_HAVE_API_FUNC_"
-
-PYX_NAN          = "__PYX_NAN()"
-
-def py_version_hex(major, minor=0, micro=0, release_level=0, release_serial=0):
-    return (major << 24) | (minor << 16) | (micro << 8) | (release_level << 4) | (release_serial)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/ado_consts.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/ado_consts.py
deleted file mode 100644
index ecb2147dc4c3568789c8f6901bb1be2c61ddcacf..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/ado_consts.py
+++ /dev/null
@@ -1,281 +0,0 @@
-# ADO enumerated constants documented on MSDN:
-# http://msdn.microsoft.com/en-us/library/ms678353(VS.85).aspx
-
-# IsolationLevelEnum
-adXactUnspecified = -1
-adXactBrowse = 0x100
-adXactChaos = 0x10
-adXactCursorStability = 0x1000
-adXactIsolated = 0x100000
-adXactReadCommitted = 0x1000
-adXactReadUncommitted = 0x100
-adXactRepeatableRead = 0x10000
-adXactSerializable = 0x100000
-
-# CursorLocationEnum
-adUseClient = 3
-adUseServer = 2
-
-# CursorTypeEnum
-adOpenDynamic = 2
-adOpenForwardOnly = 0
-adOpenKeyset = 1
-adOpenStatic = 3
-adOpenUnspecified = -1
-
-# CommandTypeEnum
-adCmdText = 1
-adCmdStoredProc = 4
-adSchemaTables = 20
-
-# ParameterDirectionEnum
-adParamInput = 1
-adParamInputOutput = 3
-adParamOutput = 2
-adParamReturnValue = 4
-adParamUnknown = 0
-directions = {
-    0: "Unknown",
-    1: "Input",
-    2: "Output",
-    3: "InputOutput",
-    4: "Return",
-}
-
-
-def ado_direction_name(ado_dir):
-    try:
-        return "adParam" + directions[ado_dir]
-    except:
-        return "unknown direction (" + str(ado_dir) + ")"
-
-
-# ObjectStateEnum
-adStateClosed = 0
-adStateOpen = 1
-adStateConnecting = 2
-adStateExecuting = 4
-adStateFetching = 8
-
-# FieldAttributeEnum
-adFldMayBeNull = 0x40
-
-# ConnectModeEnum
-adModeUnknown = 0
-adModeRead = 1
-adModeWrite = 2
-adModeReadWrite = 3
-adModeShareDenyRead = 4
-adModeShareDenyWrite = 8
-adModeShareExclusive = 12
-adModeShareDenyNone = 16
-adModeRecursive = 0x400000
-
-# XactAttributeEnum
-adXactCommitRetaining = 131072
-adXactAbortRetaining = 262144
-
-ado_error_TIMEOUT = -2147217871
-
-# DataTypeEnum - ADO Data types documented at:
-# http://msdn2.microsoft.com/en-us/library/ms675318.aspx
-adArray = 0x2000
-adEmpty = 0x0
-adBSTR = 0x8
-adBigInt = 0x14
-adBinary = 0x80
-adBoolean = 0xB
-adChapter = 0x88
-adChar = 0x81
-adCurrency = 0x6
-adDBDate = 0x85
-adDBTime = 0x86
-adDBTimeStamp = 0x87
-adDate = 0x7
-adDecimal = 0xE
-adDouble = 0x5
-adError = 0xA
-adFileTime = 0x40
-adGUID = 0x48
-adIDispatch = 0x9
-adIUnknown = 0xD
-adInteger = 0x3
-adLongVarBinary = 0xCD
-adLongVarChar = 0xC9
-adLongVarWChar = 0xCB
-adNumeric = 0x83
-adPropVariant = 0x8A
-adSingle = 0x4
-adSmallInt = 0x2
-adTinyInt = 0x10
-adUnsignedBigInt = 0x15
-adUnsignedInt = 0x13
-adUnsignedSmallInt = 0x12
-adUnsignedTinyInt = 0x11
-adUserDefined = 0x84
-adVarBinary = 0xCC
-adVarChar = 0xC8
-adVarNumeric = 0x8B
-adVarWChar = 0xCA
-adVariant = 0xC
-adWChar = 0x82
-# Additional constants used by introspection but not ADO itself
-AUTO_FIELD_MARKER = -1000
-
-adTypeNames = {
-    adBSTR: "adBSTR",
-    adBigInt: "adBigInt",
-    adBinary: "adBinary",
-    adBoolean: "adBoolean",
-    adChapter: "adChapter",
-    adChar: "adChar",
-    adCurrency: "adCurrency",
-    adDBDate: "adDBDate",
-    adDBTime: "adDBTime",
-    adDBTimeStamp: "adDBTimeStamp",
-    adDate: "adDate",
-    adDecimal: "adDecimal",
-    adDouble: "adDouble",
-    adEmpty: "adEmpty",
-    adError: "adError",
-    adFileTime: "adFileTime",
-    adGUID: "adGUID",
-    adIDispatch: "adIDispatch",
-    adIUnknown: "adIUnknown",
-    adInteger: "adInteger",
-    adLongVarBinary: "adLongVarBinary",
-    adLongVarChar: "adLongVarChar",
-    adLongVarWChar: "adLongVarWChar",
-    adNumeric: "adNumeric",
-    adPropVariant: "adPropVariant",
-    adSingle: "adSingle",
-    adSmallInt: "adSmallInt",
-    adTinyInt: "adTinyInt",
-    adUnsignedBigInt: "adUnsignedBigInt",
-    adUnsignedInt: "adUnsignedInt",
-    adUnsignedSmallInt: "adUnsignedSmallInt",
-    adUnsignedTinyInt: "adUnsignedTinyInt",
-    adUserDefined: "adUserDefined",
-    adVarBinary: "adVarBinary",
-    adVarChar: "adVarChar",
-    adVarNumeric: "adVarNumeric",
-    adVarWChar: "adVarWChar",
-    adVariant: "adVariant",
-    adWChar: "adWChar",
-}
-
-
-def ado_type_name(ado_type):
-    return adTypeNames.get(ado_type, "unknown type (" + str(ado_type) + ")")
-
-
-# here in decimal, sorted by value
-# adEmpty 0 Specifies no value (DBTYPE_EMPTY).
-# adSmallInt 2 Indicates a two-byte signed integer (DBTYPE_I2).
-# adInteger 3 Indicates a four-byte signed integer (DBTYPE_I4).
-# adSingle 4 Indicates a single-precision floating-point value (DBTYPE_R4).
-# adDouble 5 Indicates a double-precision floating-point value (DBTYPE_R8).
-# adCurrency 6 Indicates a currency value (DBTYPE_CY). Currency is a fixed-point number
-#   with four digits to the right of the decimal point. It is stored in an eight-byte signed integer scaled by 10,000.
-# adDate 7 Indicates a date value (DBTYPE_DATE). A date is stored as a double, the whole part of which is
-#   the number of days since December 30, 1899, and the fractional part of which is the fraction of a day.
-# adBSTR 8 Indicates a null-terminated character string (Unicode) (DBTYPE_BSTR).
-# adIDispatch 9 Indicates a pointer to an IDispatch interface on a COM object (DBTYPE_IDISPATCH).
-# adError 10 Indicates a 32-bit error code (DBTYPE_ERROR).
-# adBoolean 11 Indicates a boolean value (DBTYPE_BOOL).
-# adVariant 12 Indicates an Automation Variant (DBTYPE_VARIANT).
-# adIUnknown 13 Indicates a pointer to an IUnknown interface on a COM object (DBTYPE_IUNKNOWN).
-# adDecimal 14 Indicates an exact numeric value with a fixed precision and scale (DBTYPE_DECIMAL).
-# adTinyInt 16 Indicates a one-byte signed integer (DBTYPE_I1).
-# adUnsignedTinyInt 17 Indicates a one-byte unsigned integer (DBTYPE_UI1).
-# adUnsignedSmallInt 18 Indicates a two-byte unsigned integer (DBTYPE_UI2).
-# adUnsignedInt 19 Indicates a four-byte unsigned integer (DBTYPE_UI4).
-# adBigInt 20 Indicates an eight-byte signed integer (DBTYPE_I8).
-# adUnsignedBigInt 21 Indicates an eight-byte unsigned integer (DBTYPE_UI8).
-# adFileTime 64 Indicates a 64-bit value representing the number of 100-nanosecond intervals since
-#    January 1, 1601 (DBTYPE_FILETIME).
-# adGUID 72 Indicates a globally unique identifier (GUID) (DBTYPE_GUID).
-# adBinary 128 Indicates a binary value (DBTYPE_BYTES).
-# adChar 129 Indicates a string value (DBTYPE_STR).
-# adWChar 130 Indicates a null-terminated Unicode character string (DBTYPE_WSTR).
-# adNumeric 131 Indicates an exact numeric value with a fixed precision and scale (DBTYPE_NUMERIC).
-#   adUserDefined 132 Indicates a user-defined variable (DBTYPE_UDT).
-# adUserDefined 132 Indicates a user-defined variable (DBTYPE_UDT).
-# adDBDate 133 Indicates a date value (yyyymmdd) (DBTYPE_DBDATE).
-# adDBTime 134 Indicates a time value (hhmmss) (DBTYPE_DBTIME).
-# adDBTimeStamp 135 Indicates a date/time stamp (yyyymmddhhmmss plus a fraction in billionths) (DBTYPE_DBTIMESTAMP).
-# adChapter 136 Indicates a four-byte chapter value that identifies rows in a child rowset (DBTYPE_HCHAPTER).
-# adPropVariant 138 Indicates an Automation PROPVARIANT (DBTYPE_PROP_VARIANT).
-# adVarNumeric 139 Indicates a numeric value (Parameter object only).
-# adVarChar 200 Indicates a string value (Parameter object only).
-# adLongVarChar 201 Indicates a long string value (Parameter object only).
-# adVarWChar 202 Indicates a null-terminated Unicode character string (Parameter object only).
-# adLongVarWChar 203 Indicates a long null-terminated Unicode string value (Parameter object only).
-# adVarBinary 204 Indicates a binary value (Parameter object only).
-# adLongVarBinary 205 Indicates a long binary value (Parameter object only).
-# adArray (Does not apply to ADOX.) 0x2000 A flag value, always combined with another data type constant,
-#   that indicates an array of that other data type.
-
-# Error codes to names
-adoErrors = {
-    0xE7B: "adErrBoundToCommand",
-    0xE94: "adErrCannotComplete",
-    0xEA4: "adErrCantChangeConnection",
-    0xC94: "adErrCantChangeProvider",
-    0xE8C: "adErrCantConvertvalue",
-    0xE8D: "adErrCantCreate",
-    0xEA3: "adErrCatalogNotSet",
-    0xE8E: "adErrColumnNotOnThisRow",
-    0xD5D: "adErrDataConversion",
-    0xE89: "adErrDataOverflow",
-    0xE9A: "adErrDelResOutOfScope",
-    0xEA6: "adErrDenyNotSupported",
-    0xEA7: "adErrDenyTypeNotSupported",
-    0xCB3: "adErrFeatureNotAvailable",
-    0xEA5: "adErrFieldsUpdateFailed",
-    0xC93: "adErrIllegalOperation",
-    0xCAE: "adErrInTransaction",
-    0xE87: "adErrIntegrityViolation",
-    0xBB9: "adErrInvalidArgument",
-    0xE7D: "adErrInvalidConnection",
-    0xE7C: "adErrInvalidParamInfo",
-    0xE82: "adErrInvalidTransaction",
-    0xE91: "adErrInvalidURL",
-    0xCC1: "adErrItemNotFound",
-    0xBCD: "adErrNoCurrentRecord",
-    0xE83: "adErrNotExecuting",
-    0xE7E: "adErrNotReentrant",
-    0xE78: "adErrObjectClosed",
-    0xD27: "adErrObjectInCollection",
-    0xD5C: "adErrObjectNotSet",
-    0xE79: "adErrObjectOpen",
-    0xBBA: "adErrOpeningFile",
-    0xE80: "adErrOperationCancelled",
-    0xE96: "adErrOutOfSpace",
-    0xE88: "adErrPermissionDenied",
-    0xE9E: "adErrPropConflicting",
-    0xE9B: "adErrPropInvalidColumn",
-    0xE9C: "adErrPropInvalidOption",
-    0xE9D: "adErrPropInvalidValue",
-    0xE9F: "adErrPropNotAllSettable",
-    0xEA0: "adErrPropNotSet",
-    0xEA1: "adErrPropNotSettable",
-    0xEA2: "adErrPropNotSupported",
-    0xBB8: "adErrProviderFailed",
-    0xE7A: "adErrProviderNotFound",
-    0xBBB: "adErrReadFile",
-    0xE93: "adErrResourceExists",
-    0xE92: "adErrResourceLocked",
-    0xE97: "adErrResourceOutOfScope",
-    0xE8A: "adErrSchemaViolation",
-    0xE8B: "adErrSignMismatch",
-    0xE81: "adErrStillConnecting",
-    0xE7F: "adErrStillExecuting",
-    0xE90: "adErrTreePermissionDenied",
-    0xE8F: "adErrURLDoesNotExist",
-    0xE99: "adErrURLNamedRowDoesNotExist",
-    0xE98: "adErrUnavailable",
-    0xE84: "adErrUnsafeOperation",
-    0xE95: "adErrVolumeNotFound",
-    0xBBC: "adErrWriteFile",
-}
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/audioread/rawread.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/audioread/rawread.py
deleted file mode 100644
index 66afdaf5f3c90ae21deb004aea0a6eb48c04be5a..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/audioread/rawread.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# This file is part of audioread.
-# Copyright 2011, Adrian Sampson.
-#
-# 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.
-
-"""Uses standard-library modules to read AIFF, AIFF-C, and WAV files."""
-import wave
-import aifc
-import sunau
-import audioop
-import struct
-import sys
-
-from .exceptions import DecodeError
-
-# Produce two-byte (16-bit) output samples.
-TARGET_WIDTH = 2
-
-# Python 3.4 added support for 24-bit (3-byte) samples.
-if sys.version_info > (3, 4, 0):
-    SUPPORTED_WIDTHS = (1, 2, 3, 4)
-else:
-    SUPPORTED_WIDTHS = (1, 2, 4)
-
-
-class UnsupportedError(DecodeError):
-    """File is not an AIFF, WAV, or Au file."""
-
-
-class BitWidthError(DecodeError):
-    """The file uses an unsupported bit width."""
-
-
-def byteswap(s):
-    """Swaps the endianness of the bytestring s, which must be an array
-    of shorts (16-bit signed integers). This is probably less efficient
-    than it should be.
-    """
-    assert len(s) % 2 == 0
-    parts = []
-    for i in range(0, len(s), 2):
-        chunk = s[i:i + 2]
-        newchunk = struct.pack('<h', *struct.unpack('>h', chunk))
-        parts.append(newchunk)
-    return b''.join(parts)
-
-
-class RawAudioFile(object):
-    """An AIFF, WAV, or Au file that can be read by the Python standard
-    library modules ``wave``, ``aifc``, and ``sunau``.
-    """
-    def __init__(self, filename):
-        self._fh = open(filename, 'rb')
-
-        try:
-            self._file = aifc.open(self._fh)
-        except aifc.Error:
-            # Return to the beginning of the file to try the next reader.
-            self._fh.seek(0)
-        else:
-            self._needs_byteswap = True
-            self._check()
-            return
-
-        try:
-            self._file = wave.open(self._fh)
-        except wave.Error:
-            self._fh.seek(0)
-            pass
-        else:
-            self._needs_byteswap = False
-            self._check()
-            return
-
-        try:
-            self._file = sunau.open(self._fh)
-        except sunau.Error:
-            self._fh.seek(0)
-            pass
-        else:
-            self._needs_byteswap = True
-            self._check()
-            return
-
-        # None of the three libraries could open the file.
-        self._fh.close()
-        raise UnsupportedError()
-
-    def _check(self):
-        """Check that the files' parameters allow us to decode it and
-        raise an error otherwise.
-        """
-        if self._file.getsampwidth() not in SUPPORTED_WIDTHS:
-            self.close()
-            raise BitWidthError()
-
-    def close(self):
-        """Close the underlying file."""
-        self._file.close()
-        self._fh.close()
-
-    @property
-    def channels(self):
-        """Number of audio channels."""
-        return self._file.getnchannels()
-
-    @property
-    def samplerate(self):
-        """Sample rate in Hz."""
-        return self._file.getframerate()
-
-    @property
-    def duration(self):
-        """Length of the audio in seconds (a float)."""
-        return float(self._file.getnframes()) / self.samplerate
-
-    def read_data(self, block_samples=1024):
-        """Generates blocks of PCM data found in the file."""
-        old_width = self._file.getsampwidth()
-
-        while True:
-            data = self._file.readframes(block_samples)
-            if not data:
-                break
-
-            # Make sure we have the desired bitdepth and endianness.
-            data = audioop.lin2lin(data, old_width, TARGET_WIDTH)
-            if self._needs_byteswap and self._file.getcomptype() != 'sowt':
-                # Big-endian data. Swap endianness.
-                data = byteswap(data)
-            yield data
-
-    # Context manager.
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.close()
-        return False
-
-    # Iteration.
-    def __iter__(self):
-        return self.read_data()
diff --git a/spaces/aseifert/writing-assistant/highlighter.py b/spaces/aseifert/writing-assistant/highlighter.py
deleted file mode 100644
index 958b6f20815fd52cae13c6197ee6a50883901392..0000000000000000000000000000000000000000
--- a/spaces/aseifert/writing-assistant/highlighter.py
+++ /dev/null
@@ -1,122 +0,0 @@
-"""
-This module is taken with slight modifications from:
-https://github.com/PrithivirajDamodaran/Gramformer/blob/main/gramformer/gramformer.py
-"""
-
-import re
-
-import pandas as pd
-from annotated_text import annotated_text
-from bs4 import BeautifulSoup
-
-
-def show_highlights(annotator, input_text, corrected_sentence):
-    strikeout = lambda x: "\u0336".join(x) + "\u0336"
-    highlight_text = highlight(annotator, input_text, corrected_sentence)
-    color_map = {"d": "#faa", "a": "#afa", "c": "#fea"}
-    tokens = re.split(r"(<[dac]\s.*?<\/[dac]>)", highlight_text)
-    annotations = []
-    for token in tokens:
-        soup = BeautifulSoup(token, "html.parser")
-        tags = soup.findAll()
-        if tags:
-            _tag = tags[0].name
-            _type = tags[0]["type"]
-            _text = tags[0]["edit"]
-            _color = color_map[_tag]
-            if _tag == "d":
-                _text = strikeout(tags[0].text)
-            annotations.append((_text, _type, _color))
-        else:
-            annotations.append(token)
-    annotated_text(*annotations)
-
-
-def show_edits(annotator, input_text, corrected_sentence):
-    edits = get_edits(annotator, input_text, corrected_sentence)
-    df = pd.DataFrame(
-        edits,
-        columns=[
-            "type",
-            "original word",
-            "original start",
-            "original end",
-            "correct word",
-            "correct start",
-            "correct end",
-        ],
-    )
-    return df.set_index("type")
-
-
-def highlight(annotator, orig, cor):
-    edits = get_edits(annotator, orig, cor)
-    orig_tokens = orig.split()
-    ignore_indexes = []
-    for edit in edits:
-        edit_type = edit[0]
-        edit_str_start = edit[1]
-        edit_spos = edit[2]
-        edit_epos = edit[3]
-        edit_str_end = edit[4]
-        for i in range(edit_spos + 1, edit_epos):
-            ignore_indexes.append(i)
-        if edit_str_start == "":
-            if edit_spos >= 1:
-                new_edit_str = orig_tokens[edit_spos - 1]
-                # print("edit_spos >= 1", new_edit_str)
-                edit_spos -= 1
-            else:
-                new_edit_str = orig_tokens[edit_spos + 1]
-                # print("new", new_edit_str)
-                edit_spos += 1
-
-            if edit_type == "PUNCT":
-                st = (
-                    "<a type='"
-                    + edit_type
-                    + "' edit='"
-                    + edit_str_end
-                    + "'>"
-                    + new_edit_str
-                    + "</a>"
-                )
-            else:
-                st = (
-                    "<a type='"
-                    + edit_type
-                    + "' edit='"
-                    + new_edit_str
-                    + " "
-                    + edit_str_end
-                    + "'>"
-                    + new_edit_str
-                    + "</a>"
-                )
-        elif edit_str_end == "":
-            st = "<d type='" + edit_type + "' edit=''>" + edit_str_start + "</d>"
-        else:
-            st = (
-                "<c type='" + edit_type + "' edit='" + edit_str_end + "'>" + edit_str_start + "</c>"
-            )
-        orig_tokens[edit_spos] = st
-    for i in sorted(ignore_indexes, reverse=True):
-        del orig_tokens[i]
-    return " ".join(orig_tokens)
-
-
-def get_edits(annotator, orig, cor):
-    orig = annotator.parse(orig)
-    cor = annotator.parse(cor)
-    alignment = annotator.align(orig, cor)
-    edits = annotator.merge(alignment)
-    if len(edits) == 0:
-        return []
-    edit_annotations = []
-    for e in edits:
-        e = annotator.classify(e)
-        edit_annotations.append(
-            (e.type[2:], e.o_str, e.o_start, e.o_end, e.c_str, e.c_start, e.c_end)
-        )
-
-    return edit_annotations or []
diff --git a/spaces/augmentedimaginationhackathon/paperstocode/promptStuff/prompts.py b/spaces/augmentedimaginationhackathon/paperstocode/promptStuff/prompts.py
deleted file mode 100644
index 1ca5a9902671c0110f9da2dfc0a2af9dabb820db..0000000000000000000000000000000000000000
--- a/spaces/augmentedimaginationhackathon/paperstocode/promptStuff/prompts.py
+++ /dev/null
@@ -1,9 +0,0 @@
-layoutPrompt = """Given the text of the Arxiv paper you now know about, can you give me a proposed structure for a Jupyter notebook that would summarize the papers key contributions and findings. The Jupyter notebook should be structured in a logical and coherent way, with sections and sub-sections that reflect the papers organization. Please assume that the Jupyter notebook will be used by someone who is familiar with the general field of AI, but may not be familiar with the specific topic of the paper. The output should be in this format:
-- each section should be numbered and have a title (e.g., Training and Inference)
-- each subsection should start with a dash (e.g., - Overview of the training process)
-
-Only give the structured layout -- do not describe or discuss the paper."""
-
-sectionPrompt = """Given the text of the Arxiv paper that you have context for, can you generate code for t
-"""
-
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/ldmlib/modules/diffusionmodules/openaimodel.py b/spaces/awaawawawa/iurf7irfuyytruyyugb/ldmlib/modules/diffusionmodules/openaimodel.py
deleted file mode 100644
index 84b01c5e473605cc592dfdbeb03279a2103effae..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/ldmlib/modules/diffusionmodules/openaimodel.py
+++ /dev/null
@@ -1,960 +0,0 @@
-from abc import abstractmethod
-from functools import partial
-import math
-from typing import Iterable
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ldmlib.modules.diffusionmodules.util import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-from ldmlib.modules.attention import SpatialTransformer
-
-
-# dummy replace
-def convert_module_to_f16(x):
-    pass
-
-def convert_module_to_f32(x):
-    pass
-
-
-## go
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1)  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb, context=None):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            elif isinstance(layer, SpatialTransformer):
-                x = layer(x, context)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-class TransposedUpsample(nn.Module):
-    'Learned 2x upsampling without padding'
-    def __init__(self, channels, out_channels=None, ks=5):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-
-        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
-
-    def forward(self,x):
-        return self.up(x)
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
-        #return pt_checkpoint(self._forward, x)  # pytorch
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        h = self.attention(qkv)
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        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,
-    ):
-        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.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        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),
-        )
-
-        if self.num_classes is not None:
-            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, 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 _ in range(num_res_blocks):
-                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
-                    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
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                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)
-                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(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(num_res_blocks + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = model_channels * mult
-                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
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads_upsample,
-                            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
-                        )
-                    )
-                if level and i == num_res_blocks:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
-        )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-            normalization(ch),
-            conv_nd(dims, model_channels, n_embed, 1),
-            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-        )
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-        hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-        emb = self.time_embed(t_emb)
-
-        if self.num_classes is not None:
-            assert y.shape == (x.shape[0],)
-            emb = emb + self.label_emb(y)
-
-        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)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context)
-        h = h.type(x.dtype)
-        if self.predict_codebook_ids:
-            return self.id_predictor(h)
-        else:
-            return self.out(h)
-
-
-class EncoderUNetModel(nn.Module):
-    """
-    The half UNet model with attention and timestep embedding.
-    For usage, see UNet.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_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,
-        pool="adaptive",
-        *args,
-        **kwargs
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        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
-
-        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._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                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:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                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)
-                ds *= 2
-                self._feature_size += ch
-
-        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=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-        self.pool = pool
-        if pool == "adaptive":
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                nn.AdaptiveAvgPool2d((1, 1)),
-                zero_module(conv_nd(dims, ch, out_channels, 1)),
-                nn.Flatten(),
-            )
-        elif pool == "attention":
-            assert num_head_channels != -1
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                AttentionPool2d(
-                    (image_size // ds), ch, num_head_channels, out_channels
-                ),
-            )
-        elif pool == "spatial":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                nn.ReLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        elif pool == "spatial_v2":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                normalization(2048),
-                nn.SiLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        else:
-            raise NotImplementedError(f"Unexpected {pool} pooling")
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :return: an [N x K] Tensor of outputs.
-        """
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        results = []
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            if self.pool.startswith("spatial"):
-                results.append(h.type(x.dtype).mean(dim=(2, 3)))
-        h = self.middle_block(h, emb)
-        if self.pool.startswith("spatial"):
-            results.append(h.type(x.dtype).mean(dim=(2, 3)))
-            h = th.cat(results, axis=-1)
-            return self.out(h)
-        else:
-            h = h.type(x.dtype)
-            return self.out(h)
diff --git a/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology/app.py b/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology/app.py
deleted file mode 100644
index 706bd9475c92d36d9016e1f271dbb4ea003c39ea..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology/app.py
+++ /dev/null
@@ -1,269 +0,0 @@
-import gradio as gr
-import pandas as pd
-import json
-from collections import defaultdict
-
-# Create tokenizer for biomed model
-from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
-tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")    # https://huggingface.co/d4data/biomedical-ner-all?text=asthma
-model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
-pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
-
-# Matplotlib for entity graph
-import matplotlib.pyplot as plt
-plt.switch_backend("Agg")
-
-# Load examples from JSON
-import os 
-
-# Load terminology datasets:
-basedir = os.path.dirname(__file__)
-#dataLOINC = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')
-#dataPanels = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
-#dataSNOMED = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-#dataOMS = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
-#dataICD10 = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv') 
-
-dataLOINC = pd.read_csv(f'LoincTableCore.csv')
-dataPanels = pd.read_csv(f'PanelsAndForms-ACW1208Labeled.csv')     
-dataSNOMED = pd.read_csv(f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-dataOMS = pd.read_csv(f'SnomedOMS.csv')   
-dataICD10 = pd.read_csv(f'ICD10Diagnosis.csv')   
-
-dir_path = os.path.dirname(os.path.realpath(__file__))
-EXAMPLES = {}
-#with open(dir_path + "\\" + "examples.json", "r") as f:
-with open("examples.json", "r") as f:
-    example_json = json.load(f)
-    EXAMPLES = {x["text"]: x["label"] for x in example_json}
-
-def MatchLOINC(name):
-    #basedir = os.path.dirname(__file__)
-    pd.set_option("display.max_rows", None)
-    #data = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')    
-    data = dataLOINC
-    swith=data.loc[data['COMPONENT'].str.contains(name, case=False, na=False)]
-    return swith
-    
-def MatchLOINCPanelsandForms(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
-    data = dataPanels
-    # Assessment Name:
-    #swith=data.loc[data['ParentName'].str.contains(name, case=False, na=False)]
-    # Assessment Question:
-    swith=data.loc[data['LoincName'].str.contains(name, case=False, na=False)]
-    return swith
-    
-def MatchSNOMED(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-    data = dataSNOMED
-    swith=data.loc[data['term'].str.contains(name, case=False, na=False)]
-    return swith
-
-def MatchOMS(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
-    data = dataOMS
-    swith=data.loc[data['SNOMED CT'].str.contains(name, case=False, na=False)]
-    return swith
-
-def MatchICD10(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv')   
-    data = dataICD10
-    swith=data.loc[data['Description'].str.contains(name, case=False, na=False)]
-    return swith
-
-def SaveResult(text, outputfileName):
-    #try:
-    basedir = os.path.dirname(__file__)
-    savePath = outputfileName
-    print("Saving: " + text + " to " + savePath)
-    from os.path import exists
-    file_exists = exists(savePath)
-    if file_exists:
-        with open(outputfileName, "a") as f: #append
-            #for line in text:
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    else:
-        with open(outputfileName, "w") as f: #write
-            #for line in text:
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    #except ValueError as err:
-    #    raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return
-
-def loadFile(filename):
-    try:
-        basedir = os.path.dirname(__file__)
-        loadPath = basedir + "\\" + filename
-
-        print("Loading: " + loadPath)
-
-        from os.path import exists
-        file_exists = exists(loadPath)
-
-        if file_exists:
-            with open(loadPath, "r") as f: #read
-                contents = f.read()
-                print(contents)
-                return contents
-
-    except ValueError as err:
-        raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return ""
-
-def get_today_filename():
-    from datetime import datetime
-    date = datetime.now().strftime("%Y_%m_%d-%I.%M.%S.%p")
-    #print(f"filename_{date}")  'filename_2023_01_12-03-29-22_AM'
-    return f"MedNER_{date}.csv"
-
-def get_base(filename): 
-        basedir = os.path.dirname(__file__)
-        loadPath = basedir + "\\" + filename
-        #print("Loading: " + loadPath)
-        return loadPath
-
-def group_by_entity(raw):
-    outputFile = get_base(get_today_filename())
-    out = defaultdict(int)
-
-    for ent in raw:
-        out[ent["entity_group"]] += 1
-        myEntityGroup = ent["entity_group"]
-        print("Found entity group type: " + myEntityGroup)
-
-#        if (myEntityGroup in ['Sign_symptom', 'Detailed_description', 'History', 'Activity', 'Medication', 'DISEASE_DISORDER' ]):
-        if (myEntityGroup not in ['Match All']):
-            eterm = ent["word"].replace('#','')
-            minlength = 3
-            if len(eterm) > minlength:
-                print("Found eterm: " + eterm)
-                eterm.replace("#","")
-                g1=MatchLOINC(eterm)
-                g2=MatchLOINCPanelsandForms(eterm)
-                g3=MatchSNOMED(eterm)
-                g4=MatchOMS(eterm)
-                g5=MatchICD10(eterm)
-                sAll = ""
-
-                print("Saving to output file " + outputFile)
-                # Create harmonisation output format of input to output code, name, Text
-
-                try: # 18 fields, output to labeled CSV dataset for results teaching on scored regret changes to action plan with data inputs
-                    col = "          1                            2            3         4            5                     6                    7                       8                   9              10                   11                         12       13               14                      15                  16                            17                    18                       19"
-                    
-                    #LOINC
-                    g11 = g1['LOINC_NUM'].to_string().replace(","," ").replace("\n"," ")
-                    g12 = g1['COMPONENT'].to_string().replace(","," ").replace("\n"," ")
-                    s1 = ("LOINC," + myEntityGroup + "," + eterm + ",questions of ," + g12 + "," + g11 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g11 != 'Series([]  )': SaveResult(s1, outputFile)
-
-                    #LOINC Panels
-                    g21 = g2['Loinc'].to_string().replace(","," ").replace("\n"," ")
-                    g22 = g2['LoincName'].to_string().replace(","," ").replace("\n"," ")
-                    g23 = g2['ParentLoinc'].to_string().replace(","," ").replace("\n"," ")
-                    g24 = g2['ParentName'].to_string().replace(","," ").replace("\n"," ")
-                    # s2 = ("LOINC Panel," + myEntityGroup + "," + eterm + ",name of ," + g22 + "," + g21 + ", and Parent codes of ," + g23 + ", with Parent names of ," + g24 + ", Label,Value  ")
-                    s2 = ("LOINC Panel," + myEntityGroup + "," + eterm + ",name of ," + g22 + "," + g21 + "," + g24 + ", and Parent codes of ," + g23 + "," + ", Label,Value  ")
-                    if g21 != 'Series([]  )': SaveResult(s2, outputFile)
-
-                    #SNOMED
-                    g31 = g3['conceptId'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
-                    g32 = g3['term'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
-                    s3 = ("SNOMED Concept," + myEntityGroup + "," + eterm + ",terms of ," + g32 + "," + g31 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g31 != 'Series([]  )': SaveResult(s3, outputFile)
-
-                    #OMS
-                    g41 = g4['Omaha Code'].to_string().replace(","," ").replace("\n"," ")
-                    g42 = g4['SNOMED CT concept ID'].to_string().replace(","," ").replace("\n"," ")
-                    g43 = g4['SNOMED CT'].to_string().replace(","," ").replace("\n"," ")
-                    g44 = g4['PR'].to_string().replace(","," ").replace("\n"," ")
-                    g45 = g4['S&S'].to_string().replace(","," ").replace("\n"," ")
-                    s4 = ("OMS," + myEntityGroup + "," + eterm + ",concepts of ," + g44 + "," + g45 + ", and SNOMED codes of ," + g43 + ", and OMS problem of ," + g42 + ", and OMS Sign Symptom of ," + g41)
-                    if g41 != 'Series([]  )': SaveResult(s4, outputFile)
-
-                    #ICD10
-                    g51 = g5['Code'].to_string().replace(","," ").replace("\n"," ")
-                    g52 = g5['Description'].to_string().replace(","," ").replace("\n"," ")
-                    s5 = ("ICD10," + myEntityGroup + "," + eterm + ",descriptions of ," + g52 + "," + g51 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g51 != 'Series([]  )': SaveResult(s5, outputFile)
-
-                except ValueError as err:
-                    raise ValueError("Error in group by entity \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return outputFile
-
-
-def plot_to_figure(grouped):
-    fig = plt.figure()
-    plt.bar(x=list(grouped.keys()), height=list(grouped.values()))
-    plt.margins(0.2)
-    plt.subplots_adjust(bottom=0.4)
-    plt.xticks(rotation=90)
-    return fig
-
-
-def ner(text):
-    raw = pipe(text)
-    ner_content = {
-        "text": text,
-        "entities": [
-            {
-                "entity": x["entity_group"],
-                "word": x["word"],
-                "score": x["score"],
-                "start": x["start"],
-                "end": x["end"],
-            }
-            for x in raw
-        ],
-    }
-    
-    outputFile = group_by_entity(raw)
-    label = EXAMPLES.get(text, "Unknown")
-    outputDataframe = pd.read_csv(outputFile)
-    return (ner_content, outputDataframe, outputFile)
-
-demo = gr.Blocks()
-with demo:
-    gr.Markdown(
-    """
-    # 🩺⚕️NLP Clinical Ontology Biomedical NER 
-    """
-    )
-    input = gr.Textbox(label="Note text", value="")
-
-    with gr.Tab("Biomedical Entity Recognition"):
-        output=[
-            gr.HighlightedText(label="NER", combine_adjacent=True),
-            #gr.JSON(label="Entity Counts"),
-            #gr.Label(label="Rating"),
-            #gr.Plot(label="Bar"),
-            gr.Dataframe(label="Dataframe"),
-            gr.File(label="File"),
-        ]
-        examples=list(EXAMPLES.keys())  
-        gr.Examples(examples, inputs=input)
-        input.change(fn=ner, inputs=input, outputs=output)
-        
-    with gr.Tab("Clinical Terminology Resolution"):
-        with gr.Row(variant="compact"):
-            btnLOINC = gr.Button("LOINC")
-            btnPanels = gr.Button("Panels")
-            btnSNOMED = gr.Button("SNOMED")
-            btnOMS = gr.Button("OMS")
-            btnICD10 = gr.Button("ICD10")
-
-        examples=list(EXAMPLES.keys())  
-        gr.Examples(examples, inputs=input)
-        input.change(fn=ner, inputs=input, outputs=output)
-#layout="vertical"
-demo.launch(debug=True)
diff --git a/spaces/awacke1/BlackjackSimulatorCardGameAI/app.py b/spaces/awacke1/BlackjackSimulatorCardGameAI/app.py
deleted file mode 100644
index 7ede90b985e8fdda4a93d08f6777dd2d7c636711..0000000000000000000000000000000000000000
--- a/spaces/awacke1/BlackjackSimulatorCardGameAI/app.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import streamlit as st
-from PIL import Image
-from components import GamePlay, Player, Dealer, Deck
-
-# PersistDataset -----
-import os
-import csv
-
-import huggingface_hub
-from huggingface_hub import Repository, hf_hub_download, upload_file
-from datetime import datetime
-
-DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/Carddata.csv"
-DATASET_REPO_ID = "awacke1/Carddata.csv"
-DATA_FILENAME = "Carddata.csv"
-DATA_FILE = os.path.join("data", DATA_FILENAME)
-HF_TOKEN = os.environ.get("HF_TOKEN")
-# overriding/appending to the gradio template
-
-def generate_html() -> str:
-    with open(DATA_FILE) as csvfile:
-        reader = csv.DictReader(csvfile)
-        rows = []
-        for row in reader:
-            rows.append(row)
-        rows.reverse()
-        if len(rows) == 0:
-            return "no messages yet"
-        else:
-            html = "<div class='chatbot'>"
-            for row in rows:
-                html += "<div>"
-                html += f"<span>{row['name']}</span>"
-                html += f"<span class='message'>{row['message']}</span>"
-                html += "</div>"
-            html += "</div>"
-            return html
-            
-def store_message(name: str, message: str):
-    if name and message:
-        with open(DATA_FILE, "a") as csvfile:
-            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
-            writer.writerow(
-                {"name": name, "message": message, "time": str(datetime.now())}
-            )
-        commit_url = repo.push_to_hub()
-    return generate_html()
-    
-# Game settings
-number_of_decks = 6
-blackjack_multiplier = 1.5
-
-# Initialize player, dealer, deck and game play. Cache these variables
-@st.cache(allow_output_mutation=True, suppress_st_warning=True)
-def start_game():
-    game_deck = Deck(number_of_decks)
-    dealer = Dealer()
-    player = Player()
-    game_play = GamePlay(player, dealer, game_deck, blackjack_multiplier)
-    return game_deck, dealer, player, game_play
-
-game_deck, dealer, player, game_play = start_game()
-
-st.title('🃏Blackjack Simulator AI♠2️⃣1️⃣')
-
-if st.button('New hand?'):
-    game_play.deal_in()
-
-player_stats = st.empty()
-player_images = st.empty()
-player_hit_option = st.empty()
-player_double_down_option = st.empty()
-player_stand_option = st.empty()
-dealer_stats = st.empty()
-dealer_images = st.empty()
-result = st.empty()
-
-
-if 'Hit' in player.possible_actions:
-    if player_hit_option.button('Hit'):
-        player.player_hit(game_deck, game_play)
-        if 'Hit' not in player.possible_actions:
-            player_hit_option.empty()
-if 'Double Down' in player.possible_actions:
-    if player_double_down_option.button('Double Down'):
-        player.double_down(game_deck, game_play)
-        player_double_down_option.empty()
-        player_hit_option.empty()
-        player_stand_option.empty()
-if 'Stand' in player.possible_actions:
-    if player_stand_option.button('Stand'):
-        player.stand(game_play)
-        player_hit_option.empty()
-        player_double_down_option.empty()
-        player_stand_option.empty()
-
-
-game_play.update()
-player_stats.write(player)
-player_images.image([Image.open(card.image_location)
-                     for card in player.cards], width=100)
-dealer_stats.write(dealer)
-dealer_images.image([Image.open(card.image_location)
-                     for card in dealer.cards], width=100)
-                     
-result.write(game_play)
diff --git a/spaces/awacke1/GradioDoubleChatbotTasteTest/README.md b/spaces/awacke1/GradioDoubleChatbotTasteTest/README.md
deleted file mode 100644
index b756ddc56ed24ad8e08fee4327fa9ed065caa15e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GradioDoubleChatbotTasteTest/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: GradioDoubleChatbotTasteTest
-emoji: ⚡
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.24
-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/awacke1/Llama2ProWriterDrafter/README.md b/spaces/awacke1/Llama2ProWriterDrafter/README.md
deleted file mode 100644
index a3bb96ea088ad7514fae110778d6ea3ab03d8224..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Llama2ProWriterDrafter/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Llama2ProWriterDrafter
-emoji: 👀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.40.1
-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/awacke1/Streamlit-Google-Maps-California/README.md b/spaces/awacke1/Streamlit-Google-Maps-California/README.md
deleted file mode 100644
index 802257ea44d90238a60adfd0297c754b3182bd3b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Google-Maps-California/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 🏥 California Medical Centers 🌴
-emoji: 🏥🌴
-colorFrom: green
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.28.0
-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/ayoolaolafenwa/ChatLM/app.py b/spaces/ayoolaolafenwa/ChatLM/app.py
deleted file mode 100644
index c3e7400c531c909e82b74dc290f1449dccb0c230..0000000000000000000000000000000000000000
--- a/spaces/ayoolaolafenwa/ChatLM/app.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
-import time
-import numpy as np
-from torch.nn import functional as F
-import os
-from threading import Thread
-
-model_path = "ayoolaolafenwa/ChatLM"
-
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-
-model = AutoModelForCausalLM.from_pretrained(model_path, device_map = "auto", torch_dtype=torch.bfloat16, load_in_8bit=True)
-
-class StopOnTokens(StoppingCriteria):
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        stop_ids = [0]
-        for stop_id in stop_ids:
-            if input_ids[0][-1] == stop_id:
-                return True
-        return False
-
-
-def user(message, history):
-    # Append the user's message to the conversation history
-    return "", history + [[message, ""]]
-
-
-def chat(curr_system_message, history):
-    # Initialize a StopOnTokens object
-    stop = StopOnTokens()
-
-    # Construct the input message string for the model by concatenating the current system message and conversation history
-    messages = curr_system_message + \
-        "".join(["".join(["<user>: "+item[0], "<chatbot>: "+item[1]])
-                for item in history])
-        
-    # Tokenize the messages string
-    tokens = tokenizer([messages], return_tensors="pt").to("cuda")
-    streamer = TextIteratorStreamer(
-        tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
-   
-    token_ids = tokens.input_ids
-    attention_mask=tokens.attention_mask
-    
-    generate_kwargs = dict(
-       input_ids=token_ids, 
-        attention_mask = attention_mask,  
-        streamer = streamer,
-        max_length=2048,
-        do_sample=True,
-        num_return_sequences=1,
-        eos_token_id=tokenizer.eos_token_id, 
-        temperature = 0.5,
-        stopping_criteria=StoppingCriteriaList([stop])
-    ) 
-    t = Thread(target=model.generate, kwargs=generate_kwargs)
-    t.start()    
-
-    #Initialize an empty string to store the generated text
-    partial_text = ""
-    for new_text in streamer:
-        # print(new_text)
-        partial_text += new_text
-        history[-1][1] = partial_text
-        # Yield an empty string to cleanup the message textbox and the updated conversation history
-        yield history
-    return partial_text
-    
-
-
-
-with gr.Blocks() as demo:
-    # history = gr.State([])
-
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(
-                    """
-                    ChatLM is a chat Large Language model finetuned with pretrained [Falcon-1B model](https://huggingface.co/tiiuae/falcon-rw-1b).
-                    It was trained on a dataset containing normal day to day human conversations, due to limited data used in training it will not generalize well for tasks like coding, current affairs and hallucinations may occur. 
-                    """
-                )
-            gr.Markdown(""" # Github Repo
-            https://github.com/ayoolaolafenwa/ChatLM/tree/main """)
- 
-    chatbot = gr.Chatbot().style(height=400)
-    with gr.Row():
-        with gr.Column():
-            msg = gr.Textbox(label="Chat Message Box", placeholder="Chat Message Box",
-                             show_label=False).style(container=False)
-        with gr.Column():
-            with gr.Row():
-                submit = gr.Button("Run")
-                stop = gr.Button("Stop")
-                clear = gr.Button("Clear")
-    system_msg = gr.Textbox(
-        label="Response Message", interactive=False, visible=False)
-
-    submit_event = msg.submit(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
-        fn=chat, inputs=[system_msg, chatbot], outputs=[chatbot], queue=True)
-    submit_click_event = submit.click(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
-        fn=chat, inputs=[system_msg, chatbot], outputs=[chatbot], queue=True)
-    stop.click(fn=None, inputs=None, outputs=None, cancels=[
-               submit_event, submit_click_event], queue=False)
-    clear.click(lambda: None, None, [chatbot], queue=False)
-
-
-demo.queue(max_size=32, concurrency_count=2)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/badayvedat/LLaVA/llava/constants.py b/spaces/badayvedat/LLaVA/llava/constants.py
deleted file mode 100644
index be8cf0204969a6c973f442b383d8e425d684e826..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/LLaVA/llava/constants.py
+++ /dev/null
@@ -1,12 +0,0 @@
-CONTROLLER_HEART_BEAT_EXPIRATION = 30
-WORKER_HEART_BEAT_INTERVAL = 15
-
-LOGDIR = "."
-
-# Model Constants
-IGNORE_INDEX = -100
-IMAGE_TOKEN_INDEX = -200
-DEFAULT_IMAGE_TOKEN = "<image>"
-DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
-DEFAULT_IM_START_TOKEN = "<im_start>"
-DEFAULT_IM_END_TOKEN = "<im_end>"
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/ConvexObjectBreaker.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/ConvexObjectBreaker.js
deleted file mode 100644
index 4996dbb9c6616fbf64f9fd2a670d0299fd151031..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/ConvexObjectBreaker.js
+++ /dev/null
@@ -1,522 +0,0 @@
-/**
- * @author yomboprime https://github.com/yomboprime
- *
- * @fileoverview This class can be used to subdivide a convex Geometry object into pieces.
- *
- * Usage:
- *
- * Use the function prepareBreakableObject to prepare a Mesh object to be broken.
- *
- * Then, call the various functions to subdivide the object (subdivideByImpact, cutByPlane)
- *
- * Sub-objects that are product of subdivision don't need prepareBreakableObject to be called on them.
- *
- * Requisites for the object:
- *
- *  - Mesh object must have a BufferGeometry (not Geometry) and a Material
- *
- *  - Vertex normals must be planar (not smoothed)
- *
- *  - The geometry must be convex (this is not checked in the library). You can create convex
- *  geometries with THREE.ConvexBufferGeometry. The BoxBufferGeometry, SphereBufferGeometry and other convex primitives
- *  can also be used.
- *
- * Note: This lib adds member variables to object's userData member (see prepareBreakableObject function)
- * Use with caution and read the code when using with other libs.
- *
- * @param {double} minSizeForBreak Min size a debris can have to break.
- * @param {double} smallDelta Max distance to consider that a point belongs to a plane.
- *
-*/
-
-THREE.ConvexObjectBreaker = function ( minSizeForBreak, smallDelta ) {
-
-	this.minSizeForBreak = minSizeForBreak || 1.4;
-	this.smallDelta = smallDelta || 0.0001;
-
-	this.tempLine1 = new THREE.Line3();
-	this.tempPlane1 = new THREE.Plane();
-	this.tempPlane2 = new THREE.Plane();
-	this.tempPlane_Cut = new THREE.Plane();
-	this.tempCM1 = new THREE.Vector3();
-	this.tempCM2 = new THREE.Vector3();
-	this.tempVector3 = new THREE.Vector3();
-	this.tempVector3_2 = new THREE.Vector3();
-	this.tempVector3_3 = new THREE.Vector3();
-	this.tempVector3_P0 = new THREE.Vector3();
-	this.tempVector3_P1 = new THREE.Vector3();
-	this.tempVector3_P2 = new THREE.Vector3();
-	this.tempVector3_N0 = new THREE.Vector3();
-	this.tempVector3_N1 = new THREE.Vector3();
-	this.tempVector3_AB = new THREE.Vector3();
-	this.tempVector3_CB = new THREE.Vector3();
-	this.tempResultObjects = { object1: null, object2: null };
-
-	this.segments = [];
-	var n = 30 * 30;
-	for ( var i = 0; i < n; i ++ ) this.segments[ i ] = false;
-
-};
-
-THREE.ConvexObjectBreaker.prototype = {
-
-	constructor: THREE.ConvexObjectBreaker,
-
-	prepareBreakableObject: function ( object, mass, velocity, angularVelocity, breakable ) {
-
-		// object is a THREE.Object3d (normally a Mesh), must have a BufferGeometry, and it must be convex.
-		// Its material property is propagated to its children (sub-pieces)
-		// mass must be > 0
-
-		if ( ! object.geometry.isBufferGeometry ) {
-
-			console.error( 'THREE.ConvexObjectBreaker.prepareBreakableObject(): Parameter object must have a BufferGeometry.' );
-
-		}
-
-		var userData = object.userData;
-		userData.mass = mass;
-		userData.velocity = velocity.clone();
-		userData.angularVelocity = angularVelocity.clone();
-		userData.breakable = breakable;
-
-	},
-
-	/*
-	 * @param {int} maxRadialIterations Iterations for radial cuts.
-	 * @param {int} maxRandomIterations Max random iterations for not-radial cuts
-	 *
-	 * Returns the array of pieces
-	 */
-	subdivideByImpact: function ( object, pointOfImpact, normal, maxRadialIterations, maxRandomIterations ) {
-
-		var debris = [];
-
-		var tempPlane1 = this.tempPlane1;
-		var tempPlane2 = this.tempPlane2;
-
-		this.tempVector3.addVectors( pointOfImpact, normal );
-		tempPlane1.setFromCoplanarPoints( pointOfImpact, object.position, this.tempVector3 );
-
-		var maxTotalIterations = maxRandomIterations + maxRadialIterations;
-
-		var scope = this;
-
-		function subdivideRadial( subObject, startAngle, endAngle, numIterations ) {
-
-			if ( Math.random() < numIterations * 0.05 || numIterations > maxTotalIterations ) {
-
-				debris.push( subObject );
-
-				return;
-
-			}
-
-			var angle = Math.PI;
-
-			if ( numIterations === 0 ) {
-
-				tempPlane2.normal.copy( tempPlane1.normal );
-				tempPlane2.constant = tempPlane1.constant;
-
-			} else {
-
-				if ( numIterations <= maxRadialIterations ) {
-
-					angle = ( endAngle - startAngle ) * ( 0.2 + 0.6 * Math.random() ) + startAngle;
-
-					// Rotate tempPlane2 at impact point around normal axis and the angle
-					scope.tempVector3_2.copy( object.position ).sub( pointOfImpact ).applyAxisAngle( normal, angle ).add( pointOfImpact );
-					tempPlane2.setFromCoplanarPoints( pointOfImpact, scope.tempVector3, scope.tempVector3_2 );
-
-				} else {
-
-					angle = ( ( 0.5 * ( numIterations & 1 ) ) + 0.2 * ( 2 - Math.random() ) ) * Math.PI;
-
-					// Rotate tempPlane2 at object position around normal axis and the angle
-					scope.tempVector3_2.copy( pointOfImpact ).sub( subObject.position ).applyAxisAngle( normal, angle ).add( subObject.position );
-					scope.tempVector3_3.copy( normal ).add( subObject.position );
-					tempPlane2.setFromCoplanarPoints( subObject.position, scope.tempVector3_3, scope.tempVector3_2 );
-
-				}
-
-			}
-
-			// Perform the cut
-			scope.cutByPlane( subObject, tempPlane2, scope.tempResultObjects );
-
-			var obj1 = scope.tempResultObjects.object1;
-			var obj2 = scope.tempResultObjects.object2;
-
-			if ( obj1 ) {
-
-				subdivideRadial( obj1, startAngle, angle, numIterations + 1 );
-
-			}
-
-			if ( obj2 ) {
-
-				subdivideRadial( obj2, angle, endAngle, numIterations + 1 );
-
-			}
-
-		}
-
-		subdivideRadial( object, 0, 2 * Math.PI, 0 );
-
-		return debris;
-
-	},
-
-	cutByPlane: function ( object, plane, output ) {
-
-		// Returns breakable objects in output.object1 and output.object2 members, the resulting 2 pieces of the cut.
-		// object2 can be null if the plane doesn't cut the object.
-		// object1 can be null only in case of internal error
-		// Returned value is number of pieces, 0 for error.
-
-		var geometry = object.geometry;
-		var coords = geometry.attributes.position.array;
-		var normals = geometry.attributes.normal.array;
-
-		var numPoints = coords.length / 3;
-		var numFaces = numPoints / 3;
-
-		var indices = geometry.getIndex();
-
-		if ( indices ) {
-
-			indices = indices.array;
-			numFaces = indices.length / 3;
-
-		}
-
-		function getVertexIndex( faceIdx, vert ) {
-
-			// vert = 0, 1 or 2.
-
-			var idx = faceIdx * 3 + vert;
-
-			return indices ? indices[ idx ] : idx;
-
-		}
-
-		var points1 = [];
-		var points2 = [];
-
-		var delta = this.smallDelta;
-
-		// Reset segments mark
-		var numPointPairs = numPoints * numPoints;
-		for ( var i = 0; i < numPointPairs; i ++ ) this.segments[ i ] = false;
-
-		var p0 = this.tempVector3_P0;
-		var p1 = this.tempVector3_P1;
-		var n0 = this.tempVector3_N0;
-		var n1 = this.tempVector3_N1;
-
-		// Iterate through the faces to mark edges shared by coplanar faces
-		for ( var i = 0; i < numFaces - 1; i ++ ) {
-
-			var a1 = getVertexIndex( i, 0 );
-			var b1 = getVertexIndex( i, 1 );
-			var c1 = getVertexIndex( i, 2 );
-
-			// Assuming all 3 vertices have the same normal
-			n0.set( normals[ a1 ], normals[ a1 ] + 1, normals[ a1 ] + 2 );
-
-			for ( var j = i + 1; j < numFaces; j ++ ) {
-
-				var a2 = getVertexIndex( j, 0 );
-				var b2 = getVertexIndex( j, 1 );
-				var c2 = getVertexIndex( j, 2 );
-
-				// Assuming all 3 vertices have the same normal
-				n1.set( normals[ a2 ], normals[ a2 ] + 1, normals[ a2 ] + 2 );
-
-				var coplanar = 1 - n0.dot( n1 ) < delta;
-
-				if ( coplanar ) {
-
-					if ( a1 === a2 || a1 === b2 || a1 === c2 ) {
-
-						if ( b1 === a2 || b1 === b2 || b1 === c2 ) {
-
-							this.segments[ a1 * numPoints + b1 ] = true;
-							this.segments[ b1 * numPoints + a1 ] = true;
-
-						}	else {
-
-							this.segments[ c1 * numPoints + a1 ] = true;
-							this.segments[ a1 * numPoints + c1 ] = true;
-
-						}
-
-					}	else if ( b1 === a2 || b1 === b2 || b1 === c2 ) {
-
-						this.segments[ c1 * numPoints + b1 ] = true;
-						this.segments[ b1 * numPoints + c1 ] = true;
-
-					}
-
-				}
-
-			}
-
-		}
-
-		// Transform the plane to object local space
-		var localPlane = this.tempPlane_Cut;
-		object.updateMatrix();
-		THREE.ConvexObjectBreaker.transformPlaneToLocalSpace( plane, object.matrix, localPlane );
-
-		// Iterate through the faces adding points to both pieces
-		for ( var i = 0; i < numFaces; i ++ ) {
-
-			var va = getVertexIndex( i, 0 );
-			var vb = getVertexIndex( i, 1 );
-			var vc = getVertexIndex( i, 2 );
-
-			for ( var segment = 0; segment < 3; segment ++ ) {
-
-				var i0 = segment === 0 ? va : ( segment === 1 ? vb : vc );
-				var i1 = segment === 0 ? vb : ( segment === 1 ? vc : va );
-
-				var segmentState = this.segments[ i0 * numPoints + i1 ];
-
-				if ( segmentState ) continue; // The segment already has been processed in another face
-
-				// Mark segment as processed (also inverted segment)
-				this.segments[ i0 * numPoints + i1 ] = true;
-				this.segments[ i1 * numPoints + i0 ] = true;
-
-				p0.set( coords[ 3 * i0 ], coords[ 3 * i0 + 1 ], coords[ 3 * i0 + 2 ] );
-				p1.set( coords[ 3 * i1 ], coords[ 3 * i1 + 1 ], coords[ 3 * i1 + 2 ] );
-
-				// mark: 1 for negative side, 2 for positive side, 3 for coplanar point
-				var mark0 = 0;
-
-				var d = localPlane.distanceToPoint( p0 );
-
-				if ( d > delta ) {
-
-					mark0 = 2;
-					points2.push( p0.clone() );
-
-				} else if ( d < - delta ) {
-
-					mark0 = 1;
-					points1.push( p0.clone() );
-
-				} else {
-
-					mark0 = 3;
-					points1.push( p0.clone() );
-					points2.push( p0.clone() );
-
-				}
-
-				// mark: 1 for negative side, 2 for positive side, 3 for coplanar point
-				var mark1 = 0;
-
-				var d = localPlane.distanceToPoint( p1 );
-
-				if ( d > delta ) {
-
-					mark1 = 2;
-					points2.push( p1.clone() );
-
-				} else if ( d < - delta ) {
-
-					mark1 = 1;
-					points1.push( p1.clone() );
-
-				}	else {
-
-					mark1 = 3;
-					points1.push( p1.clone() );
-					points2.push( p1.clone() );
-
-				}
-
-				if ( ( mark0 === 1 && mark1 === 2 ) || ( mark0 === 2 && mark1 === 1 ) ) {
-
-					// Intersection of segment with the plane
-
-					this.tempLine1.start.copy( p0 );
-					this.tempLine1.end.copy( p1 );
-
-					var intersection = new THREE.Vector3();
-					intersection = localPlane.intersectLine( this.tempLine1, intersection );
-
-					if ( intersection === undefined ) {
-
-						// Shouldn't happen
-						console.error( "Internal error: segment does not intersect plane." );
-						output.segmentedObject1 = null;
-						output.segmentedObject2 = null;
-						return 0;
-
-					}
-
-					points1.push( intersection );
-					points2.push( intersection.clone() );
-
-				}
-
-			}
-
-		}
-
-		// Calculate debris mass (very fast and imprecise):
-		var newMass = object.userData.mass * 0.5;
-
-		// Calculate debris Center of Mass (again fast and imprecise)
-		this.tempCM1.set( 0, 0, 0 );
-		var radius1 = 0;
-		var numPoints1 = points1.length;
-
-		if ( numPoints1 > 0 ) {
-
-			for ( var i = 0; i < numPoints1; i ++ ) this.tempCM1.add( points1[ i ] );
-
-			this.tempCM1.divideScalar( numPoints1 );
-			for ( var i = 0; i < numPoints1; i ++ ) {
-
-				var p = points1[ i ];
-				p.sub( this.tempCM1 );
-				radius1 = Math.max( radius1, p.x, p.y, p.z );
-
-			}
-			this.tempCM1.add( object.position );
-
-		}
-
-		this.tempCM2.set( 0, 0, 0 );
-		var radius2 = 0;
-		var numPoints2 = points2.length;
-		if ( numPoints2 > 0 ) {
-
-			for ( var i = 0; i < numPoints2; i ++ ) this.tempCM2.add( points2[ i ] );
-
-			this.tempCM2.divideScalar( numPoints2 );
-			for ( var i = 0; i < numPoints2; i ++ ) {
-
-				var p = points2[ i ];
-				p.sub( this.tempCM2 );
-				radius2 = Math.max( radius2, p.x, p.y, p.z );
-
-			}
-			this.tempCM2.add( object.position );
-
-		}
-
-		var object1 = null;
-		var object2 = null;
-
-		var numObjects = 0;
-
-		if ( numPoints1 > 4 ) {
-
-			object1 = new THREE.Mesh( new THREE.ConvexBufferGeometry( points1 ), object.material );
-			object1.position.copy( this.tempCM1 );
-			object1.quaternion.copy( object.quaternion );
-
-			this.prepareBreakableObject( object1, newMass, object.userData.velocity, object.userData.angularVelocity, 2 * radius1 > this.minSizeForBreak );
-
-			numObjects ++;
-
-		}
-
-		if ( numPoints2 > 4 ) {
-
-			object2 = new THREE.Mesh( new THREE.ConvexBufferGeometry( points2 ), object.material );
-			object2.position.copy( this.tempCM2 );
-			object2.quaternion.copy( object.quaternion );
-
-			this.prepareBreakableObject( object2, newMass, object.userData.velocity, object.userData.angularVelocity, 2 * radius2 > this.minSizeForBreak );
-
-			numObjects ++;
-
-		}
-
-		output.object1 = object1;
-		output.object2 = object2;
-
-		return numObjects;
-
-	}
-
-};
-
-THREE.ConvexObjectBreaker.transformFreeVector = function ( v, m ) {
-
-	// input:
-	// vector interpreted as a free vector
-	// THREE.Matrix4 orthogonal matrix (matrix without scale)
-
-	var x = v.x, y = v.y, z = v.z;
-	var e = m.elements;
-
-	v.x = e[ 0 ] * x + e[ 4 ] * y + e[ 8 ] * z;
-	v.y = e[ 1 ] * x + e[ 5 ] * y + e[ 9 ] * z;
-	v.z = e[ 2 ] * x + e[ 6 ] * y + e[ 10 ] * z;
-
-	return v;
-
-};
-
-THREE.ConvexObjectBreaker.transformFreeVectorInverse = function ( v, m ) {
-
-	// input:
-	// vector interpreted as a free vector
-	// THREE.Matrix4 orthogonal matrix (matrix without scale)
-
-	var x = v.x, y = v.y, z = v.z;
-	var e = m.elements;
-
-	v.x = e[ 0 ] * x + e[ 1 ] * y + e[ 2 ] * z;
-	v.y = e[ 4 ] * x + e[ 5 ] * y + e[ 6 ] * z;
-	v.z = e[ 8 ] * x + e[ 9 ] * y + e[ 10 ] * z;
-
-	return v;
-
-};
-
-THREE.ConvexObjectBreaker.transformTiedVectorInverse = function ( v, m ) {
-
-	// input:
-	// vector interpreted as a tied (ordinary) vector
-	// THREE.Matrix4 orthogonal matrix (matrix without scale)
-
-	var x = v.x, y = v.y, z = v.z;
-	var e = m.elements;
-
-	v.x = e[ 0 ] * x + e[ 1 ] * y + e[ 2 ] * z - e[ 12 ];
-	v.y = e[ 4 ] * x + e[ 5 ] * y + e[ 6 ] * z - e[ 13 ];
-	v.z = e[ 8 ] * x + e[ 9 ] * y + e[ 10 ] * z - e[ 14 ];
-
-	return v;
-
-};
-
-THREE.ConvexObjectBreaker.transformPlaneToLocalSpace = function () {
-
-	var v1 = new THREE.Vector3();
-
-	return function transformPlaneToLocalSpace( plane, m, resultPlane ) {
-
-		resultPlane.normal.copy( plane.normal );
-		resultPlane.constant = plane.constant;
-
-		var referencePoint = THREE.ConvexObjectBreaker.transformTiedVectorInverse( plane.coplanarPoint( v1 ), m );
-
-		THREE.ConvexObjectBreaker.transformFreeVectorInverse( resultPlane.normal, m );
-
-		// recalculate constant (like in setFromNormalAndCoplanarPoint)
-		resultPlane.constant = - referencePoint.dot( resultPlane.normal );
-
-
-	};
-
-}();
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/misc/BumpMapNode.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/misc/BumpMapNode.js
deleted file mode 100644
index f5f45b1df98c77062fa600adb625aa66f8073c96..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/misc/BumpMapNode.js
+++ /dev/null
@@ -1,166 +0,0 @@
-/**
- * @author sunag / http://www.sunag.com.br/
- */
-
-import { TempNode } from '../core/TempNode.js';
-import { FloatNode } from '../inputs/FloatNode.js';
-import { FunctionNode } from '../core/FunctionNode.js';
-import { NormalNode } from '../accessors/NormalNode.js';
-import { PositionNode } from '../accessors/PositionNode.js';
-
-function BumpMapNode( value, scale ) {
-
-	TempNode.call( this, 'v3' );
-
-	this.value = value;
-	this.scale = scale || new FloatNode( 1 );
-
-	this.toNormalMap = false;
-
-}
-
-BumpMapNode.Nodes = ( function () {
-
-	var dHdxy_fwd = new FunctionNode( [
-
-		// Bump Mapping Unparametrized Surfaces on the GPU by Morten S. Mikkelsen
-		// http://api.unrealengine.com/attachments/Engine/Rendering/LightingAndShadows/BumpMappingWithoutTangentSpace/mm_sfgrad_bump.pdf
-
-		// Evaluate the derivative of the height w.r.t. screen-space using forward differencing (listing 2)
-
-		"vec2 dHdxy_fwd( sampler2D bumpMap, vec2 vUv, float bumpScale ) {",
-
-		// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988
-
-		"	vec2 dSTdx = dFdx( vUv );",
-		"	vec2 dSTdy = dFdy( vUv );",
-
-		"	float Hll = bumpScale * texture2D( bumpMap, vUv ).x;",
-		"	float dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;",
-		"	float dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;",
-
-		"	return vec2( dBx, dBy );",
-
-		"}"
-
-	].join( "\n" ), null, { derivatives: true } );
-
-	var perturbNormalArb = new FunctionNode( [
-
-		"vec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy ) {",
-
-		// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988
-
-		"	vec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );",
-		"	vec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );",
-		"	vec3 vN = surf_norm;", // normalized
-
-		"	vec3 R1 = cross( vSigmaY, vN );",
-		"	vec3 R2 = cross( vN, vSigmaX );",
-
-		"	float fDet = dot( vSigmaX, R1 );",
-
-		"	fDet *= ( float( gl_FrontFacing ) * 2.0 - 1.0 );",
-
-		"	vec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );",
-
-		"	return normalize( abs( fDet ) * surf_norm - vGrad );",
-
-		"}"
-
-	].join( "\n" ), [ dHdxy_fwd ], { derivatives: true } );
-
-	var bumpToNormal = new FunctionNode( [
-		"vec3 bumpToNormal( sampler2D bumpMap, vec2 uv, float scale ) {",
-
-		"	vec2 dSTdx = dFdx( uv );",
-		"	vec2 dSTdy = dFdy( uv );",
-
-		"	float Hll = texture2D( bumpMap, uv ).x;",
-		"	float dBx = texture2D( bumpMap, uv + dSTdx ).x - Hll;",
-		"	float dBy = texture2D( bumpMap, uv + dSTdy ).x - Hll;",
-
-		"	return vec3( .5 - ( dBx * scale ), .5 - ( dBy * scale ), 1.0 );",
-
-		"}"
-	].join( "\n" ), null, { derivatives: true } );
-
-	return {
-		dHdxy_fwd: dHdxy_fwd,
-		perturbNormalArb: perturbNormalArb,
-		bumpToNormal: bumpToNormal
-	};
-
-} )();
-
-BumpMapNode.prototype = Object.create( TempNode.prototype );
-BumpMapNode.prototype.constructor = BumpMapNode;
-BumpMapNode.prototype.nodeType = "BumpMap";
-
-BumpMapNode.prototype.generate = function ( builder, output ) {
-
-	if ( builder.isShader( 'fragment' ) ) {
-
-		if ( this.toNormalMap ) {
-
-			var bumpToNormal = builder.include( BumpMapNode.Nodes.bumpToNormal );
-
-			return builder.format( bumpToNormal + '( ' + this.value.build( builder, 'sampler2D' ) + ', ' +
-				this.value.uv.build( builder, 'v2' ) + ', ' +
-				this.scale.build( builder, 'f' ) + ' )', this.getType( builder ), output );
-
-		} else {
-
-			var derivativeHeight = builder.include( BumpMapNode.Nodes.dHdxy_fwd ),
-				perturbNormalArb = builder.include( BumpMapNode.Nodes.perturbNormalArb );
-
-			this.normal = this.normal || new NormalNode();
-			this.position = this.position || new PositionNode( PositionNode.VIEW );
-
-			var derivativeHeightCode = derivativeHeight + '( ' + this.value.build( builder, 'sampler2D' ) + ', ' +
-				this.value.uv.build( builder, 'v2' ) + ', ' +
-				this.scale.build( builder, 'f' ) + ' )';
-
-			return builder.format( perturbNormalArb + '( -' + this.position.build( builder, 'v3' ) + ', ' +
-				this.normal.build( builder, 'v3' ) + ', ' +
-				derivativeHeightCode + ' )', this.getType( builder ), output );
-
-		}
-
-	} else {
-
-		console.warn( "THREE.BumpMapNode is not compatible with " + builder.shader + " shader." );
-
-		return builder.format( 'vec3( 0.0 )', this.getType( builder ), output );
-
-	}
-
-};
-
-BumpMapNode.prototype.copy = function ( source ) {
-
-	TempNode.prototype.copy.call( this, source );
-
-	this.value = source.value;
-	this.scale = source.scale;
-
-};
-
-BumpMapNode.prototype.toJSON = function ( meta ) {
-
-	var data = this.getJSONNode( meta );
-
-	if ( ! data ) {
-
-		data = this.createJSONNode( meta );
-
-		data.value = this.value.toJSON( meta ).uuid;
-		data.scale = this.scale.toJSON( meta ).uuid;
-
-	}
-
-	return data;
-
-};
-
-export { BumpMapNode };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/UniformsUtils.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/UniformsUtils.js
deleted file mode 100644
index a9fed401db5c33ba43bf00dcc3e9e6e4bad0e2f4..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/UniformsUtils.js
+++ /dev/null
@@ -1,66 +0,0 @@
-/**
- * Uniform Utilities
- */
-
-export function cloneUniforms( src ) {
-
-	var dst = {};
-
-	for ( var u in src ) {
-
-		dst[ u ] = {};
-
-		for ( var p in src[ u ] ) {
-
-			var property = src[ u ][ p ];
-
-			if ( property && ( property.isColor ||
-				property.isMatrix3 || property.isMatrix4 ||
-				property.isVector2 || property.isVector3 || property.isVector4 ||
-				property.isTexture ) ) {
-
-				dst[ u ][ p ] = property.clone();
-
-			} else if ( Array.isArray( property ) ) {
-
-				dst[ u ][ p ] = property.slice();
-
-			} else {
-
-				dst[ u ][ p ] = property;
-
-			}
-
-		}
-
-	}
-
-	return dst;
-
-}
-
-export function mergeUniforms( uniforms ) {
-
-	var merged = {};
-
-	for ( var u = 0; u < uniforms.length; u ++ ) {
-
-		var tmp = cloneUniforms( uniforms[ u ] );
-
-		for ( var p in tmp ) {
-
-			merged[ p ] = tmp[ p ];
-
-		}
-
-	}
-
-	return merged;
-
-}
-
-// Legacy
-
-var UniformsUtils = { clone: cloneUniforms, merge: mergeUniforms };
-
-export { UniformsUtils };
diff --git a/spaces/bayartsogt/real-time-tokenizer/app.py b/spaces/bayartsogt/real-time-tokenizer/app.py
deleted file mode 100644
index 0e4eaf36311ac9ffef4b8488e8178840f7c1207c..0000000000000000000000000000000000000000
--- a/spaces/bayartsogt/real-time-tokenizer/app.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import os
-from typing import List
-import gradio as gr
-from transformers import AutoTokenizer
-import tiktoken
-
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-hf_tokenizer_list = [
-    ("tugstugi/bert-large-mongolian-cased", False),
-    ("tugstugi/bert-large-mongolian-uncased", False),
-    ("bayartsogt/mongolian-roberta-large", True),
-    ("meta-llama/Llama-2-13b-hf", True),
-    ("tiiuae/falcon-7b", True),
-    ("bigscience/bloom", True),
-    
-]
-
-openai_tokenizer_list = [
-    "text-davinci-003",
-    "gpt-4"
-]
-
-# load tokenizers
-hf_tokenizers = [
-    AutoTokenizer.from_pretrained(model_name_or_id, use_fast=use_fast, trust_remote_code=True, token=HF_TOKEN)
-    for model_name_or_id, use_fast in hf_tokenizer_list
-]
-
-openai_tokenizers = [
-    tiktoken.encoding_for_model(name)
-    for name in openai_tokenizer_list
-]
-
-
-def do_tokenize(tokenizer: AutoTokenizer, text: str) -> List[str]:
-    return [(tokenizer.decode([token_id]), str(i)) for i, token_id in enumerate(tokenizer.encode(text))]
-
-def do_simple_split(text: str):
-    return [(x, str(i)) for i, x in enumerate(text.split())]
-
-def do_function(text: str):
-    return (
-        text,
-        len(text),
-        do_simple_split(text),
-        *[do_tokenize(tokenizer, text) for tokenizer in hf_tokenizers],
-        *[do_tokenize(tokenizer, text) for tokenizer in openai_tokenizers],
-    )
-
-
-demo = gr.Interface(
-    do_function,
-    [
-        gr.Text("", placeholder="Мөнгөө тушаачихсаныхаа дараа мэдэгдээрэй")
-    ],
-    [
-        gr.Text("", label="input"),
-        gr.Number(0, label="Character Count"),
-        gr.HighlightedText("", label="Simple Split"),
-        *[gr.HighlightedText("", label=tokenizer_name) for tokenizer_name, _ in hf_tokenizer_list],
-        *[gr.HighlightedText("", label="openai/" + tokenizer_name) for tokenizer_name in openai_tokenizer_list],
-    ],
-    live=True,
-    allow_flagging="never",
-    title="Real-Time Tokenizer",
-    description=(
-        "**Tokenizers:**\n" +
-        "\n".join(
-            [
-                f"🤗 [{x}](https://huggingface.co/{x})"
-                for x, _ in hf_tokenizer_list
-            ] + [
-                f"⏳ [{x}](https://github.com/openai/tiktoken)"
-                for x in openai_tokenizer_list
-            ])
-    ),
-)
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/beihai/PDF-Table-Extractor/.history/app_20220621104714.py b/spaces/beihai/PDF-Table-Extractor/.history/app_20220621104714.py
deleted file mode 100644
index 6a03729050f8d3f4dab7580aec970265f67dc733..0000000000000000000000000000000000000000
--- a/spaces/beihai/PDF-Table-Extractor/.history/app_20220621104714.py
+++ /dev/null
@@ -1,53 +0,0 @@
-#-*- coding : utf-8-*-
-import base64
-from subprocess import STDOUT 
-import streamlit as st
-import pandas as pd 
-import camelot as cam # extracting tables from PDFs 
-
-st.title("PDF Table Extractor")
-
-input_pdf = st.file_uploader(label = "", type = 'pdf')
-
-background = st.selectbox("表格线条是否隐藏",(False,True))
-extractor_mode = st.selectbox("单页抽取  OR   全文抽取",("单页抽取","全文抽取"))
-
-def extractor(page,result_name):
-        tables_all= cam.read_pdf("input.pdf", pages=page, process_background=background)
-        result_all = pd.ExcelWriter(result_name, engine='xlsxwriter') 
-        for i in range(0,len(tables_all)):
-            table = tables_all[i].df
-            sheetname = str(i)
-            table.to_excel(result_all, sheetname,index=False) 
-        with open(result_name,'rb') as f:
-           st.download_button('抽取完成,点击下载！', f,file_name='result_all.xlsx',mime="application/vnd.ms-excel")
-
-
-if input_pdf is not None:
-    # byte object into a PDF file 
-    with open("input.pdf", "wb") as f:
-        base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8')
-        f.write(base64.b64decode(base64_pdf))
-    f.close()
-    if extractor_mode == "单页抽取":
-        page_number = st.text_input("请填写表格所在PDF页码，eg: 3", value = 1)
-        # read the pdf and parse it using stream
-        tables = cam.read_pdf("input.pdf", pages=page_number, process_background=background)
-        result = pd.ExcelWriter('result.xlsx', engine='xlsxwriter') 
-        #tables[1].to_excel(result,index=False) 
-        for i in range(0,len(tables)):
-            table = tables[i].df
-            sheetname = str(i)
-            table.to_excel(result, sheetname,index=False) 
-    
-        with open('result.xlsx','rb') as f:
-           st.download_button('提取完成，点击下载！', f,file_name='result.xlsx',mime="application/vnd.ms-excel")
-    if extractor_mode == "全文抽取":
-        tables_all= cam.read_pdf("input.pdf", pages="all", process_background=background)
-        result_all = pd.ExcelWriter('result_all.xlsx', engine='xlsxwriter') 
-        for i in range(0,len(tables_all)):
-            table = tables_all[i].df
-            sheetname = str(i)
-            table.to_excel(result_all, sheetname,index=False) 
-        with open('result_all.xlsx','rb') as f:
-           st.download_button('抽取完成,点击下载！', f,file_name='result_all.xlsx',mime="application/vnd.ms-excel")
diff --git a/spaces/better57/CHATGPT/assets/custom.js b/spaces/better57/CHATGPT/assets/custom.js
deleted file mode 100644
index b8071034f3618c541e3f4169c7fc6d6593d56f44..0000000000000000000000000000000000000000
--- a/spaces/better57/CHATGPT/assets/custom.js
+++ /dev/null
@@ -1,224 +0,0 @@
-
-// custom javascript here
-
-const MAX_HISTORY_LENGTH = 32;
-
-var key_down_history = [];
-var currentIndex = -1;
-var user_input_ta;
-
-var gradioContainer = null;
-var user_input_ta = null;
-var user_input_tb = null;
-var userInfoDiv = null;
-var appTitleDiv = null;
-var chatbot = null;
-var apSwitch = null;
-
-var ga = document.getElementsByTagName("gradio-app");
-var targetNode = ga[0];
-var isInIframe = (window.self !== window.top);
-
-// gradio 页面加载好了么??? 我能动你的元素了么??
-function gradioLoaded(mutations) {
-    for (var i = 0; i < mutations.length; i++) {
-        if (mutations[i].addedNodes.length) {  
-            gradioContainer = document.querySelector(".gradio-container");
-            user_input_tb = document.getElementById('user_input_tb');
-            userInfoDiv = document.getElementById("user_info");
-            appTitleDiv = document.getElementById("app_title");
-            chatbot = document.querySelector('#chuanhu_chatbot');
-            apSwitch = document.querySelector('.apSwitch input[type="checkbox"]');
-
-            if (gradioContainer && apSwitch) {  // gradioCainter 加载出来了没?
-                adjustDarkMode();
-            }
-            if (user_input_tb) {  // user_input_tb 加载出来了没?
-                selectHistory();
-            }
-            if (userInfoDiv && appTitleDiv) {  // userInfoDiv 和 appTitleDiv 加载出来了没?
-                setTimeout(showOrHideUserInfo(), 2000);
-            }
-            if (chatbot) {  // chatbot 加载出来了没?
-                setChatbotHeight()
-            }
-        }
-    }
-}
-
-function selectHistory() {
-    user_input_ta = user_input_tb.querySelector("textarea");
-    if (user_input_ta) {
-        observer.disconnect(); // 停止监听
-        // 在 textarea 上监听 keydown 事件
-        user_input_ta.addEventListener("keydown", function (event) {
-            var value = user_input_ta.value.trim();
-            // 判断按下的是否为方向键
-            if (event.code === 'ArrowUp' || event.code === 'ArrowDown') {
-                // 如果按下的是方向键，且输入框中有内容，且历史记录中没有该内容，则不执行操作
-                if (value && key_down_history.indexOf(value) === -1)
-                    return;
-                // 对于需要响应的动作，阻止默认行为。
-                event.preventDefault();
-                var length = key_down_history.length;
-                if (length === 0) {
-                    currentIndex = -1; // 如果历史记录为空，直接将当前选中的记录重置
-                    return;
-                }
-                if (currentIndex === -1) {
-                    currentIndex = length;
-                }
-                if (event.code === 'ArrowUp' && currentIndex > 0) {
-                    currentIndex--;
-                    user_input_ta.value = key_down_history[currentIndex];
-                } else if (event.code === 'ArrowDown' && currentIndex < length - 1) {
-                    currentIndex++;
-                    user_input_ta.value = key_down_history[currentIndex];
-                }
-                user_input_ta.selectionStart = user_input_ta.value.length;
-                user_input_ta.selectionEnd = user_input_ta.value.length;
-                const input_event = new InputEvent("input", { bubbles: true, cancelable: true });
-                user_input_ta.dispatchEvent(input_event);
-            } else if (event.code === "Enter") {
-                if (value) {
-                    currentIndex = -1;
-                    if (key_down_history.indexOf(value) === -1) {
-                        key_down_history.push(value);
-                        if (key_down_history.length > MAX_HISTORY_LENGTH) {
-                            key_down_history.shift();
-                        }
-                    }
-                }
-            }
-        });
-    }
-}
-
-function toggleUserInfoVisibility(shouldHide) {
-    if (userInfoDiv) {
-        if (shouldHide) {
-            userInfoDiv.classList.add("hideK");
-        } else {
-            userInfoDiv.classList.remove("hideK");
-        }
-    }
-}
-function showOrHideUserInfo() {
-    var sendBtn = document.getElementById("submit_btn");
-
-    // Bind mouse/touch events to show/hide user info
-    appTitleDiv.addEventListener("mouseenter", function () {
-        toggleUserInfoVisibility(false);
-    });
-    userInfoDiv.addEventListener("mouseenter", function () {
-        toggleUserInfoVisibility(false);
-    });
-    sendBtn.addEventListener("mouseenter", function () {
-        toggleUserInfoVisibility(false);
-    });
-
-    appTitleDiv.addEventListener("mouseleave", function () {
-        toggleUserInfoVisibility(true);
-    });
-    userInfoDiv.addEventListener("mouseleave", function () {
-        toggleUserInfoVisibility(true);
-    });
-    sendBtn.addEventListener("mouseleave", function () {
-        toggleUserInfoVisibility(true);
-    });
-
-    appTitleDiv.ontouchstart = function () {
-        toggleUserInfoVisibility(false);
-    };
-    userInfoDiv.ontouchstart = function () {
-        toggleUserInfoVisibility(false);
-    };
-    sendBtn.ontouchstart = function () {
-        toggleUserInfoVisibility(false);
-    };
-
-    appTitleDiv.ontouchend = function () {
-        setTimeout(function () {
-            toggleUserInfoVisibility(true);
-        }, 3000); 
-    };
-    userInfoDiv.ontouchend = function () {
-        setTimeout(function () {
-            toggleUserInfoVisibility(true);
-        }, 3000); 
-    };
-    sendBtn.ontouchend = function () {
-        setTimeout(function () {
-            toggleUserInfoVisibility(true);
-        }, 3000); // Delay 1 second to hide user info
-    };
-
-    // Hide user info after 2 second
-    setTimeout(function () {
-        toggleUserInfoVisibility(true);
-    }, 2000);
-}
-
-function toggleDarkMode(isEnabled) {
-    if (isEnabled) {
-        gradioContainer.classList.add("dark");
-        document.body.style.setProperty("background-color", "var(--neutral-950)", "important");
-    } else {
-        gradioContainer.classList.remove("dark");
-        document.body.style.backgroundColor = "";
-    }
-}
-function adjustDarkMode() {
-    const darkModeQuery = window.matchMedia("(prefers-color-scheme: dark)");
-
-    // 根据当前颜色模式设置初始状态
-    apSwitch.checked = darkModeQuery.matches;
-    toggleDarkMode(darkModeQuery.matches);
-    // 监听颜色模式变化
-    darkModeQuery.addEventListener("change", (e) => {
-        apSwitch.checked = e.matches;
-        toggleDarkMode(e.matches);
-    });
-    // apSwitch = document.querySelector('.apSwitch input[type="checkbox"]');
-    apSwitch.addEventListener("change", (e) => {
-        toggleDarkMode(e.target.checked);
-    });
-}
-
-function setChatbotHeight() {
-    const screenWidth = window.innerWidth;
-    const statusDisplay = document.querySelector('#status_display');
-    const statusDisplayHeight = statusDisplay ? statusDisplay.offsetHeight : 0;
-    const wrap = chatbot.querySelector('.wrap');
-    const vh = window.innerHeight * 0.01;
-    document.documentElement.style.setProperty('--vh', `${vh}px`);
-    if (isInIframe) {
-        chatbot.style.height = `700px`;
-        wrap.style.maxHeight = `calc(700px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`
-    } else {
-        if (screenWidth <= 320) {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px)`;
-            wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        } else if (screenWidth <= 499) {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px)`;
-            wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        } else {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px)`;
-            wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        }
-    }
-}
-
-// 监视页面内部 DOM 变动
-var observer = new MutationObserver(function (mutations) {
-    gradioLoaded(mutations);
-});
-observer.observe(targetNode, { childList: true, subtree: true });
-
-// 监视页面变化
-window.addEventListener("DOMContentLoaded", function () {
-    isInIframe = (window.self !== window.top);
-});
-window.addEventListener('resize', setChatbotHeight);
-window.addEventListener('scroll', setChatbotHeight);
-window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", adjustDarkMode);
\ No newline at end of file
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--- a/spaces/bioriAsaeru/text-to-voice/IObit Advanced SystemCare Ultimate V10.0.1.82 Final Serial 64 Bit.md	
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deleted file mode 100644
index e7c9c5767c94685019c0b3a48d14ebe8b898fcbb..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Le Nouveau Taxi 3 Guide Pedagogique Pdf Download Learn French with a Modern and Effective Method.md	
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diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/datasets/cityscapes.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/datasets/cityscapes.py
deleted file mode 100644
index 1e84a5bdb3d4e410d8eef4b80a5d4c099a180104..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/datasets/cityscapes.py
+++ /dev/null
@@ -1,329 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import functools
-import json
-import logging
-import multiprocessing as mp
-import numpy as np
-import os
-from itertools import chain
-import pycocotools.mask as mask_util
-from PIL import Image
-
-from detectron2.structures import BoxMode
-from detectron2.utils.comm import get_world_size
-from detectron2.utils.file_io import PathManager
-from detectron2.utils.logger import setup_logger
-
-try:
-    import cv2  # noqa
-except ImportError:
-    # OpenCV is an optional dependency at the moment
-    pass
-
-
-logger = logging.getLogger(__name__)
-
-
-def _get_cityscapes_files(image_dir, gt_dir):
-    files = []
-    # scan through the directory
-    cities = PathManager.ls(image_dir)
-    logger.info(f"{len(cities)} cities found in '{image_dir}'.")
-    for city in cities:
-        city_img_dir = os.path.join(image_dir, city)
-        city_gt_dir = os.path.join(gt_dir, city)
-        for basename in PathManager.ls(city_img_dir):
-            image_file = os.path.join(city_img_dir, basename)
-
-            suffix = "leftImg8bit.png"
-            assert basename.endswith(suffix), basename
-            basename = basename[: -len(suffix)]
-
-            instance_file = os.path.join(city_gt_dir, basename + "gtFine_instanceIds.png")
-            label_file = os.path.join(city_gt_dir, basename + "gtFine_labelIds.png")
-            json_file = os.path.join(city_gt_dir, basename + "gtFine_polygons.json")
-
-            files.append((image_file, instance_file, label_file, json_file))
-    assert len(files), "No images found in {}".format(image_dir)
-    for f in files[0]:
-        assert PathManager.isfile(f), f
-    return files
-
-
-def load_cityscapes_instances(image_dir, gt_dir, from_json=True, to_polygons=True):
-    """
-    Args:
-        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
-        gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train".
-        from_json (bool): whether to read annotations from the raw json file or the png files.
-        to_polygons (bool): whether to represent the segmentation as polygons
-            (COCO's format) instead of masks (cityscapes's format).
-
-    Returns:
-        list[dict]: a list of dicts in Detectron2 standard format. (See
-        `Using Custom Datasets </tutorials/datasets.html>`_ )
-    """
-    if from_json:
-        assert to_polygons, (
-            "Cityscapes's json annotations are in polygon format. "
-            "Converting to mask format is not supported now."
-        )
-    files = _get_cityscapes_files(image_dir, gt_dir)
-
-    logger.info("Preprocessing cityscapes annotations ...")
-    # This is still not fast: all workers will execute duplicate works and will
-    # take up to 10m on a 8GPU server.
-    pool = mp.Pool(processes=max(mp.cpu_count() // get_world_size() // 2, 4))
-
-    ret = pool.map(
-        functools.partial(_cityscapes_files_to_dict, from_json=from_json, to_polygons=to_polygons),
-        files,
-    )
-    logger.info("Loaded {} images from {}".format(len(ret), image_dir))
-
-    # Map cityscape ids to contiguous ids
-    from cityscapesscripts.helpers.labels import labels
-
-    labels = [l for l in labels if l.hasInstances and not l.ignoreInEval]
-    dataset_id_to_contiguous_id = {l.id: idx for idx, l in enumerate(labels)}
-    for dict_per_image in ret:
-        for anno in dict_per_image["annotations"]:
-            anno["category_id"] = dataset_id_to_contiguous_id[anno["category_id"]]
-    return ret
-
-
-def load_cityscapes_semantic(image_dir, gt_dir):
-    """
-    Args:
-        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
-        gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train".
-
-    Returns:
-        list[dict]: a list of dict, each has "file_name" and
-            "sem_seg_file_name".
-    """
-    ret = []
-    # gt_dir is small and contain many small files. make sense to fetch to local first
-    gt_dir = PathManager.get_local_path(gt_dir)
-    for image_file, _, label_file, json_file in _get_cityscapes_files(image_dir, gt_dir):
-        label_file = label_file.replace("labelIds", "labelTrainIds")
-
-        with PathManager.open(json_file, "r") as f:
-            jsonobj = json.load(f)
-        ret.append(
-            {
-                "file_name": image_file,
-                "sem_seg_file_name": label_file,
-                "height": jsonobj["imgHeight"],
-                "width": jsonobj["imgWidth"],
-            }
-        )
-    assert len(ret), f"No images found in {image_dir}!"
-    assert PathManager.isfile(
-        ret[0]["sem_seg_file_name"]
-    ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py"  # noqa
-    return ret
-
-
-def _cityscapes_files_to_dict(files, from_json, to_polygons):
-    """
-    Parse cityscapes annotation files to a instance segmentation dataset dict.
-
-    Args:
-        files (tuple): consists of (image_file, instance_id_file, label_id_file, json_file)
-        from_json (bool): whether to read annotations from the raw json file or the png files.
-        to_polygons (bool): whether to represent the segmentation as polygons
-            (COCO's format) instead of masks (cityscapes's format).
-
-    Returns:
-        A dict in Detectron2 Dataset format.
-    """
-    from cityscapesscripts.helpers.labels import id2label, name2label
-
-    image_file, instance_id_file, _, json_file = files
-
-    annos = []
-
-    if from_json:
-        from shapely.geometry import MultiPolygon, Polygon
-
-        with PathManager.open(json_file, "r") as f:
-            jsonobj = json.load(f)
-        ret = {
-            "file_name": image_file,
-            "image_id": os.path.basename(image_file),
-            "height": jsonobj["imgHeight"],
-            "width": jsonobj["imgWidth"],
-        }
-
-        # `polygons_union` contains the union of all valid polygons.
-        polygons_union = Polygon()
-
-        # CityscapesScripts draw the polygons in sequential order
-        # and each polygon *overwrites* existing ones. See
-        # (https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/json2instanceImg.py) # noqa
-        # We use reverse order, and each polygon *avoids* early ones.
-        # This will resolve the ploygon overlaps in the same way as CityscapesScripts.
-        for obj in jsonobj["objects"][::-1]:
-            if "deleted" in obj:  # cityscapes data format specific
-                continue
-            label_name = obj["label"]
-
-            try:
-                label = name2label[label_name]
-            except KeyError:
-                if label_name.endswith("group"):  # crowd area
-                    label = name2label[label_name[: -len("group")]]
-                else:
-                    raise
-            if label.id < 0:  # cityscapes data format
-                continue
-
-            # Cityscapes's raw annotations uses integer coordinates
-            # Therefore +0.5 here
-            poly_coord = np.asarray(obj["polygon"], dtype="f4") + 0.5
-            # CityscapesScript uses PIL.ImageDraw.polygon to rasterize
-            # polygons for evaluation. This function operates in integer space
-            # and draws each pixel whose center falls into the polygon.
-            # Therefore it draws a polygon which is 0.5 "fatter" in expectation.
-            # We therefore dilate the input polygon by 0.5 as our input.
-            poly = Polygon(poly_coord).buffer(0.5, resolution=4)
-
-            if not label.hasInstances or label.ignoreInEval:
-                # even if we won't store the polygon it still contributes to overlaps resolution
-                polygons_union = polygons_union.union(poly)
-                continue
-
-            # Take non-overlapping part of the polygon
-            poly_wo_overlaps = poly.difference(polygons_union)
-            if poly_wo_overlaps.is_empty:
-                continue
-            polygons_union = polygons_union.union(poly)
-
-            anno = {}
-            anno["iscrowd"] = label_name.endswith("group")
-            anno["category_id"] = label.id
-
-            if isinstance(poly_wo_overlaps, Polygon):
-                poly_list = [poly_wo_overlaps]
-            elif isinstance(poly_wo_overlaps, MultiPolygon):
-                poly_list = poly_wo_overlaps.geoms
-            else:
-                raise NotImplementedError("Unknown geometric structure {}".format(poly_wo_overlaps))
-
-            poly_coord = []
-            for poly_el in poly_list:
-                # COCO API can work only with exterior boundaries now, hence we store only them.
-                # TODO: store both exterior and interior boundaries once other parts of the
-                # codebase support holes in polygons.
-                poly_coord.append(list(chain(*poly_el.exterior.coords)))
-            anno["segmentation"] = poly_coord
-            (xmin, ymin, xmax, ymax) = poly_wo_overlaps.bounds
-
-            anno["bbox"] = (xmin, ymin, xmax, ymax)
-            anno["bbox_mode"] = BoxMode.XYXY_ABS
-
-            annos.append(anno)
-    else:
-        # See also the official annotation parsing scripts at
-        # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/instances2dict.py  # noqa
-        with PathManager.open(instance_id_file, "rb") as f:
-            inst_image = np.asarray(Image.open(f), order="F")
-        # ids < 24 are stuff labels (filtering them first is about 5% faster)
-        flattened_ids = np.unique(inst_image[inst_image >= 24])
-
-        ret = {
-            "file_name": image_file,
-            "image_id": os.path.basename(image_file),
-            "height": inst_image.shape[0],
-            "width": inst_image.shape[1],
-        }
-
-        for instance_id in flattened_ids:
-            # For non-crowd annotations, instance_id // 1000 is the label_id
-            # Crowd annotations have <1000 instance ids
-            label_id = instance_id // 1000 if instance_id >= 1000 else instance_id
-            label = id2label[label_id]
-            if not label.hasInstances or label.ignoreInEval:
-                continue
-
-            anno = {}
-            anno["iscrowd"] = instance_id < 1000
-            anno["category_id"] = label.id
-
-            mask = np.asarray(inst_image == instance_id, dtype=np.uint8, order="F")
-
-            inds = np.nonzero(mask)
-            ymin, ymax = inds[0].min(), inds[0].max()
-            xmin, xmax = inds[1].min(), inds[1].max()
-            anno["bbox"] = (xmin, ymin, xmax, ymax)
-            if xmax <= xmin or ymax <= ymin:
-                continue
-            anno["bbox_mode"] = BoxMode.XYXY_ABS
-            if to_polygons:
-                # This conversion comes from D4809743 and D5171122,
-                # when Mask-RCNN was first developed.
-                contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[
-                    -2
-                ]
-                polygons = [c.reshape(-1).tolist() for c in contours if len(c) >= 3]
-                # opencv's can produce invalid polygons
-                if len(polygons) == 0:
-                    continue
-                anno["segmentation"] = polygons
-            else:
-                anno["segmentation"] = mask_util.encode(mask[:, :, None])[0]
-            annos.append(anno)
-    ret["annotations"] = annos
-    return ret
-
-
-if __name__ == "__main__":
-    """
-    Test the cityscapes dataset loader.
-
-    Usage:
-        python -m detectron2.data.datasets.cityscapes \
-            cityscapes/leftImg8bit/train cityscapes/gtFine/train
-    """
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("image_dir")
-    parser.add_argument("gt_dir")
-    parser.add_argument("--type", choices=["instance", "semantic"], default="instance")
-    args = parser.parse_args()
-    from detectron2.data.catalog import Metadata
-    from detectron2.utils.visualizer import Visualizer
-    from cityscapesscripts.helpers.labels import labels
-
-    logger = setup_logger(name=__name__)
-
-    dirname = "cityscapes-data-vis"
-    os.makedirs(dirname, exist_ok=True)
-
-    if args.type == "instance":
-        dicts = load_cityscapes_instances(
-            args.image_dir, args.gt_dir, from_json=True, to_polygons=True
-        )
-        logger.info("Done loading {} samples.".format(len(dicts)))
-
-        thing_classes = [k.name for k in labels if k.hasInstances and not k.ignoreInEval]
-        meta = Metadata().set(thing_classes=thing_classes)
-
-    else:
-        dicts = load_cityscapes_semantic(args.image_dir, args.gt_dir)
-        logger.info("Done loading {} samples.".format(len(dicts)))
-
-        stuff_classes = [k.name for k in labels if k.trainId != 255]
-        stuff_colors = [k.color for k in labels if k.trainId != 255]
-        meta = Metadata().set(stuff_classes=stuff_classes, stuff_colors=stuff_colors)
-
-    for d in dicts:
-        img = np.array(Image.open(PathManager.open(d["file_name"], "rb")))
-        visualizer = Visualizer(img, metadata=meta)
-        vis = visualizer.draw_dataset_dict(d)
-        # cv2.imshow("a", vis.get_image()[:, :, ::-1])
-        # cv2.waitKey()
-        fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
-        vis.save(fpath)
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/video/__init__.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/video/__init__.py
deleted file mode 100644
index 72406e153b688461bfcb0ef21e35020399239309..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/video/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from .frame_selector import (
-    FrameSelectionStrategy,
-    RandomKFramesSelector,
-    FirstKFramesSelector,
-    LastKFramesSelector,
-    FrameTsList,
-    FrameSelector,
-)
-
-from .video_keyframe_dataset import (
-    VideoKeyframeDataset,
-    video_list_from_file,
-    list_keyframes,
-    read_keyframes,
-)
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/COCO/cascade_mask_rcnn_mvitv2_h_in21k_36ep.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/COCO/cascade_mask_rcnn_mvitv2_h_in21k_36ep.py
deleted file mode 100644
index 577045043b960384953a00eac4dc45ee43c1045e..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/COCO/cascade_mask_rcnn_mvitv2_h_in21k_36ep.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from fvcore.common.param_scheduler import MultiStepParamScheduler
-
-from detectron2.config import LazyCall as L
-from detectron2.solver import WarmupParamScheduler
-
-from .cascade_mask_rcnn_mvitv2_b_in21k_100ep import (
-    dataloader,
-    lr_multiplier,
-    model,
-    train,
-    optimizer,
-)
-
-model.backbone.bottom_up.embed_dim = 192
-model.backbone.bottom_up.depth = 80
-model.backbone.bottom_up.num_heads = 3
-model.backbone.bottom_up.last_block_indexes = (3, 11, 71, 79)
-model.backbone.bottom_up.drop_path_rate = 0.6
-model.backbone.bottom_up.use_act_checkpoint = True
-
-
-train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_H_in21k.pyth"
-
-
-# 36 epochs
-train.max_iter = 67500
-lr_multiplier = L(WarmupParamScheduler)(
-    scheduler=L(MultiStepParamScheduler)(
-        values=[1.0, 0.1, 0.01],
-        milestones=[
-            52500,
-            62500,
-            67500,
-        ],
-    ),
-    warmup_length=250 / train.max_iter,
-    warmup_factor=0.001,
-)
-optimizer.lr = 1.6e-4
diff --git a/spaces/cadige/03-Streamlit-Video/app.py b/spaces/cadige/03-Streamlit-Video/app.py
deleted file mode 100644
index e0f03cf2557eba112bf95ebf5eb582da8d8a0fe3..0000000000000000000000000000000000000000
--- a/spaces/cadige/03-Streamlit-Video/app.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from collections import deque
-import streamlit as st
-import torch
-from streamlit_player import st_player
-from transformers import AutoModelForCTC, Wav2Vec2Processor
-from streaming import ffmpeg_stream
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-player_options = {
-    "events": ["onProgress"],
-    "progress_interval": 200,
-    "volume": 1.0,
-    "playing": True,
-    "loop": False,
-    "controls": False,
-    "muted": False,
-    "config": {"youtube": {"playerVars": {"start": 1}}},
-}
-
-# disable rapid fading in and out on `st.code` updates
-st.markdown("<style>.element-container{opacity:1 !important}</style>", unsafe_allow_html=True)
-
-@st.cache(hash_funcs={torch.nn.parameter.Parameter: lambda _: None})
-def load_model(model_path="facebook/wav2vec2-large-robust-ft-swbd-300h"):
-    processor = Wav2Vec2Processor.from_pretrained(model_path)
-    model = AutoModelForCTC.from_pretrained(model_path).to(device)
-    return processor, model
-    
-processor, model = load_model()
-
-def stream_text(url, chunk_duration_ms, pad_duration_ms):
-    sampling_rate = processor.feature_extractor.sampling_rate
-
-    # calculate the length of logits to cut from the sides of the output to account for input padding
-    output_pad_len = model._get_feat_extract_output_lengths(int(sampling_rate * pad_duration_ms / 1000))
-
-    # define the audio chunk generator
-    stream = ffmpeg_stream(url, sampling_rate, chunk_duration_ms=chunk_duration_ms, pad_duration_ms=pad_duration_ms)
-
-    leftover_text = ""
-    for i, chunk in enumerate(stream):
-        input_values = processor(chunk, sampling_rate=sampling_rate, return_tensors="pt").input_values
-
-        with torch.no_grad():
-            logits = model(input_values.to(device)).logits[0]
-            if i > 0:
-                logits = logits[output_pad_len : len(logits) - output_pad_len]
-            else:  # don't count padding at the start of the clip
-                logits = logits[: len(logits) - output_pad_len]
-
-            predicted_ids = torch.argmax(logits, dim=-1).cpu().tolist()
-            if processor.decode(predicted_ids).strip():
-                leftover_ids = processor.tokenizer.encode(leftover_text)
-                # concat the last word (or its part) from the last frame with the current text
-                text = processor.decode(leftover_ids + predicted_ids)
-                # don't return the last word in case it's just partially recognized
-                text, leftover_text = text.rsplit(" ", 1)
-                yield text
-            else:
-                yield leftover_text
-                leftover_text = ""
-    yield leftover_text
-
-def main():
-    state = st.session_state
-    st.header("Video ASR Streamlit from Youtube Link")
-
-    with st.form(key="inputs_form"):
-    
-        # Our worlds best teachers on subjects of AI, Cognitive, Neuroscience for our Behavioral and Medical Health
-        ytJoschaBach="https://youtu.be/cC1HszE5Hcw?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=8984"
-        ytSamHarris="https://www.youtube.com/watch?v=4dC_nRYIDZU&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=2"
-        ytJohnAbramson="https://www.youtube.com/watch?v=arrokG3wCdE&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=3"
-        ytElonMusk="https://www.youtube.com/watch?v=DxREm3s1scA&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=4"
-        ytJeffreyShainline="https://www.youtube.com/watch?v=EwueqdgIvq4&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=5"
-        ytJeffHawkins="https://www.youtube.com/watch?v=Z1KwkpTUbkg&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=6"
-        ytSamHarris="https://youtu.be/Ui38ZzTymDY?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L"
-        ytSamHarris="https://youtu.be/4dC_nRYIDZU?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=7809"
-        ytSamHarris="https://youtu.be/4dC_nRYIDZU?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=7809"
-        ytSamHarris="https://youtu.be/4dC_nRYIDZU?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=7809"
-        ytTimelapseAI="https://www.youtube.com/watch?v=63yr9dlI0cU&list=PLHgX2IExbFovQybyfltywXnqZi5YvaSS-"
-        state.youtube_url = st.text_input("YouTube URL", ytTimelapseAI)
-        
-        
-        state.chunk_duration_ms = st.slider("Audio chunk duration (ms)", 2000, 10000, 3000, 100)
-        state.pad_duration_ms = st.slider("Padding duration (ms)", 100, 5000, 1000, 100)
-        submit_button = st.form_submit_button(label="Submit")
-
-    if submit_button or "asr_stream" not in state:
-        # a hack to update the video player on value changes
-        state.youtube_url = (
-            state.youtube_url.split("&hash=")[0]
-            + f"&hash={state.chunk_duration_ms}-{state.pad_duration_ms}"
-        )
-        state.asr_stream = stream_text(
-            state.youtube_url, state.chunk_duration_ms, state.pad_duration_ms
-        )
-        state.chunks_taken = 0
-        
-        
-        state.lines = deque([], maxlen=100)  # limit to the last n lines of subs
-        
-
-    player = st_player(state.youtube_url, **player_options, key="youtube_player")
-
-    if "asr_stream" in state and player.data and player.data["played"] < 1.0:
-        # check how many seconds were played, and if more than processed - write the next text chunk
-        processed_seconds = state.chunks_taken * (state.chunk_duration_ms / 1000)
-        if processed_seconds < player.data["playedSeconds"]:
-            text = next(state.asr_stream)
-            state.lines.append(text)
-            state.chunks_taken += 1
-    if "lines" in state:
-        # print the lines of subs
-        st.code("\n".join(state.lines))
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/clap/open_clip/__init__.py b/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/clap/open_clip/__init__.py
deleted file mode 100644
index e9f728f2f273be5d5fdbec6c6cc41d737176a8c0..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/clap/open_clip/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from .factory import (
-    list_models,
-    create_model,
-    create_model_and_transforms,
-    add_model_config,
-)
-from .loss import ClipLoss, gather_features, LPLoss, lp_gather_features, LPMetrics
-from .model import (
-    CLAP,
-    CLAPTextCfg,
-    CLAPVisionCfg,
-    CLAPAudioCfp,
-    convert_weights_to_fp16,
-    trace_model,
-)
-from .openai import load_openai_model, list_openai_models
-from .pretrained import (
-    list_pretrained,
-    list_pretrained_tag_models,
-    list_pretrained_model_tags,
-    get_pretrained_url,
-    download_pretrained,
-)
-from .tokenizer import SimpleTokenizer, tokenize
-from .transform import image_transform
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/common/optim.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/common/optim.py
deleted file mode 100644
index 6cf43e835f55739fbb80102b870efab950a0486d..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/common/optim.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import torch
-
-from detectron2.config import LazyCall as L
-from detectron2.solver.build import get_default_optimizer_params
-
-SGD = L(torch.optim.SGD)(
-    params=L(get_default_optimizer_params)(
-        # params.model is meant to be set to the model object, before instantiating
-        # the optimizer.
-        weight_decay_norm=0.0
-    ),
-    lr=0.02,
-    momentum=0.9,
-    weight_decay=1e-4,
-)
-
-
-AdamW = L(torch.optim.AdamW)(
-    params=L(get_default_optimizer_params)(
-        # params.model is meant to be set to the model object, before instantiating
-        # the optimizer.
-        base_lr="${..lr}",
-        weight_decay_norm=0.0,
-    ),
-    lr=1e-4,
-    betas=(0.9, 0.999),
-    weight_decay=0.1,
-)
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/ViTDet/configs/COCO/mask_rcnn_vitdet_h_75ep.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/ViTDet/configs/COCO/mask_rcnn_vitdet_h_75ep.py
deleted file mode 100644
index 9fe752c611bff09c5cdbce936927fc2c0fbcbd94..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/ViTDet/configs/COCO/mask_rcnn_vitdet_h_75ep.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from functools import partial
-
-from .mask_rcnn_vitdet_b_100ep import (
-    dataloader,
-    lr_multiplier,
-    model,
-    train,
-    optimizer,
-    get_vit_lr_decay_rate,
-)
-
-train.init_checkpoint = "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_huge_p14to16.pth"
-
-model.backbone.net.embed_dim = 1280
-model.backbone.net.depth = 32
-model.backbone.net.num_heads = 16
-model.backbone.net.drop_path_rate = 0.5
-# 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.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
-
-train.max_iter = train.max_iter * 3 // 4  # 100ep -> 75ep
-lr_multiplier.scheduler.milestones = [
-    milestone * 3 // 4 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter
diff --git a/spaces/chansung/textual-inversion-pipeline/share_btn.py b/spaces/chansung/textual-inversion-pipeline/share_btn.py
deleted file mode 100644
index 34227f85459e1f5b7655e739189304fb3093e5d0..0000000000000000000000000000000000000000
--- a/spaces/chansung/textual-inversion-pipeline/share_btn.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""
-Credits: https://huggingface.co/spaces/stabilityai/stable-diffusion/blob/main/share_btn.py
-"""
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-    const gradioEl = document.querySelector('body > gradio-app');
-    const imgEls = gradioEl.querySelectorAll('#gallery img');
-    const promptTxt = gradioEl.querySelector('#prompt-text-input input').value;
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!imgEls.length){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const files = await Promise.all(
-        [...imgEls].map(async (imgEl) => {
-            const res = await fetch(imgEl.src);
-            const blob = await res.blob();
-            const imgId = Date.now() % 200;
-            const fileName = `diffuse-the-rest-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        })
-    );
-    const urls = await Promise.all(files.map((f) => uploadFile(f)));
-	const htmlImgs = urls.map(url => `<img src='${url}' width='400' height='400'>`);
-	const descriptionMd = `<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
-${htmlImgs.join(`\n`)}
-</div>`;
-    const params = new URLSearchParams({
-        title: promptTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/stabilityai/stable-diffusion/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/transformers/examples/tensorflow/translation/run_translation.py b/spaces/chendl/compositional_test/transformers/examples/tensorflow/translation/run_translation.py
deleted file mode 100644
index 77fc80f4e2fdce9fff8e88a4c7108573004ffa5b..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/tensorflow/translation/run_translation.py
+++ /dev/null
@@ -1,702 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 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.
-"""
-Fine-tuning the library models for translation.
-"""
-# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
-
-import json
-import logging
-import os
-import sys
-from dataclasses import dataclass, field
-from typing import Optional
-
-import datasets
-import evaluate
-import numpy as np
-import tensorflow as tf
-from datasets import load_dataset
-
-import transformers
-from transformers import (
-    AutoConfig,
-    AutoTokenizer,
-    DataCollatorForSeq2Seq,
-    HfArgumentParser,
-    KerasMetricCallback,
-    M2M100Tokenizer,
-    MBart50Tokenizer,
-    MBart50TokenizerFast,
-    MBartTokenizer,
-    MBartTokenizerFast,
-    PushToHubCallback,
-    TFAutoModelForSeq2SeqLM,
-    TFTrainingArguments,
-    create_optimizer,
-    set_seed,
-)
-from transformers.trainer_utils import get_last_checkpoint
-from transformers.utils import check_min_version, send_example_telemetry
-from transformers.utils.versions import require_version
-
-
-# region Dependencies and constants
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.28.0")
-
-require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
-
-logger = logging.getLogger(__name__)
-MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast, M2M100Tokenizer]
-# endregion
-
-
-# region Arguments
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
-    )
-    use_fast_tokenizer: bool = field(
-        default=True,
-        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
-    )
-    model_revision: str = field(
-        default="main",
-        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
-    )
-    use_auth_token: bool = field(
-        default=False,
-        metadata={
-            "help": (
-                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
-                "with private models)."
-            )
-        },
-    )
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    source_lang: str = field(default=None, metadata={"help": "Source language id for translation."})
-    target_lang: str = field(default=None, metadata={"help": "Target language id for translation."})
-    dataset_name: Optional[str] = field(
-        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
-    )
-    dataset_config_name: Optional[str] = field(
-        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
-    )
-    train_file: Optional[str] = field(
-        default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
-    )
-    validation_file: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": (
-                "An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
-            )
-        },
-    )
-    test_file: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
-        },
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-    preprocessing_num_workers: Optional[int] = field(
-        default=None,
-        metadata={"help": "The number of processes to use for the preprocessing."},
-    )
-    max_source_length: Optional[int] = field(
-        default=1024,
-        metadata={
-            "help": (
-                "The maximum total input sequence length after tokenization. Sequences longer "
-                "than this will be truncated, sequences shorter will be padded."
-            )
-        },
-    )
-    max_target_length: Optional[int] = field(
-        default=128,
-        metadata={
-            "help": (
-                "The maximum total sequence length for target text after tokenization. Sequences longer "
-                "than this will be truncated, sequences shorter will be padded."
-            )
-        },
-    )
-    val_max_target_length: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": (
-                "The maximum total sequence length for validation target text after tokenization. Sequences longer "
-                "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
-                "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
-                "during ``evaluate`` and ``predict``."
-            )
-        },
-    )
-    pad_to_max_length: bool = field(
-        default=False,
-        metadata={
-            "help": (
-                "Whether to pad all samples to model maximum sentence length. "
-                "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
-                "efficient on GPU but very bad for TPU."
-            )
-        },
-    )
-    max_train_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": (
-                "For debugging purposes or quicker training, truncate the number of training examples to this "
-                "value if set."
-            )
-        },
-    )
-    max_eval_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": (
-                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
-                "value if set."
-            )
-        },
-    )
-    max_predict_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": (
-                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
-                "value if set."
-            )
-        },
-    )
-    num_beams: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": (
-                "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
-                "which is used during ``evaluate`` and ``predict``."
-            )
-        },
-    )
-    ignore_pad_token_for_loss: bool = field(
-        default=True,
-        metadata={
-            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
-        },
-    )
-    source_prefix: Optional[str] = field(
-        default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
-    )
-    forced_bos_token: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": (
-                "The token to force as the first generated token after the :obj:`decoder_start_token_id`.Useful for"
-                " multilingual models like :doc:`mBART <../model_doc/mbart>` where the first generated token needs to"
-                " be the target language token.(Usually it is the target language token)"
-            )
-        },
-    )
-
-    def __post_init__(self):
-        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
-            raise ValueError("Need either a dataset name or a training/validation file.")
-        else:
-            if self.train_file is not None:
-                extension = self.train_file.split(".")[-1]
-                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
-            if self.validation_file is not None:
-                extension = self.validation_file.split(".")[-1]
-                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
-        if self.val_max_target_length is None:
-            self.val_max_target_length = self.max_target_length
-
-
-# endregion
-
-
-def main():
-    # region Argument parsing
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
-    # information sent is the one passed as arguments along with your Python/PyTorch versions.
-    send_example_telemetry("run_translation", model_args, data_args, framework="tensorflow")
-    # endregion
-
-    # region Logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        handlers=[logging.StreamHandler(sys.stdout)],
-    )
-    logger.setLevel(logging.INFO)
-    datasets.utils.logging.set_verbosity(logging.INFO)
-    transformers.utils.logging.set_verbosity(logging.INFO)
-
-    # Log on each process the small summary:
-    logger.info(f"Training/evaluation parameters {training_args}")
-    # endregion
-
-    # region Detecting last checkpoint
-    last_checkpoint = None
-    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
-        last_checkpoint = get_last_checkpoint(training_args.output_dir)
-        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
-            raise ValueError(
-                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
-                "Use --overwrite_output_dir to overcome."
-            )
-        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
-            logger.info(
-                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
-                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
-            )
-    # endregion
-
-    # Set seed before initializing model.
-    set_seed(training_args.seed)
-
-    # region Load datasets
-    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
-    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
-    # (the dataset will be downloaded automatically from the datasets Hub).
-    #
-    # For CSV/JSON files this script will use the first column for the full texts and the second column for the
-    # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
-    #
-    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
-    # download the dataset.
-    if data_args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        raw_datasets = load_dataset(
-            data_args.dataset_name,
-            data_args.dataset_config_name,
-            cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
-        )
-    else:
-        data_files = {}
-        if data_args.train_file is not None:
-            data_files["train"] = data_args.train_file
-            extension = data_args.train_file.split(".")[-1]
-        if data_args.validation_file is not None:
-            data_files["validation"] = data_args.validation_file
-            extension = data_args.validation_file.split(".")[-1]
-        raw_datasets = load_dataset(
-            extension,
-            data_files=data_files,
-            cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
-        )
-    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
-    # https://huggingface.co/docs/datasets/loading_datasets.html.
-    # endregion
-
-    # region Load model config and tokenizer
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        use_fast=model_args.use_fast_tokenizer,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-
-    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
-    # endregion
-
-    # region Dataset preprocessing
-    # We need to tokenize inputs and targets.
-    if training_args.do_train:
-        column_names = raw_datasets["train"].column_names
-    elif training_args.do_eval:
-        column_names = raw_datasets["validation"].column_names
-    else:
-        logger.info("There is nothing to do. Please pass `do_train`, and/or `do_eval`.")
-        return
-
-    column_names = raw_datasets["train"].column_names
-
-    # For translation we set the codes of our source and target languages (only useful for mBART, the others will
-    # ignore those attributes).
-    if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
-        assert data_args.target_lang is not None and data_args.source_lang is not None, (
-            f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --source_lang and "
-            "--target_lang arguments."
-        )
-        tokenizer.src_lang = data_args.source_lang
-        tokenizer.tgt_lang = data_args.target_lang
-        forced_bos_token_id = (
-            tokenizer.lang_code_to_id[data_args.forced_bos_token] if data_args.forced_bos_token is not None else None
-        )
-
-    # Get the language codes for input/target.
-    source_lang = data_args.source_lang.split("_")[0]
-    target_lang = data_args.target_lang.split("_")[0]
-
-    padding = "max_length" if data_args.pad_to_max_length else False
-
-    # Temporarily set max_target_length for training.
-    max_target_length = data_args.max_target_length
-    padding = "max_length" if data_args.pad_to_max_length else False
-
-    def preprocess_function(examples):
-        inputs = [ex[source_lang] for ex in examples["translation"]]
-        targets = [ex[target_lang] for ex in examples["translation"]]
-        inputs = [prefix + inp for inp in inputs]
-        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)
-
-        # Tokenize targets with the `text_target` keyword argument
-        labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)
-
-        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
-        # padding in the loss.
-        if padding == "max_length" and data_args.ignore_pad_token_for_loss:
-            labels["input_ids"] = [
-                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
-            ]
-
-        model_inputs["labels"] = labels["input_ids"]
-        return model_inputs
-
-    if training_args.do_train:
-        if "train" not in raw_datasets:
-            raise ValueError("--do_train requires a train dataset")
-        train_dataset = raw_datasets["train"]
-        if data_args.max_train_samples is not None:
-            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
-            train_dataset = train_dataset.select(range(max_train_samples))
-        with training_args.main_process_first(desc="train dataset map pre-processing"):
-            train_dataset = train_dataset.map(
-                preprocess_function,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                remove_columns=column_names,
-                load_from_cache_file=not data_args.overwrite_cache,
-                desc="Running tokenizer on train dataset",
-            )
-    else:
-        train_dataset = None
-
-    if training_args.do_eval:
-        max_target_length = data_args.val_max_target_length
-        if "validation" not in raw_datasets:
-            raise ValueError("--do_eval requires a validation dataset")
-        eval_dataset = raw_datasets["validation"]
-        if data_args.max_eval_samples is not None:
-            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
-            eval_dataset = eval_dataset.select(range(max_eval_samples))
-        with training_args.main_process_first(desc="validation dataset map pre-processing"):
-            eval_dataset = eval_dataset.map(
-                preprocess_function,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                remove_columns=column_names,
-                load_from_cache_file=not data_args.overwrite_cache,
-                desc="Running tokenizer on validation dataset",
-            )
-    else:
-        eval_dataset = None
-    # endregion
-
-    with training_args.strategy.scope():
-        # region Prepare model
-        model = TFAutoModelForSeq2SeqLM.from_pretrained(
-            model_args.model_name_or_path,
-            config=config,
-            cache_dir=model_args.cache_dir,
-            revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
-        )
-
-        # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
-        # on a small vocab and want a smaller embedding size, remove this test.
-        embeddings = model.get_input_embeddings()
-
-        # Matt: This is a temporary workaround as we transition our models to exclusively using Keras embeddings.
-        #       As soon as the transition is complete, all embeddings should be keras.Embeddings layers, and
-        #       the weights will always be in embeddings.embeddings.
-        if hasattr(embeddings, "embeddings"):
-            embedding_size = embeddings.embeddings.shape[0]
-        else:
-            embedding_size = embeddings.weight.shape[0]
-        if len(tokenizer) > embedding_size:
-            model.resize_token_embeddings(len(tokenizer))
-
-        if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
-            model.config.forced_bos_token_id = forced_bos_token_id
-        # endregion
-
-        # region Set decoder_start_token_id
-        if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
-            assert (
-                data_args.target_lang is not None and data_args.source_lang is not None
-            ), "mBart requires --target_lang and --source_lang"
-            if isinstance(tokenizer, MBartTokenizer):
-                model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.target_lang]
-            else:
-                model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.target_lang)
-
-        if model.config.decoder_start_token_id is None:
-            raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
-        # endregion
-
-        # region Prepare TF Dataset objects
-        label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
-        data_collator = DataCollatorForSeq2Seq(
-            tokenizer,
-            model=model,
-            label_pad_token_id=label_pad_token_id,
-            pad_to_multiple_of=64,  # Reduce the number of unique shapes for XLA, especially for generation
-            return_tensors="np",
-        )
-        num_replicas = training_args.strategy.num_replicas_in_sync
-        total_train_batch_size = training_args.per_device_train_batch_size * num_replicas
-        total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas
-
-        dataset_options = tf.data.Options()
-        dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
-
-        # model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in
-        # training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also
-        # use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names
-        # yourself if you use this method, whereas they are automatically inferred from the model input names when
-        # using model.prepare_tf_dataset()
-        # For more info see the docs:
-        # https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset
-        # https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset
-
-        tf_train_dataset = model.prepare_tf_dataset(
-            train_dataset,
-            collate_fn=data_collator,
-            batch_size=total_train_batch_size,
-            shuffle=True,
-        ).with_options(dataset_options)
-        tf_eval_dataset = model.prepare_tf_dataset(
-            eval_dataset, collate_fn=data_collator, batch_size=total_eval_batch_size, shuffle=False
-        ).with_options(dataset_options)
-        # endregion
-
-        # region Optimizer and LR scheduling
-        num_train_steps = int(len(tf_train_dataset) * training_args.num_train_epochs)
-        if training_args.warmup_steps > 0:
-            num_warmup_steps = training_args.warmup_steps
-        elif training_args.warmup_ratio > 0:
-            num_warmup_steps = int(num_train_steps * training_args.warmup_ratio)
-        else:
-            num_warmup_steps = 0
-        if training_args.do_train:
-            optimizer, lr_schedule = create_optimizer(
-                init_lr=training_args.learning_rate,
-                num_train_steps=num_train_steps,
-                num_warmup_steps=num_warmup_steps,
-                adam_beta1=training_args.adam_beta1,
-                adam_beta2=training_args.adam_beta2,
-                adam_epsilon=training_args.adam_epsilon,
-                weight_decay_rate=training_args.weight_decay,
-                adam_global_clipnorm=training_args.max_grad_norm,
-            )
-        else:
-            optimizer = None
-        # endregion
-
-        # region Metric and postprocessing
-        if training_args.do_eval:
-            metric = evaluate.load("sacrebleu")
-
-            if data_args.val_max_target_length is None:
-                data_args.val_max_target_length = data_args.max_target_length
-
-            gen_kwargs = {
-                "max_length": data_args.val_max_target_length,
-                "num_beams": data_args.num_beams,
-                "no_repeat_ngram_size": 0,  # Not supported under XLA right now, and some models set it by default
-            }
-
-            def postprocess_text(preds, labels):
-                preds = [pred.strip() for pred in preds]
-                labels = [[label.strip()] for label in labels]
-
-                return preds, labels
-
-            def compute_metrics(preds):
-                predictions, labels = preds
-                if isinstance(predictions, tuple):
-                    predictions = predictions[0]
-                decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
-                labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
-                decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
-                decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
-                metrics = metric.compute(predictions=decoded_preds, references=decoded_labels)
-                return {"bleu": metrics["score"]}
-
-            # The KerasMetricCallback allows metrics that are too complex to write as standard Keras metrics
-            # to be computed each epoch. Any Python code can be included in the metric_fn. This is especially
-            # useful for metrics like BLEU and ROUGE that perform string comparisons on decoded model outputs.
-            # For more information, see the docs at
-            # https://huggingface.co/docs/transformers/main_classes/keras_callbacks#transformers.KerasMetricCallback
-
-            metric_callback = KerasMetricCallback(
-                metric_fn=compute_metrics,
-                eval_dataset=tf_eval_dataset,
-                predict_with_generate=True,
-                use_xla_generation=True,
-                generate_kwargs=gen_kwargs,
-            )
-            callbacks = [metric_callback]
-        else:
-            callbacks = []
-
-        # endregion
-
-        # region Preparing push_to_hub and model card
-        push_to_hub_model_id = training_args.push_to_hub_model_id
-        model_name = model_args.model_name_or_path.split("/")[-1]
-        if not push_to_hub_model_id:
-            push_to_hub_model_id = f"{model_name}-finetuned-{data_args.source_lang}-{data_args.target_lang}"
-
-        model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "translation"}
-        if data_args.dataset_name is not None:
-            model_card_kwargs["dataset_tags"] = data_args.dataset_name
-            if data_args.dataset_config_name is not None:
-                model_card_kwargs["dataset_args"] = data_args.dataset_config_name
-                model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
-            else:
-                model_card_kwargs["dataset"] = data_args.dataset_name
-
-        languages = [l for l in [data_args.source_lang, data_args.target_lang] if l is not None]
-        if len(languages) > 0:
-            model_card_kwargs["language"] = languages
-
-        if training_args.push_to_hub:
-            # Because this training can be quite long, we save once per epoch.
-            callbacks.append(
-                PushToHubCallback(
-                    output_dir=training_args.output_dir,
-                    hub_model_id=push_to_hub_model_id,
-                    hub_token=training_args.push_to_hub_token,
-                    tokenizer=tokenizer,
-                    **model_card_kwargs,
-                )
-            )
-        # endregion
-
-        # region Training
-        eval_metrics = None
-        model.compile(optimizer=optimizer, jit_compile=training_args.xla)
-
-        if training_args.do_train:
-            logger.info("***** Running training *****")
-            logger.info(f"  Num examples = {len(train_dataset)}")
-            logger.info(f"  Num Epochs = {training_args.num_train_epochs}")
-            logger.info(f"  Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
-            logger.info(f"  Total train batch size = {total_train_batch_size}")
-            logger.info(f"  Total optimization steps = {num_train_steps}")
-
-            if training_args.xla and not data_args.pad_to_max_length:
-                logger.warning(
-                    "XLA training may be slow at first when --pad_to_max_length is not set "
-                    "until all possible shapes have been compiled."
-                )
-
-            history = model.fit(tf_train_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks)
-            eval_metrics = {key: val[-1] for key, val in history.history.items()}
-        # endregion
-
-        # region Validation
-        if training_args.do_eval and not training_args.do_train:
-            # Compiling generation with XLA yields enormous speedups, see https://huggingface.co/blog/tf-xla-generate
-            @tf.function(jit_compile=True)
-            def generate(**kwargs):
-                return model.generate(**kwargs)
-
-            if training_args.do_eval:
-                logger.info("Evaluation...")
-                for batch, labels in tf_eval_dataset:
-                    batch.update(gen_kwargs)
-                    generated_tokens = generate(**batch)
-                    if isinstance(generated_tokens, tuple):
-                        generated_tokens = generated_tokens[0]
-                    decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-                    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
-                    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
-                    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
-
-                    metric.add_batch(predictions=decoded_preds, references=decoded_labels)
-
-                eval_metrics = metric.compute()
-                logger.info({"bleu": eval_metrics["score"]})
-        # endregion
-
-        if training_args.output_dir is not None and eval_metrics is not None:
-            output_eval_file = os.path.join(training_args.output_dir, "all_results.json")
-            with open(output_eval_file, "w") as writer:
-                writer.write(json.dumps(eval_metrics))
-
-        if training_args.output_dir is not None and not training_args.push_to_hub:
-            # If we're not pushing to hub, at least save a local copy when we're done
-            model.save_pretrained(training_args.output_dir)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/models/__init__.py b/spaces/chendl/compositional_test/transformers/src/transformers/models/__init__.py
deleted file mode 100644
index 667f3718fa4213c3e23eff2a0bbe939abc67dcb1..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/models/__init__.py
+++ /dev/null
@@ -1,215 +0,0 @@
-# Copyright 2020 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.
-
-from . import (
-    albert,
-    align,
-    altclip,
-    audio_spectrogram_transformer,
-    auto,
-    bart,
-    barthez,
-    bartpho,
-    beit,
-    bert,
-    bert_generation,
-    bert_japanese,
-    bertweet,
-    big_bird,
-    bigbird_pegasus,
-    biogpt,
-    bit,
-    blenderbot,
-    blenderbot_small,
-    blip,
-    blip_2,
-    bloom,
-    bort,
-    bridgetower,
-    byt5,
-    camembert,
-    canine,
-    chinese_clip,
-    clap,
-    clip,
-    clipseg,
-    codegen,
-    conditional_detr,
-    convbert,
-    convnext,
-    convnextv2,
-    cpm,
-    cpmant,
-    ctrl,
-    cvt,
-    data2vec,
-    deberta,
-    deberta_v2,
-    decision_transformer,
-    deformable_detr,
-    deit,
-    deta,
-    detr,
-    dialogpt,
-    dinat,
-    distilbert,
-    dit,
-    donut,
-    dpr,
-    dpt,
-    efficientformer,
-    efficientnet,
-    electra,
-    encoder_decoder,
-    ernie,
-    ernie_m,
-    esm,
-    flaubert,
-    flava,
-    fnet,
-    fsmt,
-    funnel,
-    git,
-    glpn,
-    gpt2,
-    gpt_bigcode,
-    gpt_neo,
-    gpt_neox,
-    gpt_neox_japanese,
-    gpt_sw3,
-    gptj,
-    gptsan_japanese,
-    graphormer,
-    groupvit,
-    herbert,
-    hubert,
-    ibert,
-    imagegpt,
-    informer,
-    jukebox,
-    layoutlm,
-    layoutlmv2,
-    layoutlmv3,
-    layoutxlm,
-    led,
-    levit,
-    lilt,
-    llama,
-    longformer,
-    longt5,
-    luke,
-    lxmert,
-    m2m_100,
-    marian,
-    markuplm,
-    mask2former,
-    maskformer,
-    mbart,
-    mbart50,
-    mctct,
-    mega,
-    megatron_bert,
-    megatron_gpt2,
-    mgp_str,
-    mluke,
-    mmbt,
-    mobilebert,
-    mobilenet_v1,
-    mobilenet_v2,
-    mobilevit,
-    mpnet,
-    mt5,
-    mvp,
-    nat,
-    nezha,
-    nllb,
-    nllb_moe,
-    nystromformer,
-    oneformer,
-    openai,
-    opt,
-    owlvit,
-    pegasus,
-    pegasus_x,
-    perceiver,
-    phobert,
-    pix2struct,
-    plbart,
-    poolformer,
-    prophetnet,
-    qdqbert,
-    rag,
-    realm,
-    reformer,
-    regnet,
-    rembert,
-    resnet,
-    retribert,
-    roberta,
-    roberta_prelayernorm,
-    roc_bert,
-    roformer,
-    segformer,
-    sew,
-    sew_d,
-    speech_encoder_decoder,
-    speech_to_text,
-    speech_to_text_2,
-    speecht5,
-    splinter,
-    squeezebert,
-    swin,
-    swin2sr,
-    swinv2,
-    switch_transformers,
-    t5,
-    table_transformer,
-    tapas,
-    tapex,
-    time_series_transformer,
-    timesformer,
-    trajectory_transformer,
-    transfo_xl,
-    trocr,
-    tvlt,
-    unispeech,
-    unispeech_sat,
-    upernet,
-    van,
-    videomae,
-    vilt,
-    vision_encoder_decoder,
-    vision_text_dual_encoder,
-    visual_bert,
-    vit,
-    vit_hybrid,
-    vit_mae,
-    vit_msn,
-    wav2vec2,
-    wav2vec2_conformer,
-    wav2vec2_phoneme,
-    wav2vec2_with_lm,
-    wavlm,
-    whisper,
-    x_clip,
-    xglm,
-    xlm,
-    xlm_prophetnet,
-    xlm_roberta,
-    xlm_roberta_xl,
-    xlnet,
-    xmod,
-    yolos,
-    yoso,
-)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Image-003ee87c.css b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Image-003ee87c.css
deleted file mode 100644
index 60f45635043d082881d8d8a529c1142ee028a68b..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Image-003ee87c.css
+++ /dev/null
@@ -1 +0,0 @@
-img.svelte-gqt00k{border-radius:var(--radius-lg);max-width:none}img.selected.svelte-gqt00k{border-color:var(--border-color-accent)}.table.svelte-gqt00k{margin:0 auto;border:2px solid var(--border-color-primary);border-radius:var(--radius-lg);width:var(--size-20);height:var(--size-20);object-fit:cover}.gallery.svelte-gqt00k{border:2px solid var(--border-color-primary);max-height:var(--size-20);object-fit:cover}
diff --git a/spaces/cihyFjudo/fairness-paper-search/Android Bulk SMS Sender 6.0.1.17 With Crack Download TOP [Latest].md b/spaces/cihyFjudo/fairness-paper-search/Android Bulk SMS Sender 6.0.1.17 With Crack Download TOP [Latest].md
deleted file mode 100644
index 5c1d2a9d04636f2fb14671dbd8a446d1ea356976..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Android Bulk SMS Sender 6.0.1.17 With Crack Download TOP [Latest].md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>Android Bulk SMS Sender for Windows is a useful application that allows users to send messages with different or multiple android phones at once. Its an amazing tool for sending bulk messages and SMS to a wide range of receivers. It has simple process steps users just need to install the application in their system and also need to install the Android & Desktop SMS Sender Modem app on their Android phones. You can also download <strong>Tenorshare iCareFone Free Download.</strong></p>
-<h2>Android Bulk SMS Sender 6.0.1.17 With Crack Download [Latest]</h2><br /><p><b><b>Download Zip</b> &bull;&bull;&bull; <a href="https://tinurli.com/2uwiCV">https://tinurli.com/2uwiCV</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/The Best Way to Convert Mixtapes to MP3s without Losing Quality.md b/spaces/cihyFjudo/fairness-paper-search/The Best Way to Convert Mixtapes to MP3s without Losing Quality.md
deleted file mode 100644
index dfe205f5637adf5e1615773eb7f052055b177fea..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/The Best Way to Convert Mixtapes to MP3s without Losing Quality.md	
+++ /dev/null
@@ -1,33 +0,0 @@
-
-<p>MP3 CDs: You can play these discs on MP3-capable CD players or your computer. To burn an MP3 CD, all the songs on it must be in MP3 format. If your playlist includes songs in other formats, such as songs purchased from the iTunes Store, you need to convert them to MP3 format before you can burn them on a CD.</p>
-<h2>Convert mixtapes to MP3s</h2><br /><p><b><b>Download Zip</b> &middot;&middot;&middot; <a href="https://tinurli.com/2uwiuG">https://tinurli.com/2uwiuG</a></b></p><br /><br />
-<p>You can convert a song to a different file format (and keep a copy of the original). For example, you can save a copy of a compressed song file such as MP3 or AAC in an uncompressed song format (AIFF or WAV).</p>
-<p>This audio converter enables you to convert any audio files to MP3 format or other formats with ease. Therefore you can convert your mixtapes in other audio formats to MP3 before uploading them to Datpiff, or convert the downloaded music on Datpiff from MP3 format to other audio formats for portable devices. Aiseesoft Video Converter Ultimate offers many default output settings for iPhone/iPad/iPod/Apple TV, Android devices, Samsung devices and other popular devices.</p>
-<p>So why not digitize them? Not only will converting your vinyl records to a digital music format preserve that rare Beatles U.K. first pressing you're afraid to take out of its sleeve and protect it from further wear and tear, but you'll also have portable versions of your favorite records, including those you just can't find on streaming services, for you to enjoy anywhere.</p>
-<p>Transferring your precious platters from grooves to sound waves is easier than you might think. From USB turntables and digital audio converters to the various software options and how to use them, we'll take you through all the steps you need for going digital in this article.</p>
-<p>Lastly, however, if you wanted to go one step further and are concerned with having more control over the input and sound quality, you should consider connecting your turntable to a stand-alone analog digital converter (ADC), such as the Focusrite Scarlett 2i2, which has a higher quality of digital conversion and connects to your computer via USB. You can even use it for recording your own music, podcasts, or anything else you want to connect to it.</p>
-<p></p>
-<p>There are definitely more ways than one to convert your records to digital format, including other recording software such as GarageBand, Pro Tools, and Logic, but we wouldn't suggest using these unless you're experienced with them. For our money (free!), Audacity has proven over the years to be the best and easiest way to do this. If you have little extra money to spend, Vinyl Studio is a bit easier and is dedicated to ripping vinyl, but the process is basically the same. Plus, you can try it our free first, with some saving restrictions. Happy converting!</p>
-<p>Now we have learned that the songs are downloaded via Deezer app are all in encrypted formats. If you want to download and keep your Deezer forever for offline listening, then the best way is to convert Deezer songs to normal audio format, such as MP3. And we need the help from a professional Deezer Music downloader tool - DeeKeep Deezer Music Converter.</p>
-<p>DeeKeep is a simple yet powerful Deezer music downloader and music converter. No need to install Deezer app, you can directly convert Deezer songs, albums and playlists to <strong>MP3, AAC, FLAC, WAV, AIFF and ALAC</strong> at 10X fast conversion speed with lossless output audio quality while keeping the ID3 tags after conversion.</p>
-<p>Click on the <strong>"Convert"</strong> button to start downloading deezer music. Wait patiently until it finished, you can also setup "Put system to sleep" under "After converting" settings which you can see on the left corner of the downloading windows.</p>
-<p>Among all the Deezer downloading tools, DeeKeep is definitely the best which can easily help you download Deezer songs and convert to MP3, keeping the ID3 tags and original audio quality. Just download and start a free trial.</p>
-<p>Tunelf Spotibeat Music Converter is a professional and powerful music converter for all Spotify users. It can help you download all kinds of Spotify content, including tracks, albums, playlists, artists, podcasts, and audiobooks, and save them to several common audio formats at a faster speed. Furthermore, it can keep the lossless audio quality and ID3 tags.</p>
-<p>The most important part of downloading and converting Spotify music to MP3 has been completed through Tunelf Spotibeat Music Converter. What you need to do now is to burn Spotify playlist to CD. There are several ways to help you burn music to CD. And you could follow the below three methods to copy from Spotify to CD.</p>
-<p>There are lots of programs that can convert songs from one format to another, including from iTunes to MP3. That's great, but in most cases, you don't need them. Unless you have very specific needs (such as FLAC; if you don't know that is, chances are you don't need it), don't spend the money on audio-conversion software. Just use iTunes.</p>
-<p>Converting songs can degrade sound quality. Before you convert iTunes to MP3, it's important to know that doing this slightly lessens the music's sound quality. This is because both AAC and MP3 are compressed versions of the original song file and thus already lower quality. Converting from AAC to another compressed format like MP3 means there will be even more compression and loss of quality.</p>
-<p>NoteBurner Spotify Music Converter is a professional music tool which is able to decode Spotify songs, albums and playlists and convert them to MP3, AAC, WAV, AIFF, FLAC, or ALAC format, keeping ID3 tags and 100% lossless quality after conversion. Here are the key features of the program:</p>
-<p>As one of the best mixtape distribution service, LiveMixTapes brings together excellent mixtape music, hip-hop tracks, and rap songs for users. And it also supports registers uploading their mixtapes to share. If you enjoy the music on it and want to get <strong>LiveMixTaps download</strong>, this post could be a reference for you. We list 3 methods to help you get LiveMixTaps MP3 download on your PC or mobile phone. Let's dive in.</p>
-<p><strong>Step 2.</strong> You can pick one of the trending mixtapes on the homepage, or search for your favorite one in the search box. When you find the preferred one, click on it and go to the detail page. Here you can are able to check the entire MixTapes track and listen to them.</p>
-<p>LiveMixTapes also has the official app both on Android and iOS devices. It brings all the mixtapes from the LiveMixTapes website for free and has different sections like the LiveMixTapes official website, such as mixtapes new today, new this week, and unreleased or mixtapes coming soon.</p>
-<p>When it seemed clear that analog formats, of which vinyl is one, would give way to digital, some consumers rushed out to buy CDs. Others scrambled to convert their sometimes massive libraries of vinyl records to mp3s.</p>
-<p>With vinyl resurgent, some consumers now want to convert their digital music back into analog format. Enter a new crop of entrepreneurs looking to make a buck by helping consumers retrofit their music collections. For a price, websites like vinyl-pressing.com, edsvinyl.com, and vinylrecorder.com allow consumers to convert their mp3 files into vinyl records. Vinylrecorder, a German company, uses a machine of its own invention called the T-560 to accomplish this feat. Simply plug an audio source into the machine, the company claims, and it will "listen" to the input and cut the corresponding grooves into a blank vinyl record. The machine is available for purchase, but it will cost you nearly $4,000.</p>
-<p>Still, Guttenberg says he doesn't understand why these services exist. "Why wouldn't you just buy the record, as opposed to taking your crappy mp3 and making it into a record?" he asks. Would he consider listening to vinyls converted from mp3? "Only for some sort of perverse pleasure."</p>
-<p>Mp3tag is superb if you have a large number of rogue MP3 files. This is because it has a really cool batch converter. That means you can grab a whole bunch of your MP3s and let Mp3tag get busy sorting through them. You can also edit the metadata yourself. It is pretty similar to MusicBrainz Picard in terms of what it is capable of, aside from the batch converter. It can actually use the MusicBrainz database to grab file info.</p>
-<p><strong>The M4A format, also known as MPEG-4 Audio, has been popularised in recent times by Apple with its iTunes service. However, you may need to convert your music tracks to the better-supported MP3 format to enjoy them on any device.</strong></p>
-<p><strong>Sidify Music Converter</strong> is a simple yet quite efficient Spotify music downloader, which can download and convert Spotify songs to MP3/AAC/WAV/FLAC/AIFF/ALAC format. Sidify stands out for its reputation for useful programs & excellent after-sales service and has gained a great number of users in recent years.</p>
-<p>Currently, Sidify Music Converter offers Windows & Mac versions, which work for both Spotify Free users and Premium subscribers. Apart from converting Spotify songs, albums, and playlists to MP3/AAC/WAV/FLAC/AIFF/ALAC, <strong>Sidify is also a good tool to burn Spotify songs to CD, upload Spotify music to OneDrive, edit ID3 tags, etc.</strong></p>
-<p>So I also have many audio cassettes to digitize onto my IMAC. I want high quality WAV files at up to 192kHz resolution. I have a Nakamichi cassette deck. I'm looking at the Berenger UCA202 and the Focusright Scarlet 2i2 as the interface. Do either of these provide a WAV file I am looking for? Or do I convert the files to WAV once they are digitized into my IMAC? Thanks, Scott</p>
-<p>Hi - I have bought a Behringer UCA 222, hoping it would be the device I needed to convert my almost 100 mixtapes. However, I cannot make it work. I connect my cassette player's RCA 'out' plugs to the Behringer device's corresponding RCA 'in' plugs, and the Behinger's cable to my MaBook Air's USB. I press play on the cassette player, click the record button in Audacity, but it does not register any signal. Switchen the Behringer's monitor on and off, or changing its output volume does not help. Oddly, if I plug a headphone in the Behringer's headphone jack, I can hear the music from the cassette without problems. What am I doing wrong?</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/clip-italian/clip-italian-demo/precompute_features.py b/spaces/clip-italian/clip-italian-demo/precompute_features.py
deleted file mode 100644
index d54615390b27ce6368c6be0d66fa073c28b5a041..0000000000000000000000000000000000000000
--- a/spaces/clip-italian/clip-italian-demo/precompute_features.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from tqdm import tqdm
-from argparse import ArgumentParser
-from jax import numpy as jnp
-from torchvision import datasets, transforms
-from torchvision.transforms import CenterCrop, Normalize, Resize, ToTensor
-from torchvision.transforms.functional import InterpolationMode
-from transformers import AutoTokenizer
-from modeling_hybrid_clip import FlaxHybridCLIP
-import utils
-import torch
-
-
-if __name__ == "__main__":
-    parser = ArgumentParser()
-    parser.add_argument("in_dir")
-    parser.add_argument("out_file")
-    args = parser.parse_args()
-
-    model = FlaxHybridCLIP.from_pretrained("clip-italian/clip-italian")
-
-    tokenizer = AutoTokenizer.from_pretrained(
-        "dbmdz/bert-base-italian-xxl-uncased", cache_dir=None, use_fast=True
-    )
-
-    image_size = model.config.vision_config.image_size
-
-    val_preprocess = transforms.Compose(
-        [
-            Resize([image_size], interpolation=InterpolationMode.BICUBIC),
-            CenterCrop(image_size),
-            ToTensor(),
-            Normalize(
-                (0.48145466, 0.4578275, 0.40821073),
-                (0.26862954, 0.26130258, 0.27577711),
-            ),
-        ]
-    )
-
-    dataset = utils.CustomDataSet(args.in_dir, transform=val_preprocess)
-
-    loader = torch.utils.data.DataLoader(
-        dataset,
-        batch_size=256,
-        shuffle=False,
-        num_workers=16,
-        drop_last=False,
-    )
-
-    image_features = utils.precompute_image_features(model, loader)
-    jnp.save(f"static/features/{args.out_file}", image_features)
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ImageSequence.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ImageSequence.py
deleted file mode 100644
index c4bb6334acfde7d245c5bb1722b7c2381661e4ca..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ImageSequence.py
+++ /dev/null
@@ -1,76 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# sequence support classes
-#
-# history:
-# 1997-02-20 fl     Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1997 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-##
-
-
-class Iterator:
-    """
-    This class implements an iterator object that can be used to loop
-    over an image sequence.
-
-    You can use the ``[]`` operator to access elements by index. This operator
-    will raise an :py:exc:`IndexError` if you try to access a nonexistent
-    frame.
-
-    :param im: An image object.
-    """
-
-    def __init__(self, im):
-        if not hasattr(im, "seek"):
-            msg = "im must have seek method"
-            raise AttributeError(msg)
-        self.im = im
-        self.position = getattr(self.im, "_min_frame", 0)
-
-    def __getitem__(self, ix):
-        try:
-            self.im.seek(ix)
-            return self.im
-        except EOFError as e:
-            raise IndexError from e  # end of sequence
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        try:
-            self.im.seek(self.position)
-            self.position += 1
-            return self.im
-        except EOFError as e:
-            raise StopIteration from e
-
-
-def all_frames(im, func=None):
-    """
-    Applies a given function to all frames in an image or a list of images.
-    The frames are returned as a list of separate images.
-
-    :param im: An image, or a list of images.
-    :param func: The function to apply to all of the image frames.
-    :returns: A list of images.
-    """
-    if not isinstance(im, list):
-        im = [im]
-
-    ims = []
-    for imSequence in im:
-        current = imSequence.tell()
-
-        ims += [im_frame.copy() for im_frame in Iterator(imSequence)]
-
-        imSequence.seek(current)
-    return [func(im) for im in ims] if func else ims
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/T_S_I_S_.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/T_S_I_S_.py
deleted file mode 100644
index 667eb0e53473c1566d4b45e5621d8897ebd7b9fe..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/T_S_I_S_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .T_S_I_V_ import table_T_S_I_V_
-
-
-class table_T_S_I_S_(table_T_S_I_V_):
-    pass
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/otTables.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/otTables.py
deleted file mode 100644
index 5cabd4b4fcbdc0377660b387dc7ab2d3e4380bc7..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/otTables.py
+++ /dev/null
@@ -1,2274 +0,0 @@
-# coding: utf-8
-"""fontTools.ttLib.tables.otTables -- A collection of classes representing the various
-OpenType subtables.
-
-Most are constructed upon import from data in otData.py, all are populated with
-converter objects from otConverters.py.
-"""
-import copy
-from enum import IntEnum
-from functools import reduce
-from math import radians
-import itertools
-from collections import defaultdict, namedtuple
-from fontTools.ttLib.tables.otTraverse import dfs_base_table
-from fontTools.misc.arrayTools import quantizeRect
-from fontTools.misc.roundTools import otRound
-from fontTools.misc.transform import Transform, Identity
-from fontTools.misc.textTools import bytesjoin, pad, safeEval
-from fontTools.pens.boundsPen import ControlBoundsPen
-from fontTools.pens.transformPen import TransformPen
-from .otBase import (
-    BaseTable,
-    FormatSwitchingBaseTable,
-    ValueRecord,
-    CountReference,
-    getFormatSwitchingBaseTableClass,
-)
-from fontTools.feaLib.lookupDebugInfo import LookupDebugInfo, LOOKUP_DEBUG_INFO_KEY
-import logging
-import struct
-from typing import TYPE_CHECKING, Iterator, List, Optional, Set
-
-if TYPE_CHECKING:
-    from fontTools.ttLib.ttGlyphSet import _TTGlyphSet
-
-
-log = logging.getLogger(__name__)
-
-
-class AATStateTable(object):
-    def __init__(self):
-        self.GlyphClasses = {}  # GlyphID --> GlyphClass
-        self.States = []  # List of AATState, indexed by state number
-        self.PerGlyphLookups = []  # [{GlyphID:GlyphID}, ...]
-
-
-class AATState(object):
-    def __init__(self):
-        self.Transitions = {}  # GlyphClass --> AATAction
-
-
-class AATAction(object):
-    _FLAGS = None
-
-    @staticmethod
-    def compileActions(font, states):
-        return (None, None)
-
-    def _writeFlagsToXML(self, xmlWriter):
-        flags = [f for f in self._FLAGS if self.__dict__[f]]
-        if flags:
-            xmlWriter.simpletag("Flags", value=",".join(flags))
-            xmlWriter.newline()
-        if self.ReservedFlags != 0:
-            xmlWriter.simpletag("ReservedFlags", value="0x%04X" % self.ReservedFlags)
-            xmlWriter.newline()
-
-    def _setFlag(self, flag):
-        assert flag in self._FLAGS, "unsupported flag %s" % flag
-        self.__dict__[flag] = True
-
-
-class RearrangementMorphAction(AATAction):
-    staticSize = 4
-    actionHeaderSize = 0
-    _FLAGS = ["MarkFirst", "DontAdvance", "MarkLast"]
-
-    _VERBS = {
-        0: "no change",
-        1: "Ax ⇒ xA",
-        2: "xD ⇒ Dx",
-        3: "AxD ⇒ DxA",
-        4: "ABx ⇒ xAB",
-        5: "ABx ⇒ xBA",
-        6: "xCD ⇒ CDx",
-        7: "xCD ⇒ DCx",
-        8: "AxCD ⇒ CDxA",
-        9: "AxCD ⇒ DCxA",
-        10: "ABxD ⇒ DxAB",
-        11: "ABxD ⇒ DxBA",
-        12: "ABxCD ⇒ CDxAB",
-        13: "ABxCD ⇒ CDxBA",
-        14: "ABxCD ⇒ DCxAB",
-        15: "ABxCD ⇒ DCxBA",
-    }
-
-    def __init__(self):
-        self.NewState = 0
-        self.Verb = 0
-        self.MarkFirst = False
-        self.DontAdvance = False
-        self.MarkLast = False
-        self.ReservedFlags = 0
-
-    def compile(self, writer, font, actionIndex):
-        assert actionIndex is None
-        writer.writeUShort(self.NewState)
-        assert self.Verb >= 0 and self.Verb <= 15, self.Verb
-        flags = self.Verb | self.ReservedFlags
-        if self.MarkFirst:
-            flags |= 0x8000
-        if self.DontAdvance:
-            flags |= 0x4000
-        if self.MarkLast:
-            flags |= 0x2000
-        writer.writeUShort(flags)
-
-    def decompile(self, reader, font, actionReader):
-        assert actionReader is None
-        self.NewState = reader.readUShort()
-        flags = reader.readUShort()
-        self.Verb = flags & 0xF
-        self.MarkFirst = bool(flags & 0x8000)
-        self.DontAdvance = bool(flags & 0x4000)
-        self.MarkLast = bool(flags & 0x2000)
-        self.ReservedFlags = flags & 0x1FF0
-
-    def toXML(self, xmlWriter, font, attrs, name):
-        xmlWriter.begintag(name, **attrs)
-        xmlWriter.newline()
-        xmlWriter.simpletag("NewState", value=self.NewState)
-        xmlWriter.newline()
-        self._writeFlagsToXML(xmlWriter)
-        xmlWriter.simpletag("Verb", value=self.Verb)
-        verbComment = self._VERBS.get(self.Verb)
-        if verbComment is not None:
-            xmlWriter.comment(verbComment)
-        xmlWriter.newline()
-        xmlWriter.endtag(name)
-        xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        self.NewState = self.Verb = self.ReservedFlags = 0
-        self.MarkFirst = self.DontAdvance = self.MarkLast = False
-        content = [t for t in content if isinstance(t, tuple)]
-        for eltName, eltAttrs, eltContent in content:
-            if eltName == "NewState":
-                self.NewState = safeEval(eltAttrs["value"])
-            elif eltName == "Verb":
-                self.Verb = safeEval(eltAttrs["value"])
-            elif eltName == "ReservedFlags":
-                self.ReservedFlags = safeEval(eltAttrs["value"])
-            elif eltName == "Flags":
-                for flag in eltAttrs["value"].split(","):
-                    self._setFlag(flag.strip())
-
-
-class ContextualMorphAction(AATAction):
-    staticSize = 8
-    actionHeaderSize = 0
-    _FLAGS = ["SetMark", "DontAdvance"]
-
-    def __init__(self):
-        self.NewState = 0
-        self.SetMark, self.DontAdvance = False, False
-        self.ReservedFlags = 0
-        self.MarkIndex, self.CurrentIndex = 0xFFFF, 0xFFFF
-
-    def compile(self, writer, font, actionIndex):
-        assert actionIndex is None
-        writer.writeUShort(self.NewState)
-        flags = self.ReservedFlags
-        if self.SetMark:
-            flags |= 0x8000
-        if self.DontAdvance:
-            flags |= 0x4000
-        writer.writeUShort(flags)
-        writer.writeUShort(self.MarkIndex)
-        writer.writeUShort(self.CurrentIndex)
-
-    def decompile(self, reader, font, actionReader):
-        assert actionReader is None
-        self.NewState = reader.readUShort()
-        flags = reader.readUShort()
-        self.SetMark = bool(flags & 0x8000)
-        self.DontAdvance = bool(flags & 0x4000)
-        self.ReservedFlags = flags & 0x3FFF
-        self.MarkIndex = reader.readUShort()
-        self.CurrentIndex = reader.readUShort()
-
-    def toXML(self, xmlWriter, font, attrs, name):
-        xmlWriter.begintag(name, **attrs)
-        xmlWriter.newline()
-        xmlWriter.simpletag("NewState", value=self.NewState)
-        xmlWriter.newline()
-        self._writeFlagsToXML(xmlWriter)
-        xmlWriter.simpletag("MarkIndex", value=self.MarkIndex)
-        xmlWriter.newline()
-        xmlWriter.simpletag("CurrentIndex", value=self.CurrentIndex)
-        xmlWriter.newline()
-        xmlWriter.endtag(name)
-        xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        self.NewState = self.ReservedFlags = 0
-        self.SetMark = self.DontAdvance = False
-        self.MarkIndex, self.CurrentIndex = 0xFFFF, 0xFFFF
-        content = [t for t in content if isinstance(t, tuple)]
-        for eltName, eltAttrs, eltContent in content:
-            if eltName == "NewState":
-                self.NewState = safeEval(eltAttrs["value"])
-            elif eltName == "Flags":
-                for flag in eltAttrs["value"].split(","):
-                    self._setFlag(flag.strip())
-            elif eltName == "ReservedFlags":
-                self.ReservedFlags = safeEval(eltAttrs["value"])
-            elif eltName == "MarkIndex":
-                self.MarkIndex = safeEval(eltAttrs["value"])
-            elif eltName == "CurrentIndex":
-                self.CurrentIndex = safeEval(eltAttrs["value"])
-
-
-class LigAction(object):
-    def __init__(self):
-        self.Store = False
-        # GlyphIndexDelta is a (possibly negative) delta that gets
-        # added to the glyph ID at the top of the AAT runtime
-        # execution stack. It is *not* a byte offset into the
-        # morx table. The result of the addition, which is performed
-        # at run time by the shaping engine, is an index into
-        # the ligature components table. See 'morx' specification.
-        # In the AAT specification, this field is called Offset;
-        # but its meaning is quite different from other offsets
-        # in either AAT or OpenType, so we use a different name.
-        self.GlyphIndexDelta = 0
-
-
-class LigatureMorphAction(AATAction):
-    staticSize = 6
-
-    # 4 bytes for each of {action,ligComponents,ligatures}Offset
-    actionHeaderSize = 12
-
-    _FLAGS = ["SetComponent", "DontAdvance"]
-
-    def __init__(self):
-        self.NewState = 0
-        self.SetComponent, self.DontAdvance = False, False
-        self.ReservedFlags = 0
-        self.Actions = []
-
-    def compile(self, writer, font, actionIndex):
-        assert actionIndex is not None
-        writer.writeUShort(self.NewState)
-        flags = self.ReservedFlags
-        if self.SetComponent:
-            flags |= 0x8000
-        if self.DontAdvance:
-            flags |= 0x4000
-        if len(self.Actions) > 0:
-            flags |= 0x2000
-        writer.writeUShort(flags)
-        if len(self.Actions) > 0:
-            actions = self.compileLigActions()
-            writer.writeUShort(actionIndex[actions])
-        else:
-            writer.writeUShort(0)
-
-    def decompile(self, reader, font, actionReader):
-        assert actionReader is not None
-        self.NewState = reader.readUShort()
-        flags = reader.readUShort()
-        self.SetComponent = bool(flags & 0x8000)
-        self.DontAdvance = bool(flags & 0x4000)
-        performAction = bool(flags & 0x2000)
-        # As of 2017-09-12, the 'morx' specification says that
-        # the reserved bitmask in ligature subtables is 0x3FFF.
-        # However, the specification also defines a flag 0x2000,
-        # so the reserved value should actually be 0x1FFF.
-        # TODO: Report this specification bug to Apple.
-        self.ReservedFlags = flags & 0x1FFF
-        actionIndex = reader.readUShort()
-        if performAction:
-            self.Actions = self._decompileLigActions(actionReader, actionIndex)
-        else:
-            self.Actions = []
-
-    @staticmethod
-    def compileActions(font, states):
-        result, actions, actionIndex = b"", set(), {}
-        for state in states:
-            for _glyphClass, trans in state.Transitions.items():
-                actions.add(trans.compileLigActions())
-        # Sort the compiled actions in decreasing order of
-        # length, so that the longer sequence come before the
-        # shorter ones.  For each compiled action ABCD, its
-        # suffixes BCD, CD, and D do not be encoded separately
-        # (in case they occur); instead, we can just store an
-        # index that points into the middle of the longer
-        # sequence. Every compiled AAT ligature sequence is
-        # terminated with an end-of-sequence flag, which can
-        # only be set on the last element of the sequence.
-        # Therefore, it is sufficient to consider just the
-        # suffixes.
-        for a in sorted(actions, key=lambda x: (-len(x), x)):
-            if a not in actionIndex:
-                for i in range(0, len(a), 4):
-                    suffix = a[i:]
-                    suffixIndex = (len(result) + i) // 4
-                    actionIndex.setdefault(suffix, suffixIndex)
-                result += a
-        result = pad(result, 4)
-        return (result, actionIndex)
-
-    def compileLigActions(self):
-        result = []
-        for i, action in enumerate(self.Actions):
-            last = i == len(self.Actions) - 1
-            value = action.GlyphIndexDelta & 0x3FFFFFFF
-            value |= 0x80000000 if last else 0
-            value |= 0x40000000 if action.Store else 0
-            result.append(struct.pack(">L", value))
-        return bytesjoin(result)
-
-    def _decompileLigActions(self, actionReader, actionIndex):
-        actions = []
-        last = False
-        reader = actionReader.getSubReader(actionReader.pos + actionIndex * 4)
-        while not last:
-            value = reader.readULong()
-            last = bool(value & 0x80000000)
-            action = LigAction()
-            actions.append(action)
-            action.Store = bool(value & 0x40000000)
-            delta = value & 0x3FFFFFFF
-            if delta >= 0x20000000:  # sign-extend 30-bit value
-                delta = -0x40000000 + delta
-            action.GlyphIndexDelta = delta
-        return actions
-
-    def fromXML(self, name, attrs, content, font):
-        self.NewState = self.ReservedFlags = 0
-        self.SetComponent = self.DontAdvance = False
-        self.ReservedFlags = 0
-        self.Actions = []
-        content = [t for t in content if isinstance(t, tuple)]
-        for eltName, eltAttrs, eltContent in content:
-            if eltName == "NewState":
-                self.NewState = safeEval(eltAttrs["value"])
-            elif eltName == "Flags":
-                for flag in eltAttrs["value"].split(","):
-                    self._setFlag(flag.strip())
-            elif eltName == "ReservedFlags":
-                self.ReservedFlags = safeEval(eltAttrs["value"])
-            elif eltName == "Action":
-                action = LigAction()
-                flags = eltAttrs.get("Flags", "").split(",")
-                flags = [f.strip() for f in flags]
-                action.Store = "Store" in flags
-                action.GlyphIndexDelta = safeEval(eltAttrs["GlyphIndexDelta"])
-                self.Actions.append(action)
-
-    def toXML(self, xmlWriter, font, attrs, name):
-        xmlWriter.begintag(name, **attrs)
-        xmlWriter.newline()
-        xmlWriter.simpletag("NewState", value=self.NewState)
-        xmlWriter.newline()
-        self._writeFlagsToXML(xmlWriter)
-        for action in self.Actions:
-            attribs = [("GlyphIndexDelta", action.GlyphIndexDelta)]
-            if action.Store:
-                attribs.append(("Flags", "Store"))
-            xmlWriter.simpletag("Action", attribs)
-            xmlWriter.newline()
-        xmlWriter.endtag(name)
-        xmlWriter.newline()
-
-
-class InsertionMorphAction(AATAction):
-    staticSize = 8
-    actionHeaderSize = 4  # 4 bytes for actionOffset
-    _FLAGS = [
-        "SetMark",
-        "DontAdvance",
-        "CurrentIsKashidaLike",
-        "MarkedIsKashidaLike",
-        "CurrentInsertBefore",
-        "MarkedInsertBefore",
-    ]
-
-    def __init__(self):
-        self.NewState = 0
-        for flag in self._FLAGS:
-            setattr(self, flag, False)
-        self.ReservedFlags = 0
-        self.CurrentInsertionAction, self.MarkedInsertionAction = [], []
-
-    def compile(self, writer, font, actionIndex):
-        assert actionIndex is not None
-        writer.writeUShort(self.NewState)
-        flags = self.ReservedFlags
-        if self.SetMark:
-            flags |= 0x8000
-        if self.DontAdvance:
-            flags |= 0x4000
-        if self.CurrentIsKashidaLike:
-            flags |= 0x2000
-        if self.MarkedIsKashidaLike:
-            flags |= 0x1000
-        if self.CurrentInsertBefore:
-            flags |= 0x0800
-        if self.MarkedInsertBefore:
-            flags |= 0x0400
-        flags |= len(self.CurrentInsertionAction) << 5
-        flags |= len(self.MarkedInsertionAction)
-        writer.writeUShort(flags)
-        if len(self.CurrentInsertionAction) > 0:
-            currentIndex = actionIndex[tuple(self.CurrentInsertionAction)]
-        else:
-            currentIndex = 0xFFFF
-        writer.writeUShort(currentIndex)
-        if len(self.MarkedInsertionAction) > 0:
-            markedIndex = actionIndex[tuple(self.MarkedInsertionAction)]
-        else:
-            markedIndex = 0xFFFF
-        writer.writeUShort(markedIndex)
-
-    def decompile(self, reader, font, actionReader):
-        assert actionReader is not None
-        self.NewState = reader.readUShort()
-        flags = reader.readUShort()
-        self.SetMark = bool(flags & 0x8000)
-        self.DontAdvance = bool(flags & 0x4000)
-        self.CurrentIsKashidaLike = bool(flags & 0x2000)
-        self.MarkedIsKashidaLike = bool(flags & 0x1000)
-        self.CurrentInsertBefore = bool(flags & 0x0800)
-        self.MarkedInsertBefore = bool(flags & 0x0400)
-        self.CurrentInsertionAction = self._decompileInsertionAction(
-            actionReader, font, index=reader.readUShort(), count=((flags & 0x03E0) >> 5)
-        )
-        self.MarkedInsertionAction = self._decompileInsertionAction(
-            actionReader, font, index=reader.readUShort(), count=(flags & 0x001F)
-        )
-
-    def _decompileInsertionAction(self, actionReader, font, index, count):
-        if index == 0xFFFF or count == 0:
-            return []
-        reader = actionReader.getSubReader(actionReader.pos + index * 2)
-        return font.getGlyphNameMany(reader.readUShortArray(count))
-
-    def toXML(self, xmlWriter, font, attrs, name):
-        xmlWriter.begintag(name, **attrs)
-        xmlWriter.newline()
-        xmlWriter.simpletag("NewState", value=self.NewState)
-        xmlWriter.newline()
-        self._writeFlagsToXML(xmlWriter)
-        for g in self.CurrentInsertionAction:
-            xmlWriter.simpletag("CurrentInsertionAction", glyph=g)
-            xmlWriter.newline()
-        for g in self.MarkedInsertionAction:
-            xmlWriter.simpletag("MarkedInsertionAction", glyph=g)
-            xmlWriter.newline()
-        xmlWriter.endtag(name)
-        xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        self.__init__()
-        content = [t for t in content if isinstance(t, tuple)]
-        for eltName, eltAttrs, eltContent in content:
-            if eltName == "NewState":
-                self.NewState = safeEval(eltAttrs["value"])
-            elif eltName == "Flags":
-                for flag in eltAttrs["value"].split(","):
-                    self._setFlag(flag.strip())
-            elif eltName == "CurrentInsertionAction":
-                self.CurrentInsertionAction.append(eltAttrs["glyph"])
-            elif eltName == "MarkedInsertionAction":
-                self.MarkedInsertionAction.append(eltAttrs["glyph"])
-            else:
-                assert False, eltName
-
-    @staticmethod
-    def compileActions(font, states):
-        actions, actionIndex, result = set(), {}, b""
-        for state in states:
-            for _glyphClass, trans in state.Transitions.items():
-                if trans.CurrentInsertionAction is not None:
-                    actions.add(tuple(trans.CurrentInsertionAction))
-                if trans.MarkedInsertionAction is not None:
-                    actions.add(tuple(trans.MarkedInsertionAction))
-        # Sort the compiled actions in decreasing order of
-        # length, so that the longer sequence come before the
-        # shorter ones.
-        for action in sorted(actions, key=lambda x: (-len(x), x)):
-            # We insert all sub-sequences of the action glyph sequence
-            # into actionIndex. For example, if one action triggers on
-            # glyph sequence [A, B, C, D, E] and another action triggers
-            # on [C, D], we return result=[A, B, C, D, E] (as list of
-            # encoded glyph IDs), and actionIndex={('A','B','C','D','E'): 0,
-            # ('C','D'): 2}.
-            if action in actionIndex:
-                continue
-            for start in range(0, len(action)):
-                startIndex = (len(result) // 2) + start
-                for limit in range(start, len(action)):
-                    glyphs = action[start : limit + 1]
-                    actionIndex.setdefault(glyphs, startIndex)
-            for glyph in action:
-                glyphID = font.getGlyphID(glyph)
-                result += struct.pack(">H", glyphID)
-        return result, actionIndex
-
-
-class FeatureParams(BaseTable):
-    def compile(self, writer, font):
-        assert (
-            featureParamTypes.get(writer["FeatureTag"]) == self.__class__
-        ), "Wrong FeatureParams type for feature '%s': %s" % (
-            writer["FeatureTag"],
-            self.__class__.__name__,
-        )
-        BaseTable.compile(self, writer, font)
-
-    def toXML(self, xmlWriter, font, attrs=None, name=None):
-        BaseTable.toXML(self, xmlWriter, font, attrs, name=self.__class__.__name__)
-
-
-class FeatureParamsSize(FeatureParams):
-    pass
-
-
-class FeatureParamsStylisticSet(FeatureParams):
-    pass
-
-
-class FeatureParamsCharacterVariants(FeatureParams):
-    pass
-
-
-class Coverage(FormatSwitchingBaseTable):
-
-    # manual implementation to get rid of glyphID dependencies
-
-    def populateDefaults(self, propagator=None):
-        if not hasattr(self, "glyphs"):
-            self.glyphs = []
-
-    def postRead(self, rawTable, font):
-        if self.Format == 1:
-            self.glyphs = rawTable["GlyphArray"]
-        elif self.Format == 2:
-            glyphs = self.glyphs = []
-            ranges = rawTable["RangeRecord"]
-            # Some SIL fonts have coverage entries that don't have sorted
-            # StartCoverageIndex.  If it is so, fixup and warn.  We undo
-            # this when writing font out.
-            sorted_ranges = sorted(ranges, key=lambda a: a.StartCoverageIndex)
-            if ranges != sorted_ranges:
-                log.warning("GSUB/GPOS Coverage is not sorted by glyph ids.")
-                ranges = sorted_ranges
-            del sorted_ranges
-            for r in ranges:
-                start = r.Start
-                end = r.End
-                startID = font.getGlyphID(start)
-                endID = font.getGlyphID(end) + 1
-                glyphs.extend(font.getGlyphNameMany(range(startID, endID)))
-        else:
-            self.glyphs = []
-            log.warning("Unknown Coverage format: %s", self.Format)
-        del self.Format  # Don't need this anymore
-
-    def preWrite(self, font):
-        glyphs = getattr(self, "glyphs", None)
-        if glyphs is None:
-            glyphs = self.glyphs = []
-        format = 1
-        rawTable = {"GlyphArray": glyphs}
-        if glyphs:
-            # find out whether Format 2 is more compact or not
-            glyphIDs = font.getGlyphIDMany(glyphs)
-            brokenOrder = sorted(glyphIDs) != glyphIDs
-
-            last = glyphIDs[0]
-            ranges = [[last]]
-            for glyphID in glyphIDs[1:]:
-                if glyphID != last + 1:
-                    ranges[-1].append(last)
-                    ranges.append([glyphID])
-                last = glyphID
-            ranges[-1].append(last)
-
-            if brokenOrder or len(ranges) * 3 < len(glyphs):  # 3 words vs. 1 word
-                # Format 2 is more compact
-                index = 0
-                for i in range(len(ranges)):
-                    start, end = ranges[i]
-                    r = RangeRecord()
-                    r.StartID = start
-                    r.Start = font.getGlyphName(start)
-                    r.End = font.getGlyphName(end)
-                    r.StartCoverageIndex = index
-                    ranges[i] = r
-                    index = index + end - start + 1
-                if brokenOrder:
-                    log.warning("GSUB/GPOS Coverage is not sorted by glyph ids.")
-                    ranges.sort(key=lambda a: a.StartID)
-                for r in ranges:
-                    del r.StartID
-                format = 2
-                rawTable = {"RangeRecord": ranges}
-            # else:
-            # 	fallthrough; Format 1 is more compact
-        self.Format = format
-        return rawTable
-
-    def toXML2(self, xmlWriter, font):
-        for glyphName in getattr(self, "glyphs", []):
-            xmlWriter.simpletag("Glyph", value=glyphName)
-            xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        glyphs = getattr(self, "glyphs", None)
-        if glyphs is None:
-            glyphs = []
-            self.glyphs = glyphs
-        glyphs.append(attrs["value"])
-
-
-# The special 0xFFFFFFFF delta-set index is used to indicate that there
-# is no variation data in the ItemVariationStore for a given variable field
-NO_VARIATION_INDEX = 0xFFFFFFFF
-
-
-class DeltaSetIndexMap(getFormatSwitchingBaseTableClass("uint8")):
-    def populateDefaults(self, propagator=None):
-        if not hasattr(self, "mapping"):
-            self.mapping = []
-
-    def postRead(self, rawTable, font):
-        assert (rawTable["EntryFormat"] & 0xFFC0) == 0
-        self.mapping = rawTable["mapping"]
-
-    @staticmethod
-    def getEntryFormat(mapping):
-        ored = 0
-        for idx in mapping:
-            ored |= idx
-
-        inner = ored & 0xFFFF
-        innerBits = 0
-        while inner:
-            innerBits += 1
-            inner >>= 1
-        innerBits = max(innerBits, 1)
-        assert innerBits <= 16
-
-        ored = (ored >> (16 - innerBits)) | (ored & ((1 << innerBits) - 1))
-        if ored <= 0x000000FF:
-            entrySize = 1
-        elif ored <= 0x0000FFFF:
-            entrySize = 2
-        elif ored <= 0x00FFFFFF:
-            entrySize = 3
-        else:
-            entrySize = 4
-
-        return ((entrySize - 1) << 4) | (innerBits - 1)
-
-    def preWrite(self, font):
-        mapping = getattr(self, "mapping", None)
-        if mapping is None:
-            mapping = self.mapping = []
-        self.Format = 1 if len(mapping) > 0xFFFF else 0
-        rawTable = self.__dict__.copy()
-        rawTable["MappingCount"] = len(mapping)
-        rawTable["EntryFormat"] = self.getEntryFormat(mapping)
-        return rawTable
-
-    def toXML2(self, xmlWriter, font):
-        # Make xml dump less verbose, by omitting no-op entries like:
-        #   <Map index="..." outer="65535" inner="65535"/>
-        xmlWriter.comment("Omitted values default to 0xFFFF/0xFFFF (no variations)")
-        xmlWriter.newline()
-        for i, value in enumerate(getattr(self, "mapping", [])):
-            attrs = [("index", i)]
-            if value != NO_VARIATION_INDEX:
-                attrs.extend(
-                    [
-                        ("outer", value >> 16),
-                        ("inner", value & 0xFFFF),
-                    ]
-                )
-            xmlWriter.simpletag("Map", attrs)
-            xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        mapping = getattr(self, "mapping", None)
-        if mapping is None:
-            self.mapping = mapping = []
-        index = safeEval(attrs["index"])
-        outer = safeEval(attrs.get("outer", "0xFFFF"))
-        inner = safeEval(attrs.get("inner", "0xFFFF"))
-        assert inner <= 0xFFFF
-        mapping.insert(index, (outer << 16) | inner)
-
-
-class VarIdxMap(BaseTable):
-    def populateDefaults(self, propagator=None):
-        if not hasattr(self, "mapping"):
-            self.mapping = {}
-
-    def postRead(self, rawTable, font):
-        assert (rawTable["EntryFormat"] & 0xFFC0) == 0
-        glyphOrder = font.getGlyphOrder()
-        mapList = rawTable["mapping"]
-        mapList.extend([mapList[-1]] * (len(glyphOrder) - len(mapList)))
-        self.mapping = dict(zip(glyphOrder, mapList))
-
-    def preWrite(self, font):
-        mapping = getattr(self, "mapping", None)
-        if mapping is None:
-            mapping = self.mapping = {}
-
-        glyphOrder = font.getGlyphOrder()
-        mapping = [mapping[g] for g in glyphOrder]
-        while len(mapping) > 1 and mapping[-2] == mapping[-1]:
-            del mapping[-1]
-
-        rawTable = {"mapping": mapping}
-        rawTable["MappingCount"] = len(mapping)
-        rawTable["EntryFormat"] = DeltaSetIndexMap.getEntryFormat(mapping)
-        return rawTable
-
-    def toXML2(self, xmlWriter, font):
-        for glyph, value in sorted(getattr(self, "mapping", {}).items()):
-            attrs = (
-                ("glyph", glyph),
-                ("outer", value >> 16),
-                ("inner", value & 0xFFFF),
-            )
-            xmlWriter.simpletag("Map", attrs)
-            xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        mapping = getattr(self, "mapping", None)
-        if mapping is None:
-            mapping = {}
-            self.mapping = mapping
-        try:
-            glyph = attrs["glyph"]
-        except:  # https://github.com/fonttools/fonttools/commit/21cbab8ce9ded3356fef3745122da64dcaf314e9#commitcomment-27649836
-            glyph = font.getGlyphOrder()[attrs["index"]]
-        outer = safeEval(attrs["outer"])
-        inner = safeEval(attrs["inner"])
-        assert inner <= 0xFFFF
-        mapping[glyph] = (outer << 16) | inner
-
-
-class VarRegionList(BaseTable):
-    def preWrite(self, font):
-        # The OT spec says VarStore.VarRegionList.RegionAxisCount should always
-        # be equal to the fvar.axisCount, and OTS < v8.0.0 enforces this rule
-        # even when the VarRegionList is empty. We can't treat RegionAxisCount
-        # like a normal propagated count (== len(Region[i].VarRegionAxis)),
-        # otherwise it would default to 0 if VarRegionList is empty.
-        # Thus, we force it to always be equal to fvar.axisCount.
-        # https://github.com/khaledhosny/ots/pull/192
-        fvarTable = font.get("fvar")
-        if fvarTable:
-            self.RegionAxisCount = len(fvarTable.axes)
-        return {
-            **self.__dict__,
-            "RegionAxisCount": CountReference(self.__dict__, "RegionAxisCount"),
-        }
-
-
-class SingleSubst(FormatSwitchingBaseTable):
-    def populateDefaults(self, propagator=None):
-        if not hasattr(self, "mapping"):
-            self.mapping = {}
-
-    def postRead(self, rawTable, font):
-        mapping = {}
-        input = _getGlyphsFromCoverageTable(rawTable["Coverage"])
-        if self.Format == 1:
-            delta = rawTable["DeltaGlyphID"]
-            inputGIDS = font.getGlyphIDMany(input)
-            outGIDS = [(glyphID + delta) % 65536 for glyphID in inputGIDS]
-            outNames = font.getGlyphNameMany(outGIDS)
-            for inp, out in zip(input, outNames):
-                mapping[inp] = out
-        elif self.Format == 2:
-            assert (
-                len(input) == rawTable["GlyphCount"]
-            ), "invalid SingleSubstFormat2 table"
-            subst = rawTable["Substitute"]
-            for inp, sub in zip(input, subst):
-                mapping[inp] = sub
-        else:
-            assert 0, "unknown format: %s" % self.Format
-        self.mapping = mapping
-        del self.Format  # Don't need this anymore
-
-    def preWrite(self, font):
-        mapping = getattr(self, "mapping", None)
-        if mapping is None:
-            mapping = self.mapping = {}
-        items = list(mapping.items())
-        getGlyphID = font.getGlyphID
-        gidItems = [(getGlyphID(a), getGlyphID(b)) for a, b in items]
-        sortableItems = sorted(zip(gidItems, items))
-
-        # figure out format
-        format = 2
-        delta = None
-        for inID, outID in gidItems:
-            if delta is None:
-                delta = (outID - inID) % 65536
-
-            if (inID + delta) % 65536 != outID:
-                break
-        else:
-            if delta is None:
-                # the mapping is empty, better use format 2
-                format = 2
-            else:
-                format = 1
-
-        rawTable = {}
-        self.Format = format
-        cov = Coverage()
-        input = [item[1][0] for item in sortableItems]
-        subst = [item[1][1] for item in sortableItems]
-        cov.glyphs = input
-        rawTable["Coverage"] = cov
-        if format == 1:
-            assert delta is not None
-            rawTable["DeltaGlyphID"] = delta
-        else:
-            rawTable["Substitute"] = subst
-        return rawTable
-
-    def toXML2(self, xmlWriter, font):
-        items = sorted(self.mapping.items())
-        for inGlyph, outGlyph in items:
-            xmlWriter.simpletag("Substitution", [("in", inGlyph), ("out", outGlyph)])
-            xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        mapping = getattr(self, "mapping", None)
-        if mapping is None:
-            mapping = {}
-            self.mapping = mapping
-        mapping[attrs["in"]] = attrs["out"]
-
-
-class MultipleSubst(FormatSwitchingBaseTable):
-    def populateDefaults(self, propagator=None):
-        if not hasattr(self, "mapping"):
-            self.mapping = {}
-
-    def postRead(self, rawTable, font):
-        mapping = {}
-        if self.Format == 1:
-            glyphs = _getGlyphsFromCoverageTable(rawTable["Coverage"])
-            subst = [s.Substitute for s in rawTable["Sequence"]]
-            mapping = dict(zip(glyphs, subst))
-        else:
-            assert 0, "unknown format: %s" % self.Format
-        self.mapping = mapping
-        del self.Format  # Don't need this anymore
-
-    def preWrite(self, font):
-        mapping = getattr(self, "mapping", None)
-        if mapping is None:
-            mapping = self.mapping = {}
-        cov = Coverage()
-        cov.glyphs = sorted(list(mapping.keys()), key=font.getGlyphID)
-        self.Format = 1
-        rawTable = {
-            "Coverage": cov,
-            "Sequence": [self.makeSequence_(mapping[glyph]) for glyph in cov.glyphs],
-        }
-        return rawTable
-
-    def toXML2(self, xmlWriter, font):
-        items = sorted(self.mapping.items())
-        for inGlyph, outGlyphs in items:
-            out = ",".join(outGlyphs)
-            xmlWriter.simpletag("Substitution", [("in", inGlyph), ("out", out)])
-            xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        mapping = getattr(self, "mapping", None)
-        if mapping is None:
-            mapping = {}
-            self.mapping = mapping
-
-        # TTX v3.0 and earlier.
-        if name == "Coverage":
-            self.old_coverage_ = []
-            for element in content:
-                if not isinstance(element, tuple):
-                    continue
-                element_name, element_attrs, _ = element
-                if element_name == "Glyph":
-                    self.old_coverage_.append(element_attrs["value"])
-            return
-        if name == "Sequence":
-            index = int(attrs.get("index", len(mapping)))
-            glyph = self.old_coverage_[index]
-            glyph_mapping = mapping[glyph] = []
-            for element in content:
-                if not isinstance(element, tuple):
-                    continue
-                element_name, element_attrs, _ = element
-                if element_name == "Substitute":
-                    glyph_mapping.append(element_attrs["value"])
-            return
-
-            # TTX v3.1 and later.
-        outGlyphs = attrs["out"].split(",") if attrs["out"] else []
-        mapping[attrs["in"]] = [g.strip() for g in outGlyphs]
-
-    @staticmethod
-    def makeSequence_(g):
-        seq = Sequence()
-        seq.Substitute = g
-        return seq
-
-
-class ClassDef(FormatSwitchingBaseTable):
-    def populateDefaults(self, propagator=None):
-        if not hasattr(self, "classDefs"):
-            self.classDefs = {}
-
-    def postRead(self, rawTable, font):
-        classDefs = {}
-
-        if self.Format == 1:
-            start = rawTable["StartGlyph"]
-            classList = rawTable["ClassValueArray"]
-            startID = font.getGlyphID(start)
-            endID = startID + len(classList)
-            glyphNames = font.getGlyphNameMany(range(startID, endID))
-            for glyphName, cls in zip(glyphNames, classList):
-                if cls:
-                    classDefs[glyphName] = cls
-
-        elif self.Format == 2:
-            records = rawTable["ClassRangeRecord"]
-            for rec in records:
-                cls = rec.Class
-                if not cls:
-                    continue
-                start = rec.Start
-                end = rec.End
-                startID = font.getGlyphID(start)
-                endID = font.getGlyphID(end) + 1
-                glyphNames = font.getGlyphNameMany(range(startID, endID))
-                for glyphName in glyphNames:
-                    classDefs[glyphName] = cls
-        else:
-            log.warning("Unknown ClassDef format: %s", self.Format)
-        self.classDefs = classDefs
-        del self.Format  # Don't need this anymore
-
-    def _getClassRanges(self, font):
-        classDefs = getattr(self, "classDefs", None)
-        if classDefs is None:
-            self.classDefs = {}
-            return
-        getGlyphID = font.getGlyphID
-        items = []
-        for glyphName, cls in classDefs.items():
-            if not cls:
-                continue
-            items.append((getGlyphID(glyphName), glyphName, cls))
-        if items:
-            items.sort()
-            last, lastName, lastCls = items[0]
-            ranges = [[lastCls, last, lastName]]
-            for glyphID, glyphName, cls in items[1:]:
-                if glyphID != last + 1 or cls != lastCls:
-                    ranges[-1].extend([last, lastName])
-                    ranges.append([cls, glyphID, glyphName])
-                last = glyphID
-                lastName = glyphName
-                lastCls = cls
-            ranges[-1].extend([last, lastName])
-            return ranges
-
-    def preWrite(self, font):
-        format = 2
-        rawTable = {"ClassRangeRecord": []}
-        ranges = self._getClassRanges(font)
-        if ranges:
-            startGlyph = ranges[0][1]
-            endGlyph = ranges[-1][3]
-            glyphCount = endGlyph - startGlyph + 1
-            if len(ranges) * 3 < glyphCount + 1:
-                # Format 2 is more compact
-                for i in range(len(ranges)):
-                    cls, start, startName, end, endName = ranges[i]
-                    rec = ClassRangeRecord()
-                    rec.Start = startName
-                    rec.End = endName
-                    rec.Class = cls
-                    ranges[i] = rec
-                format = 2
-                rawTable = {"ClassRangeRecord": ranges}
-            else:
-                # Format 1 is more compact
-                startGlyphName = ranges[0][2]
-                classes = [0] * glyphCount
-                for cls, start, startName, end, endName in ranges:
-                    for g in range(start - startGlyph, end - startGlyph + 1):
-                        classes[g] = cls
-                format = 1
-                rawTable = {"StartGlyph": startGlyphName, "ClassValueArray": classes}
-        self.Format = format
-        return rawTable
-
-    def toXML2(self, xmlWriter, font):
-        items = sorted(self.classDefs.items())
-        for glyphName, cls in items:
-            xmlWriter.simpletag("ClassDef", [("glyph", glyphName), ("class", cls)])
-            xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        classDefs = getattr(self, "classDefs", None)
-        if classDefs is None:
-            classDefs = {}
-            self.classDefs = classDefs
-        classDefs[attrs["glyph"]] = int(attrs["class"])
-
-
-class AlternateSubst(FormatSwitchingBaseTable):
-    def populateDefaults(self, propagator=None):
-        if not hasattr(self, "alternates"):
-            self.alternates = {}
-
-    def postRead(self, rawTable, font):
-        alternates = {}
-        if self.Format == 1:
-            input = _getGlyphsFromCoverageTable(rawTable["Coverage"])
-            alts = rawTable["AlternateSet"]
-            assert len(input) == len(alts)
-            for inp, alt in zip(input, alts):
-                alternates[inp] = alt.Alternate
-        else:
-            assert 0, "unknown format: %s" % self.Format
-        self.alternates = alternates
-        del self.Format  # Don't need this anymore
-
-    def preWrite(self, font):
-        self.Format = 1
-        alternates = getattr(self, "alternates", None)
-        if alternates is None:
-            alternates = self.alternates = {}
-        items = list(alternates.items())
-        for i in range(len(items)):
-            glyphName, set = items[i]
-            items[i] = font.getGlyphID(glyphName), glyphName, set
-        items.sort()
-        cov = Coverage()
-        cov.glyphs = [item[1] for item in items]
-        alternates = []
-        setList = [item[-1] for item in items]
-        for set in setList:
-            alts = AlternateSet()
-            alts.Alternate = set
-            alternates.append(alts)
-        # a special case to deal with the fact that several hundred Adobe Japan1-5
-        # CJK fonts will overflow an offset if the coverage table isn't pushed to the end.
-        # Also useful in that when splitting a sub-table because of an offset overflow
-        # I don't need to calculate the change in the subtable offset due to the change in the coverage table size.
-        # Allows packing more rules in subtable.
-        self.sortCoverageLast = 1
-        return {"Coverage": cov, "AlternateSet": alternates}
-
-    def toXML2(self, xmlWriter, font):
-        items = sorted(self.alternates.items())
-        for glyphName, alternates in items:
-            xmlWriter.begintag("AlternateSet", glyph=glyphName)
-            xmlWriter.newline()
-            for alt in alternates:
-                xmlWriter.simpletag("Alternate", glyph=alt)
-                xmlWriter.newline()
-            xmlWriter.endtag("AlternateSet")
-            xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        alternates = getattr(self, "alternates", None)
-        if alternates is None:
-            alternates = {}
-            self.alternates = alternates
-        glyphName = attrs["glyph"]
-        set = []
-        alternates[glyphName] = set
-        for element in content:
-            if not isinstance(element, tuple):
-                continue
-            name, attrs, content = element
-            set.append(attrs["glyph"])
-
-
-class LigatureSubst(FormatSwitchingBaseTable):
-    def populateDefaults(self, propagator=None):
-        if not hasattr(self, "ligatures"):
-            self.ligatures = {}
-
-    def postRead(self, rawTable, font):
-        ligatures = {}
-        if self.Format == 1:
-            input = _getGlyphsFromCoverageTable(rawTable["Coverage"])
-            ligSets = rawTable["LigatureSet"]
-            assert len(input) == len(ligSets)
-            for i in range(len(input)):
-                ligatures[input[i]] = ligSets[i].Ligature
-        else:
-            assert 0, "unknown format: %s" % self.Format
-        self.ligatures = ligatures
-        del self.Format  # Don't need this anymore
-
-    def preWrite(self, font):
-        self.Format = 1
-        ligatures = getattr(self, "ligatures", None)
-        if ligatures is None:
-            ligatures = self.ligatures = {}
-
-        if ligatures and isinstance(next(iter(ligatures)), tuple):
-            # New high-level API in v3.1 and later.  Note that we just support compiling this
-            # for now.  We don't load to this API, and don't do XML with it.
-
-            # ligatures is map from components-sequence to lig-glyph
-            newLigatures = dict()
-            for comps, lig in sorted(
-                ligatures.items(), key=lambda item: (-len(item[0]), item[0])
-            ):
-                ligature = Ligature()
-                ligature.Component = comps[1:]
-                ligature.CompCount = len(comps)
-                ligature.LigGlyph = lig
-                newLigatures.setdefault(comps[0], []).append(ligature)
-            ligatures = newLigatures
-
-        items = list(ligatures.items())
-        for i in range(len(items)):
-            glyphName, set = items[i]
-            items[i] = font.getGlyphID(glyphName), glyphName, set
-        items.sort()
-        cov = Coverage()
-        cov.glyphs = [item[1] for item in items]
-
-        ligSets = []
-        setList = [item[-1] for item in items]
-        for set in setList:
-            ligSet = LigatureSet()
-            ligs = ligSet.Ligature = []
-            for lig in set:
-                ligs.append(lig)
-            ligSets.append(ligSet)
-        # Useful in that when splitting a sub-table because of an offset overflow
-        # I don't need to calculate the change in subtabl offset due to the coverage table size.
-        # Allows packing more rules in subtable.
-        self.sortCoverageLast = 1
-        return {"Coverage": cov, "LigatureSet": ligSets}
-
-    def toXML2(self, xmlWriter, font):
-        items = sorted(self.ligatures.items())
-        for glyphName, ligSets in items:
-            xmlWriter.begintag("LigatureSet", glyph=glyphName)
-            xmlWriter.newline()
-            for lig in ligSets:
-                xmlWriter.simpletag(
-                    "Ligature", glyph=lig.LigGlyph, components=",".join(lig.Component)
-                )
-                xmlWriter.newline()
-            xmlWriter.endtag("LigatureSet")
-            xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        ligatures = getattr(self, "ligatures", None)
-        if ligatures is None:
-            ligatures = {}
-            self.ligatures = ligatures
-        glyphName = attrs["glyph"]
-        ligs = []
-        ligatures[glyphName] = ligs
-        for element in content:
-            if not isinstance(element, tuple):
-                continue
-            name, attrs, content = element
-            lig = Ligature()
-            lig.LigGlyph = attrs["glyph"]
-            components = attrs["components"]
-            lig.Component = components.split(",") if components else []
-            lig.CompCount = len(lig.Component)
-            ligs.append(lig)
-
-
-class COLR(BaseTable):
-    def decompile(self, reader, font):
-        # COLRv0 is exceptional in that LayerRecordCount appears *after* the
-        # LayerRecordArray it counts, but the parser logic expects Count fields
-        # to always precede the arrays. Here we work around this by parsing the
-        # LayerRecordCount before the rest of the table, and storing it in
-        # the reader's local state.
-        subReader = reader.getSubReader(offset=0)
-        for conv in self.getConverters():
-            if conv.name != "LayerRecordCount":
-                subReader.advance(conv.staticSize)
-                continue
-            reader[conv.name] = conv.read(subReader, font, tableDict={})
-            break
-        else:
-            raise AssertionError("LayerRecordCount converter not found")
-        return BaseTable.decompile(self, reader, font)
-
-    def preWrite(self, font):
-        # The writer similarly assumes Count values precede the things counted,
-        # thus here we pre-initialize a CountReference; the actual count value
-        # will be set to the lenght of the array by the time this is assembled.
-        self.LayerRecordCount = None
-        return {
-            **self.__dict__,
-            "LayerRecordCount": CountReference(self.__dict__, "LayerRecordCount"),
-        }
-
-    def computeClipBoxes(self, glyphSet: "_TTGlyphSet", quantization: int = 1):
-        if self.Version == 0:
-            return
-
-        clips = {}
-        for rec in self.BaseGlyphList.BaseGlyphPaintRecord:
-            try:
-                clipBox = rec.Paint.computeClipBox(self, glyphSet, quantization)
-            except Exception as e:
-                from fontTools.ttLib import TTLibError
-
-                raise TTLibError(
-                    f"Failed to compute COLR ClipBox for {rec.BaseGlyph!r}"
-                ) from e
-
-            if clipBox is not None:
-                clips[rec.BaseGlyph] = clipBox
-
-        hasClipList = hasattr(self, "ClipList") and self.ClipList is not None
-        if not clips:
-            if hasClipList:
-                self.ClipList = None
-        else:
-            if not hasClipList:
-                self.ClipList = ClipList()
-                self.ClipList.Format = 1
-            self.ClipList.clips = clips
-
-
-class LookupList(BaseTable):
-    @property
-    def table(self):
-        for l in self.Lookup:
-            for st in l.SubTable:
-                if type(st).__name__.endswith("Subst"):
-                    return "GSUB"
-                if type(st).__name__.endswith("Pos"):
-                    return "GPOS"
-        raise ValueError
-
-    def toXML2(self, xmlWriter, font):
-        if (
-            not font
-            or "Debg" not in font
-            or LOOKUP_DEBUG_INFO_KEY not in font["Debg"].data
-        ):
-            return super().toXML2(xmlWriter, font)
-        debugData = font["Debg"].data[LOOKUP_DEBUG_INFO_KEY][self.table]
-        for conv in self.getConverters():
-            if conv.repeat:
-                value = getattr(self, conv.name, [])
-                for lookupIndex, item in enumerate(value):
-                    if str(lookupIndex) in debugData:
-                        info = LookupDebugInfo(*debugData[str(lookupIndex)])
-                        tag = info.location
-                        if info.name:
-                            tag = f"{info.name}: {tag}"
-                        if info.feature:
-                            script, language, feature = info.feature
-                            tag = f"{tag} in {feature} ({script}/{language})"
-                        xmlWriter.comment(tag)
-                        xmlWriter.newline()
-
-                    conv.xmlWrite(
-                        xmlWriter, font, item, conv.name, [("index", lookupIndex)]
-                    )
-            else:
-                if conv.aux and not eval(conv.aux, None, vars(self)):
-                    continue
-                value = getattr(
-                    self, conv.name, None
-                )  # TODO Handle defaults instead of defaulting to None!
-                conv.xmlWrite(xmlWriter, font, value, conv.name, [])
-
-
-class BaseGlyphRecordArray(BaseTable):
-    def preWrite(self, font):
-        self.BaseGlyphRecord = sorted(
-            self.BaseGlyphRecord, key=lambda rec: font.getGlyphID(rec.BaseGlyph)
-        )
-        return self.__dict__.copy()
-
-
-class BaseGlyphList(BaseTable):
-    def preWrite(self, font):
-        self.BaseGlyphPaintRecord = sorted(
-            self.BaseGlyphPaintRecord, key=lambda rec: font.getGlyphID(rec.BaseGlyph)
-        )
-        return self.__dict__.copy()
-
-
-class ClipBoxFormat(IntEnum):
-    Static = 1
-    Variable = 2
-
-    def is_variable(self):
-        return self is self.Variable
-
-    def as_variable(self):
-        return self.Variable
-
-
-class ClipBox(getFormatSwitchingBaseTableClass("uint8")):
-    formatEnum = ClipBoxFormat
-
-    def as_tuple(self):
-        return tuple(getattr(self, conv.name) for conv in self.getConverters())
-
-    def __repr__(self):
-        return f"{self.__class__.__name__}{self.as_tuple()}"
-
-
-class ClipList(getFormatSwitchingBaseTableClass("uint8")):
-    def populateDefaults(self, propagator=None):
-        if not hasattr(self, "clips"):
-            self.clips = {}
-
-    def postRead(self, rawTable, font):
-        clips = {}
-        glyphOrder = font.getGlyphOrder()
-        for i, rec in enumerate(rawTable["ClipRecord"]):
-            if rec.StartGlyphID > rec.EndGlyphID:
-                log.warning(
-                    "invalid ClipRecord[%i].StartGlyphID (%i) > "
-                    "EndGlyphID (%i); skipped",
-                    i,
-                    rec.StartGlyphID,
-                    rec.EndGlyphID,
-                )
-                continue
-            redefinedGlyphs = []
-            missingGlyphs = []
-            for glyphID in range(rec.StartGlyphID, rec.EndGlyphID + 1):
-                try:
-                    glyph = glyphOrder[glyphID]
-                except IndexError:
-                    missingGlyphs.append(glyphID)
-                    continue
-                if glyph not in clips:
-                    clips[glyph] = copy.copy(rec.ClipBox)
-                else:
-                    redefinedGlyphs.append(glyphID)
-            if redefinedGlyphs:
-                log.warning(
-                    "ClipRecord[%i] overlaps previous records; "
-                    "ignoring redefined clip boxes for the "
-                    "following glyph ID range: [%i-%i]",
-                    i,
-                    min(redefinedGlyphs),
-                    max(redefinedGlyphs),
-                )
-            if missingGlyphs:
-                log.warning(
-                    "ClipRecord[%i] range references missing " "glyph IDs: [%i-%i]",
-                    i,
-                    min(missingGlyphs),
-                    max(missingGlyphs),
-                )
-        self.clips = clips
-
-    def groups(self):
-        glyphsByClip = defaultdict(list)
-        uniqueClips = {}
-        for glyphName, clipBox in self.clips.items():
-            key = clipBox.as_tuple()
-            glyphsByClip[key].append(glyphName)
-            if key not in uniqueClips:
-                uniqueClips[key] = clipBox
-        return {
-            frozenset(glyphs): uniqueClips[key] for key, glyphs in glyphsByClip.items()
-        }
-
-    def preWrite(self, font):
-        if not hasattr(self, "clips"):
-            self.clips = {}
-        clipBoxRanges = {}
-        glyphMap = font.getReverseGlyphMap()
-        for glyphs, clipBox in self.groups().items():
-            glyphIDs = sorted(
-                glyphMap[glyphName] for glyphName in glyphs if glyphName in glyphMap
-            )
-            if not glyphIDs:
-                continue
-            last = glyphIDs[0]
-            ranges = [[last]]
-            for glyphID in glyphIDs[1:]:
-                if glyphID != last + 1:
-                    ranges[-1].append(last)
-                    ranges.append([glyphID])
-                last = glyphID
-            ranges[-1].append(last)
-            for start, end in ranges:
-                assert (start, end) not in clipBoxRanges
-                clipBoxRanges[(start, end)] = clipBox
-
-        clipRecords = []
-        for (start, end), clipBox in sorted(clipBoxRanges.items()):
-            record = ClipRecord()
-            record.StartGlyphID = start
-            record.EndGlyphID = end
-            record.ClipBox = clipBox
-            clipRecords.append(record)
-        rawTable = {
-            "ClipCount": len(clipRecords),
-            "ClipRecord": clipRecords,
-        }
-        return rawTable
-
-    def toXML(self, xmlWriter, font, attrs=None, name=None):
-        tableName = name if name else self.__class__.__name__
-        if attrs is None:
-            attrs = []
-        if hasattr(self, "Format"):
-            attrs.append(("Format", self.Format))
-        xmlWriter.begintag(tableName, attrs)
-        xmlWriter.newline()
-        # sort clips alphabetically to ensure deterministic XML dump
-        for glyphs, clipBox in sorted(
-            self.groups().items(), key=lambda item: min(item[0])
-        ):
-            xmlWriter.begintag("Clip")
-            xmlWriter.newline()
-            for glyphName in sorted(glyphs):
-                xmlWriter.simpletag("Glyph", value=glyphName)
-                xmlWriter.newline()
-            xmlWriter.begintag("ClipBox", [("Format", clipBox.Format)])
-            xmlWriter.newline()
-            clipBox.toXML2(xmlWriter, font)
-            xmlWriter.endtag("ClipBox")
-            xmlWriter.newline()
-            xmlWriter.endtag("Clip")
-            xmlWriter.newline()
-        xmlWriter.endtag(tableName)
-        xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        clips = getattr(self, "clips", None)
-        if clips is None:
-            self.clips = clips = {}
-        assert name == "Clip"
-        glyphs = []
-        clipBox = None
-        for elem in content:
-            if not isinstance(elem, tuple):
-                continue
-            name, attrs, content = elem
-            if name == "Glyph":
-                glyphs.append(attrs["value"])
-            elif name == "ClipBox":
-                clipBox = ClipBox()
-                clipBox.Format = safeEval(attrs["Format"])
-                for elem in content:
-                    if not isinstance(elem, tuple):
-                        continue
-                    name, attrs, content = elem
-                    clipBox.fromXML(name, attrs, content, font)
-        if clipBox:
-            for glyphName in glyphs:
-                clips[glyphName] = clipBox
-
-
-class ExtendMode(IntEnum):
-    PAD = 0
-    REPEAT = 1
-    REFLECT = 2
-
-
-# Porter-Duff modes for COLRv1 PaintComposite:
-# https://github.com/googlefonts/colr-gradients-spec/tree/off_sub_1#compositemode-enumeration
-class CompositeMode(IntEnum):
-    CLEAR = 0
-    SRC = 1
-    DEST = 2
-    SRC_OVER = 3
-    DEST_OVER = 4
-    SRC_IN = 5
-    DEST_IN = 6
-    SRC_OUT = 7
-    DEST_OUT = 8
-    SRC_ATOP = 9
-    DEST_ATOP = 10
-    XOR = 11
-    PLUS = 12
-    SCREEN = 13
-    OVERLAY = 14
-    DARKEN = 15
-    LIGHTEN = 16
-    COLOR_DODGE = 17
-    COLOR_BURN = 18
-    HARD_LIGHT = 19
-    SOFT_LIGHT = 20
-    DIFFERENCE = 21
-    EXCLUSION = 22
-    MULTIPLY = 23
-    HSL_HUE = 24
-    HSL_SATURATION = 25
-    HSL_COLOR = 26
-    HSL_LUMINOSITY = 27
-
-
-class PaintFormat(IntEnum):
-    PaintColrLayers = 1
-    PaintSolid = 2
-    PaintVarSolid = (3,)
-    PaintLinearGradient = 4
-    PaintVarLinearGradient = 5
-    PaintRadialGradient = 6
-    PaintVarRadialGradient = 7
-    PaintSweepGradient = 8
-    PaintVarSweepGradient = 9
-    PaintGlyph = 10
-    PaintColrGlyph = 11
-    PaintTransform = 12
-    PaintVarTransform = 13
-    PaintTranslate = 14
-    PaintVarTranslate = 15
-    PaintScale = 16
-    PaintVarScale = 17
-    PaintScaleAroundCenter = 18
-    PaintVarScaleAroundCenter = 19
-    PaintScaleUniform = 20
-    PaintVarScaleUniform = 21
-    PaintScaleUniformAroundCenter = 22
-    PaintVarScaleUniformAroundCenter = 23
-    PaintRotate = 24
-    PaintVarRotate = 25
-    PaintRotateAroundCenter = 26
-    PaintVarRotateAroundCenter = 27
-    PaintSkew = 28
-    PaintVarSkew = 29
-    PaintSkewAroundCenter = 30
-    PaintVarSkewAroundCenter = 31
-    PaintComposite = 32
-
-    def is_variable(self):
-        return self.name.startswith("PaintVar")
-
-    def as_variable(self):
-        if self.is_variable():
-            return self
-        try:
-            return PaintFormat.__members__[f"PaintVar{self.name[5:]}"]
-        except KeyError:
-            return None
-
-
-class Paint(getFormatSwitchingBaseTableClass("uint8")):
-    formatEnum = PaintFormat
-
-    def getFormatName(self):
-        try:
-            return self.formatEnum(self.Format).name
-        except ValueError:
-            raise NotImplementedError(f"Unknown Paint format: {self.Format}")
-
-    def toXML(self, xmlWriter, font, attrs=None, name=None):
-        tableName = name if name else self.__class__.__name__
-        if attrs is None:
-            attrs = []
-        attrs.append(("Format", self.Format))
-        xmlWriter.begintag(tableName, attrs)
-        xmlWriter.comment(self.getFormatName())
-        xmlWriter.newline()
-        self.toXML2(xmlWriter, font)
-        xmlWriter.endtag(tableName)
-        xmlWriter.newline()
-
-    def iterPaintSubTables(self, colr: COLR) -> Iterator[BaseTable.SubTableEntry]:
-        if self.Format == PaintFormat.PaintColrLayers:
-            # https://github.com/fonttools/fonttools/issues/2438: don't die when no LayerList exists
-            layers = []
-            if colr.LayerList is not None:
-                layers = colr.LayerList.Paint
-            yield from (
-                BaseTable.SubTableEntry(name="Layers", value=v, index=i)
-                for i, v in enumerate(
-                    layers[self.FirstLayerIndex : self.FirstLayerIndex + self.NumLayers]
-                )
-            )
-            return
-
-        if self.Format == PaintFormat.PaintColrGlyph:
-            for record in colr.BaseGlyphList.BaseGlyphPaintRecord:
-                if record.BaseGlyph == self.Glyph:
-                    yield BaseTable.SubTableEntry(name="BaseGlyph", value=record.Paint)
-                    return
-            else:
-                raise KeyError(f"{self.Glyph!r} not in colr.BaseGlyphList")
-
-        for conv in self.getConverters():
-            if conv.tableClass is not None and issubclass(conv.tableClass, type(self)):
-                value = getattr(self, conv.name)
-                yield BaseTable.SubTableEntry(name=conv.name, value=value)
-
-    def getChildren(self, colr) -> List["Paint"]:
-        # this is kept for backward compatibility (e.g. it's used by the subsetter)
-        return [p.value for p in self.iterPaintSubTables(colr)]
-
-    def traverse(self, colr: COLR, callback):
-        """Depth-first traversal of graph rooted at self, callback on each node."""
-        if not callable(callback):
-            raise TypeError("callback must be callable")
-
-        for path in dfs_base_table(
-            self, iter_subtables_fn=lambda paint: paint.iterPaintSubTables(colr)
-        ):
-            paint = path[-1].value
-            callback(paint)
-
-    def getTransform(self) -> Transform:
-        if self.Format == PaintFormat.PaintTransform:
-            t = self.Transform
-            return Transform(t.xx, t.yx, t.xy, t.yy, t.dx, t.dy)
-        elif self.Format == PaintFormat.PaintTranslate:
-            return Identity.translate(self.dx, self.dy)
-        elif self.Format == PaintFormat.PaintScale:
-            return Identity.scale(self.scaleX, self.scaleY)
-        elif self.Format == PaintFormat.PaintScaleAroundCenter:
-            return (
-                Identity.translate(self.centerX, self.centerY)
-                .scale(self.scaleX, self.scaleY)
-                .translate(-self.centerX, -self.centerY)
-            )
-        elif self.Format == PaintFormat.PaintScaleUniform:
-            return Identity.scale(self.scale)
-        elif self.Format == PaintFormat.PaintScaleUniformAroundCenter:
-            return (
-                Identity.translate(self.centerX, self.centerY)
-                .scale(self.scale)
-                .translate(-self.centerX, -self.centerY)
-            )
-        elif self.Format == PaintFormat.PaintRotate:
-            return Identity.rotate(radians(self.angle))
-        elif self.Format == PaintFormat.PaintRotateAroundCenter:
-            return (
-                Identity.translate(self.centerX, self.centerY)
-                .rotate(radians(self.angle))
-                .translate(-self.centerX, -self.centerY)
-            )
-        elif self.Format == PaintFormat.PaintSkew:
-            return Identity.skew(radians(-self.xSkewAngle), radians(self.ySkewAngle))
-        elif self.Format == PaintFormat.PaintSkewAroundCenter:
-            return (
-                Identity.translate(self.centerX, self.centerY)
-                .skew(radians(-self.xSkewAngle), radians(self.ySkewAngle))
-                .translate(-self.centerX, -self.centerY)
-            )
-        if PaintFormat(self.Format).is_variable():
-            raise NotImplementedError(f"Variable Paints not supported: {self.Format}")
-
-        return Identity
-
-    def computeClipBox(
-        self, colr: COLR, glyphSet: "_TTGlyphSet", quantization: int = 1
-    ) -> Optional[ClipBox]:
-        pen = ControlBoundsPen(glyphSet)
-        for path in dfs_base_table(
-            self, iter_subtables_fn=lambda paint: paint.iterPaintSubTables(colr)
-        ):
-            paint = path[-1].value
-            if paint.Format == PaintFormat.PaintGlyph:
-                transformation = reduce(
-                    Transform.transform,
-                    (st.value.getTransform() for st in path),
-                    Identity,
-                )
-                glyphSet[paint.Glyph].draw(TransformPen(pen, transformation))
-
-        if pen.bounds is None:
-            return None
-
-        cb = ClipBox()
-        cb.Format = int(ClipBoxFormat.Static)
-        cb.xMin, cb.yMin, cb.xMax, cb.yMax = quantizeRect(pen.bounds, quantization)
-        return cb
-
-
-# For each subtable format there is a class. However, we don't really distinguish
-# between "field name" and "format name": often these are the same. Yet there's
-# a whole bunch of fields with different names. The following dict is a mapping
-# from "format name" to "field name". _buildClasses() uses this to create a
-# subclass for each alternate field name.
-#
-_equivalents = {
-    "MarkArray": ("Mark1Array",),
-    "LangSys": ("DefaultLangSys",),
-    "Coverage": (
-        "MarkCoverage",
-        "BaseCoverage",
-        "LigatureCoverage",
-        "Mark1Coverage",
-        "Mark2Coverage",
-        "BacktrackCoverage",
-        "InputCoverage",
-        "LookAheadCoverage",
-        "VertGlyphCoverage",
-        "HorizGlyphCoverage",
-        "TopAccentCoverage",
-        "ExtendedShapeCoverage",
-        "MathKernCoverage",
-    ),
-    "ClassDef": (
-        "ClassDef1",
-        "ClassDef2",
-        "BacktrackClassDef",
-        "InputClassDef",
-        "LookAheadClassDef",
-        "GlyphClassDef",
-        "MarkAttachClassDef",
-    ),
-    "Anchor": (
-        "EntryAnchor",
-        "ExitAnchor",
-        "BaseAnchor",
-        "LigatureAnchor",
-        "Mark2Anchor",
-        "MarkAnchor",
-    ),
-    "Device": (
-        "XPlaDevice",
-        "YPlaDevice",
-        "XAdvDevice",
-        "YAdvDevice",
-        "XDeviceTable",
-        "YDeviceTable",
-        "DeviceTable",
-    ),
-    "Axis": (
-        "HorizAxis",
-        "VertAxis",
-    ),
-    "MinMax": ("DefaultMinMax",),
-    "BaseCoord": (
-        "MinCoord",
-        "MaxCoord",
-    ),
-    "JstfLangSys": ("DefJstfLangSys",),
-    "JstfGSUBModList": (
-        "ShrinkageEnableGSUB",
-        "ShrinkageDisableGSUB",
-        "ExtensionEnableGSUB",
-        "ExtensionDisableGSUB",
-    ),
-    "JstfGPOSModList": (
-        "ShrinkageEnableGPOS",
-        "ShrinkageDisableGPOS",
-        "ExtensionEnableGPOS",
-        "ExtensionDisableGPOS",
-    ),
-    "JstfMax": (
-        "ShrinkageJstfMax",
-        "ExtensionJstfMax",
-    ),
-    "MathKern": (
-        "TopRightMathKern",
-        "TopLeftMathKern",
-        "BottomRightMathKern",
-        "BottomLeftMathKern",
-    ),
-    "MathGlyphConstruction": ("VertGlyphConstruction", "HorizGlyphConstruction"),
-}
-
-#
-# OverFlow logic, to automatically create ExtensionLookups
-# XXX This should probably move to otBase.py
-#
-
-
-def fixLookupOverFlows(ttf, overflowRecord):
-    """Either the offset from the LookupList to a lookup overflowed, or
-    an offset from a lookup to a subtable overflowed.
-    The table layout is:
-    GPSO/GUSB
-            Script List
-            Feature List
-            LookUpList
-                    Lookup[0] and contents
-                            SubTable offset list
-                                    SubTable[0] and contents
-                                    ...
-                                    SubTable[n] and contents
-                    ...
-                    Lookup[n] and contents
-                            SubTable offset list
-                                    SubTable[0] and contents
-                                    ...
-                                    SubTable[n] and contents
-    If the offset to a lookup overflowed (SubTableIndex is None)
-            we must promote the *previous*	lookup to an Extension type.
-    If the offset from a lookup to subtable overflowed, then we must promote it
-            to an Extension Lookup type.
-    """
-    ok = 0
-    lookupIndex = overflowRecord.LookupListIndex
-    if overflowRecord.SubTableIndex is None:
-        lookupIndex = lookupIndex - 1
-    if lookupIndex < 0:
-        return ok
-    if overflowRecord.tableType == "GSUB":
-        extType = 7
-    elif overflowRecord.tableType == "GPOS":
-        extType = 9
-
-    lookups = ttf[overflowRecord.tableType].table.LookupList.Lookup
-    lookup = lookups[lookupIndex]
-    # If the previous lookup is an extType, look further back. Very unlikely, but possible.
-    while lookup.SubTable[0].__class__.LookupType == extType:
-        lookupIndex = lookupIndex - 1
-        if lookupIndex < 0:
-            return ok
-        lookup = lookups[lookupIndex]
-
-    for lookupIndex in range(lookupIndex, len(lookups)):
-        lookup = lookups[lookupIndex]
-        if lookup.LookupType != extType:
-            lookup.LookupType = extType
-            for si in range(len(lookup.SubTable)):
-                subTable = lookup.SubTable[si]
-                extSubTableClass = lookupTypes[overflowRecord.tableType][extType]
-                extSubTable = extSubTableClass()
-                extSubTable.Format = 1
-                extSubTable.ExtSubTable = subTable
-                lookup.SubTable[si] = extSubTable
-    ok = 1
-    return ok
-
-
-def splitMultipleSubst(oldSubTable, newSubTable, overflowRecord):
-    ok = 1
-    oldMapping = sorted(oldSubTable.mapping.items())
-    oldLen = len(oldMapping)
-
-    if overflowRecord.itemName in ["Coverage", "RangeRecord"]:
-        # Coverage table is written last. Overflow is to or within the
-        # the coverage table. We will just cut the subtable in half.
-        newLen = oldLen // 2
-
-    elif overflowRecord.itemName == "Sequence":
-        # We just need to back up by two items from the overflowed
-        # Sequence index to make sure the offset to the Coverage table
-        # doesn't overflow.
-        newLen = overflowRecord.itemIndex - 1
-
-    newSubTable.mapping = {}
-    for i in range(newLen, oldLen):
-        item = oldMapping[i]
-        key = item[0]
-        newSubTable.mapping[key] = item[1]
-        del oldSubTable.mapping[key]
-
-    return ok
-
-
-def splitAlternateSubst(oldSubTable, newSubTable, overflowRecord):
-    ok = 1
-    if hasattr(oldSubTable, "sortCoverageLast"):
-        newSubTable.sortCoverageLast = oldSubTable.sortCoverageLast
-
-    oldAlts = sorted(oldSubTable.alternates.items())
-    oldLen = len(oldAlts)
-
-    if overflowRecord.itemName in ["Coverage", "RangeRecord"]:
-        # Coverage table is written last. overflow is to or within the
-        # the coverage table. We will just cut the subtable in half.
-        newLen = oldLen // 2
-
-    elif overflowRecord.itemName == "AlternateSet":
-        # We just need to back up by two items
-        # from the overflowed AlternateSet index to make sure the offset
-        # to the Coverage table doesn't overflow.
-        newLen = overflowRecord.itemIndex - 1
-
-    newSubTable.alternates = {}
-    for i in range(newLen, oldLen):
-        item = oldAlts[i]
-        key = item[0]
-        newSubTable.alternates[key] = item[1]
-        del oldSubTable.alternates[key]
-
-    return ok
-
-
-def splitLigatureSubst(oldSubTable, newSubTable, overflowRecord):
-    ok = 1
-    oldLigs = sorted(oldSubTable.ligatures.items())
-    oldLen = len(oldLigs)
-
-    if overflowRecord.itemName in ["Coverage", "RangeRecord"]:
-        # Coverage table is written last. overflow is to or within the
-        # the coverage table. We will just cut the subtable in half.
-        newLen = oldLen // 2
-
-    elif overflowRecord.itemName == "LigatureSet":
-        # We just need to back up by two items
-        # from the overflowed AlternateSet index to make sure the offset
-        # to the Coverage table doesn't overflow.
-        newLen = overflowRecord.itemIndex - 1
-
-    newSubTable.ligatures = {}
-    for i in range(newLen, oldLen):
-        item = oldLigs[i]
-        key = item[0]
-        newSubTable.ligatures[key] = item[1]
-        del oldSubTable.ligatures[key]
-
-    return ok
-
-
-def splitPairPos(oldSubTable, newSubTable, overflowRecord):
-    st = oldSubTable
-    ok = False
-    newSubTable.Format = oldSubTable.Format
-    if oldSubTable.Format == 1 and len(oldSubTable.PairSet) > 1:
-        for name in "ValueFormat1", "ValueFormat2":
-            setattr(newSubTable, name, getattr(oldSubTable, name))
-
-        # Move top half of coverage to new subtable
-
-        newSubTable.Coverage = oldSubTable.Coverage.__class__()
-
-        coverage = oldSubTable.Coverage.glyphs
-        records = oldSubTable.PairSet
-
-        oldCount = len(oldSubTable.PairSet) // 2
-
-        oldSubTable.Coverage.glyphs = coverage[:oldCount]
-        oldSubTable.PairSet = records[:oldCount]
-
-        newSubTable.Coverage.glyphs = coverage[oldCount:]
-        newSubTable.PairSet = records[oldCount:]
-
-        oldSubTable.PairSetCount = len(oldSubTable.PairSet)
-        newSubTable.PairSetCount = len(newSubTable.PairSet)
-
-        ok = True
-
-    elif oldSubTable.Format == 2 and len(oldSubTable.Class1Record) > 1:
-        if not hasattr(oldSubTable, "Class2Count"):
-            oldSubTable.Class2Count = len(oldSubTable.Class1Record[0].Class2Record)
-        for name in "Class2Count", "ClassDef2", "ValueFormat1", "ValueFormat2":
-            setattr(newSubTable, name, getattr(oldSubTable, name))
-
-        # The two subtables will still have the same ClassDef2 and the table
-        # sharing will still cause the sharing to overflow.  As such, disable
-        # sharing on the one that is serialized second (that's oldSubTable).
-        oldSubTable.DontShare = True
-
-        # Move top half of class numbers to new subtable
-
-        newSubTable.Coverage = oldSubTable.Coverage.__class__()
-        newSubTable.ClassDef1 = oldSubTable.ClassDef1.__class__()
-
-        coverage = oldSubTable.Coverage.glyphs
-        classDefs = oldSubTable.ClassDef1.classDefs
-        records = oldSubTable.Class1Record
-
-        oldCount = len(oldSubTable.Class1Record) // 2
-        newGlyphs = set(k for k, v in classDefs.items() if v >= oldCount)
-
-        oldSubTable.Coverage.glyphs = [g for g in coverage if g not in newGlyphs]
-        oldSubTable.ClassDef1.classDefs = {
-            k: v for k, v in classDefs.items() if v < oldCount
-        }
-        oldSubTable.Class1Record = records[:oldCount]
-
-        newSubTable.Coverage.glyphs = [g for g in coverage if g in newGlyphs]
-        newSubTable.ClassDef1.classDefs = {
-            k: (v - oldCount) for k, v in classDefs.items() if v > oldCount
-        }
-        newSubTable.Class1Record = records[oldCount:]
-
-        oldSubTable.Class1Count = len(oldSubTable.Class1Record)
-        newSubTable.Class1Count = len(newSubTable.Class1Record)
-
-        ok = True
-
-    return ok
-
-
-def splitMarkBasePos(oldSubTable, newSubTable, overflowRecord):
-    # split half of the mark classes to the new subtable
-    classCount = oldSubTable.ClassCount
-    if classCount < 2:
-        # oh well, not much left to split...
-        return False
-
-    oldClassCount = classCount // 2
-    newClassCount = classCount - oldClassCount
-
-    oldMarkCoverage, oldMarkRecords = [], []
-    newMarkCoverage, newMarkRecords = [], []
-    for glyphName, markRecord in zip(
-        oldSubTable.MarkCoverage.glyphs, oldSubTable.MarkArray.MarkRecord
-    ):
-        if markRecord.Class < oldClassCount:
-            oldMarkCoverage.append(glyphName)
-            oldMarkRecords.append(markRecord)
-        else:
-            markRecord.Class -= oldClassCount
-            newMarkCoverage.append(glyphName)
-            newMarkRecords.append(markRecord)
-
-    oldBaseRecords, newBaseRecords = [], []
-    for rec in oldSubTable.BaseArray.BaseRecord:
-        oldBaseRecord, newBaseRecord = rec.__class__(), rec.__class__()
-        oldBaseRecord.BaseAnchor = rec.BaseAnchor[:oldClassCount]
-        newBaseRecord.BaseAnchor = rec.BaseAnchor[oldClassCount:]
-        oldBaseRecords.append(oldBaseRecord)
-        newBaseRecords.append(newBaseRecord)
-
-    newSubTable.Format = oldSubTable.Format
-
-    oldSubTable.MarkCoverage.glyphs = oldMarkCoverage
-    newSubTable.MarkCoverage = oldSubTable.MarkCoverage.__class__()
-    newSubTable.MarkCoverage.glyphs = newMarkCoverage
-
-    # share the same BaseCoverage in both halves
-    newSubTable.BaseCoverage = oldSubTable.BaseCoverage
-
-    oldSubTable.ClassCount = oldClassCount
-    newSubTable.ClassCount = newClassCount
-
-    oldSubTable.MarkArray.MarkRecord = oldMarkRecords
-    newSubTable.MarkArray = oldSubTable.MarkArray.__class__()
-    newSubTable.MarkArray.MarkRecord = newMarkRecords
-
-    oldSubTable.MarkArray.MarkCount = len(oldMarkRecords)
-    newSubTable.MarkArray.MarkCount = len(newMarkRecords)
-
-    oldSubTable.BaseArray.BaseRecord = oldBaseRecords
-    newSubTable.BaseArray = oldSubTable.BaseArray.__class__()
-    newSubTable.BaseArray.BaseRecord = newBaseRecords
-
-    oldSubTable.BaseArray.BaseCount = len(oldBaseRecords)
-    newSubTable.BaseArray.BaseCount = len(newBaseRecords)
-
-    return True
-
-
-splitTable = {
-    "GSUB": {
-        # 					1: splitSingleSubst,
-        2: splitMultipleSubst,
-        3: splitAlternateSubst,
-        4: splitLigatureSubst,
-        # 					5: splitContextSubst,
-        # 					6: splitChainContextSubst,
-        # 					7: splitExtensionSubst,
-        # 					8: splitReverseChainSingleSubst,
-    },
-    "GPOS": {
-        # 					1: splitSinglePos,
-        2: splitPairPos,
-        # 					3: splitCursivePos,
-        4: splitMarkBasePos,
-        # 					5: splitMarkLigPos,
-        # 					6: splitMarkMarkPos,
-        # 					7: splitContextPos,
-        # 					8: splitChainContextPos,
-        # 					9: splitExtensionPos,
-    },
-}
-
-
-def fixSubTableOverFlows(ttf, overflowRecord):
-    """
-    An offset has overflowed within a sub-table. We need to divide this subtable into smaller parts.
-    """
-    table = ttf[overflowRecord.tableType].table
-    lookup = table.LookupList.Lookup[overflowRecord.LookupListIndex]
-    subIndex = overflowRecord.SubTableIndex
-    subtable = lookup.SubTable[subIndex]
-
-    # First, try not sharing anything for this subtable...
-    if not hasattr(subtable, "DontShare"):
-        subtable.DontShare = True
-        return True
-
-    if hasattr(subtable, "ExtSubTable"):
-        # We split the subtable of the Extension table, and add a new Extension table
-        # to contain the new subtable.
-
-        subTableType = subtable.ExtSubTable.__class__.LookupType
-        extSubTable = subtable
-        subtable = extSubTable.ExtSubTable
-        newExtSubTableClass = lookupTypes[overflowRecord.tableType][
-            extSubTable.__class__.LookupType
-        ]
-        newExtSubTable = newExtSubTableClass()
-        newExtSubTable.Format = extSubTable.Format
-        toInsert = newExtSubTable
-
-        newSubTableClass = lookupTypes[overflowRecord.tableType][subTableType]
-        newSubTable = newSubTableClass()
-        newExtSubTable.ExtSubTable = newSubTable
-    else:
-        subTableType = subtable.__class__.LookupType
-        newSubTableClass = lookupTypes[overflowRecord.tableType][subTableType]
-        newSubTable = newSubTableClass()
-        toInsert = newSubTable
-
-    if hasattr(lookup, "SubTableCount"):  # may not be defined yet.
-        lookup.SubTableCount = lookup.SubTableCount + 1
-
-    try:
-        splitFunc = splitTable[overflowRecord.tableType][subTableType]
-    except KeyError:
-        log.error(
-            "Don't know how to split %s lookup type %s",
-            overflowRecord.tableType,
-            subTableType,
-        )
-        return False
-
-    ok = splitFunc(subtable, newSubTable, overflowRecord)
-    if ok:
-        lookup.SubTable.insert(subIndex + 1, toInsert)
-    return ok
-
-
-# End of OverFlow logic
-
-
-def _buildClasses():
-    import re
-    from .otData import otData
-
-    formatPat = re.compile(r"([A-Za-z0-9]+)Format(\d+)$")
-    namespace = globals()
-
-    # populate module with classes
-    for name, table in otData:
-        baseClass = BaseTable
-        m = formatPat.match(name)
-        if m:
-            # XxxFormatN subtable, we only add the "base" table
-            name = m.group(1)
-            # the first row of a format-switching otData table describes the Format;
-            # the first column defines the type of the Format field.
-            # Currently this can be either 'uint16' or 'uint8'.
-            formatType = table[0][0]
-            baseClass = getFormatSwitchingBaseTableClass(formatType)
-        if name not in namespace:
-            # the class doesn't exist yet, so the base implementation is used.
-            cls = type(name, (baseClass,), {})
-            if name in ("GSUB", "GPOS"):
-                cls.DontShare = True
-            namespace[name] = cls
-
-    # link Var{Table} <-> {Table} (e.g. ColorStop <-> VarColorStop, etc.)
-    for name, _ in otData:
-        if name.startswith("Var") and len(name) > 3 and name[3:] in namespace:
-            varType = namespace[name]
-            noVarType = namespace[name[3:]]
-            varType.NoVarType = noVarType
-            noVarType.VarType = varType
-
-    for base, alts in _equivalents.items():
-        base = namespace[base]
-        for alt in alts:
-            namespace[alt] = base
-
-    global lookupTypes
-    lookupTypes = {
-        "GSUB": {
-            1: SingleSubst,
-            2: MultipleSubst,
-            3: AlternateSubst,
-            4: LigatureSubst,
-            5: ContextSubst,
-            6: ChainContextSubst,
-            7: ExtensionSubst,
-            8: ReverseChainSingleSubst,
-        },
-        "GPOS": {
-            1: SinglePos,
-            2: PairPos,
-            3: CursivePos,
-            4: MarkBasePos,
-            5: MarkLigPos,
-            6: MarkMarkPos,
-            7: ContextPos,
-            8: ChainContextPos,
-            9: ExtensionPos,
-        },
-        "mort": {
-            4: NoncontextualMorph,
-        },
-        "morx": {
-            0: RearrangementMorph,
-            1: ContextualMorph,
-            2: LigatureMorph,
-            # 3: Reserved,
-            4: NoncontextualMorph,
-            5: InsertionMorph,
-        },
-    }
-    lookupTypes["JSTF"] = lookupTypes["GPOS"]  # JSTF contains GPOS
-    for lookupEnum in lookupTypes.values():
-        for enum, cls in lookupEnum.items():
-            cls.LookupType = enum
-
-    global featureParamTypes
-    featureParamTypes = {
-        "size": FeatureParamsSize,
-    }
-    for i in range(1, 20 + 1):
-        featureParamTypes["ss%02d" % i] = FeatureParamsStylisticSet
-    for i in range(1, 99 + 1):
-        featureParamTypes["cv%02d" % i] = FeatureParamsCharacterVariants
-
-    # add converters to classes
-    from .otConverters import buildConverters
-
-    for name, table in otData:
-        m = formatPat.match(name)
-        if m:
-            # XxxFormatN subtable, add converter to "base" table
-            name, format = m.groups()
-            format = int(format)
-            cls = namespace[name]
-            if not hasattr(cls, "converters"):
-                cls.converters = {}
-                cls.convertersByName = {}
-            converters, convertersByName = buildConverters(table[1:], namespace)
-            cls.converters[format] = converters
-            cls.convertersByName[format] = convertersByName
-            # XXX Add staticSize?
-        else:
-            cls = namespace[name]
-            cls.converters, cls.convertersByName = buildConverters(table, namespace)
-            # XXX Add staticSize?
-
-
-_buildClasses()
-
-
-def _getGlyphsFromCoverageTable(coverage):
-    if coverage is None:
-        # empty coverage table
-        return []
-    else:
-        return coverage.glyphs
diff --git a/spaces/cmotions/beatlify/app.py b/spaces/cmotions/beatlify/app.py
deleted file mode 100644
index 555338fc50bbd27d5081d8e71fb5eb690205176d..0000000000000000000000000000000000000000
--- a/spaces/cmotions/beatlify/app.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# Import libraries
-from transformers import pipeline
-from numpy import random
-import gradio as gr
-import re
-import torch
-from torch import autocast
-import os
-
-# Array with song cover art styles
-image_input_styles = ["Random", "Pencil sketch", "Oil painting", "Pop art", "Piet Mondriaan"]
-
-# Get image type for image input
-"""
-The default setting for the art style dropdown is "Random". The below function determines which style is chosen
-If set to "Random", copy the art style array and remove "Random" to prevent "Random" from being a chosen art style
-"""
-def get_image_input(title, given_input_style):
-    if given_input_style == 'Random':
-      image_input_styles_new = image_input_styles.copy()
-      image_input_styles_new.pop(0)
-      random_choice = random.randint(len(image_input_styles_new)-1)
-      final_style = image_input_styles_new[random_choice]
-    else:
-      final_style = given_input_style
-    image_input = 'Cover for ' + title.lower() + ' in style of ' + final_style 
-    return image_input, final_style
-
-# Available models for generate lyrics pipeline
-# checkpoint = 'wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics'
-checkpoint = 'wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics'
-# checkpoint = 'wvangils/BLOOM-560m-Beatles-Lyrics-finetuned'
-
-# Setup all the pipelines we need
-title_generator = pipeline('summarization', model='czearing/story-to-title')
-lyrics_generator = pipeline("text-generation", model=checkpoint)
-
-# For the image generator we use stable diffusion from an existing HuggingFace space, Gradio accelerated backend
-stable_diffusion = gr.Blocks.load(name="spaces/stabilityai/stable-diffusion-1")
-
-# Create 4 images for the given prompt and receive the first one
-# This function uses an existing HuggingFace space where the number of created images cannot be modified
-def get_image(prompt):
-    gallery_dir = stable_diffusion(prompt, fn_index=2)
-    images = [os.path.join(gallery_dir, img) for img in os.listdir(gallery_dir)]
-    return [images[0]]
-
-# Lyrics generation
-def generate_beatles(input_prompt, temperature, top_p, given_input_style):
-    # Create generator for different models
-    generated_lyrics = lyrics_generator(input_prompt
-                              , max_length = 100
-                              , num_return_sequences = 1
-                              , return_full_text = True
-                              , temperature = temperature    
-                              , top_p = top_p    # Default 1.0
-                              , no_repeat_ngram_size = 3   # Default = 0
-                              , repetition_penalty = 1.0   # Default = 1.0
-                              )[0]["generated_text"]
-
-    # Put lyrics in the right form
-    lyrics_sentences = re.sub('\n', '. ', generated_lyrics)
-
-    # Create a title based on the generated lyrics
-    title = title_generator(lyrics_sentences, min_length=1, max_length=10, repetition_penalty=2.5)[0]['summary_text']
-
-    # Create an image based on the generated title
-    image_input, image_style = get_image_input(title, given_input_style)
-
-    # Generate the image
-    image = get_image(image_input)
-    return (title, generated_lyrics, image, image_style)
-    
-# Create textboxes for input and output
-input_box = gr.Textbox(label="Write the start of a song here", placeholder="Write the start of a new song here", value="It's been three days ", lines=2, max_lines=5)
-gen_lyrics = gr.Textbox(label="Song lyrics", lines=15)
-gen_title = gr.Textbox(label="Proposed songtitle", lines=1)
-gen_image = gr.Gallery(label="Proposed song cover").style(grid=1, height="auto")
-gen_image_style = gr.Textbox(label="Image style", lines=1)
-
-# Layout and text around the app
-title='Beatles lyrics generator'
-description="<p style='text-align: center'>We've fine-tuned multiple language models on lyrics from The Beatles to generate Beatles-like text. Below are the results we obtained fine-tuning a GPT Neo model. After generation a title is generated using <a href='https://huggingface.co/czearing/story-to-title' target='_blank'>this model</a>. On top we use the generated title to suggest an album cover using <a href='https://huggingface.co/CompVis/stable-diffusion-v1-4' target='_blank'>Stable Diffusion 1.4</a>. Give it a try!</p>"
-article="""<p style='text-align: left'>These text generation models that output Beatles-like text were created by data
-    scientists working for <a href='https://cmotions.nl/' target="_blank">Cmotions.</a>
-    We tried several text generation models that we were able to load in Colab: a general <a
-        href='https://huggingface.co/gpt2-medium' target='_blank'>GPT2-medium</a> model, the Eleuther AI small-sized GPT
-    model <a href='https://huggingface.co/EleutherAI/gpt-neo-125M' target='_blank'>GPT-Neo</a> and the new kid on the
-    block build by the <a
-        href='https://bigscience.notion.site/BLOOM-BigScience-176B-Model-ad073ca07cdf479398d5f95d88e218c4'
-        target='_blank'>Bigscience</a> initiative <a href='https://huggingface.co/bigscience/bloom-560m'
-        target='_blank'>BLOOM 560m</a>.
-    Further we've put together a <a href='https://huggingface.co/datasets/cmotions/Beatles_lyrics' target='_blank'>
-        Huggingface dataset</a> containing all known lyrics created by The Beatles. A general blog on building this text generator can be found <a
-        href='https://www.theanalyticslab.nl/building-a-beatles-lyrics-generator/' target='_blank'>here. </a> If you are interested in how we evaluated the results of these models <a
-        href='https://www.theanalyticslab.nl/how-to-evaluate-a-text-generation-model-strengths-and-limitations-of-popular-evaluation-metrics/' target='_blank'>this blog</a> will enlighten you. Finally we also wrote on our experience to showcase the results of our model
-        by using HF spaces, details can be found <a href='https://www.theanalyticslab.nl/how-to-showcase-your-demo-on-a-hugging-face-space/' target='_blank'>here. </a>
-    The default output contains 100 tokens and has a repetition penalty of 1.0.
-</p>"""
-css = """
-        .gr-button-primary {
-          text-indent: -9999px;
-          line-height: 0;
-        }  
-        .gr-button-primary:after {
-          content: "Beatlify!";
-          text-indent: 0;
-          display: block;
-          line-height: initial;
-        }
-""" 
-
-# Let users select their own temperature and top-p
-temperature = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, label="Change the temperature \r\n (higher temperature = more creative in lyrics generation, but posibbly less Beatly)", value=0.7, show_label=True)  #high = sensitive for low probability tokens
-top_p = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, label="Change top probability of the next word \n (higher top probability = more words to choose from for the next word, but possibly less Beatly)", value=0.5, show_label=True)
-given_input_style = gr.Dropdown(choices=image_input_styles, value="Random", label="Choose the art style for the lyrics cover", show_label=True)
-
-# Use generate Beatles function in demo-app Gradio
-gr.Interface(fn=generate_beatles
-             , inputs=[input_box, temperature, top_p, given_input_style]
-             , outputs=[gen_title, gen_lyrics, gen_image, gen_image_style]
-             , title=title
-             , css=css
-             , description=description
-             , article=article
-             , allow_flagging='never'
-             ).launch()
\ No newline at end of file
diff --git "a/spaces/codertoro/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/codertoro/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/codertoro/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/colakin/video-generater/public/ffmpeg/libavcodec/codec_desc.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/codec_desc.h
deleted file mode 100644
index 126b52df47629c51816b510cf9ee5c67f6f0f498..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/codec_desc.h
+++ /dev/null
@@ -1,128 +0,0 @@
-/*
- * Codec descriptors public API
- *
- * 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
- */
-
-#ifndef AVCODEC_CODEC_DESC_H
-#define AVCODEC_CODEC_DESC_H
-
-#include "libavutil/avutil.h"
-
-#include "codec_id.h"
-
-/**
- * @addtogroup lavc_core
- * @{
- */
-
-/**
- * This struct describes the properties of a single codec described by an
- * AVCodecID.
- * @see avcodec_descriptor_get()
- */
-typedef struct AVCodecDescriptor {
-    enum AVCodecID     id;
-    enum AVMediaType type;
-    /**
-     * Name of the codec described by this descriptor. It is non-empty and
-     * unique for each codec descriptor. It should contain alphanumeric
-     * characters and '_' only.
-     */
-    const char      *name;
-    /**
-     * A more descriptive name for this codec. May be NULL.
-     */
-    const char *long_name;
-    /**
-     * Codec properties, a combination of AV_CODEC_PROP_* flags.
-     */
-    int             props;
-    /**
-     * MIME type(s) associated with the codec.
-     * May be NULL; if not, a NULL-terminated array of MIME types.
-     * The first item is always non-NULL and is the preferred MIME type.
-     */
-    const char *const *mime_types;
-    /**
-     * If non-NULL, an array of profiles recognized for this codec.
-     * Terminated with FF_PROFILE_UNKNOWN.
-     */
-    const struct AVProfile *profiles;
-} AVCodecDescriptor;
-
-/**
- * Codec uses only intra compression.
- * Video and audio codecs only.
- */
-#define AV_CODEC_PROP_INTRA_ONLY    (1 << 0)
-/**
- * Codec supports lossy compression. Audio and video codecs only.
- * @note a codec may support both lossy and lossless
- * compression modes
- */
-#define AV_CODEC_PROP_LOSSY         (1 << 1)
-/**
- * Codec supports lossless compression. Audio and video codecs only.
- */
-#define AV_CODEC_PROP_LOSSLESS      (1 << 2)
-/**
- * Codec supports frame reordering. That is, the coded order (the order in which
- * the encoded packets are output by the encoders / stored / input to the
- * decoders) may be different from the presentation order of the corresponding
- * frames.
- *
- * For codecs that do not have this property set, PTS and DTS should always be
- * equal.
- */
-#define AV_CODEC_PROP_REORDER       (1 << 3)
-/**
- * Subtitle codec is bitmap based
- * Decoded AVSubtitle data can be read from the AVSubtitleRect->pict field.
- */
-#define AV_CODEC_PROP_BITMAP_SUB    (1 << 16)
-/**
- * Subtitle codec is text based.
- * Decoded AVSubtitle data can be read from the AVSubtitleRect->ass field.
- */
-#define AV_CODEC_PROP_TEXT_SUB      (1 << 17)
-
-/**
- * @return descriptor for given codec ID or NULL if no descriptor exists.
- */
-const AVCodecDescriptor *avcodec_descriptor_get(enum AVCodecID id);
-
-/**
- * Iterate over all codec descriptors known to libavcodec.
- *
- * @param prev previous descriptor. NULL to get the first descriptor.
- *
- * @return next descriptor or NULL after the last descriptor
- */
-const AVCodecDescriptor *avcodec_descriptor_next(const AVCodecDescriptor *prev);
-
-/**
- * @return codec descriptor with the given name or NULL if no such descriptor
- *         exists.
- */
-const AVCodecDescriptor *avcodec_descriptor_get_by_name(const char *name);
-
-/**
- * @}
- */
-
-#endif // AVCODEC_CODEC_DESC_H
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/d3d11va.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/d3d11va.h
deleted file mode 100644
index 6816b6c1e68494bc6d6f0b14cbd232bbcc714a05..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/d3d11va.h
+++ /dev/null
@@ -1,112 +0,0 @@
-/*
- * Direct3D11 HW acceleration
- *
- * copyright (c) 2009 Laurent Aimar
- * copyright (c) 2015 Steve Lhomme
- *
- * 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
- */
-
-#ifndef AVCODEC_D3D11VA_H
-#define AVCODEC_D3D11VA_H
-
-/**
- * @file
- * @ingroup lavc_codec_hwaccel_d3d11va
- * Public libavcodec D3D11VA header.
- */
-
-#if !defined(_WIN32_WINNT) || _WIN32_WINNT < 0x0602
-#undef _WIN32_WINNT
-#define _WIN32_WINNT 0x0602
-#endif
-
-#include <stdint.h>
-#include <d3d11.h>
-
-/**
- * @defgroup lavc_codec_hwaccel_d3d11va Direct3D11
- * @ingroup lavc_codec_hwaccel
- *
- * @{
- */
-
-#define FF_DXVA2_WORKAROUND_SCALING_LIST_ZIGZAG 1 ///< Work around for Direct3D11 and old UVD/UVD+ ATI video cards
-#define FF_DXVA2_WORKAROUND_INTEL_CLEARVIDEO    2 ///< Work around for Direct3D11 and old Intel GPUs with ClearVideo interface
-
-/**
- * This structure is used to provides the necessary configurations and data
- * to the Direct3D11 FFmpeg HWAccel implementation.
- *
- * The application must make it available as AVCodecContext.hwaccel_context.
- *
- * Use av_d3d11va_alloc_context() exclusively to allocate an AVD3D11VAContext.
- */
-typedef struct AVD3D11VAContext {
-    /**
-     * D3D11 decoder object
-     */
-    ID3D11VideoDecoder *decoder;
-
-    /**
-      * D3D11 VideoContext
-      */
-    ID3D11VideoContext *video_context;
-
-    /**
-     * D3D11 configuration used to create the decoder
-     */
-    D3D11_VIDEO_DECODER_CONFIG *cfg;
-
-    /**
-     * The number of surface in the surface array
-     */
-    unsigned surface_count;
-
-    /**
-     * The array of Direct3D surfaces used to create the decoder
-     */
-    ID3D11VideoDecoderOutputView **surface;
-
-    /**
-     * A bit field configuring the workarounds needed for using the decoder
-     */
-    uint64_t workaround;
-
-    /**
-     * Private to the FFmpeg AVHWAccel implementation
-     */
-    unsigned report_id;
-
-    /**
-      * Mutex to access video_context
-      */
-    HANDLE  context_mutex;
-} AVD3D11VAContext;
-
-/**
- * Allocate an AVD3D11VAContext.
- *
- * @return Newly-allocated AVD3D11VAContext or NULL on failure.
- */
-AVD3D11VAContext *av_d3d11va_alloc_context(void);
-
-/**
- * @}
- */
-
-#endif /* AVCODEC_D3D11VA_H */
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Buildcraft The Ultimate Guide to Automation in Minecraft.md b/spaces/congsaPfin/Manga-OCR/logs/Buildcraft The Ultimate Guide to Automation in Minecraft.md
deleted file mode 100644
index a1e30b78a0789b58f41777d6f525bf40272b090a..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Buildcraft The Ultimate Guide to Automation in Minecraft.md	
+++ /dev/null
@@ -1,131 +0,0 @@
-<br />
-<h1>Buildcraft: A Minecraft Mod for Automation and Industrialization</h1>
-<p>If you are a fan of Minecraft and you want to add more machines, pipes, and automation to your game, then you might want to try Buildcraft. Buildcraft is a mod that allows you to mine resources, craft items, and build structures with ease. In this article, we will explain what Buildcraft is, how to install it, and how to use it.</p>
-<h2>buildcraft</h2><br /><p><b><b>DOWNLOAD</b> &#10037;&#10037;&#10037; <a href="https://urlca.com/2uO7fn">https://urlca.com/2uO7fn</a></b></p><br /><br />
- <h2>What is Buildcraft?</h2>
-<p>Buildcraft is a mod that adds various blocks and items to Minecraft that can be used for automation and industrialization. With Buildcraft, you can create machines that can mine, transport, process, and store resources, as well as craft items and build structures automatically. You can also use pipes to connect different machines and inventories, and use MJ (Minecraft Joules) as a form of energy to power your machines.</p>
- <h3>The main features of Buildcraft</h3>
-<p>Some of the main features of Buildcraft are:</p>
-<ul>
-<li>Pipes: Pipes are used to transport items, fluids, and MJ between inventories and machines. There are many types of pipes with different functions and properties, such as wooden, cobblestone, iron, gold, diamond, emerald, and obsidian pipes. You can also use pipe sealant to make fluid pipes or redstone to make kinesis pipes.</li>
-<li>Machines: Machines are blocks that can perform various tasks, such as mining, crafting, sorting, pumping, refining, smelting, and more. Some examples of machines are the quarry, the assembly table, the refinery, the furnace, the builder, and the filler.</li>
-<li>Tools: Tools are items that can help you with your automation and building projects. Some examples of tools are the wrench, the blueprint, the template, the filler pattern, the gate, the pipe plug, and the paintbrush.</li>
-<li>Robots: Robots are entities that can perform various tasks for you, such as harvesting crops, chopping trees, picking up items, placing blocks, fighting mobs, and more. You can program robots with triggers and actions using gates and zone planners.</li>
-</ul>
- <h3>The modules of Buildcraft</h3>
-<p>Buildcraft is divided into several modules that can be installed separately or together. The modules are:</p>
-<ul>
-<li>Core: The core module contains the basic blocks and items that are required for all other modules.</li>
-<li>Builders: The builders module contains the blocks and items that can help you build structures automatically, such as the builder, the filler, the architect table, the blueprint library, and the landmarks.</li>
-<li>Energy: The energy module contains the blocks and items that can generate and transport MJ (Minecraft Joules), such as the engines, the kinesis pipes, the oil spring, and the oil bucket.</li>
-<li>Factory: The factory module contains the blocks and items that can process resources and craft items automatically, such as the refinery, the auto workbench, the mining well, the pump, and the flood gate.</li>
-<li>Robotics: The robotics module contains the blocks and items that can create and control robots, such as the zone planner, the docking station, the requester gate, the robot programmer,</li>
-<li>Silicon: The silicon module contains the blocks and items that can create advanced circuits and chips for gates and robots, such as the assembly table, the integration table, the packager, the stamper, and the laser.</li>
-<li>Transport: The transport module contains the blocks and items that can transport items, fluids, and MJ using pipes, such as the wooden pipe, the iron pipe, the diamond pipe, the emerald pipe, and the pipe wire.</li>
-</ul>
- <h2>How to install Buildcraft</h2>
-<p>If you want to install Buildcraft, you will need some requirements and steps to follow. Here are the requirements and steps for installing Buildcraft:</p>
- <h3>The requirements for Buildcraft</h3>
-<p>Before you install Buildcraft, you will need to have the following:</p>
-<p>How to install Buildcraft mod for Minecraft<br />
-Buildcraft pipes tutorial and tips<br />
-Best machines and blueprints for Buildcraft<br />
-Buildcraft vs IndustrialCraft: which mod is better?<br />
-How to use Buildcraft robots and zones<br />
-Buildcraft energy sources and converters<br />
-Buildcraft quarry and mining guide<br />
-Buildcraft compatible mods and addons<br />
-How to automate crafting with Buildcraft<br />
-Buildcraft assembly table and integration table<br />
-Buildcraft filler and builder blocks<br />
-Buildcraft oil and fuel production<br />
-Buildcraft tanks and fluid pipes<br />
-Buildcraft modpacks and servers<br />
-How to use Buildcraft paintbrushes and facades<br />
-Buildcraft silicon and redstone chips<br />
-Buildcraft gates and wires<br />
-Buildcraft emerald pipes and filters<br />
-Buildcraft diamond pipes and sorting<br />
-Buildcraft stripes pipes and actions<br />
-How to use Buildcraft landmarks and markers<br />
-Buildcraft kinesis pipes and power transfer<br />
-Buildcraft wooden pipes and extraction<br />
-Buildcraft cobblestone pipes and collision<br />
-Buildcraft stone pipes and speed<br />
-Buildcraft sandstone pipes and voiding<br />
-Buildcraft iron pipes and direction<br />
-Buildcraft gold pipes and acceleration<br />
-Buildcraft obsidian pipes and suction<br />
-How to make a nuclear reactor with Buildcraft<br />
-How to use the Zone Planner in Buildcraft<br />
-How to make a space station with Buildcraft<br />
-How to use the Programmable Rednet Controller with Buildcraft<br />
-How to make a solar farm with Buildcraft<br />
-How to use the Architect Table and Blueprint Library in Buildcraft<br />
-How to make a windmill with Buildcraft<br />
-How to use the Packager and Stamper in Buildcraft<br />
-How to make a water wheel with Buildcraft<br />
-How to use the Pipe Plug in Buildcraft<br />
-How to make a steam engine with Buildcraft</p>
-<ul>
-<li>Minecraft: You will need to have Minecraft installed on your computer. You can download Minecraft from the official website. The latest version of Buildcraft is compatible with Minecraft 1.12.2.</li>
-<li>Forge: You will need to have Forge installed on your Minecraft. Forge is a mod loader that allows you to run multiple mods at the same time. You can download Forge from the official website. Make sure you download the version that matches your Minecraft version.</li>
-<li>Buildcraft: You will need to download the Buildcraft mod files from the official website. You can choose to download the full package or the individual modules that you want.</li>
-</ul>
- <h3>The steps to install Buildcraft</h3>
-<p>After you have the requirements, you can follow these steps to install Buildcraft:</p>
-<ol>
-<li>Open your Minecraft launcher and select the profile that has Forge installed.</li>
-<li>Click on "Play" and wait for Minecraft to load.</li>
-<li>Once Minecraft is loaded, click on "Quit Game".</li>
-<li>Go to your Minecraft folder. You can find it by typing %appdata% in your Windows search bar and opening the .minecraft folder.</li>
-<li>Open the "mods" folder. If you don't have one, create one.</li>
-<li>Copy and paste the Buildcraft mod files that you downloaded into the "mods" folder.</li>
-<li>Launch Minecraft again and select the Forge profile.</li>
-<li>Click on "Mods" in the main menu and check if Buildcraft is listed. If it is, congratulations, you have successfully installed Buildcraft!</li>
-</ol>
- <h2>How to use Buildcraft</h2>
-<p>Now that you have installed Buildcraft, you can start using it to automate and industrialize your Minecraft world. Here are some tips on how to use Buildcraft:</p>
- <h3>The basics of pipes and MJ</h3>
-<p>Pipes are essential for transporting items, fluids, and MJ between machines and inventories. To use pipes, you will need to craft them using different materials, such as wood, stone, iron, gold, diamond, emerald, and obsidian. You can also use pipe sealant to make fluid pipes or redstone to make kinesis pipes. To connect pipes, simply place them next to each other or use pipe plugs to block unwanted connections.</p>
- <p>MJ (Minecraft Joules) is the form of energy that powers most of the machines in Buildcraft. To generate MJ, you will need to use engines, such as redstone engines, stirling engines, combustion engines, or creative engines. To transport MJ, you will need to use kinesis pipes, which are made by combining any pipe with redstone. To store MJ, you will need to use energy cells or batteries.</p>
- <h3>The machines and tools of Buildcraft</h3>
-<p>Machines are blocks that can perform various tasks for you, such as mining, crafting, sorting, pumping, refining, smelting, and more. To use machines, you will need to place them in the world and provide them with MJ and inputs (items or fluids). Some machines also have outputs (items or fluids) that can be transported with pipes. Some machines also have GUIs (graphical user interfaces) that allow you to configure them or see their status.</p>
- <p>Tools are items that can help you with your automation and building projects. To use tools, you will need to hold them in your hand and right-click on blocks or entities. Some tools also have GUIs that allow you to set parameters or see information.</p>
- <p>Here is a table that shows some of the machines and tools of Buildcraft and their functions:</p>
- <table>
-<tr><th>Machines/Tools</th><th>Functions</th></tr>
-<tr><td>Quarry</td><td>Mines a large area of land and collects the resources</td></tr>
-<tr><td>Assembly Table</td><td>Crafts advanced circuits and chips using lasers</td></tr>
-<tr><td>Refinery</td><td>Refines oil into fuel</td></tr>
-<tr><td>Furnace</td><td>Smelts items using MJ</td></tr>
-<tr><td>Builder</td><td>Builds structures using blueprints or templates</td></tr>
-<tr><td>Filler</td><td>Fills or clears areas using filler patterns</td></tr>
-<tr><td>Wrench</td><td>Rotates or removes machines or pipes</td></tr <tr><td>Blueprint</td><td>Stores the layout and materials of a structure</td></tr>
-<tr><td>Template</td><td>Stores the layout of a structure</td></tr>
-<tr><td>Filler Pattern</td><td>Defines how the filler fills or clears an area</td></tr>
-<tr><td>Gate</td><td>Controls the flow of items, fluids, and MJ using logic</td></tr>
-<tr><td>Pipe Plug</td><td>Blocks pipe connections</td></tr>
-<tr><td>Paintbrush</td><td>Paints pipes with different colors</td></tr>
-</table>
- <h3>The blueprints and robots of Buildcraft</h3>
-<p>Blueprints and robots are features that can help you create and control complex structures and tasks. To use blueprints, you will need to craft them using paper and lapis lazuli. You can then use an architect table to scan a structure and save it as a blueprint or a template. You can also use a blueprint library to store and share your blueprints or templates. To use a builder, you will need to place it in the world and provide it with a blueprint or a template, MJ, and the required materials. The builder will then build the structure according to the blueprint or template.</p>
- <p>Robots are entities that can perform various tasks for you, such as harvesting crops, chopping trees, picking up items, placing blocks, fighting mobs, and more. To use robots, you will need to craft them using an assembly table and different materials, such as iron ingots, redstone, diamonds, emeralds, and eyes of ender. You can also use an integration table to upgrade your robots with different modules, such as shields, weapons, tools, and sensors. To control robots, you will need to use a zone planner to define zones for different tasks, a docking station to charge and deploy robots, and a gate to program robots with triggers and actions.</p>
- <h2>Conclusion</h2>
-<p>Buildcraft is a mod that adds automation and industrialization to Minecraft. With Buildcraft, you can create machines that can mine, transport, process, and store resources, as well as craft items and build structures automatically. You can also use pipes to connect different machines and inventories, and use MJ as a form of energy to power your machines. You can also use blueprints and robots to create and control complex structures and tasks. Buildcraft is a mod that can enhance your Minecraft experience and challenge your creativity and engineering skills.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Buildcraft:</p>
-<ul>
-<li><b>Q: Is Buildcraft compatible with other mods?</b></li>
-<li>A: Yes, Buildcraft is compatible with many other mods that use Forge as a mod loader. Some examples of compatible mods are IndustrialCraft 2, Thermal Expansion, Forestry, Project Red, Railcraft, and ComputerCraft.</li>
-<li><b>Q: How can I get more information about Buildcraft?</b></li>
-<li>A: You can get more information about Buildcraft from the official website, the wiki, the forums, the Discord server, or the YouTube channel.</li>
-<li><b>Q: How can I report bugs or suggest features for Buildcraft?</b></li>
-<li>A: You can report bugs or suggest features for Buildcraft on the GitHub page or the Discord server.</li>
-<li><b>Q: How can I support the development of Buildcraft?</b></li>
-<li>A: You can support the development of Buildcraft by donating on Patreon or PayPal.</li>
-<li><b>Q: How can I learn more about automation and industrialization in real life?</b></li>
-<li>A: You can learn more about automation and industrialization in real life by reading books, watching documentaries, taking online courses, or visiting museums.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Aplikasi Mad Skills Motocross 3 and Enjoy Realistic Physics and Graphics.md b/spaces/congsaPfin/Manga-OCR/logs/Download Aplikasi Mad Skills Motocross 3 and Enjoy Realistic Physics and Graphics.md
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-<br />
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-<p>If you are a fan of motocross games, you must have heard of Mad Skills Motocross 3, the latest installment in the popular series by Turborilla. This game is one of the best motocross simulators on the market, with realistic physics, stunning graphics, and addictive gameplay. But what if you want to enjoy the game without any limitations or interruptions? That's where Mad Skills Motocross 3 mod apk comes in handy. In this article, we will tell you everything you need to know about this modded version of the game, including its features, benefits, and how to download and install it on your device. Let's get started!</p>
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-<h2>What is Mad Skills Motocross 3?</h2>
-<p>Mad Skills Motocross 3 is a racing game that lets you experience the thrill of motocross on your mobile device. You can choose from dozens of different bikes and riders, customize them to your liking, and compete against other players from around the world. You can also participate in weekly competitions and events, where you can win prizes and fame. The game has a simple control system that is easy to learn but hard to master. You can use the buttons on the screen to accelerate, brake, lean forward, and lean backward. You have to balance your speed and your posture to avoid crashing and perform tricks. The game has a realistic physics engine that makes every jump, landing, and collision feel authentic. The game also has amazing graphics that bring the tracks and the environments to life.</p>
-<h3>Features of Mad Skills Motocross 3</h3>
-<p>Mad Skills Motocross 3 has many features that make it one of the best motocross games on the market. Here are some of them:</p>
-<h4>Realistic physics</h4>
-<p>The game has a realistic physics engine that simulates the behavior of the bikes and the riders. You can feel the weight, the momentum, and the gravity of your bike as you race through the tracks. You can also see how your bike reacts to different terrains, such as dirt, sand, mud, grass, and water. You have to adjust your speed and your angle to avoid crashing or losing control.</p>
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-<p>The game allows you to customize your bike and your rider to suit your style and preferences. You can choose from dozens of different bikes, each with its own characteristics and performance. You can also change the color, the decals, the wheels, and the exhaust of your bike. You can also customize your rider's appearance, such as their helmet, their suit, their gloves, and their boots. You can even create your own custom number plate.</p>
-<h4>Online multiplayer and leaderboards</h4>
-<p>The game has an online multiplayer mode that lets you race against other players from around the world. You can join or create a room with up to eight players and compete in different modes, such as time trial, head-to-head, or team race. You can also chat with other players and send them emojis. The game also has leaderboards that rank you based on your performance in different categories, such as global, regional, national, or friends. You can also see your stats and achievements in your profile.</p>
-<h4>Weekly competitions and events</h4>
-<p>The game has weekly competitions and events that give you a chance to win prizes and fame. You can join different events, such as jam, cash jam, boss fight, or special series. Each event has its own rules, rewards, and challenges. You can also see how you rank among other players in each event.</p>
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-<p>Mad Skills Motocross 3 is a free-to-play game, but it also has some in-app purchases and ads that can limit your enjoyment. For example, you need to spend money to buy new bikes, upgrade them, or unlock new tracks. You also have to watch ads to get extra lives, coins, or bonuses. If you want to avoid these hassles and enjoy the game to the fullest, you should download Mad Skills Motocross 3 mod apk. This is a modified version of the game that gives you some advantages and benefits, such as:</p>
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-<p>With Mad Skills Motocross 3 mod apk, you will not see any ads in the game. You don't have to watch annoying videos or banners that interrupt your gameplay or waste your time. You can play the game smoothly and without any distractions.</p>
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-<p>If you are interested in downloading and installing Mad Skills Motocross 3 mod apk on your device, you can follow these simple steps:</p>
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-<p>The next thing you need to do is to enable unknown sources on your device. This is a security setting that allows you to install apps from sources other than the Google Play Store. To enable unknown sources, you can follow these steps:</p>
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-<h2>Conclusion</h2>
-<p>Mad Skills Motocross 3 is a great game for motocross lovers who want to experience realistic and exciting racing on their mobile devices. However, if you want to enjoy the game without any limitations or interruptions, you should download Mad Skills Motocross 3 mod apk. This is a modified version of the game that gives you unlimited money, unlocked all bikes and tracks, and no ads. You can download and install it easily by following our guide above. We hope this article was helpful for you. If you have any questions or feedback, feel free to leave a comment below.</p>
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- <ul>
- <li><b>Is Mad Skills Motocross 3 mod apk safe?</b></li> <p>Yes, Mad Skills Motocross 3 mod apk is safe to use, as long as you download it from a trusted source. We have tested the mod apk file and found no viruses or malware. However, you should always be careful when downloading and installing apps from unknown sources, as they may contain harmful or unwanted content. You should also backup your data before installing the mod apk, in case something goes wrong.</p>
- <li><b>What are the minimum requirements to play Mad Skills Motocross 3?</b></li>
-<p>Mad Skills Motocross 3 is a relatively lightweight game that can run on most Android devices. However, you need to have at least Android 4.4 or higher and 2 GB of RAM to play the game smoothly. You also need to have a stable internet connection to access the online features of the game.</p>
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-<p>Mad Skills Motocross 3 mod apk is not available on the Google Play Store, so you cannot update it automatically. You need to check for updates manually from the source where you downloaded the mod apk file. If there is a new version available, you need to download and install it again, following the same steps as before. You may also need to uninstall the previous version of the mod apk before installing the new one.</p>
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-<p>Yes, you can play Mad Skills Motocross 3 mod apk with your friends, as long as they also have the mod apk installed on their devices. You can join or create a room with up to eight players and race against each other in different modes. You can also chat with your friends and send them emojis during the game.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download No Love X Aaja We Mahiya X Against All Odd - The Best Mashup of 2023 by Shubh and Friends.md b/spaces/congsaPfin/Manga-OCR/logs/Download No Love X Aaja We Mahiya X Against All Odd - The Best Mashup of 2023 by Shubh and Friends.md
deleted file mode 100644
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--- a/spaces/congsaPfin/Manga-OCR/logs/Download No Love X Aaja We Mahiya X Against All Odd - The Best Mashup of 2023 by Shubh and Friends.md	
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@@ -1,83 +0,0 @@
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-<h1>No Love x Aaja We Mahiya Song Download: A Mashup of Punjabi Hits</h1>
- <p>If you are a fan of Punjabi music, you might have heard of the song No Love x Aaja We Mahiya, a mashup of three songs by different artists. The song is a blend of No Love by Shubh, Aaja We Mahiya by Imran Khan, and Against All Odds by AP Dhillon, Gurinder Gill, and Shinda Kahlon. The mashup was created by Saurabh Chaudhary, a music producer and DJ, and uploaded on YouTube in December 2022. The song has gained over 21 million views and 219 thousand likes so far, making it one of the most popular Punjabi mashups of recent times.</p>
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- <p>In this article, we will tell you how to download the song from various platforms, why it is popular and what are the reviews, what are the lyrics and the meaning of the song, and some FAQs that you might have. So, let's get started!</p>
- <h2>How to download the song from various platforms?</h2>
- <p>If you want to download the song No Love x Aaja We Mahiya for offline listening, you have several options. Here are some of them:</p>
- <ul>
-<li>Apple Music: If you have an Apple Music subscription, you can stream or download the song from [Apple Music](^2^). You can also get up to 3 months free trial if you are a new user.</li>
-<li>Shazam: If you use Shazam, a music recognition app, you can identify the song by playing it or humming it. Then, you can tap on the song title and choose to play it on Apple Music or Spotify. You can also see the lyrics and other information about the song on Shazam.</li>
-<li>YouTube: If you prefer YouTube, you can watch or download the song from [YouTube](^1^). You can also use a YouTube downloader tool or website to save the song as an MP3 file on your device.</li>
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-</ul>
- <h2>Why is the song popular and what are the reviews?</h2>
- <p>The song No Love x Aaja We Mahiya is popular because it combines three hit songs by talented Punjabi artists. The mashup showcases the diversity and versatility of Punjabi music, as it blends different genres, styles, and moods. The song also appeals to a wide audience, as it has elements of romance, sadness, motivation, and celebration.</p>
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-<p>"This is one of the best mashups I have ever heard. Respect to Saurabh Chaudhary for making this masterpiece."</p>
-<p>"No love x Aaja we mahiya x Against all odds = Fire emoji Fire emoji Fire emoji"</p>
-<p>"All three songs are amazing in their own way. But when they come together like this, it's just mind-blowing."</p>
-<p>"I can't stop listening to this mashup. It's so addictive and catchy."</p>
-</blockquote>
- <h2>What are the lyrics and the meaning of the song?</h2>
- jabi, and they express different emotions and situations. The song starts with No Love by Shubh, which is a sad song about a broken relationship. The chorus goes like this:</p>
- <pre><code>
-No love, no love I don't need your love You can keep your love 'Cause I don't feel your love </code></pre>
- <p>The song then transitions to Aaja We Mahiya by Imran Khan, which is a romantic song about a lover who is waiting for his beloved. The chorus goes like this:</p>
- <pre><code>
-Aaja we mahiya Come my love Tere bin dil lagda nahi My heart doesn't feel without you Aaja we mahiya Come my love Tere bin dil mera lagda nahi My heart doesn't feel without you </code></pre>
- <p>The song then switches to Against All Odds by AP Dhillon, Gurinder Gill, and Shinda Kahlon, which is a motivational song about overcoming challenges and achieving success. The chorus goes like this:</p>
- <pre><code>
-Against all odds we made it happen We never gave up we kept on grinding Against all odds we made it happen We never gave up we kept on shining </code></pre>
- <p>The song then repeats the three parts in different orders, creating a dynamic and captivating mashup. The song ends with a part from No Love, where Shubh sings:</p>
- <pre><code>
-I don't need you in my life I don't need you by my side I don't need you in my sight I don't need you in my mind </code></pre>
- <h2>Conclusion</h2>
- <p>No Love x Aaja We Mahiya is a mashup of three Punjabi songs that have different themes and vibes. The song is a creative and unique blend of music that showcases the talent and diversity of Punjabi artists. The song is also popular and well-received by the listeners, who enjoy the catchy and emotional lyrics and the smooth and energetic beats. If you want to download the song, you can use various platforms such as Apple Music, Shazam, YouTube, or Rytmp3.fun. We hope you enjoyed this article and learned something new about this amazing mashup.</p>
- <p>If you liked this article, please share it with your friends and family who might be interested in Punjabi music. Also, don't forget to leave a comment below and let us know what you think of the song No Love x Aaja We Mahiya. Thank you for reading!</p>
- <h2>FAQs</h2>
- <h3>Who made the mashup of No Love x Aaja We Mahiya?</h3>
-<p>The mashup was made by Saurabh Chaudhary, a music producer and DJ from India.</p>
- <h3>When was the mashup released?</h3>
-<p>The mashup was released on December 12, 2022 on YouTube.</p>
- <h3>How many views and likes does the mashup have on YouTube?</h3>
-<p>The mashup has over 21 million views and 219 thousand likes as of June 2023.</p>
- <h3>What are the original songs that are used in the mashup?</h3>
-<p>The original songs are No Love by Shubh, Aaja We Mahiya by Imran Khan, and Against All Odds by AP Dhillon, Gurinder Gill, and Shinda Kahlon.</p>
- <h3>What are the genres and styles of the original songs?</h3>
-<p>No Love is a hip hop/R&B song, Aaja We Mahiya is a pop/Bhangra song, and Against All Odds is a rap/trap song.</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/FNAF 3 The Best Tips and Tricks to Beat the Game.md b/spaces/congsaPfin/Manga-OCR/logs/FNAF 3 The Best Tips and Tricks to Beat the Game.md
deleted file mode 100644
index f30c6cf0981c66268268f8779c185f5846815974..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/FNAF 3 The Best Tips and Tricks to Beat the Game.md	
+++ /dev/null
@@ -1,157 +0,0 @@
-
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-<p>If you are a fan of horror games, you have probably heard of <strong>Five Nights at Freddy's</strong>, a series of indie point-and-click survival horror games developed by Scott Cawthon. The series revolves around a fictional chain of pizza restaurants called Freddy Fazbear's Pizza, where animatronic animals entertain children during the day, but become murderous at night. The player takes on the role of a night guard who must survive five nights (or more) while avoiding being killed by the animatronics.</p>
-<p>The third installment of the series, <strong>Five Nights at Freddy's 3</strong> (<strong>fnaf 3</strong> for short), was released in March 2015 for Windows, Android, iOS, Nintendo Switch, PlayStation 4, and Xbox One. The game is set thirty years after the events of the first game, in a horror-themed attraction called Fazbear's Fright, which is based on the legend of Freddy Fazbear's Pizza. The player must monitor two sets of security cameras, one for the rooms and corridors of the attraction, and one for the ventilation ducts, while also managing three operating systems that control the cameras, audio devices, and ventilation. The player must also fend off a single animatronic called Springtrap, as well as several phantom versions of previous animatronics that can cause malfunctions and jump scares.</p>
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-<tr>
-<th>Name</th>
-<th>Appearance</th>
-<th>Behavior</th>
-<th>Role</th>
-</tr>
-<tr>
-<td>Springtrap</td>
-<td>A decayed and patchwork rabbit-like animatronic with damp and moldy fur. He has loose metal, wires, and his exoskeleton exposed in many areas. He also contains the corpse of William Afton, the Purple Guy, inside him.</td>
-<td>He is the main antagonist of the game and the only animatronic that can kill the player directly. He roams around the attraction and tries to enter the office through the door or the vents. He can be lured away by playing audio of a child laughing in adjacent rooms. He can also be blocked by sealing off certain vents.</td>
-<td>He is the main attraction of Fazbear's Fright and a remnant of the original Freddy Fazbear's Pizza. He was once a springlock suit that could be worn by employees or used as an animatronic, but it malfunctioned and killed William Afton, who was hiding from the ghosts of his victims inside it.</td>
-</tr>
-<tr>
-<td>Phantom Freddy</td>
-<td>A damaged and burnt hallucination of Freddy Fazbear from the original Freddy Fazbear's Pizza. He still carries his signature microphone and wears his hat and bowtie, but he has a much more decayed and withered appearance. He has sunken-in black sclera and soulless white pupils.</td>
-<td>He can appear randomly on the cameras or in front of the player. He walks slowly across the window from right to left, then leans into the office and jump scares the player. He can make the player's vision blurry for a few seconds.</td>
-<td>He is one of the phantoms that haunt the player in fnaf 3. He represents one of the children that were killed by William Afton and stuffed into an animatronic suit.</td>
-</tr>
-<tr>
-<td>Phantom Chica</td>
-<td>A burnt and heavily damaged hallucination of Chica from Freddy Fazbear's Pizza. She is missing her entire left arm and has blacked-out eyes and a wide open mouth that reveals her monstrous teeth.</td>
-<td>She can appear randomly on the cameras or in front of the player. She appears on the arcade machine screen in CAM 07, then jumps out and scares the player. She can disable all cameras for a few seconds.</td>
-<td>She is another phantom that haunts the player in fnaf 3. She represents another child that was killed by William Afton and stuffed into an animatronic suit.</td>
-</tr>
-<tr>
-<td>Phantom Foxy</td>
-<td>A hallucination of Foxy from Freddy Fazbear's Pizza. He is missing his right arm, with most of his body missing his fur and withered down to his metal skeleton. He has a hollowed-out eye and a haunting white pupil, as well as large, monstrous fangs.</td>
-<td>He can appear randomly on the cameras or in front of the player. He appears in front of the office window or behind one of the monitors, then lunges at the player and jump scares them. He can cause an audio error that requires rebooting.</td>
-<td>He is another phantom that haunts the player in fnaf 3. He represents another child that was killed by William Afton and stuffed into an animatronic suit.</td>
-</tr>
-<tr>
-<td>Phantom Balloon Boy</td>
-<td>A hallucination of Balloon Boy from Freddy Fazbear's Pizza. He is burnt and damaged beyond relief, missing his balloon and sign. He has disturbing black eyes and a creepy smile.</td>
-<td>He can appear randomly on the cameras or in front of the player. He appears on any camera feed, then jumps out and scares the player. He can cause a ventilation error that requires rebooting.</td>
-<td>He is another phantom that haunts the player in fnaf 3. He represents another child that was killed by William Afton and stuffed into an animatronic suit.</td>
-</tr>
- <tr> <td>Phantom Puppet</td>
-<td>A hallucination of Puppet from Freddy Fazbear's Pizza. He is a tall and slender marionette-like animatronic with black and white stripes, a white face, and black tear marks. He has a red smile and red cheeks, as well as black eyes with white pupils.</td>
-<td>He can appear randomly on the cameras or in front of the player. He appears in front of the office window or behind one of the monitors, then blocks the player's view for a few seconds. He can also cause the ventilation to fail.</td>
-<td>He is another phantom that haunts the player in fnaf 3. He represents the first child that was killed by William Afton and became the leader of the other ghosts.</td>
-</tr>
-<tr>
-<td>Phantom Mangle</td>
-<td>A hallucination of Mangle from Freddy Fazbear's Pizza. He is a mangled and twisted animatronic fox with pink and white fur. He has two heads, one with an endoskeleton jaw and one with a normal jaw. He has multiple limbs and wires hanging from his body.</td>
-<td>He can appear randomly on the cameras or in front of the player. He appears on the camera feed of CAM 04, then hangs from the ceiling of the office and emits a loud static noise. He can cause an audio error that requires rebooting.</td>
-<td>He is another phantom that haunts the player in fnaf 3. He represents another child that was killed by William Afton and stuffed into an animatronic suit.</td>
-</tr>
-</table>
- <h2>Story</h2>
-<p>The story of fnaf 3 is told through various cutscenes, minigames, phone calls, and newspaper clippings. The game takes place in 2023, thirty years after the closure of Freddy Fazbear's Pizza. A group of entrepreneurs have decided to create a horror-themed attraction called Fazbear's Fright, which is based on the legend of the haunted pizzeria. They have collected various relics and artifacts from the original locations, such as masks, posters, props, and animatronics. They have also hired a security guard to watch over the place at night, which is the role of the player.</p>
-<p>The main plot of the game revolves around Springtrap, who is revealed to be the corpse of William Afton, also known as the Purple Guy, inside a springlock suit. William Afton was the murderer of several children at Freddy Fazbear's Pizza, who used a yellow rabbit suit to lure them into a back room and kill them. He then stuffed their bodies into animatronic suits, which caused them to become possessed by their spirits. The spirits of the children then haunted the pizzeria and tried to kill anyone who wore a security uniform, mistaking them for their killer.</p>
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-<p>In fnaf 3, it is shown that after Freddy Fazbear's Pizza was closed down, William Afton returned to the location to destroy the animatronics and free their souls. However, he was confronted by their ghosts, who chased him into a hidden room where he found a springlock suit. He put on the suit to hide from them, but it malfunctioned and crushed him inside it, killing him slowly and painfully. The room was then sealed off by the company, leaving his body to rot for decades.</p>
-<p>The attraction owners eventually found his body and brought it to Fazbear's Fright, unaware of its true nature. Springtrap then became active at night and tried to kill the player, while also being haunted by his own victims in the form of phantoms. The player can also discover various minigames that reveal more details about the past events and allow them to free the souls of the children by giving them cake and playing with them.</p>
-<p>The game has two endings: a bad ending and a good ending. The bad ending is achieved by completing all five nights without completing all the minigames. In this ending, it is shown that Springtrap survived the fire that burned down Fazbear's Fright, and that the souls of the children are still trapped inside their animatronic heads. The good ending is achieved by completing all five nights and all the minigames. In this ending, it is shown that Springtrap was destroyed by the fire, and that the souls of the children were finally freed and able to move on to the afterlife.</p>
- <h2>Tips and tricks</h2>
-<p>Fnaf 3 is a challenging game that requires quick reflexes, strategy, and patience. Here are some useful tips and tricks to help you survive the nights in fnaf 3:</p>
-<ul>
-<li>Keep an eye on Springtrap at all times. Use the cameras to track his movements and use the audio devices to lure him away from <td>PlayStation Universe</td>
-<td>8/10 (PS4)</td>
-<td>"Fnaf 3 is a horror game that will keep you on the edge of your seat with its tense and immersive gameplay. It has a great story that reveals more about the lore of the series and a lot of replay value with different endings and modes. It is a must-play for fans of the genre and the series."</td>
-</tr>
-<tr>
-<td>Xbox Tavern</td>
-<td>7.5/10 (Xbox One)</td>
-<td>"Fnaf 3 is a decent horror game that offers some new twists and scares to the formula. It has a good presentation, sound design, and atmosphere that create a sense of dread and suspense. It also has some interesting minigames and secrets that add to the depth and fun. However, it also has some flaws, such as a steep difficulty curve, a lack of variety, and some bugs and glitches."</td>
-</tr>
-</table>
- <h2>Download</h2>
-<p>If you are interested in playing fnaf 3, you can download it from various sources depending on your platform of choice. Here are some of the download options for fnaf 3:</p>
-<ul>
-<li>For Windows, you can download fnaf 3 from Steam for $7.99. You will need a minimum of Windows XP, 2 GB of RAM, 1 GB of storage space, and a DirectX 9.0 compatible graphics card.</li>
-<li>For Android, you can download fnaf 3 from Google Play for $2.99. You will need a minimum of Android 5.0, 1 GB of RAM, and 50 MB of storage space.</li>
-<li>For iOS, you can download fnaf 3 from App Store for $2.99. You will need a minimum of iOS 8.0, 1 GB of RAM, and 103 MB of storage space.</li>
-<li>For Nintendo Switch, you can download fnaf 3 from Nintendo eShop for $7.99. You will need a minimum of Nintendo Switch system version 10.0.0, 1 GB of RAM, and 1 GB of storage space.</li>
-<li>For PlayStation 4, you can download fnaf 3 from PlayStation Store for $7.99. You will need a minimum of PlayStation 4 system version 5.05, 1 GB of RAM, and 1 GB of storage space.</li>
-<li>For Xbox One, you can download fnaf 3 from Microsoft Store for $7.99. You will need a minimum of Xbox One system version 10.0.19041.5485, 1 GB of RAM, and 1 GB of storage space.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Fnaf 3 is a horror game that will test your nerves and skills as you try to survive five nights (or more) in a haunted attraction filled with animatronics and phantoms. It is the third installment of the Five Nights at Freddy's series, which is known for its creepy atmosphere, intriguing story, and challenging gameplay. Fnaf 3 offers some new features and mechanics that make it different from the previous games, such as the audio devices, the ventilation system, and the phantom animatronics. It also reveals more about the lore and history of the series, as well as the fate of the characters involved.</p>
-<p>If you are looking for a game that will scare you and keep you entertained for hours, fnaf 3 is a great choice. It has received mostly positive reviews from critics and players alike, who praised its presentation, sound design, story, and gameplay innovations. However, it also has some drawbacks, such as its difficulty, jump scares, bugs, and lack of replay value.</p>
-<p>You can download fnaf 3 from various sources depending on your platform of choice. The game costs $7.99 for Windows, Nintendo Switch, PlayStation 4, and Xbox One; and $2.99 for Android and iOS. The game has some system requirements and compatibility issues that you should check before downloading it.</p>
-<p>We hope that this article has given you everything you need to know about fnaf 3. Whether you are new to the series or a veteran player, you will find something useful and interesting in this comprehensive guide.</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the frequently asked questions about fnaf 3:</p>
-<ol>
-<li><strong>What is fnaf?</strong></li>
-<p>Fnaf stands for Five Nights at Freddy's, which is a series of indie point-and-click survival horror games developed by Scott Cawthon.</p>
-<li><strong>What is the story of fnaf?</strong></li>
-<p>The story of fnaf revolves around a fictional chain of pizza restaurants called Freddy Fazbear's Pizza, where animatronic animals entertain children during the day, but become murderous at night. The player takes on the role of a night guard who must survive five nights (or more) while avoiding being killed by the animatronics. The series also has a complex and mysterious lore that involves murders, hauntings, secrets, and mysteries.</p>
-<li><strong>How many games are in the fnaf series?</strong></li>
-<p>There are currently nine main games in the fnaf series: Five Nights at Freddy's, Five Nights at Freddy's 2, Five Nights at Freddy's 3, Five Nights at Freddy's 4, Five Nights at Freddy's: Sister Location, Freddy Fazbear's Pizzeria Simulator, Ultimate Custom Night, Five Nights at Freddy's VR: Help Wanted, and Five Nights at Freddy's AR: Special Delivery. There are also several spin-off games, such as FNaF World, Freddy in Space 2, and Security Breach: Fury's Rage.</p>
-<li><strong>What is the order of the fnaf games?</strong></li>
-<p>The order of the fnaf games is not the same as the order of their release dates. The chronological order of the fnaf games according to the lore is as follows: Five Nights at Freddy's 4, Five Nights at Freddy's: Sister Location, Five Nights at Freddy's 2, Five Nights at Freddy's, Five Nights at Freddy's 3, Freddy Fazbear's Pizzeria Simulator, Ultimate Custom Night, Five Nights at Freddy's VR: Help Wanted, and Five Nights at Freddy's AR: Special Delivery. However, some of the games have multiple timelines and endings that can alter the order.</p>
-<li><strong>Who is Scott Cawthon?</strong></li>
-<p>Scott Cawthon is the creator and developer of the fnaf series. He is an independent game developer who started making games in 1994. He is also a Christian and a father of four children. He has announced his retirement from game development in June 2021.</p>
-<li><strong>Is fnaf 3 scary?</strong></li>
-<p>Fnaf 3 is a horror game that can be very scary for some players. It has a dark and creepy atmosphere, disturbing sound effects and music, jump scares and gore, and a tense and immersive gameplay. However, some players may find it less scary than the previous games in the series, as it has fewer animatronics and more predictable patterns. The level of scariness may also depend on the player's personal preferences and tolerance.</p> 401be4b1e0<br />
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-
-<h1>How to Download MinGW: A Free and Open-Source Programming Environment for Windows</h1>
-<p>If you are looking for a simple and effective way to develop native Windows applications using C/C++ or other languages, you might want to try MinGW. MinGW stands for Minimalist GNU for Windows, and it is a port of the GNU Compiler Collection (GCC) and GNU Binutils for Windows. In this article, we will show you how to download, install, and use MinGW on Windows 10, as well as some alternatives that you can explore.</p>
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- <h2>What is MinGW and why use it?</h2>
-<p>MinGW is a free and open-source programming environment for the Microsoft Windows operating system. It integrates a lot of GNU tools for building and debugging programs, such as GCC (GNU Compiler Collection), as well as runtime libraries and development headers. Here are some of the reasons why you might want to use MinGW:</p>
-<h3>MinGW is a port of the GNU Compiler Collection (GCC) and GNU Binutils for Windows</h3>
-<p>GCC is one of the most popular and widely used compilers in the world, supporting many languages such as C, C++, Objective-C, Fortran, Ada, Go, and more. GCC is also known for its high performance, portability, and compliance with standards. Binutils is a collection of binary tools that work with GCC, such as assembler, linker, debugger, etc. By using MinGW, you can access these powerful tools on your Windows system.</p>
-<h3>MinGW provides a complete programming toolset for developing native Windows applications</h3>
-<p>MinGW allows you to create programs that run on Windows without depending on any third-party libraries or frameworks. You can use the standard Windows API functions and libraries, as well as extensions to the MSVC runtime that support C99 functionality. You can also use other libraries that are compatible with MinGW, such as SDL, GTK+, Qt, Boost, etc.</p>
-<h3>MinGW is easy to use, compatible, and customizable</h3>
-<p>One of the main advantages of MinGW is how simple it is to use. The software is designed to be easy to install and configure, making it an excellent choice for beginners new to Windows programming development. Additionally, MinGW shares a command-line interface with operating systems that are similar to Unix. Thus, users who are already familiar with the commands and tools of Unix-like systems, such as Linux or macOS, will find it easy to adapt to MinGW. Moreover, MinGW is compatible with many other development tools and environments, such as Visual Studio, Code::Blocks, Eclipse, etc. You can also customize your MinGW installation by choosing which components and packages you want to install, depending on your needs and preferences. <h2>How to download and install MinGW on Windows 10</h2>
-<p>Downloading and installing MinGW on Windows 10 is a straightforward process that should not take more than a few minutes. Here are the steps you need to follow:</p>
-<h3>Download the installer from the official link</h3>
-<p>The first thing you need to do is to download the MinGW installer from the official website: <a href="">https://sourceforge.net/projects/mingw/</a>. This is a small executable file that will allow you to download and install the MinGW components you want. You can also download the installer from other mirrors or sources, but make sure they are trustworthy and up-to-date.</p>
-<h3>Run the installer and select the components to install</h3>
-<p>Once you have downloaded the installer, run it as an administrator and follow the instructions on the screen. You will be asked to choose a folder where you want to install MinGW. The default location is C:\MinGW, but you can change it if you want. You will also be asked to select which components you want to install. The minimum requirement is to install the base system and the GCC compiler suite. However, you can also install other packages, such as GDB (GNU Debugger), MSYS (Minimal System), etc. You can also add or remove packages later using the MinGW Installation Manager.</p>
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-<h3>Add the MinGW bin folder to the Windows PATH environment variable</h3>
-<p>After installing MinGW, you need to add its bin folder to the Windows PATH environment variable. This will allow you to use the MinGW tools from any command prompt without having to specify their full path. To do this, follow these steps:</p>
-<ul>
-<li>Open the Control Panel and go to System and Security > System.</li>
-<li>Click on Advanced system settings on the left side.</li>
-<li>Click on Environment Variables at the bottom of the System Properties window.</li>
-<li>In the System variables section, find and select the variable named PATH and click on Edit.</li>
-<li>In the Edit environment variable window, click on New and type or paste the path to your MinGW bin folder. For example, if you installed MinGW in C:\MinGW, type C:\MinGW\bin.</li>
-<li>Click on OK to save the changes and close all the windows.</li>
-</ul>
-<h3>Check your MinGW installation by typing gcc --version in a command prompt</h3>
-<p>To verify that your MinGW installation is working properly, open a command prompt and type gcc --version. You should see something like this:</p>
- <pre><code>C:\Users\user&gt;gcc --version gcc (MinGW.org GCC-8.2.0-5) 8.2.0 Copyright (C) 2018 Free Software Foundation, Inc. This is free software; see the source for copying conditions.  There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. </code></pre>
- <p>If you see an error message or a different output, make sure you have followed the previous steps correctly and that your PATH variable is set correctly.</p>
- <h2>How to use MinGW to compile and run C/C++ programs on Windows</h2>
-<p>Now that you have installed MinGW, you can use it to compile and run C/C++ programs on Windows. Here is a simple example of how to do that:</p>
-<h3>Create a simple Hello World program in a text editor and save it as hello.c</h3>
-<p>Open your favorite text editor and type the following code:</p>
- <pre><code>#include &lt;stdio.h&gt; int main()      printf("Hello, world!\n");     return 0;  </code></pre>
- <p>This is a simple C program that prints "Hello, world!" on the screen. Save it as hello.c in a folder of your choice.</p>
-<h3>Open a command prompt and navigate to the folder where you saved hello.c</h3>
-<p>Open a command prompt and use the cd command to change your current directory to the folder where you saved hello.c. For example, if you saved it in C:\Users\user\Documents\MinGW\hello.c, type:</p>
- <pre><code>C:\Users\user&gt;cd Documents\MinGW C:\Users\user\Documents\MinGW&gt; </code></pre>
- <p>You can also drag and drop the hello.c file to the command prompt window to automatically insert its full path.</p>
-<h3>Compile the program by typing gcc hello.c -o hello.exe</h3>
-<p>To compile the program, use the gcc command with the name of the source file (hello.c) and the name of the output file (hello.exe). The -o option specifies the name of the output file. If you omit it, the default name will be a.exe. Type the following command and press Enter:</p>
- <pre><code>C:\Users\user\Documents\MinGW&gt;gcc hello.c -o hello.exe </code></pre>
- <p>If there are no errors, the command will create an executable file named hello.exe in the same folder as hello.c.</p>
-<h3>Run the program by typing hello.exe</h3>
-<p>To run the program, simply type its name and press Enter:</p>
- <pre><code>C:\Users\user\Documents\MinGW&gt;hello.exe Hello, world! </code></pre>
- <p>You should see the output of the program on the screen. Congratulations, you have successfully compiled and run your first C program using MinGW!</p>
- <h2>Alternatives to MinGW for Windows programming</h2>
-<p>MinGW is not the only option for Windows programming using GCC and GNU tools. There are some alternatives that provide different features and functionalities for different needs and preferences. Here are some of them:</p>
-<h3>MinGW-w64 is a fork of MinGW that supports 64-bit and 32-bit Windows applications</h3>
-<p>MinGW-w64 is a project that was created to extend MinGW with support for 64-bit Windows applications, as well as improved support for 32-bit ones. It also includes more libraries and headers, such as DirectX, Windows 8 API, etc. You can download MinGW-w64 from <a href="">https://www.mingw-w64.org/</a>.</p>
-<h3>Cygwin is a Unix-like environment and command-line interface for Windows that allows running POSIX programs</h3>
-<p>Cygwin is a software that provides a Unix-like environment and command-line interface for Windows. It allows you to run many POSIX programs, such as bash, awk, sed, etc., as well as compile and run programs using GCC and other tools. Cygwin also provides a POSIX emulation layer that allows programs to access Windows functions and libraries. You can download Cygwin from <a href="">https://www.cygwin.com/</a>.</p>
-<h3>MSYS2 is an independent rewrite of MSYS that provides a modern Cygwin and MinGW-w64 environment</h3>
-<p>MSYS2 is a software that provides a modern Cygwin and MinGW-w64 environment for Windows. It includes a package manager that allows you to install and update various tools and libraries, such as GCC, GDB, Python, Ruby, etc. MSYS2 also provides a terminal emulator that supports colors, tabs, etc. You can download MSYS2 from <a href="">https://www.msys2.org/</a>.</p>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download, install, and use MinGW on Windows 10. MinGW is a free and open-source programming environment for Windows that allows you to develop native Windows applications using GCC and GNU tools. MinGW is easy to use, compatible, and customizable, and offers many benefits for both novice and experienced developers. MinGW also has some alternatives that provide different features and functionalities for Windows programming.</p>
-<p>We hope you have found this article helpful and informative. If you have any questions or comments, please feel free to leave them below. Happy coding!</p>
- <h2>Frequently Asked Questions</h2>
-<ul>
-<li><b>What is the difference between MinGW and MinGW-w64?</b></li>
-<li>MinGW is the original project that provides a port of GCC and GNU Binutils for Windows. MinGW-w64 is a fork of MinGW that adds support for 64-bit Windows applications, as well as improved support for 32-bit ones.</li>
-<li><b>What is the difference between MinGW and Cygwin?</b></li>
-<li>MinGW is a programming environment that allows you to create native Windows applications using GCC and GNU tools. Cygwin is a Unix-like environment and command-line interface that allows you to run POSIX programs on Windows.</li>
-<li><b>What is the difference between MinGW and MSYS2?</b></li>
-<li>MinGW is a programming environment that provides GCC and GNU Binutils for Windows. MSYS2 is an independent rewrite of MSYS that provides a modern Cygwin and MinGW-w64 environment for Windows.</li>
-<li><b>How do I update my MinGW installation ?</b></li>
-<li>You can update your MinGW installation by using the MinGW Installation Manager. This is a graphical tool that allows you to add or remove packages from your MinGW installation. You can access it by running mingw-get.exe from your MinGW bin folder.</li>
-<li><b>How do I uninstall MinGW from my Windows system?</b></li>
-<li>You can uninstall MinGW from your Windows system by following these steps:</li>
-<ul>
-<li>Delete the MinGW folder from your installation location.</li>
-<li>Remove the MinGW bin folder from your Windows PATH environment variable.</li>
-<li>Delete any shortcuts or icons related to MinGW from your desktop or start menu.</li>
-</ul>
-</ul></p> 197e85843d<br />
-<br />
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deleted file mode 100644
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--- a/spaces/congsaPfin/Manga-OCR/logs/How to Get Diablo Immortal Mod Apk 2022 for Free on Android and iOS.md	
+++ /dev/null
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-
-<table>
-<tr>
-<td>
- <h1>Diablo Immortal Mod APK 2022: Everything You Need to Know</h1>
-<h2>Introduction</h2>
-<p>Are you a fan of Diablo, the legendary action role-playing game series by Blizzard Entertainment? If so, you might have heard of Diablo Immortal, the first official Blizzard mobile game, available on both Android and iOS. It was developed in collaboration with Netease Games. It is one of the most anticipated mobile games of 2022. The final, global version of this game was released to users in June 2022.</p>
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-<p>But what if we told you that there is a way to make your gaming experience even better? Yes, we are talking about Diablo Immortal Mod APK, a modified version of the original game that gives you access to unlimited features and resources. With this mod apk, you can enjoy Diablo Immortal without any restrictions or limitations.</p>
-<p>In this article, we will tell you everything you need to know about Diablo Immortal Mod APK. We will explain what it is, what features it offers, how to download and install it, and why you should try it out. So, without further ado, let's get started!</p>
-<h2>Features of Diablo Immortal Mod APK</h2>
-<h3 <h3>Full Unlocked</h3>
-<p>One of the best features of Diablo Immortal Mod APK is that it gives you full access to all the content and features of the game. You can choose from six different classes: Barbarian, Crusader, Demon Hunter, Monk, Necromancer, and Wizard. Each class has its own unique skills, abilities, and playstyle. You can also customize your character's appearance, gear, and talents.</p>
-<p>Moreover, you can explore all the regions and zones of the game, from the peaceful town of Wortham to the hellish realm of Pandemonium. You can also participate in all the events and quests that the game offers, such as the Siege of Westmarch, the Caverns of Echoes, and the Tomb of Fahir. You will never run out of things to do in Diablo Immortal.</p>
-<h3>High-Quality Graphics and Sound</h3>
-<p>Another feature that makes Diablo Immortal Mod APK stand out is its high-quality graphics and sound. The game uses a powerful engine that delivers stunning visuals and smooth performance on your mobile device. You will be amazed by the level of detail and realism that the game provides. You will feel like you are in the dark and epic world of Sanctuary.</p>
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-<p>The game also features immersive sound effects and music that enhance your gaming experience. You will hear the roar of demons, the clash of weapons, and the voice of your allies and enemies. You will also enjoy the original soundtrack composed by Russell Brower, the award-winning composer of Diablo III. The game's sound design will make you feel like you are part of the action.</p>
-<h3>Online Multiplayer and Co-op</h3>
-<p>Diablo Immortal Mod APK also offers online multiplayer and co-op modes that let you play with millions of other players around the world. You can join or create a guild with your friends or make new ones in the game. You can also chat with other players, trade items, and share tips and strategies.</p>
-<p>You can also team up with other players to take on challenging dungeons and events that require teamwork and coordination. You can also compete with other players in PvP modes such as Battlegrounds and Cycle of Strife. You will never get bored in Diablo Immortal.</p>
-<h3>Customizable Controls and UI</h3>
-<p>The last feature that we want to mention is the customizable controls and UI of Diablo Immortal Mod APK. The game allows you to adjust the game settings to suit your preferences and device specifications. You can choose from different control schemes and layouts that fit your playstyle. You can also change the graphics quality, sound volume, language, and other options.</p>
-<p>The game also has a user-friendly and intuitive UI that makes it easy to navigate and access the game's features. You can easily switch between skills, items, menus, and chat with a simple tap or swipe. You can also view your character's stats, inventory, quests, achievements, and more with a glance.</p>
-<h2>How to Download and Install Diablo Immortal Mod APK</h2>
-<h3>Downloading the APK File</h3>
-<p>If you are interested in downloading Diablo Immortal Mod APK, you need to find a reliable source for downloading the APK file. There are many websites that claim to offer modded versions of popular games, but not all of them are safe and trustworthy. Some of them may contain viruses, malware, or spyware that can harm your device or steal your personal information.</p>
-<p>One of the websites that we recommend for downloading Diablo Immortal Mod APK is [text]. This website provides a secure and fast download link for the latest version of Diablo Immortal Mod APK. It also provides detailed information about the mod apk, such as its features, requirements, installation steps, screenshots, and more.</p>
-<p>To download Diablo Immortal Mod APK from [text], you need to follow these simple steps:</p>
-<ol>
-<li>Visit [text] on your browser.</li>
-<li>Scroll down to find the download button for Diablo Immortal Mod APK.</li>
-<li>Click on the download button and wait for the file to be saved on your device.</li>
-</ol>
-<h3>Installing the APK File</h3>
-<p>After downloading Diablo Immortal Mod APK from [text], you need to install it on your device. To do so, you need to enable unknown sources on your device settings to allow installation of third-party apps. This is a necessary step because Diablo Immortal Mod APK is not available on Google Play Store or App Store.</p>
-<p>To enable unknown sources on your device settings, you need to follow these simple steps:</p>
-<ol>
-<li>Go to your device settings.</li>
-<li>Find security or privacy options.</li>
-<li>Look <li>Toggle on the option to allow installation of apps from unknown sources.</li>
-</ol>
-<p>Once you have enabled unknown sources on your device settings, you can proceed to install Diablo Immortal Mod APK. To do so, you need to follow these simple steps:</p>
-<ol>
-<li>Locate the APK file on your device storage and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Diablo Immortal Mod APK is a modified version of Diablo Immortal, the first official Blizzard mobile game. It gives you access to unlimited features and resources that enhance your gaming experience. You can play with all classes, skills, items, and regions. You can also enjoy the high-quality graphics and sound, online multiplayer and co-op, and customizable controls and UI of the game.</p>
-<p>To download and install Diablo Immortal Mod APK, you need to find a reliable source for downloading the APK file, such as [text]. You also need to enable unknown sources on your device settings to allow installation of third-party apps. Then, you can follow the simple steps that we have provided in this article.</p>
-<p>If you are a fan of Diablo, you should definitely try out Diablo Immortal Mod APK. It will give you a new and exciting way to experience the dark and epic world of Sanctuary on your mobile device. You will not regret it!</p>
-<h2>Frequently Asked Questions</h2>
-<h3>Is Diablo Immortal Mod APK safe to use?</h3>
-<p>Yes, Diablo Immortal Mod APK is safe to use as long as you download it from a trusted source, such as [text]. It does not contain any viruses, malware, or spyware that can harm your device or steal your personal information. However, you should always be careful when downloading and installing any modded apps from unknown sources.</p>
-<h3>Is Diablo Immortal Mod APK free to use?</h3>
-<p>Yes, Diablo Immortal Mod APK is free to use. You do not need to pay any money to download or install it. You also do not need to spend any money to access or use any of the features or resources of the game. However, you may encounter some ads or in-app purchases in the game that are optional and not required.</p>
-<h3>Do I need to root or jailbreak my device to use Diablo Immortal Mod APK?</h3>
-<p>No, you do not need to root or jailbreak your device to use Diablo Immortal Mod APK. It works on both rooted and non-rooted devices, as well as both Android and iOS devices. You just need to enable unknown sources on your device settings to allow installation of third-party apps.</p>
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-<p>There is a low risk of getting banned for using Diablo Immortal Mod APK. The game does not have a strict anti-cheat system that can detect or prevent modded apps from running. However, you should always be careful and respectful when playing online with other players. Do not abuse or exploit the features or resources of the mod apk. Do not cheat or hack in PvP modes. Do not brag or boast about using the mod apk. Do not report or complain about other players who are using the mod apk. If you follow these tips, you should be able to avoid getting banned for using Diablo Immortal Mod APK.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Switch from MT5 to MT4 after Downloading the Wrong Platform.md b/spaces/congsaPfin/Manga-OCR/logs/How to Switch from MT5 to MT4 after Downloading the Wrong Platform.md
deleted file mode 100644
index 7cdef1d9fc3215bf3f30b198891d6f514e762c22..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/How to Switch from MT5 to MT4 after Downloading the Wrong Platform.md	
+++ /dev/null
@@ -1,139 +0,0 @@
-
-<h1>I download MT4 but install MT5: What happened and how to fix it</h1>
-<p>If you are a forex trader, you might have heard of MetaTrader 4 (MT4) and MetaTrader 5 (MT5), two of the most popular and widely used trading platforms in the online space. But what if you download MT4 but install MT5 by mistake? How can you tell the difference between the two platforms and how can you switch to the one you want? In this article, we will explain what are MT4 and MT5, why you might have installed the wrong platform, and how to uninstall MT5 and install MT4 in a few simple steps.</p>
- <h2>What are MT4 and MT5?</h2>
-<p>MT4 and MT5 are third-party trading platforms that allow you to access the forex market and other financial instruments through your chosen broker. They offer advanced technical analysis tools, charting features, automated trading robots, and various order types. However, they are not the same platform and have some key differences that you should be aware of.</p>
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- <h3>MT4: The most popular forex trading platform</h3>
-<p>MT4 was launched in 2005 by MetaQuotes Software and quickly became the industry standard for forex trading. It is designed mainly for forex traders who want to use technical indicators, custom scripts, and expert advisors (EAs) to automate their trading strategies. It supports four types of pending orders (buy limit, sell limit, buy stop, sell stop) and two execution modes (instant and market). It also has a user-friendly interface, a built-in economic calendar, an email system, and a chat function. According to MetaQuotes, more than 1,000 brokers offer MT4 to their clients.</p>
- <h3>MT5: The advanced multi-asset trading platform</h3>
-<p>MT5 was released in 2010 as an upgrade to MT4, but it is not a successor or a replacement. It is a separate platform that offers more features and capabilities than MT4. It is suitable for traders who want to trade not only forex but also stocks, futures, options, bonds, and cryptocurrencies. It supports six types of pending orders (buy limit, sell limit, buy stop, sell stop, buy stop limit, sell stop limit) and four execution modes (instant, market, request, exchange). It also has a depth of market feature, a time & sales report, 38 technical indicators, 44 graphical objects, 21 timeframes, an economic calendar, an email system with attachments, a chat function, and a built-in MQL5 community.</p>
- <h2>Why did I install MT5 instead of MT4?</h2>
-<p>If you download MT4 but install MT5 on your device, you might be confused and frustrated. How did this happen and how can you fix it? Here are some possible reasons why you installed the wrong platform and how to check which one you have.</p>
- <h3>Possible reasons for installing the wrong platform</h3>
-<ul>
-<li>You downloaded the installer from an unreliable source that mislabeled or mixed up the files.</li>
-<li>You clicked on the wrong link or button on the broker's website that directed you to the wrong platform.</li>
-<li>You did not pay attention to the installation wizard or the file name that indicated which platform you were installing.</li>
-<li>You already had MT5 installed on your device and you accidentally ran it instead of installing MT4.</li>
-</ul>
- <h3>How to check which platform I have installed</h3>
-<ul>
-<li>Check the icon on your desktop or taskbar. If it is blue with four numbers (MetaTrader 4), then you have installed MT4. If it is red with five numbers (MetaTrader 5), then you have installed MT5.</li>
-<li>Check the title bar of the platform window. If it says MetaTrader 4, then you have installed MT4. If it says MetaTrader 5, then you have installed MT5.</li>
-<li>Check the version number of the platform in the Help menu. If it starts with 4, then you have installed MT4. If it starts with 5, then you have installed MT5.</li>
-</ul>
- <h2>How to uninstall MT5 and install MT4</h2>
-<p>If you have confirmed that you have installed MT5 instead of MT4, and you want to switch to the platform you prefer, you need to uninstall MT5 and install MT4. Here are the steps to do that for different devices and operating systems.</p>
- <h3>How to uninstall MT5 from Windows, Mac OS, Android, and iOS</h3>
-<table>
-<tr>
-<th>Device</th>
-<th>Steps</th>
-</tr>
-<tr>
-<td>Windows</td>
-<td><ol>
-<li>Go to Control Panel > Programs > Uninstall a program.</li>
-<li>Select MetaTrader 5 and click Uninstall.</li>
-<li>Follow the instructions on the screen to complete the process.</li>
-</ol></td>
-</tr>
-<tr>
-<td>Mac OS</td>
-<td><ol>
-<li>Open Finder and go to Applications.</li>
-<li>Drag MetaTrader 5 to the Trash.</li>
-<li>Empty the Trash to delete the program.</li>
-</ol></td>
-</tr>
-<tr>
-<td>Android</td>
-<td><ol>
-<li>Open Settings and go to Apps or Applications.</li>
-<li>Select MetaTrader 5 and tap Uninstall.</li>
-<li>Confirm your action by tapping OK.</li>
-</ol></td>
-</tr>
-<tr>
-<td>iOS</td>
-<td><ol>
-<li>Tap and hold the MetaTrader 5 icon on your home screen until it wiggles.</li>
-<li>Tap the X on the top left corner of the icon.</li>
-<li>Confirm your action by tapping Delete.</li>
-</ol></td>
-</tr>
-</table>
- <h3>How to download and install MT4 from a reliable broker</h3>
- <p>After you have uninstalled MT5 from your device, you can download and install MT4 from a reliable broker that offers this platform. Here are some tips to do that:</p>
- <ul>
- <li>Choose a broker that is regulated, reputable, and has good reviews from other traders. You can use online resources such as [Forex Peace Army] or [Trustpilot] to check the ratings and feedback of different brokers.</li>
- <li>Visit the broker's website and look for the download link for MT4. Make sure it is clearly labeled and not mixed up with MT5 or other platforms. You can also contact the broker's customer support if you have any doubts or questions.</li>
- <li>Download the installer file for your device and operating system. Check the file name and size to make sure it is correct and not corrupted. You can also scan the file with an antivirus program before running it.</li>
- <li>Run the installer file and follow the instructions on the screen. Choose a destination folder for the program and agree to the terms and conditions. Wait for the installation to finish and launch the platform.</li>
- <li>Login to your trading account using your credentials provided by the broker. If you don't have an account yet, you can register for one on the broker's website or on the platform itself. You can also use a demo account to practice trading without risking real money.</li>
- <li>Customize your platform settings, charts, indicators, EAs, and other features according to your preferences and trading style. You can also access tutorials, guides, and videos on how to use MT4 on the broker's website or on [MetaQuotes' official website].</li>
- </ul>
- <h2>Conclusion</h2>
- <p>In conclusion, if you download MT4 but install MT5 by mistake, don't panic. You can easily fix this issue by uninstalling MT5 and installing MT4 from a reliable broker. Just make sure you check which platform you have installed before trading and use the one that suits your needs and goals. Both platforms are powerful and popular, but they have some differences that you should be aware of. We hope this article helped you understand what are MT4 and MT5, why you might have installed the wrong platform, and how to uninstall MT5 and install MT4 in a few simple steps.</p>
- <h2>FAQs</h2>
- <ol>
- <li><b>What is the difference between MT4 and MT5?</b></li>
- <p>The main difference between MT4 and MT5 is that MT4 is designed mainly for forex trading, while MT5 is suitable for multi-asset trading. MT5 also has more features and capabilities than MT4, such as more pending order types, more execution modes, more technical indicators, more timeframes, a depth of market feature, a time & sales report, and a built-in MQL5 community.</p>
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- <li><b>Can I use both MT4 and MT5 on the same device?</b></li>
- <p>Yes, you can use both MT4 and MT5 on the same device, as they are separate platforms that do not interfere with each other. However, you need to download and install them from different sources and use different login credentials for each platform. You also need to make sure you have enough storage space and memory on your device to run both platforms smoothly.</p>
- <li><b>Which platform is better: MT4 or MT5?</b></li>
- <p>There is no definitive answer to this question, as it depends on your personal preferences, trading style, and goals. Some traders prefer MT4 for its simplicity, familiarity, and compatibility with many brokers and EAs. Others prefer MT5 for its versatility, functionality, and access to more markets and instruments. You can try both platforms for free using a demo account and see which one suits you better.</p>
- <li><b>How can I transfer my data and settings from MT4 to MT5 or vice versa?</b></li>
- <p>Unfortunately, you cannot transfer your data and settings from MT4 to MT5 or vice versa, as they are not compatible with each other. This means that you cannot use your MT4 indicators, scripts, EAs, templates, profiles, or history on MT5 or vice versa. You need to recreate them manually or find equivalent versions on the MQL4 or MQL5 marketplaces.</p>
- <li><b>How can I contact MetaQuotes for support or feedback?</b></li>
- <p>If you have any questions, issues, or suggestions regarding MT4 or MT5, you can contact MetaQuotes directly through their official website. You can also join their MQL4 or MQL5 community forums  and interact with other traders and developers who use their platforms.</p>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Ludo Download Ludo King Enjoy the Fun of Dice and Strategy.md b/spaces/congsaPfin/Manga-OCR/logs/Ludo Download Ludo King Enjoy the Fun of Dice and Strategy.md
deleted file mode 100644
index e756fee3c31910b0106129bbc6451d8e7a0a884a..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Ludo Download Ludo King Enjoy the Fun of Dice and Strategy.md	
+++ /dev/null
@@ -1,111 +0,0 @@
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-<h1>Ludo Download Ludo King: How to Play the Most Popular Ludo Game Online</h1>
- <p>Ludo is a classic board game that has been played for centuries by people of all ages and cultures. It is a simple game of dice and strategy, where you have to move your four tokens from the start to the finish line before your opponents do. But what if you want to play Ludo online with your friends and family, or even with strangers from around the world? That's where Ludo King comes in.</p>
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- <p>Ludo King is a cross-platform multiplayer game that allows you to play Ludo online on your smartphone, tablet, or computer. It is one of the most popular and downloaded games on the internet, with over 900 million downloads and millions of active players. It is also the first Ludo game of its kind available on mobile since 2016.</p>
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-<li>Voice chat: You can talk to your opponents while playing, making it more interactive and social.</li>
-<li>6 player online multiplayer: You can play with up to 6 players in the online mode, making it more challenging and competitive.</li>
-<li>Quick mode: You can play a fast game that finishes quickly, without losing the fun of the full-length game.</li>
-<li>Team up mode: You can team up with your friends and have 2 vs 2 matches.</li>
-<li>Amazing themes: You can choose from different themes that change the look and feel of the game, such as disco, night, nature, Egypt, pinball, candy, Christmas, penguin, battle, Diwali, pirate, Sui Dhaaga, marble, alien, octopus, Taj Mahal, etc.</li>
-<li>Tournament mode: You can participate in an 8 player tournament and win 6 times the rewards.</li>
-<li>Ludo King inventory: You can access various items such as themes, dices, emojis, voice notes, rewards, etc. that enhance your gaming experience.</li>
-</ul>
- <h3>How to download Ludo King on different devices</h3>
- <p>Ludo King is available for free on various platforms such as Android, iOS, HTML5, and Windows. You can download it from the following links:</p>
- <ul>
-<li>For Android devices: <a href="(^1^)">Ludo King™ - Apps on Google Play</a></li>
-<li>For iOS devices: <a href="(^3^)">‎Ludo King on the App Store</a></li>
-<li>For HTML5 devices: <a href="(^2^)">Ludo King™ - India's No.1 Online Board Game</a></li>
-<li>For Windows devices: <a href="">Get Ludo King - Microsoft Store</a></li>
-</ul>
- <h2>How to play Ludo King online with friends and family</h2>
- <p>One of the best features of Ludo King is that you can play it online with your friends and family, no matter where they are. You can also make new friends and buddies from around the world. Here are some steps on how to play Ludo King online:</p>
- <h3>How to create a private game room</h3>
- <ol>
-<li>Open the app and tap ladder, it will climb up to the square that has the top of the ladder. This is called getting a boost from a ladder.</li>
-<li>The player who reaches the top right corner of the board first will win the game.</li>
-</ol>
- <h3>How to play Quick Ludo mode</h3>
- <p>Quick Ludo mode is a fast and fun way to play Ludo with only one token per player. You can play it with up to 6 players in the online or offline mode. Here are some steps on how to play Quick Ludo mode:</p>
- <ol>
-<li>Select the theme (classic, nature, Egypt, disco, etc.) that you want to play on.</li>
-<li>Each player will have a token of their color that will start from their home base.</li>
-<li>The player who rolls the highest number on the dice will go first. The order of the players will be clockwise.</li>
-<li>The player will move their token according to the number shown on the dice. The token must start from its home base and move clockwise around the board.</li>
-<li>If the player rolls a 6, they can roll again or move their token by 6 steps.</li>
-<li>If a player's token lands on a square that is already occupied by another player's token, they can capture that token and send it back to its home base.</li>
-<li>The player who moves their token to their finish line first will win the game.</li>
-</ol>
- <h3>How to play Tournament mode</h3>
- <p>Tournament mode is a competitive and rewarding way to play Ludo with 8 players in a knockout format. You can play it in the online mode only. Here are some steps on how to play Tournament mode:</p>
- <ol>
-<li>Select the entry fee (1000, 5000, 10000, or 50000 coins) that you want to pay to join the tournament.</li>
-<li>You will be matched with 7 other players who have paid the same entry fee.</li>
-<li>You will be divided into 4 groups of 2 players each. You will play a Ludo game with your group partner in the first round.</li>
-<li>The winner of each group will advance to the second round, where they will play another Ludo game with another winner from another group.</li>
-<li>The winner of each second round game will advance to the final round, where they will play a Ludo game with the other finalist.</li>
-<li>The winner of the final round will win the tournament and get 6 times the entry fee as a reward. The runner-up will get 2 times the entry fee as a reward. The other players will get nothing.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Ludo King is a great game that you can play online or offline with your friends and family, or with anyone from around the world. It is easy to learn and fun to play, and it has many features that make it more enjoyable and exciting. You can also play other games like Snake and Ladders, Quick Ludo mode, and Tournament mode in Ludo King. So what are you waiting for? Download Ludo King today and start playing!</p>
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- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Ludo King:</p>
- <ol>
-<li>Q: How can I get more coins in Ludo King?</li>
-<li>A: You can get more coins by winning games, watching ads, spinning the wheel, completing missions, or buying them with real money.</li>
-<li>Q: How can I change my profile picture and name in Ludo King?</li>
-<li>A: You can change your profile picture and name by tapping on the settings icon on the top right corner of the home screen and then tapping on edit profile.</li>
-<li>Q: How can I report or block a player who is abusive or cheating in Ludo King?</li>
-<li>A: You can report or block a player by tapping on their profile picture during or after a game and then tapping on report or block.</li>
-<li>Q: How can I restore my progress if I lose my data or change my device in Ludo King?</li>
-<li>A: You can restore your progress by logging in with your Facebook account or your email address that you used to register in Ludo King.</li>
-<li>Q: How can I contact the support team if I have any issues or feedback about Ludo King?</li>
-<li>A: You can contact the support team by tapping on the settings icon on the top right corner of the home screen and then tapping on help & support.</li>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Murder us APK A Free and Fun Game for Android Lovers.md b/spaces/congsaPfin/Manga-OCR/logs/Murder us APK A Free and Fun Game for Android Lovers.md
deleted file mode 100644
index 3f0fb0f1999dcb8c86708a2ef87a81d8f794d986..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Murder us APK A Free and Fun Game for Android Lovers.md	
+++ /dev/null
@@ -1,50 +0,0 @@
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Play with your friends online and discover who is the murderer", "url": "[2](https://apkcombo.com/murder-us/com.apt.murderus/)"], "advertisement_results": [], "image_search_results": [], "news_search_results": [], "question_answering_results": [], "weather_results": "alerts": [], "time_range": null [assistant](#message) `<h2>How to Play Murder Us APK?</h2>` `<p>Playing Murder Us APK is easy and fun. You just need to follow these steps:</p>` `<ol>` `<li>Download and install the game from <a href="">Google Play</a> or <a href="">APKCombo</a>.</li>` `<li>Open the game and choose a nickname and a language.</li>` `<li>Select a game mode: Classic, Hide and Seek, or Tag.</li>` `<li>Join a room or create your own. You can invite your friends by sharing the room code.</li>` `<li>Wait for the game to start and find out your role: crewmate or impostor in Classic mode, hider or seeker in Hide and Seek mode, runner or chaser in Tag mode.</li>` `<li>If you are a crewmate, hider, or runner, your goal is to complete tasks around the map while avoiding the impostor, seeker, or chaser. You can also use items to help you survive.</li>` `<li>If you are an impostor, seeker, or chaser, your goal is to kill all the crewmates, hiders, or runners before they complete their tasks. You can also use items to help you hunt them down.</li>` `<li>In Classic mode, you can report dead bodies or call emergency meetings to discuss and vote out the impostor. You can also chat with other players during the game.</li>` `<li>In Hide and Seek and Tag modes, you cannot report bodies or call meetings, but you can chat with other players after the game.</li>` `<li>The game ends when either the crewmates, hiders, or runners complete their tasks, or when the impostor, seeker, or chaser kills them all.</li>` `</ol>` `<h2>Why You Should Download Murder Us APK?</h2>` `<p>Murder Us APK is not just another clone of Among Us. It is a game that offers you more fun, variety, and customization. Here are some of the reasons why you should download Murder Us APK:</p>` `<ul>` `<li>It is free to play and easy to install. You don't need to pay anything or register an account to enjoy the game.</li>` `<li>It has more game modes and roles than Among Us. You can choose from Classic, Hide and Seek, or Tag, and play as a crewmate, impostor, hider, seeker, runner, or chaser.</li>` `<li>It has more maps and tasks than Among Us. 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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Project Drift 2.0 MOD APK Unlock All Cars and Customizations.md b/spaces/congsaPfin/Manga-OCR/logs/Project Drift 2.0 MOD APK Unlock All Cars and Customizations.md
deleted file mode 100644
index d37a77d48f0be22f5ff2578c076e5f4ba9eb26f5..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Project Drift 2.0 MOD APK Unlock All Cars and Customizations.md	
+++ /dev/null
@@ -1,84 +0,0 @@
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-<h3>Step 2: Enable unknown sources on your device settings</h3>
-<p>The second step is to enable unknown sources on your device settings, which will allow you to install apps from sources other than the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and toggle it on. You might see a warning message, but just ignore it and proceed.</p>
-<h3>Step 3: Install the mod apk file and launch the game</h3>
-<p>The third step is to install the mod apk file and launch the game. To do this, locate the downloaded mod apk file on your device storage, then tap on it and follow the installation prompts. Once the installation is complete, launch the game from your app drawer or home screen.</p>
-<h3>Step 4: Enjoy the game with all the mod features</h3>
-<p>The fourth and final step is to enjoy the game with all the mod features. You will see that you have unlimited money and unlocked cars in your garage, as well as access to all the tracks and game modes in the menu. You can also play without any ads or root requirements, and enjoy a smooth and stable performance on your device.</p>
-<h2>Conclusion</h2>
-<p>Project Drift 2.0 is a great racing game that lets you experience the thrill of drifting with realistic physics and graphics, various game modes and challenges, and customizable cars and tracks. However, if you want to take your drifting experience to a new level, you should download Project Drift 2.0 Mod Apk, which gives you unlimited money and unlocked cars, no ads and no root required, and easy installation and compatibility. With Project Drift 2.0 Mod Apk, you can enjoy racing games like never before.</p>
- FAQs - Q: Is Project Drift 2.0 Mod Apk safe to download? - A: Yes, Project Drift 2.0 Mod Apk is safe to download from a trusted source, such as [this link]. You can also scan the file with an antivirus program before downloading it to ensure its safety and security. - Q: What are the requirements for Project Drift 2.0 Mod Apk? - A: The requirements for Project Drift 2.0 Mod Apk are an Android device with 4.1 or higher version, at least - A: The requirements for Project Drift 2.0 Mod Apk are an Android device with 4.1 or higher version, at least 200 MB of free storage space, and a stable internet connection. - Q: How can I update Project Drift 2.0 Mod Apk? - A: You can update Project Drift 2.0 Mod Apk by downloading the latest version of the mod apk file from the same source you downloaded it from, and installing it over the existing one. You don't have to uninstall the previous version, as the new one will overwrite it. - Q: How can I contact the developers of Project Drift 2.0? - A: You can contact the developers of Project Drift 2.0 by visiting their official website, [this link], or by following them on their social media accounts, such as Facebook, Instagram, and Twitter. - Q: What are some tips and tricks for Project Drift 2.0? - A: Some tips and tricks for Project Drift 2.0 are:   - Use the handbrake to initiate a drift and control the angle of your car.   - Adjust your steering sensitivity and camera angle to suit your preference and visibility.   - Try different cars and tracks to find the ones that match your style and skill level.   - Practice your drifting skills in Free Ride mode before entering Career or Multiplayer mode.   - Watch videos of other players or tutorials to learn from their techniques and strategies. - Q: What are some alternatives to Project Drift 2.0? - A: Some alternatives to Project Drift 2.0 are:   - CarX Drift Racing 2, a realistic and addictive drifting game with over 100 cars and tracks, online multiplayer mode, and advanced tuning options.   - Real Drift Car Racing, a realistic and fun drifting game with over 40 cars and tracks, customizable difficulty settings, and leaderboards and achievements.   - Drift Max Pro, a stylish and dynamic drifting game with over 50 cars and tracks, career mode, daily challenges, and car customization features.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Temple Run 2 MOD APK Everything You Need to Know About This Amazing Game.md b/spaces/congsaPfin/Manga-OCR/logs/Temple Run 2 MOD APK Everything You Need to Know About This Amazing Game.md
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-<br />
-<h1>Temple Run 2 2020 Mod Apk: How to Download and Play the Best Running Game on Your Android Device</h1>
- <p>Do you love running games? Do you want to experience the thrill of escaping from evil monkeys while collecting treasures and power-ups? If yes, then you should try Temple Run 2, one of the most popular and addictive running games on the Google Play Store. And if you want to make your gaming experience even more fun and exciting, you should download and install Temple Run 2 2020 Mod Apk, a modified version of the game that gives you unlimited currency, menu access, and other features. In this article, we will tell you everything you need to know about Temple Run 2 and Temple Run 2 2020 Mod Apk, including how to download, install, and play them on your Android device. Let's get started!</p>
- <h2>What is Temple Run 2?</h2>
- <p>Temple Run 2 is the sequel to the successful smartphone running game Temple Run, developed by Imangi Studios. As in the previous work, the hero who received the idol escapes from the demon monkey. The operation is the same as the previous work, slide up and down to jump and slide, and slide left and right to rotate.</p>
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- <h3>The gameplay of Temple Run 2</h3>
- <p>The gameplay of Temple Run 2 is simple but addictive. You have to run as far as you can while avoiding obstacles, such as cliffs, rivers, fire, traps, and more. You also have to collect coins and gems along the way, which you can use to upgrade your abilities and power-ups. You can also unlock different characters and outfits, each with their own special skills and attributes. The game has various maps, such as the jungle, the frozen shadows, the lost jungle, the blazing sands, and more. Each map has its own challenges and surprises, such as zip lines, mine carts, waterfalls, volcanoes, etc. The game also has daily and weekly challenges, global leaderboards, achievements, and more.</p>
- <h3>The features of Temple Run 2</h3>
- <p>Temple Run 2 has many features that make it one of the best running games on the market. Some of these features are:</p>
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-<ul>
-<li>Beautiful graphics and animations</li>
-<li>Smooth and responsive controls</li>
-<li>Diverse and dynamic environments</li>
-<li>Various characters and outfits to choose from</li>
-<li>Multiple abilities and power-ups to enhance your performance</li>
-<li>Challenging obstacles and enemies</li>
-<li>Daily and weekly challenges to test your skills</li>
-<li>Global leaderboards to compete with other players</li>
-<li>Achievements to unlock and show off</li>
-</ul>
- <h2>What is Temple Run 2 2020 Mod Apk?</h2>
- <p> Temple Run 2 2020 Mod Apk is a modified version of Temple Run 2 that gives you some advantages and features that are not available in the original game. For example, with Temple Run 2 2020 Mod Apk, you can get unlimited coins and gems, which you can use to buy anything you want in the game. You can also access the menu, where you can activate cheats and hacks, such as invincibility, speed boost, coin magnet, and more. You can also unlock all the characters and outfits without spending any money or completing any tasks. Temple Run 2 2020 Mod Apk is a great way to enjoy the game without any limitations or restrictions.</p>
- <h3>The benefits of using Temple Run 2 2020 Mod Apk</h3>
- <p>There are many benefits of using Temple Run 2 2020 Mod Apk, such as:</p>
-<ul>
-<li>You can have unlimited coins and gems, which you can use to upgrade your abilities and power-ups, buy new characters and outfits, and more.</li>
-<li>You can access the menu, where you can activate cheats and hacks, such as invincibility, speed boost, coin magnet, and more. These can help you run faster, farther, and safer.</li>
-<li>You can unlock all the characters and outfits without spending any money or completing any tasks. You can choose your favorite character and outfit and enjoy their special skills and attributes.</li>
-<li>You can have more fun and excitement playing the game without any limitations or restrictions. You can explore different maps, complete different challenges, and compete with other players without any worries.</li>
-</ul>
- <h3>The drawbacks of using Temple Run 2 2020 Mod Apk</h3>
- <p>However, there are also some drawbacks of using Temple Run 2 2020 Mod Apk, such as:</p>
-<ul>
-<li>You may face some technical issues or errors while downloading or installing the mod apk. You may also need to allow some permissions or disable some security features on your device to make it work.</li>
-<li>You may lose the originality and authenticity of the game. You may miss out on the thrill and challenge of playing the game as it is intended to be played. You may also lose the sense of achievement and satisfaction that comes from earning coins and gems, unlocking characters and outfits, and completing tasks.</li>
-<li>You may violate the terms and conditions of the game developer and the Google Play Store. You may risk getting banned or suspended from the game or the store for using an unauthorized or modified version of the game. You may also expose your device to malware or viruses that may harm your data or privacy.</li>
-</ul>
- <h2>How to download and install Temple Run 2 2020 Mod Apk?</h2>
- <p>If you want to download and install Temple Run 2 2020 Mod Apk on your Android device, you need to follow these steps:</p>
- <h3>The steps to download and install Temple Run 2 2020 Mod Apk</h3>
- <ol>
-<li>First, you need to uninstall the original Temple Run 2 game from your device if you have it installed already.</li>
-<li>Second, you need to find a reliable and trustworthy source to download the Temple Run 2 2020 Mod Apk file. You can search online for some websites that offer this file for free. Make sure to check the reviews and ratings of the website before downloading anything from it.</li>
-<li>Third, you need to enable the unknown sources option on your device settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Fourth, you need to locate the downloaded Temple Run 2 2020 Mod Apk file on your device storage. You can use a file manager app to do this. Then, tap on the file and follow the instructions to install it on your device.</li>
-<li>Fifth, you need to launch the game from your app drawer or home screen. You will see a menu icon on the top left corner of the screen. Tap on it to access the cheats and hacks. You will also see unlimited coins and gems on your account.</li>
-</ol>
- <h3>The precautions to take before downloading and installing Temple Run 2 2020 Mod Apk</h3>
- <p>Before downloading and installing Temple Run 2 2020 Mod Apk on your device, you need to take some precautions, such as:</p>
-<ul>
-<li>Make sure that your device has enough storage space and battery life to download and install the mod apk file.</li>
-<li>Make sure that your device is compatible with the mod apk file. The mod apk file may not work on some devices or Android versions.</li>
-<li>Make sure that you have a stable internet connection to download the mod apk file. You may encounter some errors or interruptions if your connection is slow or unstable.</li>
-<li>Make sure that you backup your data and settings before uninstalling the original game or installing the mod apk file. You may lose some progress or preferences if something goes wrong during the process.</li>
-<li>Make sure that you download and install the mod apk file at your own risk. You may face some legal or ethical issues for using an unauthorized or modified version of the game. You may also expose your device to malware or viruses that may harm your data or privacy.</li>
-</ul>
- <h2>How to play Temple Run 2 2020 Mod Apk?</h2>
- <p>Once you have downloaded and installed Temple Run 2 2020 Mod Apk on your device, you can start playing the game and enjoy its features. Here are some tips on how to play Temple Run 2 2020 Mod Apk:</p>
- <h3>The controls of Temple Run 2 2020 Mod Apk</h3>
- <p>The controls of Temple Run 2 2020 Mod Apk are the same as the original game. You have to use your finger to swipe up, down, left, or right on the screen to make your character jump, slide, turn, or tilt. You can also use the menu icon to access the cheats and hacks. You can tap on the icon to open the menu, and then tap on the options you want to activate or deactivate. For example, you can tap on the invincibility option to make your character immune to any damage or obstacle. You can also tap on the speed boost option to make your character run faster than normal.</p>
- <h3>The tips and tricks to play Temple Run 2 2020 Mod Apk</h3>
- <p>Here are some tips and tricks to play Temple Run 2 2020 Mod Apk and get the most out of it:</p>
- <h4>Tip #1: Collect coins and gems</h4>
- <p>Coins and gems are the currency of the game, which you can use to buy new characters, outfits, abilities, power-ups, and more. You can collect coins and gems by running through them on the path, or by using the coin magnet power-up, which attracts them to you automatically. You can also get unlimited coins and gems by using the Temple Run 2 2020 Mod Apk, which gives you an infinite amount of them. You can use them to buy anything you want in the game without any limitations.</p>
- <h4>Tip #2: Upgrade your abilities and power-ups</h4>
- <p>Abilities and power-ups are essential to enhance your performance and survival in the game. Abilities are passive skills that improve your character's attributes, such as coin value, score multiplier, shield duration, etc. Power-ups are active items that give you a temporary boost or advantage, such as coin magnet, speed boost, shield, etc. You can upgrade your abilities and power-ups by spending coins and gems in the store. You can also use the Temple Run 2 2020 Mod Apk to get unlimited coins and gems and upgrade them to the maximum level.</p>
- <h4>Tip #3: Choose your character and outfit wisely</h4>
- <p>Character and outfit are not only cosmetic features in the game, but also affect your gameplay. Each character has a special skill that gives them a unique ability or power-up. For example, Guy Dangerous has a shield power-up that protects him from one hit, Scarlett Fox has a speed boost power-up that makes her run faster for a short time, etc. Each outfit also has a bonus effect that improves your character's attributes, such as coin value, score multiplier, gem bonus, etc. You can unlock new characters and outfits by spending coins and gems in the store. You can also use the Temple Run 2 2020 Mod Apk to unlock all of them without any cost or effort.</p>
- <h4>Tip #4: Use the menu to access cheats and hacks</h4>
- <p>The menu is a feature that is exclusive to Temple Run 2 2020 Mod Apk. It allows you to access cheats and hacks that give you an unfair advantage over the game. You can use the menu to activate or deactivate options such as invincibility, speed boost, coin magnet, unlimited currency, menu access, etc. These options can help you run faster, farther, and safer than normal. However, you should be careful not to abuse them too much, as they may ruin the fun and challenge of the game.</p>
- <h4>Tip #5: Avoid obstacles and enemies</h4>
- <p>Obstacles and enemies are the main threats that can end your run in the game. Obstacles are anything that can block your path or harm you, such as cliffs, rivers, fire, traps, etc. Enemies are anything that can chase you or attack you, such as monkeys, eagles, snakes, etc. You have to avoid obstacles and enemies by using your controls and power-ups. You can also use the Temple Run 2 2020 Mod Apk to activate the invincibility option, which makes you immune to any damage or obstacle. However, you should still be careful not to fall off the edge or run out of the path, as these can still end your run.</p>
- <h2>Conclusion</h2>
- <p>Temple Run 2 is one of the best running games on the Google Play Store, with millions of downloads and positive reviews. It offers a simple but addictive gameplay, beautiful graphics and animations, diverse and dynamic environments, various characters and outfits, multiple abilities and power-ups, challenging obstacles and enemies, daily and weekly challenges, global leaderboards, achievements, and more. Temple Run 2 2020 Mod Apk is a modified version of Temple Run 2 that gives you unlimited currency, menu access, and other features that make your gaming experience even more fun and exciting. However, it also has some drawbacks, such as technical issues, loss of originality, and legal or ethical risks. Therefore, you should use it at your own discretion and responsibility.</p>
- <p>If you want to download and install Temple Run 2 2020 Mod Apk on your Android device, you need to follow the steps and precautions we have mentioned in this article. You also need to know how to play Temple Run 2 2020 Mod Apk and use its features wisely. We hope this article has helped you learn everything you need to know about Temple Run 2 and Temple Run 2 2020 Mod Apk. Now, what are you waiting for? Go ahead and download Temple Run 2 2020 Mod Apk and enjoy the best running game on your Android device!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Temple Run 2 and Temple Run 2 2020 Mod Apk:</p>
- <ol>
-<li>Is Temple Run 2 free to play?</li>
-<p>Yes, Temple Run 2 is free to play on the Google Play Store. However, it contains some in-app purchases that require real money to buy coins and gems.</p>
-<li>Is Temple Run 2 2020 Mod Apk safe to use?</li>
-<p>Temple Run 2 2020 Mod Apk is not an official or authorized version of Temple Run 2. It is a modified version that may contain some malware or viruses that may harm your device or data. Therefore, you should use it at your own risk and responsibility.</p>
-<li>How can I update Temple Run 2 2020 Mod Apk?</li>
-<p>Temple Run 2 2020 Mod Apk may not be compatible with the latest version of Temple Run 2. Therefore, you may need to download and install a new version of Temple Run 2 2020 Mod Apk from a reliable source whenever there is an update available.</p>
-<li>Can I play Temple Run 2 offline?</li>
-<p>Yes, you can play Temple Run 2 offline without an internet connection. However, you may not be able to access some features that require an internet connection, such as daily and weekly challenges, global leaderboards, achievements, etc.</p>
-<li>Can I play Temple Run 2 with friends?</li>
-<p>Yes, you can play Temple Run 2 with friends by connecting your game account to Facebook or Google Play Games. You can then see your friends' scores and achievements on the leaderboards and challenge them to beat your records.</p>
-</ol></p> 197e85843d<br />
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-<p>If you are a fan of Japanese action movies, you might have heard of High and Low The Worst X Cross, the latest installment of the popular High and Low franchise. This movie is a crossover between the High and Low series and the Crows Zero series, both of which are based on manga about delinquent high school students who fight for supremacy. In this article, we will tell you what this movie is about, why you should watch it, and how to download it with Indonesian subtitles.</p>
- <h2>What is High and Low The Worst X Cross?</h2>
-<p>High and Low The Worst X Cross is a 2022 Japanese action movie directed by Hiranuma Norihisa. It is the seventh original movie of the High and Low franchise, which started in 2015 as a collaboration between EXILE TRIBE, a collective of musicians and actors, and Shonen Magazine, a manga publisher. It is also the fourth work of the new storyline in the High and Low series called High and Low The Worst, which is a collaboration with Crows Zero, another manga series by Takahashi Hiroshi.</p>
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- <h3>The plot of the movie</h3>
-<p>The movie revolves around the battle between two high schools in the SWORD area, Oya High School and Senomon Technical High School. The head of Senomon Technical High School builds a "three-school alliance" with two other schools, Kamasaka High School and Ebara Commercial High School, and expands its power to aim for Oya High School. Men of Oya High are suddenly attacked and captured. Can Fujio, the leader of Oya High, protect them with his own fists? The battle to decide the real top is about to begin.</p>
- <h3>The cast and crew of the movie</h3>
-<p>The movie features a large cast of actors, many of whom are members of EXILE TRIBE or other prominent artists. Some of the main roles are:</p>
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-<ul>
-<li>Kawamura Kazuma as Hanaoka Fujio, the leader of Oya High School</li>
-<li>Yuta Suzaki as Ryo, Fujio's right-hand man</li>
-<li>Yoshino Hokuto as Takajo Tsukasa, the leader of Senomon Technical High School</li>
-<li>Miyama Ryoki as Amagai Kohei, Tsukasa's right-hand man</li>
-<li>Maeda Goki as Todoroki Yosuke, the leader of Kamasaka High School</li>
-<li>Shiono Akihisa as Odajima Yuken, the leader of Ebara Commercial High School</li>
-</ul>
-<p>The movie also has some guest appearances from actors who played in previous works of the High and Low or Crows Zero series, such as Shison Jun, Yabe Kyosuke, Sato Ryuji, Kamio Fuju, Fujiwara Itsuki, Oka Hiroaki, Zin Kazami, Naganuma Ikuya, Aoi Yo, Koyanagi Shin, Arai Atsushi, Sakaguchi Ryotaro, Daichi Mikami, Itagaki Mizuki, Tokito Yuki, Yagi Yusei, Kimura Keito, Takahashi Yuuri, Sakurai Yuki, Asano Shunya, Minato Yoshiki.</p>
- <h3>The connection to the High and Low franchise</h3>
-<p>The movie is part of the High and Low franchise, which consists of several movies, dramas, spin-offs, specials, and music videos. The franchise is set in a fictional area called SWORD, which stands for Sannoh Rengokai, White Rascals, Oya Kohkoh, Rude Boys, and Daruma Ikka. These are the names of the five gangs that rule the area. The franchise depicts the conflicts and alliances among these gangs, as well as their encounters with other groups such as Mighty Warriors, Amamiya Brothers, Doubt, Prison Gang, and Kuryu Group. The movie is also connected to the Crows Zero series, which is based on a manga by Takahashi Hiroshi. The series follows the students of Suzuran All-Boys High School, also known as the School of Crows, where the students are constantly fighting for dominance. The series has three live-action movies and two spin-off movies. High and Low The Worst X Cross is a crossover between these two series, where the characters from both worlds meet and clash. The movie is a sequel to the High and Low The Worst drama, which aired in 2019 and introduced the characters from Crows Zero to the High and Low universe. <h2>Why should you watch High and Low The Worst X Cross?</h2>
-<p>If you are a fan of Japanese action movies, there are many reasons why you should watch High and Low The Worst X Cross. Here are some of them:</p>
- <h3>The action scenes and stunts</h3>
-<p>The movie is full of thrilling and spectacular action scenes and stunts that will keep you on the edge of your seat. You will see the characters fight with fists, kicks, weapons, motorcycles, cars, and even helicopters. The movie showcases the skills and talents of the actors, many of whom are trained in martial arts or have experience in performing stunts. The movie also uses minimal CGI effects and relies more on practical effects and real locations.</p>
- <h3>The comedy and drama elements</h3>
-<p>The movie is not only about action, but also about comedy and drama. You will laugh at the hilarious interactions and dialogues among the characters, especially when they are from different gangs or schools. You will also feel the emotions and struggles of the characters, as they face their enemies, their pasts, their futures, and their friendships. The movie balances the humor and the seriousness of the story well, making it enjoyable for different kinds of audiences.</p>
- <h3>The friendship and rivalry themes</h3>
-<p>The movie explores the themes of friendship and rivalry among the characters, both within and across the gangs and schools. You will see how the characters bond with their comrades, support each other, protect each other, and sometimes betray each other. You will also see how they compete with their rivals, challenge each other, respect each other, and sometimes cooperate with each other. The movie shows how friendship and rivalry can be both positive and negative forces in life.</p>
- <h2>How to download High and Low The Worst X Cross sub indo?</h2>
-<p>If you want to watch High and Low The Worst X Cross with Indonesian subtitles, you might be wondering how to download it. There are two main options: legal and safe options or illegal and risky options.</p>
- <h3>The legal and safe options</h3>
-<p>The legal and safe options are those that respect the rights of the creators and distributors of the movie, as well as protect your device from viruses or malware. These options usually require you to pay a fee or subscribe to a service to access the movie.</p>
- <h4>Netflix</h4>
-<p>One of the legal and safe options is Netflix, a popular streaming platform that offers a wide range of movies and shows from different countries and genres. Netflix has acquired the rights to stream High and Low The Worst X Cross in Indonesia and other countries. You can watch the movie with Indonesian subtitles on Netflix by following these steps:</p>
-<ol>
-<li>Go to the Netflix website or app and sign in with your account. If you don't have an account, you can create one and choose a plan that suits your needs.</li>
-<li>Search for High and Low The Worst X Cross in the search bar or browse the categories to find it.</li>
-<li>Click on the movie and select the play button. You can also add it to your list or download it for offline viewing.</li>
-<li>Click on the subtitles icon and choose Indonesian as the language. You can also adjust the size, color, and font of the subtitles.</li>
-<li>Enjoy watching the movie with Indonesian subtitles.</li>
-</ol>
- <h4>Other streaming platforms</h4>
-<p>Another legal and safe option is to use other streaming platforms that have the rights to stream High and Low The Worst X Cross in Indonesia or other countries. Some of these platforms are:</p>
-<ul>
-<li>Viu, a streaming service that offers Asian movies and shows, including High and Low The Worst X Cross. You can watch the movie with Indonesian subtitles on Viu by subscribing to a premium plan or using a free trial.</li>
-<li>iQiyi, a streaming service that offers Chinese and Korean movies and shows, including High and Low The Worst X Cross. You can watch the movie with Indonesian subtitles on iQiyi by subscribing to a VIP plan or using a free trial.</li>
-<li>WeTV, a streaming service that offers Thai and Indonesian movies and shows, including High and Low The Worst X Cross. You can watch the movie with Indonesian subtitles on WeTV by subscribing to a VIP plan or using a free trial.</li>
-</ul>
-<p>To watch the movie with Indonesian subtitles on these platforms, you need to follow similar steps as Netflix, such as signing in, searching for the movie, selecting the play button, choosing the subtitles language, and enjoying the movie.</p>
- <h3>The illegal and risky options</h3>
-<p>The illegal and risky options are those that violate the rights of the creators and distributors of the movie, as well as expose your device to viruses or malware. These options usually do not require you to pay a fee or subscribe to a service to access the movie, but they come with many drawbacks and dangers.</p>
- <h4>Torrent sites</h4>
-<p>One of the illegal and risky options is to use torrent sites, which are websites that allow users to share files through peer-to-peer networks. You can download High and Low The Worst X Cross sub indo from torrent sites by following these steps:</p>
-<ol>
-<li>Go to a torrent site that has High and Low The Worst X Cross sub indo available, such as 1337x, RARBG, or YTS. Be careful of fake or malicious sites that may harm your device.</li>
-<li>Search for High and Low The Worst X Cross sub indo in the search bar or browse the categories to find it.</li>
-<li>Click on the torrent file that has good quality, seeders, and comments. Avoid files that have low quality, seeders, or comments, as they may be corrupted or infected.</li>
-<li>Download the torrent file and open it with a torrent client, such as BitTorrent or uTorrent. Make sure you have enough space on your device and a stable internet connection.</li>
-<li>Wait for the download to finish and enjoy watching the movie with Indonesian subtitles.</li>
-</ol>
-<p>However, using torrent sites is illegal and risky because:</p>
-<ul>
-<li>You are infringing on the intellectual property rights of the creators and distributors of the movie, which may result in legal consequences or penalties.</li>
-<li>You are exposing your device to viruses or malware that may damage your data or compromise your security.</li>
-<li>You are consuming a lot of bandwidth and resources that may slow down your device or internet speed.</li>
-</ul>
- <h4>Piracy sites</h4>
-<p>Another illegal and risky option is to use piracy sites, which are websites that host or stream pirated content without permission. You can watch High and Low The Worst X Cross sub indo from piracy sites by following these steps:</p>
-<ol>
-<li>Go to a piracy site that has High and Low The Worst X Cross sub indo available, such as IndoXXI, LayarKaca21, or Dunia21. Be careful of fake or malicious sites that may harm your device.</li>
-<li>Search for High and Low The Worst X Cross sub indo in the search bar or browse the categories to find it.</li>
-<li>Click on the movie and select the play button. You may have to close some pop-up ads or windows that may appear.</li>
-<li>Choose Indonesian as the subtitles language if available. You may also have to adjust the quality or speed of the video if needed.</li>
-<li>Enjoy watching the movie with Indonesian subtitles.</li>
-</ol>
-<p>However, using piracy sites is illegal and risky because:</p>
-<ul>
-<li>You are infringing on the intellectual property rights of the creators and distributors of the movie, which may result in legal consequences or penalties.</li>
-<li>You are exposing your device to viruses or malware that may damage your data or compromise your security.</li>
-<li>You are supporting the piracy industry that harms the entertainment industry and the artists who work hard to produce quality content.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>High and Low The Worst X Cross is a Japanese action movie that is a crossover between the High and Low series and the Crows Zero series. It is a movie that offers exciting action scenes, hilarious comedy moments, and touching drama elements. It is a movie that explores the themes of friendship and rivalry among delinquent high school students. It is a movie that you should watch if you are a fan of Japanese action movies.</p>
- <p>However, you should be careful when you want to watch it with Indonesian subtitles. You should choose the legal and safe options, such as Netflix or other streaming platforms, that respect the rights of the creators and distributors of the movie, as well as protect your device from viruses or malware. You should avoid the illegal and risky options, such as torrent sites or piracy sites, that violate the rights of the creators and distributors of the movie, as well as expose your device to viruses or malware.</p>
- <p>We hope this article has helped you learn more about High and Low The Worst X Cross and how to download it with Indonesian subtitles. If you have any questions or comments, please feel free to leave them below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about High and Low The Worst X Cross:</p>
- <h4>Q: When was High and Low The Worst X Cross released?</h4>
-<p>A: High and Low The Worst X Cross was released on January 8, 2022 in Japan. It was also released in other countries, such as Indonesia, Malaysia, Singapore, Thailand, Taiwan, Hong Kong, and South Korea.</p>
- <h4>Q: How long is High and Low The Worst X Cross?</h4>
-<p>A: High and Low The Worst X Cross has a runtime of 125 minutes.</p>
- <h4>Q: How much did High and Low The Worst X Cross earn at the box office?</h4>
-<p>A: High and Low The Worst X Cross earned 1.5 billion yen (about 13.6 million USD) at the Japanese box office as of February 6, 2022. It ranked second in its opening weekend and third in its second weekend.</p>
- <h4>Q: Where can I watch High and Low The Worst X Cross with English subtitles?</h4>
-<p>A: You can watch High and Low The Worst X Cross with English subtitles on Netflix or other streaming platforms that have the rights to stream it in your country. You can also buy or rent the DVD or Blu-ray of the movie that has English subtitles.</p>
- <h4>Q: What are some other movies or shows that are similar to High and Low The Worst X Cross?</h4>
-<p>A: Some other movies or shows that are similar to High and Low The Worst X Cross are:</p>
-<ul>
-<li>The Raid (2011) and The Raid 2 (2014), Indonesian action movies that feature intense fights and stunts in a crime-ridden setting.</li>
-<li>Gokusen (2002-2009), a Japanese drama series that follows a female teacher who tries to reform her delinquent students at an all-boys high school.</li>
-<li>The Outsiders (1983), an American movie based on a novel by S.E. Hinton that depicts the rivalry between two gangs of teenagers in 1960s Oklahoma.</li>
-</ul></p> 401be4b1e0<br />
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-<h1>How to Download 3y3 Mea by Tutulapato</h1>
-<p>If you are a fan of Ghanaian music, you might have heard of Tutulapato, the young rapper who has been making waves in the industry. He recently released a new song called 3y3 Mea, which has been gaining popularity among music lovers. In this article, we will tell you everything you need to know about Tutulapato, his song 3y3 Mea, and how to download it.</p>
-<h2>download 3y3 mea by tutulapato</h2><br /><p><b><b>Download File</b> &#10001; <a href="https://urlca.com/2uO8Uv">https://urlca.com/2uO8Uv</a></b></p><br /><br />
- <h2>Who is Tutulapato?</h2>
-<p>Tutulapato is a Ghanaian rapper who was born in 2005. His real name is Cosmos Boako, and he hails from the Volta Region of Ghana. He started rapping at the age of seven, and rose to fame after winning the TV reality show Talented Kidz in 2012. He has since released several songs and albums, such as King Size, Special Day, Anansesem, and Gye Nyame.</p>
- <h3>His background and achievements</h3>
-<p>Tutulapato was born into a musical family, as his father was a drummer and his mother was a singer. He developed a passion for rap music at an early age, and was inspired by artists like Sarkodie, Pope Skinny, and Lil Wayne. He started performing at school events and local shows, and caught the attention of Pope Skinny, who invited him to his radio show Kasa Hari Level. There, he impressed the listeners with his freestyle skills and witty lyrics.</p>
-<p>He then auditioned for Talented Kidz, a TV reality show that showcases young talents in Ghana. He won the hearts of the judges and the audience with his rap performances, and emerged as the winner of the season four. He became the youngest rapper in Ghana to win a major award, and received a cash prize, a laptop, and a scholarship. He also got the opportunity to collaborate with some of the big names in Ghanaian music, such as Kwaw Kese, M.anifest, Guru, Yaa Pono, and MzVee.</p>
- <h3>His style and influences</h3>
-<p>Tutulapato is known for his unique style of rap, which combines Akan language, English language, and pidgin English. He raps about various topics, such as social issues, education, religion, love, and entertainment. He also incorporates humor, sarcasm, wordplay, and metaphors in his lyrics. He has been praised for his creativity, confidence, charisma, and delivery.</p>
-<p>He has cited Sarkodie as his biggest influence in rap music, as he admires his flow, versatility, and success. He has also mentioned Pope Skinny as his mentor and role model, as he helped him to develop his rap skills and career. He has also expressed his admiration for other artists like Lil Wayne, Eminem, Jay-Z, Kwaw Kese, M.anifest, Guru, Yaa Pono, MzVee, Efya, R2Bees, Shatta Wale, Stonebwoy, Samini, Kuami Eugene, King Promise, and Kidi.</p>
- <h2>What is 3y3 Mea?</h2>
-<p>3y3 Mea is one of the latest songs by Tutulapato. It was released in June 2021 under Muse Media Networks. It is a hip-hop song that showcases Tutulapato's rap prowess and lyrical dexterity.</p>
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- <h3>The meaning and message of the song</h3>
-<p>The title of the song means "It's mine" in Akan language, which is one of the most widely spoken languages in Ghana. The song is a self-praise and motivational song, in which Tutulapato asserts his confidence and ambition. He claims that he is the best rapper in Ghana, and that he has everything he wants in life. He also encourages his fans to believe in themselves and pursue their dreams. He uses the phrase "3y3 mea" to express his ownership and pride in his achievements and possessions.</p>
- <h3>The features and production of the song</h3>
-<p>The song features two other Ghanaian rappers, namely Kwame Yogot and Article Wan. They both deliver impressive verses that complement Tutulapato's rap style and theme. They also add some flavor and diversity to the song with their different voices and flows. The song was produced by Article Wan, who is also a singer, songwriter, and record producer. He is known for his hit songs such as Solo, That Thing, Hallelujah, and Borkor. He created a catchy and upbeat instrumental for the song, which blends hip-hop beats with African rhythms and melodies.</p>
- <h2>How to download 3y3 Mea by Tutulapato</h2>
-<p>If you want to download 3y3 Mea by Tutulapato, you have several options to choose from. You can either download it from online platforms or sources, or you can use a mobile app or software to download it.</p>
- <h3>The platforms and sources available</h3>
-<p>There are many online platforms and sources that offer 3y3 Mea by Tutulapato for download. Some of them are:</p>
-<ul>
-<li><a href="">Muse Africa</a>: This is the official website of Muse Media Networks, the label that released the song. You can find the song under the music section, and download it for free by clicking on the download button.</li>
-<li><a href="">Ghana Songs</a>: This is one of the most popular websites for Ghanaian music. You can search for the song by typing its name in the search box, and download it by clicking on the download icon.</li>
-<li><a href="">Ghana Motion</a>: This is another website that provides Ghanaian music. You can find the song under the hip-hop category, and download it by clicking on the download link.</li>
-<li><a href="">YouTube</a>: This is the most popular video-sharing platform in the world. You can watch the official video of the song on YouTube, and download it by using a YouTube downloader tool or software.</li>
-</ul>
- <h3>The steps and tips to follow</h3>
-<p>To download 3y3 Mea by Tutulapato from any of the platforms or sources mentioned above, you need to follow these steps:</p>
-<ol>
-<li>Open your web browser and go to the website of your choice.</li>
-<li>Search for the song by typing its name in the search box or browsing through the categories.</li>
-<li>Click on the song title or image to open its page.</li>
-<li>Click on the download button, icon, or link to start downloading the song.</li>
-<li>Choose a folder or location on your device where you want to save the song.</li>
-<li>Wait for the download to complete, and enjoy listening to the song.</li>
-</ol>
-<p>Some tips to keep in mind when downloading 3y3 Mea by Tutulapato are:</p>
-<ul>
-<li>Make sure you have a stable internet connection and enough storage space on your device.</li>
-<li>Check the quality and format of the song before downloading it. You can choose between MP3, MP4, WAV, or other formats depending on your preference and device compatibility.</li>
-<li>Be careful of malware or viruses that might infect your device when downloading from untrusted or unknown sources. Use an antivirus software or scan your device regularly to prevent any damage.</li>
-<li>Respect the intellectual property rights of the artist and the producer. Do not use the song for commercial purposes or distribute it without their permission.</li>
-</ul>
- <h2>Why you should download 3y3 Mea by Tutulapato</h2>
-<p>There are many reasons why you should download 3y3 Mea by Tutulapato. Here are some of them:</p>
- <h3>The benefits and advantages of downloading the song</h3>
-<p>By downloading 3y3 Mea by Tutulapato, you can enjoy these benefits and advantages:</p>
-<ul>
-<li>You can listen to the song anytime and anywhere you want, even when you are offline or have no internet access.</li>
-<li>You can add the song to your personal playlist or library, and organize it according to your preference and mood.</li>
-<li>You can share the song with your friends and family, and enjoy it together.</li>
-<li>You can support the artist and the producer, and show your appreciation for their work.</li>
-</ul>
- <h3>The reviews and ratings of the song</h3>
-<p>3y3 Mea by Tutulapato has received positive reviews and ratings from critics and fans alike. Here are some of them:</p>
-<table>
-<tr>
-<th>Source</th>
-<th>Review</th>
-<th>Rating</th>
-</tr>
-<tr>
-<td><a href="">Ghana Music Awards</a></td>
-<td>"Tutulapato proves once again that he is a force to reckon with in the Ghanaian rap scene. He delivers a catchy and confident song that showcases his talent and personality. He is joined by Kwame Yogot and Article Wan, who add their own flavor and style to the song. The production by Article Wan is top-notch, as he creates a vibrant and energetic instrumental that matches the mood and theme of the song. 3y3 Mea is a song that will make you nod your head, dance your feet, and sing along."</td>
-<td>4.5/5</td>
-</tr>
-<tr>
-<td><a href="">Ghana Web</a></td>
-<td>"3y3 Mea is a self-praise and motivational song that will inspire you to chase your dreams and celebrate your achievements. Tutulapato raps with confidence and charisma, as he claims that he is the best rapper in Ghana, and that he has everything he wants in life. He also encourages his fans to believe in themselves and pursue their goals. He is supported by Kwame Yogot and Article Wan, who also rap with skill and flair. The song is produced by Article Wan, who blends hip-hop beats with African rhythms and melodies. 3y3 Mea is a song that will uplift your spirit and boost your morale."</td>
-<td>4/5</td>
-</tr>
-<tr>
-<td><a href="">Ghana Celebrities</a></td>
-<td>"Tutulapato is back with a bang with his new song 3y3 Mea. He teams up with Kwame Yogot and Article Wan, who are also rising stars in the Ghanaian music industry. They rap about their success and pride, and how they have overcome the challenges and obstacles in their way. They also motivate their listeners to be confident and ambitious, and to enjoy their life. The song is produced by Article Wan, who makes a catchy and upbeat instrumental that suits the rap style and theme of the song. 3y3 Mea is a song that will make you feel good about yourself and your achievements."</td>
-<td>4/5</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>In conclusion, 3y3 Mea by Tutulapato is a great song that you should download and listen to. It is a hip-hop song that features Tutulapato, Kwame Yogot, and Article Wan. It is a self-praise and motivational song that expresses confidence and ambition. It is produced by Article Wan, who creates a catchy and upbeat instrumental. It has received positive reviews and ratings from critics and fans alike. It is available on various online platforms and sources, such as Muse Africa, Ghana Songs, Ghana Motion, YouTube, etc. You can download it easily by following some simple steps and tips.</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>Q: How old is Tutulapato?</b></li>
-<li>A: Tutulapato is 18 years old as of 2021.</li>
-<li><b>Q: What does 3y3 Mea mean?</b></li>
-<li>A: 3y3 Mea means "It's mine" in Akan language.</li>
-<li><b>Q: Who are Kwame Yogot and Article Wan?</b></li>
-<li>A: Kwame Yogot and Article Wan are Ghanaian rappers who feature on the song 3y3 Mea.</li>
-<li><b>Q: Where can I download 3y3 Mea by Tutulapato?</b></li>
-<li>A: You can download 3y3 Mea by Tutulapato from various online platforms and sources, such as Muse Africa, Ghana Songs, Ghana Motion, YouTube, etc.</li>
-<li><b>Q: Why should I download 3y3 Mea by Tutulapato?</b></li>
-<li>A: You should download 3y3 Mea by Tutulapato because it is a great song that showcases Tutulapato's rap prowess and lyrical dexterity. It is also a self-praise and motivational song that will inspire you to be confident and ambitious. It is also produced by Article Wan, who creates a catchy and upbeat instrumental that will make you dance and sing along.</li>
-</ul>
- <p>I hope you enjoyed reading this article and learned something new. If you have any questions or feedback, please feel free to leave a comment below. Thank you for your time and attention.</p> 401be4b1e0<br />
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@@ -1,11 +0,0 @@
-
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diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/utils/up_conv_block.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/utils/up_conv_block.py
deleted file mode 100644
index 86328011a9704d17e9f9d0d54994719ead5caa56..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/utils/up_conv_block.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.nn as nn
-from annotator.mmpkg.mmcv.cnn import ConvModule, build_upsample_layer
-
-
-class UpConvBlock(nn.Module):
-    """Upsample convolution block in decoder for UNet.
-
-    This upsample convolution block consists of one upsample module
-    followed by one convolution block. The upsample module expands the
-    high-level low-resolution feature map and the convolution block fuses
-    the upsampled high-level low-resolution feature map and the low-level
-    high-resolution feature map from encoder.
-
-    Args:
-        conv_block (nn.Sequential): Sequential of convolutional layers.
-        in_channels (int): Number of input channels of the high-level
-        skip_channels (int): Number of input channels of the low-level
-        high-resolution feature map from encoder.
-        out_channels (int): Number of output channels.
-        num_convs (int): Number of convolutional layers in the conv_block.
-            Default: 2.
-        stride (int): Stride of convolutional layer in conv_block. Default: 1.
-        dilation (int): Dilation rate of convolutional layer in conv_block.
-            Default: 1.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-        conv_cfg (dict | None): Config dict for convolution layer.
-            Default: None.
-        norm_cfg (dict | None): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict | None): Config dict for activation layer in ConvModule.
-            Default: dict(type='ReLU').
-        upsample_cfg (dict): The upsample config of the upsample module in
-            decoder. Default: dict(type='InterpConv'). If the size of
-            high-level feature map is the same as that of skip feature map
-            (low-level feature map from encoder), it does not need upsample the
-            high-level feature map and the upsample_cfg is None.
-        dcn (bool): Use deformable convolution in convolutional layer or not.
-            Default: None.
-        plugins (dict): plugins for convolutional layers. Default: None.
-    """
-
-    def __init__(self,
-                 conv_block,
-                 in_channels,
-                 skip_channels,
-                 out_channels,
-                 num_convs=2,
-                 stride=1,
-                 dilation=1,
-                 with_cp=False,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 upsample_cfg=dict(type='InterpConv'),
-                 dcn=None,
-                 plugins=None):
-        super(UpConvBlock, self).__init__()
-        assert dcn is None, 'Not implemented yet.'
-        assert plugins is None, 'Not implemented yet.'
-
-        self.conv_block = conv_block(
-            in_channels=2 * skip_channels,
-            out_channels=out_channels,
-            num_convs=num_convs,
-            stride=stride,
-            dilation=dilation,
-            with_cp=with_cp,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg,
-            dcn=None,
-            plugins=None)
-        if upsample_cfg is not None:
-            self.upsample = build_upsample_layer(
-                cfg=upsample_cfg,
-                in_channels=in_channels,
-                out_channels=skip_channels,
-                with_cp=with_cp,
-                norm_cfg=norm_cfg,
-                act_cfg=act_cfg)
-        else:
-            self.upsample = ConvModule(
-                in_channels,
-                skip_channels,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                act_cfg=act_cfg)
-
-    def forward(self, skip, x):
-        """Forward function."""
-
-        x = self.upsample(x)
-        out = torch.cat([skip, x], dim=1)
-        out = self.conv_block(out)
-
-        return out
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/evaluation.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/evaluation.py
deleted file mode 100644
index 4d00999ce5665c53bded8de9e084943eee2d230d..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/evaluation.py
+++ /dev/null
@@ -1,509 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import warnings
-from math import inf
-
-import torch.distributed as dist
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.utils.data import DataLoader
-
-from annotator.uniformer.mmcv.fileio import FileClient
-from annotator.uniformer.mmcv.utils import is_seq_of
-from .hook import Hook
-from .logger import LoggerHook
-
-
-class EvalHook(Hook):
-    """Non-Distributed evaluation hook.
-
-    This hook will regularly perform evaluation in a given interval when
-    performing in non-distributed environment.
-
-    Args:
-        dataloader (DataLoader): A PyTorch dataloader, whose dataset has
-            implemented ``evaluate`` function.
-        start (int | None, optional): Evaluation starting epoch. It enables
-            evaluation before the training starts if ``start`` <= the resuming
-            epoch. If None, whether to evaluate is merely decided by
-            ``interval``. Default: None.
-        interval (int): Evaluation interval. Default: 1.
-        by_epoch (bool): Determine perform evaluation by epoch or by iteration.
-            If set to True, it will perform by epoch. Otherwise, by iteration.
-            Default: True.
-        save_best (str, optional): If a metric is specified, it would measure
-            the best checkpoint during evaluation. The information about best
-            checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep
-            best score value and best checkpoint path, which will be also
-            loaded when resume checkpoint. Options are the evaluation metrics
-            on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox
-            detection and instance segmentation. ``AR@100`` for proposal
-            recall. If ``save_best`` is ``auto``, the first key of the returned
-            ``OrderedDict`` result will be used. Default: None.
-        rule (str | None, optional): Comparison rule for best score. If set to
-            None, it will infer a reasonable rule. Keys such as 'acc', 'top'
-            .etc will be inferred by 'greater' rule. Keys contain 'loss' will
-            be inferred by 'less' rule. Options are 'greater', 'less', None.
-            Default: None.
-        test_fn (callable, optional): test a model with samples from a
-            dataloader, and return the test results. If ``None``, the default
-            test function ``mmcv.engine.single_gpu_test`` will be used.
-            (default: ``None``)
-        greater_keys (List[str] | None, optional): Metric keys that will be
-            inferred by 'greater' comparison rule. If ``None``,
-            _default_greater_keys will be used. (default: ``None``)
-        less_keys (List[str] | None, optional): Metric keys that will be
-            inferred by 'less' comparison rule. If ``None``, _default_less_keys
-            will be used. (default: ``None``)
-        out_dir (str, optional): The root directory to save checkpoints. If not
-            specified, `runner.work_dir` will be used by default. If specified,
-            the `out_dir` will be the concatenation of `out_dir` and the last
-            level directory of `runner.work_dir`.
-            `New in version 1.3.16.`
-        file_client_args (dict): Arguments to instantiate a FileClient.
-            See :class:`mmcv.fileio.FileClient` for details. Default: None.
-            `New in version 1.3.16.`
-        **eval_kwargs: Evaluation arguments fed into the evaluate function of
-            the dataset.
-
-    Notes:
-        If new arguments are added for EvalHook, tools/test.py,
-        tools/eval_metric.py may be affected.
-    """
-
-    # Since the key for determine greater or less is related to the downstream
-    # tasks, downstream repos may need to overwrite the following inner
-    # variable accordingly.
-
-    rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y}
-    init_value_map = {'greater': -inf, 'less': inf}
-    _default_greater_keys = [
-        'acc', 'top', 'AR@', 'auc', 'precision', 'mAP', 'mDice', 'mIoU',
-        'mAcc', 'aAcc'
-    ]
-    _default_less_keys = ['loss']
-
-    def __init__(self,
-                 dataloader,
-                 start=None,
-                 interval=1,
-                 by_epoch=True,
-                 save_best=None,
-                 rule=None,
-                 test_fn=None,
-                 greater_keys=None,
-                 less_keys=None,
-                 out_dir=None,
-                 file_client_args=None,
-                 **eval_kwargs):
-        if not isinstance(dataloader, DataLoader):
-            raise TypeError(f'dataloader must be a pytorch DataLoader, '
-                            f'but got {type(dataloader)}')
-
-        if interval <= 0:
-            raise ValueError(f'interval must be a positive number, '
-                             f'but got {interval}')
-
-        assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean'
-
-        if start is not None and start < 0:
-            raise ValueError(f'The evaluation start epoch {start} is smaller '
-                             f'than 0')
-
-        self.dataloader = dataloader
-        self.interval = interval
-        self.start = start
-        self.by_epoch = by_epoch
-
-        assert isinstance(save_best, str) or save_best is None, \
-            '""save_best"" should be a str or None ' \
-            f'rather than {type(save_best)}'
-        self.save_best = save_best
-        self.eval_kwargs = eval_kwargs
-        self.initial_flag = True
-
-        if test_fn is None:
-            from annotator.uniformer.mmcv.engine import single_gpu_test
-            self.test_fn = single_gpu_test
-        else:
-            self.test_fn = test_fn
-
-        if greater_keys is None:
-            self.greater_keys = self._default_greater_keys
-        else:
-            if not isinstance(greater_keys, (list, tuple)):
-                greater_keys = (greater_keys, )
-            assert is_seq_of(greater_keys, str)
-            self.greater_keys = greater_keys
-
-        if less_keys is None:
-            self.less_keys = self._default_less_keys
-        else:
-            if not isinstance(less_keys, (list, tuple)):
-                less_keys = (less_keys, )
-            assert is_seq_of(less_keys, str)
-            self.less_keys = less_keys
-
-        if self.save_best is not None:
-            self.best_ckpt_path = None
-            self._init_rule(rule, self.save_best)
-
-        self.out_dir = out_dir
-        self.file_client_args = file_client_args
-
-    def _init_rule(self, rule, key_indicator):
-        """Initialize rule, key_indicator, comparison_func, and best score.
-
-        Here is the rule to determine which rule is used for key indicator
-        when the rule is not specific (note that the key indicator matching
-        is case-insensitive):
-        1. If the key indicator is in ``self.greater_keys``, the rule will be
-           specified as 'greater'.
-        2. Or if the key indicator is in ``self.less_keys``, the rule will be
-           specified as 'less'.
-        3. Or if the key indicator is equal to the substring in any one item
-           in ``self.greater_keys``, the rule will be specified as 'greater'.
-        4. Or if the key indicator is equal to the substring in any one item
-           in ``self.less_keys``, the rule will be specified as 'less'.
-
-        Args:
-            rule (str | None): Comparison rule for best score.
-            key_indicator (str | None): Key indicator to determine the
-                comparison rule.
-        """
-        if rule not in self.rule_map and rule is not None:
-            raise KeyError(f'rule must be greater, less or None, '
-                           f'but got {rule}.')
-
-        if rule is None:
-            if key_indicator != 'auto':
-                # `_lc` here means we use the lower case of keys for
-                # case-insensitive matching
-                key_indicator_lc = key_indicator.lower()
-                greater_keys = [key.lower() for key in self.greater_keys]
-                less_keys = [key.lower() for key in self.less_keys]
-
-                if key_indicator_lc in greater_keys:
-                    rule = 'greater'
-                elif key_indicator_lc in less_keys:
-                    rule = 'less'
-                elif any(key in key_indicator_lc for key in greater_keys):
-                    rule = 'greater'
-                elif any(key in key_indicator_lc for key in less_keys):
-                    rule = 'less'
-                else:
-                    raise ValueError(f'Cannot infer the rule for key '
-                                     f'{key_indicator}, thus a specific rule '
-                                     f'must be specified.')
-        self.rule = rule
-        self.key_indicator = key_indicator
-        if self.rule is not None:
-            self.compare_func = self.rule_map[self.rule]
-
-    def before_run(self, runner):
-        if not self.out_dir:
-            self.out_dir = runner.work_dir
-
-        self.file_client = FileClient.infer_client(self.file_client_args,
-                                                   self.out_dir)
-
-        # if `self.out_dir` is not equal to `runner.work_dir`, it means that
-        # `self.out_dir` is set so the final `self.out_dir` is the
-        # concatenation of `self.out_dir` and the last level directory of
-        # `runner.work_dir`
-        if self.out_dir != runner.work_dir:
-            basename = osp.basename(runner.work_dir.rstrip(osp.sep))
-            self.out_dir = self.file_client.join_path(self.out_dir, basename)
-            runner.logger.info(
-                (f'The best checkpoint will be saved to {self.out_dir} by '
-                 f'{self.file_client.name}'))
-
-        if self.save_best is not None:
-            if runner.meta is None:
-                warnings.warn('runner.meta is None. Creating an empty one.')
-                runner.meta = dict()
-            runner.meta.setdefault('hook_msgs', dict())
-            self.best_ckpt_path = runner.meta['hook_msgs'].get(
-                'best_ckpt', None)
-
-    def before_train_iter(self, runner):
-        """Evaluate the model only at the start of training by iteration."""
-        if self.by_epoch or not self.initial_flag:
-            return
-        if self.start is not None and runner.iter >= self.start:
-            self.after_train_iter(runner)
-        self.initial_flag = False
-
-    def before_train_epoch(self, runner):
-        """Evaluate the model only at the start of training by epoch."""
-        if not (self.by_epoch and self.initial_flag):
-            return
-        if self.start is not None and runner.epoch >= self.start:
-            self.after_train_epoch(runner)
-        self.initial_flag = False
-
-    def after_train_iter(self, runner):
-        """Called after every training iter to evaluate the results."""
-        if not self.by_epoch and self._should_evaluate(runner):
-            # Because the priority of EvalHook is higher than LoggerHook, the
-            # training log and the evaluating log are mixed. Therefore,
-            # we need to dump the training log and clear it before evaluating
-            # log is generated. In addition, this problem will only appear in
-            # `IterBasedRunner` whose `self.by_epoch` is False, because
-            # `EpochBasedRunner` whose `self.by_epoch` is True calls
-            # `_do_evaluate` in `after_train_epoch` stage, and at this stage
-            # the training log has been printed, so it will not cause any
-            # problem. more details at
-            # https://github.com/open-mmlab/mmsegmentation/issues/694
-            for hook in runner._hooks:
-                if isinstance(hook, LoggerHook):
-                    hook.after_train_iter(runner)
-            runner.log_buffer.clear()
-
-            self._do_evaluate(runner)
-
-    def after_train_epoch(self, runner):
-        """Called after every training epoch to evaluate the results."""
-        if self.by_epoch and self._should_evaluate(runner):
-            self._do_evaluate(runner)
-
-    def _do_evaluate(self, runner):
-        """perform evaluation and save ckpt."""
-        results = self.test_fn(runner.model, self.dataloader)
-        runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
-        key_score = self.evaluate(runner, results)
-        # the key_score may be `None` so it needs to skip the action to save
-        # the best checkpoint
-        if self.save_best and key_score:
-            self._save_ckpt(runner, key_score)
-
-    def _should_evaluate(self, runner):
-        """Judge whether to perform evaluation.
-
-        Here is the rule to judge whether to perform evaluation:
-        1. It will not perform evaluation during the epoch/iteration interval,
-           which is determined by ``self.interval``.
-        2. It will not perform evaluation if the start time is larger than
-           current time.
-        3. It will not perform evaluation when current time is larger than
-           the start time but during epoch/iteration interval.
-
-        Returns:
-            bool: The flag indicating whether to perform evaluation.
-        """
-        if self.by_epoch:
-            current = runner.epoch
-            check_time = self.every_n_epochs
-        else:
-            current = runner.iter
-            check_time = self.every_n_iters
-
-        if self.start is None:
-            if not check_time(runner, self.interval):
-                # No evaluation during the interval.
-                return False
-        elif (current + 1) < self.start:
-            # No evaluation if start is larger than the current time.
-            return False
-        else:
-            # Evaluation only at epochs/iters 3, 5, 7...
-            # if start==3 and interval==2
-            if (current + 1 - self.start) % self.interval:
-                return False
-        return True
-
-    def _save_ckpt(self, runner, key_score):
-        """Save the best checkpoint.
-
-        It will compare the score according to the compare function, write
-        related information (best score, best checkpoint path) and save the
-        best checkpoint into ``work_dir``.
-        """
-        if self.by_epoch:
-            current = f'epoch_{runner.epoch + 1}'
-            cur_type, cur_time = 'epoch', runner.epoch + 1
-        else:
-            current = f'iter_{runner.iter + 1}'
-            cur_type, cur_time = 'iter', runner.iter + 1
-
-        best_score = runner.meta['hook_msgs'].get(
-            'best_score', self.init_value_map[self.rule])
-        if self.compare_func(key_score, best_score):
-            best_score = key_score
-            runner.meta['hook_msgs']['best_score'] = best_score
-
-            if self.best_ckpt_path and self.file_client.isfile(
-                    self.best_ckpt_path):
-                self.file_client.remove(self.best_ckpt_path)
-                runner.logger.info(
-                    (f'The previous best checkpoint {self.best_ckpt_path} was '
-                     'removed'))
-
-            best_ckpt_name = f'best_{self.key_indicator}_{current}.pth'
-            self.best_ckpt_path = self.file_client.join_path(
-                self.out_dir, best_ckpt_name)
-            runner.meta['hook_msgs']['best_ckpt'] = self.best_ckpt_path
-
-            runner.save_checkpoint(
-                self.out_dir, best_ckpt_name, create_symlink=False)
-            runner.logger.info(
-                f'Now best checkpoint is saved as {best_ckpt_name}.')
-            runner.logger.info(
-                f'Best {self.key_indicator} is {best_score:0.4f} '
-                f'at {cur_time} {cur_type}.')
-
-    def evaluate(self, runner, results):
-        """Evaluate the results.
-
-        Args:
-            runner (:obj:`mmcv.Runner`): The underlined training runner.
-            results (list): Output results.
-        """
-        eval_res = self.dataloader.dataset.evaluate(
-            results, logger=runner.logger, **self.eval_kwargs)
-
-        for name, val in eval_res.items():
-            runner.log_buffer.output[name] = val
-        runner.log_buffer.ready = True
-
-        if self.save_best is not None:
-            # If the performance of model is pool, the `eval_res` may be an
-            # empty dict and it will raise exception when `self.save_best` is
-            # not None. More details at
-            # https://github.com/open-mmlab/mmdetection/issues/6265.
-            if not eval_res:
-                warnings.warn(
-                    'Since `eval_res` is an empty dict, the behavior to save '
-                    'the best checkpoint will be skipped in this evaluation.')
-                return None
-
-            if self.key_indicator == 'auto':
-                # infer from eval_results
-                self._init_rule(self.rule, list(eval_res.keys())[0])
-            return eval_res[self.key_indicator]
-
-        return None
-
-
-class DistEvalHook(EvalHook):
-    """Distributed evaluation hook.
-
-    This hook will regularly perform evaluation in a given interval when
-    performing in distributed environment.
-
-    Args:
-        dataloader (DataLoader): A PyTorch dataloader, whose dataset has
-            implemented ``evaluate`` function.
-        start (int | None, optional): Evaluation starting epoch. It enables
-            evaluation before the training starts if ``start`` <= the resuming
-            epoch. If None, whether to evaluate is merely decided by
-            ``interval``. Default: None.
-        interval (int): Evaluation interval. Default: 1.
-        by_epoch (bool): Determine perform evaluation by epoch or by iteration.
-            If set to True, it will perform by epoch. Otherwise, by iteration.
-            default: True.
-        save_best (str, optional): If a metric is specified, it would measure
-            the best checkpoint during evaluation. The information about best
-            checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep
-            best score value and best checkpoint path, which will be also
-            loaded when resume checkpoint. Options are the evaluation metrics
-            on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox
-            detection and instance segmentation. ``AR@100`` for proposal
-            recall. If ``save_best`` is ``auto``, the first key of the returned
-            ``OrderedDict`` result will be used. Default: None.
-        rule (str | None, optional): Comparison rule for best score. If set to
-            None, it will infer a reasonable rule. Keys such as 'acc', 'top'
-            .etc will be inferred by 'greater' rule. Keys contain 'loss' will
-            be inferred by 'less' rule. Options are 'greater', 'less', None.
-            Default: None.
-        test_fn (callable, optional): test a model with samples from a
-            dataloader in a multi-gpu manner, and return the test results. If
-            ``None``, the default test function ``mmcv.engine.multi_gpu_test``
-            will be used. (default: ``None``)
-        tmpdir (str | None): Temporary directory to save the results of all
-            processes. Default: None.
-        gpu_collect (bool): Whether to use gpu or cpu to collect results.
-            Default: False.
-        broadcast_bn_buffer (bool): Whether to broadcast the
-            buffer(running_mean and running_var) of rank 0 to other rank
-            before evaluation. Default: True.
-        out_dir (str, optional): The root directory to save checkpoints. If not
-            specified, `runner.work_dir` will be used by default. If specified,
-            the `out_dir` will be the concatenation of `out_dir` and the last
-            level directory of `runner.work_dir`.
-        file_client_args (dict): Arguments to instantiate a FileClient.
-            See :class:`mmcv.fileio.FileClient` for details. Default: None.
-        **eval_kwargs: Evaluation arguments fed into the evaluate function of
-            the dataset.
-    """
-
-    def __init__(self,
-                 dataloader,
-                 start=None,
-                 interval=1,
-                 by_epoch=True,
-                 save_best=None,
-                 rule=None,
-                 test_fn=None,
-                 greater_keys=None,
-                 less_keys=None,
-                 broadcast_bn_buffer=True,
-                 tmpdir=None,
-                 gpu_collect=False,
-                 out_dir=None,
-                 file_client_args=None,
-                 **eval_kwargs):
-
-        if test_fn is None:
-            from annotator.uniformer.mmcv.engine import multi_gpu_test
-            test_fn = multi_gpu_test
-
-        super().__init__(
-            dataloader,
-            start=start,
-            interval=interval,
-            by_epoch=by_epoch,
-            save_best=save_best,
-            rule=rule,
-            test_fn=test_fn,
-            greater_keys=greater_keys,
-            less_keys=less_keys,
-            out_dir=out_dir,
-            file_client_args=file_client_args,
-            **eval_kwargs)
-
-        self.broadcast_bn_buffer = broadcast_bn_buffer
-        self.tmpdir = tmpdir
-        self.gpu_collect = gpu_collect
-
-    def _do_evaluate(self, runner):
-        """perform evaluation and save ckpt."""
-        # Synchronization of BatchNorm's buffer (running_mean
-        # and running_var) is not supported in the DDP of pytorch,
-        # which may cause the inconsistent performance of models in
-        # different ranks, so we broadcast BatchNorm's buffers
-        # of rank 0 to other ranks to avoid this.
-        if self.broadcast_bn_buffer:
-            model = runner.model
-            for name, module in model.named_modules():
-                if isinstance(module,
-                              _BatchNorm) and module.track_running_stats:
-                    dist.broadcast(module.running_var, 0)
-                    dist.broadcast(module.running_mean, 0)
-
-        tmpdir = self.tmpdir
-        if tmpdir is None:
-            tmpdir = osp.join(runner.work_dir, '.eval_hook')
-
-        results = self.test_fn(
-            runner.model,
-            self.dataloader,
-            tmpdir=tmpdir,
-            gpu_collect=self.gpu_collect)
-        if runner.rank == 0:
-            print('\n')
-            runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
-            key_score = self.evaluate(runner, results)
-            # the key_score may be `None` so it needs to skip the action to
-            # save the best checkpoint
-            if self.save_best and key_score:
-                self._save_ckpt(runner, key_score)
diff --git a/spaces/csuer/vits/text/thai.py b/spaces/csuer/vits/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/csuer/vits/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'เอ'),
-    ('b','บี'),
-    ('c','ซี'),
-    ('d','ดี'),
-    ('e','อี'),
-    ('f','เอฟ'),
-    ('g','จี'),
-    ('h','เอช'),
-    ('i','ไอ'),
-    ('j','เจ'),
-    ('k','เค'),
-    ('l','แอล'),
-    ('m','เอ็ม'),
-    ('n','เอ็น'),
-    ('o','โอ'),
-    ('p','พี'),
-    ('q','คิว'),
-    ('r','แอร์'),
-    ('s','เอส'),
-    ('t','ที'),
-    ('u','ยู'),
-    ('v','วี'),
-    ('w','ดับเบิลยู'),
-    ('x','เอ็กซ์'),
-    ('y','วาย'),
-    ('z','ซี')
-]]
-
-
-def num_to_thai(text):
-    return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
-    for regex, replacement in _latin_to_thai:
-        text = re.sub(regex, replacement, text)
-    return text
diff --git a/spaces/cugiahuy/CB-GR-Chatbot-Blenderbot-AW03/README.md b/spaces/cugiahuy/CB-GR-Chatbot-Blenderbot-AW03/README.md
deleted file mode 100644
index caf71fa74576d046dca11b86ab2760a68fc8c088..0000000000000000000000000000000000000000
--- a/spaces/cugiahuy/CB-GR-Chatbot-Blenderbot-AW03/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CB GR Chatbot Blenderbot AW03
-emoji: 🏢
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.6
-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/cvlab/zero123-live/taming-transformers/taming/lr_scheduler.py b/spaces/cvlab/zero123-live/taming-transformers/taming/lr_scheduler.py
deleted file mode 100644
index e598ed120159c53da6820a55ad86b89f5c70c82d..0000000000000000000000000000000000000000
--- a/spaces/cvlab/zero123-live/taming-transformers/taming/lr_scheduler.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import numpy as np
-
-
-class LambdaWarmUpCosineScheduler:
-    """
-    note: use with a base_lr of 1.0
-    """
-    def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
-        self.lr_warm_up_steps = warm_up_steps
-        self.lr_start = lr_start
-        self.lr_min = lr_min
-        self.lr_max = lr_max
-        self.lr_max_decay_steps = max_decay_steps
-        self.last_lr = 0.
-        self.verbosity_interval = verbosity_interval
-
-    def schedule(self, n):
-        if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
-        if n < self.lr_warm_up_steps:
-            lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
-            self.last_lr = lr
-            return lr
-        else:
-            t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
-            t = min(t, 1.0)
-            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
-                    1 + np.cos(t * np.pi))
-            self.last_lr = lr
-            return lr
-
-    def __call__(self, n):
-        return self.schedule(n)
-
diff --git a/spaces/cymic/Waifu_Diffusion_Webui/modules/sd_hijack.py b/spaces/cymic/Waifu_Diffusion_Webui/modules/sd_hijack.py
deleted file mode 100644
index 9e1601c795ac5e2fb8788d80eaf3323b5f264520..0000000000000000000000000000000000000000
--- a/spaces/cymic/Waifu_Diffusion_Webui/modules/sd_hijack.py
+++ /dev/null
@@ -1,321 +0,0 @@
-import math
-import os
-import sys
-import traceback
-import torch
-import numpy as np
-from torch import einsum
-from torch.nn.functional import silu
-
-import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
-from modules.shared import opts, device, cmd_opts
-
-import ldm.modules.attention
-import ldm.modules.diffusionmodules.model
-
-attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
-diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
-diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
-
-
-def apply_optimizations():
-    undo_optimizations()
-
-    ldm.modules.diffusionmodules.model.nonlinearity = silu
-
-    if cmd_opts.opt_split_attention_v1:
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
-
-
-def undo_optimizations():
-    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
-    ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
-    ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
-
-
-class StableDiffusionModelHijack:
-    fixes = None
-    comments = []
-    layers = None
-    circular_enabled = False
-    clip = None
-
-    embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
-
-    def hijack(self, m):
-        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
-
-        model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
-        m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-
-        self.clip = m.cond_stage_model
-
-        apply_optimizations()
-
-        def flatten(el):
-            flattened = [flatten(children) for children in el.children()]
-            res = [el]
-            for c in flattened:
-                res += c
-            return res
-
-        self.layers = flatten(m)
-
-    def undo_hijack(self, m):
-        if type(m.cond_stage_model) == FrozenCLIPEmbedderWithCustomWords:
-            m.cond_stage_model = m.cond_stage_model.wrapped
-
-        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
-        if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
-            model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
-
-    def apply_circular(self, enable):
-        if self.circular_enabled == enable:
-            return
-
-        self.circular_enabled = enable
-
-        for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
-            layer.padding_mode = 'circular' if enable else 'zeros'
-
-    def tokenize(self, text):
-        max_length = self.clip.max_length - 2
-        _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
-        return remade_batch_tokens[0], token_count, max_length
-
-
-class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
-    def __init__(self, wrapped, hijack):
-        super().__init__()
-        self.wrapped = wrapped
-        self.hijack: StableDiffusionModelHijack = hijack
-        self.tokenizer = wrapped.tokenizer
-        self.max_length = wrapped.max_length
-        self.token_mults = {}
-
-        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
-        for text, ident in tokens_with_parens:
-            mult = 1.0
-            for c in text:
-                if c == '[':
-                    mult /= 1.1
-                if c == ']':
-                    mult *= 1.1
-                if c == '(':
-                    mult *= 1.1
-                if c == ')':
-                    mult /= 1.1
-
-            if mult != 1.0:
-                self.token_mults[ident] = mult
-
-    def tokenize_line(self, line, used_custom_terms, hijack_comments):
-        id_start = self.wrapped.tokenizer.bos_token_id
-        id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = self.wrapped.max_length
-
-        if opts.enable_emphasis:
-            parsed = prompt_parser.parse_prompt_attention(line)
-        else:
-            parsed = [[line, 1.0]]
-
-        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
-
-        fixes = []
-        remade_tokens = []
-        multipliers = []
-
-        for tokens, (text, weight) in zip(tokenized, parsed):
-            i = 0
-            while i < len(tokens):
-                token = tokens[i]
-
-                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
-
-                if embedding is None:
-                    remade_tokens.append(token)
-                    multipliers.append(weight)
-                    i += 1
-                else:
-                    emb_len = int(embedding.vec.shape[0])
-                    fixes.append((len(remade_tokens), embedding))
-                    remade_tokens += [0] * emb_len
-                    multipliers += [weight] * emb_len
-                    used_custom_terms.append((embedding.name, embedding.checksum()))
-                    i += embedding_length_in_tokens
-
-        if len(remade_tokens) > maxlen - 2:
-            vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
-            ovf = remade_tokens[maxlen - 2:]
-            overflowing_words = [vocab.get(int(x), "") for x in ovf]
-            overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
-            hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
-        token_count = len(remade_tokens)
-        remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
-        remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
-
-        multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
-        multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
-
-        return remade_tokens, fixes, multipliers, token_count
-
-    def process_text(self, texts):
-        used_custom_terms = []
-        remade_batch_tokens = []
-        hijack_comments = []
-        hijack_fixes = []
-        token_count = 0
-
-        cache = {}
-        batch_multipliers = []
-        for line in texts:
-            if line in cache:
-                remade_tokens, fixes, multipliers = cache[line]
-            else:
-                remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
-
-                cache[line] = (remade_tokens, fixes, multipliers)
-
-            remade_batch_tokens.append(remade_tokens)
-            hijack_fixes.append(fixes)
-            batch_multipliers.append(multipliers)
-
-        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-
-
-    def process_text_old(self, text):
-        id_start = self.wrapped.tokenizer.bos_token_id
-        id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = self.wrapped.max_length
-        used_custom_terms = []
-        remade_batch_tokens = []
-        overflowing_words = []
-        hijack_comments = []
-        hijack_fixes = []
-        token_count = 0
-
-        cache = {}
-        batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
-        batch_multipliers = []
-        for tokens in batch_tokens:
-            tuple_tokens = tuple(tokens)
-
-            if tuple_tokens in cache:
-                remade_tokens, fixes, multipliers = cache[tuple_tokens]
-            else:
-                fixes = []
-                remade_tokens = []
-                multipliers = []
-                mult = 1.0
-
-                i = 0
-                while i < len(tokens):
-                    token = tokens[i]
-
-                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
-
-                    mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
-                    if mult_change is not None:
-                        mult *= mult_change
-                        i += 1
-                    elif embedding is None:
-                        remade_tokens.append(token)
-                        multipliers.append(mult)
-                        i += 1
-                    else:
-                        emb_len = int(embedding.vec.shape[0])
-                        fixes.append((len(remade_tokens), embedding))
-                        remade_tokens += [0] * emb_len
-                        multipliers += [mult] * emb_len
-                        used_custom_terms.append((embedding.name, embedding.checksum()))
-                        i += embedding_length_in_tokens
-
-                if len(remade_tokens) > maxlen - 2:
-                    vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
-                    ovf = remade_tokens[maxlen - 2:]
-                    overflowing_words = [vocab.get(int(x), "") for x in ovf]
-                    overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
-                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
-                token_count = len(remade_tokens)
-                remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
-                remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
-                cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
-
-            multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
-            multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
-
-            remade_batch_tokens.append(remade_tokens)
-            hijack_fixes.append(fixes)
-            batch_multipliers.append(multipliers)
-        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-
-    def forward(self, text):
-
-        if opts.use_old_emphasis_implementation:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
-        else:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
-
-        self.hijack.fixes = hijack_fixes
-        self.hijack.comments = hijack_comments
-
-        if len(used_custom_terms) > 0:
-            self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
-
-        tokens = torch.asarray(remade_batch_tokens).to(device)
-        outputs = self.wrapped.transformer(input_ids=tokens)
-        z = outputs.last_hidden_state
-
-        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers = torch.asarray(batch_multipliers).to(device)
-        original_mean = z.mean()
-        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
-        new_mean = z.mean()
-        z *= original_mean / new_mean
-
-        return z
-
-
-class EmbeddingsWithFixes(torch.nn.Module):
-    def __init__(self, wrapped, embeddings):
-        super().__init__()
-        self.wrapped = wrapped
-        self.embeddings = embeddings
-
-    def forward(self, input_ids):
-        batch_fixes = self.embeddings.fixes
-        self.embeddings.fixes = None
-
-        inputs_embeds = self.wrapped(input_ids)
-
-        if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
-            return inputs_embeds
-
-        vecs = []
-        for fixes, tensor in zip(batch_fixes, inputs_embeds):
-            for offset, embedding in fixes:
-                emb = embedding.vec
-                emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
-                tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
-
-            vecs.append(tensor)
-
-        return torch.stack(vecs)
-
-
-def add_circular_option_to_conv_2d():
-    conv2d_constructor = torch.nn.Conv2d.__init__
-
-    def conv2d_constructor_circular(self, *args, **kwargs):
-        return conv2d_constructor(self, *args, padding_mode='circular', **kwargs)
-
-    torch.nn.Conv2d.__init__ = conv2d_constructor_circular
-
-
-model_hijack = StableDiffusionModelHijack()
diff --git a/spaces/daikooo/DialoGPT-finetune-mental-health-chatbot/README.md b/spaces/daikooo/DialoGPT-finetune-mental-health-chatbot/README.md
deleted file mode 100644
index 45882d08e84a47388873da59ac1b2c7ee45c6613..0000000000000000000000000000000000000000
--- a/spaces/daikooo/DialoGPT-finetune-mental-health-chatbot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Daikooo DialogGPT Finetune Mental Health Chatbot
-emoji: 🏃
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.21.0
-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/davidfischer/ea-classifier/README.md b/spaces/davidfischer/ea-classifier/README.md
deleted file mode 100644
index 1ffc6211ecef86096f02e03efec524a29777602f..0000000000000000000000000000000000000000
--- a/spaces/davidfischer/ea-classifier/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Ea Classifier
-emoji: 🐨
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/S__i_l_l.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/S__i_l_l.py
deleted file mode 100644
index 12b0b8f6cc55b337db857df99e27e7bb69bb5bbc..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/S__i_l_l.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.fixedTools import floatToFixedToStr
-from fontTools.misc.textTools import safeEval
-from . import DefaultTable
-from . import grUtils
-import struct
-
-Sill_hdr = """
-    >
-    version:    16.16F
-"""
-
-
-class table_S__i_l_l(DefaultTable.DefaultTable):
-    def __init__(self, tag=None):
-        DefaultTable.DefaultTable.__init__(self, tag)
-        self.langs = {}
-
-    def decompile(self, data, ttFont):
-        (_, data) = sstruct.unpack2(Sill_hdr, data, self)
-        self.version = float(floatToFixedToStr(self.version, precisionBits=16))
-        (numLangs,) = struct.unpack(">H", data[:2])
-        data = data[8:]
-        maxsetting = 0
-        langinfo = []
-        for i in range(numLangs):
-            (langcode, numsettings, offset) = struct.unpack(
-                ">4sHH", data[i * 8 : (i + 1) * 8]
-            )
-            offset = int(offset / 8) - (numLangs + 1)
-            langcode = langcode.replace(b"\000", b"")
-            langinfo.append((langcode.decode("utf-8"), numsettings, offset))
-            maxsetting = max(maxsetting, offset + numsettings)
-        data = data[numLangs * 8 :]
-        finfo = []
-        for i in range(maxsetting):
-            (fid, val, _) = struct.unpack(">LHH", data[i * 8 : (i + 1) * 8])
-            finfo.append((fid, val))
-        self.langs = {}
-        for c, n, o in langinfo:
-            self.langs[c] = []
-            for i in range(o, o + n):
-                self.langs[c].append(finfo[i])
-
-    def compile(self, ttFont):
-        ldat = b""
-        fdat = b""
-        offset = len(self.langs)
-        for c, inf in sorted(self.langs.items()):
-            ldat += struct.pack(">4sHH", c.encode("utf8"), len(inf), 8 * offset + 20)
-            for fid, val in inf:
-                fdat += struct.pack(">LHH", fid, val, 0)
-            offset += len(inf)
-        ldat += struct.pack(">LHH", 0x80808080, 0, 8 * offset + 20)
-        return (
-            sstruct.pack(Sill_hdr, self)
-            + grUtils.bininfo(len(self.langs))
-            + ldat
-            + fdat
-        )
-
-    def toXML(self, writer, ttFont):
-        writer.simpletag("version", version=self.version)
-        writer.newline()
-        for c, inf in sorted(self.langs.items()):
-            writer.begintag("lang", name=c)
-            writer.newline()
-            for fid, val in inf:
-                writer.simpletag("feature", fid=grUtils.num2tag(fid), val=val)
-                writer.newline()
-            writer.endtag("lang")
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "version":
-            self.version = float(safeEval(attrs["version"]))
-        elif name == "lang":
-            c = attrs["name"]
-            self.langs[c] = []
-            for element in content:
-                if not isinstance(element, tuple):
-                    continue
-                tag, a, subcontent = element
-                if tag == "feature":
-                    self.langs[c].append(
-                        (grUtils.tag2num(a["fid"]), int(safeEval(a["val"])))
-                    )
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_subprocess.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_subprocess.py
deleted file mode 100644
index 5ec7936549c11f432c2b98a2f88a7a87d1b38772..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_subprocess.py
+++ /dev/null
@@ -1,142 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 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
-"""Contains utilities to easily handle subprocesses in `huggingface_hub`."""
-import os
-import subprocess
-import sys
-from contextlib import contextmanager
-from io import StringIO
-from pathlib import Path
-from typing import IO, Generator, List, Optional, Tuple, Union
-
-from .logging import get_logger
-
-
-logger = get_logger(__name__)
-
-
-@contextmanager
-def capture_output() -> Generator[StringIO, None, None]:
-    """Capture output that is printed to terminal.
-
-    Taken from https://stackoverflow.com/a/34738440
-
-    Example:
-    ```py
-    >>> with capture_output() as output:
-    ...     print("hello world")
-    >>> assert output.getvalue() == "hello world\n"
-    ```
-    """
-    output = StringIO()
-    previous_output = sys.stdout
-    sys.stdout = output
-    yield output
-    sys.stdout = previous_output
-
-
-def run_subprocess(
-    command: Union[str, List[str]],
-    folder: Optional[Union[str, Path]] = None,
-    check=True,
-    **kwargs,
-) -> subprocess.CompletedProcess:
-    """
-    Method to run subprocesses. Calling this will capture the `stderr` and `stdout`,
-    please call `subprocess.run` manually in case you would like for them not to
-    be captured.
-
-    Args:
-        command (`str` or `List[str]`):
-            The command to execute as a string or list of strings.
-        folder (`str`, *optional*):
-            The folder in which to run the command. Defaults to current working
-            directory (from `os.getcwd()`).
-        check (`bool`, *optional*, defaults to `True`):
-            Setting `check` to `True` will raise a `subprocess.CalledProcessError`
-            when the subprocess has a non-zero exit code.
-        kwargs (`Dict[str]`):
-            Keyword arguments to be passed to the `subprocess.run` underlying command.
-
-    Returns:
-        `subprocess.CompletedProcess`: The completed process.
-    """
-    if isinstance(command, str):
-        command = command.split()
-
-    if isinstance(folder, Path):
-        folder = str(folder)
-
-    return subprocess.run(
-        command,
-        stderr=subprocess.PIPE,
-        stdout=subprocess.PIPE,
-        check=check,
-        encoding="utf-8",
-        errors="replace",  # if not utf-8, replace char by �
-        cwd=folder or os.getcwd(),
-        **kwargs,
-    )
-
-
-@contextmanager
-def run_interactive_subprocess(
-    command: Union[str, List[str]],
-    folder: Optional[Union[str, Path]] = None,
-    **kwargs,
-) -> Generator[Tuple[IO[str], IO[str]], None, None]:
-    """Run a subprocess in an interactive mode in a context manager.
-
-    Args:
-        command (`str` or `List[str]`):
-            The command to execute as a string or list of strings.
-        folder (`str`, *optional*):
-            The folder in which to run the command. Defaults to current working
-            directory (from `os.getcwd()`).
-        kwargs (`Dict[str]`):
-            Keyword arguments to be passed to the `subprocess.run` underlying command.
-
-    Returns:
-        `Tuple[IO[str], IO[str]]`: A tuple with `stdin` and `stdout` to interact
-        with the process (input and output are utf-8 encoded).
-
-    Example:
-    ```python
-    with _interactive_subprocess("git credential-store get") as (stdin, stdout):
-        # Write to stdin
-        stdin.write("url=hf.co\nusername=obama\n".encode("utf-8"))
-        stdin.flush()
-
-        # Read from stdout
-        output = stdout.read().decode("utf-8")
-    ```
-    """
-    if isinstance(command, str):
-        command = command.split()
-
-    with subprocess.Popen(
-        command,
-        stdin=subprocess.PIPE,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT,
-        encoding="utf-8",
-        errors="replace",  # if not utf-8, replace char by �
-        cwd=folder or os.getcwd(),
-        **kwargs,
-    ) as process:
-        assert process.stdin is not None, "subprocess is opened as subprocess.PIPE"
-        assert process.stdout is not None, "subprocess is opened as subprocess.PIPE"
-        yield process.stdin, process.stdout
diff --git a/spaces/declare-lab/tango/diffusers/docker/diffusers-onnxruntime-cuda/Dockerfile b/spaces/declare-lab/tango/diffusers/docker/diffusers-onnxruntime-cuda/Dockerfile
deleted file mode 100644
index 2129dbcaf68c57755485e1e54e867af05b937336..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/docker/diffusers-onnxruntime-cuda/Dockerfile
+++ /dev/null
@@ -1,44 +0,0 @@
-FROM nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04
-LABEL maintainer="Hugging Face"
-LABEL repository="diffusers"
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt update && \
-    apt install -y bash \
-                   build-essential \
-                   git \
-                   git-lfs \
-                   curl \
-                   ca-certificates \
-                   libsndfile1-dev \
-                   python3.8 \
-                   python3-pip \
-                   python3.8-venv && \
-    rm -rf /var/lib/apt/lists
-
-# make sure to use venv
-RUN python3 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3 -m pip install --no-cache-dir --upgrade pip && \
-    python3 -m pip install --no-cache-dir \
-        torch \
-        torchvision \
-        torchaudio \
-        "onnxruntime-gpu>=1.13.1" \
-        --extra-index-url https://download.pytorch.org/whl/cu117 && \
-    python3 -m pip install --no-cache-dir \
-        accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        Jinja2 \
-        librosa \
-        numpy \
-        scipy \
-        tensorboard \
-        transformers
-
-CMD ["/bin/bash"]
\ No newline at end of file
diff --git a/spaces/deniandriancode/zephyr-7b-alpha-chatbot/README.md b/spaces/deniandriancode/zephyr-7b-alpha-chatbot/README.md
deleted file mode 100644
index a3d9d49d63e9cd48d0a8fd9e1d45661adba122ec..0000000000000000000000000000000000000000
--- a/spaces/deniandriancode/zephyr-7b-alpha-chatbot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Zephyr 7b Alpha Chatbot
-emoji: ⚡
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.47.1
-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/dexrm/Weewee/Dockerfile b/spaces/dexrm/Weewee/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/dexrm/Weewee/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Bosch Kts 520 Drivers.md b/spaces/diacanFperku/AutoGPT/Bosch Kts 520 Drivers.md
deleted file mode 100644
index 18c487623b38bec03b844c1ada4b5aeda3385dc2..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Bosch Kts 520 Drivers.md	
+++ /dev/null
@@ -1,92 +0,0 @@
-
-<h1>Bosch KTS 520 Drivers: What You Need to Know</h1>
-<p>If you are looking for a reliable and versatile tool for ECU diagnosis and reprogramming, you might want to consider the Bosch KTS 520. This device is a communication module that connects to your PC or laptop and allows you to access the original manufacturer data and online portals of various vehicle brands. However, to use the Bosch KTS 520 effectively, you need to have the right drivers installed and updated on your computer. In this article, we will explain what Bosch KTS 520 drivers are, why they are important, how to install and update them, and where to find more information and support.</p>
-<h2>What are Bosch KTS 520 Drivers?</h2>
-<p>Bosch KTS 520 drivers are software components that enable your computer to communicate with the Bosch KTS 520 device. They are essential for the proper functioning of the device and its compatibility with different vehicle models and protocols. Bosch KTS 520 drivers also allow you to use the Bosch PassThru software, which is a program that enables you to access the online portals of the vehicle manufacturers and use their original data for diagnosis and reprogramming.</p>
-<h2>bosch kts 520 drivers</h2><br /><p><b><b>Download File</b> &#10001; &#10001; &#10001; <a href="https://gohhs.com/2uFVIJ">https://gohhs.com/2uFVIJ</a></b></p><br /><br />
-<h2>Why are Bosch KTS 520 Drivers Important?</h2>
-<p>Bosch KTS 520 drivers are important because they ensure that your device can perform ECU diagnosis and reprogramming accurately and efficiently. Without the correct drivers, your device might not be able to connect to your computer or recognize certain vehicle interfaces or protocols. This could result in errors, malfunctions, or incomplete or incorrect data. Bosch KTS 520 drivers also ensure that your device is up to date with the latest standards and regulations, such as the Euro 5 Standard, which requires that vehicle manufacturers provide technical repair information via their online portals also for all independent workshops.</p>
-<h2>How to Install and Update Bosch KTS 520 Drivers?</h2>
-<p>To install and update Bosch KTS 520 drivers, you need to follow these steps:</p>
-<ol>
-<li>Download the latest version of Bosch KTS 520 drivers from the Bosch Automotive Aftermarket website. You can find them in the Download Centre section under Services and Support.</li>
-<li>Run the downloaded file and follow the instructions on the screen to install the drivers on your computer.</li>
-<li>Connect your Bosch KTS 520 device to your computer using a USB cable.</li>
-<li>Launch the Bosch PassThru software and select your vehicle brand from the list.</li>
-<li>Log in to the online portal of the vehicle manufacturer using your credentials.</li>
-<li>Follow the instructions on the screen to perform ECU diagnosis and reprogramming using the original manufacturer data.</li>
-</ol>
-<p>You should check for updates regularly to make sure that your drivers are compatible with the latest vehicles and protocols. You can do this by visiting the Bosch Automotive Aftermarket website or by using the automatic update feature of the Bosch PassThru software.</p>
-<h2>Where to Find More Information and Support?</h2>
-<p>If you need more information or support regarding Bosch KTS 520 drivers, you can visit the following sources:</p>
-<ul>
-<li>The Bosch Automotive Aftermarket website, where you can find manuals, brochures, flyers, technical documents, software updates, and more.</li>
-<li>The Euro 5 / PassThru section of the website, where you can find general information, reprogramming procedures, manufacturer portal links, and more.</li>
-<li>The additional download area of the website, where you can find documents for the latest products and for products that are not available in your country or anymore in general.</li>
-<li>The customer service hotline or email address of Bosch Automotive Aftermarket in your country or region.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Bosch KTS 520 drivers are software components that enable your computer to communicate with the Bosch KTS 520 device. They are essential for ECU diagnosis and reprogramming using original manufacturer data and online portals. To use Bosch KTS 520 drivers effectively, you need to install and update them regularly on your computer. You can find them on the Bosch Automotive Aftermarket website along with other useful information and support. With Bosch KTS 520 drivers, you can enhance your workshop's output and offer professional service to your customers.</p>
-<h2>How to use Bosch KTS 520 for ECU diagnosis and reprogramming?</h2>
-<p>To use Bosch KTS 520 for ECU diagnosis and reprogramming, you need to follow these steps:</p>
-<ol>
-<li>Install and update Bosch KTS 520 drivers on your computer. You can find them on the Bosch Automotive Aftermarket website or in the Download Centre section.</li>
-<li>Connect your Bosch KTS 520 device to your computer using a USB cable.</li>
-<li>Launch the Bosch PassThru software and select your vehicle brand from the list.</li>
-<li>Log in to the online portal of the vehicle manufacturer using your credentials.</li>
-<li>Select the vehicle model and year from the menu.</li>
-<li>Select the ECU diagnosis or reprogramming option from the menu.</li>
-<li>Follow the instructions on the screen to perform ECU diagnosis or reprogramming using the original manufacturer data.</li>
-</ol>
-<h2>What are some of the best practices for Bosch KTS 520 drivers?</h2>
-<p>To ensure optimal performance and safety of Bosch KTS 520 drivers, you should follow some of these best practices:</p>
-<ul>
-<li>Always use the official Bosch KTS 520 drivers from the Bosch Automotive Aftermarket website or the Download Centre section. Do not use unofficial or modified drivers from other sources.</li>
-<li>Always check for updates regularly and install them as soon as possible. Updates can fix bugs, improve compatibility, and add new features and functions.</li>
-<li>Always backup your data before performing ECU diagnosis or reprogramming. ECU diagnosis or reprogramming can alter or erase your data, so it is advisable to have a backup in case of any errors or failures.</li>
-<li>Always follow the manufacturer's instructions and recommendations when performing ECU diagnosis or reprogramming. Do not attempt to modify or override any settings or parameters that are not authorized by the manufacturer.</li>
-<li>Always disconnect your device from your computer and vehicle when not in use. This can prevent any accidental damage or interference from external factors.</li>
-</ul>
-<h2>What are some of the advantages and disadvantages of Bosch KTS 520 drivers?</h2>
-<p>Bosch KTS 520 drivers have some advantages and disadvantages that you should be aware of before using them. Here are some of them:</p>
-<p></p>
-<h3>Advantages</h3>
-<ul>
-<li>They allow you to access the original manufacturer data and online portals of various vehicle brands, which can provide you with accurate and comprehensive information for ECU diagnosis and reprogramming.</li>
-<li>They support various vehicle interfaces and protocols, which can increase your compatibility and coverage with different vehicle models and systems.</li>
-<li>They enable you to use the Bosch PassThru software, which is a program that facilitates the communication between the device and the online portals, and provides you with clear and user-friendly interface and instructions.</li>
-<li>They provide you with fast and reliable data transfer and processing, which can improve your efficiency and productivity in your workshop.</li>
-<li>They are easy to install and update, and you can find them on the Bosch Automotive Aftermarket website or in the Download Centre section.</li>
-</ul>
-<h3>Disadvantages</h3>
-<ul>
-<li>They require a computer and an internet connection to work, which can limit your mobility and flexibility in your workshop.</li>
-<li>They depend on the availability and quality of the online portals of the vehicle manufacturers, which can vary depending on the brand, model, year, region, and subscription.</li>
-<li>They can encounter errors or failures due to various factors, such as compatibility issues, software bugs, network problems, or human errors.</li>
-<li>They can alter or erase your data during ECU diagnosis or reprogramming, which can cause problems or damages to your vehicle or device.</li>
-<li>They can expose you to legal and ethical issues if you use them without permission or authorization from the vehicle manufacturers or owners.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Bosch KTS 520 drivers are software components that enable your computer to communicate with the Bosch KTS 520 device. They are essential for ECU diagnosis and reprogramming using original manufacturer data and online portals. To use Bosch KTS 520 drivers effectively, you need to install and update them regularly on your computer. You can find them on the Bosch Automotive Aftermarket website along with other useful information and support. You should also follow some best practices and be aware of some advantages and disadvantages of Bosch KTS 520 drivers. With Bosch KTS 520 drivers, you can enhance your workshop's output and offer professional service to your customers.</p>
-<h2>How to compare Bosch KTS 520 drivers with other similar products?</h2>
-<p>If you want to compare Bosch KTS 520 drivers with other similar products, you need to consider some factors such as:</p>
-<ul>
-<li>The features and functions of the products, such as the supported vehicle interfaces and protocols, the access to the original manufacturer data and online portals, the compatibility and coverage with different vehicle models and systems, and the data transfer and processing speed and reliability.</li>
-<li>The quality and performance of the products, such as the accuracy and completeness of the information for ECU diagnosis and reprogramming, the user-friendliness and clarity of the interface and instructions, the robustness and durability of the design and materials, and the frequency and availability of updates and support.</li>
-<li>The price and value of the products, such as the initial cost of purchase, the ongoing cost of maintenance and subscription, the return on investment and profitability, and the customer satisfaction and loyalty.</li>
-</ul>
-<p>You can find some information and reviews about different products on the internet, such as on websites, blogs, forums, or social media. You can also ask for recommendations or feedback from other professionals or experts in your field. However, you should always verify the credibility and validity of the sources and opinions before making a decision. You should also test and evaluate the products yourself to see if they meet your needs and expectations.</p>
-<h2>What are some of the future trends and developments for Bosch KTS 520 drivers?</h2>
-<p>Bosch KTS 520 drivers are constantly evolving and improving to keep up with the changing demands and standards of the automotive industry. Some of the future trends and developments for Bosch KTS 520 drivers are:</p>
-<ul>
-<li>The integration of artificial intelligence and machine learning, which can enhance the data analysis and processing capabilities, provide more intelligent and personalized solutions, and enable more proactive and predictive diagnosis and reprogramming.</li>
-<li>The adoption of cloud computing and internet of things, which can increase the data storage and accessibility options, provide more seamless and secure communication between devices and online portals, and enable more remote and mobile diagnosis and reprogramming.</li>
-<li>The expansion of vehicle interfaces and protocols, which can support more advanced and complex vehicle systems and functions, such as electric vehicles, hybrid vehicles, autonomous vehicles, connected vehicles, and smart vehicles.</li>
-<li>The compliance with new standards and regulations, which can ensure more safety and quality for ECU diagnosis and reprogramming, such as the Euro 6 Standard, which requires more stringent emission limits for vehicles.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Bosch KTS 520 drivers are software components that enable your computer to communicate with the Bosch KTS 520 device. They are essential for ECU diagnosis and reprogramming using original manufacturer data and online portals. To use Bosch KTS 520 drivers effectively, you need to install
-<h2>Conclusion</h2>
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diff --git a/spaces/diacanFperku/AutoGPT/Optsim-52-Software-Free-Download.md b/spaces/diacanFperku/AutoGPT/Optsim-52-Software-Free-Download.md
deleted file mode 100644
index 76609f3bcd9bb12da957363f23d090c6d5b6744e..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Optsim-52-Software-Free-Download.md
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diff --git a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/short_audio_transcribe.py b/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/short_audio_transcribe.py
deleted file mode 100644
index f1e8b30671f2c2f2fa3c93feb1f4edd3fbe2f545..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/short_audio_transcribe.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import whisper
-import os
-import json
-import torchaudio
-import argparse
-import torch
-
-lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-def transcribe_one(audio_path):
-    # load audio and pad/trim it to fit 30 seconds
-    audio = whisper.load_audio(audio_path)
-    audio = whisper.pad_or_trim(audio)
-
-    # make log-Mel spectrogram and move to the same device as the model
-    mel = whisper.log_mel_spectrogram(audio).to(model.device)
-
-    # detect the spoken language
-    _, probs = model.detect_language(mel)
-    print(f"Detected language: {max(probs, key=probs.get)}")
-    lang = max(probs, key=probs.get)
-    # decode the audio
-    options = whisper.DecodingOptions(beam_size=5)
-    result = whisper.decode(model, mel, options)
-
-    # print the recognized text
-    print(result.text)
-    return lang, result.text
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--languages", default="CJE")
-    parser.add_argument("--whisper_size", default="medium")
-    args = parser.parse_args()
-    if args.languages == "CJE":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-    elif args.languages == "CJ":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-        }
-    elif args.languages == "C":
-        lang2token = {
-            'zh': "[ZH]",
-        }
-    assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
-    model = whisper.load_model(args.whisper_size)
-    parent_dir = "./custom_character_voice/"
-    speaker_names = list(os.walk(parent_dir))[0][1]
-    speaker_annos = []
-    total_files = sum([len(files) for r, d, files in os.walk(parent_dir)])
-    # resample audios
-    # 2023/4/21: Get the target sampling rate
-    with open("./configs/config.json", 'r', encoding='utf-8') as f:
-        hps = json.load(f)
-    target_sr = hps['data']['sampling_rate']
-    processed_files = 0
-    for speaker in speaker_names:
-        for i, wavfile in enumerate(list(os.walk(parent_dir + speaker))[0][2]):
-            # try to load file as audio
-            if wavfile.startswith("processed_"):
-                continue
-            try:
-                wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True,
-                                          channels_first=True)
-                wav = wav.mean(dim=0).unsqueeze(0)
-                if sr != target_sr:
-                    wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
-                if wav.shape[1] / sr > 20:
-                    print(f"{wavfile} too long, ignoring\n")
-                save_path = parent_dir + speaker + "/" + f"processed_{i}.wav"
-                torchaudio.save(save_path, wav, target_sr, channels_first=True)
-                # transcribe text
-                lang, text = transcribe_one(save_path)
-                if lang not in list(lang2token.keys()):
-                    print(f"{lang} not supported, ignoring\n")
-                    continue
-                text = "ZH|" + text + "\n"#
-                #text = lang2token[lang] + text + lang2token[lang] + "\n"
-                speaker_annos.append(save_path + "|" + speaker + "|" + text)
-                
-                processed_files += 1
-                print(f"Processed: {processed_files}/{total_files}")
-            except:
-                continue
-
-    # # clean annotation
-    # import argparse
-    # import text
-    # from utils import load_filepaths_and_text
-    # for i, line in enumerate(speaker_annos):
-    #     path, sid, txt = line.split("|")
-    #     cleaned_text = text._clean_text(txt, ["cjke_cleaners2"])
-    #     cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-    #     speaker_annos[i] = path + "|" + sid + "|" + cleaned_text
-    # write into annotation
-    if len(speaker_annos) == 0:
-        print("Warning: no short audios found, this IS expected if you have only uploaded long audios, videos or video links.")
-        print("this IS NOT expected if you have uploaded a zip file of short audios. Please check your file structure or make sure your audio language is supported.")
-    with open("./filelists/short_character_anno.list", 'w', encoding='utf-8') as f:
-        for line in speaker_annos:
-            f.write(line)
-
-    # import json
-    # # generate new config
-    # with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
-    #     hps = json.load(f)
-    # # modify n_speakers
-    # hps['data']["n_speakers"] = 1000 + len(speaker2id)
-    # # add speaker names
-    # for speaker in speaker_names:
-    #     hps['speakers'][speaker] = speaker2id[speaker]
-    # # save modified config
-    # with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
-    #     json.dump(hps, f, indent=2)
-    # print("finished")
diff --git a/spaces/digitalxingtong/Jiuxia-Bert-Vits2/transforms.py b/spaces/digitalxingtong/Jiuxia-Bert-Vits2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Jiuxia-Bert-Vits2/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/dpe1/can_this_pokemon_evolve/app.py b/spaces/dpe1/can_this_pokemon_evolve/app.py
deleted file mode 100644
index 55f89144c66a2738aa08b5c56a434127f35d8c0f..0000000000000000000000000000000000000000
--- a/spaces/dpe1/can_this_pokemon_evolve/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import wahtever
-import gradio as gr
-from gradio.components import Textbox
-
-demo = gr.Interface(
-    wahtever.isisisisis,
-    [
-Textbox(label="enter pokemons name (it uses fuzzy search)"),
-    ],
-    [
-        Textbox(label="can it evolve????"),
-    ],
-    live=True,
-    title='Can this Pokemon evolve?',
-    description="This shows you if a Pokemon can evolve and how. Unlike using Wiki, this doesn't spoil what it evolves into and at what level.\nI also used real time fuzzy search so that you don't have to enter exact name of a Pokemon.",
-    article="""NOTE I used a hole Bunch of data from https://pokemondb.net/evolution/level.
-
-Most pokemon evolve upon reaching a certain level. A few Pokémon also need to meet a condition such as gender to evolve; these are also listed.
-
-To evolve a Pokémon via stone, simply select the item to use in your bag, then select the Pokémon to apply it to - they will evolve straight away.
-
-A Pokémon's Friendship (aka happiness) can be increased in many ways. The simplest is walking around with the Pokémon in your party, battling and growing them, but not letting them faint. Massages and haircuts increase Friendship by a larger amount. Giving them a Soothe Bell to hold increases the effect of the other methods. See more in the glossary or this PokéBase question.
-
-For special conditions, Once the condition is met, the Pokémon needs to level up once more in order to evolve."""
-)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/util/inference.py b/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/util/inference.py
deleted file mode 100644
index 943befc517b2ae1137e6df082eaa76b41cb1ef3a..0000000000000000000000000000000000000000
--- a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/util/inference.py
+++ /dev/null
@@ -1,255 +0,0 @@
-from typing import Tuple, List
-
-import cv2
-import numpy as np
-import supervision as sv
-import torch
-from PIL import Image
-from torchvision.ops import box_convert
-
-import groundingdino.datasets.transforms as T
-from groundingdino.models import build_model
-from groundingdino.util.misc import clean_state_dict
-from groundingdino.util.slconfig import SLConfig
-from groundingdino.util.utils import get_phrases_from_posmap
-
-# ----------------------------------------------------------------------------------------------------------------------
-# OLD API
-# ----------------------------------------------------------------------------------------------------------------------
-
-
-def preprocess_caption(caption: str) -> str:
-    result = caption.lower().strip()
-    if result.endswith("."):
-        return result
-    return result + "."
-
-
-def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"):
-    args = SLConfig.fromfile(model_config_path)
-    args.device = device
-    model = build_model(args)
-    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
-    model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
-    model.eval()
-    return model
-
-
-def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
-    transform = T.Compose(
-        [
-            T.RandomResize([800], max_size=1333),
-            T.ToTensor(),
-            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-        ]
-    )
-    image_source = Image.open(image_path).convert("RGB")
-    image = np.asarray(image_source)
-    image_transformed, _ = transform(image_source, None)
-    return image, image_transformed
-
-
-def predict(
-        model,
-        image: torch.Tensor,
-        caption: str,
-        box_threshold: float,
-        text_threshold: float,
-        device: str = "cuda"
-) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
-    caption = preprocess_caption(caption=caption)
-
-    model = model.to(device)
-    image = image.to(device)
-
-    with torch.no_grad():
-        outputs = model(image[None], captions=[caption])
-
-    prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0]  # prediction_logits.shape = (nq, 256)
-    prediction_boxes = outputs["pred_boxes"].cpu()[0]  # prediction_boxes.shape = (nq, 4)
-
-    mask = prediction_logits.max(dim=1)[0] > box_threshold
-    logits = prediction_logits[mask]  # logits.shape = (n, 256)
-    boxes = prediction_boxes[mask]  # boxes.shape = (n, 4)
-
-    tokenizer = model.tokenizer
-    tokenized = tokenizer(caption)
-
-    phrases = [
-        get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')
-        for logit
-        in logits
-    ]
-
-    return boxes, logits.max(dim=1)[0], phrases
-
-
-def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray:
-    h, w, _ = image_source.shape
-    boxes = boxes * torch.Tensor([w, h, w, h])
-    xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
-    detections = sv.Detections(xyxy=xyxy)
-
-    labels = [
-        f"{phrase} {logit:.2f}"
-        for phrase, logit
-        in zip(phrases, logits)
-    ]
-
-    box_annotator = sv.BoxAnnotator()
-    annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)
-    annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
-    return annotated_frame
-
-
-# ----------------------------------------------------------------------------------------------------------------------
-# NEW API
-# ----------------------------------------------------------------------------------------------------------------------
-
-
-class Model:
-
-    def __init__(
-        self,
-        model_config_path: str,
-        model_checkpoint_path: str,
-        device: str = "cuda"
-    ):
-        self.model = load_model(
-            model_config_path=model_config_path,
-            model_checkpoint_path=model_checkpoint_path,
-            device=device
-        ).to(device)
-        self.device = device
-
-    def predict_with_caption(
-        self,
-        image: np.ndarray,
-        caption: str,
-        box_threshold: float = 0.35,
-        text_threshold: float = 0.25
-    ) -> Tuple[sv.Detections, List[str]]:
-        """
-        import cv2
-
-        image = cv2.imread(IMAGE_PATH)
-
-        model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)
-        detections, labels = model.predict_with_caption(
-            image=image,
-            caption=caption,
-            box_threshold=BOX_THRESHOLD,
-            text_threshold=TEXT_THRESHOLD
-        )
-
-        import supervision as sv
-
-        box_annotator = sv.BoxAnnotator()
-        annotated_image = box_annotator.annotate(scene=image, detections=detections, labels=labels)
-        """
-        processed_image = Model.preprocess_image(image_bgr=image).to(self.device)
-        boxes, logits, phrases = predict(
-            model=self.model,
-            image=processed_image,
-            caption=caption,
-            box_threshold=box_threshold,
-            text_threshold=text_threshold, 
-            device=self.device)
-        source_h, source_w, _ = image.shape
-        detections = Model.post_process_result(
-            source_h=source_h,
-            source_w=source_w,
-            boxes=boxes,
-            logits=logits)
-        return detections, phrases
-
-    def predict_with_classes(
-        self,
-        image: np.ndarray,
-        classes: List[str],
-        box_threshold: float,
-        text_threshold: float
-    ) -> sv.Detections:
-        """
-        import cv2
-
-        image = cv2.imread(IMAGE_PATH)
-
-        model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)
-        detections = model.predict_with_classes(
-            image=image,
-            classes=CLASSES,
-            box_threshold=BOX_THRESHOLD,
-            text_threshold=TEXT_THRESHOLD
-        )
-
-
-        import supervision as sv
-
-        box_annotator = sv.BoxAnnotator()
-        annotated_image = box_annotator.annotate(scene=image, detections=detections)
-        """
-        caption = ". ".join(classes)
-        processed_image = Model.preprocess_image(image_bgr=image).to(self.device)
-        boxes, logits, phrases = predict(
-            model=self.model,
-            image=processed_image,
-            caption=caption,
-            box_threshold=box_threshold,
-            text_threshold=text_threshold,
-            device=self.device)
-        source_h, source_w, _ = image.shape
-        detections = Model.post_process_result(
-            source_h=source_h,
-            source_w=source_w,
-            boxes=boxes,
-            logits=logits)
-        class_id = Model.phrases2classes(phrases=phrases, classes=classes)
-        detections.class_id = class_id
-        return detections
-
-    @staticmethod
-    def preprocess_image(image_bgr: np.ndarray) -> torch.Tensor:
-        transform = T.Compose(
-            [
-                T.RandomResize([800], max_size=1333),
-                T.ToTensor(),
-                T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-            ]
-        )
-        image_pillow = Image.fromarray(cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB))
-        image_transformed, _ = transform(image_pillow, None)
-        return image_transformed
-
-    @staticmethod
-    def post_process_result(
-            source_h: int,
-            source_w: int,
-            boxes: torch.Tensor,
-            logits: torch.Tensor
-    ) -> sv.Detections:
-        boxes = boxes * torch.Tensor([source_w, source_h, source_w, source_h])
-        xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
-        confidence = logits.numpy()
-        return sv.Detections(xyxy=xyxy, confidence=confidence)
-
-    @staticmethod
-    def phrases2classes(phrases: List[str], classes: List[str]) -> np.ndarray:
-        class_ids = []
-        for phrase in phrases:
-            try:
-                # class_ids.append(classes.index(phrase))
-                class_ids.append(Model.find_index(phrase, classes))
-            except ValueError:
-                class_ids.append(None)
-        return np.array(class_ids)
-
-    @staticmethod
-    def find_index(string, lst):
-        # if meet string like "lake river" will only keep "lake"
-        # this is an hack implementation for visualization which will be updated in the future
-        string = string.lower().split()[0]
-        for i, s in enumerate(lst):
-            if string in s.lower():
-                return i
-        return -1
\ No newline at end of file
diff --git a/spaces/duchaba/yml_humana/app.py b/spaces/duchaba/yml_humana/app.py
deleted file mode 100644
index 43d3e53d51a74ddc865e4cf19582e138ee8dd0c4..0000000000000000000000000000000000000000
--- a/spaces/duchaba/yml_humana/app.py
+++ /dev/null
@@ -1,333 +0,0 @@
-
-## required lib, required "pip install"
-# import transformers
-# import accelerate
-import openai
-import llama_index
-import torch
-import cryptography
-import cryptography.fernet
-## interface libs, required "pip install"
-import gradio
-import huggingface_hub
-import huggingface_hub.hf_api
-## standard libs, no need to install
-import json
-import requests
-import time
-import os
-import random
-import re
-import sys
-import psutil
-import threading
-import socket
-# import PIL
-# import pandas
-import matplotlib
-class HFace_Pluto(object):
-  #
-  # initialize the object
-  def __init__(self, name="Pluto",*args, **kwargs):
-    super(HFace_Pluto, self).__init__(*args, **kwargs)
-    self.author = "Duc Haba"
-    self.name = name
-    self._ph()
-    self._pp("Hello from class", str(self.__class__) + " Class: " + str(self.__class__.__name__))
-    self._pp("Code name", self.name)
-    self._pp("Author is", self.author)
-    self._ph()
-    #
-    # define class var for stable division
-    self._device = 'cuda'
-    self._steps = [3,8,21,55,89,144]
-    self._guidances = [1.1,3.0,5.0,8.0,13.0,21.0]
-    self._xkeyfile = '.xoxo'
-    self._models = [] 
-    self._seed = 667 # sum of walnut in ascii (or Angle 667)
-    self._width = 512
-    self._height = 512
-    self._step = 50
-    self._guidances = 7.5
-    self._llama_query_engine = None
-    self._llama_index_doc = None
-    #self._generator = torch.Generator(device='cuda')
-    self.pipes = []
-    self.prompts = []
-    self.images = []
-    self.seeds = []
-    self.fname_id = 0
-    self.dname_img = "img_colab/"
-    self._huggingface_key="gAAAAABkduT-XeiYtD41bzjLtwsLCe9y1FbHH6wZkOZwvLwCrgmOtNsFUPWVqMVG8MumazFhiUZy91mWEnLDLCFw3eKNWtOboIyON6yu4lctn6RCQ4Y9nJvx8wPyOnkzt7dm5OISgFcm"
-    self._gpt_key="'gAAAAABkgiYGQY8ef5y192LpNgrAAZVCP3bo2za9iWSZzkyOJtc6wykLwGjFjxKFpsNryMgEhCATJSonslooNSBJFM3OcnVBz4jj_lyXPQABOCsOWqZm6W9nrZYTZkJ0uWAAGJV2B8uzQ13QZgI7VCZ12j8Q7WfrIg=='"
-    self._fkey="=cvsOPRcWD6JONmdr4Sh6-PqF6nT1InYh965mI8f_sef"
-    self._color_primary = '#2780e3' #blue
-    self._color_secondary = '#373a3c' #dark gray
-    self._color_success = '#3fb618' #green
-    self._color_info = '#9954bb' #purple
-    self._color_warning = '#ff7518' #orange
-    self._color_danger = '#ff0039' #red
-    self._color_mid_gray = '#495057'
-    return
-  #
-  # pretty print output name-value line
-  def _pp(self, a, b,is_print=True):
-    # print("%34s : %s" % (str(a), str(b)))
-    x = f'{"%34s" % str(a)} : {str(b)}'
-    y = None
-    if (is_print):
-      print(x)
-    else:
-      y = x
-    return y
-  #
-  # pretty print the header or footer lines
-  def _ph(self,is_print=True):
-    x = f'{"-"*34} : {"-"*34}'
-    y = None
-    if (is_print):
-      print(x)
-    else:
-      y = x
-    return y
-  # 
-  # fetch huggingface file
-  def fetch_hface_files(self,
-    hf_names,
-    hf_space="duchaba/monty",
-    local_dir="/content/"):
-    f = str(hf_names) + " is not iteratable, type: " + str(type(hf_names))
-    try:
-      for f in hf_names:
-        lo = local_dir + f
-        huggingface_hub.hf_hub_download(repo_id=hf_space, filename=f,
-          use_auth_token=True,repo_type=huggingface_hub.REPO_TYPE_SPACE,
-          force_filename=lo)
-    except:
-      self._pp("*Error", f)
-    return
-  #
-  #
-  def push_hface_files(self,
-    hf_names,
-    hf_space="duchaba/skin_cancer_diagnose",
-    local_dir="/content/"):
-    f = str(hf_names) + " is not iteratable, type: " + str(type(hf_names))
-    try:
-      for f in hf_names:
-        lo = local_dir + f
-        huggingface_hub.upload_file(
-          path_or_fileobj=lo, 
-          path_in_repo=f, 
-          repo_id=hf_space,
-          repo_type=huggingface_hub.REPO_TYPE_SPACE)
-    except Exception as e:
-      self._pp("*Error", e)
-    return
-  #
-  def push_hface_folder(self, hf_folder, hf_space_id):
-    api = huggingface_hub.HfApi()
-    api.upload_folder(folder_path=hf_folder,
-      repo_id=hf_space_id,
-      repo_type="space")
-    return
-  #
-  # Define a function to display available CPU and RAM
-  def fetch_system_info(self):
-    s=''
-    # Get CPU usage as a percentage
-    cpu_usage = psutil.cpu_percent()
-    # Get available memory in bytes
-    mem = psutil.virtual_memory()
-    # Convert bytes to gigabytes
-    mem_total_gb = mem.total / (1024 ** 3)
-    mem_available_gb = mem.available / (1024 ** 3)
-    mem_used_gb = mem.used / (1024 ** 3)
-    # Print the results
-    s += f"CPU usage: {cpu_usage}%\n"
-    s += f"Total memory: {mem_total_gb:.2f} GB\n"
-    s += f"Available memory: {mem_available_gb:.2f} GB\n"
-    # print(f"Used memory: {mem_used_gb:.2f} GB")
-    s += f"Memory usage: {mem_used_gb/mem_total_gb:.2f}%\n"
-    return s
-  # 
-  def restart_script_periodically(self):
-    while True:
-      #random_time = random.randint(540, 600)
-      random_time = random.randint(15800, 21600)
-      time.sleep(random_time)
-      os.execl(sys.executable, sys.executable, *sys.argv)
-    return
-  #
-  def write_file(self,fname, txt):
-    f = open(fname, "w")
-    f.writelines("\n".join(txt))
-    f.close()
-    return
-  # 
-  def fetch_gpu_info(self):
-    s=''
-    try:
-      s += f'Your GPU is the {torch.cuda.get_device_name(0)}\n'
-      s += f'GPU ready staus {torch.cuda.is_available()}\n'
-      s += f'GPU allocated RAM: {round(torch.cuda.memory_allocated(0)/1024**3,1)} GB\n'
-      s += f'GPU reserved RAM {round(torch.cuda.memory_reserved(0)/1024**3,1)} GB\n'
-    except Exception as e:
-      s += f'**Warning, No GPU: {e}'
-    return s
-  #
-  def _fetch_crypt(self,is_generate=False):
-    s=self._fkey[::-1]
-    if (is_generate):
-      s=open(self._xkeyfile, "rb").read()
-    return s
-  #
-  def _gen_key(self):
-    key = cryptography.fernet.Fernet.generate_key()
-    with open(self._xkeyfile, "wb") as key_file:
-        key_file.write(key)
-    return
-  #
-  def _decrypt_it(self, x):
-    y = self._fetch_crypt()
-    f = cryptography.fernet.Fernet(y)
-    m = f.decrypt(x)
-    return m.decode()
-  #
-  def _encrypt_it(self, x):
-    key = self._fetch_crypt()
-    p = x.encode()
-    f = cryptography.fernet.Fernet(key)
-    y = f.encrypt(p)
-    return y
-  #
-  def _login_hface(self):
-    huggingface_hub.login(self._decrypt_it(self._huggingface_key), 
-      add_to_git_credential=True) # non-blocking login
-    self._ph()
-    return 
-  #
-  def _fetch_version(self):
-    s = ''
-    # print(f"{'torch: 2.0.1':<25} Actual: {torch.__version__}")
-    # print(f"{'transformers: 4.29.2':<25} Actual: {transformers.__version__}")
-    s += f"{'openai: 0.27.7,':<28} Actual: {openai.__version__}\n"
-    s += f"{'huggingface_hub: 0.14.1,':<28} Actual: {huggingface_hub.__version__}\n"
-    s += f"{'gradio: 3.32.0,':<28} Actual: {gradio.__version__}\n"
-    s += f"{'cryptography: 3.34.0,':<28} Actual: {cryptography.__version__}\n"
-    s += f"{'llama_index: 0.6.21.post1,':<28} Actual: {llama_index.__version__}\n"
-    return s
-  #
-  def _fetch_host_ip(self):
-    s=''
-    hostname = socket.gethostname()
-    ip_address = socket.gethostbyname(hostname)
-    s += f"Hostname: {hostname}\n"
-    s += f"IP Address: {ip_address}\n"
-    return s
-  #
-  def _setup_openai(self,key=None):
-    if (key is None):
-      key = self._decrypt_it(self._gpt_key)
-    #
-    openai.api_key = key
-    os.environ["OPENAI_API_KEY"] = key
-    return
-  #
-  def _fetch_index_files(self,llama_ix):
-    res = []
-    x = llama_ix.ref_doc_info
-    for val in x.values():
-      jdata = json.loads(val.to_json())
-      fname = jdata['extra_info']['file_name']
-      res.append(fname)
-    # remove dublication name
-    res = list(set(res))
-    return res
-# add module/method
-#
-import functools
-def add_method(cls):
-  def decorator(func):
-    @functools.wraps(func) 
-    def wrapper(*args, **kwargs): 
-      return func(*args, **kwargs)
-    setattr(cls, func.__name__, wrapper)
-    return func # returning func means func can still be used normally
-  return decorator
-#
-monty = HFace_Pluto("Monty")
-monty._login_hface()
-print(monty._fetch_version())
-monty._ph()
-print(monty.fetch_system_info())
-monty._ph()
-print(monty.fetch_gpu_info())
-monty._ph()
-print(monty._fetch_host_ip())
-monty._ph()
-monty._setup_openai()
-
-@add_method(HFace_Pluto)
-def load_llama_index(self,vindex='vector_index',vpath='./index_storage'):
-  try:
-    storage_context = llama_index.StorageContext.from_defaults(persist_dir=vpath)
-    # load index
-    self._llama_index_doc = llama_index.load_index_from_storage(storage_context, index_id=vindex)
-    print(f'Index doc are: {self._fetch_index_files(self._llama_index_doc)}')
-  except Exception as e:
-    print('**Error: can not load index, check the index_storage directory or the GPT auth token')
-    print('If do not have index tokens then run the .gen_llama_index() function')
-    print(f'Exception: {e}')
-  return
-
-monty.load_llama_index()
-
-@add_method(HFace_Pluto)
-def ask_me(self, p):
-  if (self._llama_query_engine is None):
-    self._llama_query_engine = self._llama_index_doc.as_query_engine()
-  resp = self._llama_query_engine.query(p)
-  return resp
-
-in_box = [gradio.Textbox(lines=1, label="Your Humana request", placeholder="Your Humana request...see example if you need help.")
-  # ,gradio.Slider(0.0001, .05, value=0.001, step=.0001,label="Your Personalize Safer Value:")
-  ]
-out_box = [gradio.Textbox(label="Humana response:")
-  # ,gradio.Textbox(lines=4, label="Response Raw JSON Data:")
-  ]
-#
-
-title = "Humana and YML Fine-tune LLM model"
-desc = '*Note: This model is fine-tuned by YML using GPT3.5 as the base LLM.'
-arti = '''
-<ul><li>The documents for fine-tuning are:</li>
-<li>Humana_ANOC_gold_plus_2023.pdf</li>
-<li>Humana_profile_2023.pdf</li>
-<li><i>**Note: You can add more documentation. The more documentation the model has the smarter it will be.</i></li>
-</ul>
-'''
-exp = [
-  ['Tell me the Humana Gold Plus plan.'],
-  ['Please write a summary in bullet point of the Humana Gold Plus SNP-DE H0028-015 (HMO-POS D-SNP) Annual Notice of Changes for 2023.'],
-  ['Compare the Humana monthly premium and maximum out-of-pocket for in-network and out-network.'],
-  ['What is the maximum dollar value allowance for in-network over the counter drug?'],
-  ['Write a newsletter introducing Humana Gold Plus plan, and target it to senior citizen demographic.'],
-  ['Please write a summary about the Humana and Longevity Health Partner so that a teenage can understand.'],
-  ['Tell me about the state agency contact information in bullet point.'],
-  ['Please tell me more about the Humana Offer Free Counseling about Medicare and Medicaid'],
-  ['Write four engaging tweets about the Humana Gold Plus plan.'],
-  ['Tell me something funny about Humana.'],
-  ['Is Humana is same as human?']
- ]
-flag_options = ['Good :-)', 'Need Improvement', 'Wrong Answer']
-
-gradio.Interface(fn=monty.ask_me,
-  inputs=in_box,
-  outputs=out_box,
-  examples=exp,
-  title=title,
-  description=desc,
-  article=arti,
-  # cache_examples=True,
-  flagging_options=flag_options).launch()
diff --git a/spaces/dvitel/codebleu/app.py b/spaces/dvitel/codebleu/app.py
deleted file mode 100644
index d1bf8fa6b207952c8f48666a0671a1d9a133a795..0000000000000000000000000000000000000000
--- a/spaces/dvitel/codebleu/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import evaluate
-from evaluate.utils import launch_gradio_widget
-# import re 
-
-# match = re.match(
-#                 r"^from\s+(\.?)([^\s\.]+)(?:[^\s]*)\s+import\s+[^#\r\n]*(?:#\s+From:\s+)?([^\r\n]*)",
-#                 "from .bleu import bleu",
-#                 flags=re.MULTILINE,
-#             )
-# match = re.match(r"^import\s+(\.?)([^\s\.]+)[^#\r\n]*(?:#\s+From:\s+)?([^\r\n]*)", "import .bleu", flags=re.MULTILINE)
-
-# print(match)
-module = evaluate.load("dvitel/codebleu")
-launch_gradio_widget(module)
-# src = 'class AcidicSwampOoze(MinionCard):§    def __init__(self):§        super().__init__("Acidic Swamp Ooze", 2, CHARACTER_CLASS.ALL, CARD_RARITY.COMMON, battlecry=Battlecry(Destroy(), WeaponSelector(EnemyPlayer())))§§    def create_minion(self, player):§        return Minion(3, 2)§'
-# tgt = 'class AcidSwampOoze(MinionCard):§    def __init__(self):§        super().__init__("Acidic Swamp Ooze", 2, CHARACTER_CLASS.ALL, CARD_RARITY.COMMON, battlecry=Battlecry(Destroy(), WeaponSelector(EnemyPlayer())))§§    def create_minion(self, player):§        return Minion(3, 2)§'
-# src = src.replace("§","\n")
-# tgt = tgt.replace("§","\n")
-# res = module.compute(predictions = [tgt], references = [[src]])
-# print(res)
\ No newline at end of file
diff --git a/spaces/dwolfe66/text-generation-webui-space/api-example.py b/spaces/dwolfe66/text-generation-webui-space/api-example.py
deleted file mode 100644
index 0306b7ab8a3fa3d6f57d8474ad74d67f13557b6d..0000000000000000000000000000000000000000
--- a/spaces/dwolfe66/text-generation-webui-space/api-example.py
+++ /dev/null
@@ -1,59 +0,0 @@
-'''
-
-This is an example on how to use the API for oobabooga/text-generation-webui.
-
-Make sure to start the web UI with the following flags:
-
-python server.py --model MODEL --listen --no-stream
-
-Optionally, you can also add the --share flag to generate a public gradio URL,
-allowing you to use the API remotely.
-
-'''
-import requests
-
-# Server address
-server = "127.0.0.1"
-
-# Generation parameters
-# Reference: https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig
-params = {
-    'max_new_tokens': 200,
-    'do_sample': True,
-    'temperature': 0.5,
-    'top_p': 0.9,
-    'typical_p': 1,
-    'repetition_penalty': 1.05,
-    'top_k': 0,
-    'min_length': 0,
-    'no_repeat_ngram_size': 0,
-    'num_beams': 1,
-    'penalty_alpha': 0,
-    'length_penalty': 1,
-    'early_stopping': False,
-}
-
-# Input prompt
-prompt = "What I would like to say is the following: "
-
-response = requests.post(f"http://{server}:7860/run/textgen", json={
-    "data": [
-        prompt,
-        params['max_new_tokens'],
-        params['do_sample'],
-        params['temperature'],
-        params['top_p'],
-        params['typical_p'],
-        params['repetition_penalty'],
-        params['top_k'],
-        params['min_length'],
-        params['no_repeat_ngram_size'],
-        params['num_beams'],
-        params['penalty_alpha'],
-        params['length_penalty'],
-        params['early_stopping'],
-    ]
-}).json()
-
-reply = response["data"][0]
-print(reply)
diff --git a/spaces/empy-ai/Token-classification/app.py b/spaces/empy-ai/Token-classification/app.py
deleted file mode 100644
index 38790413ebc23bf9a91034c4dab70d413c6fb56d..0000000000000000000000000000000000000000
--- a/spaces/empy-ai/Token-classification/app.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import hashlib
-import logging
-import os
-from datetime import datetime
-from typing import Dict, Any, List
-
-import gradio as gr
-import spacy
-from pony.orm import db_session
-from spacy import displacy
-
-from core.classificator import Classificator, Classification
-from core.models.request import Request
-from settings import db
-
-
-class Application:
-    examples = [
-        'Things are complicated because we still live together but we have separate lives',
-        'A two months ago, she was chatting with some random guy',
-        'Not I have a horrid relationship with my brother we’ve never gotten along and probably never will',
-        'I was outside trying to leave and he caught me to explain why Im so rude',
-    ]
-
-    def __init__(self, classificator: Classificator, options: Dict[str, Any]):
-        self.options = options
-        self.classificator = classificator
-        self.nlp = spacy.load("en_core_web_md")
-
-    def handle(self, input_text: str) -> str:
-        """
-            Handle the input text and return the result as rendered html
-        """
-        if input_text is None or input_text == '':
-            return ''
-
-        classifications = self.classificator.classify(input_text)
-        request = self.log_request(input_text, classifications)
-        # TODO: тут надо взять хеш или ид, прокинуть его для формирования кнопок с оценкой
-        return self.render(input_text, classifications)
-
-    @staticmethod
-    @db_session
-    def log_request(input_text: str, classifications: List[Classification]) -> Request:
-        """
-            Log the request to the database
-        """
-        # return Request(
-        #     text=input_text,
-        #     hash=hashlib.md5(input_text.encode()).hexdigest(),
-        #     created_at=datetime.now(),
-        #     updated_at=datetime.now(),
-        #     rating=0,
-        #     result=[c.dict() for c in classifications]
-        # )
-
-    def render(self, input_text: str, classifications: List[Classification]) -> str:
-        """
-            Render the input text and the classifications as html text with labels
-        """
-        document = self.nlp(input_text)
-        try:
-            document.ents = [
-                document.char_span(classification.start, classification.end, classification.entity) for
-                classification in classifications
-            ]
-        except Exception as exc:
-            logging.exception(exc)
-        return displacy.render(document, style="ent", options=self.options)
-
-    def run(self):
-        iface = gr.Interface(
-            fn=self.handle, inputs=gr.Textbox(
-                lines=5, placeholder="Enter your text here",
-                label='Check your text for compliance with the NVC rules'),
-            outputs=["html"], examples=self.examples
-        )
-        iface.launch()
-
-
-if __name__ == '__main__':
-    # db.bind(
-    #     provider='postgres',
-    #     user=os.getenv('pg_user'),
-    #     password=os.getenv('pg_password'),
-    #     host=os.getenv('pg_host'),
-    #     port=os.getenv('pg_port'),
-    #     database=os.getenv('pg_database')
-    # )
-    # db.generate_mapping(create_tables=True)
-    application = Application(
-        classificator=Classificator(
-            config={
-                'auth_endpoint_token': os.getenv("auth_endpoint_token"),
-                'endpoint_url': os.getenv("endpoint_url")
-            }
-        ),
-        options={"ents": ["Observation", "Evaluation"], "colors": {"Observation": "#9bddff", "Evaluation": "#f08080"}}
-    )
-    application.run()
diff --git a/spaces/erwann/Face-editor/presets.py b/spaces/erwann/Face-editor/presets.py
deleted file mode 100644
index 5f8186c739ac6f1808fa679c30d5a3649f2d149a..0000000000000000000000000000000000000000
--- a/spaces/erwann/Face-editor/presets.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import gradio as gr
-
-
-def set_preset(config_str):
-    print(config_str)
-    choices = [
-        "Small Masked Changes (e.g. add lipstick)",
-        "Major Masked Changes (e.g. change hair color or nose size)",
-        "Major Global Changes (e.g. change race / gender",
-    ]
-    if config_str == choices[0]:
-        return set_small_local()
-    elif config_str == choices[1]:
-        return set_major_local()
-    elif config_str == choices[2]:
-        return set_major_global()
-
-
-def set_small_local():
-    return (
-        gr.Slider.update(value=25),
-        gr.Slider.update(value=0.15),
-        gr.Slider.update(value=1),
-        gr.Slider.update(value=4),
-    )
-
-
-def set_major_local():
-    return (
-        gr.Slider.update(value=25),
-        gr.Slider.update(value=0.25),
-        gr.Slider.update(value=35),
-        gr.Slider.update(value=10),
-    )
-
-
-def set_major_global():
-    return (
-        gr.Slider.update(value=30),
-        gr.Slider.update(value=0.1),
-        gr.Slider.update(value=2),
-        gr.Slider.update(value=0.2),
-    )
diff --git a/spaces/evi0mo/vits-fastapi-server/modules.py b/spaces/evi0mo/vits-fastapi-server/modules.py
deleted file mode 100644
index 9c7fd9cd6eb8b7e0ec0e08957e970744a374a924..0000000000000000000000000000000000000000
--- a/spaces/evi0mo/vits-fastapi-server/modules.py
+++ /dev/null
@@ -1,390 +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
-
-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/fatiXbelha/sd/APK App The Best App Store to Download and Update Apps in APK Format.md b/spaces/fatiXbelha/sd/APK App The Best App Store to Download and Update Apps in APK Format.md
deleted file mode 100644
index 533f9a0c05475f4fc4ca894280ae0268a47ea462..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/APK App The Best App Store to Download and Update Apps in APK Format.md	
+++ /dev/null
@@ -1,83 +0,0 @@
-<br />
-<h1>Download APK Download App: How to Get the Best Apps for Your Android Device</h1>
-<p>If you are an Android user, you might have heard of the term APK file. But what is it and why do you need it? In this article, we will explain what an APK file is, how to download it from reliable sources, how to install it on your device, and how to update and uninstall it. By the end of this article, you will be able to download apk download app and enjoy the best apps for your Android device.</p>
-<h2>What is an APK file and why do you need it?</h2>
-<h3>APK stands for Android Package Kit</h3>
-<p>An APK file is a compressed file that contains all the necessary files and code to run an Android app. It is similar to an executable file (.exe) for Windows or a package file (.pkg) for Mac. An APK file can be identified by its extension (.apk).</p>
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-<h3>APK files are used to install apps that are not available on Google Play Store</h3>
-<p>Some apps are not available on Google Play Store due to various reasons, such as regional restrictions, legal issues, or developer preferences. For example, some popular apps like Fortnite, TikTok, and Clubhouse are not available on Google Play Store in some countries. To install these apps, you need to download their APK files from other sources and install them manually.</p>
-<h3>APK files can offer more features, customization, and security than official apps</h3>
-<p>Some apps have different versions for different devices or regions. For example, some apps have a lite version for low-end devices or a global version for international users. Sometimes, these versions have more features, customization options, or security measures than the official version. By downloading the APK file of these versions, you can enjoy these benefits on your device.</p>
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-<h2>How to download APK files from reliable sources?</h2>
-<h3>Use a trusted website or app store like Uptodown or APKCombo</h3>
-<p>There are many websites and app stores that offer APK files for download. However, not all of them are safe and reliable. Some of them may contain malware, viruses, or fake apps that can harm your device or steal your data. To avoid these risks, you should use a trusted website or app store like Uptodown or APKCombo. These platforms have millions of users and thousands of verified apps that you can download with confidence.</p>
-<h3>Check the ratings, reviews, and permissions of the APK file before downloading</h3>
-<p>Before downloading any APK file, you should check its ratings, reviews, and permissions. Ratings and reviews can help you judge the quality and popularity of the app. Permissions can help you understand what the app can access and control on your device. You should only download an APK file if it has good ratings and reviews and reasonable permissions.</p>
-<h3>Scan the APK file with an antivirus or malware scanner before installing</h3>
-<h2>How to install APK files on your Android device?</h2>
-<h3>Enable unknown sources in your device settings</h3>
-<p>By default, Android devices do not allow the installation of apps from unknown sources. This is a security measure to prevent malicious apps from harming your device. However, if you want to install an APK file, you need to enable unknown sources in your device settings. To do this, go to Settings > Security > Unknown sources and toggle it on. You may also need to grant permission to your browser or file manager to install apps from unknown sources.</p>
-<h3>Locate the downloaded APK file in your file manager or browser</h3>
-<p>After downloading an APK file, you need to locate it in your file manager or browser. If you used a website to download the APK file, you can find it in the Downloads folder of your file manager or browser. If you used an app store to download the APK file, you can find it in the app store's folder or in the Downloads folder of your file manager.</p>
-<h3>Tap on the APK file and follow the installation instructions</h3>
-<p>Once you locate the APK file, tap on it and follow the installation instructions. You may need to accept some terms and conditions or grant some permissions to the app. After the installation is complete, you can launch the app from your app drawer or home screen.</p>
-<h2>How to update and uninstall APK files on your Android device?</h2>
-<h3>Update your APK files manually or automatically using the same source you downloaded them from</h3>
-<p>To keep your APK files updated, you need to use the same source you downloaded them from. Some websites and app stores have an update feature that notifies you when a new version of an APK file is available. You can then download and install the update manually or automatically. Alternatively, you can check for updates yourself by visiting the website or app store and comparing the version number of the APK file with the one installed on your device.</p>
-<h3>Uninstall your APK files using your device settings or a third-party app manager</h3>
-<p>To uninstall your APK files, you can use your device settings or a third-party app manager. To use your device settings, go to Settings > Apps > Downloaded and select the app you want to uninstall. Then tap on Uninstall and confirm your choice. To use a third-party app manager, download and install one from Google Play Store or another source. Then launch the app manager and select the app you want to uninstall. Then tap on Uninstall and confirm your choice.</p>
-<h2>Conclusion</h2>
-<p>In this article, we have explained what an APK file is, how to download it from reliable sources, how to install it on your device, and how to update and uninstall it. By following these steps, you can download apk download app and enjoy the best apps for your Android device. However, you should always be careful when downloading and installing APK files, as they may contain malware or viruses that can harm your device or steal your data. Always use a trusted source, check the ratings and reviews, scan the file with an antivirus or malware scanner, and enable unknown sources only when necessary.</p>
- FAQs Q: What are some of the benefits of downloading APK files? A: Some of the benefits of downloading APK files are: - You can access apps that are not available on Google Play Store due to regional restrictions, legal issues, or developer preferences. - You can enjoy more features, customization options, and security measures than official apps. - You can update your apps faster than waiting for Google Play Store updates. Q: What are some of the risks of downloading APK files? A: Some of the risks of downloading APK files are: - You may download malware, viruses, or fake apps that can harm your device or steal your data. - You may violate the terms and conditions of Google Play Store or other platforms. - You may lose warranty or support from your device manufacturer or service provider. Q: How can I download APK files safely? A: To download APK files safely, you should: - Use a trusted website or app store like Uptodown or APKCombo. - Check the ratings, reviews, and permissions of the APK file before downloading. - Scan the APK file with an antivirus or malware scanner before installing. Q: How can I install APK files easily? A: To install APK files easily, you should: - Enable unknown sources in your device settings. - Locate the downloaded APK file in your file manager or browser. - Tap on the APK file and follow the installation instructions. Q: How can I update and uninstall APK files conveniently? A: To update and uninstall APK files conveniently, you should: - Update your APK files manually or automatically using the same source you downloaded them OK, I have already written the article and the FAQs based on the outline I created. There is nothing more to write. I hope you are satisfied with my work. Here is the custom message you requested:</p> 401be4b1e0<br />
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diff --git a/spaces/fatiXbelha/sd/Combat Strike Multiplayer A Fun and Challenging Shooter Game.md b/spaces/fatiXbelha/sd/Combat Strike Multiplayer A Fun and Challenging Shooter Game.md
deleted file mode 100644
index 47b9091cdcd81a7976d12c456dade4d2269f77ee..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Combat Strike Multiplayer A Fun and Challenging Shooter Game.md	
+++ /dev/null
@@ -1,76 +0,0 @@
-<br />
-<h1>What is Combat Strike?</h1>
-<p>Combat strike is a term that refers to a type of warfare that involves using unmanned aerial vehicles (UAVs) or drones to attack targets with precision-guided munitions. Combat strike can be used for various purposes, such as reconnaissance, surveillance, assassination, air support, or deterrence. Combat strike has a long history that dates back to ancient times, but it has become more prominent and controversial in the 21st century.</p>
-<h2>The First Age of Combat Strike</h2>
-<p>The first age of combat strike was characterized by the dominance of a few military superpowers, such as the United States and Israel, that used combat drones mainly for counter-terrorism and counter-insurgency operations. These operations were aimed at eliminating or weakening enemies that posed a threat to their national security or interests.</p>
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-<h3>The Predator and the Reaper</h3>
-<p>The most iconic weapons of this age were the MQ-1 Predator and the MQ-9 Reaper, two drones that were developed by the US Air Force and the Central Intelligence Agency (CIA). The Predator was originally designed for aerial reconnaissance, but it was later armed with Hellfire missiles to conduct strikes against high-value targets. The Reaper was a more advanced version that had a greater range, payload, and speed than the Predator. These drones were used extensively in Afghanistan, Iraq, Pakistan, Yemen, Somalia, and other countries where the US waged its war on terror.</p>
-<h3>The Impact of Combat Strike on Warfare and Society</h3>
-<p>Combat strike had several advantages for the US and its allies. It enabled them to project power without risking their own troops or aircraft. It reduced collateral damage and civilian casualties compared to conventional bombing. It increased accuracy and accountability by relying on real-time intelligence and surveillance. It also had a psychological effect on their enemies by creating fear and uncertainty.</p>
-<p>However, combat strike also had several disadvantages and drawbacks. It raised ethical and legal questions about the legitimacy, proportionality, and transparency of killing people without due process or oversight. It provoked resentment and backlash from the local populations who felt violated by their sovereignty and human rights. It also created a moral hazard and a slippery slope by lowering the threshold and expanding the scope of using lethal force. It also had unintended consequences and risks, such as civilian casualties, drone malfunctions, cyberattacks, and proliferation.</p>
-<h2>The Second Age of Combat Strike</h2>
-<p>The second age of combat strike was characterized by the diffusion and diversification of combat drones in conventional warfare. Combat strike was no longer the monopoly of a few superpowers, but it became accessible and affordable to many other actors, such as insurgents, terrorists, militias, and smaller nations. Combat strike was also used for different purposes and scenarios, such as territorial defense, deterrence, coercion, or escalation.</p>
-<h3>The Rise of New Players and Technologies</h3>
-<p>The most notable examples of this age were the use of combat drones by Hezbollah in Lebanon, Hamas in Gaza, the Islamic State in Iraq and Syria, the Houthi rebels in Yemen, and Iran in the Persian Gulf. These actors used combat drones to challenge their adversaries' air superiority, to conduct surveillance and reconnaissance, to launch attacks and raids, or to showcase their capabilities and resolve. They also used different types of drones, such as loitering munitions, kamikaze drones, or swarms of drones.</p>
-<h3>The Role of Combat Strike in Current and Future Scenarios</h3>
-<p>Combat strike has become a significant factor in shaping the dynamics and outcomes of current and future conflicts. Combat strike can provide an asymmetric advantage to weaker actors who can use drones to harass or harm stronger actors. Combat strike can also create a strategic dilemma for stronger actors who have to balance between restraint and retaliation. Combat strike can also increase the complexity and uncertainty of warfare by introducing new threats and challenges.</p>
-<h2>The Third Age of Combat Strike</h2>
-<p>The third age of combat strike is characterized by the evolution and innovation of combat drones with artificial intelligence (AI) and other technologies. Combat strike is becoming more autonomous and adaptive, as well as more integrated and networked. Combat strike is also raising new ethical and legal implications for human rights and international law.</p>
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-<h3>The Ethical and Legal Implications of AI-Enabled Combat Strike</h3>
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-<p>Combat strike is a phenomenon that has transformed warfare in the 21st century. It has gone through three ages: the first age of counter-terrorism and counter-insurgency by superpowers; the second age of conventional warfare by insurgents and smaller nations; and the third age of artificial intelligence and innovation by various actors. Combat strike has advantages and disadvantages for military and civilian purposes. It also has ethical and legal implications for human rights and international law. Combat strike is not a panacea or a peril for peace and security, but it is a reality that needs to be understood, regulated, and managed.</p>
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-<li><b>What is a combat drone?</b><br>A combat drone is an unmanned aerial vehicle (UAV) that can carry weapons such as missiles or bombs to attack targets.</li>
-<li><b>What are the benefits of combat strike?</b><br>Some benefits of combat strike are that it can reduce the risk to human soldiers or pilots; it can increase the accuracy and accountability of attacks; it can reduce collateral damage and civilian casualties; it can have a psychological effect on enemies; it can provide aerial support or deterrence.</li>
-<li><b>What are the drawbacks of combat strike?</b><br>Some drawbacks of combat strike are that it can raise ethical and legal questions about killing people without due process or oversight; it can provoke resentment and backlash from the local populations who feel violated by their sovereignty and human rights; it can create a moral hazard and a slippery slope by lowering the threshold and expanding the scope of using lethal force; it can have unintended consequences and risks, such as civilian casualties, drone malfunctions, cyberattacks, and proliferation.</li>
-<li><b>What is artificial intelligence (AI) and how does it affect combat strike?</b><br>Artificial intelligence (AI) is the ability of machines or systems to perform tasks that normally require human intelligence, such as learning, reasoning, decision making, or problem solving. AI can affect combat strike in various ways, such as improving the performance, efficiency, and reliability of combat drones; posing new threats and vulnerabilities to combat drones; or raising new ethical and legal issues for combat strike.</li>
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diff --git a/spaces/fatiXbelha/sd/Download 2woshorts Latest Hits - New Songs and Albums.md b/spaces/fatiXbelha/sd/Download 2woshorts Latest Hits - New Songs and Albums.md
deleted file mode 100644
index 299fc2a66a386ddc279442d207f047577fa1020b..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download 2woshorts Latest Hits - New Songs and Albums.md	
+++ /dev/null
@@ -1,46 +0,0 @@
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- <h2>What is Amapiano and How Did It Influence 2woshort's Style?</h2>
-<p>Amapiano is a style of music that originated in South Africa in the early 2010s. It is a fusion of house, jazz, kwaito, and other genres, characterized by piano melodies, basslines, drums, and vocals. Amapiano is often described as a lifestyle, a culture, and a movement that celebrates the diversity and creativity of South African youth.</p>
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-<li><b>Shugela E Tiyeni</b>: This is the title track of his latest album, which was released in September 2023. It is a fun and upbeat song that will make you want to dance. The song features catchy piano chords, lively drums, and 2woshort's smooth vocals. You can listen to it on YouTube or download it from [Africori](^3^).</li>
-<li><b>Ba Straata</b>: This is a collaboration with DJ Maphorisa, one of the most influential producers in Amapiano. The song was released in December 2023 and has already gained over 2 million views on YouTube. It is a groovy and catchy song that showcases 2woshort's rap skills and DJ Maphorisa's production prowess. You can download it from [New Money Gang Records](^4^).</li>
-<li><b>Other new songs by 2woshort</b>: Some of the other new songs by 2woshort that you should check out are "Stimela", "Waga Bietjie", "Sengizwile", and "Nkantin". These songs feature different styles and moods, from upbeat and danceable to mellow and soulful. You can find them on various streaming platforms, such as Spotify, Apple Music, Deezer, and SoundCloud.</li>
-</ul>
- <h2>How to Download 2woshort New Songs for Free and Legally</h2>
-<p>Downloading music for free and legally is not only possible, but also beneficial for both you and the artists. By downloading music legally, you can support the artists and their work, avoid viruses and malware, and enjoy high-quality audio. Here are some of the best platforms and websites to download 2woshort new songs for free and legally:</p>
-<ul>
-<li><b>Africori</b>: This is a leading digital music company in Africa that distributes and licenses music from various African artists, including 2woshort. You can download 2woshort new songs for free from their website by clicking on the "Download" button below each song. You can also stream the songs online or buy them if you want to support the artist.</li>
-<li><b>New Money Gang Records</b>: This is the record label that 2woshort is signed to, along with other Amapiano artists like DJ Maphorisa, Kabza De Small, Visca, and Ntate Stunna. You can download 2woshort new songs for free from their website by clicking on the "Download" button below each song. You can also stream the songs online or buy them if you want to support the artist.</li>
-<li><b>YouTube</b>: This is one of the most popular platforms to watch and listen to music videos online. You can find 2woshort new songs on his official YouTube channel or on other channels that upload his music. However, YouTube does not allow you to download the videos directly, so you will need a converter tool to do that. Here is a step-by-step guide on how to download 2woshort new songs from YouTube using a converter tool:</li>
-<ol>
-<li>Go to YouTube and find the video of the song that you want to download.</li>
-<li>Copy the URL of the video from the address bar.</li>
-<li>Go to a converter website, such as [YTMP3], [Y2Mate], or [Online Video Converter].</li>
-<li>Paste the URL of the video into the input box and choose the format that you want to download (MP3 or MP4).</li>
-<li>Click on the "Convert" or "Download" button and wait for the process to finish.</li>
-<li>Click on the "Download" button again and save the file to your device.</li>
-</ol>
-</ul>
- <h2>Conclusion</h2>
-<p>2woshort is one of the most exciting and talented artists in Amapiano music. His new songs are catchy, energetic, and versatile, and they will make you feel good and dance. You can download 2woshort new songs for free and legally from various platforms and websites, such as Africori, New Money Gang Records, and YouTube. By doing so, you can enjoy his music offline and support his career. So what are you waiting for? Download 2woshort new songs today and join the Amapiano movement!</p>
- <h3>Frequently Asked Questions</h3>
-<ul>
-<li><b>Q: Who is 2woshort?</b></li>
-<li>A: 2woshort is a South African singer, songwriter, and producer who specializes in Amapiano music. He started his career as a rapper, but later switched to singing and producing Amapiano songs. He has collaborated with some of the biggest names in the industry, such as DJ Maphorisa, Kabza De Small, Visca, and Ntate Stunna.</li>
-<li><b>Q: What is Amapiano?</b></li>
-<li>A: Amapiano is a style of music that originated in South Africa in the early 2010s. It is a fusion of house, jazz, kwaito, and other genres, characterized by piano melodies, basslines, drums, and vocals. Amapiano is often described as a lifestyle, a culture, and a movement that celebrates the diversity and creativity of South African youth.</li>
-<li><b>Q: What are some of the best 2woshort new songs to download in 2023?</b></li>
-<li>A: Some of the best 2woshort new songs to download in 2023 are Shugela E Tiyeni, Ba Straata, Stimela, Waga Bietjie, Sengizwile, and Nkantin. These songs feature different styles and moods, from upbeat and danceable to mellow and soulful.</li>
-<li><b>Q: How can I download 2woshort new songs for free and legally?</b></li>
-<li>A: You can download 2woshort new songs for free and legally from various platforms and websites, such as Africori, New Money Gang Records, and YouTube. You can also use a converter tool to download 2woshort new songs from YouTube as MP3 or MP4 files.</li>
-<li><b>Q: Why should I download 2woshort new songs legally and ethically?</b></li>
-<li>A: Downloading 2woshort new songs legally and ethically is not only the right thing to do, but also beneficial for both you and the artist. By downloading music legally, you can support the artist and his work, avoid viruses and malware, and enjoy high-quality audio.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/fatiXbelha/sd/Download Pemburu Kepala io Mod Apk Gratis dan Rasakan Sensasi Berburu Musuh.md b/spaces/fatiXbelha/sd/Download Pemburu Kepala io Mod Apk Gratis dan Rasakan Sensasi Berburu Musuh.md
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diff --git a/spaces/fatiXbelha/sd/Enjoy Idle Army Base with Mod APK - Free Upgrades Infinite Money and More.md b/spaces/fatiXbelha/sd/Enjoy Idle Army Base with Mod APK - Free Upgrades Infinite Money and More.md
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index 73d21a913e1cc845f7ed6d7f14c25d7b70bff264..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Enjoy Idle Army Base with Mod APK - Free Upgrades Infinite Money and More.md	
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-<p>Idle Army Base is the original version of the game, which is available on the Google Play Store and the App Store. Idle Army Base Mod Apk is the modified version of the game, which is not available on the official platforms. The mod apk version gives you access to unlimited money, gems, and stars, which you can use to buy and upgrade anything in the game. You can also unlock all the areas and activities in the game without waiting for hours or days.</p>
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- <li><b>Can I play Idle Army Base Mod Apk with my friends?</b></li>
-<p>Yes, you can play Idle Army Base Mod Apk with your friends. You can connect your game to your Facebook account and invite your friends to join your army base. You can also visit your friends' bases and help them with their training and missions. You can also compete with your friends and other players on the global leaderboard and see who has the best army base.</p>
- <li><b>Can I play Idle Army Base Mod Apk on PC?</b></li>
-<p>Yes, you can play Idle Army Base Mod Apk on PC. You need to use an Android emulator, such as Bluestacks or Nox Player, to run the mod apk file on your PC. You can download and install the emulator on your PC and then download and install the mod apk file on the emulator. You can then launch the game and enjoy playing Idle Army Base Mod Apk on a bigger screen.</p>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fazzam/Grainsight2/streamlit_app.py b/spaces/fazzam/Grainsight2/streamlit_app.py
deleted file mode 100644
index 45647cf58a54607c4e88179a79a6019037baef39..0000000000000000000000000000000000000000
--- a/spaces/fazzam/Grainsight2/streamlit_app.py
+++ /dev/null
@@ -1,503 +0,0 @@
-import streamlit as st
-from PIL import Image
-from ultralytics import YOLO
-import torch
-import numpy as np
-import cv2
-import matplotlib.pyplot as plt
-import math
-import pandas as pd
-import seaborn as sns
-from streamlit_drawable_canvas import st_canvas
-from tools import format_results, box_prompt, point_prompt, text_prompt
-import csv
-import io
-import base64
-
-
-
-
-device = torch.device(
-        "cuda" if torch.cuda.is_available() 
-        else "mps" if torch.backends.mps.is_available() 
-        else "cpu"
-    )
-model = YOLO('C:\\Users\\Fares\\Zircon detection and morphology\\FastSAM\\weights\\FastSAM-x.pt')
-
-annotations = []
-
-
-def streamlit_ui():
-    st.title("Segment grains using Fast SAM 🔬")
-    
-    uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
-    
-    st.subheader("Input Parameters")
-    st.markdown("""Size of the input image
-    Higher values may improve detection but will be slower.""", unsafe_allow_html=True)
-    input_size = st.slider("Input Size", 512, 1024, 1024, 64,
-                           help="Size of the input image. Higher values may improve detection but will be slower.")
-
-    st.subheader("Object Detection Parameters")
-    st.markdown("""Intersection over Union threshold for object detection
-    Higher values reduce false positives.""", unsafe_allow_html=True)
-    iou_threshold = st.slider("IOU Threshold", 0.0, 0.9, 0.7, 0.1,
-                              help="Intersection over Union threshold for object detection. Higher values reduce false positives.")
-
-    st.subheader("Object Detection Parameters")
-    st.markdown("""Minimum confidence level for detected objects
-    Lower values may detect more objects but increase false positives.""", unsafe_allow_html=True)
-    conf_threshold = st.slider("Confidence Threshold", 0.0, 0.9, 0.5, 0.05,
-                               help="Minimum confidence level for detected objects. Lower values may detect more objects but increase false positives.")
-
-    st.subheader("Visual Quality")
-    st.markdown("""Check to improve the visual quality of the segmentation
-    May be slower.""", unsafe_allow_html=True)
-    better_quality = st.checkbox("Better Visual Quality", True, 
-                                 help="Check to improve the visual quality of the segmentation. May be slower.")
-
-    contour_thickness = st.slider("Contour Thickness", 1, 50, 1, 
-                                  help="Thickness of the contour lines around detected objects.")
-                 
-    st.subheader("Real-world Length")
-    st.markdown("""Length of the reference line in the real world, used for scaling object parameters.""", unsafe_allow_html=True)
-    real_world_length = st.number_input("Enter the real-world length of the line in micrometers:", min_value=1, value=100, 
-                                       help="Length of the reference line in the real world, used for scaling object parameters.")
-
-    return uploaded_image, input_size, iou_threshold, conf_threshold, better_quality, contour_thickness, real_world_length
-
-def calculate_pixel_length(start_point, end_point):
-    x1, y1 = start_point
-    x2, y2 = end_point
-    return math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
-
-def drawable_canvas(uploaded_image):
-    st.write("Draw a line to set the scale:")
-    background_image = Image.open(uploaded_image)
-    canvas_result = st_canvas(
-        fill_color="rgba(255, 165, 0, 0.3)",
-        stroke_width=10,
-        stroke_color="#e00",
-        background_image=background_image,
-        height=550,
-        drawing_mode="line",
-        key="canvas",
-    )
-    return canvas_result
-
-
-def fast_process(annotations,
-    image,
-    device,
-    scale,
-    better_quality=False,
-    mask_random_color=True,
-    bbox=None,
-    use_retina=True,
-    withContours=True,
-    contour_thickness=2
-):
-    if isinstance(annotations[0], dict):
-        annotations = [annotation['segmentation'] for annotation in annotations]
-
-    original_h = image.height
-    original_w = image.width
-    if better_quality:
-        if isinstance(annotations[0], torch.Tensor):
-            annotations = np.array(annotations.cpu())
-        for i, mask in enumerate(annotations):
-            mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((3, 3), np.uint8))
-            annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
-    if device == 'cpu':
-        annotations = np.array(annotations)
-        inner_mask = fast_show_mask(
-            annotations,
-            plt.gca(),
-            random_color=mask_random_color,
-            bbox=bbox,
-            retinamask=use_retina,
-            target_height=original_h,
-            target_width=original_w,
-        )
-    else:
-        if isinstance(annotations[0], np.ndarray):
-            annotations = torch.from_numpy(annotations)
-        inner_mask = fast_show_mask_gpu(
-            annotations,
-            plt.gca(),
-            random_color=mask_random_color,
-            bbox=bbox,
-            retinamask=use_retina,
-            target_height=original_h,
-            target_width=original_w,
-        )
-    if isinstance(annotations, torch.Tensor):
-        annotations = annotations.cpu().numpy()
-
-    kernel = np.ones((5, 5), np.uint8)
-    
-    if withContours:
-        contour_all = []
-        temp = np.zeros((original_h, original_w, 1))
-        for i, mask in enumerate(annotations):
-            if type(mask) == dict:
-                mask = mask['segmentation']
-
-            # Convert to uint8
-            annotation = mask.astype(np.uint8)
-
-            # Perform morphological operations for separating connected objects and smoothing contours
-            kernel = np.ones((5,5),np.uint8)
-            annotation = cv2.morphologyEx(annotation, cv2.MORPH_OPEN, kernel)
-
-            annotation = cv2.GaussianBlur(annotation, (5, 5), 0) # Gaussian blur
-
-            # Find contours
-            contours, _ = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
-            # Add contour approximation here
-            for contour in contours:
-                hull = cv2.convexHull(contour)
-                epsilon = 0.005 * cv2.arcLength(contour, True)
-                approx = cv2.approxPolyDP(contour, epsilon, True)
-                contour_all.append(approx)  # Append the approximated contour
-
-        # New code to display object indices
-        for i, contour in enumerate(contour_all):
-            # Calculate the centroid of the object to place the index text
-            M = cv2.moments(contour)
-            if M["m00"] != 0:
-                cX = int(M["m10"] / M["m00"])
-                cY = int(M["m01"] / M["m00"])
-            else:
-                cX, cY = 0, 0
-
-            # Put the index at the centroid of the object
-            cv2.putText(temp, str(i), (cX, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 125, 255), 2)
-
-        # Increase contour thickness (you can also make this a variable)
-        cv2.drawContours(temp, contour_all, -1, (255, 255, 255), contour_thickness)
-
-        # Change the color here (this example sets it to red)
-        color = np.array([255 / 255, 0 / 255, 0 / 255, 1])  # RGBA
-
-        contour_mask = temp / 255 * color.reshape(1, 1, -1)
-
-
-    image = image.convert('RGBA')
-    overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), 'RGBA')
-    image.paste(overlay_inner, (0, 0), overlay_inner)
-
-    if withContours:  # Make sure contour_mask is defined when this block is executed
-        overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), 'RGBA')
-        image.paste(overlay_contour, (0, 0), overlay_contour)
-
-    return image
-
-
-# CPU post process
-def fast_show_mask(
-    annotation,
-    ax,
-    random_color=False,
-    bbox=None,
-    retinamask=True,
-    target_height=960,
-    target_width=960,
-):
-    mask_sum = annotation.shape[0]
-    height = annotation.shape[1]
-    weight = annotation.shape[2]
-    # 将annotation 按照面积 排序
-    areas = np.sum(annotation, axis=(1, 2))
-    sorted_indices = np.argsort(areas)[::1]
-    annotation = annotation[sorted_indices]
-
-    index = (annotation != 0).argmax(axis=0)
-    if random_color:
-        color = np.random.random((mask_sum, 1, 1, 3))
-    else:
-        color = np.ones((mask_sum, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 255 / 255])
-    transparency = np.ones((mask_sum, 1, 1, 1)) * 0.6
-    visual = np.concatenate([color, transparency], axis=-1)
-    mask_image = np.expand_dims(annotation, -1) * visual
-
-    mask = np.zeros((height, weight, 4))
-
-    h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij')
-    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
-
-    mask[h_indices, w_indices, :] = mask_image[indices]
-    if bbox is not None:
-        x1, y1, x2, y2 = bbox
-        ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
-
-    if not retinamask:
-        mask = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
-
-    return mask
-
-
-def fast_show_mask_gpu(
-    annotation,
-    ax,
-    random_color=False,
-    bbox=None,
-    retinamask=True,
-    target_height=960,
-    target_width=960,
-):
-    device = annotation.device
-    mask_sum = annotation.shape[0]
-    height = annotation.shape[1]
-    weight = annotation.shape[2]
-    areas = torch.sum(annotation, dim=(1, 2))
-    sorted_indices = torch.argsort(areas, descending=False)
-    annotation = annotation[sorted_indices]
-    # 找每个位置第一个非零值下标 (translate to english: find the first non-zero value index for each position)
-    index = (annotation != 0).to(torch.long).argmax(dim=0)
-    if random_color:
-        color = torch.rand((mask_sum, 1, 1, 3)).to(device)
-    else:
-        color = torch.ones((mask_sum, 1, 1, 3)).to(device) * torch.tensor(
-            [30 / 255, 144 / 255, 255 / 255]
-        ).to(device)
-    transparency = torch.ones((mask_sum, 1, 1, 1)).to(device) * 0.6
-    visual = torch.cat([color, transparency], dim=-1)
-    mask_image = torch.unsqueeze(annotation, -1) * visual
-    # 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式(translate to english: use vectorization to get the value of the batch)
-    mask = torch.zeros((height, weight, 4)).to(device)
-    h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight))
-    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
-    # 使用向量化索引更新show的值
-    mask[h_indices, w_indices, :] = mask_image[indices]
-    mask_cpu = mask.cpu().numpy()
-    if bbox is not None:
-        x1, y1, x2, y2 = bbox
-        ax.add_patch(
-            plt.Rectangle(
-                (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1
-            )
-        )
-    if not retinamask:
-        mask_cpu = cv2.resize(
-            mask_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST
-        )
-    return mask_cpu
-
-
-# segment_everything function
-def segment_everything(_input, input_size=1024, iou_threshold=0.7, conf_threshold=0.25, better_quality=False, contour_thickness=1):
-    input = _input
-    input_size = int(input_size)
-    w, h = input.size
-    scale = input_size / max(w, h)
-    new_w = int(w * scale)
-    new_h = int(h * scale)
-    input = input.resize((new_w, new_h))
-
-    results = model(input,
-                    retina_masks=True,
-                    iou=iou_threshold,
-                    conf=conf_threshold,
-                    imgsz=input_size)
-
-    annotations = results[0].masks.data
-
-    fig = fast_process(annotations=annotations,device=device,
-                       image=input,
-                       scale=(1024 // input_size),
-                       better_quality=better_quality,
-                       contour_thickness=contour_thickness)  
-    return fig,annotations
-
-def calculate_parameters(annotations, scale_factor):
-    # Initialize an empty DataFrame
-    df = pd.DataFrame(columns=['Object', 'Area', 'Perimeter', 'Roundness', 'Aspect Ratio', 'Longest Length'])
-    
-    if len(annotations) > 0:  # Check if annotations list is not empty
-        for i, mask in enumerate(annotations):
-            # Convert mask to binary image
-            binary_mask = mask.cpu().numpy().astype(np.uint8)
-            
-            # Calculate area in pixels
-            area_pixel = np.sum(binary_mask)
-            
-            # Convert area to microns
-            area_micron = area_pixel * (scale_factor ** 2)
-            
-            # Find contours
-            contours, _ = cv2.findContours(binary_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-            
-            # Calculate Perimeter in pixels
-            perimeter_pixel = cv2.arcLength(contours[0], True)
-            
-            # Convert perimeter to microns
-            perimeter_micron = perimeter_pixel * scale_factor
-            
-            # Fit an ellipse to the object
-            if len(contours[0]) >= 5:  # Check if there are enough points to fit an ellipse
-                ellipse = cv2.fitEllipse(contours[0])
-                major_axis = max(ellipse[1])
-                minor_axis = min(ellipse[1])
-            else:
-                major_axis = minor_axis = 0  # Default values if not enough points
-
-            # Convert major and minor axis to microns
-            major_axis_micron = major_axis * scale_factor
-            minor_axis_micron = minor_axis * scale_factor
-            
-            # Calculate Roundness
-            roundness = 4 * area_micron / (np.pi * (major_axis_micron ** 2))
-            
-            # Calculate Aspect Ratio
-            if minor_axis_micron != 0:  # Check to avoid division by zero
-                aspect_ratio = major_axis_micron / minor_axis_micron
-            else:
-                aspect_ratio = "Undefined due to zero minor axis"
-
-            # Longest Length (Major Axis)
-            longest_length_micron = major_axis_micron
-            
-            # Add to DataFrame
-            new_row = pd.DataFrame({
-                'Object': [f"Object {i+1}"],
-                'Area': [area_micron],
-                'Perimeter': [perimeter_micron],
-                'Roundness': [roundness],
-                'Aspect Ratio': [aspect_ratio],
-                'Longest Length': [longest_length_micron]
-            })
-            
-            df = pd.concat([df, new_row], ignore_index=True)
-            
-            # Display in Streamlit
-            #st.write(f"Object {i+1}: Area = {area_micron:.2f} µm², Perimeter = {perimeter_micron:.2f} µm, Roundness = {roundness:.2f}, Aspect Ratio = {aspect_ratio}, Longest Length = {longest_length_micron:.2f} µm")
-    
-    return df
-
-# Function to plot distribution
-def plot_distribution(df, selected_parameter):
-    try:
-        fig, ax = plt.subplots()
-        
-        # Plot histogram
-        sns.histplot(df[selected_parameter], kde=True, ax=ax, label='Histogram')
-        
-        # Plot cumulative frequency curve
-        sns.histplot(df[selected_parameter], kde=False, cumulative=True, 
-                     element='step', stat='density', ax=ax, label='Cumulative Frequency')
-        
-        ax.set_title(f'Distribution of {selected_parameter}')
-        ax.set_xlabel(selected_parameter)
-        ax.set_ylabel('Frequency')
-        ax.legend()
-        
-        st.pyplot(fig)
-    except Exception as e:
-        st.write(f"An error occurred while plotting: {e}")
-
-
-# Function to convert DataFrame to CSV string
-def dataframe_to_csv(df):
-    """Convert DataFrame to CSV string."""
-    csv_buffer = io.StringIO()
-    df.to_csv(csv_buffer, index=False)
-    return csv_buffer.getvalue()
-
-def get_csv_download_link(df, filename="data.csv"):
-    """Generate a link to download the DataFrame as a csv file."""
-    csv = df.to_csv(index=False)
-    b64 = base64.b64encode(csv.encode()).decode()
-    return f'<a href="data:file/csv;base64,{b64}" download="{filename}">Download CSV File</a>'
-
-
-def main():
-    uploaded_image, input_size, iou_threshold, conf_threshold, better_quality, contour_thickness, real_world_length = streamlit_ui()
-    if uploaded_image is not None:
-        canvas_result = drawable_canvas(uploaded_image)
-        pixel_length = None  # Initialize pixel_length
-
-        # Check if line is drawn on canvas and pixel_length is not None
-        if canvas_result.json_data is not None and "objects" in canvas_result.json_data:
-            if len(canvas_result.json_data["objects"]) > 0:
-                line_object = canvas_result.json_data["objects"][0]
-                start_point = [line_object['x1'], line_object['y1']]
-                end_point = [line_object['x2'], line_object['y2']]
-                pixel_length = calculate_pixel_length(start_point, end_point)
-                
-                # Display pixel length
-                st.write(f"Pixel length of the line: {pixel_length}")
-                
-                # Check if scale_factor is available
-                if 'scale_factor' in locals() or 'scale_factor' in globals():
-                    # Convert pixel length to micrometers using scale_factor
-                    length_micron = pixel_length * scale_factor
-                    
-                    # Display length in micrometers
-                    st.write(f"Length of the line in µm: {length_micron:.2f}")
-                else:
-                    st.write("Scale factor is not available. Cannot convert to µm.")
-            
-            else:
-                st.write("Please draw a line to set the scale or enter the real-world length.")
-        else:
-            st.write("Please draw a line to set the scale or enter the real-world length.")
-        
-        if pixel_length is not None and real_world_length is not None:
-            scale_factor = real_world_length / pixel_length  # Calculate scale_factor
-        else:
-            st.write("Scale factor could not be calculated. Make sure to draw a line and enter the real-world length.")
-            return  # Exit the function if scale_factor can't be calculated
-        
-        input_image = Image.open(uploaded_image)
-        segmented_image, annotations = segment_everything(
-        input_image, 
-        input_size=input_size, 
-        iou_threshold=iou_threshold, 
-        conf_threshold=conf_threshold, 
-        better_quality=better_quality,
-        contour_thickness=contour_thickness)
-        st.image(segmented_image, caption="Segmented Image", use_column_width=True)
-
-        # Call your new function
-        df = calculate_parameters(annotations, scale_factor)
-        
-        # Display DataFrame in Streamlit
-        if not df.empty:
-            st.write("Summary of Object Parameters:")
-            st.dataframe(df)
-
-            # Add download button
-            csv_data = dataframe_to_csv(df)
-            st.download_button(
-            label="Download CSV",
-            data=csv_data,
-            file_name="object_parameters.csv",
-            mime="text/csv",
-            )
-
-            # Add download link
-            st.markdown(get_csv_download_link(df), unsafe_allow_html=True)
-            
-            # Select the parameter to plot
-            filtered_columns = [col for col in df.columns.tolist() if col != 'Object']
-            selected_parameter = st.selectbox("Select a parameter to see its distribution:", filtered_columns)
-            
-            # Check if selected_parameter is defined
-            if selected_parameter:
-                # Plot the distribution of the selected parameter
-                plot_distribution(df, selected_parameter)
-            else:
-                st.write("No parameter selected for plotting.")
-        else:
-            st.write("No objects detected.")
-    else:
-        st.write("Please upload an image.")
-
-if __name__ == "__main__":
-    main()
-
-
-
-    
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Orange Image with No Attribution Required Explore Thousands of Orange Pictures.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Orange Image with No Attribution Required Explore Thousands of Orange Pictures.md
deleted file mode 100644
index 3de294486579759635f3c7b9395d6b3936a71409..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Orange Image with No Attribution Required Explore Thousands of Orange Pictures.md	
+++ /dev/null
@@ -1,155 +0,0 @@
-<br />
-<h1>How to Download Orange Images</h1>
-<p>Orange is a vibrant and cheerful color that can brighten up any project. Whether you need orange images for your website, blog, social media, presentation, flyer, poster, or any other creative work, you might wonder how to find and download them easily. In this article, we will show you how to do that in a few simple steps.</p>
-<p>Orange images are photos or graphics that feature the color orange prominently. They can be anything from orange fruits, flowers, animals, objects, backgrounds, patterns, textures, gradients, etc. You might want to download orange images for various reasons. For example:</p>
-<h2>download orange image</h2><br /><p><b><b>Download File</b> &#10026; <a href="https://gohhs.com/2uPqbs">https://gohhs.com/2uPqbs</a></b></p><br /><br />
-<ul>
-<li>Orange images can create a warm, energetic, and positive mood for your project.</li>
-<li>Orange images can attract attention and stimulate interest among your audience.</li>
-<li>Orange images can convey different meanings and emotions depending on the context. For instance, orange can represent creativity, enthusiasm, fun, adventure, optimism, health, happiness, etc.</li>
-</ul>
-<p>However, finding and downloading orange images can also pose some challenges. For example:</p>
-<ul>
-<li>Orange images can be hard to find among the millions of images available online.</li>
-<li>Orange images can vary in quality, size, format, license, etc., making it difficult to choose the best ones for your project.</li>
-<li>Orange images can require some editing before you can use them for your project.</li>
-</ul>
-<p>Don't worry though. In this article, we will guide you through the process of finding and downloading orange images online. We will also show you how to edit them on your computer using some popular image editing software. By the end of this article, you will be able to download and edit orange images like a pro.</p>
- <h2>How to Find Orange Images Online</h2>
-<p>The first step is to find some sources of orange images online. There are two main ways to do that: using a search engine or using a stock photo website.</p>
- <h3>Use a search -free images, you should still be careful and respectful when using them. Some images might have some terms and conditions that you need to follow, such as giving credit, not modifying, not selling, etc. You should always read the license and the terms of use of each image before you download and use it. You should also support the photographers and the creators of the images that you use by giving them feedback, rating, donation, etc.</p>
- <h2>How to Edit Orange Images on Your Computer</h2>
-<p>The next step is to edit your orange images on your computer. You might want to do that for various reasons. For example:</p>
-<p>download orange fruit image<br />
-download orange background image<br />
-download orange slice image<br />
-download orange juice image<br />
-download orange color image<br />
-download orange flower image<br />
-download orange peel image<br />
-download orange wallpaper image<br />
-download orange abstract image<br />
-download orange gradient image<br />
-download orange sunset image<br />
-download orange texture image<br />
-download orange vector image<br />
-download orange logo image<br />
-download orange pattern image<br />
-download orange leaf image<br />
-download orange balloon image<br />
-download orange butterfly image<br />
-download orange tree image<br />
-download orange paint image<br />
-download orange splash image<br />
-download orange sky image<br />
-download orange citrus image<br />
-download orange watercolor image<br />
-download orange light image<br />
-download orange smoke image<br />
-download orange fire image<br />
-download orange art image<br />
-download orange design image<br />
-download orange illustration image<br />
-download orange geometric image<br />
-download orange neon image<br />
-download orange glow image<br />
-download orange mandarin image<br />
-download orange bird image<br />
-download orange cat image<br />
-download orange ribbon image<br />
-download orange heart image<br />
-download orange star image<br />
-download orange circle image<br />
-download orange line image<br />
-download orange dot image<br />
-download orange square image<br />
-download orange triangle image<br />
-download orange polygon image<br />
-download orange diamond image</p>
-<ul>
-<li>You might want to change the size, format, resolution, or orientation of your orange images to fit your project.</li>
-<li>You might want to enhance the quality, color, brightness, contrast, or sharpness of your orange images to make them more appealing.</li>
-<li>You might want to add some text, shapes, filters, effects, layers, masks, or other elements to your orange images to make them more creative.</li>
-</ul>
-<p>To edit your orange images on your computer, you will need two things: an image file format and an image editing software.</p>
- <h3>Choose an image file format that suits your needs</h3>
-<p>An image file format is a way of storing and displaying an image on a computer. There are many different image file formats available, each with its own characteristics and advantages. The most common ones are:</p>
-<table>
-<tr>
-<th>Image File Format</th>
-<th>Description</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>JPEG (Joint Photographic Experts Group)</td>
-<td>A raster image format that uses lossy compression to reduce the file size.</td>
-<td>Widely supported by most devices and applications. Good for photos and realistic images.</td>
-<td>Reduces the quality and detail of the image. Not good for text or graphics with sharp edges.</td>
-</tr>
-<tr>
-<td>PNG (Portable Network Graphics)</td>
-<td>A raster image format that uses lossless compression to preserve the quality and detail of the image.</td>
-<td>Supports transparency and alpha channel. Good for text and graphics with sharp edges.</td>
-<td>Larger file size than JPEG. Not supported by some older devices and applications.</td>
-</tr>
-<tr>
-<td>GIF (Graphics Interchange Format)</td>
-<td>A raster image format that supports animation and a limited color palette of 256 colors.</td>
-<td>Good for simple animations and icons. Small file size.</td>
-<td>Low quality and color depth. Not good for photos or realistic images.</td>
-</tr>
-<tr>
-<td>SVG (Scalable Vector Graphics)</td>
-<td>A vector image format that uses mathematical equations to define the shapes and colors of the image.</td>
-<td>Scalable without losing quality or resolution. Good for logos and illustrations.</td>
-<td>Not supported by some older devices and applications. Not good for photos or realistic images.</td>
-</tr>
-</table>
- <p>The choice of an image file format depends on your needs and preferences. You should consider factors such as the quality, size, compatibility, and purpose of your orange images. You can also convert between different image file formats using online tools or software if needed.</p>
- <h3>Choose an image editing software that matches your skill level and budget</h3> <p>An image editing software is a program that allows you to manipulate and modify your orange images on your computer. There are many different image editing software available, each with its own features and functions. The most popular ones are:</p>
-<table>
-<tr>
-<th>Image Editing Software</th>
-<th>Description</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>GIMP (GNU Image Manipulation Program)</td>
-<td>A free and open-source raster image editing software that offers a variety of tools and options for editing images.</td>
-<td>Free and easy to use. Supports many image file formats. Has a large community of users and developers.</td>
-<td>Not as powerful or professional as some paid software. Has a steep learning curve for some advanced features.</td>
-</tr>
-<tr>
-<td>Photoshop (Adobe Photoshop)</td>
-<td>A paid and proprietary raster image editing software that is widely used by professionals and amateurs for editing images.</td>
-<td>Powerful and professional. Offers a wide range of tools and options for editing images. Has a large market share and compatibility.</td>
-<td>Expensive and requires a subscription. Has a steep learning curve for some advanced features.</td>
-</tr>
-<tr>
-<td>PaintShop Pro (Corel PaintShop Pro)</td>
-<td>A paid and proprietary raster image editing software that is similar to Photoshop but more affordable and user-friendly.</td>
-<td>Affordable and user-friendly. Offers a good range of tools and options for editing images. Has a loyal fan base and support.</td>
-<td>Not as powerful or professional as Photoshop. Has a smaller market share and compatibility.</td>
-</tr>
-</table>
- <p>The choice of an image editing software depends on your skill level and budget. You should consider factors such as the features, functions, ease of use, cost, support, etc. of each software. You can also try some free trials or demos of each software before you buy or download them. You should also install and update your image editing software regularly to ensure its performance and security.</p>
- <h3>Apply basic and advanced editing techniques to your orange images</h3>
-<p>The final step is to apply some basic and advanced editing techniques to your orange images. You can do that using the tools and options provided by your image editing software. Here are some examples of what you can do:</p>
- - How to crop, resize, rotate, flip, and adjust the brightness, contrast, saturation, hue, etc. of your orange images   - Cropping is the process of cutting out unwanted parts of your orange image to focus on the main subject or fit a specific size or shape.   - Resizing is the process of changing the dimensions or resolution of your orange image to fit a specific size or quality.   - Rotating is the process of turning your orange image clockwise or counterclockwise by a certain angle or degree.   - Flipping is the process of mirroring your orange image horizontally or vertically.   - Adjusting the brightness, contrast, saturation, hue, etc. is the process of changing the lightness, darkness, intensity, color, etc. of your orange image to enhance its appearance or mood.   - To perform these basic editing techniques, you can use the crop, resize, rotate, flip, adjust tools or options in your image editing software. You can also use the undo, redo, zoom, pan tools or options to correct any mistakes or view any changes. - How to add text, shapes, filters, effects, layers, masks, etc. to your orange images   - Adding text is the process of inserting words or phrases to your orange image to convey a message or information.   - Adding shapes is the process of inserting geometric forms or symbols to your orange image to create patterns or designs.   - Adding filters is the process of applying predefined settings or presets to your orange image to create different styles or looks.   - Adding effects is the process of applying custom settings or adjustments to your orange image to create different moods or atmospheres.   - Adding layers is the process of stacking multiple images or elements on top of each other to create complex compositions or manipulations.   - Adding masks is the process of hiding or revealing parts of an image or element using another image or element as a guide.   - To perform these advanced editing techniques, you can use the text, shape, filter, effect, layer, mask tools or options in your image editing software. You can also use the move, transform, blend tools or options to modify any elements you add. <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download and edit orange images for your projects. We have covered how to find orange images online using a search engine or a stock photo website. We have also covered how to edit orange images on your computer using an image file format and an image editing software. We have also given you some examples of basic and advanced editing techniques that you can apply to your orange images. We hope that this article has been helpful and informative for you.</p>
-<p>Downloading and editing orange images can be a fun and rewarding experience. You can use orange images to create stunning and unique projects that will impress and inspire your audience. You can also express your creativity and personality through orange images. However, you should also be careful and respectful when using orange images. You should always check the license and the source of the images you use. You should also cite the sources and support the creators of the images you use. You should also follow some tips and best practices for downloading and editing orange images, such as:</p>
-<ul>
-<li>Choose the right image file format for your project.</li>
-<li>Choose the right image editing software for your skill level and budget.</li>
-<li>Apply basic and advanced editing techniques to enhance your orange images.</li>
-<li>Save and export your edited orange images in different formats and resolutions.</li>
-<li>Optimize your orange images for web or print.</li>
-</ul>
-<p>If you have any feedback or questions about this article, please feel free to share them with us. We would love to hear from you and learn from you. Thank you for reading this article and happy downloading and editing!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about downloading and editing orange images:</p>
- - What are some examples of projects that use orange images?   - Some examples of projects that use orange images are:     - Websites or blogs that focus on topics like food, health, travel, culture, etc.     - Social media posts or stories that showcase your lifestyle, hobbies, interests, etc.     - Presentations or flyers that promote your products, services, events, etc.     - Posters or wallpapers that decorate your home, office, or classroom.     - Logos or illustrations that represent your brand, identity, or message. - Where can I find more inspiration and ideas for using orange images?   - Some sources of inspiration and ideas for using orange images are:     - Online galleries or portfolios that feature orange images or projects.     - Online magazines or blogs that share tips, trends, or tutorials on using orange images.     - Online communities or forums that discuss or showcase orange images or projects.     - Online courses or books that teach you how to use orange images effectively. - How can I optimize my orange images for web or print?   - Some ways to optimize your orange images for web or print are:     - Reduce the file size of your orange images to improve the loading speed and performance of your website or blog.     - Increase the resolution of your orange images to improve the quality and clarity of your printouts or displays.     - Adjust the color mode of your orange images to match the color space of your web browser or printer.     - Add some metadata to your orange images to provide some information about them. - What are some common mistakes to avoid when downloading and editing orange images?   - Some common mistakes to avoid when downloading and editing orange images are:     - Downloading or using orange images without checking the license or the source.     - Downloading or using low-quality or inappropriate orange images for your project.     - Editing or modifying orange images without saving a backup copy of the original image.     - Editing or modifying orange images without following the guidelines or instructions of your image editing software.     - Editing or modifying orange images without previewing or testing the results. - How can I learn more about image editing software and techniques?   - Some ways to learn more about image editing software and techniques are:     - Read the user manual or the help section of your image editing software.     - Watch some online videos or tutorials that demonstrate how to use your image editing software.     - Join some online courses or workshops that teach you how to use your image editing software.     - Ask some experts or peers who use your image editing software for advice or feedback.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fun-research/FC-CLIP/fcclip/modeling/pixel_decoder/ops/test.py b/spaces/fun-research/FC-CLIP/fcclip/modeling/pixel_decoder/ops/test.py
deleted file mode 100644
index 6e1b545459f6fd3235767e721eb5a1090ae14bef..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/fcclip/modeling/pixel_decoder/ops/test.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-
-from __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import time
-import torch
-import torch.nn as nn
-from torch.autograd import gradcheck
-
-from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch
-
-
-N, M, D = 1, 2, 2
-Lq, L, P = 2, 2, 2
-shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
-level_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1]))
-S = sum([(H*W).item() for H, W in shapes])
-
-
-torch.manual_seed(3)
-
-
-@torch.no_grad()
-def check_forward_equal_with_pytorch_double():
-    value = torch.rand(N, S, M, D).cuda() * 0.01
-    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
-    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
-    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
-    im2col_step = 2
-    output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu()
-    output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu()
-    fwdok = torch.allclose(output_cuda, output_pytorch)
-    max_abs_err = (output_cuda - output_pytorch).abs().max()
-    max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
-
-    print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
-
-
-@torch.no_grad()
-def check_forward_equal_with_pytorch_float():
-    value = torch.rand(N, S, M, D).cuda() * 0.01
-    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
-    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
-    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
-    im2col_step = 2
-    output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()
-    output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()
-    fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
-    max_abs_err = (output_cuda - output_pytorch).abs().max()
-    max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
-
-    print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
-
-
-def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):
-
-    value = torch.rand(N, S, M, channels).cuda() * 0.01
-    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
-    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
-    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
-    im2col_step = 2
-    func = MSDeformAttnFunction.apply
-
-    value.requires_grad = grad_value
-    sampling_locations.requires_grad = grad_sampling_loc
-    attention_weights.requires_grad = grad_attn_weight
-
-    gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))
-
-    print(f'* {gradok} check_gradient_numerical(D={channels})')
-
-
-if __name__ == '__main__':
-    check_forward_equal_with_pytorch_double()
-    check_forward_equal_with_pytorch_float()
-
-    for channels in [30, 32, 64, 71, 1025, 2048, 3096]:
-        check_gradient_numerical(channels, True, True, True)
-
-
-
diff --git a/spaces/gerhug/dalle-mini/README.md b/spaces/gerhug/dalle-mini/README.md
deleted file mode 100644
index e8d3d7e311b04d1e35e1ff572ec809613f71f09c..0000000000000000000000000000000000000000
--- a/spaces/gerhug/dalle-mini/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: DALL·E mini
-metaTitle: DALL·E mini by craiyon.com on Hugging Face
-emoji: 🥑
-colorFrom: yellow
-colorTo: green
-sdk: static
-pinned: true
-license: apache-2.0
-duplicated_from: dalle-mini/dalle-mini
----
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/GridinSoft Anti-Malware 4.0.4 Full [NEW] Keygen.md b/spaces/gotiQspiryo/whisper-ui/examples/GridinSoft Anti-Malware 4.0.4 Full [NEW] Keygen.md
deleted file mode 100644
index ef8e10a0b99d69af81a39097ec1c26b5e3445141..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/GridinSoft Anti-Malware 4.0.4 Full [NEW] Keygen.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<br />
-<p>15-Herramientas de Internet<br />BrowseEmAll.v8.3.5.Enterprise.Server.Studio.Manual.Edition-P2P<br />BWMeter.7.3.1<br />cFosSpeed.10.06.2206<br />Change Mac Aderess v.19.89.852 Final<br />CHROMECOOKIESVIEW-1.12<br />Classroom.Spy.Professional.4.4.4<br />cv6<br />DU Meter v7.20 Build 4761<br />ent4<br />Eric's TelNet98 v20.6-SSH<br />HIDE-ALL-IP-2015-3-30<br />IA_Us<br />MyLanViewer<br />NET SETMAN 401<br />Net.Monitor.for.Employees.Pro.5.4.6<br />NetBalancer.9.12.2.1558<br />NetLimiter.Pro.4.0.33.0<br />NetLimiter_Pro_4.0.48.0_Enterprise_Multilingual<br />netscan<br />NetTraffic 1.39.3<br />NetTraffic 1.46.0<br />netvizor<br />Network.LookOut.Administrator.Pro.4.2.2<br />Networx 6.1.1.0<br />NirLauncher.Package.1.20.45<br />NirSoft Package v.1.19.25 Final<br />Nsauditor Network Security Auditor 3.0.8.0<br />Nsauditor Network Security Auditor 3.0.16.0<br />SiteMonitor.Enterprise.3.89<br />SoftPerfect.Network.Scanner.7.1.4<br />WEBBRO WSERPASSVIEW<br />Windows.Firewall.Control.4.9.9.0<br />Xmanager Enterprise v5 Build 1015<br />administración de redes DameWare NT Utilities<br />deskman-network-4-1-5665-60686-multi-win<br />FileZilla_3.10.1.1_win32<br />ipnetinfo<br />NirSoft Package v.1.19.25 Final<br />BvSshServer-Inst<br />iphostmonitor<br />Ipscan v.2.4.2601.0<br />LAN_SpeedTest<br />netbalancer-8-6-2-en-win<br />NetDrive2_Setup_2.4.2<br />NETGEARGenie-install<br />total-network-inventory-3-2-1-multi-win<br />Microsoft.Remote.Server.Administration.Tools.for.Windows.8.1<br />4K Video Downloader 4.4.10.2342 (x64)<br />Abelssoft Doku Downloader 2019 v1.4 Multilingual<br />Abelssoft.YouTube.Song.Downloader.2019.v19.05-DVT<br />Air.Live.Drive.v.1.2.2<br />Ant Download Manager Pro 1.11.1<br />Free Youtube Download 4.2.10<br />Free.Download.Manager.5.1.37.7297.x64<br />Free.Download.Manager.5.1.37.7297.x86<br />Internet Download Manager 6.33 Build 3 Retail<br />MEGAsyncSetup<br />Youtube.Music.Downloader.9.8.4<br />YouTubeDownloader.3.9.9.10.1201<br />YouTubeGet.7.2.4<br />YTD Video Downloader Pro 5.9.9.3<br />Dropbox 78.4.119<br />eM Client Pro 7.2.33834.0<br />Mozilla Thunderbird 60.8.0 x64<br />Mozilla Thunderbird 60.8.0 x86<br />The.Bat.Professional.8.7.0<br />Core.FTP.Pro.2.2.Build.1922.x64<br />FileZilla.3.37.4.x64<br />FileZilla.3.37.4.x86<br />FileZilla_3.40.0_Final_x64<br />putty-64bit-0.69<br />WinSCP_v5.15.3<br />WinSCP-5.15.3<br />kw50ew<br />CAMFROG-VIDEO CHAT 6.10.454<br />iGap.Windows.5.0.7.0<br />IMO para PC<br />MessengerSetup v1-4-3<br />telegram setup.1.7.14<br />Viber 10.9.0.52<br />WhatsAppSetup-64bit<br />conferendo<br />LINE_v5.18.1.1995<br />Skype-8.49.0.49<br />yahoo-messenger-0.8.155-win32<br />BigAnt-Office-Messenger-5.0.17<br />BORGChat-Inst<br />Outlook Messenger 4.1 Full<br />pandion<br />Pidgin v.2.11.0 Final<br />pidgin-2.5.4<br />Softros.LAN.Messenger.v9.1<br />pidgin-2.5.4<br />pidgin-2.5.8<br />Install_AIM<br />openfire<br />spark_2_7_3<br />TeamTalk_v5.0<br />trillian-v5-3-0-7<br />BraveBrowserStandalone<br />BraveBrowserStandalone<br />Chromium 75.0.3770.90<br />Firefox Setup 69.0<br />Google_Chrome_(32bit)_v76.0.3809.87<br />Google_Chrome_(64bit)_v76.0.3809.87<br />Maxthon_Cloud_Browser_v5.2.7.4000<br />Opera_62.0.3331.99_x64<br />Pale_Moon_(32bit)_v28.6.1<br />Pale_Moon_(64bit)_v28.6.1<br />Tor Browser v8.0.4 [Multilingual] ES<br />Vivaldi.2.6.1566.40<br />Vivaldi.2.6.1566.40.x64<br />Waterfox 56.2.14<br />Yandex_Browser_v19.7.0.1635<br />Comodo Dragon 74.0.3729.157 x64<br />Comodo Dragon 74.0.3729.157<br />Opera_GX_60.0.3255.50747 x64<br />Ashampoo.Privacy.Protector.1.1.3<br />Connectify 8<br />Connectify 9_X PRO Plus with keygen free download full version<br />Connectify Hotspot 2015 PRO<br />Connectify Hotspot 2018<br />Connectify_Pro_3.4.0.23678<br />Connectify-me<br />MarsWifi<br />WIRELESSNETVIEW-1-66<br />wireless-network-watcher-1-80-en-win<br />Connectify_Pro_3.4.0.23678<br />virtual-wi-fi-router-3-0-1-1-en-win<br />Connectify2015<br />SHAREit-KCWEB<br />virtual-wi-fi-router-3-0-1-1-en-win<br />wirelessmon<br />wirelessnetview_setup<br />ZapyaSetup_1400_en<br />Adobe Flash Player 32.00.223 for Chromium-based browsers<br />Adobe Flash Player 32.00.223 for Firefox<br />Adobe Flash Player 32.00.223 for Internet Explorer<br />AdobeAIRInstaller 32.0.0.125<br />Cerberus FTP Server Enterprise 10.0.13.0 x64<br />Cerberus FTP Server Enterprise 10.0.13.0 x86<br />FileZilla Server v.0.9.56 Final<br />FileZilla v.3.10.3.RC1 Final<br />FileZilla.Server.0.9.60<br />Titan FTP Server Enterprise 2019 Build 3535 x64<br />Titan FTP Server Enterprise 2019 Build 3535 x86<br />Wing FTP Server Corporate 6.1.5 Multilingual<br />Wing_FTP_Server_Corporate_6.1.0_Multilingual<br />Xlight.FTP.Server.Pro.v3.8.8.x86.x64-P2P<br />CoreFTPServer<br />CoreFTPServer64<br />SmartFTP_(64bit)_v9.0.2685.0<br />Media Play Mbolie<br />hfs<br />PlexMediaServer-1.16.4.1469-6d5612c2f-x86<br />Viber v.6.4.1476 Final<br />Rosadin Tv (Instalador) Update 13,08,2016<br />BITCOMET-1.38<br />Auslogics_Browser_Care_5.0.17.0<br />Auto Hide IP 5.6.1.8<br />Auto Hide IP v5.6.2.2<br />Avira Phantom VPN Pro 2.16.1.16182<br />cFosSpeed v10.09 Build 2236<br />Complete Internet Repair 3.758<br />Hide ALL IP 2017 Full<br />Hide ALL IP 2018.01.04.180104<br />Hide ALL IP v2015.09.09.150909<br />Hide IP Easy v5.5.1.2<br />Hide.IP.Easy.v5.5.4.2-P2P<br />HideIPEasy 5.4.8.2<br />Httrack 3.47.2 ES Descargue Webs Completas<br />IP Hider Pro v5.6.0.1 Final<br />MaskMyIP<br />Net.Monitor.for.Employees.Pro.5.5.3<br />Platinum.Hide.IP.3.5.3.2<br />SuperHideIP-3.6.2.6<br />UltraSurf 2016 v160.3<br />Windows.Firewall.Control.4.9.9.4<br />ultimate-ip-changer-1-0-en<br />free-hide-ip-4-0-8-8-en<br />tango-1-6-14117-en</p>
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Hussein Who Said No Full Movie Download How an Unfair War Made One of the Most Important Effects in Islam History.md b/spaces/gotiQspiryo/whisper-ui/examples/Hussein Who Said No Full Movie Download How an Unfair War Made One of the Most Important Effects in Islam History.md
deleted file mode 100644
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--- a/spaces/gotiQspiryo/whisper-ui/examples/Hussein Who Said No Full Movie Download How an Unfair War Made One of the Most Important Effects in Islam History.md	
+++ /dev/null
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diff --git a/spaces/gradio/HuBERT/tests/test_sparse_multihead_attention.py b/spaces/gradio/HuBERT/tests/test_sparse_multihead_attention.py
deleted file mode 100644
index 3e32b25a7fb1e12295b84d0c65064f8e42b7bdd3..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/tests/test_sparse_multihead_attention.py
+++ /dev/null
@@ -1,114 +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 unittest
-
-import torch
-from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention
-
-
-class TestSparseMultiheadAttention(unittest.TestCase):
-    def test_sparse_multihead_attention(self):
-        attn_weights = torch.randn(1, 8, 8)
-        bidirectional_sparse_mask = torch.tensor(
-            [
-                [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
-                [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
-                [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
-                [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0],
-                [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
-                [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
-                [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
-                [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
-            ]
-        )
-
-        bidirectional_attention = SparseMultiheadAttention(
-            16, 1, stride=4, expressivity=1, is_bidirectional=True
-        )
-        bidirectional_attention_sparse_mask = (
-            bidirectional_attention.buffered_sparse_mask(attn_weights, 8, 8)
-        )
-        torch.all(
-            torch.eq(bidirectional_attention_sparse_mask, bidirectional_sparse_mask)
-        )
-
-        sparse_mask = torch.tensor(
-            [
-                [
-                    0,
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                ],
-                [
-                    0,
-                    0,
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                ],
-                [
-                    0,
-                    0,
-                    0,
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                ],
-                [
-                    0,
-                    0,
-                    0,
-                    0,
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                ],
-                [0, 0, 0, 0, 0, float("-inf"), float("-inf"), float("-inf")],
-                [
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    0,
-                    0,
-                    0,
-                    float("-inf"),
-                    float("-inf"),
-                ],
-                [
-                    float("-inf"),
-                    float("-inf"),
-                    float("-inf"),
-                    0,
-                    0,
-                    0,
-                    0,
-                    float("-inf"),
-                ],
-                [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0],
-            ]
-        )
-
-        attention = SparseMultiheadAttention(
-            16, 1, stride=4, expressivity=1, is_bidirectional=False
-        )
-        attention_sparse_mask = attention.buffered_sparse_mask(attn_weights, 8, 8)
-
-        torch.all(torch.eq(attention_sparse_mask, sparse_mask))
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/gradio/animeganv2/run.py b/spaces/gradio/animeganv2/run.py
deleted file mode 100644
index 5d8de0171ebe9b1c0fc7b28d8f6ba0c4d39549ff..0000000000000000000000000000000000000000
--- a/spaces/gradio/animeganv2/run.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import gradio as gr
-import torch
-
-model2 = torch.hub.load(
-    "AK391/animegan2-pytorch:main",
-    "generator",
-    pretrained=True,
-    progress=False
-)
-model1 = torch.hub.load("AK391/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1")
-face2paint = torch.hub.load(
-    'AK391/animegan2-pytorch:main', 'face2paint', 
-    size=512,side_by_side=False
-)
-
-def inference(img, ver):
-    if ver == 'version 2 (🔺 robustness,🔻 stylization)':
-        out = face2paint(model2, img)
-    else:
-        out = face2paint(model1, img)
-    return out
-
-title = "AnimeGANv2"
-description = "Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below."
-article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
-examples=[['groot.jpeg','version 2 (🔺 robustness,🔻 stylization)'],['gongyoo.jpeg','version 1 (🔺 stylization, 🔻 robustness)']]
-
-demo = gr.Interface(
-    fn=inference, 
-    inputs=[gr.Image(type="pil"),gr.Radio(['version 1 (🔺 stylization, 🔻 robustness)','version 2 (🔺 robustness,🔻 stylization)'], type="value", value='version 2 (🔺 robustness,🔻 stylization)', label='version')], 
-    outputs=gr.Image(type="pil"),
-    title=title,
-    description=description,
-    article=article,
-    examples=examples)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/gradio/fake_gan/README.md b/spaces/gradio/fake_gan/README.md
deleted file mode 100644
index e9178d856a0fa21e43e962eea248c36f9de1b511..0000000000000000000000000000000000000000
--- a/spaces/gradio/fake_gan/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
----
-title: fake_gan 
-emoji: 🔥
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.1.2
-app_file: run.py
-pinned: false
-hf_oauth: true
----
diff --git a/spaces/gradio/longformer/tvm/_ffi/base.py b/spaces/gradio/longformer/tvm/_ffi/base.py
deleted file mode 100644
index c61c5c44544236a99e3f77e70fe6ea3dd7a144e5..0000000000000000000000000000000000000000
--- a/spaces/gradio/longformer/tvm/_ffi/base.py
+++ /dev/null
@@ -1,314 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# 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 CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-# coding: utf-8
-# pylint: disable=invalid-name
-"""Base library for TVM FFI."""
-from __future__ import absolute_import
-
-import sys
-import os
-import ctypes
-import numpy as np
-from . import libinfo
-
-#----------------------------
-# library loading
-#----------------------------
-if sys.version_info[0] == 3:
-    string_types = (str,)
-    integer_types = (int, np.int32)
-    numeric_types = integer_types + (float, np.float32)
-    # this function is needed for python3
-    # to convert ctypes.char_p .value back to python str
-    if sys.platform == "win32":
-        encoding = 'cp' + str(ctypes.cdll.kernel32.GetACP())
-        py_str = lambda x: x.decode(encoding)
-    else:
-        py_str = lambda x: x.decode('utf-8')
-else:
-    string_types = (basestring,)
-    integer_types = (int, long, np.int32)
-    numeric_types = integer_types + (float, np.float32)
-    py_str = lambda x: x
-
-
-def _load_lib():
-    """Load libary by searching possible path."""
-    lib_path = libinfo.find_lib_path()
-    lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_GLOBAL)
-    # DMatrix functions
-    lib.TVMGetLastError.restype = ctypes.c_char_p
-    return lib, os.path.basename(lib_path[0])
-
-# version number
-__version__ = libinfo.__version__
-# library instance of nnvm
-_LIB, _LIB_NAME = _load_lib()
-
-# Whether we are runtime only
-_RUNTIME_ONLY = "runtime" in _LIB_NAME
-
-# The FFI mode of TVM
-_FFI_MODE = os.environ.get("TVM_FFI", "auto")
-
-#----------------------------
-# helper function in ctypes.
-#----------------------------
-def c_str(string):
-    """Create ctypes char * from a python string
-    Parameters
-    ----------
-    string : string type
-        python string
-
-    Returns
-    -------
-    str : c_char_p
-        A char pointer that can be passed to C API
-    """
-    return ctypes.c_char_p(string.encode('utf-8'))
-
-
-def c_array(ctype, values):
-    """Create ctypes array from a python array
-
-    Parameters
-    ----------
-    ctype : ctypes data type
-        data type of the array we want to convert to
-
-    values : tuple or list
-        data content
-
-    Returns
-    -------
-    out : ctypes array
-        Created ctypes array
-    """
-    return (ctype * len(values))(*values)
-
-
-def decorate(func, fwrapped):
-    """A wrapper call of decorator package, differs to call time
-
-    Parameters
-    ----------
-    func : function
-        The original function
-
-    fwrapped : function
-        The wrapped function
-    """
-    import decorator
-    return decorator.decorate(func, fwrapped)
-
-
-#-----------------------------------------
-# Base code for structured error handling.
-#-----------------------------------------
-# Maps error type to its constructor
-ERROR_TYPE = {}
-
-
-class TVMError(RuntimeError):
-    """Default error thrown by TVM functions.
-
-    TVMError will be raised if you do not give any error type specification,
-    """
-
-
-def register_error(func_name=None, cls=None):
-    """Register an error class so it can be recognized by the ffi error handler.
-
-    Parameters
-    ----------
-    func_name : str or function or class
-        The name of the error function.
-
-    cls : function
-        The function to create the class
-
-    Returns
-    -------
-    fregister : function
-        Register function if f is not specified.
-
-    Examples
-    --------
-    .. code-block:: python
-
-      @tvm.error.register_error
-      class MyError(RuntimeError):
-          pass
-
-      err_inst = tvm.error.create_ffi_error("MyError: xyz")
-      assert isinstance(err_inst, MyError)
-    """
-    if callable(func_name):
-        cls = func_name
-        func_name = cls.__name__
-
-    def register(mycls):
-        """internal register function"""
-        err_name = func_name if isinstance(func_name, str) else mycls.__name__
-        ERROR_TYPE[err_name] = mycls
-        return mycls
-    if cls is None:
-        return register
-    return register(cls)
-
-
-def _valid_error_name(name):
-    """Check whether name is a valid error name."""
-    return all(x.isalnum() or x in "_." for x in name)
-
-
-def _find_error_type(line):
-    """Find the error name given the first line of the error message.
-
-    Parameters
-    ----------
-    line : str
-        The first line of error message.
-
-    Returns
-    -------
-    name : str The error name
-    """
-    end_pos = line.find(":")
-    if end_pos == -1:
-        return None
-    err_name = line[:end_pos]
-    if _valid_error_name(err_name):
-        return err_name
-    return None
-
-
-def c2pyerror(err_msg):
-    """Translate C API error message to python style.
-
-    Parameters
-    ----------
-    err_msg : str
-        The error message.
-
-    Returns
-    -------
-    new_msg : str
-        Translated message.
-
-    err_type : str
-        Detected error type.
-    """
-    arr = err_msg.split("\n")
-    if arr[-1] == "":
-        arr.pop()
-    err_type = _find_error_type(arr[0])
-    trace_mode = False
-    stack_trace = []
-    message = []
-    for line in arr:
-        if trace_mode:
-            if line.startswith("  "):
-                stack_trace.append(line)
-            else:
-                trace_mode = False
-        if not trace_mode:
-            if line.startswith("Stack trace"):
-                trace_mode = True
-            else:
-                message.append(line)
-    out_msg = ""
-    if stack_trace:
-        out_msg += "Traceback (most recent call last):\n"
-        out_msg += "\n".join(reversed(stack_trace)) + "\n"
-    out_msg += "\n".join(message)
-    return out_msg, err_type
-
-
-def py2cerror(err_msg):
-    """Translate python style error message to C style.
-
-    Parameters
-    ----------
-    err_msg : str
-        The error message.
-
-    Returns
-    -------
-    new_msg : str
-        Translated message.
-    """
-    arr = err_msg.split("\n")
-    if arr[-1] == "":
-        arr.pop()
-    trace_mode = False
-    stack_trace = []
-    message = []
-    for line in arr:
-        if trace_mode:
-            if line.startswith("  "):
-                stack_trace.append(line)
-            else:
-                trace_mode = False
-        if not trace_mode:
-            if line.find("Traceback") != -1:
-                trace_mode = True
-            else:
-                message.append(line)
-    # Remove the first error name if there are two of them.
-    # RuntimeError: MyErrorName: message => MyErrorName: message
-    head_arr = message[0].split(":", 3)
-    if len(head_arr) >= 3 and _valid_error_name(head_arr[1].strip()):
-        head_arr[1] = head_arr[1].strip()
-        message[0] = ":".join(head_arr[1:])
-    # reverse the stack trace.
-    out_msg = "\n".join(message)
-    if stack_trace:
-        out_msg += "\nStack trace:\n"
-        out_msg += "\n".join(reversed(stack_trace)) + "\n"
-    return out_msg
-
-
-def get_last_ffi_error():
-    """Create error object given result of TVMGetLastError.
-
-    Returns
-    -------
-    err : object
-        The error object based on the err_msg
-    """
-    c_err_msg = py_str(_LIB.TVMGetLastError())
-    py_err_msg, err_type = c2pyerror(c_err_msg)
-    if err_type is not None and err_type.startswith("tvm.error."):
-        err_type = err_type[10:]
-    return ERROR_TYPE.get(err_type, TVMError)(py_err_msg)
-
-
-def check_call(ret):
-    """Check the return value of C API call
-
-    This function will raise exception when error occurs.
-    Wrap every API call with this function
-
-    Parameters
-    ----------
-    ret : int
-        return value from API calls
-    """
-    if ret != 0:
-        raise get_last_ffi_error()
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/ops/grid_sample_gradfix.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/ops/grid_sample_gradfix.py
deleted file mode 100644
index c522ae9b6f36a89203ce62f3d4487514523b5b00..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/ops/grid_sample_gradfix.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# 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.
-
-"""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 warnings
-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():
-    if not enabled:
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(
-        f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().')
-    return False
-
-# ----------------------------------------------------------------------------
-
-
-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/haofeixu/unimatch/app.py b/spaces/haofeixu/unimatch/app.py
deleted file mode 100644
index 816aa3c9ebe926dce9f7d18c4ffe44509864acbd..0000000000000000000000000000000000000000
--- a/spaces/haofeixu/unimatch/app.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from PIL import Image
-import gradio as gr
-
-from unimatch.unimatch import UniMatch
-from utils.flow_viz import flow_to_image
-from dataloader.stereo import transforms
-from utils.visualization import vis_disparity
-
-IMAGENET_MEAN = [0.485, 0.456, 0.406]
-IMAGENET_STD = [0.229, 0.224, 0.225]
-
-
-@torch.no_grad()
-def inference(image1, image2, task='flow'):
-    """Inference on an image pair for optical flow or stereo disparity prediction"""
-
-    model = UniMatch(feature_channels=128,
-                     num_scales=2,
-                     upsample_factor=4,
-                     ffn_dim_expansion=4,
-                     num_transformer_layers=6,
-                     reg_refine=True,
-                     task=task)
-
-    model.eval()
-
-    if task == 'flow':
-        checkpoint_path = 'pretrained/gmflow-scale2-regrefine6-mixdata-train320x576-4e7b215d.pth'
-    else:
-        checkpoint_path = 'pretrained/gmstereo-scale2-regrefine3-resumeflowthings-mixdata-train320x640-ft640x960-e4e291fd.pth'
-
-    checkpoint_flow = torch.load(checkpoint_path)
-    model.load_state_dict(checkpoint_flow['model'], strict=True)
-
-    padding_factor = 32
-    attn_type = 'swin' if task == 'flow' else 'self_swin2d_cross_swin1d'
-    attn_splits_list = [2, 8]
-    corr_radius_list = [-1, 4]
-    prop_radius_list = [-1, 1]
-    num_reg_refine = 6 if task == 'flow' else 3
-
-    # smaller inference size for faster speed
-    max_inference_size = [384, 768] if task == 'flow' else [640, 960]
-
-    transpose_img = False
-
-    image1 = np.array(image1).astype(np.float32)
-    image2 = np.array(image2).astype(np.float32)
-
-    if len(image1.shape) == 2:  # gray image
-        image1 = np.tile(image1[..., None], (1, 1, 3))
-        image2 = np.tile(image2[..., None], (1, 1, 3))
-    else:
-        image1 = image1[..., :3]
-        image2 = image2[..., :3]
-
-    if task == 'flow':
-        image1 = torch.from_numpy(image1).permute(2, 0, 1).float().unsqueeze(0)
-        image2 = torch.from_numpy(image2).permute(2, 0, 1).float().unsqueeze(0)
-    else:
-        val_transform_list = [transforms.ToTensor(),
-                              transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
-                              ]
-
-        val_transform = transforms.Compose(val_transform_list)
-
-        sample = {'left': image1, 'right': image2}
-        sample = val_transform(sample)
-
-        image1 = sample['left'].unsqueeze(0)  # [1, 3, H, W]
-        image2 = sample['right'].unsqueeze(0)  # [1, 3, H, W]
-
-    # the model is trained with size: width > height
-    if task == 'flow' and image1.size(-2) > image1.size(-1):
-        image1 = torch.transpose(image1, -2, -1)
-        image2 = torch.transpose(image2, -2, -1)
-        transpose_img = True
-
-    nearest_size = [int(np.ceil(image1.size(-2) / padding_factor)) * padding_factor,
-                    int(np.ceil(image1.size(-1) / padding_factor)) * padding_factor]
-
-    inference_size = [min(max_inference_size[0], nearest_size[0]), min(max_inference_size[1], nearest_size[1])]
-
-    assert isinstance(inference_size, list) or isinstance(inference_size, tuple)
-    ori_size = image1.shape[-2:]
-
-    # resize before inference
-    if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]:
-        image1 = F.interpolate(image1, size=inference_size, mode='bilinear',
-                               align_corners=True)
-        image2 = F.interpolate(image2, size=inference_size, mode='bilinear',
-                               align_corners=True)
-
-    results_dict = model(image1, image2,
-                         attn_type=attn_type,
-                         attn_splits_list=attn_splits_list,
-                         corr_radius_list=corr_radius_list,
-                         prop_radius_list=prop_radius_list,
-                         num_reg_refine=num_reg_refine,
-                         task=task,
-                         )
-
-    flow_pr = results_dict['flow_preds'][-1]  # [1, 2, H, W] or [1, H, W]
-
-    # resize back
-    if task == 'flow':
-        if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]:
-            flow_pr = F.interpolate(flow_pr, size=ori_size, mode='bilinear',
-                                    align_corners=True)
-            flow_pr[:, 0] = flow_pr[:, 0] * ori_size[-1] / inference_size[-1]
-            flow_pr[:, 1] = flow_pr[:, 1] * ori_size[-2] / inference_size[-2]
-    else:
-        if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]:
-            pred_disp = F.interpolate(flow_pr.unsqueeze(1), size=ori_size,
-                                      mode='bilinear',
-                                      align_corners=True).squeeze(1)  # [1, H, W]
-            pred_disp = pred_disp * ori_size[-1] / float(inference_size[-1])
-
-    if task == 'flow':
-        if transpose_img:
-            flow_pr = torch.transpose(flow_pr, -2, -1)
-
-        flow = flow_pr[0].permute(1, 2, 0).cpu().numpy()  # [H, W, 2]
-
-        output = flow_to_image(flow)  # [H, W, 3]
-    else:
-        disp = pred_disp[0].cpu().numpy()
-
-        output = vis_disparity(disp, return_rgb=True)
-
-    return Image.fromarray(output)
-
-
-title = "UniMatch"
-
-description = "<p style='text-align: center'>Optical flow and stereo matching demo for <a href='https://haofeixu.github.io/unimatch/' target='_blank'>Unifying Flow, Stereo and Depth Estimation</a> | <a href='https://arxiv.org/abs/2211.05783' target='_blank'>Paper</a> | <a href='https://github.com/autonomousvision/unimatch' target='_blank'>Code</a> | <a href='https://colab.research.google.com/drive/1r5m-xVy3Kw60U-m5VB-aQ98oqqg_6cab?usp=sharing' target='_blank'>Colab</a><br>Task <strong>flow</strong>: Image1: <strong>video frame t</strong>, Image2: <strong>video frame t+1</strong>; Task <strong>stereo</strong>: Image1: <strong>left</strong> image, Image2: <strong>right</strong> image<br>Simply upload your images or click one of the provided examples.<br><strong>Select the task type according to your input images</strong>.</p>"
-
-examples = [
-    ['demo/stereo_drivingstereo_test_2018-07-11-14-48-52_2018-07-11-14-58-34-673_left.jpg',
-     'demo/stereo_drivingstereo_test_2018-07-11-14-48-52_2018-07-11-14-58-34-673_right.jpg', 'stereo'],
-    ['demo/stereo_middlebury_plants_im0.png', 'demo/stereo_middlebury_plants_im1.png', 'stereo'],
-    ['demo/stereo_holopix_left.png', 'demo/stereo_holopix_right.png', 'stereo'],
-    ['demo/flow_kitti_test_000197_10.png', 'demo/flow_kitti_test_000197_11.png', 'flow'],
-    ['demo/flow_sintel_cave_3_frame_0049.png', 'demo/flow_sintel_cave_3_frame_0050.png', 'flow'],
-    ['demo/flow_davis_skate-jump_00059.jpg', 'demo/flow_davis_skate-jump_00060.jpg', 'flow']
-]
-
-gr.Interface(
-    inference,
-    [gr.Image(type="pil", label="Image1"), gr.Image(type="pil", label="Image2"), gr.Radio(choices=['flow', 'stereo'], value='flow', label='Task')],
-    gr.Image(type="pil", label="flow/disparity"),
-    title=title,
-    description=description,
-    examples=examples,
-).launch(debug=True, quiet=True)
diff --git a/spaces/hbestm/gpt-academic-play/docs/waifu_plugin/autoload.js b/spaces/hbestm/gpt-academic-play/docs/waifu_plugin/autoload.js
deleted file mode 100644
index 3464a5cd44b0d4e1b0f2528bd01fc1793275b964..0000000000000000000000000000000000000000
--- a/spaces/hbestm/gpt-academic-play/docs/waifu_plugin/autoload.js
+++ /dev/null
@@ -1,30 +0,0 @@
-try {
-    $("<link>").attr({href: "file=docs/waifu_plugin/waifu.css", rel: "stylesheet", type: "text/css"}).appendTo('head');
-    $('body').append('<div class="waifu"><div class="waifu-tips"></div><canvas id="live2d" class="live2d"></canvas><div class="waifu-tool"><span class="fui-home"></span> <span class="fui-chat"></span> <span class="fui-eye"></span> <span class="fui-user"></span> <span class="fui-photo"></span> <span class="fui-info-circle"></span> <span class="fui-cross"></span></div></div>');
-    $.ajax({url: "file=docs/waifu_plugin/waifu-tips.js", dataType:"script", cache: true, success: function() {
-        $.ajax({url: "file=docs/waifu_plugin/live2d.js", dataType:"script", cache: true, success: function() {
-            /* 可直接修改部分参数 */
-            live2d_settings['hitokotoAPI'] = "hitokoto.cn";  // 一言 API
-            live2d_settings['modelId'] = 5;                  // 默认模型 ID
-            live2d_settings['modelTexturesId'] = 1;          // 默认材质 ID
-            live2d_settings['modelStorage'] = false;         // 不储存模型 ID
-            live2d_settings['waifuSize']            = '210x187';  
-            live2d_settings['waifuTipsSize']        = '187x52';  
-            live2d_settings['canSwitchModel']       = true;
-            live2d_settings['canSwitchTextures']    = true;
-            live2d_settings['canSwitchHitokoto']    = false;
-            live2d_settings['canTakeScreenshot']    = false;
-            live2d_settings['canTurnToHomePage']    = false;
-            live2d_settings['canTurnToAboutPage']   = false;
-            live2d_settings['showHitokoto']         = false;         // 显示一言
-            live2d_settings['showF12Status']        = false;         // 显示加载状态
-            live2d_settings['showF12Message']       = false;        // 显示看板娘消息
-            live2d_settings['showF12OpenMsg']       = false;         // 显示控制台打开提示
-            live2d_settings['showCopyMessage']      = false;         // 显示 复制内容 提示
-            live2d_settings['showWelcomeMessage']   = true;         // 显示进入面页欢迎词
-
-            /* 在 initModel 前添加 */
-            initModel("file=docs/waifu_plugin/waifu-tips.json");
-        }});
-    }});
-} catch(err) { console.log("[Error] JQuery is not defined.") }
diff --git a/spaces/heiyubili/bingo/src/components/turn-counter.tsx b/spaces/heiyubili/bingo/src/components/turn-counter.tsx
deleted file mode 100644
index 08a9e488f044802a8600f4d195b106567c35aab4..0000000000000000000000000000000000000000
--- a/spaces/heiyubili/bingo/src/components/turn-counter.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import React from 'react'
-import { Throttling } from '@/lib/bots/bing/types'
-
-export interface TurnCounterProps {
-  throttling?: Throttling
-}
-
-export function TurnCounter({ throttling }: TurnCounterProps) {
-  if (!throttling) {
-    return null
-  }
-
-  return (
-    <div className="turn-counter">
-      <div className="text">
-        <span>{throttling.numUserMessagesInConversation}</span>
-        <span> 共 </span>
-        <span>{throttling.maxNumUserMessagesInConversation}</span>
-      </div>
-      <div className="indicator"></div>
-    </div>
-  )
-}
diff --git a/spaces/hf4all/bingo-api/auto-commit.js b/spaces/hf4all/bingo-api/auto-commit.js
deleted file mode 100644
index 7e307c1b781e8148a524bb9785f35b9ae0c5b061..0000000000000000000000000000000000000000
--- a/spaces/hf4all/bingo-api/auto-commit.js
+++ /dev/null
@@ -1,36 +0,0 @@
-const fs = require('fs')
-const path = require('path')
-const execSync = require('child_process').execSync
-
-function exec(command, options) {
-  try {
-    const { stdout, stderr } = execSync(command, options)
-    if (stderr) {
-      throw new Error(stderr)
-    }
-    console.log(stdout)
-  } catch (e) {
-    console.log('Exec Error:', e)
-  }
-}
-
-const root = __dirname
-function loop() {
-  console.log(new Date(), 'auto commit start')
-  const dirs = fs.readdirSync(root)
-  for (let dir of dirs) {
-    const cwd = path.join(root, dir)
-    if (fs.existsSync(path.join(cwd, '.git/config'))) {
-      console.log('auto commit', cwd)
-      exec(`git add -A`, { cwd })
-      exec(`git commit -am "[WIP] auto commit"`, { cwd })
-      exec(`git push`, { cwd })
-      console.log('done')
-    }
-  }
-}
-// loop()
-const timeout = process.env.AUTO_COMMIT || 86400
-if (timeout > 600) {
-  setInterval(loop, 1000 * timeout)
-}
\ No newline at end of file
diff --git a/spaces/hhhhardman/VITS/text/symbols.py b/spaces/hhhhardman/VITS/text/symbols.py
deleted file mode 100644
index 053a7105f7ce95aa51614f6995399fa2172b3eb2..0000000000000000000000000000000000000000
--- a/spaces/hhhhardman/VITS/text/symbols.py
+++ /dev/null
@@ -1,76 +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`→↓↑ '
-'''
-
-'''# sanskrit_cleaners
-_pad        = '_'
-_punctuation = '।'
-_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
-'''
-
-'''# cjks_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
-'''
-
-'''# thai_cleaners
-_pad        = '_'
-_punctuation = '.!? '
-_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
-'''
-
-'''# cjke_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
-'''
-
-'''# shanghainese_cleaners
-_pad        = '_'
-_punctuation = ',.!?…'
-_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
-'''
-
-'''# chinese_dialect_cleaners
-_pad        = '_'
-_punctuation = ',.!?~…─'
-_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚ᴀᴇ↑↓∅ⱼ '
-'''
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
diff --git a/spaces/hkunlp/Binder/utils/mmqa/eval_mmqa.py b/spaces/hkunlp/Binder/utils/mmqa/eval_mmqa.py
deleted file mode 100644
index 2f99709db17ce0ec15edfa5269b2acc9a5f3cf3e..0000000000000000000000000000000000000000
--- a/spaces/hkunlp/Binder/utils/mmqa/eval_mmqa.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from utils.mmqa.evaluator import evaluate_predictions
-import json
-
-ANNOTATION_RESULT_PATH = "../results_mmqa/unfiltered_mmqa_nsqls_mmqa_v2_all_standard.json"
-EXECUTION_RESULT_PATH = "../results_mmqa/unfiltered_mmqa_nsqls_mmqa_v2_all_standard_new_qa_pool_bug_fixed_v1.jsonn"
-
-if __name__ == '__main__':
-    right = 0
-    all_num = 0
-    with open(ANNOTATION_RESULT_PATH, "r") as f:
-        all_data = json.load(f)
-    with open(EXECUTION_RESULT_PATH, "r") as f:
-        pred_data = json.load(f)
-
-    pred_dict = {eid: [str(a) for a in pred_data[eid]['pred_answer']] for eid in pred_data}
-    gold_dict = {eid: [str(a) for a in pred_data[eid]['gold_answer']] for eid in pred_data}
-    eval_scores, instance_eval_results = evaluate_predictions(pred_dict, gold_dict)
-    print(eval_scores)
-    print(instance_eval_results)
\ No newline at end of file
diff --git a/spaces/hlydecker/ImageBind_zeroshot_demo/models/imagebind_model.py b/spaces/hlydecker/ImageBind_zeroshot_demo/models/imagebind_model.py
deleted file mode 100644
index 395aabf4dbd6f7624ed7e20e74b13d34d0725cfb..0000000000000000000000000000000000000000
--- a/spaces/hlydecker/ImageBind_zeroshot_demo/models/imagebind_model.py
+++ /dev/null
@@ -1,517 +0,0 @@
-#!/usr/bin/env python3
-# Portions 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 os
-import urllib
-from functools import partial
-from types import SimpleNamespace
-
-import torch
-import torch.nn as nn
-
-from models.helpers import (
-    EinOpsRearrange,
-    LearnableLogitScaling,
-    Normalize,
-    SelectElement,
-    SelectEOSAndProject,
-)
-from models.multimodal_preprocessors import (
-    AudioPreprocessor,
-    IMUPreprocessor,
-    PadIm2Video,
-    PatchEmbedGeneric,
-    RGBDTPreprocessor,
-    SpatioTemporalPosEmbeddingHelper,
-    TextPreprocessor,
-    ThermalPreprocessor,
-)
-
-from models.transformer import MultiheadAttention, SimpleTransformer
-
-
-ModalityType = SimpleNamespace(
-    VISION="vision",
-    TEXT="text",
-    AUDIO="audio",
-    THERMAL="thermal",
-    DEPTH="depth",
-    IMU="imu",
-)
-
-
-class ImageBindModel(nn.Module):
-    def __init__(
-        self,
-        video_frames=2,
-        kernel_size=(2, 14, 14),
-        audio_kernel_size=16,
-        audio_stride=10,
-        out_embed_dim=768,
-        vision_embed_dim=1024,
-        vision_num_blocks=24,
-        vision_num_heads=16,
-        audio_embed_dim=768,
-        audio_num_blocks=12,
-        audio_num_heads=12,
-        audio_num_mel_bins=128,
-        audio_target_len=204,
-        audio_drop_path=0.1,
-        text_embed_dim=768,
-        text_num_blocks=12,
-        text_num_heads=12,
-        depth_embed_dim=384,
-        depth_kernel_size=16,
-        depth_num_blocks=12,
-        depth_num_heads=8,
-        depth_drop_path=0.0,
-        thermal_embed_dim=768,
-        thermal_kernel_size=16,
-        thermal_num_blocks=12,
-        thermal_num_heads=12,
-        thermal_drop_path=0.0,
-        imu_embed_dim=512,
-        imu_kernel_size=8,
-        imu_num_blocks=6,
-        imu_num_heads=8,
-        imu_drop_path=0.7,
-    ):
-        super().__init__()
-
-        self.modality_preprocessors = self._create_modality_preprocessors(
-            video_frames,
-            vision_embed_dim,
-            kernel_size,
-            text_embed_dim,
-            audio_embed_dim,
-            audio_kernel_size,
-            audio_stride,
-            audio_num_mel_bins,
-            audio_target_len,
-            depth_embed_dim,
-            depth_kernel_size,
-            thermal_embed_dim,
-            thermal_kernel_size,
-            imu_embed_dim,
-        )
-
-        self.modality_trunks = self._create_modality_trunks(
-            vision_embed_dim,
-            vision_num_blocks,
-            vision_num_heads,
-            text_embed_dim,
-            text_num_blocks,
-            text_num_heads,
-            audio_embed_dim,
-            audio_num_blocks,
-            audio_num_heads,
-            audio_drop_path,
-            depth_embed_dim,
-            depth_num_blocks,
-            depth_num_heads,
-            depth_drop_path,
-            thermal_embed_dim,
-            thermal_num_blocks,
-            thermal_num_heads,
-            thermal_drop_path,
-            imu_embed_dim,
-            imu_num_blocks,
-            imu_num_heads,
-            imu_drop_path,
-        )
-
-        self.modality_heads = self._create_modality_heads(
-            out_embed_dim,
-            vision_embed_dim,
-            text_embed_dim,
-            audio_embed_dim,
-            depth_embed_dim,
-            thermal_embed_dim,
-            imu_embed_dim,
-        )
-
-        self.modality_postprocessors = self._create_modality_postprocessors(
-            out_embed_dim
-        )
-
-    def _create_modality_preprocessors(
-        self,
-        video_frames=2,
-        vision_embed_dim=1024,
-        kernel_size=(2, 14, 14),
-        text_embed_dim=768,
-        audio_embed_dim=768,
-        audio_kernel_size=16,
-        audio_stride=10,
-        audio_num_mel_bins=128,
-        audio_target_len=204,
-        depth_embed_dim=768,
-        depth_kernel_size=16,
-        thermal_embed_dim=768,
-        thermal_kernel_size=16,
-        imu_embed_dim=512,
-    ):
-        rgbt_stem = PatchEmbedGeneric(
-            proj_stem=[
-                PadIm2Video(pad_type="repeat", ntimes=2),
-                nn.Conv3d(
-                    in_channels=3,
-                    kernel_size=kernel_size,
-                    out_channels=vision_embed_dim,
-                    stride=kernel_size,
-                    bias=False,
-                ),
-            ]
-        )
-        rgbt_preprocessor = RGBDTPreprocessor(
-            img_size=[3, video_frames, 224, 224],
-            num_cls_tokens=1,
-            pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
-            rgbt_stem=rgbt_stem,
-            depth_stem=None,
-        )
-
-        text_preprocessor = TextPreprocessor(
-            context_length=77,
-            vocab_size=49408,
-            embed_dim=text_embed_dim,
-            causal_masking=True,
-        )
-
-        audio_stem = PatchEmbedGeneric(
-            proj_stem=[
-                nn.Conv2d(
-                    in_channels=1,
-                    kernel_size=audio_kernel_size,
-                    stride=audio_stride,
-                    out_channels=audio_embed_dim,
-                    bias=False,
-                ),
-            ],
-            norm_layer=nn.LayerNorm(normalized_shape=audio_embed_dim),
-        )
-        audio_preprocessor = AudioPreprocessor(
-            img_size=[1, audio_num_mel_bins, audio_target_len],
-            num_cls_tokens=1,
-            pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
-            audio_stem=audio_stem,
-        )
-
-        depth_stem = PatchEmbedGeneric(
-            [
-                nn.Conv2d(
-                    kernel_size=depth_kernel_size,
-                    in_channels=1,
-                    out_channels=depth_embed_dim,
-                    stride=depth_kernel_size,
-                    bias=False,
-                ),
-            ],
-            norm_layer=nn.LayerNorm(normalized_shape=depth_embed_dim),
-        )
-
-        depth_preprocessor = RGBDTPreprocessor(
-            img_size=[1, 224, 224],
-            num_cls_tokens=1,
-            pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
-            rgbt_stem=None,
-            depth_stem=depth_stem,
-        )
-
-        thermal_stem = PatchEmbedGeneric(
-            [
-                nn.Conv2d(
-                    kernel_size=thermal_kernel_size,
-                    in_channels=1,
-                    out_channels=thermal_embed_dim,
-                    stride=thermal_kernel_size,
-                    bias=False,
-                ),
-            ],
-            norm_layer=nn.LayerNorm(normalized_shape=thermal_embed_dim),
-        )
-        thermal_preprocessor = ThermalPreprocessor(
-            img_size=[1, 224, 224],
-            num_cls_tokens=1,
-            pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
-            thermal_stem=thermal_stem,
-        )
-
-        imu_stem = PatchEmbedGeneric(
-            [
-                nn.Linear(
-                    in_features=48,
-                    out_features=imu_embed_dim,
-                    bias=False,
-                ),
-            ],
-            norm_layer=nn.LayerNorm(normalized_shape=imu_embed_dim),
-        )
-
-        imu_preprocessor = IMUPreprocessor(
-            img_size=[6, 2000],
-            num_cls_tokens=1,
-            kernel_size=8,
-            embed_dim=imu_embed_dim,
-            pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
-            imu_stem=imu_stem,
-        )
-
-        modality_preprocessors = {
-            ModalityType.VISION: rgbt_preprocessor,
-            ModalityType.TEXT: text_preprocessor,
-            ModalityType.AUDIO: audio_preprocessor,
-            ModalityType.DEPTH: depth_preprocessor,
-            ModalityType.THERMAL: thermal_preprocessor,
-            ModalityType.IMU: imu_preprocessor,
-        }
-
-        return nn.ModuleDict(modality_preprocessors)
-
-    def _create_modality_trunks(
-        self,
-        vision_embed_dim=1024,
-        vision_num_blocks=24,
-        vision_num_heads=16,
-        text_embed_dim=768,
-        text_num_blocks=12,
-        text_num_heads=12,
-        audio_embed_dim=768,
-        audio_num_blocks=12,
-        audio_num_heads=12,
-        audio_drop_path=0.0,
-        depth_embed_dim=768,
-        depth_num_blocks=12,
-        depth_num_heads=12,
-        depth_drop_path=0.0,
-        thermal_embed_dim=768,
-        thermal_num_blocks=12,
-        thermal_num_heads=12,
-        thermal_drop_path=0.0,
-        imu_embed_dim=512,
-        imu_num_blocks=6,
-        imu_num_heads=8,
-        imu_drop_path=0.7,
-    ):
-        def instantiate_trunk(
-            embed_dim, num_blocks, num_heads, pre_transformer_ln, add_bias_kv, drop_path
-        ):
-            return SimpleTransformer(
-                embed_dim=embed_dim,
-                num_blocks=num_blocks,
-                ffn_dropout_rate=0.0,
-                drop_path_rate=drop_path,
-                attn_target=partial(
-                    MultiheadAttention,
-                    embed_dim=embed_dim,
-                    num_heads=num_heads,
-                    bias=True,
-                    add_bias_kv=add_bias_kv,
-                ),
-                pre_transformer_layer=nn.Sequential(
-                    nn.LayerNorm(embed_dim, eps=1e-6)
-                    if pre_transformer_ln
-                    else nn.Identity(),
-                    EinOpsRearrange("b l d -> l b d"),
-                ),
-                post_transformer_layer=EinOpsRearrange("l b d -> b l d"),
-            )
-
-        modality_trunks = {}
-        modality_trunks[ModalityType.VISION] = instantiate_trunk(
-            vision_embed_dim,
-            vision_num_blocks,
-            vision_num_heads,
-            pre_transformer_ln=True,
-            add_bias_kv=False,
-            drop_path=0.0,
-        )
-        modality_trunks[ModalityType.TEXT] = instantiate_trunk(
-            text_embed_dim,
-            text_num_blocks,
-            text_num_heads,
-            pre_transformer_ln=False,
-            add_bias_kv=False,
-            drop_path=0.0,
-        )
-        modality_trunks[ModalityType.AUDIO] = instantiate_trunk(
-            audio_embed_dim,
-            audio_num_blocks,
-            audio_num_heads,
-            pre_transformer_ln=False,
-            add_bias_kv=True,
-            drop_path=audio_drop_path,
-        )
-        modality_trunks[ModalityType.DEPTH] = instantiate_trunk(
-            depth_embed_dim,
-            depth_num_blocks,
-            depth_num_heads,
-            pre_transformer_ln=False,
-            add_bias_kv=True,
-            drop_path=depth_drop_path,
-        )
-        modality_trunks[ModalityType.THERMAL] = instantiate_trunk(
-            thermal_embed_dim,
-            thermal_num_blocks,
-            thermal_num_heads,
-            pre_transformer_ln=False,
-            add_bias_kv=True,
-            drop_path=thermal_drop_path,
-        )
-        modality_trunks[ModalityType.IMU] = instantiate_trunk(
-            imu_embed_dim,
-            imu_num_blocks,
-            imu_num_heads,
-            pre_transformer_ln=False,
-            add_bias_kv=True,
-            drop_path=imu_drop_path,
-        )
-
-        return nn.ModuleDict(modality_trunks)
-
-    def _create_modality_heads(
-        self,
-        out_embed_dim,
-        vision_embed_dim,
-        text_embed_dim,
-        audio_embed_dim,
-        depth_embed_dim,
-        thermal_embed_dim,
-        imu_embed_dim,
-    ):
-        modality_heads = {}
-
-        modality_heads[ModalityType.VISION] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=vision_embed_dim, eps=1e-6),
-            SelectElement(index=0),
-            nn.Linear(vision_embed_dim, out_embed_dim, bias=False),
-        )
-
-        modality_heads[ModalityType.TEXT] = SelectEOSAndProject(
-            proj=nn.Sequential(
-                nn.LayerNorm(normalized_shape=text_embed_dim, eps=1e-6),
-                nn.Linear(text_embed_dim, out_embed_dim, bias=False),
-            )
-        )
-
-        modality_heads[ModalityType.AUDIO] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=audio_embed_dim, eps=1e-6),
-            SelectElement(index=0),
-            nn.Linear(audio_embed_dim, out_embed_dim, bias=False),
-        )
-
-        modality_heads[ModalityType.DEPTH] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=depth_embed_dim, eps=1e-6),
-            SelectElement(index=0),
-            nn.Linear(depth_embed_dim, out_embed_dim, bias=False),
-        )
-
-        modality_heads[ModalityType.THERMAL] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=thermal_embed_dim, eps=1e-6),
-            SelectElement(index=0),
-            nn.Linear(thermal_embed_dim, out_embed_dim, bias=False),
-        )
-
-        modality_heads[ModalityType.IMU] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=imu_embed_dim, eps=1e-6),
-            SelectElement(index=0),
-            nn.Dropout(p=0.5),
-            nn.Linear(imu_embed_dim, out_embed_dim, bias=False),
-        )
-
-        return nn.ModuleDict(modality_heads)
-
-    def _create_modality_postprocessors(self, out_embed_dim):
-        modality_postprocessors = {}
-
-        modality_postprocessors[ModalityType.VISION] = Normalize(dim=-1)
-        modality_postprocessors[ModalityType.TEXT] = nn.Sequential(
-            Normalize(dim=-1), LearnableLogitScaling(learnable=True)
-        )
-        modality_postprocessors[ModalityType.AUDIO] = nn.Sequential(
-            Normalize(dim=-1),
-            LearnableLogitScaling(logit_scale_init=20.0, learnable=False),
-        )
-        modality_postprocessors[ModalityType.DEPTH] = nn.Sequential(
-            Normalize(dim=-1),
-            LearnableLogitScaling(logit_scale_init=5.0, learnable=False),
-        )
-        modality_postprocessors[ModalityType.THERMAL] = nn.Sequential(
-            Normalize(dim=-1),
-            LearnableLogitScaling(logit_scale_init=10.0, learnable=False),
-        )
-        modality_postprocessors[ModalityType.IMU] = nn.Sequential(
-            Normalize(dim=-1),
-            LearnableLogitScaling(logit_scale_init=5.0, learnable=False),
-        )
-
-        return nn.ModuleDict(modality_postprocessors)
-
-    def forward(self, inputs):
-        outputs = {}
-        for modality_key, modality_value in inputs.items():
-            reduce_list = (
-                modality_value.ndim >= 5
-            )  # Audio and Video inputs consist of multiple clips
-            if reduce_list:
-                B, S = modality_value.shape[:2]
-                modality_value = modality_value.reshape(
-                    B * S, *modality_value.shape[2:]
-                )
-
-            if modality_value is not None:
-                modality_value = self.modality_preprocessors[modality_key](
-                    **{modality_key: modality_value}
-                )
-                trunk_inputs = modality_value["trunk"]
-                head_inputs = modality_value["head"]
-                modality_value = self.modality_trunks[modality_key](**trunk_inputs)
-                modality_value = self.modality_heads[modality_key](
-                    modality_value, **head_inputs
-                )
-                modality_value = self.modality_postprocessors[modality_key](
-                    modality_value
-                )
-
-                if reduce_list:
-                    modality_value = modality_value.reshape(B, S, -1)
-                    modality_value = modality_value.mean(dim=1)
-
-                outputs[modality_key] = modality_value
-
-        return outputs
-
-
-def imagebind_huge(pretrained=False):
-    model = ImageBindModel(
-        vision_embed_dim=1280,
-        vision_num_blocks=32,
-        vision_num_heads=16,
-        text_embed_dim=1024,
-        text_num_blocks=24,
-        text_num_heads=16,
-        out_embed_dim=1024,
-        audio_drop_path=0.1,
-        imu_drop_path=0.7,
-    )
-
-    if pretrained:
-        if not os.path.exists(".checkpoints/imagebind_huge.pth"):
-            print(
-                "Downloading imagebind weights to .checkpoints/imagebind_huge.pth ..."
-            )
-            os.makedirs(".checkpoints", exist_ok=True)
-            torch.hub.download_url_to_file(
-                "https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth",
-                ".checkpoints/imagebind_huge.pth",
-                progress=True,
-            )
-
-        model.load_state_dict(torch.load(".checkpoints/imagebind_huge.pth"))
-
-    return model
diff --git a/spaces/hoshilumine/combined-GI-RVC-models/lib/infer_pack/commons.py b/spaces/hoshilumine/combined-GI-RVC-models/lib/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/hoshilumine/combined-GI-RVC-models/lib/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-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 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(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * 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 slice_segments2(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 ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/hra/Curriculum-BabyAGI/README.md b/spaces/hra/Curriculum-BabyAGI/README.md
deleted file mode 100644
index 562b84d6f62595e5032783ccd12b441adb3eb864..0000000000000000000000000000000000000000
--- a/spaces/hra/Curriculum-BabyAGI/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Curriculum BabyAGI
-emoji: 🏃
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: cc-by-nc-nd-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/huggingface/data-measurements-tool/data_measurements/streamlit_utils.py b/spaces/huggingface/data-measurements-tool/data_measurements/streamlit_utils.py
deleted file mode 100644
index fbe549e959e4eb97afb42e681bb5a52532f4729a..0000000000000000000000000000000000000000
--- a/spaces/huggingface/data-measurements-tool/data_measurements/streamlit_utils.py
+++ /dev/null
@@ -1,498 +0,0 @@
-# Copyright 2021 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 statistics
-
-import json
-import pandas as pd
-import seaborn as sns
-import streamlit as st
-#from st_aggrid import AgGrid, GridOptionsBuilder
-
-from .dataset_utils import HF_DESC_FIELD, HF_FEATURE_FIELD, HF_LABEL_FIELD
-st.set_option('deprecation.showPyplotGlobalUse', False)
-json_file_path = "cache_dir/has_cache.json"
-with open(json_file_path, "r", encoding="utf-8") as j:
-    _HAS_CACHE = json.loads(j.read())
-
-def sidebar_header():
-    st.sidebar.markdown(
-        """
-    This demo showcases the [dataset measures as we develop them](https://huggingface.co/blog/data-measurements-tool).
-    Right now this has a few pre-loaded datasets for which you can:
-    - view some general statistics about the text vocabulary, lengths, labels
-    - explore some distributional statistics to assess properties of the language
-    - view some comparison statistics and overview of the text distribution
-
-    The tool is in development, and will keep growing in utility and functionality 🤗🚧
-    """,
-        unsafe_allow_html=True,
-    )
-
-
-def sidebar_selection(ds_name_to_dict, column_id):
-    # ds_names = list(ds_name_to_dict.keys())
-    ds_names = list(_HAS_CACHE.keys())
-    with st.sidebar.expander(f"Choose dataset and field {column_id}", expanded=True):
-        # choose a dataset to analyze
-        ds_name = st.selectbox(
-            f"Choose dataset to explore{column_id}:",
-            ds_names,
-            index=ds_names.index("hate_speech18"),
-        )
-        # choose a config to analyze
-        ds_configs = ds_name_to_dict[ds_name]
-        if ds_name == "c4":
-            config_names = ['en','en.noblocklist','realnewslike']
-        else:
-            config_names = list(ds_configs.keys())
-        config_names = list(_HAS_CACHE[ds_name].keys())
-        config_name = st.selectbox(
-            f"Choose configuration{column_id}:",
-            config_names,
-            index=0,
-        )
-        # choose a subset of num_examples
-        # TODO: Handling for multiple text features
-        #ds_config = ds_configs[config_name]
-        # text_features = ds_config[HF_FEATURE_FIELD]["string"]
-        text_features = [tuple(text_field.split('-')) for text_field in _HAS_CACHE[ds_name][config_name]]
-        # TODO @yacine: Explain what this is doing and why eg tp[0] could = "id"
-        text_field = st.selectbox(
-            f"Which text feature from the{column_id} dataset would you like to analyze?",
-            [("text",)]
-            if ds_name == "c4"
-            else [tp for tp in text_features if tp[0] != "id"],
-        )
-        # Choose a split and dataset size
-        # avail_splits = list(ds_config["splits"].keys())
-        avail_splits = list(_HAS_CACHE[ds_name][config_name]['-'.join(text_field)].keys())
-        # 12.Nov note: Removing "test" because those should not be examined
-        # without discussion of pros and cons, which we haven't done yet.
-        if "test" in avail_splits:
-            avail_splits.remove("test")
-        split = st.selectbox(
-            f"Which split from the{column_id} dataset would you like to analyze?",
-            avail_splits,
-            index=0,
-        )
-        label_field, label_names = (
-            ds_name_to_dict[ds_name][config_name][HF_FEATURE_FIELD][HF_LABEL_FIELD][0]
-            if len(
-                ds_name_to_dict[ds_name][config_name][HF_FEATURE_FIELD][HF_LABEL_FIELD]
-            )
-            > 0
-            else ((), [])
-        )
-        return {
-            "dset_name": ds_name,
-            "dset_config": config_name,
-            "split_name": split,
-            "text_field": text_field,
-            "label_field": label_field,
-            "label_names": label_names,
-        }
-
-
-def expander_header(dstats, ds_name_to_dict, column_id):
-    with st.expander(f"Dataset Description{column_id}"):
-        st.markdown(
-            ds_name_to_dict[dstats.dset_name][dstats.dset_config][HF_DESC_FIELD]
-        )
-        st.dataframe(dstats.dset_peek)
-
-
-def expander_general_stats(dstats, column_id):
-    with st.expander(f"General Text Statistics{column_id}"):
-        st.caption(
-            "Use this widget to check whether the terms you see most represented"
-            " in the dataset make sense for the goals of the dataset."
-        )
-        if dstats.total_words == 0:
-            st.markdown("Eh oh...not finding the file I need. 😭  Probably it will be there soon. 🤞 Check back later!")
-        else:
-            st.markdown("There are {0} total words".format(str(dstats.total_words)))
-            st.markdown(
-                "There are {0} words after removing closed "
-                "class words".format(str(dstats.total_open_words))
-            )
-            st.markdown(
-                "The most common "
-                "[open class words](https://dictionary.apa.org/open-class-words) "
-                "and their counts are: "
-            )
-            st.dataframe(dstats.sorted_top_vocab_df)
-            st.markdown(
-                "There are {0} missing values in the dataset.".format(
-                    str(dstats.text_nan_count)
-                )
-            )
-            if dstats.dedup_total > 0:
-                st.markdown(
-                    "There are {0} duplicate items in the dataset. "
-                    "For more information about the duplicates, "
-                    "click the 'Duplicates' tab below.".format(str(dstats.dedup_total))
-                )
-            else:
-                st.markdown("There are 0 duplicate items in the dataset. ")
-
-
-### Show the label distribution from the datasets
-def expander_label_distribution(fig_labels, column_id):
-    with st.expander(f"Label Distribution{column_id}", expanded=False):
-        st.caption(
-            "Use this widget to see how balanced the labels in your dataset are."
-        )
-        if fig_labels is not None:
-            st.plotly_chart(fig_labels, use_container_width=True)
-        else:
-            st.markdown("No labels were found in the dataset")
-
-
-def expander_text_lengths(dstats, column_id):
-    _TEXT_LENGTH_CAPTION = (
-        "Use this widget to identify outliers, particularly suspiciously long outliers."
-    )
-    with st.expander(f"Text Lengths{column_id}", expanded=False):
-        st.caption(_TEXT_LENGTH_CAPTION)
-        st.markdown(
-            "Below, you can see how the lengths of the text instances in your dataset are distributed."
-        )
-        st.markdown(
-            "Any unexpected peaks or valleys in the distribution may help to identify instances you want to remove or augment."
-        )
-        st.markdown(
-            "### Here is the relative frequency of different text lengths in your dataset:"
-        )
-        try:
-            st.image(dstats.fig_tok_length_png)
-        except:
-            st.pyplot(dstats.fig_tok_length, use_container_width=True)
-        st.markdown(
-            "The average length of text instances is **"
-            + str(dstats.avg_length)
-            + " words**, with a standard deviation of **"
-            + str(dstats.std_length)
-            + "**."
-        )
-        # This is quite a large file and is breaking our ability to navigate the app development.
-        # Just passing if it's not already there for launch v0
-        if dstats.length_df is not None:
-            start_id_show_lengths = st.selectbox(
-                "Show examples of length:",
-                sorted(dstats.length_df["length"].unique().tolist()),
-                key=f"select_show_length_{column_id}",
-            )
-            st.table(
-                dstats.length_df[
-                    dstats.length_df["length"] == start_id_show_lengths
-                ].set_index("length")
-            )
-
-
-### Third, use a sentence embedding model
-def expander_text_embeddings(
-    text_dset, fig_tree, node_list, embeddings, text_field, column_id
-):
-    with st.expander(f"Text Embedding Clusters{column_id}", expanded=False):
-        _EMBEDDINGS_CAPTION = """
-        ### Hierarchical Clustering of Text Fields
-        Taking in the diversity of text represented in a dataset can be
-        challenging when it is made up of hundreds to thousands of sentences.
-        Grouping these text items based on a measure of similarity can help
-        users gain some insights into their distribution.
-        The following figure shows a hierarchical clustering of the text fields
-        in the dataset based on a
-        [Sentence-Transformer](https://hf.co/sentence-transformers/all-mpnet-base-v2)
-        model. Clusters are merged if any of the embeddings in cluster A has a
-        dot product with any of the embeddings or with the centroid of cluster B
-        higher than a threshold (one threshold per level, from 0.5 to 0.95).
-        To explore the clusters, you can:
-        - hover over a node to see the 5 most representative examples (deduplicated)
-        - enter an example in the text box below to see which clusters it is most similar to
-        - select a cluster by ID to show all of its examples
-        """
-        st.markdown(_EMBEDDINGS_CAPTION)
-        st.plotly_chart(fig_tree, use_container_width=True)
-        st.markdown("---\n")
-        if st.checkbox(
-            label="Enter text to see nearest clusters",
-            key=f"search_clusters_{column_id}",
-        ):
-            compare_example = st.text_area(
-                label="Enter some text here to see which of the clusters in the dataset it is closest to",
-                key=f"search_cluster_input_{column_id}",
-            )
-            if compare_example != "":
-                paths_to_leaves = embeddings.cached_clusters.get(
-                    compare_example,
-                    embeddings.find_cluster_beam(compare_example, beam_size=50),
-                )
-                clusters_intro = ""
-                if paths_to_leaves[0][1] < 0.3:
-                    clusters_intro += (
-                        "**Warning: no close clusters found (best score <0.3). **"
-                    )
-                clusters_intro += "The closest clusters to the text entered aboce are:"
-                st.markdown(clusters_intro)
-                for path, score in paths_to_leaves[:5]:
-                    example = text_dset[
-                        node_list[path[-1]]["sorted_examples_centroid"][0][0]
-                    ][text_field][:256]
-                    st.write(
-                        f"Cluster {path[-1]:5d} | Score: {score:.3f}  \n Example: {example}"
-                    )
-                show_node_default = paths_to_leaves[0][0][-1]
-            else:
-                show_node_default = len(node_list) // 2
-        else:
-            show_node_default = len(node_list) // 2
-        st.markdown("---\n")
-        if text_dset is None:
-            st.markdown("Missing source text to show, check back later!")
-        else:
-            show_node = st.selectbox(
-                f"Choose a leaf node to explore in the{column_id} dataset:",
-                range(len(node_list)),
-                index=show_node_default,
-            )
-            node = node_list[show_node]
-            start_id = st.slider(
-                f"Show closest sentences in cluster to the centroid{column_id} starting at index:",
-                0,
-                len(node["sorted_examples_centroid"]) - 5,
-                value=0,
-                step=5,
-            )
-            for sid, sim in node["sorted_examples_centroid"][start_id : start_id + 5]:
-                # only show the first 4 lines and the first 10000 characters
-                show_text = text_dset[sid][text_field][:10000]
-                show_text = "\n".join(show_text.split("\n")[:4])
-                st.text(f"{sim:.3f} \t {show_text}")
-
-
-### Then, show duplicates
-def expander_text_duplicates(dstats, column_id):
-    # TODO: Saving/loading figure
-    with st.expander(f"Text Duplicates{column_id}", expanded=False):
-        st.caption(
-            "Use this widget to identify text strings that appear more than once."
-        )
-        st.markdown(
-            "A model's training and testing may be negatively affected by unwarranted duplicates ([Lee et al., 2021](https://arxiv.org/abs/2107.06499))."
-        )
-        st.markdown("------")
-        st.write(
-            "### Here is the list of all the duplicated items and their counts in your dataset:"
-        )
-        if dstats.dup_counts_df is None or dstats.dup_counts_df.empty:
-            st.write("There are no duplicates in this dataset! 🥳")
-        else:
-            st.dataframe(dstats.dup_counts_df.reset_index(drop=True))
-
-
-def expander_npmi_description(min_vocab):
-    _NPMI_CAPTION = (
-        "Use this widget to identify problematic biases and stereotypes in your data."
-    )
-    _NPMI_CAPTION1 = """
-    nPMI scores for a word help to identify potentially
-    problematic associations, ranked by how close the association is."""
-    _NPMI_CAPTION2 = """
-    nPMI bias scores for paired words help to identify how word
-    associations are skewed between the selected selected words
-    ([Aka et al., 2021](https://arxiv.org/abs/2103.03417)).
-    """
-
-    st.caption(_NPMI_CAPTION)
-    st.markdown(_NPMI_CAPTION1)
-    st.markdown(_NPMI_CAPTION2)
-    st.markdown("  ")
-    st.markdown(
-        "You can select from gender and sexual orientation "
-        "identity terms that appear in the dataset at least %s "
-        "times." % min_vocab
-    )
-    st.markdown(
-        "The resulting ranked words are those that co-occur with both "
-        "identity terms.  "
-    )
-    st.markdown(
-        "The more *positive* the score, the more associated the word is with the first identity term.  "
-        "The more *negative* the score, the more associated the word is with the second identity term."
-    )
-
-
-### Finally, show Zipf stuff
-def expander_zipf(z, zipf_fig, column_id):
-    with st.expander(
-        f"Vocabulary Distribution{column_id}: Zipf's Law Fit", expanded=False
-    ):
-        try:
-            _ZIPF_CAPTION = """This shows how close the observed language is to an ideal
-            natural language distribution following [Zipf's law](https://en.wikipedia.org/wiki/Zipf%27s_law),
-            calculated by minimizing the [Kolmogorov-Smirnov (KS) statistic](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test)."""
-
-            powerlaw_eq = r"""p(x) \propto x^{- \alpha}"""
-            zipf_summary = (
-                    "The optimal alpha based on this dataset is: **"
-                    + str(round(z.alpha, 2))
-                    + "**, with a KS distance of: **"
-                    + str(round(z.distance, 2))
-            )
-            zipf_summary += (
-                    "**.  This was fit with a minimum rank value of: **"
-                    + str(int(z.xmin))
-                    + "**, which is the optimal rank *beyond which* the scaling regime of the power law fits best."
-            )
-
-            alpha_warning = "Your alpha value is a bit on the high side, which means that the distribution over words in this dataset is a bit unnatural. This could be due to non-language items throughout the dataset."
-            xmin_warning = "The minimum rank for this fit is a bit on the high side, which means that the frequencies of your most common words aren't distributed as would be expected by Zipf's law."
-            fit_results_table = pd.DataFrame.from_dict(
-                {
-                    r"Alpha:": [str("%.2f" % z.alpha)],
-                    "KS distance:": [str("%.2f" % z.distance)],
-                    "Min rank:": [str("%s" % int(z.xmin))],
-                },
-                columns=["Results"],
-                orient="index",
-            )
-            fit_results_table.index.name = column_id
-            st.caption(
-                "Use this widget for the counts of different words in your dataset, measuring the difference between the observed count and the expected count under Zipf's law."
-            )
-            st.markdown(_ZIPF_CAPTION)
-            st.write(
-                """
-            A Zipfian distribution follows the power law: $p(x) \propto x^{-α}$
-    with an ideal α value of 1."""
-            )
-            st.markdown(
-                "In general, an alpha greater than 2 or a minimum rank greater than 10 (take with a grain of salt) means that your distribution is relativaly _unnatural_ for natural language. This can be a sign of mixed artefacts in the dataset, such as HTML markup."
-            )
-            st.markdown(
-                "Below, you can see the counts of each word in your dataset vs. the expected number of counts following a Zipfian distribution."
-            )
-            st.markdown("-----")
-            st.write("### Here is your dataset's Zipf results:")
-            st.dataframe(fit_results_table)
-            st.write(zipf_summary)
-            # TODO: Nice UI version of the content in the comments.
-            # st.markdown("\nThe KS test p-value is < %.2f" % z.ks_test.pvalue)
-            # if z.ks_test.pvalue < 0.01:
-            #    st.markdown(
-            #        "\n Great news! Your data fits a powerlaw with a minimum KS " "distance of %.4f" % z.distance)
-            # else:
-            #    st.markdown("\n Sadly, your data does not fit a powerlaw. =(")
-            # st.markdown("Checking the goodness of fit of our observed distribution")
-            # st.markdown("to the hypothesized power law distribution")
-            # st.markdown("using a Kolmogorov–Smirnov (KS) test.")
-            st.plotly_chart(zipf_fig, use_container_width=True)
-            if z.alpha > 2:
-                st.markdown(alpha_warning)
-            if z.xmin > 5:
-                st.markdown(xmin_warning)
-        except:
-            st.write("Under construction! 😱 🚧")
-
-
-### Finally finally finally, show nPMI stuff.
-def npmi_widget(npmi_stats, min_vocab, column_id):
-    """
-    Part of the main app, but uses a user interaction so pulled out as its own f'n.
-    :param use_cache:
-    :param column_id:
-    :param npmi_stats:
-    :param min_vocab:
-    :return:
-    """
-    with st.expander(f"Word Association{column_id}: nPMI", expanded=False):
-        try:
-            if len(npmi_stats.available_terms) > 0:
-                expander_npmi_description(min_vocab)
-                st.markdown("-----")
-                term1 = st.selectbox(
-                    f"What is the first term you want to select?{column_id}",
-                    npmi_stats.available_terms,
-                )
-                term2 = st.selectbox(
-                    f"What is the second term you want to select?{column_id}",
-                    reversed(npmi_stats.available_terms),
-                )
-                # We calculate/grab nPMI data based on a canonical (alphabetic)
-                # subgroup ordering.
-                subgroup_pair = sorted([term1, term2])
-                try:
-                    joint_npmi_df = npmi_stats.load_or_prepare_joint_npmi(subgroup_pair)
-                    npmi_show(joint_npmi_df)
-                except KeyError:
-                    st.markdown(
-                        "**WARNING!** The nPMI for these terms has not been pre-computed, please re-run caching."
-                    )
-            else:
-                st.markdown(
-                    "No words found co-occurring with both of the selected identity terms."
-                )
-        except:
-            st.write("Under construction! 😱 🚧")
-
-
-def npmi_show(paired_results):
-    if paired_results.empty:
-        st.markdown("No words that co-occur enough times for results!  Or there's a 🐛.  Or we're still computing this one. 🤷")
-    else:
-        s = pd.DataFrame(paired_results.sort_values(by="npmi-bias", ascending=True))
-        # s.columns=pd.MultiIndex.from_arrays([['npmi','npmi','npmi','count', 'count'],['bias','man','straight','man','straight']])
-        s.index.name = "word"
-        npmi_cols = s.filter(like="npmi").columns
-        count_cols = s.filter(like="count").columns
-        if s.shape[0] > 10000:
-            bias_thres = max(abs(s["npmi-bias"][5000]), abs(s["npmi-bias"][-5000]))
-            print(f"filtering with bias threshold: {bias_thres}")
-            s_filtered = s[s["npmi-bias"].abs() > bias_thres]
-        else:
-            s_filtered = s
-        # TODO: This is very different look than the duplicates table above. Should probably standardize.
-        cm = sns.palplot(sns.diverging_palette(270, 36, s=99, l=48, n=16))
-        out_df = (
-            s_filtered.style.background_gradient(subset=npmi_cols, cmap=cm)
-            .format(subset=npmi_cols, formatter="{:,.3f}")
-            .format(subset=count_cols, formatter=int)
-            .set_properties(
-                subset=count_cols, **{"width": "10em", "text-align": "center"}
-            )
-            .set_properties(**{"align": "center"})
-            .set_caption(
-                "nPMI scores and co-occurence counts between the selected identity terms and the words they both co-occur with"
-            )
-        )  # s = pd.read_excel("output.xlsx", index_col="word")
-        st.write("### Here is your dataset's nPMI results:")
-        st.dataframe(out_df)
-
-
-### Dumping unused functions here for now
-### Second, show the distribution of text perplexities
-def expander_text_perplexities(text_label_df, sorted_sents_loss, fig_loss):
-    with st.expander("Show text perplexities A", expanded=False):
-        st.markdown("### Text perplexities A")
-        st.plotly_chart(fig_loss, use_container_width=True)
-        start_id_show_loss = st.slider(
-            "Show highest perplexity sentences in A starting at index:",
-            0,
-            text_label_df.shape[0] - 5,
-            value=0,
-            step=5,
-        )
-        for lss, sent in sorted_sents_loss[start_id_show_loss : start_id_show_loss + 5]:
-            st.text(f"{lss:.3f} {sent}")
diff --git a/spaces/huggingface/transformers-chat/app.py b/spaces/huggingface/transformers-chat/app.py
deleted file mode 100644
index b1ac822b5e672ccbc5770aa49dda2d23874af551..0000000000000000000000000000000000000000
--- a/spaces/huggingface/transformers-chat/app.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import datetime
-import os
-
-import gradio as gr
-import langchain
-import pickle
-from langchain.llms import HuggingFaceHub
-
-from chain import get_new_chain1
-
-api_token = os.environ["HF_TOKEN"]
-
-def get_faiss_store():
-    with open("docs.pkl", 'rb') as f:
-        faiss_store = pickle.load(f)
-        return faiss_store
-
-
-def load_model():
-
-    print(langchain.__file__)
-
-    vectorstore = get_faiss_store()
-
-    flan_ul = HuggingFaceHub(repo_id="google/flan-ul2", 
-                                model_kwargs={"temperature":0.1, "max_new_tokens":200},
-                                huggingfacehub_api_token=api_token)
-
-    qa_chain = get_new_chain1(vectorstore, flan_ul, flan_ul, isFlan=True)
-    return qa_chain
-
-
-def chat(inp, history, agent):
-    history = history or []
-    if agent is None:
-        history.append((inp, "Please wait for model to load (3-5 seconds)"))
-        return history, history
-    print("\n==== date/time: " + str(datetime.datetime.now()) + " ====")
-    print("inp: " + inp)
-    history = history or []
-    output = agent({"question": inp, "chat_history": history})
-    answer = output["answer"]
-    history.append((inp, answer))
-    print(history)
-    return history, history
-
-
-block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
-
-with block:
-    with gr.Row():
-        gr.Markdown("<h3><center>🤗 Transformers Chat</center></h3><p>Ask questions about the Hugging Face Transformers Library, Powered by Flan-UL2</p>")
-
-    chatbot = gr.Chatbot()
-
-    with gr.Row():
-        message = gr.Textbox(
-            label="What's your question?",
-            placeholder="What's the answer to life, the universe, and everything?",
-            lines=1,
-        )
-        submit = gr.Button(value="Send", variant="secondary").style(full_width=False)
-
-    gr.Examples(
-        examples=[
-            "How do I install transformers?",
-            "How do I load pretrained instances with an AutoClass?",
-            "How do I fine-tune a pretrained model?",
-        ],
-        inputs=message,
-    )
-
-    gr.HTML(
-        """
-    This simple application uses Langchain, an open-source LLM, and FAISS to do Q&A over the 🤗 Transformers Documentation."""
-    )
-
-    gr.HTML(
-        "<center>Powered by <a href='huggingface.co'>Hugging Face 🤗</a> and <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>"
-    )
-
-    state = gr.State()
-    agent_state = gr.State()
-
-    block.load(load_model, inputs=None, outputs=[agent_state])
-
-    submit.click(chat, inputs=[message, state, agent_state], outputs=[chatbot, state])
-    message.submit(chat, inputs=[message, state, agent_state], outputs=[chatbot, state])
-
-block.launch(debug=True)
diff --git a/spaces/hysts/ControlNet-with-Anything-v4/model.py b/spaces/hysts/ControlNet-with-Anything-v4/model.py
deleted file mode 100644
index b1d7263063a9d15e38746e56d9d128ec963e5913..0000000000000000000000000000000000000000
--- a/spaces/hysts/ControlNet-with-Anything-v4/model.py
+++ /dev/null
@@ -1,649 +0,0 @@
-# This file is adapted from gradio_*.py in https://github.com/lllyasviel/ControlNet/tree/f4748e3630d8141d7765e2bd9b1e348f47847707
-# The original license file is LICENSE.ControlNet in this repo.
-from __future__ import annotations
-
-import gc
-import pathlib
-import sys
-
-import cv2
-import numpy as np
-import PIL.Image
-import torch
-from diffusers import (ControlNetModel, DiffusionPipeline,
-                       StableDiffusionControlNetPipeline,
-                       UniPCMultistepScheduler)
-
-repo_dir = pathlib.Path(__file__).parent
-submodule_dir = repo_dir / 'ControlNet'
-sys.path.append(submodule_dir.as_posix())
-
-try:
-    from annotator.canny import apply_canny
-    from annotator.hed import apply_hed, nms
-    from annotator.midas import apply_midas
-    from annotator.mlsd import apply_mlsd
-    from annotator.openpose import apply_openpose
-    from annotator.uniformer import apply_uniformer
-    from annotator.util import HWC3, resize_image
-except Exception:
-    pass
-
-CONTROLNET_MODEL_IDS = {
-    'canny': 'lllyasviel/sd-controlnet-canny',
-    'hough': 'lllyasviel/sd-controlnet-mlsd',
-    'hed': 'lllyasviel/sd-controlnet-hed',
-    'scribble': 'lllyasviel/sd-controlnet-scribble',
-    'pose': 'lllyasviel/sd-controlnet-openpose',
-    'seg': 'lllyasviel/sd-controlnet-seg',
-    'depth': 'lllyasviel/sd-controlnet-depth',
-    'normal': 'lllyasviel/sd-controlnet-normal',
-}
-
-
-def download_all_controlnet_weights() -> None:
-    for model_id in CONTROLNET_MODEL_IDS.values():
-        ControlNetModel.from_pretrained(model_id)
-
-
-class Model:
-    def __init__(self,
-                 base_model_id: str = 'runwayml/stable-diffusion-v1-5',
-                 task_name: str = 'canny'):
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-        self.base_model_id = ''
-        self.task_name = ''
-        self.pipe = self.load_pipe(base_model_id, task_name)
-
-    def load_pipe(self, base_model_id: str, task_name) -> DiffusionPipeline:
-        if self.device.type == 'cpu':
-            return None
-        if base_model_id == self.base_model_id and task_name == self.task_name and hasattr(
-                self, 'pipe'):
-            return self.pipe
-        model_id = CONTROLNET_MODEL_IDS[task_name]
-        controlnet = ControlNetModel.from_pretrained(model_id,
-                                                     torch_dtype=torch.float16)
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            base_model_id,
-            safety_checker=None,
-            controlnet=controlnet,
-            torch_dtype=torch.float16)
-        pipe.scheduler = UniPCMultistepScheduler.from_config(
-            pipe.scheduler.config)
-        pipe.enable_xformers_memory_efficient_attention()
-        pipe.to(self.device)
-        torch.cuda.empty_cache()
-        gc.collect()
-        self.base_model_id = base_model_id
-        self.task_name = task_name
-        return pipe
-
-    def set_base_model(self, base_model_id: str) -> str:
-        if not base_model_id or base_model_id == self.base_model_id:
-            return self.base_model_id
-        del self.pipe
-        torch.cuda.empty_cache()
-        gc.collect()
-        try:
-            self.pipe = self.load_pipe(base_model_id, self.task_name)
-        except Exception:
-            self.pipe = self.load_pipe(self.base_model_id, self.task_name)
-        return self.base_model_id
-
-    def load_controlnet_weight(self, task_name: str) -> None:
-        if task_name == self.task_name:
-            return
-        if hasattr(self.pipe, 'controlnet'):
-            del self.pipe.controlnet
-        torch.cuda.empty_cache()
-        gc.collect()
-        model_id = CONTROLNET_MODEL_IDS[task_name]
-        controlnet = ControlNetModel.from_pretrained(model_id,
-                                                     torch_dtype=torch.float16)
-        controlnet.to(self.device)
-        torch.cuda.empty_cache()
-        gc.collect()
-        self.pipe.controlnet = controlnet
-        self.task_name = task_name
-
-    def get_prompt(self, prompt: str, additional_prompt: str) -> str:
-        if not prompt:
-            prompt = additional_prompt
-        else:
-            prompt = f'{prompt}, {additional_prompt}'
-        return prompt
-
-    @torch.autocast('cuda')
-    def run_pipe(
-        self,
-        prompt: str,
-        negative_prompt: str,
-        control_image: PIL.Image.Image,
-        num_images: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        if seed == -1:
-            seed = np.random.randint(0, np.iinfo(np.int64).max)
-        generator = torch.Generator().manual_seed(seed)
-        return self.pipe(prompt=prompt,
-                         negative_prompt=negative_prompt,
-                         guidance_scale=guidance_scale,
-                         num_images_per_prompt=num_images,
-                         num_inference_steps=num_steps,
-                         generator=generator,
-                         image=control_image).images
-
-    @staticmethod
-    def preprocess_canny(
-        input_image: np.ndarray,
-        image_resolution: int,
-        low_threshold: int,
-        high_threshold: int,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        image = resize_image(HWC3(input_image), image_resolution)
-        control_image = apply_canny(image, low_threshold, high_threshold)
-        control_image = HWC3(control_image)
-        vis_control_image = 255 - control_image
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            vis_control_image)
-
-    @torch.inference_mode()
-    def process_canny(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        low_threshold: int,
-        high_threshold: int,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_canny(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            low_threshold=low_threshold,
-            high_threshold=high_threshold,
-        )
-        self.load_controlnet_weight('canny')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_hough(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-        value_threshold: float,
-        distance_threshold: float,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        control_image = apply_mlsd(
-            resize_image(input_image, detect_resolution), value_threshold,
-            distance_threshold)
-        control_image = HWC3(control_image)
-        image = resize_image(input_image, image_resolution)
-        H, W = image.shape[:2]
-        control_image = cv2.resize(control_image, (W, H),
-                                   interpolation=cv2.INTER_NEAREST)
-
-        vis_control_image = 255 - cv2.dilate(
-            control_image, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
-
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            vis_control_image)
-
-    @torch.inference_mode()
-    def process_hough(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        value_threshold: float,
-        distance_threshold: float,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_hough(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-            value_threshold=value_threshold,
-            distance_threshold=distance_threshold,
-        )
-        self.load_controlnet_weight('hough')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_hed(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        control_image = apply_hed(resize_image(input_image, detect_resolution))
-        control_image = HWC3(control_image)
-        image = resize_image(input_image, image_resolution)
-        H, W = image.shape[:2]
-        control_image = cv2.resize(control_image, (W, H),
-                                   interpolation=cv2.INTER_LINEAR)
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            control_image)
-
-    @torch.inference_mode()
-    def process_hed(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_hed(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-        )
-        self.load_controlnet_weight('hed')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_scribble(
-        input_image: np.ndarray,
-        image_resolution: int,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        image = resize_image(HWC3(input_image), image_resolution)
-        control_image = np.zeros_like(image, dtype=np.uint8)
-        control_image[np.min(image, axis=2) < 127] = 255
-        vis_control_image = 255 - control_image
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            vis_control_image)
-
-    @torch.inference_mode()
-    def process_scribble(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_scribble(
-            input_image=input_image,
-            image_resolution=image_resolution,
-        )
-        self.load_controlnet_weight('scribble')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_scribble_interactive(
-        input_image: np.ndarray,
-        image_resolution: int,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        image = resize_image(HWC3(input_image['mask'][:, :, 0]),
-                             image_resolution)
-        control_image = np.zeros_like(image, dtype=np.uint8)
-        control_image[np.min(image, axis=2) > 127] = 255
-        vis_control_image = 255 - control_image
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            vis_control_image)
-
-    @torch.inference_mode()
-    def process_scribble_interactive(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_scribble_interactive(
-            input_image=input_image,
-            image_resolution=image_resolution,
-        )
-        self.load_controlnet_weight('scribble')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_fake_scribble(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        control_image = apply_hed(resize_image(input_image, detect_resolution))
-        control_image = HWC3(control_image)
-        image = resize_image(input_image, image_resolution)
-        H, W = image.shape[:2]
-
-        control_image = cv2.resize(control_image, (W, H),
-                                   interpolation=cv2.INTER_LINEAR)
-        control_image = nms(control_image, 127, 3.0)
-        control_image = cv2.GaussianBlur(control_image, (0, 0), 3.0)
-        control_image[control_image > 4] = 255
-        control_image[control_image < 255] = 0
-
-        vis_control_image = 255 - control_image
-
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            vis_control_image)
-
-    @torch.inference_mode()
-    def process_fake_scribble(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_fake_scribble(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-        )
-        self.load_controlnet_weight('scribble')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_pose(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-        is_pose_image: bool,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        if not is_pose_image:
-            control_image, _ = apply_openpose(
-                resize_image(input_image, detect_resolution))
-            control_image = HWC3(control_image)
-            image = resize_image(input_image, image_resolution)
-            H, W = image.shape[:2]
-            control_image = cv2.resize(control_image, (W, H),
-                                       interpolation=cv2.INTER_NEAREST)
-        else:
-            control_image = resize_image(input_image, image_resolution)
-
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            control_image)
-
-    @torch.inference_mode()
-    def process_pose(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        is_pose_image: bool,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_pose(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-            is_pose_image=is_pose_image,
-        )
-        self.load_controlnet_weight('pose')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_seg(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-        is_segmentation_map: bool,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        if not is_segmentation_map:
-            control_image = apply_uniformer(
-                resize_image(input_image, detect_resolution))
-            image = resize_image(input_image, image_resolution)
-            H, W = image.shape[:2]
-            control_image = cv2.resize(control_image, (W, H),
-                                       interpolation=cv2.INTER_NEAREST)
-        else:
-            control_image = resize_image(input_image, image_resolution)
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            control_image)
-
-    @torch.inference_mode()
-    def process_seg(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        is_segmentation_map: bool,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_seg(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-            is_segmentation_map=is_segmentation_map,
-        )
-        self.load_controlnet_weight('seg')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_depth(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-        is_depth_image: bool,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        if not is_depth_image:
-            control_image, _ = apply_midas(
-                resize_image(input_image, detect_resolution))
-            control_image = HWC3(control_image)
-            image = resize_image(input_image, image_resolution)
-            H, W = image.shape[:2]
-            control_image = cv2.resize(control_image, (W, H),
-                                       interpolation=cv2.INTER_LINEAR)
-        else:
-            control_image = resize_image(input_image, image_resolution)
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            control_image)
-
-    @torch.inference_mode()
-    def process_depth(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        is_depth_image: bool,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_depth(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-            is_depth_image=is_depth_image,
-        )
-        self.load_controlnet_weight('depth')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_normal(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-        bg_threshold: float,
-        is_normal_image: bool,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        if not is_normal_image:
-            _, control_image = apply_midas(resize_image(
-                input_image, detect_resolution),
-                                           bg_th=bg_threshold)
-            control_image = HWC3(control_image)
-            image = resize_image(input_image, image_resolution)
-            H, W = image.shape[:2]
-            control_image = cv2.resize(control_image, (W, H),
-                                       interpolation=cv2.INTER_LINEAR)
-        else:
-            control_image = resize_image(input_image, image_resolution)
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            control_image)
-
-    @torch.inference_mode()
-    def process_normal(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        bg_threshold: float,
-        is_normal_image: bool,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_normal(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-            bg_threshold=bg_threshold,
-            is_normal_image=is_normal_image,
-        )
-        self.load_controlnet_weight('normal')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
diff --git a/spaces/iamstolas/STOLAS/src/components/ui/sheet.tsx b/spaces/iamstolas/STOLAS/src/components/ui/sheet.tsx
deleted file mode 100644
index c9f5ce0f81a91067bb013e988a07eb1e6bf6953b..0000000000000000000000000000000000000000
--- a/spaces/iamstolas/STOLAS/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/imseldrith/FaceSwap/roop/typing.py b/spaces/imseldrith/FaceSwap/roop/typing.py
deleted file mode 100644
index 1cff7440616e20bfe7b8bc287f86d11bf1b0f083..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/FaceSwap/roop/typing.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from typing import Any
-
-from insightface.app.common import Face
-import numpy
-
-Face = Face
-Frame = numpy.ndarray[Any, Any]
diff --git a/spaces/inamXcontru/PoeticTTS/Amazing Pictures And Facts About Hong Kong The Most Amazing Fact Book For Kids About Hong Kong (Kid [TOP].md b/spaces/inamXcontru/PoeticTTS/Amazing Pictures And Facts About Hong Kong The Most Amazing Fact Book For Kids About Hong Kong (Kid [TOP].md
deleted file mode 100644
index 377441df15d8a595a3c3d76b21fabf0cf9cf9097..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Amazing Pictures And Facts About Hong Kong The Most Amazing Fact Book For Kids About Hong Kong (Kid [TOP].md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>Chinese New Year (Spring Festival) is the most important festival in China and a major event in other Asian countries. Chinese Lunar New Year 2023 will fall on January 22nd. Here are <strong></strong>15 interesting facts about Chinese New Year.</p>
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diff --git a/spaces/inamXcontru/PoeticTTS/Disk Drill Pro Crack 3.5.890 Activation Code Restore Your Deleted Files in Minutes.md b/spaces/inamXcontru/PoeticTTS/Disk Drill Pro Crack 3.5.890 Activation Code Restore Your Deleted Files in Minutes.md
deleted file mode 100644
index 1ed38b7cbbe5d675254d288330a149b3ce3400f5..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Disk Drill Pro Crack 3.5.890 Activation Code Restore Your Deleted Files in Minutes.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
-<p>* New: More flexibility in byte-to-byte backups: select specific disk sectors or indicate the size in MB<br> * New: S.M.A.R.T. monitoring is now easily accessible through the Extras menu<br> * New: New simplified two-step Disk Drill installation<br> * New: MSI files (Windows installer packages) are now supported in Deep Scan<br> * Improved: Better detection of PSD files in Deep Scan<br> * Improved: Clean Up detects and displays items stored in other user accounts on a computer<br> * Improved: More details are now displayed upon recovery completion in case there were errors during the recovery process<br> * Improved: More hints have been added to the icons and menus throughout the apps interface<br> * Improved: A click on the Home button during scan in Clean Up module now evokes a confirmation dialog, where you can decide whether to cancel the scan or not<br> * Improved: Thumbnail generation progress is back. You can now see that the previews are being generated in a convenient status label<br> * Improved: A much faster Quick Scan for ExFAT systems that contain a large number of deleted files<br> * Improved: Better detection of lost NTFS partitions and their files based on the found MFT entries<br> * Improved: Scan is automatically stopped when a FAT32 disk is disconnected from the computer<br> * Improved: The FAT32 scan is ten times faster now for files with longer names<br> * Improved: Optimized and revised Deep Scan for such file formats as JPG, PDF, and DOC<br> * Improved: Detection of Insta360 One X2 file signatures within Deep Scan<br> * Improved: Better support for compressed NTFS volumes<br> * Improved: Deep Scan now recovers video files (MP4) encoded with H.265 XAVC codec<br> * Improved: Improved detection of false exFAT volumes<br> * Improved: Improved behavior when scan and recovery run simultaneously<br> * Fixed: Minor interface enhancements throughout<br> * Fixed: Disk Drill stability when scanning Time Machine backups<br> * Fixed: Resolved compatibility issues with previously unsupported versions of osxfuse<br> * Fixed: A crash while recording a backup image to an unstable disk<br> * Fixed: A crash that occured if a recovery process was canceled during an ongoing scan<br> * Fixed: The Show in Finder button for the local Time Machine snapshots<br> * Fixed: A crash on macOS 10.11 and 10.12 that occured when scan was terminated in the Find Duplicates module<br> * Fixed: A crash that occured at the beginning of scan when the scanned disk was suddenly disconnected<br> * Fixed: Disk Drill freezing when scanning NTFS partitions with outdated Recovery Vault or Guaranteed Recovery database<br> * Fixed: A crash on macOS 10.11 if the scan results were displayed as Files and Icons<br> * Fixed: Random crashes in Hex view when loading saved sessions or repeating a scan</p>
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-<p>* New: Yet another update that improves native scanning and recovery from storage devices encrypted by M1 security chips<br> * New: Totally new highly enhanced NTFS recovery<br> * New: Full-blown scanning and recovery from Time Machine on APFS volumes<br> * New: New storage space indicators when selecting drives in Disk Drill<br> * New: S.M.A.R.T. disk info is now collected and displayed for SSD devices on M1 Macs (when enabled in Preferences)<br> * Improved: Updated system extension deployment instructions + new video tutorial<br> * Improved: More user verifications when canceling Disk Drills actions, like ongoing scans or other operations<br> * Improved: Multiple improvements in Disk Drill Extras: search for duplicates, disk clean up, backup creation, and data extraction from Time Machine<br> * Improved: Parent disk and partition listing when booting from external drives<br> * Improved: System information listing and collection of technical parameters for RAID arrays<br> * Improved: Now disks with bad sectors are marked red in Extra Tools<br> * Improved: More consistent messaging on disk updates<br> * Improved: Improved scanning of APFS Fusion Drives<br> * Improved: NTFS Quick Scan<br> * Improved: Better disk selection when saving recovery results<br> * Improved: Improved UI navigation on session auto-saves<br> * Improved: Sufficient improvements in Disk Drills internal logs for data recovery and S.M.A.R.T. status reporting<br> * Improved: Disk Drill removal impromevents<br> * Fixed: Disk Drill errors when opening and detaching DMG disk images<br> * Fixed: Issues when drives changed their attributes while being scanned<br> * Fixed: Scan error 3072 while reconnecting encrypted drives<br> * Fixed: System drive scanning error in macOS Catalina on devices with T2 chip<br> * Fixed: Corrected accidental Disk Drill removal via Clean Up and Data Shredder<br> * Fixed: Some Clean Up freezes<br> * Fixed: App activation status verification when processing disks with corrupted MBR sectors<br> * Fixed: Improper display of scan auto-resume dialog on drive disconnect<br> * Fixed: Warning messages when disk goes offline<br> * Fixed: Most extra tools are now blocked in Portable Mode<br> * Fixed: Disk stability verification procedure before Rebuild HFS+ algorithm</p> aaccfb2cb3<br />
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\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Download Game Ps 2 Iso Winning Eleven 2013 WORK.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Download Game Ps 2 Iso Winning Eleven 2013 WORK.md
deleted file mode 100644
index 5fd2cbabf5ce247252498154ac8a0f195ff0a4a3..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Download Game Ps 2 Iso Winning Eleven 2013 WORK.md	
+++ /dev/null
@@ -1,73 +0,0 @@
-<br />
-<h1>How to Download Game PS 2 ISO Winning Eleven 2013</h1>
-<p>If you are a fan of soccer games, you might have heard of Winning Eleven 2013, also known as Pro Evolution Soccer 2013. This game was released by Konami in 2012 for the PlayStation 2 console, and it features realistic graphics, gameplay, and sound effects. You can play with your favorite teams and players from around the world, and enjoy various modes such as exhibition, league, cup, and master league.</p>
-<h2>download game ps 2 iso winning eleven 2013</h2><br /><p><b><b>Download File</b> &#10003;&#10003;&#10003; <a href="https://urlin.us/2uExZZ">https://urlin.us/2uExZZ</a></b></p><br /><br />
-<p>But what if you don't have a PlayStation 2 console, or you want to play the game on your PC or laptop? Well, there is a way to do that. You can download game PS 2 ISO Winning Eleven 2013 from the internet, and use an emulator to run it on your computer. An emulator is a software that mimics the functions of a console, and allows you to play games that are not compatible with your device.</p>
-<p>In this article, we will show you how to download game PS 2 ISO Winning Eleven 2013, and how to use an emulator to play it on your PC or laptop. Follow these steps carefully, and you will be able to enjoy this amazing soccer game in no time.</p>
-<h2>Step 1: Download Game PS 2 ISO Winning Eleven 2013</h2>
-<p>The first step is to download game PS 2 ISO Winning Eleven 2013 from a reliable source. There are many websites that offer PS2 ISO downloads, but not all of them are safe and legal. Some of them may contain viruses, malware, or fake files that can harm your computer or steal your personal information.</p>
-<p></p>
-<p>Therefore, you need to be careful when choosing a website to download game PS 2 ISO Winning Eleven 2013. We recommend using CDRomance or WoWroms, as they are trusted and popular sites that provide high-quality PS2 ISO downloads. You can find the links to these sites below:</p>
-<ul>
-<li><a href="https://cdromance.com/ps2-iso/pro-evolution-soccer-2013-usa/">CDRomance - Pro Evolution Soccer 2013 (USA) PS2 ISO</a></li>
-<li><a href="https://wowroms.com/en/isos/playstation-2/pro-evolution-soccer-2013-usa/120430.html">WoWroms - PRO EVOLUTION SOCCER 2013 [USA]</a></li>
-</ul>
-<p>Once you have chosen a site, you need to click on the download link or button, and save the file to your computer. The file size is about 1.57 GB, so make sure you have enough space on your hard drive. The file name should be something like PES 2013 - Pro Evolution Soccer (USA).7z or PRO EVOLUTION SOCCER 2013 [USA].7z.</p>
-<h2>Step 2: Extract Game PS 2 ISO Winning Eleven 2013</h2>
-<p>The next step is to extract game PS 2 ISO Winning Eleven 2013 from the compressed file that you downloaded. A compressed file is a file that has been reduced in size to save space and bandwidth. You need to use a software that can extract compressed files, such as WinRAR or 7-Zip.</p>
-<p>You can download WinRAR or 7-Zip from their official websites:</p>
-<ul>
-<li><a href="https://www.win-rar.com/download.html">WinRAR Download</a></li>
-<li><a href="https://www.7-zip.org/download.html">7-Zip Download</a></li>
-</ul>
-<p>After installing WinRAR or 7-Zip, you need to right-click on the compressed file that you downloaded, and choose Extract Here or Extract Files. This will create a new folder with the same name as the compressed file, and inside it you will find the game PS 2 ISO Winning Eleven 2013 file. The file name should be something like PES_2013.iso or PRO_EVOLUTION_SOCCER_2013.iso.</p>
-<h2>Step 3: Download and Install PCSX2 Emulator</h2>
-<p>The final step is to download and install PCSX2 emulator on your computer. PCSX2 is a free and open-source emulator that can run PS2 games on Windows, Linux, and Mac OS. It has many features and options that allow you to customize your gaming experience, such as graphics settings, controller configuration, cheats, save states, and more.</p>
-<p>You can download PCSX2 emulator from its official website:</p>
-<ul>
-<li><a href="https://pcsx2.net/download.html">PCSX2 Download</a></li>
-</ul>
-<p>After downloading PCSX2 emulator, you need to run the installer and follow the instructions on the screen. You will also need to download some BIOS files for PCSX2 emulator to work properly. BIOS files are essential components of a console that contain information about its hardware and software. You can find BIOS files for PCSX2 emulator on various websites online.</p>
-<p>Once you have downloaded BIOS files for PCSX2 emulator, you need to copy them to the BIOS folder inside the PCSX2 installation directory. The default location of this folder is C:\Program Files (x86)\PCSX2\BIOS.</p>
-<h2>Step 4: Run Game PS 2 ISO Winning Eleven 2013 on PCSX2 Emulator</h2>
-<p>Now you are ready to run game PS 2 ISO Winning Eleven 2013 on PCSX2 emulator. To do this, you need to launch PCSX2 emulator from your desktop or start menu shortcut. Then you need to click on CDVD > ISO Selector > Browse, and locate the game PS 2 ISO Winning Eleven 2013 file that you extracted earlier.</p>
-<p>After selecting the game PS 2 ISO Winning Eleven 2013 file, you need to click on System > Boot CDVD (fast) or Boot CDVD (full). This will start the game PS 2 ISO Winning Eleven 2013 on PCSX2 emulator. You can use your keyboard or a controller to play the game.</p>
-<h1>Conclusion</h1>
-<p>In this article, we have shown you how to download game PS 2 ISO Winning Eleven 2013 from the internet, and how to use an emulator to play it on your PC or laptop. We hope this guide was helpful and informative for you.</p>
-<p>If you have any questions or comments about this article, feel free to leave them below. We would love to hear from you.</p>
-<h2>Step 5: Enjoy Game PS 2 ISO Winning Eleven 2013 on Your PC or Laptop</h2>
-<p>Congratulations! You have successfully downloaded and run game PS 2 ISO Winning Eleven 2013 on your PC or laptop. Now you can enjoy this amazing soccer game anytime and anywhere you want. You can play with your friends online or offline, and challenge yourself with different difficulty levels and game modes.</p>
-<p>Game PS 2 ISO Winning Eleven 2013 is one of the best soccer games ever made for the PlayStation 2 console. It has realistic graphics, gameplay, and sound effects that will make you feel like you are on the field. You can choose from hundreds of teams and players from around the world, and customize your own team with different formations, tactics, and strategies.</p>
-<p>Game PS 2 ISO Winning Eleven 2013 also has a master league mode, where you can create your own club and manage it from scratch. You can sign new players, train them, sell them, and compete with other clubs in various leagues and cups. You can also edit your own players and teams, and create your own tournaments and leagues.</p>
-<p>Game PS 2 ISO Winning Eleven 2013 is a game that will keep you entertained for hours and hours. It is a game that will test your skills and knowledge of soccer. It is a game that will make you love soccer even more.</p>
-<h1>Final Words</h1>
-<p>In this article, we have shown you how to download game PS 2 ISO Winning Eleven 2013 from the internet, and how to use an emulator to play it on your PC or laptop. We hope this guide was helpful and informative for you.</p>
-<p>If you liked this article, please share it with your friends and family who might be interested in playing game PS 2 ISO Winning Eleven 2013 on their PC or laptop. Also, don't forget to leave a comment below and let us know what you think about this game.</p>
-<p>Thank you for reading this article. Have fun playing game PS 2 ISO Winning Eleven 2013!</p>
-<h2>Step 6: Adjust the Settings of PCSX2 Emulator for Better Performance</h2>
-<p>If you want to improve the performance of game PS 2 ISO Winning Eleven 2013 on your PC or laptop, you can adjust the settings of PCSX2 emulator to suit your preferences and system specifications. PCSX2 emulator has many options and features that allow you to tweak the graphics, sound, speed, and compatibility of the game.</p>
-<p>To access the settings of PCSX2 emulator, you need to click on Config > Emulation Settings. Here you will find various tabs and menus that let you change the settings of PCSX2 emulator. Some of the most important settings are:</p>
-<ul>
-<li>GS: This is where you can change the graphics plugin and settings of PCSX2 emulator. You can choose from different renderers, resolutions, filters, hacks, and more. You can also enable or disable Vsync, frame skipping, and frame limiting.</li>
-<li>SPU2: This is where you can change the sound plugin and settings of PCSX2 emulator. You can choose from different modules, output devices, latency, volume, and more. You can also enable or disable effects processing, time stretching, and audio expansion.</li>
-<li>Speedhacks: This is where you can enable or disable some hacks that can boost the speed of game PS 2 ISO Winning Eleven 2013 on your PC or laptop. These hacks include fast CDVD access, EE cycle rate, VU cycle stealing, and more. However, be careful when using these hacks, as they may cause some glitches or crashes in the game.</li>
-<li>Game Fixes: This is where you can enable or disable some fixes that can improve the compatibility of game PS 2 ISO Winning Eleven 2013 on your PC or laptop. These fixes include skip MPEG hack, FPU negative div hack, EE timing hack, and more. However, be careful when using these fixes, as they may cause some side effects or conflicts in the game.</li>
-</ul>
-<p>You can experiment with different settings of PCSX2 emulator until you find the best combination for game PS 2 ISO Winning Eleven 2013 on your PC or laptop. You can also check online for some recommended settings for this game from other users.</p>
-<h2>Step 7: Troubleshoot Any Problems or Errors with Game PS 2 ISO Winning Eleven 2013 on PCSX2 Emulator</h2>
-<p>If you encounter any problems or errors with game PS 2 ISO Winning Eleven 2013 on PCSX2 emulator, don't panic. There are some common issues and solutions that you can try to fix them. Here are some of them:</p>
-<ul>
-<li>If the game PS 2 ISO Winning Eleven 2013 does not run or load on PCSX2 emulator, make sure you have selected the correct ISO file and BIOS file in the CDVD and BIOS menus. Also make sure you have enough disk space and RAM on your computer.</li>
-<li>If the game PS 2 ISO Winning Eleven 2013 runs too slow or too fast on PCSX2 emulator, make sure you have enabled frame limiting and disabled frame skipping in the GS menu. Also try adjusting the speedhacks and game fixes in the emulation settings.</li>
-<li>If the game PS 2 ISO Winning Eleven 2013 has graphical glitches or errors on PCSX2 emulator, make sure you have updated your graphics card drivers and DirectX software. Also try changing the graphics plugin and settings in the GS menu.</li>
-<li>If the game PS 2 ISO Winning Eleven 2013 has sound glitches or errors on PCSX2 emulator, make sure you have updated your sound card drivers and DirectX software. Also try changing the sound plugin and settings in the SPU2 menu.</li>
-<li>If the game PS 2 ISO Winning Eleven 2013 has controller issues or errors on PCSX2 emulator, make sure you have connected your controller properly and configured it in the PAD menu. Also try changing the controller plugin and settings in the PAD menu.</li>
-</ul>
-<p>If none of these solutions work for you, you can check online for more help and support from other users or developers of PCSX2 emulator. You can also report any bugs or errors that you find with game PS 2 ISO Winning Eleven 2013 on PCSX2 emulator to help improve its quality and compatibility.</p>
-<h1>Conclusion</h1>
-<p>In this article, we have shown you how to download game PS 2 ISO Winning Eleven 2013 from the internet, and how to use an emulator to play it on your PC or laptop. We have also given you some tips and tricks on how to adjust the settings of PCSX2 emulator for better performance, and how to troubleshoot any problems or errors with game PS 2 ISO Winning Eleven 2013 on PCSX2 emulator.</p>
-<p>We hope this guide was helpful and informative for you. If you have any questions or comments about this article, feel free to leave them below. We would love to hear from you.</p>
-<p>Game PS 2 ISO Winning Eleven 2013 is one of the best soccer games ever made for the PlayStation 2 console. It is a game that will make you feel like you are on the field. It is a game that will test your skills and knowledge of soccer. It is a game that will make you love soccer even more.</p>
-<p>So what are you waiting for? Download game PS 2 ISO Winning Eleven 2013 today, and enjoy this amazing soccer game on your PC or laptop. Have fun!</p> 3cee63e6c2<br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/CorelDRAW Graphics Suite X6 16.4.0.1280 SP4 Fixed (32 Bit) [Chin 64 Bit ((EXCLUSIVE)).md b/spaces/inreVtussa/clothingai/Examples/CorelDRAW Graphics Suite X6 16.4.0.1280 SP4 Fixed (32 Bit) [Chin 64 Bit ((EXCLUSIVE)).md
deleted file mode 100644
index 674378030643fa087d2bcbb944a51a5292f1194f..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/CorelDRAW Graphics Suite X6 16.4.0.1280 SP4 Fixed (32 Bit) [Chin 64 Bit ((EXCLUSIVE)).md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>With the suspected offending component now removed, the suite seemed to settle down and run smoothly, with detection taking only a couple of minutes on each file. Optimal handling of the output file was again most pleasing, with AVI files at all levels of compression taking under an hour, and uncompressed AVI files keeping their size at the low end of the scale, at maximum level of compression. In comparison, the other media types were less frugal, and with no more than a few MB difference in file size between the highest and lowest levels of compression, and AVI was usually found on the higher end of the scale.</p>
-<p>The install package was somewhat large at 128MB and the whole job took more than half a day to complete. The GUI has had a big face lift compared to the release version of 2016 and looks great, while the interface is pretty similar to the standard suite offering. It, too, had a few problems, particularly with the layout of the main panels which were impenetrable in places, and with the way the units were split up by category. The program did prove slightly less responsive than the standard suite offering, though, and was easily over three times the speed of the previous 2016 version as far as completing the test sets went.</p>
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diff --git a/spaces/iqovocn/ChuanhuChatGPT/assets/custom.css b/spaces/iqovocn/ChuanhuChatGPT/assets/custom.css
deleted file mode 100644
index 22108488886cfc8d7772214dd9b83727b3fca6a3..0000000000000000000000000000000000000000
--- a/spaces/iqovocn/ChuanhuChatGPT/assets/custom.css
+++ /dev/null
@@ -1,468 +0,0 @@
-:root {
-    --chatbot-color-light: #000000;
-    --chatbot-color-dark: #FFFFFF;
-    --chatbot-background-color-light: #F3F3F3;
-    --chatbot-background-color-dark: #121111;
-    --message-user-background-color-light: #95EC69;
-    --message-user-background-color-dark: #26B561;
-    --message-bot-background-color-light: #FFFFFF;
-    --message-bot-background-color-dark: #2C2C2C;
-}
-
-#app_title {
-    font-weight: var(--prose-header-text-weight);
-    font-size: var(--text-xxl);
-    line-height: 1.3;
-    text-align: left;
-    margin-top: 6px;
-    white-space: nowrap;
-}
-#description {
-    text-align: center;
-    margin: 32px 0 4px 0;
-}
-
-/* gradio的页脚信息 */
-footer {
-    /* display: none !important; */
-    margin-top: .2em !important;
-    font-size: 85%;
-}
-#footer {
-    text-align: center;
-}
-#footer div {
-    display: inline-block;
-}
-#footer .versions{
-    font-size: 85%;
-    opacity: 0.60;
-}
-
-#float_display {
-    position: absolute;
-    max-height: 30px;
-}
-/* user_info */
-#user_info {
-    white-space: nowrap;
-    position: absolute; left: 8em; top: .2em;
-    z-index: var(--layer-2);
-    box-shadow: var(--block-shadow);
-    border: none; border-radius: var(--block-label-radius);
-    background: var(--color-accent);
-    padding: var(--block-label-padding);
-    font-size: var(--block-label-text-size); line-height: var(--line-sm);
-    width: auto; min-height: 30px!important;
-    opacity: 1;
-    transition: opacity 0.3s ease-in-out;
-}
-#user_info .wrap {
-    opacity: 0;
-}
-#user_info p {
-    color: white;
-    font-weight: var(--block-label-text-weight);
-}
-#user_info.hideK {
-    opacity: 0;
-    transition: opacity 1s ease-in-out;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: ui-monospace, "SF Mono", "SFMono-Regular", "Menlo", "Consolas", "Liberation Mono", "Microsoft Yahei UI", "Microsoft Yahei", monospace;
-    /* Windows下中文的monospace会fallback为新宋体，实在太丑，这里折中使用微软雅黑 */
-    color: var(--body-text-color-subdued);
-}
-
-#status_display {
-    transition: all 0.6s;
-}
-#chuanhu_chatbot {
-    transition: height 0.3s ease;
-}
-
-/* usage_display */
-.insert_block {
-    position: relative;
-    margin: 0;
-    padding: .5em 1em;
-    box-shadow: var(--block-shadow);
-    border-width: var(--block-border-width);
-    border-color: var(--block-border-color);
-    border-radius: var(--block-radius);
-    background: var(--block-background-fill);
-    width: 100%;
-    line-height: var(--line-sm);
-    min-height: 2em;
-}
-#usage_display p, #usage_display span {
-    margin: 0;
-    font-size: .85em;
-    color: var(--body-text-color-subdued);
-}
-.progress-bar {
-    background-color: var(--input-background-fill);;
-    margin: .5em 0 !important;
-    height: 20px;
-    border-radius: 10px;
-    overflow: hidden;
-}
-.progress {
-    background-color: var(--block-title-background-fill);
-    height: 100%;
-    border-radius: 10px;
-    text-align: right;
-    transition: width 0.5s ease-in-out;
-}
-.progress-text {
-    /* color: white; */
-    color: var(--color-accent) !important;
-    font-size: 1em !important;
-    font-weight: bold;
-    padding-right: 10px;
-    line-height: 20px;
-}
-
-.apSwitch {
-    top: 2px;
-    display: inline-block;
-    height: 24px;
-    position: relative;
-    width: 48px;
-    border-radius: 12px;
-}
-.apSwitch input {
-    display: none !important;
-}
-.apSlider {
-    background-color: var(--neutral-200);
-    bottom: 0;
-    cursor: pointer;
-    left: 0;
-    position: absolute;
-    right: 0;
-    top: 0;
-    transition: .4s;
-    font-size: 18px;
-    border-radius: 12px;
-}
-.apSlider::before {
-    bottom: -1.5px;
-    left: 1px;
-    position: absolute;
-    transition: .4s;
-    content: "🌞";
-}
-input:checked + .apSlider {
-    background-color: var(--primary-600);
-}
-input:checked + .apSlider::before {
-    transform: translateX(23px);
-    content:"🌚";
-}
-
-/* Override Slider Styles (for webkit browsers like Safari and Chrome)
- * 好希望这份提案能早日实现 https://github.com/w3c/csswg-drafts/issues/4410
- * 进度滑块在各个平台还是太不统一了
- */
-input[type="range"] {
-    -webkit-appearance: none;
-    height: 4px;
-    background: var(--input-background-fill);
-    border-radius: 5px;
-    background-image: linear-gradient(var(--primary-500),var(--primary-500));
-    background-size: 0% 100%;
-    background-repeat: no-repeat;
-}
-input[type="range"]::-webkit-slider-thumb {
-    -webkit-appearance: none;
-    height: 20px;
-    width: 20px;
-    border-radius: 50%;
-    border: solid 0.5px #ddd;
-    background-color: white;
-    cursor: ew-resize;
-    box-shadow: var(--input-shadow);
-    transition: background-color .1s ease;
-}
-input[type="range"]::-webkit-slider-thumb:hover {
-    background: var(--neutral-50);
-}
-input[type=range]::-webkit-slider-runnable-track {
-    -webkit-appearance: none;
-    box-shadow: none;
-    border: none;
-    background: transparent;
-}
-
-#submit_btn, #cancel_btn {
-    height: 42px !important;
-}
-#submit_btn::before {
-    content: url("data:image/svg+xml, %3Csvg width='21px' height='20px' viewBox='0 0 21 20' version='1.1' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E %3Cg id='page' stroke='none' stroke-width='1' fill='none' fill-rule='evenodd'%3E %3Cg id='send' transform='translate(0.435849, 0.088463)' fill='%23FFFFFF' fill-rule='nonzero'%3E %3Cpath d='M0.579148261,0.0428666046 C0.301105539,-0.0961547561 -0.036517765,0.122307382 0.0032026237,0.420210298 L1.4927172,18.1553639 C1.5125774,18.4334066 1.79062012,18.5922882 2.04880264,18.4929872 L8.24518329,15.8913017 L11.6412765,19.7441794 C11.8597387,19.9825018 12.2370824,19.8832008 12.3165231,19.5852979 L13.9450591,13.4882182 L19.7839562,11.0255541 C20.0619989,10.8865327 20.0818591,10.4694687 19.7839562,10.3105871 L0.579148261,0.0428666046 Z M11.6138902,17.0883151 L9.85385903,14.7195502 L0.718169621,0.618812241 L12.69945,12.9346347 L11.6138902,17.0883151 Z' id='shape'%3E%3C/path%3E %3C/g%3E %3C/g%3E %3C/svg%3E");
-    height: 21px;
-}
-#cancel_btn::before {
-    content: url("data:image/svg+xml,%3Csvg width='21px' height='21px' viewBox='0 0 21 21' version='1.1' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E %3Cg id='pg' stroke='none' stroke-width='1' fill='none' fill-rule='evenodd'%3E %3Cpath d='M10.2072007,20.088463 C11.5727865,20.088463 12.8594566,19.8259823 14.067211,19.3010209 C15.2749653,18.7760595 16.3386126,18.0538087 17.2581528,17.1342685 C18.177693,16.2147282 18.8982283,15.1527965 19.4197586,13.9484733 C19.9412889,12.7441501 20.202054,11.4557644 20.202054,10.0833163 C20.202054,8.71773046 19.9395733,7.43106036 19.4146119,6.22330603 C18.8896505,5.01555169 18.1673997,3.95018885 17.2478595,3.0272175 C16.3283192,2.10424615 15.2646719,1.3837109 14.0569176,0.865611739 C12.8491633,0.34751258 11.5624932,0.088463 10.1969073,0.088463 C8.83132146,0.088463 7.54636692,0.34751258 6.34204371,0.865611739 C5.1377205,1.3837109 4.07407321,2.10424615 3.15110186,3.0272175 C2.22813051,3.95018885 1.5058797,5.01555169 0.984349419,6.22330603 C0.46281914,7.43106036 0.202054,8.71773046 0.202054,10.0833163 C0.202054,11.4557644 0.4645347,12.7441501 0.9894961,13.9484733 C1.5144575,15.1527965 2.23670831,16.2147282 3.15624854,17.1342685 C4.07578877,18.0538087 5.1377205,18.7760595 6.34204371,19.3010209 C7.54636692,19.8259823 8.83475258,20.088463 10.2072007,20.088463 Z M10.2072007,18.2562448 C9.07493099,18.2562448 8.01471483,18.0452309 7.0265522,17.6232031 C6.03838956,17.2011753 5.17031614,16.6161693 4.42233192,15.8681851 C3.6743477,15.1202009 3.09105726,14.2521274 2.67246059,13.2639648 C2.25386392,12.2758022 2.04456558,11.215586 2.04456558,10.0833163 C2.04456558,8.95104663 2.25386392,7.89083047 2.67246059,6.90266784 C3.09105726,5.9145052 3.6743477,5.04643178 4.42233192,4.29844756 C5.17031614,3.55046334 6.036674,2.9671729 7.02140552,2.54857623 C8.00613703,2.12997956 9.06463763,1.92068122 10.1969073,1.92068122 C11.329177,1.92068122 12.3911087,2.12997956 13.3827025,2.54857623 C14.3742962,2.9671729 15.2440852,3.55046334 15.9920694,4.29844756 C16.7400537,5.04643178 17.3233441,5.9145052 17.7419408,6.90266784 C18.1605374,7.89083047 18.3698358,8.95104663 18.3698358,10.0833163 C18.3698358,11.215586 18.1605374,12.2758022 17.7419408,13.2639648 C17.3233441,14.2521274 16.7400537,15.1202009 15.9920694,15.8681851 C15.2440852,16.6161693 14.3760118,17.2011753 13.3878492,17.6232031 C12.3996865,18.0452309 11.3394704,18.2562448 10.2072007,18.2562448 Z M7.65444721,13.6242324 L12.7496608,13.6242324 C13.0584616,13.6242324 13.3003556,13.5384544 13.4753427,13.3668984 C13.6503299,13.1953424 13.7378234,12.9585951 13.7378234,12.6566565 L13.7378234,7.49968276 C13.7378234,7.19774418 13.6503299,6.96099688 13.4753427,6.78944087 C13.3003556,6.61788486 13.0584616,6.53210685 12.7496608,6.53210685 L7.65444721,6.53210685 C7.33878414,6.53210685 7.09345904,6.61788486 6.91847191,6.78944087 C6.74348478,6.96099688 6.65599121,7.19774418 6.65599121,7.49968276 L6.65599121,12.6566565 C6.65599121,12.9585951 6.74348478,13.1953424 6.91847191,13.3668984 C7.09345904,13.5384544 7.33878414,13.6242324 7.65444721,13.6242324 Z' id='shape' fill='%23FF3B30' fill-rule='nonzero'%3E%3C/path%3E %3C/g%3E %3C/svg%3E");
-    height: 21px;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色（默认） */
-#chuanhu_chatbot {
-    background-color: var(--chatbot-background-color-light) !important;
-    color: var(--chatbot-color-light) !important;
-}
-[data-testid = "bot"] {
-    background-color: var(--message-bot-background-color-light) !important;
-}
-[data-testid = "user"] {
-    background-color: var(--message-user-background-color-light) !important;
-}
-/* 暗色 */
-.dark #chuanhu_chatbot {
-    background-color: var(--chatbot-background-color-dark) !important;
-    color: var(--chatbot-color-dark) !important;
-}
-.dark [data-testid = "bot"] {
-    background-color: var(--message-bot-background-color-dark) !important;
-}
-.dark [data-testid = "user"] {
-    background-color: var(--message-user-background-color-dark) !important;
-}
-
-/* 屏幕宽度大于等于500px的设备 */
-/* update on 2023.4.8: 高度的细致调整已写入JavaScript */
-@media screen and (min-width: 500px) {
-    #chuanhu_chatbot {
-        height: calc(100vh - 200px);
-    }
-    #chuanhu_chatbot .wrap {
-        max-height: calc(100vh - 200px - var(--line-sm)*1rem - 2*var(--block-label-margin) );
-    }
-}
-/* 屏幕宽度小于500px的设备 */
-@media screen and (max-width: 499px) {
-    #chuanhu_chatbot {
-        height: calc(100vh - 140px);
-    }
-    #chuanhu_chatbot .wrap {
-        max-height: calc(100vh - 140px - var(--line-sm)*1rem - 2*var(--block-label-margin) );
-    }
-    [data-testid = "bot"] {
-        max-width: 95% !important;
-    }
-    #app_title h1{
-        letter-spacing: -1px; font-size: 22px;
-    }
-}
-#chuanhu_chatbot .wrap {
-    overflow-x: hidden;
-}
-/* 对话气泡 */
-.message {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-
-.message.user p {
-    white-space: pre-wrap;
-}
-.message .user-message {
-    display: block;
-    padding: 0 !important;
-    white-space: pre-wrap;
-}
-
-.message .md-message p {
-    margin-top: 0.6em !important;
-    margin-bottom: 0.6em !important;
-}
-.message .md-message p:first-child { margin-top: 0 !important; }
-.message .md-message p:last-of-type { margin-bottom: 0 !important; }
-
-.message .md-message {
-    display: block;
-    padding: 0 !important;
-}
-.message .raw-message p {
-    margin:0 !important;
-}
-.message .raw-message {
-    display: block;
-    padding: 0 !important;
-    white-space: pre-wrap;
-}
-.raw-message.hideM, .md-message.hideM {
-    display: none;
-}
-
-/* custom buttons */
-.chuanhu-btn {
-    border-radius: 5px;
-    /* background-color: #E6E6E6 !important; */
-    color: rgba(120, 120, 120, 0.64) !important;
-    padding: 4px !important;
-    position: absolute;
-    right: -22px;
-    cursor: pointer !important;
-    transition: color .2s ease, background-color .2s ease;
-}
-.chuanhu-btn:hover {
-    background-color: rgba(167, 167, 167, 0.25) !important;
-    color: unset !important;
-}
-.chuanhu-btn:active {
-    background-color: rgba(167, 167, 167, 0.5) !important;
-}
-.chuanhu-btn:focus {
-    outline: none;
-}
-.copy-bot-btn {
-    /* top: 18px; */
-    bottom: 0;
-}
-.toggle-md-btn {
-    /* top: 0; */
-    bottom: 20px;
-}
-.copy-code-btn {
-    position: relative;
-    float: right;
-    font-size: 1em;
-    cursor: pointer;
-}
-
-.message-wrap>div img{
-    border-radius: 10px !important;
-}
-
-/* history message */
-.wrap>.history-message {
-    padding: 10px !important;
-}
-.history-message {
-    /* padding: 0 !important; */
-    opacity: 80%;
-    display: flex;
-    flex-direction: column;
-}
-.history-message>.history-message {
-    padding: 0 !important;
-}
-.history-message>.message-wrap {
-    padding: 0 !important;
-    margin-bottom: 16px;
-}
-.history-message>.message {
-    margin-bottom: 16px;
-}
-.wrap>.history-message::after {
-    content: "";
-    display: block;
-    height: 2px;
-    background-color: var(--body-text-color-subdued);
-    margin-bottom: 10px;
-    margin-top: -10px;
-    clear: both;
-}
-.wrap>.history-message>:last-child::after {
-    content: "仅供查看";
-    display: block;
-    text-align: center;
-    color: var(--body-text-color-subdued);
-    font-size: 0.8em;
-}
-
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-.message :not(pre) code {
-    display: inline;
-    white-space: break-spaces;
-    font-family: var(--font-mono);
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-.message pre,
-.message pre[class*=language-] {
-    color: #fff;
-    overflow-x: auto;
-    overflow-y: hidden;
-    margin: .8em 1em 1em 0em !important;
-    padding: var(--spacing-xl) 1.2em !important;
-    border-radius: var(--radius-lg) !important;
-}
-.message pre code,
-.message pre code[class*=language-] {
-    color: #fff;
-    padding: 0;
-    margin: 0;
-    background-color: unset;
-    text-shadow: none;
-    font-family: var(--font-mono);
-}
-/* 覆盖 gradio 丑陋的复制按钮样式 */
-pre button[title="copy"] {
-    border-radius: 5px;
-    transition: background-color .2s ease;
-}
-pre button[title="copy"]:hover {
-    background-color: #333232;
-}
-pre button .check {
-    color: #fff !important;
-    background: var(--neutral-950) !important;
-}
-
-/* 覆盖prism.css */
-.language-css .token.string,
-.style .token.string,
-.token.entity,
-.token.operator,
-.token.url {
-    background: none !important;
-}
diff --git a/spaces/ivntl/MMS/uroman/lib/NLP/Chinese.pm b/spaces/ivntl/MMS/uroman/lib/NLP/Chinese.pm
deleted file mode 100644
index ea6c52991bd1bb2e55ec851bf31537f59f57b58a..0000000000000000000000000000000000000000
--- a/spaces/ivntl/MMS/uroman/lib/NLP/Chinese.pm
+++ /dev/null
@@ -1,239 +0,0 @@
-################################################################
-#                                                              #
-# Chinese                                                      #
-#                                                              #
-################################################################
-
-package NLP::Chinese;
-
-$utf8 = NLP::UTF8;
-%empty_ht = ();
-
-sub read_chinese_tonal_pinyin_files {
-   local($caller, *ht, @filenames) = @_;
-
-   $n_kHanyuPinlu = 0;
-   $n_kXHC1983 = 0;
-   $n_kHanyuPinyin = 0;
-   $n_kMandarin = 0;
-   $n_cedict = 0;
-   $n_simple_pinyin = 0;
-
-   foreach $filename (@filenames) {
-      if ($filename =~ /unihan/i) {
-	 my $line_number = 0;
-         if (open(IN, $filename)) {
-            while (<IN>) {
-	       $line_number++;
-               next if /^#/;
-               s/\s*$//;
-               if (($u, $type, $value) = split(/\t/, $_)) {
-                  if ($type =~ /^(kHanyuPinlu|kXHC1983|kHanyuPinyin|kMandarin)$/) {
-	             $u = $util->trim($u);
-	             $type = $util->trim($type);
-	             $value = $util->trim($value);
-                     $f = $utf8->unicode_string2string($u);
-
-                     if ($type eq "kHanyuPinlu") {
-	                $value =~ s/\(.*?\)//g;
-			$value = $util->trim($value);
-                        $translit = $caller->number_to_accent_tone($value);
-	                $ht{"kHanyuPinlu"}->{$f} = $translit;
-	                $n_kHanyuPinlu++;
-                     } elsif ($type eq "kXHC1983") {
-	                @translits = ($value =~ /:(\S+)/g);
-	                $translit = join(" ", @translits);
-	                $ht{"kXHC1983"}->{$f} = $translit;
-	                $n_kXHC1983++;
-                     } elsif ($type eq "kHanyuPinyin") {
-	                $value =~ s/^.*://;
-	                $value =~ s/,/ /g;
-	                $ht{"kHanyuPinyin"}->{$f} = $value;
-	                $n_kHanyuPinyin++;
-                     } elsif ($type eq "kMandarin") {
-			$ht{"kMandarin"}->{$f} = $value;
-			$n_kMandarin++;
-                     }
-                  }
-               }
-            }
-            close(IN);
-            print "Read in $n_kHanyuPinlu kHanyuPinlu, $n_kXHC1983 n_kXHC1983, $n_kHanyuPinyin n_kHanyuPinyin $n_kMandarin n_kMandarin\n";
-         } else {
-	    print STDERR "Can't open $filename\n";
-	 }
-      } elsif ($filename =~ /cedict/i) {
-         if (open(IN, $filename)) {
-	    my $line_number = 0;
-            while (<IN>) {
-	       $line_number++;
-               next if /^#/;
-               s/\s*$//;
-               if (($f, $translit) = ($_ =~ /^\S+\s+(\S+)\s+\[([^\[\]]+)\]/)) {
-                  $translit = $utf8->extended_lower_case($translit);
-                  $translit = $caller->number_to_accent_tone($translit);
-                  $translit =~ s/\s//g;
-		  if ($old_translit = $ht{"cedict"}->{$f}) {
-                     # $ht{CONFLICT}->{("DUPLICATE " . $f)} = "CEDICT($f): $old_translit\nCEDICT($f): $translit (duplicate)\n" unless $translit eq $old_translit;
-                     $ht{"cedicts"}->{$f} = join(" ", $ht{"cedicts"}->{$f}, $translit) unless $old_translit eq $translit;
-		  } else {
-                     $ht{"cedict"}->{$f} = $translit;
-                     $ht{"cedicts"}->{$f} = $translit;
-		  }
-                  $n_cedict++;
-	       }
-            }
-            close(IN);
-            # print "Read in $n_cedict n_cedict\n";
-         } else {
-	    print STDERR "Can't open $filename";
-         }
-      } elsif ($filename =~ /chinese_to_pinyin/i) {
-	 if (open(IN, $filename)) {
-	    my $line_number = 0;
-	    while (<IN>) {
-	       $line_number++;
-	       next if /^#/;
-	       if (($f, $translit) = ($_ =~ /^(\S+)\t(\S+)\s*$/)) {
-		  $ht{"simple_pinyin"}->{$f} = $translit; 
-		  $n_simple_pinyin++;
-	       }
-	    }
-	    close(IN);
-	    # print "Read in $n_simple_pinyin n_simple_pinyin\n";
-	 } else {
-	    print STDERR "Can't open $filename";
-	 }
-      } else {
-	 print STDERR "Don't know what to do with file $filename (in read_chinese_tonal_pinyin_files)\n";
-      }
-   }
-}
-
-sub tonal_pinyin {
-   local($caller, $s, *ht, $gloss) = @_;
-
-   return $result if defined($result = $ht{COMBINED}->{$s});
-
-   $cedict_pinyin = $ht{"cedict"}->{$s} || "";
-   $cedicts_pinyin = $ht{"cedicts"}->{$s} || "";
-   $unihan_pinyin = "";
-   @characters = $utf8->split_into_utf8_characters($s, "return only chars", *empty_ht);
-   foreach $c (@characters) {
-      if ($pinyin = $ht{"simple_pinyin"}->{$c}) {
-	 $unihan_pinyin .= $pinyin;
-      } elsif ($pinyin = $ht{"kHanyuPinlu"}->{$c}) {
-	 $pinyin =~ s/^(\S+)\s.*$/$1/;
-	 $unihan_pinyin .= $pinyin;
-      } elsif ($pinyin = $ht{"kXHC1983"}->{$c}) {
-	 $pinyin =~ s/^(\S+)\s.*$/$1/;
-	 $unihan_pinyin .= $pinyin;
-      } elsif ($pinyin = $ht{"kHanyuPinyin"}->{$c}) {
-	 $pinyin =~ s/^(\S+)\s.*$/$1/;
-	 $unihan_pinyin .= $pinyin;
-      } elsif ($pinyin = $ht{"cedicts"}->{$c}) {
-	 $pinyin =~ s/^(\S+)\s.*$/$1/;
-	 $unihan_pinyin .= $pinyin;
-      # middle dot, katakana middle dot, multiplication sign
-      } elsif ($c =~ /^(\xC2\xB7|\xE3\x83\xBB|\xC3\x97)$/) {
-	 $unihan_pinyin .= $c;
-      # ASCII
-      } elsif ($c =~ /^([\x21-\x7E])$/) {
-	 $unihan_pinyin .= $c;
-      } else {
-	 $unihan_pinyin .= "?";
-	 $hex = $utf8->utf8_to_hex($c);
-	 $unicode = uc $utf8->utf8_to_4hex_unicode($c);
-	 # print STDERR "Tonal pinyin: Unknown character $c ($hex/U+$unicode) -> ?\n";
-      }
-   }
-   $pinyin_title = "";
-   if (($#characters >= 1) && $cedicts_pinyin) {
-      foreach $pinyin (split(/\s+/, $cedicts_pinyin)) {
-	 $pinyin_title .= "$s $pinyin (CEDICT)\n";
-      }
-      $pinyin_title .= "\n";
-   }
-   foreach $c (@characters) {
-      my %local_ht = ();
-      @pinyins = ();
-      foreach $type (("kHanyuPinlu", "kXHC1983", "kHanyuPinyin", "cedicts")) {
-	 if ($pinyin_s = $ht{$type}->{$c}) {
-	    foreach $pinyin (split(/\s+/, $pinyin_s)) {
-	       push(@pinyins, $pinyin) unless $util->member($pinyin, @pinyins);
-	       $type2 = ($type eq "cedicts") ? "CEDICT" : $type;
-	       $local_ht{$pinyin} = ($local_ht{$pinyin}) ? join(", ", $local_ht{$pinyin}, $type2) : $type2;
-	    }
-	 }
-      }
-      foreach $pinyin (@pinyins) {
-	 $type_s = $local_ht{$pinyin};
-	 $pinyin_title .= "$c $pinyin ($type_s)\n";
-      }
-   }
-   $pinyin_title =~ s/\n$//;
-   $pinyin_title =~ s/\n/&#xA;/g;
-   $unihan_pinyin = "" if $unihan_pinyin =~ /^\?+$/;
-   if (($#characters >= 1) && $cedict_pinyin && $unihan_pinyin && ($unihan_pinyin ne $cedict_pinyin)) {
-      $log = "Gloss($s): $gloss\nCEdict($s): $cedicts_pinyin\nUnihan($s): $unihan_pinyin\n";
-      foreach $type (("kHanyuPinlu", "kXHC1983", "kHanyuPinyin")) {
-	 $log_line = "$type($s): ";
-	 foreach $c (@characters) {
-	    $pinyin = $ht{$type}->{$c} || "";
-	    if ($pinyin =~ / /) {
-	       $log_line .= "($pinyin)";
-	    } elsif ($pinyin) {
-	       $log_line .= $pinyin;
-	    } else {
-	       $log_line .= "?";
-	    }
-	 }
-	 $log .= "$log_line\n";
-      }
-      $ht{CONFLICT}->{$s} = $log;
-   }
-   $result = $unihan_pinyin || $cedict_pinyin;
-   $result = $cedict_pinyin if ($#characters > 0) && $cedict_pinyin;
-   $ht{COMBINED}->{$s} = $result;
-   $ht{PINYIN_TITLE}->{$s} = $pinyin_title;
-   return $result;
-}
-
-%number_to_accent_tone_ht = (
-   "a1", "\xC4\x81", "a2", "\xC3\xA1", "a3", "\xC7\x8E", "a4", "\xC3\xA0",
-   "e1", "\xC4\x93", "e2", "\xC3\xA9", "e3", "\xC4\x9B", "e4", "\xC3\xA8",
-   "i1", "\xC4\xAB", "i2", "\xC3\xAD", "i3", "\xC7\x90", "i4", "\xC3\xAC",
-   "o1", "\xC5\x8D", "o2", "\xC3\xB3", "o3", "\xC7\x92", "o4", "\xC3\xB2",
-   "u1", "\xC5\xAB", "u2", "\xC3\xBA", "u3", "\xC7\x94", "u4", "\xC3\xB9",
-   "u:1","\xC7\x96", "u:2","\xC7\x98", "u:3","\xC7\x9A", "u:4","\xC7\x9C",
-   "\xC3\xBC1","\xC7\x96","\xC3\xBC2","\xC7\x98","\xC3\xBC3","\xC7\x9A","\xC3\xBC4","\xC7\x9C"
-);
-
-sub number_to_accent_tone {
-   local($caller, $s) = @_;
-
-   my $result = "";
-   while (($pre,$alpha,$tone_number,$rest) = ($s =~ /^(.*?)((?:[a-z]|u:|\xC3\xBC)+)([1-5])(.*)$/i)) {
-      if ($tone_number eq "5") {
-         $result .= "$pre$alpha";
-      } elsif ((($pre_acc,$acc_letter,$post_acc) = ($alpha =~ /^(.*)([ae])(.*)$/))
-       || (($pre_acc,$acc_letter,$post_acc) = ($alpha =~ /^(.*)(o)(u.*)$/))
-       || (($pre_acc,$acc_letter,$post_acc) = ($alpha =~ /^(.*)(u:|[iou]|\xC3\xBC)([^aeiou]*)$/))) {
-         $result .= "$pre$pre_acc" . ($number_to_accent_tone_ht{($acc_letter . $tone_number)} || ($acc_letter . $tone_number)) . $post_acc;
-      } else {
-         $result .= "$pre$alpha$tone_number";
-      }
-      $s = $rest;
-   }
-   $result .= $s;
-   $result =~ s/u:/\xC3\xBC/g;
-   return $result;
-}
-
-sub string_contains_utf8_cjk_unified_ideograph_p {
-   local($caller, $s) = @_;
-
-   return ($s =~ /([\xE4-\xE9]|\xE3[\x90-\xBF]|\xF0[\xA0-\xAC])/);
-}
-
-1;
diff --git a/spaces/jbilcke-hf/MusicGen/audiocraft/quantization/base.py b/spaces/jbilcke-hf/MusicGen/audiocraft/quantization/base.py
deleted file mode 100644
index 1b16c130d266fbd021d3fc29bb9f98c33dd3c588..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/MusicGen/audiocraft/quantization/base.py
+++ /dev/null
@@ -1,107 +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.
-
-"""
-Base class for all quantizers.
-"""
-
-from dataclasses import dataclass, field
-import typing as tp
-
-import torch
-from torch import nn
-
-
-@dataclass
-class QuantizedResult:
-    x: torch.Tensor
-    codes: torch.Tensor
-    bandwidth: torch.Tensor  # bandwidth in kb/s used, per batch item.
-    penalty: tp.Optional[torch.Tensor] = None
-    metrics: dict = field(default_factory=dict)
-
-
-class BaseQuantizer(nn.Module):
-    """Base class for quantizers.
-    """
-
-    def forward(self, x: torch.Tensor, frame_rate: int) -> QuantizedResult:
-        """
-        Given input tensor x, returns first the quantized (or approximately quantized)
-        representation along with quantized codes, bandwidth, and any penalty term for the loss.
-        Finally, this returns a dict of metrics to update logging etc.
-        Frame rate must be passed so that the bandwidth is properly computed.
-        """
-        raise NotImplementedError()
-
-    def encode(self, x: torch.Tensor) -> torch.Tensor:
-        """Encode a given input tensor with the specified sample rate at the given bandwidth.
-        """
-        raise NotImplementedError()
-
-    def decode(self, codes: torch.Tensor) -> torch.Tensor:
-        """Decode the given codes to the quantized representation.
-        """
-        raise NotImplementedError()
-
-    @property
-    def total_codebooks(self):
-        """Total number of codebooks.
-        """
-        raise NotImplementedError()
-
-    @property
-    def num_codebooks(self):
-        """Number of active codebooks.
-        """
-        raise NotImplementedError()
-
-    def set_num_codebooks(self, n: int):
-        """Set the number of active codebooks.
-        """
-        raise NotImplementedError()
-
-
-class DummyQuantizer(BaseQuantizer):
-    """Fake quantizer that actually does not perform any quantization.
-    """
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x: torch.Tensor, frame_rate: int):
-        q = x.unsqueeze(1)
-        return QuantizedResult(x, q, torch.tensor(q.numel() * 32 * frame_rate / 1000 / len(x)).to(x))
-
-    def encode(self, x: torch.Tensor) -> torch.Tensor:
-        """Encode a given input tensor with the specified sample rate at the given bandwidth.
-        In the case of the DummyQuantizer, the codes are actually identical
-        to the input and resulting quantized representation as no quantization is done.
-        """
-        return x.unsqueeze(1)
-
-    def decode(self, codes: torch.Tensor) -> torch.Tensor:
-        """Decode the given codes to the quantized representation.
-        In the case of the DummyQuantizer, the codes are actually identical
-        to the input and resulting quantized representation as no quantization is done.
-        """
-        return codes.squeeze(1)
-
-    @property
-    def total_codebooks(self):
-        """Total number of codebooks.
-        """
-        return 1
-
-    @property
-    def num_codebooks(self):
-        """Total number of codebooks.
-        """
-        return self.total_codebooks
-
-    def set_num_codebooks(self, n: int):
-        """Set the number of active codebooks.
-        """
-        raise AttributeError("Cannot override the number of codebooks for the dummy quantizer")
diff --git a/spaces/jbilcke-hf/VideoQuest/src/components/ui/select.tsx b/spaces/jbilcke-hf/VideoQuest/src/components/ui/select.tsx
deleted file mode 100644
index 704239634b359b9e680dab25275e205e72579f82..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/components/ui/select.tsx
+++ /dev/null
@@ -1,121 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as SelectPrimitive from "@radix-ui/react-select"
-import { Check, ChevronDown } from "lucide-react"
-
-import { cn } from "@/lib/utils"
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Trigger>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Trigger>
->(({ className, children, ...props }, ref) => (
-  <SelectPrimitive.Trigger
-    ref={ref}
-    className={cn(
-      "flex h-10 w-full items-center justify-between rounded-md border border-stone-200 border-stone-200 bg-transparent px-3 py-2 text-sm ring-offset-white placeholder:text-stone-500 focus:outline-none focus:ring-2 focus:ring-stone-400 focus:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50 dark:border-stone-800 dark:border-stone-800 dark:ring-offset-stone-950 dark:placeholder:text-stone-400 dark:focus:ring-stone-800",
-      className
-    )}
-    {...props}
-  >
-    {children}
-    <SelectPrimitive.Icon asChild>
-      <ChevronDown className="h-4 w-4 opacity-50" />
-    </SelectPrimitive.Icon>
-  </SelectPrimitive.Trigger>
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Content>
->(({ className, children, position = "popper", ...props }, ref) => (
-  <SelectPrimitive.Portal>
-    <SelectPrimitive.Content
-      ref={ref}
-      className={cn(
-        "relative z-50 min-w-[8rem] overflow-hidden rounded-md border border-stone-200 bg-white text-stone-950 shadow-md data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 dark:border-stone-800 dark:bg-stone-950 dark:text-stone-50",
-        position === "popper" &&
-          "data-[side=bottom]:translate-y-1 data-[side=left]:-translate-x-1 data-[side=right]:translate-x-1 data-[side=top]:-translate-y-1",
-        className
-      )}
-      position={position}
-      {...props}
-    >
-      <SelectPrimitive.Viewport
-        className={cn(
-          "p-1",
-          position === "popper" &&
-            "h-[var(--radix-select-trigger-height)] w-full min-w-[var(--radix-select-trigger-width)]"
-        )}
-      >
-        {children}
-      </SelectPrimitive.Viewport>
-    </SelectPrimitive.Content>
-  </SelectPrimitive.Portal>
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Label>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Label>
->(({ className, ...props }, ref) => (
-  <SelectPrimitive.Label
-    ref={ref}
-    className={cn("py-1.5 pl-8 pr-2 text-sm font-semibold", className)}
-    {...props}
-  />
-))
-SelectLabel.displayName = SelectPrimitive.Label.displayName
-
-const SelectItem = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Item>
->(({ className, children, ...props }, ref) => (
-  <SelectPrimitive.Item
-    ref={ref}
-    className={cn(
-      "relative flex w-full cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none focus:bg-stone-100 focus:text-stone-900 data-[disabled]:pointer-events-none data-[disabled]:opacity-50 dark:focus:bg-stone-800 dark:focus:text-stone-50",
-      className
-    )}
-    {...props}
-  >
-    <span className="absolute left-2 flex h-3.5 w-3.5 items-center justify-center">
-      <SelectPrimitive.ItemIndicator>
-        <Check className="h-4 w-4" />
-      </SelectPrimitive.ItemIndicator>
-    </span>
-
-    <SelectPrimitive.ItemText>{children}</SelectPrimitive.ItemText>
-  </SelectPrimitive.Item>
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
-  React.ElementRef<typeof SelectPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof SelectPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <SelectPrimitive.Separator
-    ref={ref}
-    className={cn("-mx-1 my-1 h-px bg-stone-100 dark:bg-stone-800", className)}
-    {...props}
-  />
-))
-SelectSeparator.displayName = SelectPrimitive.Separator.displayName
-
-export {
-  Select,
-  SelectGroup,
-  SelectValue,
-  SelectTrigger,
-  SelectContent,
-  SelectLabel,
-  SelectItem,
-  SelectSeparator,
-}
diff --git a/spaces/jeonchangbin49/De-limiter/app.py b/spaces/jeonchangbin49/De-limiter/app.py
deleted file mode 100644
index c0f8242082a47f7f0a49d71984997a4d5d287934..0000000000000000000000000000000000000000
--- a/spaces/jeonchangbin49/De-limiter/app.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import os
-import json
-import argparse
-import copy
-
-import numpy as np
-import matplotlib.pyplot as plt
-import torch
-import tqdm
-import librosa
-import librosa.display
-import soundfile as sf
-import pyloudnorm as pyln
-from dotmap import DotMap
-import gradio as gr
-
-from models import load_model_with_args
-from separate_func import (
-    conv_tasnet_separate,
-)
-from utils import db2linear
-
-
-tqdm.monitor_interval = 0
-
-
-def separate_track_with_model(
-    args, model, device, track_audio, track_name, meter, augmented_gain
-):
-    with torch.no_grad():
-        if (
-            args.model_loss_params.architecture == "conv_tasnet_mask_on_output"
-            or args.model_loss_params.architecture == "conv_tasnet"
-        ):
-            estimates = conv_tasnet_separate(
-                args,
-                model,
-                device,
-                track_audio,
-                track_name,
-                meter=meter,
-                augmented_gain=augmented_gain,
-            )
-
-        return estimates
-
-
-def parallel_mix(input, output, mix_coefficient):
-    sr = 44100
-    return sr, input[1] * mix_coefficient + output[1] * (1 - mix_coefficient)
-
-
-def int16_to_float32(wav):
-    X = wav / 32768
-    return X
-
-
-def waveform_plot(input, output, prl_mix_ouptut, figsize_x=20, figsize_y=9):
-    sr = 44100
-    fig, ax = plt.subplots(
-        nrows=3, sharex=True, sharey=True, figsize=(figsize_x, figsize_y)
-    )
-    librosa.display.waveshow(int16_to_float32(input[1]).T, sr=sr, ax=ax[0])
-    ax[0].set(title="Loudness Normalized Input")
-    ax[0].label_outer()
-    librosa.display.waveshow(int16_to_float32(output[1]).T, sr=sr, ax=ax[1])
-    ax[1].set(title="De-limiter Output")
-    ax[1].label_outer()
-    librosa.display.waveshow(int16_to_float32(prl_mix_ouptut[1]).T, sr=sr, ax=ax[2])
-    ax[2].set(title="Parallel Mix of the Input and its De-limiter Output")
-    ax[2].label_outer()
-    return fig
-
-
-def main(input, mix_coefficient):
-    parser = argparse.ArgumentParser(description="model test.py")
-    parser.add_argument("--target", type=str, default="all")
-    parser.add_argument("--weight_directory", type=str, default="weight")
-    parser.add_argument("--output_directory", type=str, default="output")
-    parser.add_argument("--use_gpu", type=bool, default=True)
-    parser.add_argument("--save_name_as_target", type=bool, default=False)
-    parser.add_argument(
-        "--loudnorm_input_lufs",
-        type=float,
-        default=None,
-        help="If you want to use loudnorm for input",
-    )
-    parser.add_argument(
-        "--save_output_loudnorm",
-        type=float,
-        default=-14.0,
-        help="Save loudness normalized outputs or not. If you want to save, input target loudness",
-    )
-    parser.add_argument(
-        "--save_mixed_output",
-        type=float,
-        default=True,
-        help="Save original+delimited-estimation mixed output with a ratio of default 0.5 (orginal) and 1 - 0.5 (estimation)",
-    )
-    parser.add_argument(
-        "--save_16k_mono",
-        type=bool,
-        default=False,
-        help="Save 16k mono wav files for FAD evaluation.",
-    )
-    parser.add_argument(
-        "--save_histogram",
-        type=bool,
-        default=False,
-        help="Save histogram of the output. Only valid when the task is 'delimit'",
-    )
-    parser.add_argument(
-        "--use_singletrackset",
-        type=bool,
-        default=False,
-        help="Use SingleTrackSet if input data is too long.",
-    )
-
-    args, _ = parser.parse_known_args()
-
-    with open(f"{args.weight_directory}/{args.target}.json", "r") as f:
-        args_dict = json.load(f)
-        args_dict = DotMap(args_dict)
-
-    for key, value in args_dict["args"].items():
-        if key in list(vars(args).keys()):
-            pass
-        else:
-            setattr(args, key, value)
-
-    args.test_output_dir = f"{args.output_directory}"
-    os.makedirs(args.test_output_dir, exist_ok=True)
-
-    device = torch.device(
-        "cuda" if torch.cuda.is_available() and args.use_gpu else "cpu"
-    )
-
-    ###################### Define Models ######################
-    our_model = load_model_with_args(args)
-    our_model = our_model.to(device)
-
-    target_model_path = f"{args.weight_directory}/{args.target}.pth"
-    checkpoint = torch.load(target_model_path, map_location=device)
-    our_model.load_state_dict(checkpoint)
-
-    our_model.eval()
-
-    meter = pyln.Meter(44100)
-
-    sr, track_audio = input
-    orig_sr = copy.deepcopy(sr)
-    track_audio = track_audio.T
-    track_name = "gradio_demo"
-
-    if sr != 44100:
-        track_audio = int16_to_float32(track_audio)
-        track_audio = librosa.resample(
-            track_audio, orig_sr=sr, target_sr=44100, res_type="soxr_vhq"
-        )
-        sr = 44100
-
-    orig_audio = track_audio.copy()
-
-    augmented_gain = None
-
-    if args.loudnorm_input_lufs:  # If you want to use loud-normalized input
-        track_lufs = meter.integrated_loudness(track_audio.T)
-        augmented_gain = args.loudnorm_input_lufs - track_lufs
-        track_audio = track_audio * db2linear(augmented_gain, eps=0.0)
-
-    track_audio = (
-        torch.as_tensor(track_audio, dtype=torch.float32).unsqueeze(0).to(device)
-    )
-
-    estimates = separate_track_with_model(
-        args, our_model, device, track_audio, track_name, meter, augmented_gain
-    )
-
-    max_value = np.abs(estimates).max()
-    if max_value >= 1.0:
-        estimates = estimates / max_value
-        estimates = estimates * db2linear(-0.1, eps=0.0)
-        args.save_output_loudnorm = meter.integrated_loudness(estimates.T)
-
-    if orig_sr == 44100:
-        orig_audio = int16_to_float32(orig_audio)
-    track_lufs = meter.integrated_loudness(orig_audio.T)
-    augmented_gain = args.save_output_loudnorm - track_lufs
-    orig_audio = orig_audio * db2linear(augmented_gain, eps=0.0)
-
-    prl_mix_out = orig_audio.T * mix_coefficient + estimates.T * (1 - mix_coefficient)
-    prl_mix_out = prl_mix_out * 32768
-    prl_mix_out = prl_mix_out.astype(np.int16)
-    estimates = estimates.T * 32768
-    estimates = estimates.astype(np.int16)
-    orig_audio = orig_audio.T * 32768
-    orig_audio = orig_audio.astype(np.int16)
-
-    return (
-        (sr, estimates),
-        (sr, orig_audio),
-        (sr, prl_mix_out),
-    )
-
-
-with gr.Blocks() as demo:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-              <div
-                style="
-                  display: inline-flex;
-                  align-items: center;
-                  gap: 0.8rem;
-                  font-size: 1.75rem;
-                "
-              >
-                <h1 style="font-weight: 900; margin-bottom: 7px;">
-                  Music De-limiter
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-                A demo for "Music De-limiter Networks via Sample-wise Gain Inversion" to appear in WASPAA 2023.<br>
-                Upload a stereo music (tested with .wav, .mp3, .m4a) file and then press "De-limit" button to apply the De-limiter.<br>
-                The processing is based on 44.1kHz sample rate. Other sample rate will be automatically resampled to 44.1kHz.<br>
-                Since we use a CPU instead of a GPU, it may require a few seconds to minutes.<br>
-                Then, you can apply a Parallel Mix technique, which is a simple linear mixing technique of "loudness normalized input" and the "de-limiter output", similar to Parallel Compression.<br>
-                If the coefficient is 0.3 then the output will be the "loudness_normalized_input * 0.3 + de-limiter_output * 0.7"<br>
-                Check our Paper <a href="https://arxiv.org/abs/2308.01187">[arXiv]</a>
-                Codes <a href="https://github.com/jeonchangbin49/De-limiter">[GitHub]</a>
-                Audio samples <a href="https://catnip-leaf-c6a.notion.site/Music-De-limiter-7072c0e725fd42249ff78cbbaedc95d7?pvs=4">[Notion]</a> <br>
-                Please let me know any issues or comments on vinyne@snu.ac.kr or the "Community" page (the upper right section of this page).
-            </div>
-        """
-    )
-    with gr.Row().style(mobile_collapse=False, equal_height=True):
-        with gr.Column():
-            with gr.Box():
-                input_audio = gr.Audio(source="upload", label="De-limiter Input")
-                btn = gr.Button("De-limit")
-        with gr.Column():
-            with gr.Box():
-                loud_norm_input = gr.Audio(
-                    label="Loudness Normalized Input",
-                    show_download_button=True,
-                )
-            with gr.Box():
-                output_audio = gr.Audio(
-                    label="De-limiter Output",
-                    show_download_button=True,
-                )
-            with gr.Box():
-                output_audio_parallel = gr.Audio(
-                    label="Parallel Mix of the Input and its De-limiter Output",
-                    show_download_button=True,
-                )
-                slider = gr.Slider(
-                    minimum=0,
-                    maximum=1,
-                    step=0.1,
-                    value=0.5,
-                    label="Parallel Mix Coefficient",
-                )
-            btn.click(
-                main,
-                inputs=[input_audio, slider],
-                outputs=[output_audio, loud_norm_input, output_audio_parallel],
-            )
-            slider.release(
-                parallel_mix,
-                inputs=[loud_norm_input, output_audio, slider],
-                outputs=output_audio_parallel,
-            )
-    with gr.Row().style(mobile_collapse=False, equal_height=True):
-        with gr.Column():
-            with gr.Box():
-                plot = gr.Plot(label="Plots")
-                btn2 = gr.Button("Show Plots")
-                slider_plot_x = gr.Slider(
-                    minimum=1,
-                    maximum=100,
-                    step=1,
-                    value=20,
-                    label="Plot X-axis size",
-                )
-                slider_plot_y = gr.Slider(
-                    minimum=1,
-                    maximum=30,
-                    step=1,
-                    value=9,
-                    label="Plot Y-axis size",
-                )
-                btn2.click(
-                    waveform_plot,
-                    inputs=[
-                        loud_norm_input,
-                        output_audio,
-                        output_audio_parallel,
-                        slider_plot_x,
-                        slider_plot_y,
-                    ],
-                    outputs=plot,
-                )
-
-if __name__ == "__main__":
-    demo.launch(debug=True)
diff --git a/spaces/joaogabriellima/Real-Time-Voice-Cloning/synthesizer/train.py b/spaces/joaogabriellima/Real-Time-Voice-Cloning/synthesizer/train.py
deleted file mode 100644
index a136cf9b38538ca7dc428adf209c0cbb40e890d7..0000000000000000000000000000000000000000
--- a/spaces/joaogabriellima/Real-Time-Voice-Cloning/synthesizer/train.py
+++ /dev/null
@@ -1,269 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import optim
-from torch.utils.data import DataLoader
-from synthesizer import audio
-from synthesizer.models.tacotron import Tacotron
-from synthesizer.synthesizer_dataset import SynthesizerDataset, collate_synthesizer
-from synthesizer.utils import ValueWindow, data_parallel_workaround
-from synthesizer.utils.plot import plot_spectrogram
-from synthesizer.utils.symbols import symbols
-from synthesizer.utils.text import sequence_to_text
-from vocoder.display import *
-from datetime import datetime
-import numpy as np
-from pathlib import Path
-import sys
-import time
-import platform
-
-
-def np_now(x: torch.Tensor): return x.detach().cpu().numpy()
-
-def time_string():
-    return datetime.now().strftime("%Y-%m-%d %H:%M")
-
-def train(run_id: str, syn_dir: str, models_dir: str, save_every: int,
-         backup_every: int, force_restart:bool, hparams):
-
-    syn_dir = Path(syn_dir)
-    models_dir = Path(models_dir)
-    models_dir.mkdir(exist_ok=True)
-
-    model_dir = models_dir.joinpath(run_id)
-    plot_dir = model_dir.joinpath("plots")
-    wav_dir = model_dir.joinpath("wavs")
-    mel_output_dir = model_dir.joinpath("mel-spectrograms")
-    meta_folder = model_dir.joinpath("metas")
-    model_dir.mkdir(exist_ok=True)
-    plot_dir.mkdir(exist_ok=True)
-    wav_dir.mkdir(exist_ok=True)
-    mel_output_dir.mkdir(exist_ok=True)
-    meta_folder.mkdir(exist_ok=True)
-    
-    weights_fpath = model_dir.joinpath(run_id).with_suffix(".pt")
-    metadata_fpath = syn_dir.joinpath("train.txt")
-    
-    print("Checkpoint path: {}".format(weights_fpath))
-    print("Loading training data from: {}".format(metadata_fpath))
-    print("Using model: Tacotron")
-    
-    # Book keeping
-    step = 0
-    time_window = ValueWindow(100)
-    loss_window = ValueWindow(100)
-    
-    
-    # From WaveRNN/train_tacotron.py
-    if torch.cuda.is_available():
-        device = torch.device("cuda")
-
-        for session in hparams.tts_schedule:
-            _, _, _, batch_size = session
-            if batch_size % torch.cuda.device_count() != 0:
-                raise ValueError("`batch_size` must be evenly divisible by n_gpus!")
-    else:
-        device = torch.device("cpu")
-    print("Using device:", device)
-
-    # Instantiate Tacotron Model
-    print("\nInitialising Tacotron Model...\n")
-    model = Tacotron(embed_dims=hparams.tts_embed_dims,
-                     num_chars=len(symbols),
-                     encoder_dims=hparams.tts_encoder_dims,
-                     decoder_dims=hparams.tts_decoder_dims,
-                     n_mels=hparams.num_mels,
-                     fft_bins=hparams.num_mels,
-                     postnet_dims=hparams.tts_postnet_dims,
-                     encoder_K=hparams.tts_encoder_K,
-                     lstm_dims=hparams.tts_lstm_dims,
-                     postnet_K=hparams.tts_postnet_K,
-                     num_highways=hparams.tts_num_highways,
-                     dropout=hparams.tts_dropout,
-                     stop_threshold=hparams.tts_stop_threshold,
-                     speaker_embedding_size=hparams.speaker_embedding_size).to(device)
-
-    # Initialize the optimizer
-    optimizer = optim.Adam(model.parameters())
-
-    # Load the weights
-    if force_restart or not weights_fpath.exists():
-        print("\nStarting the training of Tacotron from scratch\n")
-        model.save(weights_fpath)
-
-        # Embeddings metadata
-        char_embedding_fpath = meta_folder.joinpath("CharacterEmbeddings.tsv")
-        with open(char_embedding_fpath, "w", encoding="utf-8") as f:
-            for symbol in symbols:
-                if symbol == " ":
-                    symbol = "\\s"  # For visual purposes, swap space with \s
-
-                f.write("{}\n".format(symbol))
-
-    else:
-        print("\nLoading weights at %s" % weights_fpath)
-        model.load(weights_fpath, optimizer)
-        print("Tacotron weights loaded from step %d" % model.step)
-    
-    # Initialize the dataset
-    metadata_fpath = syn_dir.joinpath("train.txt")
-    mel_dir = syn_dir.joinpath("mels")
-    embed_dir = syn_dir.joinpath("embeds")
-    dataset = SynthesizerDataset(metadata_fpath, mel_dir, embed_dir, hparams)
-    test_loader = DataLoader(dataset,
-                             batch_size=1,
-                             shuffle=True,
-                             pin_memory=True)
-
-    for i, session in enumerate(hparams.tts_schedule):
-        current_step = model.get_step()
-
-        r, lr, max_step, batch_size = session
-
-        training_steps = max_step - current_step
-
-        # Do we need to change to the next session?
-        if current_step >= max_step:
-            # Are there no further sessions than the current one?
-            if i == len(hparams.tts_schedule) - 1:
-                # We have completed training. Save the model and exit
-                model.save(weights_fpath, optimizer)
-                break
-            else:
-                # There is a following session, go to it
-                continue
-
-        model.r = r
-
-        # Begin the training
-        simple_table([(f"Steps with r={r}", str(training_steps // 1000) + "k Steps"),
-                      ("Batch Size", batch_size),
-                      ("Learning Rate", lr),
-                      ("Outputs/Step (r)", model.r)])
-
-        for p in optimizer.param_groups:
-            p["lr"] = lr
-
-        data_loader = DataLoader(dataset,
-                                 collate_fn=lambda batch: collate_synthesizer(batch, r, hparams),
-                                 batch_size=batch_size,
-                                 num_workers=2 if platform.system() != "Windows" else 0,
-                                 shuffle=True,
-                                 pin_memory=True)
-
-        total_iters = len(dataset) 
-        steps_per_epoch = np.ceil(total_iters / batch_size).astype(np.int32)
-        epochs = np.ceil(training_steps / steps_per_epoch).astype(np.int32)
-
-        for epoch in range(1, epochs+1):
-            for i, (texts, mels, embeds, idx) in enumerate(data_loader, 1):
-                start_time = time.time()
-
-                # Generate stop tokens for training
-                stop = torch.ones(mels.shape[0], mels.shape[2])
-                for j, k in enumerate(idx):
-                    stop[j, :int(dataset.metadata[k][4])-1] = 0
-
-                texts = texts.to(device)
-                mels = mels.to(device)
-                embeds = embeds.to(device)
-                stop = stop.to(device)
-
-                # Forward pass
-                # Parallelize model onto GPUS using workaround due to python bug
-                if device.type == "cuda" and torch.cuda.device_count() > 1:
-                    m1_hat, m2_hat, attention, stop_pred = data_parallel_workaround(model, texts,
-                                                                                    mels, embeds)
-                else:
-                    m1_hat, m2_hat, attention, stop_pred = model(texts, mels, embeds)
-
-                # Backward pass
-                m1_loss = F.mse_loss(m1_hat, mels) + F.l1_loss(m1_hat, mels)
-                m2_loss = F.mse_loss(m2_hat, mels)
-                stop_loss = F.binary_cross_entropy(stop_pred, stop)
-
-                loss = m1_loss + m2_loss + stop_loss
-
-                optimizer.zero_grad()
-                loss.backward()
-
-                if hparams.tts_clip_grad_norm is not None:
-                    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), hparams.tts_clip_grad_norm)
-                    if np.isnan(grad_norm.cpu()):
-                        print("grad_norm was NaN!")
-
-                optimizer.step()
-
-                time_window.append(time.time() - start_time)
-                loss_window.append(loss.item())
-
-                step = model.get_step()
-                k = step // 1000
-
-                msg = f"| Epoch: {epoch}/{epochs} ({i}/{steps_per_epoch}) | Loss: {loss_window.average:#.4} | {1./time_window.average:#.2} steps/s | Step: {k}k | "
-                stream(msg)
-
-                # Backup or save model as appropriate
-                if backup_every != 0 and step % backup_every == 0 : 
-                    backup_fpath = Path("{}/{}_{}k.pt".format(str(weights_fpath.parent), run_id, k))
-                    model.save(backup_fpath, optimizer)
-
-                if save_every != 0 and step % save_every == 0 : 
-                    # Must save latest optimizer state to ensure that resuming training
-                    # doesn't produce artifacts
-                    model.save(weights_fpath, optimizer)
-
-                # Evaluate model to generate samples
-                epoch_eval = hparams.tts_eval_interval == -1 and i == steps_per_epoch  # If epoch is done
-                step_eval = hparams.tts_eval_interval > 0 and step % hparams.tts_eval_interval == 0  # Every N steps
-                if epoch_eval or step_eval:
-                    for sample_idx in range(hparams.tts_eval_num_samples):
-                        # At most, generate samples equal to number in the batch
-                        if sample_idx + 1 <= len(texts):
-                            # Remove padding from mels using frame length in metadata
-                            mel_length = int(dataset.metadata[idx[sample_idx]][4])
-                            mel_prediction = np_now(m2_hat[sample_idx]).T[:mel_length]
-                            target_spectrogram = np_now(mels[sample_idx]).T[:mel_length]
-                            attention_len = mel_length // model.r
-
-                            eval_model(attention=np_now(attention[sample_idx][:, :attention_len]),
-                                       mel_prediction=mel_prediction,
-                                       target_spectrogram=target_spectrogram,
-                                       input_seq=np_now(texts[sample_idx]),
-                                       step=step,
-                                       plot_dir=plot_dir,
-                                       mel_output_dir=mel_output_dir,
-                                       wav_dir=wav_dir,
-                                       sample_num=sample_idx + 1,
-                                       loss=loss,
-                                       hparams=hparams)
-
-                # Break out of loop to update training schedule
-                if step >= max_step:
-                    break
-
-            # Add line break after every epoch
-            print("")
-
-def eval_model(attention, mel_prediction, target_spectrogram, input_seq, step,
-               plot_dir, mel_output_dir, wav_dir, sample_num, loss, hparams):
-    # Save some results for evaluation
-    attention_path = str(plot_dir.joinpath("attention_step_{}_sample_{}".format(step, sample_num)))
-    save_attention(attention, attention_path)
-
-    # save predicted mel spectrogram to disk (debug)
-    mel_output_fpath = mel_output_dir.joinpath("mel-prediction-step-{}_sample_{}.npy".format(step, sample_num))
-    np.save(str(mel_output_fpath), mel_prediction, allow_pickle=False)
-
-    # save griffin lim inverted wav for debug (mel -> wav)
-    wav = audio.inv_mel_spectrogram(mel_prediction.T, hparams)
-    wav_fpath = wav_dir.joinpath("step-{}-wave-from-mel_sample_{}.wav".format(step, sample_num))
-    audio.save_wav(wav, str(wav_fpath), sr=hparams.sample_rate)
-
-    # save real and predicted mel-spectrogram plot to disk (control purposes)
-    spec_fpath = plot_dir.joinpath("step-{}-mel-spectrogram_sample_{}.png".format(step, sample_num))
-    title_str = "{}, {}, step={}, loss={:.5f}".format("Tacotron", time_string(), step, loss)
-    plot_spectrogram(mel_prediction, str(spec_fpath), title=title_str,
-                     target_spectrogram=target_spectrogram,
-                     max_len=target_spectrogram.size // hparams.num_mels)
-    print("Input at step {}: {}".format(step, sequence_to_text(input_seq)))
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/PublicKey/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/PublicKey/__init__.py
deleted file mode 100644
index cf3a238477632ada1767a800455b5fb4c61d863a..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/PublicKey/__init__.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain.  To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# 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.
-# ===================================================================
-
-from Crypto.Util.asn1 import (DerSequence, DerInteger, DerBitString,
-                             DerObjectId, DerNull)
-
-
-def _expand_subject_public_key_info(encoded):
-    """Parse a SubjectPublicKeyInfo structure.
-
-    It returns a triple with:
-        * OID (string)
-        * encoded public key (bytes)
-        * Algorithm parameters (bytes or None)
-    """
-
-    #
-    # SubjectPublicKeyInfo  ::=  SEQUENCE  {
-    #   algorithm         AlgorithmIdentifier,
-    #   subjectPublicKey  BIT STRING
-    # }
-    #
-    # AlgorithmIdentifier  ::=  SEQUENCE  {
-    #   algorithm   OBJECT IDENTIFIER,
-    #   parameters  ANY DEFINED BY algorithm OPTIONAL
-    # }
-    #
-
-    spki = DerSequence().decode(encoded, nr_elements=2)
-    algo = DerSequence().decode(spki[0], nr_elements=(1,2))
-    algo_oid = DerObjectId().decode(algo[0])
-    spk = DerBitString().decode(spki[1]).value
-
-    if len(algo) == 1:
-        algo_params = None
-    else:
-        try:
-            DerNull().decode(algo[1])
-            algo_params = None
-        except:
-            algo_params = algo[1]
-
-    return algo_oid.value, spk, algo_params
-
-
-def _create_subject_public_key_info(algo_oid, public_key, params):
-
-    if params is None:
-        algorithm = DerSequence([DerObjectId(algo_oid)])
-    else:
-        algorithm = DerSequence([DerObjectId(algo_oid), params])
-
-    spki = DerSequence([algorithm,
-                        DerBitString(public_key)
-                        ])
-    return spki.encode()
-
-
-def _extract_subject_public_key_info(x509_certificate):
-    """Extract subjectPublicKeyInfo from a DER X.509 certificate."""
-
-    certificate = DerSequence().decode(x509_certificate, nr_elements=3)
-    tbs_certificate = DerSequence().decode(certificate[0],
-                                           nr_elements=range(6, 11))
-
-    index = 5
-    try:
-        tbs_certificate[0] + 1
-        # Version not present
-        version = 1
-    except TypeError:
-        version = DerInteger(explicit=0).decode(tbs_certificate[0]).value
-        if version not in (2, 3):
-            raise ValueError("Incorrect X.509 certificate version")
-        index = 6
-
-    return tbs_certificate[index]
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/XbmImagePlugin.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/XbmImagePlugin.py
deleted file mode 100644
index 3c12564c963d8b6342fa6ef1d7fc1892af30ffff..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/XbmImagePlugin.py
+++ /dev/null
@@ -1,94 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# XBM File handling
-#
-# History:
-# 1995-09-08 fl   Created
-# 1996-11-01 fl   Added save support
-# 1997-07-07 fl   Made header parser more tolerant
-# 1997-07-22 fl   Fixed yet another parser bug
-# 2001-02-17 fl   Use 're' instead of 'regex' (Python 2.1) (0.4)
-# 2001-05-13 fl   Added hotspot handling (based on code from Bernhard Herzog)
-# 2004-02-24 fl   Allow some whitespace before first #define
-#
-# Copyright (c) 1997-2004 by Secret Labs AB
-# Copyright (c) 1996-1997 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import re
-
-from . import Image, ImageFile
-
-# XBM header
-xbm_head = re.compile(
-    rb"\s*#define[ \t]+.*_width[ \t]+(?P<width>[0-9]+)[\r\n]+"
-    b"#define[ \t]+.*_height[ \t]+(?P<height>[0-9]+)[\r\n]+"
-    b"(?P<hotspot>"
-    b"#define[ \t]+[^_]*_x_hot[ \t]+(?P<xhot>[0-9]+)[\r\n]+"
-    b"#define[ \t]+[^_]*_y_hot[ \t]+(?P<yhot>[0-9]+)[\r\n]+"
-    b")?"
-    rb"[\000-\377]*_bits\[]"
-)
-
-
-def _accept(prefix):
-    return prefix.lstrip()[:7] == b"#define"
-
-
-##
-# Image plugin for X11 bitmaps.
-
-
-class XbmImageFile(ImageFile.ImageFile):
-    format = "XBM"
-    format_description = "X11 Bitmap"
-
-    def _open(self):
-        m = xbm_head.match(self.fp.read(512))
-
-        if not m:
-            msg = "not a XBM file"
-            raise SyntaxError(msg)
-
-        xsize = int(m.group("width"))
-        ysize = int(m.group("height"))
-
-        if m.group("hotspot"):
-            self.info["hotspot"] = (int(m.group("xhot")), int(m.group("yhot")))
-
-        self.mode = "1"
-        self._size = xsize, ysize
-
-        self.tile = [("xbm", (0, 0) + self.size, m.end(), None)]
-
-
-def _save(im, fp, filename):
-    if im.mode != "1":
-        msg = f"cannot write mode {im.mode} as XBM"
-        raise OSError(msg)
-
-    fp.write(f"#define im_width {im.size[0]}\n".encode("ascii"))
-    fp.write(f"#define im_height {im.size[1]}\n".encode("ascii"))
-
-    hotspot = im.encoderinfo.get("hotspot")
-    if hotspot:
-        fp.write(f"#define im_x_hot {hotspot[0]}\n".encode("ascii"))
-        fp.write(f"#define im_y_hot {hotspot[1]}\n".encode("ascii"))
-
-    fp.write(b"static char im_bits[] = {\n")
-
-    ImageFile._save(im, fp, [("xbm", (0, 0) + im.size, 0, None)])
-
-    fp.write(b"};\n")
-
-
-Image.register_open(XbmImageFile.format, XbmImageFile, _accept)
-Image.register_save(XbmImageFile.format, _save)
-
-Image.register_extension(XbmImageFile.format, ".xbm")
-
-Image.register_mime(XbmImageFile.format, "image/xbm")
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiohttp/http_parser.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiohttp/http_parser.py
deleted file mode 100644
index 5a66ce4b9eec19777800ddc3c0f5e66b2270f9d3..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/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/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/keyword_table/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/keyword_table/__init__.py
deleted file mode 100644
index 43a973b9b0920e2d6a499bee82a9aee62fe2321f..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/keyword_table/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""Keyword Table Index Data Structures."""
-
-# indices
-from gpt_index.indices.keyword_table.base import GPTKeywordTableIndex
-from gpt_index.indices.keyword_table.rake_base import GPTRAKEKeywordTableIndex
-from gpt_index.indices.keyword_table.simple_base import GPTSimpleKeywordTableIndex
-
-__all__ = [
-    "GPTKeywordTableIndex",
-    "GPTSimpleKeywordTableIndex",
-    "GPTRAKEKeywordTableIndex",
-]
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/components/button.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/components/button.py
deleted file mode 100644
index e64db3827b2e03eb08555011dac44e8c22b34469..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/components/button.py
+++ /dev/null
@@ -1,124 +0,0 @@
-"""gr.Button() component."""
-
-from __future__ import annotations
-
-import warnings
-from typing import Callable, Literal
-
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import StringSerializable
-
-from gradio.components.base import IOComponent, _Keywords
-from gradio.deprecation import warn_deprecation, warn_style_method_deprecation
-from gradio.events import Clickable
-
-set_documentation_group("component")
-
-
-@document()
-class Button(Clickable, IOComponent, StringSerializable):
-    """
-    Used to create a button, that can be assigned arbitrary click() events. The label (value) of the button can be used as an input or set via the output of a function.
-
-    Preprocessing: passes the button value as a {str} into the function
-    Postprocessing: expects a {str} to be returned from a function, which is set as the label of the button
-    Demos: blocks_inputs, blocks_kinematics
-    """
-
-    def __init__(
-        self,
-        value: str | Callable = "Run",
-        *,
-        variant: Literal["primary", "secondary", "stop"] = "secondary",
-        size: Literal["sm", "lg"] | None = None,
-        icon: str | None = None,
-        link: str | None = None,
-        visible: bool = True,
-        interactive: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            value: Default text for the button to display. If callable, the function will be called whenever the app loads to set the initial value of the component.
-            variant: 'primary' for main call-to-action, 'secondary' for a more subdued style, 'stop' for a stop button.
-            size: Size of the button. Can be "sm" or "lg".
-            icon: URL or path to the icon file to display within the button. If None, no icon will be displayed. Must be within the working directory of the Gradio app or an external URL.
-            link: URL to open when the button is clicked. If None, no link will be used.
-            visible: If False, component will be hidden.
-            interactive: If False, the Button will be in a disabled state.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-        """
-        IOComponent.__init__(
-            self,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            value=value,
-            interactive=interactive,
-            scale=scale,
-            min_width=min_width,
-            **kwargs,
-        )
-        if variant == "plain":
-            warn_deprecation("'plain' variant deprecated, using 'secondary' instead.")
-            variant = "secondary"
-        self.variant = variant
-        self.size = size
-        self.icon = icon
-        self.link = link
-
-    @staticmethod
-    def update(
-        value: str | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
-        variant: Literal["primary", "secondary", "stop"] | None = None,
-        size: Literal["sm", "lg"] | None = None,
-        icon: str | None = None,
-        link: str | None = None,
-        visible: bool | None = None,
-        interactive: bool | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-    ):
-        warnings.warn(
-            "Using the update method is deprecated. Simply return a new object instead, e.g. `return gr.Button(...)` instead of `return gr.Button.update(...)`."
-        )
-        return {
-            "variant": variant,
-            "size": size,
-            "visible": visible,
-            "value": value,
-            "icon": icon,
-            "link": link,
-            "interactive": interactive,
-            "scale": scale,
-            "min_width": min_width,
-            "__type__": "update",
-        }
-
-    def style(
-        self,
-        *,
-        full_width: bool | None = None,
-        size: Literal["sm", "lg"] | None = None,
-        **kwargs,
-    ):
-        """
-        This method is deprecated. Please set these arguments in the constructor instead.
-        """
-        warn_style_method_deprecation()
-        if full_width is not None:
-            warn_deprecation(
-                "Use `scale` in place of full_width in the constructor. "
-                "scale=1 will make the button expand, whereas 0 will not."
-            )
-            self.scale = 1 if full_width else None
-        if size is not None:
-            self.size = size
-        return self
diff --git a/spaces/johnowhitaker/whistlegen_v2/app.py b/spaces/johnowhitaker/whistlegen_v2/app.py
deleted file mode 100644
index 410fb161f608f47f5d8ea9b67ee013a4154fe5cc..0000000000000000000000000000000000000000
--- a/spaces/johnowhitaker/whistlegen_v2/app.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import gradio as gr
-import json, urllib
-from model import GPT, GPTConfig
-from utils import sample
-import torch
-import pickle
-
-device = torch.device('cpu')
-
-
-# Create the model
-vocab_size=147
-block_size=128
-mconf = GPTConfig(vocab_size, block_size,
-                  n_layer=6, n_head=8, n_embd=256)
-model = GPT(mconf)
-
-# Load checkpoint
-model.load_state_dict(torch.load('another_epoch_1.75total.ckpt', map_location=device))
-
-# Vocab
-stoi = pickle.load(open('stoi.pkl', 'rb'))
-itos = pickle.load(open('itos.pkl', 'rb'))
-
-# Post-process generation
-# Completion
-def completion_to_song(c):
-  lines = c.split('\n')
-  kept_lines = []
-  notes = False
-  for l in lines:
-
-    # Record if we've hit music
-    if '|' in l:
-      notes = True
-
-    # Stop if we then go back to the start of another song
-    if 'T' in l and notes:
-      break
-    if 'T' in l and notes:
-      break
-
-    # Stop on an empty line
-    if len(l.strip()) < 2 and notes:
-      break
-    
-    # Otherwise keep the line
-    kept_lines.append(l)
-
-  return '\n'.join(kept_lines)
-
-# Generate function
-def generate_song(randomize, title, nu, ks, key):
-  # Start sequence
-  context = b"""T:"""
-
-  if not randomize:
-    context += bytes(title+'\n', 'utf-8')
-    context += bytes('M:'+ks+'\n', 'utf-8')
-    context += bytes('K:'+key+'\n', 'utf-8')
-    context += bytes('L:'+nu+'\n', 'utf-8')
-
-
-  # Model inputs
-  x = torch.tensor([stoi[s] for s in context], dtype=torch.long)[None,...].to(device)
-
-  # Completion
-  y = sample(model, x, 400, temperature=1.0, sample=True, top_k=10)[0]
-  completion = ''.join([chr(itos[int(i)]) for i in y])
-
-  # Return the first song
-  song = completion_to_song(completion)
-
-  html_song = song.replace('\n', '<br>')
-  url_song = urllib.parse.quote(song, safe='~@#$&()*!+=:;,?/\'')
-
-  html_text = '<p><a href="https://editor.drawthedots.com?t='+url_song+'" target="_blank"><b>EDIT LINK - click to open abcjs editor (allows download and playback)</b></a></p>'+"<p>"+html_song+'</p>'
-  return html_text
-
-# Gradio demo
-demo = gr.Blocks()
-
-with demo:
-    gr.Markdown("Quick demo for [WhistleGen v2](https://wandb.ai/johnowhitaker/whistlegen_v2/reports/WhistleGen-v2--VmlldzoyMTAwNjAz) which lets you generate folk music using a transformer model. I can't get the javascript needed for rendering and playback working with gradio, so this shows the raw ABC notation from the model and a link to view it properly in an external editor.")
-    with gr.Row():
-        title = gr.Text(label='Title', value='The March of AI')
-        with gr.Column():
-          nu = gr.Text(label='Note unit', value='1/8')
-    with gr.Row():
-        key_signature = gr.Dropdown(['3/4', '4/4', '6/8', 'Random'], value='4/4', label='Time Signature')
-        with gr.Column():
-          key = gr.Text(label='Key', value='D')
-    with gr.Row():
-      randomize = gr.Checkbox(label='Randomize (ignores settings above)', value=True)
-    with gr.Row():
-        out = gr.HTML(label="Output", value='Output should appear here (takes ~30s)')
-    btn = gr.Button("Run")
-    btn.click(fn=generate_song, inputs=[randomize, title, nu, key_signature, key], outputs=out)
-    
-    with gr.Row():
-        gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=whistlegen_v2_space)")
-        gr.Markdown("This is currently using an early model. See the [report](https://wandb.ai/johnowhitaker/whistlegen_v2/reports/WhistleGen-v2--VmlldzoyMTAwNjAz) for training info and updates.")
-
-demo.launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/joushe/moe-tts/text/japanese.py b/spaces/joushe/moe-tts/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/joushe/moe-tts/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ts', 'ʦ'),
-    ('u', 'ɯ'),
-    ('j', 'ʥ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('u', 'ɯ'),
-    ('ʧ', 'tʃ'),
-    ('j', 'dʑ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_romaji_with_accent(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = ''
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            if text != '':
-                text += ' '
-            labels = pyopenjtalk.extract_fullcontext(sentence)
-            for n, label in enumerate(labels):
-                phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-                if phoneme not in ['sil', 'pau']:
-                    text += phoneme.replace('ch', 'ʧ').replace('sh',
-                                                               'ʃ').replace('cl', 'Q')
-                else:
-                    continue
-                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-                a3 = int(re.search(r"\+(\d+)/", label).group(1))
-                if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
-                    a2_next = -1
-                else:
-                    a2_next = int(
-                        re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-                # Accent phrase boundary
-                if a3 == 1 and a2_next == 1:
-                    text += ' '
-                # Falling
-                elif a1 == 0 and a2_next == a2 + 1:
-                    text += '↓'
-                # Rising
-                elif a2 == 1 and a2_next == 2:
-                    text += '↑'
-        if i < len(marks):
-            text += unidecode(marks[i]).replace(' ', '')
-    return text
-
-
-def get_real_sokuon(text):
-    for regex, replacement in _real_sokuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def get_real_hatsuon(text):
-    for regex, replacement in _real_hatsuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = re.sub(
-        r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa2(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa3(text):
-    text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
-        'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
-    text = re.sub(
-        r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
-    return text
diff --git a/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/models/modeling_moss.py b/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/models/modeling_moss.py
deleted file mode 100644
index b7adea5bca857f7fdd6399dde7ce359f8f8cecfe..0000000000000000000000000000000000000000
--- a/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/models/modeling_moss.py
+++ /dev/null
@@ -1,711 +0,0 @@
-""" PyTorch Moss model."""
-
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from transformers.activations import ACT2FN
-from transformers.modeling_utils import PreTrainedModel
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from transformers.utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging
-)
-
-from .configuration_moss import MossConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "fnlp/moss-moon-003-base"
-_CONFIG_FOR_DOC = "MossConfig"
-
-
-MOSS_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "fnlp/moss-moon-003-base",
-    "fnlp/moss-moon-003-sft",
-    "fnlp/moss-moon-003-sft-plugin",
-]
-
-
-# Copied from transformers.models.gptj.modeling_gptj.create_sinusoidal_positions
-def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
-    inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
-    sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.float), inv_freq).float()
-    return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
-
-
-# Copied from transformers.models.gptj.modeling_gptj.rotate_every_two
-def rotate_every_two(x: torch.Tensor) -> torch.Tensor:
-    x1 = x[:, :, :, ::2]
-    x2 = x[:, :, :, 1::2]
-    x = torch.stack((-x2, x1), dim=-1)
-    return x.flatten(-2)  # in einsum notation: rearrange(x, '... d j -> ... (d j)')
-
-
-# Copied from transformers.models.gptj.modeling_gptj.apply_rotary_pos_emb
-def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor:
-    sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
-    cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
-    return (tensor * cos) + (rotate_every_two(tensor) * sin)
-
-
-class MossAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        max_positions = config.max_position_embeddings
-        self.register_buffer(
-            "causal_mask",
-            torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
-                1, 1, max_positions, max_positions
-            ),
-        )
-
-        self.attn_dropout = nn.Dropout(config.attn_pdrop)
-        self.resid_dropout = nn.Dropout(config.resid_pdrop)
-
-        self.embed_dim = config.hidden_size
-        self.num_attention_heads = config.num_attention_heads
-        self.head_dim = self.embed_dim // self.num_attention_heads
-        if self.head_dim * self.num_attention_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
-                f" `num_attention_heads`: {self.num_attention_heads})."
-            )
-        self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype())
-        self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=False)
-
-        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
-        self.rotary_dim = config.rotary_dim
-        pos_embd_dim = self.rotary_dim or self.embed_dim
-        self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim)
-
-    def _split_heads(self, x, n_head, dim_head, mp_num):
-        reshaped = x.reshape(x.shape[:-1] + (n_head // mp_num, dim_head))
-        reshaped = reshaped.reshape(x.shape[:-2] + (-1,) + reshaped.shape[-1:])
-        return reshaped
-
-    def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
-        """
-        Merges attn_head_size dim and num_attn_heads dim into n_ctx
-        """
-        if len(tensor.shape) == 5:
-            tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
-        elif len(tensor.shape) == 4:
-            tensor = tensor.permute(0, 2, 1, 3).contiguous()
-        else:
-            raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
-        new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
-        return tensor.view(new_shape)
-
-    def _attn(
-        self,
-        query,
-        key,
-        value,
-        attention_mask=None,
-        head_mask=None,
-    ):
-        # compute causal mask from causal mask buffer
-        query_length, key_length = query.size(-2), key.size(-2)
-        causal_mask = self.causal_mask[:, :, key_length - query_length : key_length, :key_length]
-
-        # Keep the attention weights computation in fp32 to avoid overflow issues
-        query = query.to(torch.float32)
-        key = key.to(torch.float32)
-
-        attn_weights = torch.matmul(query, key.transpose(-1, -2))
-
-        attn_weights = attn_weights / self.scale_attn
-        mask_value = torch.finfo(attn_weights.dtype).min
-        # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
-        # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
-        mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
-        attn_weights = torch.where(causal_mask, attn_weights, mask_value)
-
-        if attention_mask is not None:
-            # Apply the attention mask
-            attn_weights = attn_weights + attention_mask
-
-        attn_weights = nn.Softmax(dim=-1)(attn_weights)
-        attn_weights = attn_weights.to(value.dtype)
-        attn_weights = self.attn_dropout(attn_weights)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attn_weights = attn_weights * head_mask
-
-        attn_output = torch.matmul(attn_weights, value)
-
-        return attn_output, attn_weights
-
-    def forward(
-        self,
-        hidden_states: Optional[torch.FloatTensor],
-        layer_past: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = False,
-        output_attentions: Optional[bool] = False,
-    ) -> Union[
-        Tuple[torch.Tensor, Tuple[torch.Tensor]],
-        Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
-    ]:
-        qkv = self.qkv_proj(hidden_states)
-        # TODO(enijkamp): factor out number of logical TPU-v4 cores or make forward pass agnostic
-        mp_num = 4
-        qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))
-
-        local_dim = self.head_dim * self.num_attention_heads // mp_num
-        query, value, key = torch.split(qkv_split, local_dim, dim=-1)
-        query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num)
-        key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num)
-
-        value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num)
-        value = value.permute(0, 2, 1, 3)
-
-        embed_positions = self.embed_positions
-        if embed_positions.device != position_ids.device:
-            embed_positions = embed_positions.to(position_ids.device)
-            self.embed_positions = embed_positions
-
-        sincos = embed_positions[position_ids]
-        sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
-
-        if self.rotary_dim is not None:
-            k_rot = key[:, :, :, : self.rotary_dim]
-            k_pass = key[:, :, :, self.rotary_dim :]
-
-            q_rot = query[:, :, :, : self.rotary_dim]
-            q_pass = query[:, :, :, self.rotary_dim :]
-
-            k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
-            q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
-
-            key = torch.cat([k_rot, k_pass], dim=-1)
-            query = torch.cat([q_rot, q_pass], dim=-1)
-        else:
-            key = apply_rotary_pos_emb(key, sin, cos)
-            query = apply_rotary_pos_emb(query, sin, cos)
-
-        key = key.permute(0, 2, 1, 3)
-        query = query.permute(0, 2, 1, 3)
-
-        if layer_past is not None:
-            past_key = layer_past[0]
-            past_value = layer_past[1]
-            key = torch.cat((past_key, key), dim=-2)
-            value = torch.cat((past_value, value), dim=-2)
-
-        if use_cache is True:
-            present = (key, value)
-        else:
-            present = None
-
-        # compute self-attention: V x Softmax(QK^T)
-        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
-
-        attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
-        attn_output = self.out_proj(attn_output)
-        attn_output = self.resid_dropout(attn_output)
-
-        outputs = (attn_output, present)
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs  # a, present, (attentions)
-
-
-# Copied from transformers.models.gptj.modeling_gptj.GPTJMLP with GPTJ->Moss
-class MossMLP(nn.Module):
-    def __init__(self, intermediate_size, config):  # in MLP: intermediate_size= 4 * embed_dim
-        super().__init__()
-        embed_dim = config.n_embd
-
-        self.fc_in = nn.Linear(embed_dim, intermediate_size)
-        self.fc_out = nn.Linear(intermediate_size, embed_dim)
-
-        self.act = ACT2FN[config.activation_function]
-        self.dropout = nn.Dropout(config.resid_pdrop)
-
-    def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
-        hidden_states = self.fc_in(hidden_states)
-        hidden_states = self.act(hidden_states)
-        hidden_states = self.fc_out(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.gptj.modeling_gptj.GPTJBlock with GPTJ->Moss
-class MossBlock(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
-        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.attn = MossAttention(config)
-        self.mlp = MossMLP(inner_dim, config)
-
-    def forward(
-        self,
-        hidden_states: Optional[torch.FloatTensor],
-        layer_past: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = False,
-        output_attentions: Optional[bool] = False,
-    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
-        residual = hidden_states
-        hidden_states = self.ln_1(hidden_states)
-        attn_outputs = self.attn(
-            hidden_states=hidden_states,
-            layer_past=layer_past,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-        )
-        attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
-        outputs = attn_outputs[1:]
-
-        feed_forward_hidden_states = self.mlp(hidden_states)
-        hidden_states = attn_output + feed_forward_hidden_states + residual
-
-        if use_cache:
-            outputs = (hidden_states,) + outputs
-        else:
-            outputs = (hidden_states,) + outputs[1:]
-
-        return outputs  # hidden_states, present, (attentions)
-
-
-class MossPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = MossConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["MossBlock"]
-
-    def __init__(self, *inputs, **kwargs):
-        super().__init__(*inputs, **kwargs)
-
-    def _init_weights(self, module):
-        """Initialize the weights."""
-        if isinstance(module, (nn.Linear,)):
-            # Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, MossModel):
-            module.gradient_checkpointing = value
-
-
-MOSS_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
-    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`MossConfig`]): 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.
-"""
-
-MOSS_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoProcenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.FloatTensor` of shape `({0})`, *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)
-        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
-            1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        head_mask (`torch.FloatTensor` of shape `(num_attention_heads,)` or `(n_layer, num_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the self-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 `({0}, hidden_dim)`, *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.
-"""
-
-
-@add_start_docstrings(
-    "The bare Moss Model transformer outputting raw hidden-states without any specific head on top.",
-    MOSS_START_DOCSTRING,
-)
-class MossModel(MossPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.embed_dim = config.n_embd
-        self.vocab_size = config.vocab_size
-        self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
-        self.drop = nn.Dropout(config.embd_pdrop)
-        self.h = nn.ModuleList([MossBlock(config) for _ in range(config.n_layer)])
-        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
-        self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.num_attention_heads)
-
-        self.gradient_checkpointing = False
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.wte
-
-    def set_input_embeddings(self, new_embeddings):
-        self.wte = new_embeddings
-
-    @add_start_docstrings_to_model_forward(MOSS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPast,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        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, BaseModelOutputWithPast]:
-        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 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:
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
-            batch_size = input_ids.shape[0]
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size = inputs_embeds.shape[0]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if token_type_ids is not None:
-            token_type_ids = token_type_ids.view(-1, input_shape[-1])
-
-        if position_ids is not None:
-            position_ids = position_ids.view(-1, input_shape[-1]).long()
-
-        if past_key_values is None:
-            past_length = 0
-            past_key_values = tuple([None] * len(self.h))
-        else:
-            past_length = past_key_values[0][0].size(-2)
-
-        if position_ids is None:
-            position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
-            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
-
-        # Attention mask.
-        if attention_mask is not None:
-            if batch_size <= 0:
-                raise ValueError("batch_size has to be defined and > 0")
-            attention_mask = attention_mask.view(batch_size, -1)
-            # We create a 3D attention mask from a 2D tensor mask.
-            # Sizes are [batch_size, 1, 1, to_seq_length]
-            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-            # this attention mask is more simple than the triangular masking of causal attention
-            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-            attention_mask = attention_mask[:, None, None, :]
-
-            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-            # masked positions, this operation will create a tensor which is 0.0 for
-            # positions we want to attend and the dtype's smallest value for masked positions.
-            # Since we are adding it to the raw scores before the softmax, this is
-            # effectively the same as removing these entirely.
-            attention_mask = attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x num_attention_heads x N x N
-        # head_mask has shape n_layer x batch x num_attention_heads x N x N
-        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.wte(input_ids)
-
-        hidden_states = inputs_embeds
-
-        if token_type_ids is not None:
-            token_type_embeds = self.wte(token_type_ids)
-            hidden_states = hidden_states + token_type_embeds
-
-        hidden_states = self.drop(hidden_states)
-
-        output_shape = input_shape + (hidden_states.size(-1),)
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
-                    "`use_cache=False`..."
-                )
-                use_cache = False
-
-        presents = () if use_cache else None
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, use_cache, output_attentions)
-
-                    return custom_forward
-
-                outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    None,
-                    attention_mask,
-                    position_ids,
-                    head_mask[i],
-                )
-            else:
-                outputs = block(
-                    hidden_states=hidden_states,
-                    layer_past=layer_past,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    head_mask=head_mask[i],
-                    use_cache=use_cache,
-                    output_attentions=output_attentions,
-                )
-
-            hidden_states = outputs[0]
-            if use_cache is True:
-                presents = presents + (outputs[1],)
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
-
-        hidden_states = self.ln_f(hidden_states)
-
-        hidden_states = hidden_states.view(output_shape)
-        # Add last hidden state
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
-
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=presents,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The Moss Model transformer with a language modeling head on top.
-    """,
-    MOSS_START_DOCSTRING,
-)
-class MossForCausalLM(MossPreTrainedModel):
-    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.causal_mask"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.transformer = MossModel(config)
-        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
-        token_type_ids = kwargs.get("token_type_ids", None)
-        # only last token for inputs_ids if past is defined in kwargs
-        if past_key_values:
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-            if token_type_ids is not None:
-                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
-
-        attention_mask = kwargs.get("attention_mask", None)
-        position_ids = kwargs.get("position_ids", None)
-
-        if attention_mask is not None and position_ids is None:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            if past_key_values:
-                position_ids = position_ids[:, -1].unsqueeze(-1)
-
-        return {
-            "input_ids": input_ids,
-            "past_key_values": past_key_values,
-            "use_cache": kwargs.get("use_cache"),
-            "position_ids": position_ids,
-            "attention_mask": attention_mask,
-            "token_type_ids": token_type_ids,
-        }
-
-    @add_start_docstrings_to_model_forward(MOSS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=CausalLMOutputWithPast,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[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, CausalLMOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
-            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
-            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.transformer(
-            input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-
-        # make sure sampling in fp16 works correctly and
-        # compute loss in fp32 to match with mesh-tf version
-        # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
-        lm_logits = self.lm_head(hidden_states).to(torch.float32)
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
-
-            loss = loss.to(hidden_states.dtype)
-
-        if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=lm_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-    @staticmethod
-    def _reorder_cache(
-        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
-    ) -> Tuple[Tuple[torch.Tensor]]:
-        """
-        This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
-        [`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
-        beam_idx at every generation step.
-        """
-        return tuple(
-            tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
-            for layer_past in past_key_values
-        )
diff --git a/spaces/kdrkdrkdr/HinaTTS/attentions.py b/spaces/kdrkdrkdr/HinaTTS/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/HinaTTS/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **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
-
-    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):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      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., 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., 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):
-    max_relative_position = 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()
-    # padd 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., 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/kdrkdrkdr/LisaTTS/text/cleaners.py b/spaces/kdrkdrkdr/LisaTTS/text/cleaners.py
deleted file mode 100644
index bd3dcf1ea644ba3351eba2cca0ab11ee0b69ebf1..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/LisaTTS/text/cleaners.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import re
-
-def japanese_cleaners(text):
-    from text.japanese import japanese_to_romaji_with_accent
-    text = japanese_to_romaji_with_accent(text)
-    if len(text) == 0 or re.match('[A-Za-z]', text[-1]):
-        text += '.'
-
-    return text
-
-
-def japanese_cleaners2(text):
-    text = text.replace('・・・', '…').replace('・', ' ')
-    text = japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…') \
-                                    .replace('(', '').replace(')', '') \
-                                    .replace('[', '').replace(']', '') \
-                                    .replace('*', ' ').replace('{', '').replace('}', '')
-    return text
-
-
-def ko2kata(text):
-    return text
-
-def en2kata(text):
-    return text
-
-
-
-def jke_cleaners(text):
-    japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
-    korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
-    english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
-    
-    for japanese_text in japanese_texts:
-        cleaned_text = japanese_text[4:-4]
-        text = text.replace(japanese_text, cleaned_text+' ', 1)
-
-    for korean_text in korean_texts:
-        cleaned_text = ko2kata(korean_text[4:-4])
-        text = text.replace(korean_text, cleaned_text+' ', 1)
-
-    for english_text in english_texts:
-        cleaned_text = en2kata(english_text[4:-4])
-        text = text.replace(english_text, cleaned_text+' ', 1)
-
-    text = japanese_cleaners2(text)
-
-    text = text[:-1]
-    if re.match(r'[^\.,!\?\-…~]', text[-1]):
-        text += '.'
-    return text
-
-
diff --git a/spaces/keneonyeachonam/Docker-FlanT5-TextGeneratorTranslator-021623/main.py b/spaces/keneonyeachonam/Docker-FlanT5-TextGeneratorTranslator-021623/main.py
deleted file mode 100644
index 5000cdaca953ce182c37c955940b38296c401d21..0000000000000000000000000000000000000000
--- a/spaces/keneonyeachonam/Docker-FlanT5-TextGeneratorTranslator-021623/main.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from fastapi import FastAPI
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
-
-from transformers import pipeline
-
-app = FastAPI()
-
-pipe_flan = pipeline("text2text-generation", model="google/flan-t5-small")
-
-@app.get("/infer_t5")
-def t5(input):
-    output = pipe_flan(input)
-    return {"output": output[0]["generated_text"]}
-
-app.mount("/", StaticFiles(directory="static", html=True), name="static")
-
-@app.get("/")
-def index() -> FileResponse:
-    return FileResponse(path="/app/static/index.html", media_type="text/html")
diff --git a/spaces/kevinwang676/M4Singer/data_gen/tts/txt_processors/base_text_processor.py b/spaces/kevinwang676/M4Singer/data_gen/tts/txt_processors/base_text_processor.py
deleted file mode 100644
index 84a9772fee5bcd910fca4ca7396945b03ed9982e..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/M4Singer/data_gen/tts/txt_processors/base_text_processor.py
+++ /dev/null
@@ -1,8 +0,0 @@
-class BaseTxtProcessor:
-    @staticmethod
-    def sp_phonemes():
-        return ['|']
-
-    @classmethod
-    def process(cls, txt, pre_align_args):
-        raise NotImplementedError
diff --git a/spaces/kevinwang676/VALLE/data/tokenizer.py b/spaces/kevinwang676/VALLE/data/tokenizer.py
deleted file mode 100644
index 9b8e889641b6715b9b4fa3cffd3dd7bef06ad7e9..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VALLE/data/tokenizer.py
+++ /dev/null
@@ -1,117 +0,0 @@
-#!/usr/bin/env python3
-# Copyright    2023                            (authors: Feiteng Li)
-#
-# 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 re
-from dataclasses import asdict, dataclass
-from typing import Any, Dict, List, Optional, Pattern, Union
-
-import numpy as np
-import torch
-import torchaudio
-from encodec import EncodecModel
-from encodec.utils import convert_audio
-
-def remove_encodec_weight_norm(model):
-    from encodec.modules import SConv1d
-    from encodec.modules.seanet import SConvTranspose1d, SEANetResnetBlock
-    from torch.nn.utils import remove_weight_norm
-
-    encoder = model.encoder.model
-    for key in encoder._modules:
-        if isinstance(encoder._modules[key], SEANetResnetBlock):
-            remove_weight_norm(encoder._modules[key].shortcut.conv.conv)
-            block_modules = encoder._modules[key].block._modules
-            for skey in block_modules:
-                if isinstance(block_modules[skey], SConv1d):
-                    remove_weight_norm(block_modules[skey].conv.conv)
-        elif isinstance(encoder._modules[key], SConv1d):
-            remove_weight_norm(encoder._modules[key].conv.conv)
-
-    decoder = model.decoder.model
-    for key in decoder._modules:
-        if isinstance(decoder._modules[key], SEANetResnetBlock):
-            remove_weight_norm(decoder._modules[key].shortcut.conv.conv)
-            block_modules = decoder._modules[key].block._modules
-            for skey in block_modules:
-                if isinstance(block_modules[skey], SConv1d):
-                    remove_weight_norm(block_modules[skey].conv.conv)
-        elif isinstance(decoder._modules[key], SConvTranspose1d):
-            remove_weight_norm(decoder._modules[key].convtr.convtr)
-        elif isinstance(decoder._modules[key], SConv1d):
-            remove_weight_norm(decoder._modules[key].conv.conv)
-
-
-class AudioTokenizer:
-    """EnCodec audio."""
-
-    def __init__(
-        self,
-        device: Any = None,
-    ) -> None:
-        # Instantiate a pretrained EnCodec model
-        model = EncodecModel.encodec_model_24khz()
-        model.set_target_bandwidth(6.0)
-        remove_encodec_weight_norm(model)
-
-        if not device:
-            device = torch.device("cpu")
-            if torch.cuda.is_available():
-                device = torch.device("cuda:0")
-
-        self._device = device
-
-        self.codec = model.to(device)
-        self.sample_rate = model.sample_rate
-        self.channels = model.channels
-
-    @property
-    def device(self):
-        return self._device
-
-    def encode(self, wav: torch.Tensor) -> torch.Tensor:
-        return self.codec.encode(wav.to(self.device))
-
-    def decode(self, frames: torch.Tensor) -> torch.Tensor:
-        return self.codec.decode(frames)
-
-
-def tokenize_audio(tokenizer: AudioTokenizer, audio):
-    # Load and pre-process the audio waveform
-    if isinstance(audio, str):
-        wav, sr = torchaudio.load(audio)
-    else:
-        wav, sr = audio
-    wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels)
-    wav = wav.unsqueeze(0)
-
-    # Extract discrete codes from EnCodec
-    with torch.no_grad():
-        encoded_frames = tokenizer.encode(wav)
-    return encoded_frames
-
-
-if __name__ == "__main__":
-    model = EncodecModel.encodec_model_24khz()
-    model.set_target_bandwidth(6.0)
-
-    samples = torch.from_numpy(np.random.random([4, 1, 1600])).type(
-        torch.float32
-    )
-    codes_raw = model.encode(samples)
-
-    remove_encodec_weight_norm(model)
-    codes_norm = model.encode(samples)
-
-    assert torch.allclose(codes_raw[0][0], codes_norm[0][0])
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/encoder/inference.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/encoder/inference.py
deleted file mode 100644
index af9a529f13878d939054625b64413e5060b6028e..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/encoder/inference.py
+++ /dev/null
@@ -1,195 +0,0 @@
-from encoder.params_data import *
-from encoder.model import SpeakerEncoder
-from encoder.audio import preprocess_wav   # We want to expose this function from here
-from matplotlib import cm
-from encoder import audio
-from pathlib import Path
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-_model = None # type: SpeakerEncoder
-_device = None # type: torch.device
-
-
-def load_model(weights_fpath: Path, device=None):
-    """
-    Loads the model in memory. If this function is not explicitely called, it will be run on the 
-    first call to embed_frames() with the default weights file.
-    
-    :param weights_fpath: the path to saved model weights.
-    :param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda"). The 
-    model will be loaded and will run on this device. Outputs will however always be on the cpu. 
-    If None, will default to your GPU if it"s available, otherwise your CPU.
-    """
-    # TODO: I think the slow loading of the encoder might have something to do with the device it
-    #   was saved on. Worth investigating.
-    global _model, _device
-    if device is None:
-        _device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    elif isinstance(device, str):
-        _device = torch.device(device)
-    _model = SpeakerEncoder(_device, torch.device("cpu"))
-    checkpoint = torch.load(weights_fpath, _device)
-    _model.load_state_dict(checkpoint["model_state"])
-    _model.eval()
-    print("Loaded encoder \"%s\" trained to step %d" % (weights_fpath.name, checkpoint["step"]))
-    return _model
-    
-def set_model(model, device=None):
-    global _model, _device
-    _model = model
-    if device is None:
-        _device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    _device = device
-    _model.to(device)
-
-def is_loaded():
-    return _model is not None
-
-
-def embed_frames_batch(frames_batch):
-    """
-    Computes embeddings for a batch of mel spectrogram.
-    
-    :param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape 
-    (batch_size, n_frames, n_channels)
-    :return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size)
-    """
-    if _model is None:
-        raise Exception("Model was not loaded. Call load_model() before inference.")
-    
-    frames = torch.from_numpy(frames_batch).to(_device)
-    embed = _model.forward(frames).detach().cpu().numpy()
-    return embed
-
-
-def compute_partial_slices(n_samples, partial_utterance_n_frames=partials_n_frames,
-                           min_pad_coverage=0.75, overlap=0.5, rate=None):
-    """
-    Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain 
-    partial utterances of <partial_utterance_n_frames> each. Both the waveform and the mel 
-    spectrogram slices are returned, so as to make each partial utterance waveform correspond to 
-    its spectrogram. This function assumes that the mel spectrogram parameters used are those 
-    defined in params_data.py.
-    
-    The returned ranges may be indexing further than the length of the waveform. It is 
-    recommended that you pad the waveform with zeros up to wave_slices[-1].stop.
-    
-    :param n_samples: the number of samples in the waveform
-    :param partial_utterance_n_frames: the number of mel spectrogram frames in each partial 
-    utterance
-    :param min_pad_coverage: when reaching the last partial utterance, it may or may not have 
-    enough frames. If at least <min_pad_coverage> of <partial_utterance_n_frames> are present, 
-    then the last partial utterance will be considered, as if we padded the audio. Otherwise, 
-    it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial 
-    utterance, this parameter is ignored so that the function always returns at least 1 slice.
-    :param overlap: by how much the partial utterance should overlap. If set to 0, the partial 
-    utterances are entirely disjoint. 
-    :return: the waveform slices and mel spectrogram slices as lists of array slices. Index 
-    respectively the waveform and the mel spectrogram with these slices to obtain the partial 
-    utterances.
-    """
-    assert 0 <= overlap < 1
-    assert 0 < min_pad_coverage <= 1
-    
-    if rate != None:
-        samples_per_frame = int((sampling_rate * mel_window_step / 1000))
-        n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
-        frame_step = int(np.round((sampling_rate / rate) / samples_per_frame))
-    else: 
-        samples_per_frame = int((sampling_rate * mel_window_step / 1000))
-        n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
-        frame_step = max(int(np.round(partial_utterance_n_frames * (1 - overlap))), 1)
-
-    assert 0 < frame_step, "The rate is too high"
-    assert frame_step <= partials_n_frames, "The rate is too low, it should be %f at least" % \
-        (sampling_rate / (samples_per_frame * partials_n_frames))
-
-    # Compute the slices
-    wav_slices, mel_slices = [], []
-    steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1)
-    for i in range(0, steps, frame_step):
-        mel_range = np.array([i, i + partial_utterance_n_frames])
-        wav_range = mel_range * samples_per_frame
-        mel_slices.append(slice(*mel_range))
-        wav_slices.append(slice(*wav_range))
-        
-    # Evaluate whether extra padding is warranted or not
-    last_wav_range = wav_slices[-1]
-    coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start)
-    if coverage < min_pad_coverage and len(mel_slices) > 1:
-        mel_slices = mel_slices[:-1]
-        wav_slices = wav_slices[:-1]
-    
-    return wav_slices, mel_slices
-
-
-def embed_utterance(wav, using_partials=True, return_partials=False, **kwargs):
-    """
-    Computes an embedding for a single utterance.
-    
-    # TODO: handle multiple wavs to benefit from batching on GPU
-    :param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32
-    :param using_partials: if True, then the utterance is split in partial utterances of 
-    <partial_utterance_n_frames> frames and the utterance embedding is computed from their 
-    normalized average. If False, the utterance is instead computed from feeding the entire 
-    spectogram to the network.
-    :param return_partials: if True, the partial embeddings will also be returned along with the 
-    wav slices that correspond to the partial embeddings.
-    :param kwargs: additional arguments to compute_partial_splits()
-    :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If 
-    <return_partials> is True, the partial utterances as a numpy array of float32 of shape 
-    (n_partials, model_embedding_size) and the wav partials as a list of slices will also be 
-    returned. If <using_partials> is simultaneously set to False, both these values will be None 
-    instead.
-    """
-    # Process the entire utterance if not using partials
-    if not using_partials:
-        frames = audio.wav_to_mel_spectrogram(wav)
-        embed = embed_frames_batch(frames[None, ...])[0]
-        if return_partials:
-            return embed, None, None
-        return embed
-    
-    # Compute where to split the utterance into partials and pad if necessary
-    wave_slices, mel_slices = compute_partial_slices(len(wav), **kwargs)
-    max_wave_length = wave_slices[-1].stop
-    if max_wave_length >= len(wav):
-        wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
-    
-    # Split the utterance into partials
-    frames = audio.wav_to_mel_spectrogram(wav)
-    frames_batch = np.array([frames[s] for s in mel_slices])
-    partial_embeds = embed_frames_batch(frames_batch)
-    
-    # Compute the utterance embedding from the partial embeddings
-    raw_embed = np.mean(partial_embeds, axis=0)
-    embed = raw_embed / np.linalg.norm(raw_embed, 2)
-    
-    if return_partials:
-        return embed, partial_embeds, wave_slices
-    return embed
-
-
-def embed_speaker(wavs, **kwargs):
-    raise NotImplemented()
-
-
-def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)):
-    if ax is None:
-        ax = plt.gca()
-    
-    if shape is None:
-        height = int(np.sqrt(len(embed)))
-        shape = (height, -1)
-    embed = embed.reshape(shape)
-    
-    cmap = cm.get_cmap()
-    mappable = ax.imshow(embed, cmap=cmap)
-    cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04)
-    sm = cm.ScalarMappable(cmap=cmap)
-    sm.set_clim(*color_range)
-    
-    ax.set_xticks([]), ax.set_yticks([])
-    ax.set_title(title)
diff --git a/spaces/kira4424/VITS-fast-fine-tuning/download_video.py b/spaces/kira4424/VITS-fast-fine-tuning/download_video.py
deleted file mode 100644
index 05ccce79e03f8507ec6d40a29cae4789051e0a22..0000000000000000000000000000000000000000
--- a/spaces/kira4424/VITS-fast-fine-tuning/download_video.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import os
-import random
-import shutil
-from concurrent.futures import ThreadPoolExecutor
-from google.colab import files
-
-basepath = os.getcwd()
-uploaded = files.upload()  # 上传文件
-for filename in uploaded.keys():
-    assert (filename.endswith(".txt")), "speaker-videolink info could only be .txt file!"
-    shutil.move(os.path.join(basepath, filename), os.path.join("./speaker_links.txt"))
-
-
-def generate_infos():
-    infos = []
-    with open("./speaker_links.txt", 'r', encoding='utf-8') as f:
-        lines = f.readlines()
-    for line in lines:
-        line = line.replace("\n", "").replace(" ", "")
-        if line == "":
-            continue
-        speaker, link = line.split("|")
-        filename = speaker + "_" + str(random.randint(0, 1000000))
-        infos.append({"link": link, "filename": filename})
-    return infos
-
-
-def download_video(info):
-    link = info["link"]
-    filename = info["filename"]
-    os.system(f"youtube-dl -f 0 {link} -o ./video_data/{filename}.mp4")
-
-
-if __name__ == "__main__":
-    infos = generate_infos()
-    with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor:
-        executor.map(download_video, infos)
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/adaptive_span/__init__.py b/spaces/koajoel/PolyFormer/fairseq/examples/adaptive_span/__init__.py
deleted file mode 100644
index e0a142a769360e1140bf814c532eaf841f1d52d8..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/adaptive_span/__init__.py
+++ /dev/null
@@ -1,19 +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 importlib
-import os
-
-# automatically import any Python files in the current directory
-cur_dir = os.path.dirname(__file__)
-for file in os.listdir(cur_dir):
-    path = os.path.join(cur_dir, file)
-    if (
-        not file.startswith("_")
-        and not file.startswith(".")
-        and (file.endswith(".py") or os.path.isdir(path))
-    ):
-        mod_name = file[: file.find(".py")] if file.endswith(".py") else file
-        module = importlib.import_module(__name__ + "." + mod_name)
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py b/spaces/koajoel/PolyFormer/fairseq/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py
deleted file mode 100644
index 8cc2a7174b765b7ad8808489196e12082a91a2d7..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py
+++ /dev/null
@@ -1,195 +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 fairseq.tasks import register_task
-from fairseq.tasks.multilingual_translation import MultilingualTranslationTask
-from fairseq.utils import safe_hasattr
-
-from .loss.latent_depth import LatentLayersKLLoss, LatentLayersSparsityLoss
-
-
-@register_task("multilingual_translation_latent_depth")
-class MultilingualTranslationTaskLatentDepth(MultilingualTranslationTask):
-    """A task for multiple translation with latent depth.
-
-    See `"Deep Transformer with Latent Depth"
-        (Li et al., 2020) <https://arxiv.org/pdf/2009.13102.pdf>`_.
-    """
-
-    @staticmethod
-    def add_args(parser):
-        """Add task-specific arguments to the parser."""
-        # fmt: off
-        MultilingualTranslationTask.add_args(parser)
-        parser.add_argument('--encoder-latent-layer', action='store_true', help='latent layer selection in encoder')
-        parser.add_argument('--decoder-latent-layer', action='store_true', help='latent layer selection in decoder')
-        parser.add_argument('--target-layers', default=-1, type=int,
-                            help='number of effective layers to learn; -1 means no constraint')
-        parser.add_argument('--sparsity-weight', default=0.0, type=float,
-                            help='weight for sparsity loss')
-        parser.add_argument('--share-weight', default=0.0, type=float,
-                            help='weight for sharing loss')
-        parser.add_argument('--soft-update', default=1, type=int,
-                            help='number of updates with soft sampling')
-        parser.add_argument('--anneal-updates', default=1, type=int,
-                            help='number of updates to anneal the KL loss weight')
-        parser.add_argument('--prior', default="uniform", type=str,
-                            help='prior used for computing KL loss')
-        # fmt: on
-
-    def __init__(self, args, dicts, training):
-        super().__init__(args, dicts, training)
-        self.src_langs, self.tgt_langs = zip(
-            *[(lang.split("-")[0], lang.split("-")[1]) for lang in args.lang_pairs]
-        )
-        if self.training and self.encoder_latent_layer:
-            assert self.args.share_encoders
-        if self.training and self.decoder_latent_layer:
-            assert self.args.share_decoders
-        if training or self.encoder_latent_layer or self.decoder_latent_layer:
-            self.lang_pairs = args.lang_pairs
-        else:
-            self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)]
-        self.eval_lang_pairs = self.lang_pairs
-        self.model_lang_pairs = self.lang_pairs
-        if self.training and (self.encoder_latent_layer or self.decoder_latent_layer):
-            self.kl_loss = LatentLayersKLLoss(self.args)
-            self.sparsity_loss = LatentLayersSparsityLoss(self.args)
-
-    def _per_lang_pair_train_loss(
-        self, lang_pair, model, update_num, criterion, sample, optimizer, ignore_grad
-    ):
-        src, tgt = lang_pair.split("-")
-        if self.encoder_latent_layer:
-            src_lang_idx = self.src_lang_idx_dict[src]
-            model.models[lang_pair].encoder.set_lang_idx(src_lang_idx)
-            model.models[lang_pair].encoder.layer_select.hard_select = (
-                update_num > self.args.soft_update
-            )
-        if self.decoder_latent_layer:
-            tgt_lang_idx = self.tgt_lang_idx_dict[tgt]
-            model.models[lang_pair].decoder.set_lang_idx(tgt_lang_idx)
-            model.models[lang_pair].decoder.layer_select.hard_select = (
-                update_num > self.args.soft_update
-            )
-
-        loss, sample_size, logging_output = criterion(
-            model.models[lang_pair], sample[lang_pair]
-        )
-        if self.encoder_latent_layer:
-            none_samples = sum(
-                1 if x is None else 0
-                for x in model.models[lang_pair].encoder.layer_select.layer_samples
-            )
-            if none_samples == 0 or self.args.prior != "agged_posterior":
-                loss += self.kl_loss(
-                    model.models[lang_pair].encoder.layer_select.layer_samples,
-                    src_lang_idx,
-                    update_num,
-                    sample_size,
-                )
-        if self.decoder_latent_layer:
-            none_samples = sum(
-                1 if x is None else 0
-                for x in model.models[lang_pair].decoder.layer_select.layer_samples
-            )
-            if none_samples == 0 or self.args.prior != "agged_posterior":
-                loss += self.kl_loss(
-                    model.models[lang_pair].decoder.layer_select.layer_samples,
-                    tgt_lang_idx,
-                    update_num,
-                    sample_size,
-                )
-        if ignore_grad:
-            loss *= 0
-
-        if hasattr(self, "sparsity_loss") and self.sparsity_loss.is_valid(update_num):
-            # need to retain the graph if sparsity loss needs to be added
-            loss.backward(retain_graph=True)
-        else:
-            optimizer.backward(loss)
-
-        return loss, sample_size, logging_output
-
-    def train_step(
-        self, sample, model, criterion, optimizer, update_num, ignore_grad=False
-    ):
-        agg_loss, agg_sample_size, agg_logging_output = super().train_step(
-            sample, model, criterion, optimizer, update_num, ignore_grad
-        )
-        # compute auxiliary loss from layere sparsity, based on all samples from all languages
-        if hasattr(self, "sparsity_loss") and self.sparsity_loss.is_valid(update_num):
-            sparsity_loss = 0
-            if self.encoder_latent_layer:
-                sparsity_loss += self.sparsity_loss(
-                    next(
-                        iter(model.models.values())
-                    ).encoder.layer_select.layer_samples,
-                    update_num,
-                    agg_sample_size,
-                )
-            if self.decoder_latent_layer:
-                sparsity_loss += self.sparsity_loss(
-                    next(
-                        iter(model.models.values())
-                    ).decoder.layer_select.layer_samples,
-                    update_num,
-                    agg_sample_size,
-                )
-            if sparsity_loss > 0:
-                optimizer.backward(sparsity_loss)
-        return agg_loss, agg_sample_size, agg_logging_output
-
-    def _per_lang_pair_valid_loss(self, lang_pair, model, criterion, sample):
-        src, tgt = lang_pair.split("-")
-        if self.encoder_latent_layer:
-            src_lang_idx = self.src_lang_idx_dict[src]
-            model.models[lang_pair].encoder.set_lang_idx(src_lang_idx)
-        if self.decoder_latent_layer:
-            tgt_lang_idx = self.tgt_lang_idx_dict[tgt]
-            model.models[lang_pair].decoder.set_lang_idx(tgt_lang_idx)
-        loss, sample_size, logging_output = criterion(
-            model.models[lang_pair], sample[lang_pair]
-        )
-        return loss, sample_size, logging_output
-
-    def inference_step(
-        self, generator, models, sample, prefix_tokens=None, constraints=None
-    ):
-        if self.encoder_latent_layer or self.decoder_latent_layer:
-            for model in models:
-                if self.encoder_latent_layer:
-                    assert model.encoder.layer_select is not None
-                    src_lang_idx = self.src_lang_idx_dict[self.args.source_lang]
-                    model.encoder.set_lang_idx(src_lang_idx)
-                if self.decoder_latent_layer:
-                    assert model.decoder.layer_select is not None
-                    tgt_lang_idx = self.tgt_lang_idx_dict[self.args.target_lang]
-                    model.decoder.set_lang_idx(tgt_lang_idx)
-        return super().inference_step(
-            generator, models, sample, prefix_tokens, constraints
-        )
-
-    @property
-    def encoder_latent_layer(self):
-        return (
-            safe_hasattr(self.args, "encoder_latent_layer")
-            and self.args.encoder_latent_layer
-        )
-
-    @property
-    def decoder_latent_layer(self):
-        return (
-            safe_hasattr(self.args, "decoder_latent_layer")
-            and self.args.decoder_latent_layer
-        )
-
-    @property
-    def src_lang_idx_dict(self):
-        return {lang: lang_idx for lang_idx, lang in enumerate(self.src_langs)}
-
-    @property
-    def tgt_lang_idx_dict(self):
-        return {lang: lang_idx for lang_idx, lang in enumerate(self.tgt_langs)}
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/tests/zipdata01/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/tests/zipdata01/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/lhnrx/bai/Dockerfile b/spaces/lhnrx/bai/Dockerfile
deleted file mode 100644
index 7b77fccb3333b3516261a14b6663de8d7e8f9434..0000000000000000000000000000000000000000
--- a/spaces/lhnrx/bai/Dockerfile
+++ /dev/null
@@ -1,33 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符-仅可进行对话，如需绘画，需要修改为自己的token
-ENV Go_Proxy_BingAI_USER_TOKEN_1="5kxiDYDA3TsDAURSzJA3TsDAeKUrk168a"
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
diff --git a/spaces/librarian-bots/collection-reading-list-generator/README.md b/spaces/librarian-bots/collection-reading-list-generator/README.md
deleted file mode 100644
index 54ac7d96868c793181c09b08f14d43fc612989aa..0000000000000000000000000000000000000000
--- a/spaces/librarian-bots/collection-reading-list-generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Collection Reading List Generator
-emoji: 📚🚀
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/litagin/rvc_okiba_TTS/lib/infer_pack/modules.py b/spaces/litagin/rvc_okiba_TTS/lib/infer_pack/modules.py
deleted file mode 100644
index c83289df7c79a4810dacd15c050148544ba0b6a9..0000000000000000000000000000000000000000
--- a/spaces/litagin/rvc_okiba_TTS/lib/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 lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from lib.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/litagin/vits-japros-webui-demo/model.py b/spaces/litagin/vits-japros-webui-demo/model.py
deleted file mode 100644
index 80957ec5c5607f50e2c5f09912e08190e87bde6c..0000000000000000000000000000000000000000
--- a/spaces/litagin/vits-japros-webui-demo/model.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from typing import List, Optional, Tuple
-
-import numpy as np
-import pyworld
-import torch
-import yaml
-from espnet2.bin.tts_inference import Text2Speech
-from espnet2.text.token_id_converter import TokenIDConverter
-
-from text import p2tokens
-
-
-class VITSJaProsModel:
-    def __init__(self, name: str, model_path: str, config_path: Optional[str] = None):
-        self.name = name
-        if config_path is None:
-            config_path = "conf/config.yaml"
-        if torch.cuda.is_available():
-            self.device = "cuda"
-        else:
-            self.device = "cpu"
-        self.model = Text2Speech(
-            train_config=config_path, model_file=model_path, device=self.device
-        )
-
-        with open(config_path, "r") as f:
-            config = yaml.safe_load(f)
-        if config["tts"] != "vits":
-            raise ValueError("このモデルはVITSではありません。")
-        if config["g2p"] != "pyopenjtalk_prosody":
-            raise ValueError("このモデルのg2pはpyopenjtalk_prosodyではありません。")
-        self.converter = TokenIDConverter(config["token_list"])
-
-    def tokens2speech(
-        self,
-        tokens: List[str],
-        speed_scale: float = 1,
-        noise_scale: float = 0.667,
-        noise_scale_dur: float = 0.8,
-    ) -> Tuple[int, np.ndarray]:
-        self.model.decode_conf.update(
-            alpha=1 / speed_scale,
-            noise_scale=noise_scale,
-            noise_scale_dur=noise_scale_dur,
-        )
-        int_tensor = np.array(self.converter.tokens2ids(tokens))
-        with torch.no_grad():
-            tensor = self.model(int_tensor)["wav"]
-        return self.model.fs, tensor.view(-1).cpu().numpy()
-
-    def p2speech(
-        self,
-        p: str,
-        speed_scale: float = 1,
-        pitch_scale: float = 1,
-        intonation_scale: float = 1,
-        noise_scale: float = 0.667,
-        noise_scale_dur: float = 0.8,
-    ) -> Tuple[int, np.ndarray]:
-        fs, wave = self.tokens2speech(
-            p2tokens(p), speed_scale, noise_scale, noise_scale_dur
-        )
-
-        if pitch_scale == 1 and intonation_scale == 1:
-            return fs, wave
-
-        # pyworldでf0を加工して合成
-        # pyworldよりもよいのがあるかもしれないが……
-
-        wave = wave.astype(np.double)
-        f0, t = pyworld.harvest(wave, fs)
-        # 質が高そうだしとりあえずharvestにしておく
-        # rmvpeが使えたらそれがいいのかも……？
-        sp = pyworld.cheaptrick(wave, f0, t, fs)
-        ap = pyworld.d4c(wave, f0, t, fs)
-
-        non_zero_f0 = [f for f in f0 if f != 0]
-        f0_mean = sum(non_zero_f0) / len(non_zero_f0)
-
-        for i, f in enumerate(f0):
-            if f == 0:
-                continue
-            f0[i] = pitch_scale * f0_mean + intonation_scale * (f - f0_mean)
-
-        wave = pyworld.synthesize(f0, sp, ap, fs)
-        return fs, wave
diff --git a/spaces/llds/shengweibing/Dockerfile b/spaces/llds/shengweibing/Dockerfile
deleted file mode 100644
index 3698c7cb7938e025afc53b18a571ae2961fbdffe..0000000000000000000000000000000000000000
--- a/spaces/llds/shengweibing/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="kJs8hD92ncMzLaoQWYtX5rG6bE3fZ4iO"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/lordvader31/almithal/takeaways.py b/spaces/lordvader31/almithal/takeaways.py
deleted file mode 100644
index 7283c553448a864aa0355c84939e6e613728b4bb..0000000000000000000000000000000000000000
--- a/spaces/lordvader31/almithal/takeaways.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import openai
-import os
-
-def extract_bullet_points(text):
-    """
-    Extract bullet points from a string and return a list of bullet points.
-    
-    Args:
-        text (str): The input text containing bullet points.
-        
-    Returns:
-        list: A list of bullet points.
-    """
-    bullet_points = []
-    lines = text.split("\n")
-    for line in lines:
-        # Check if the line starts with a bullet point (e.g. "1. ", "2. ", etc.)
-        if line.strip().startswith(("* ", "- ", "• ", "· ", "1. ", "2. ", "3. ", "4. ", "5. ", "6. ", "7. ", "8. ", "9. ")):
-            bullet_points.append(line.strip()[2:])
-    return bullet_points
-
-
-class KeyTakeaways:
-    
-    def __init__(self):
-        openai.api_key = os.getenv("OPENAI_API_KEY")
-        
-    def generate_key_takeaways(self, text_chunks_lib:dict) -> list:
-        PROMPT = """
-            You are a super intelligent human and helpful assistant. 
-            I am giving you parts of a video transcription that I want to learn from.
-            In bullet points, give me at most 3 key takeaways from this text.
-        """
-        
-        final_takeaways = []
-        for key in text_chunks_lib:
-            for text_chunk in text_chunks_lib[key]:
-                response = openai.Completion.create(
-                    engine="text-davinci-003",
-                    prompt=PROMPT + text_chunk,
-                    temperature=0.4,
-                    max_tokens=1024,
-                    top_p=1,
-                    frequency_penalty=0.0,
-                    presence_penalty=0.6,
-                )
-                takeaways = extract_bullet_points(response.choices[0].text.strip())
-                final_takeaways.extend(takeaways)
-        
-        
-        return final_takeaways
\ No newline at end of file
diff --git a/spaces/m3hrdadfi/gpt2-persian-qa/regexes/number.py b/spaces/m3hrdadfi/gpt2-persian-qa/regexes/number.py
deleted file mode 100644
index 3d70b6af8c693c33215507d9c7d39ebacf2a65cb..0000000000000000000000000000000000000000
--- a/spaces/m3hrdadfi/gpt2-persian-qa/regexes/number.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import re
-
-NUMBERS_REGEX = re.compile(
-    r"(?:^|(?<=[^\w,.]))[+–-]?(([1-9]\d{0,2}(,\d{3})+(\.\d*)?)|([1-9]\d{0,2}([ .]\d{3})+(,\d*)?)|(\d*?[.,]\d+)|\d+)(?:$|(?=\b))"
-)
diff --git a/spaces/ma-xu/LIVE/thrust/CONTRIBUTING.md b/spaces/ma-xu/LIVE/thrust/CONTRIBUTING.md
deleted file mode 100644
index 5ab75fa6650687c849caf75deb2f926cd628f239..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/CONTRIBUTING.md
+++ /dev/null
@@ -1,490 +0,0 @@
-# Table of Contents
-
-1. [Contributing to Thrust](#contributing-to-thrust)
-1. [CMake Options](#cmake-options)
-1. [Development Model](#development-model)
-
-# Contributing to Thrust
-
-Thrust uses Github to manage all open-source development, including bug
-tracking, pull requests, and design discussions. This document details how to get
-started as a Thrust contributor.
-
-An overview of this process is:
-
-1. [Clone the Thrust repository](#clone-the-thrust-repository)
-1. [Setup a fork of Thrust](#setup-a-fork-of-thrust)
-1. [Setup your environment](#setup-your-environment)
-1. [Create a development branch](#create-a-development-branch)
-1. [Local development loop](#local-development-loop)
-1. [Push development branch to your fork](#push-development-branch-to-your-fork)
-1. [Create pull request](#create-pull-request)
-1. [Address feedback and update pull request](#address-feedback-and-update-pull-request)
-1. [When your PR is approved...](#when-your-pr-is-approved)
-
-## Clone the Thrust Repository
-
-To get started, clone the main repository to your local computer. Thrust should
-be cloned recursively to setup the CUB submodule (required for `CUDA`
-acceleration).
-
-```
-git clone --recursive https://github.com/thrust/thrust.git
-cd thrust
-```
-
-## Setup a Fork of Thrust
-
-You'll need a fork of Thrust on Github to create a pull request. To setup your
-fork:
-
-1. Create a Github account (if needed)
-2. Go to [the Thrust Github page](https://github.com/thrust/thrust)
-3. Click "Fork" and follow any prompts that appear.
-
-Once your fork is created, setup a new remote repo in your local Thrust clone:
-
-```
-git remote add github-fork git@github.com:<GITHUB_USERNAME>/thrust.git
-```
-
-If you need to modify CUB, too, go to
-[the CUB Github page](https://github.com/thrust/cub) and repeat this process.
-Create CUB's `github-fork` remote in the `thrust/dependencies/cub` submodule.
-
-## Setup Your Environment
-
-### Git Environment
-
-If you haven't already, this is a good time to tell git who you are. This
-information is used to fill out authorship information on your git commits.
-
-```
-git config --global user.name "John Doe"
-git config --global user.email johndoe@example.com
-```
-
-### Configure CMake builds
-
-Thrust uses [CMake](https://www.cmake.org) for its developer build system. To
-configure, build, and test your checkout of Thrust:
-
-```
-# Create build directory:
-mkdir build
-cd build
-
-# Configure -- use one of the following:
-cmake ..   # Command line interface.
-ccmake ..  # ncurses GUI (Linux only)
-cmake-gui  # Graphical UI, set source/build directories in the app
-
-# Build:
-cmake --build . -j <num jobs>   # invokes make (or ninja, etc)
-
-# Run tests and examples:
-ctest
-```
-
-See [CMake Options](#cmake-options) for details on customizing the build.
-
-## Create a Development Branch
-
-All work should be done in a development branch (also called a "topic branch")
-and not directly in the `master` branch. This makes it easier to manage multiple
-in-progress patches at once, and provides a descriptive label for your patch
-as it passes through the review system.
-
-To create a new branch based on the current `master`:
-
-```
-# Checkout local master branch:
-cd /path/to/thrust/sources
-git checkout master
-
-# Sync local master branch with github:
-git pull
-
-# Create a new branch named `my_descriptive_branch_name` based on master:
-git checkout -b my_descriptive_branch_name
-
-# Verify that the branch has been created and is currently checked out:
-git branch
-```
-
-Thrust branch names should follow a particular pattern:
-
-- For new features, name the branch `feature/<name>`
-- For bugfixes associated with a github issue, use `bug/github/<bug-description>-<bug-id>`
-  - Internal nvidia and gitlab bugs should use `nvidia` or `gitlab` in place of
-    `github`.
-
-If you plan to work on CUB as part of your patch, repeat this process in the
-`thrust/dependencies/cub` submodule.
-
-## Local Development Loop
-
-### Edit, Build, Test, Repeat
-
-Once the topic branch is created, you're all set to start working on Thrust
-code. Make some changes, then build and test them:
-
-```
-# Implement changes:
-cd /path/to/thrust/sources
-emacs thrust/some_file.h # or whatever editor you prefer
-
-# Create / update a unit test for your changes:
-emacs testing/some_test.cu
-
-# Check that everything builds and tests pass:
-cd /path/to/thrust/build/directory
-cmake --build . -j <num jobs>
-ctest
-```
-
-### Creating a Commit
-
-Once you're satisfied with your patch, commit your changes:
-
-#### Thrust-only Changes
-
-```
-# Manually add changed files and create a commit:
-cd /path/to/thrust
-git add thrust/some_file.h
-git add testing/some_test.cu
-git commit
-
-# Or, if possible, use git-gui to review your changes while building your patch:
-git gui
-```
-
-#### Thrust and CUB Changes
-
-```
-# Create CUB patch first:
-cd /path/to/thrust/dependencies/cub
-# Manually add changed files and create a commit:
-git add cub/some_file.cuh
-git commit
-
-# Create Thrust patch, including submodule update:
-cd /path/to/thrust/
-git add dependencies/cub # Updates submodule info
-git add thrust/some_file.h
-git add testing/some_test.cu
-git commit
-
-# Or, if possible, use git-gui to review your changes while building your patch:
-cd /path/to/thrust/dependencies/cub
-git gui
-cd /path/to/thrust
-git gui # Include dependencies/cub as part of your commit
-
-```
-
-#### Writing a Commit Message
-
-Your commit message will communicate the purpose and rationale behind your
-patch to other developers, and will be used to populate the initial description
-of your Github pull request.
-
-When writing a commit message, the following standard format should be used,
-since tools in the git ecosystem are designed to parse this correctly:
-
-```
-First line of commit message is a short summary (<80 char)
-<Second line left blank>
-Detailed description of change begins on third line. This portion can
-span multiple lines, try to manually wrap them at something reasonable.
-
-Blank lines can be used to separate multiple paragraphs in the description.
-
-If your patch is associated with another pull request or issue in the main
-Thrust repository, you should reference it with a `#` symbol, e.g.
-#1023 for issue 1023.
-
-For issues / pull requests in a different github repo, reference them using
-the full syntax, e.g. thrust/cub#4 for issue 4 in the thrust/cub repo.
-
-Markdown is recommended for formatting more detailed messages, as these will
-be nicely rendered on Github, etc.
-```
-
-## Push Development Branch to your Fork
-
-Once you've committed your changes to a local development branch, it's time to
-push them to your fork:
-
-```
-cd /path/to/thrust/checkout
-git checkout my_descriptive_branch_name # if not already checked out
-git push --set-upstream github-fork my_descriptive_branch_name
-```
-
-`--set-upstream github-fork` tells git that future pushes/pulls on this branch
-should target your `github-fork` remote by default.
-
-If have CUB changes to commit as part of your patch, repeat this process in the
-`thrust/dependencies/cub` submodule.
-
-## Create Pull Request
-
-To create a pull request for your freshly pushed branch, open your github fork
-in a browser by going to `https://www.github.com/<GITHUB_USERNAME>/thrust`. A
-prompt may automatically appear asking you to create a pull request if you've
-recently pushed a branch.
-
-If there's no prompt, go to "Code" > "Branches" and click the appropriate
-"New pull request" button for your branch.
-
-If you would like a specific developer to review your patch, feel free to
-request them as a reviewer at this time.
-
-The Thrust team will review your patch, test it on NVIDIA's internal CI, and
-provide feedback.
-
-
-If have CUB changes to commit as part of your patch, repeat this process with
-your CUB branch and fork.
-
-## Address Feedback and Update Pull Request
-
-If the reviewers request changes to your patch, use the following process to
-update the pull request:
-
-```
-# Make changes:
-cd /path/to/thrust/sources
-git checkout my_descriptive_branch_name
-emacs thrust/some_file.h
-emacs testing/some_test.cu
-
-# Build + test
-cd /path/to/thrust/build/directory
-cmake --build . -j <num jobs>
-ctest
-
-# Amend commit:
-cd /path/to/thrust/sources
-git add thrust/some_file.h
-git add testing/some_test.cu
-git commit --amend
-# Or
-git gui # Check the "Amend Last Commit" box
-
-# Update the branch on your fork:
-git push -f
-```
-
-At this point, the pull request should show your recent changes.
-
-If have CUB changes to commit as part of your patch, repeat this process in the
-`thrust/dependencies/cub` submodule, and be sure to include any CUB submodule
-updates as part of your commit.
-
-## When Your PR is Approved
-
-Once your pull request is approved by the Thrust team, no further action is
-needed from you. We will handle integrating it since we must coordinate changes
-to `master` with NVIDIA's internal perforce repository.
-
-# CMake Options
-
-A Thrust build is configured using CMake options. These may be passed to CMake
-using
-
-```
-cmake -D<option_name>=<value> /path/to/thrust/sources
-```
-
-or configured interactively with the `ccmake` or `cmake-gui` interfaces.
-
-Thrust supports two build modes. By default, a single configuration is built
-that targets a specific host system, device system, and C++ dialect.
-When `THRUST_ENABLE_MULTICONFIG` is `ON`, multiple configurations
-targeting a variety of systems and dialects are generated.
-
-The CMake options are divided into these categories:
-
-1. [Generic CMake Options](#generic-cmake-options): Options applicable to all
-   Thrust builds.
-1. [Single Config CMake Options](#single-config-cmake-options) Options
-   applicable only when `THRUST_ENABLE_MULTICONFIG` is disabled.
-1. [Multi Config CMake Options](#multi-config-cmake-options) Options applicable
-   only when `THRUST_ENABLE_MULTICONFIG` is enabled.
-1. [CUDA Specific CMake Options](#cuda-specific-cmake-options) Options that
-   control CUDA compilation. Only available when one or more configurations
-   targets the CUDA system.
-1. [TBB Specific CMake Options](#tbb-specific-cmake-options) Options that
-   control TBB compilation. Only available when one or more configurations
-   targets the TBB system.
-
-## Generic CMake Options
-
-- `CMAKE_BUILD_TYPE={Release, Debug, RelWithDebInfo, MinSizeRel}`
-  - Standard CMake build option. Default: `RelWithDebInfo`
-- `THRUST_ENABLE_HEADER_TESTING={ON, OFF}`
-  - Whether to test compile public headers. Default is `ON`.
-- `THRUST_ENABLE_TESTING={ON, OFF}`
-  - Whether to build unit tests. Default is `ON`.
-- `THRUST_ENABLE_EXAMPLES={ON, OFF}`
-  - Whether to build examples. Default is `ON`.
-- `THRUST_ENABLE_MULTICONFIG={ON, OFF}`
-  - Toggles single-config and multi-config modes. Default is `OFF` (single config).
-- `THRUST_ENABLE_EXAMPLE_FILECHECK={ON, OFF}`
-  - Enable validation of example outputs using the LLVM FileCheck utility.
-    Default is `OFF`.
-
-## Single Config CMake Options
-
-- `THRUST_HOST_SYSTEM={CPP, TBB, OMP}`
-  - Selects the host system. Default: `CPP`
-- `THRUST_DEVICE_SYSTEM={CUDA, TBB, OMP, CPP}`
-  - Selects the device system. Default: `CUDA`
-- `THRUST_CPP_DIALECT={11, 14, 17}`
-  - Selects the C++ standard dialect to use. Default is `14` (C++14).
-
-## Multi Config CMake Options
-
-- `THRUST_MULTICONFIG_ENABLE_DIALECT_CPPXX={ON, OFF}`
-  - Toggle whether a specific C++ dialect will be targeted.
-  - Possible values of `XX` are `{11, 14, 17}`.
-  - By default, only C++14 is enabled.
-- `THRUST_MULTICONFIG_ENABLE_SYSTEM_XXXX={ON, OFF}`
-  - Toggle whether a specific system will be targeted.
-  - Possible values of `XXXX` are `{CPP, CUDA, TBB, OMP}`
-  - By default, only `CPP` and `CUDA` are enabled.
-- `THRUST_MULTICONFIG_WORKLOAD={SMALL, MEDIUM, LARGE, FULL}`
-  - Restricts the host/device combinations that will be targeted.
-  - By default, the `SMALL` workload is used.
-  - The full cross product of `host x device` systems results in 12
-    configurations, some of which are more important than others.
-    This option can be used to prune some of the less important ones.
-  - `SMALL`: (3 configs) Minimal coverage and validation of each device system against the `CPP` host.
-  - `MEDIUM`: (6 configs) Cheap extended coverage.
-  - `LARGE`: (8 configs) Expensive extended coverage. Includes all useful build configurations.
-  - `FULL`: (12 configs) The complete cross product of all possible build configurations.
-
-| Config   | Workloads | Value      | Expense   | Note                         |
-|----------|-----------|------------|-----------|------------------------------|
-| CPP/CUDA | `F L M S` | Essential  | Expensive | Validates CUDA against CPP   |
-| CPP/OMP  | `F L M S` | Essential  | Cheap     | Validates OMP against CPP    |
-| CPP/TBB  | `F L M S` | Essential  | Cheap     | Validates TBB against CPP    |
-| CPP/CPP  | `F L M  ` | Important  | Cheap     | Tests CPP as device          |
-| OMP/OMP  | `F L M  ` | Important  | Cheap     | Tests OMP as host            |
-| TBB/TBB  | `F L M  ` | Important  | Cheap     | Tests TBB as host            |
-| TBB/CUDA | `F L    ` | Important  | Expensive | Validates TBB/CUDA interop   |
-| OMP/CUDA | `F L    ` | Important  | Expensive | Validates OMP/CUDA interop   |
-| TBB/OMP  | `F      ` | Not useful | Cheap     | Mixes CPU-parallel systems   |
-| OMP/TBB  | `F      ` | Not useful | Cheap     | Mixes CPU-parallel systems   |
-| TBB/CPP  | `F      ` | Not Useful | Cheap     | Parallel host, serial device |
-| OMP/CPP  | `F      ` | Not Useful | Cheap     | Parallel host, serial device |
-
-## CUDA Specific CMake Options
-
-- `THRUST_INCLUDE_CUB_CMAKE={ON, OFF}`
-  - If enabled, the CUB project will be built as part of Thrust. Default is
-    `OFF`.
-  - This adds CUB tests, etc. Useful for working on both CUB and Thrust
-    simultaneously.
-  - CUB configurations will be generated for each C++ dialect targeted by
-    the current Thrust build.
-- `THRUST_ENABLE_COMPUTE_XX={ON, OFF}`
-  - Controls the targeted CUDA architecture(s)
-  - Multiple options may be selected when using NVCC as the CUDA compiler.
-  - Valid values of `XX` are:
-    `{35, 37, 50, 52, 53, 60, 61, 62, 70, 72, 75, 80}`
-  - Default value depends on `THRUST_DISABLE_ARCH_BY_DEFAULT`:
-- `THRUST_ENABLE_COMPUTE_FUTURE={ON, OFF}`
-  - If enabled, CUDA objects will target the most recent virtual architecture
-    in addition to the real architectures specified by the
-    `THRUST_ENABLE_COMPUTE_XX` options.
-  - Default value depends on `THRUST_DISABLE_ARCH_BY_DEFAULT`:
-- `THRUST_DISABLE_ARCH_BY_DEFAULT={ON, OFF}`
-  - When `ON`, all `THRUST_ENABLE_COMPUTE_*` options are initially `OFF`.
-  - Default: `OFF` (meaning all architectures are enabled by default)
-- `THRUST_ENABLE_TESTS_WITH_RDC={ON, OFF}`
-  - Whether to enable Relocatable Device Code when building tests.
-    Default is `OFF`.
-- `THRUST_ENABLE_EXAMPLES_WITH_RDC={ON, OFF}`
-  - Whether to enable Relocatable Device Code when building examples.
-    Default is `OFF`.
-
-## TBB Specific CMake Options
-
-- `THRUST_TBB_ROOT=<path to tbb root>`
-  - When the TBB system is requested, set this to the root of the TBB installation
-    (e.g. the location of `lib/`, `bin/` and `include/` for the TBB libraries).
-
-# Development Model
-
-The following is a description of the basic development process that Thrust follows. This is a living
-document that will evolve as our process evolves.
-
-Thrust is distributed in three ways:
-
-   * On GitHub.
-   * In the NVIDIA HPC SDK.
-   * In the CUDA Toolkit.
-
-## Trunk Based Development
-
-Thrust uses [trunk based development](https://trunkbaseddevelopment.com). There is a single long-lived
-branch called `master`. Engineers may create branches for feature development. Such branches always
-merge into `master`. There are no release branches. Releases are produced by taking a snapshot of
-`master` ("snapping"). After a release has been snapped from `master`, it will never be changed.
-
-## Repositories
-
-As Thrust is developed both on GitHub and internally at NVIDIA, there are three main places where code lives:
-
-   * The Source of Truth, the [public Thrust repository](https://github.com/thrust/thrust), referred to as
-     `github` later in this document.
-   * An internal GitLab repository, referred to as `gitlab` later in this document.
-   * An internal Perforce repository, referred to as `perforce` later in this document.
-
-## Versioning
-
-Thrust has its own versioning system for releases, independent of the versioning scheme of the NVIDIA
-HPC SDK or the CUDA Toolkit.
-
-Today, Thrust version numbers have a specific [semantic meaning](https://semver.org/).
-Releases prior to 1.10.0 largely, but not strictly, followed these semantic meanings.
-
-The version number for a Thrust release uses the following format: `MMM.mmm.ss-ppp`, where:
-
-   * `THRUST_VERSION_MAJOR`/`MMM`: Major version, up to 3 decimal digits. It is incremented
-     when changes that are API-backwards-incompatible are made.
-   * `THRUST_VERSION_MINOR`/`mmm`: Minor version, up to 3 decimal digits. It is incremented when
-     breaking API, ABI, or semantic changes are made.
-   * `THRUST_VERSION_SUBMINOR`/`ss`: Subminor version, up to 2 decimal digits. It is incremented
-     when notable new features or bug fixes or features that are API-backwards-compatible are made.
-   * `THRUST_PATCH_NUMBER`/`ppp`: Patch number, up to 3 decimal digits. It is incremented if any
-     change in the repo whatsoever is made and no other version component has been incremented.
-
-The `<thrust/version.h>` header defines `THRUST_*` macros for all of the version components mentioned
-above. Additionally, a `THRUST_VERSION` macro is defined, which is an integer literal containing all
-of the version components except for `THRUST_PATCH_NUMBER`.
-
-## Branches and Tags
-
-The following tag names are used in the Thrust project:
-
-  * `github/nvhpc-X.Y`: the tag that directly corresponds to what has been shipped in the NVIDIA HPC SDK release X.Y.
-  * `github/cuda-X.Y`: the tag that directly corresponds to what has been shipped in the CUDA Toolkit release X.Y.
-  * `github/A.B.C`: the tag that directly corresponds to a Thrust version A.B.C.
-
-The following branch names are used in the Thrust project:
-
-  * `github/master`: the Source of Truth development branch of Thrust.
-  * `github/old-master`: the old Source of Truth branch, before unification of public and internal repositories.
-  * `github/feature/<name>`: feature branch for a feature under development.
-  * `github/bug/<bug-system>/<bug-description>-<bug-id>`: bug fix branch, where `bug-system` is `github` or `nvidia`.
-  * `gitlab/master`: mirror of `github/master`.
-  * `perforce/private`: mirrored `github/master`, plus files necessary for internal NVIDIA testing systems.
-
-On the rare occasion that we cannot do work in the open, for example when developing a change specific to an
-unreleased product, these branches may exist on `gitlab` instead of `github`. By default, everything should be
-in the open on `github` unless there is a strong motivation for it to not be open.
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/fill.h b/spaces/ma-xu/LIVE/thrust/thrust/fill.h
deleted file mode 100644
index 8503138023fcd87a3d45f34f7d9f476badfc00cb..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/fill.h
+++ /dev/null
@@ -1,209 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file fill.h
- *  \brief Fills a range with a constant value
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-
-namespace thrust
-{
-
-
-/*! \addtogroup transformations
- *  \addtogroup filling
- *  \ingroup transformations
- *  \{
- */
-
-
-/*! \p fill assigns the value \p value to every element in
- *  the range <tt>[first, last)</tt>. That is, for every
- *  iterator \c i in <tt>[first, last)</tt>, it performs
- *  the assignment <tt>*i = value</tt>.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param last The end of the sequence.
- *  \param value The value to be copied.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator is mutable.
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and \p T's \c value_type is convertible to \p ForwardIterator's \c value_type.
- *
- *  The following code snippet demonstrates how to use \p fill to set a thrust::device_vector's
- *  elements to a given value using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/fill.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> v(4);
- *  thrust::fill(thrust::device, v.begin(), v.end(), 137);
- *
- *  // v[0] == 137, v[1] == 137, v[2] == 137, v[3] == 137
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/fill.html
- *  \see \c fill_n
- *  \see \c uninitialized_fill
- */
-template<typename DerivedPolicy, typename ForwardIterator, typename T>
-__host__ __device__
-  void fill(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-            ForwardIterator first,
-            ForwardIterator last,
-            const T &value);
-
-
-/*! \p fill assigns the value \p value to every element in
- *  the range <tt>[first, last)</tt>. That is, for every
- *  iterator \c i in <tt>[first, last)</tt>, it performs
- *  the assignment <tt>*i = value</tt>.
- *
- *  \param first The beginning of the sequence.
- *  \param last The end of the sequence.
- *  \param value The value to be copied.
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator is mutable.
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and \p T's \c value_type is convertible to \p ForwardIterator's \c value_type.
- *
- *  The following code snippet demonstrates how to use \p fill to set a thrust::device_vector's
- *  elements to a given value.
- *
- *  \code
- *  #include <thrust/fill.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> v(4);
- *  thrust::fill(v.begin(), v.end(), 137);
- *
- *  // v[0] == 137, v[1] == 137, v[2] == 137, v[3] == 137
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/fill.html
- *  \see \c fill_n
- *  \see \c uninitialized_fill
- */
-template<typename ForwardIterator, typename T>
-__host__ __device__
-  void fill(ForwardIterator first,
-            ForwardIterator last,
-            const T &value);
-
-
-/*! \p fill_n assigns the value \p value to every element in
- *  the range <tt>[first, first+n)</tt>. That is, for every
- *  iterator \c i in <tt>[first, first+n)</tt>, it performs
- *  the assignment <tt>*i = value</tt>.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param n The size of the sequence.
- *  \param value The value to be copied.
- *  \return <tt>first + n</tt>
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and \p T's \c value_type is convertible to a type in \p OutputIterator's set of \c value_type.
- *
- *  The following code snippet demonstrates how to use \p fill to set a thrust::device_vector's
- *  elements to a given value using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/fill.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> v(4);
- *  thrust::fill_n(thrust::device, v.begin(), v.size(), 137);
- *
- *  // v[0] == 137, v[1] == 137, v[2] == 137, v[3] == 137
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/fill_n.html
- *  \see \c fill
- *  \see \c uninitialized_fill_n
- */
-template<typename DerivedPolicy, typename OutputIterator, typename Size, typename T>
-__host__ __device__
-  OutputIterator fill_n(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                        OutputIterator first,
-                        Size n,
-                        const T &value);
-
-
-/*! \p fill_n assigns the value \p value to every element in
- *  the range <tt>[first, first+n)</tt>. That is, for every
- *  iterator \c i in <tt>[first, first+n)</tt>, it performs
- *  the assignment <tt>*i = value</tt>.
- *
- *  \param first The beginning of the sequence.
- *  \param n The size of the sequence.
- *  \param value The value to be copied.
- *  \return <tt>first + n</tt>
- *
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and \p T's \c value_type is convertible to a type in \p OutputIterator's set of \c value_type.
- *
- *  The following code snippet demonstrates how to use \p fill to set a thrust::device_vector's
- *  elements to a given value.
- *
- *  \code
- *  #include <thrust/fill.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> v(4);
- *  thrust::fill_n(v.begin(), v.size(), 137);
- *
- *  // v[0] == 137, v[1] == 137, v[2] == 137, v[3] == 137
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/fill_n.html
- *  \see \c fill
- *  \see \c uninitialized_fill_n
- */
-template<typename OutputIterator, typename Size, typename T>
-__host__ __device__
-  OutputIterator fill_n(OutputIterator first,
-                        Size n,
-                        const T &value);
-
-
-/*! \} // end filling
- *  \} // transformations
- */
-
-} // end namespace thrust
-
-#include <thrust/detail/fill.inl>
-
diff --git a/spaces/magicr/BuboGPT/ram/models/bert.py b/spaces/magicr/BuboGPT/ram/models/bert.py
deleted file mode 100644
index cb90b794284d2262d171aa0f93fdf20854a9059b..0000000000000000000000000000000000000000
--- a/spaces/magicr/BuboGPT/ram/models/bert.py
+++ /dev/null
@@ -1,1035 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
- * Based on huggingface code base
- * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
-'''
-
-import math
-import os
-import warnings
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import torch
-from torch import Tensor, device, dtype, nn
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-import torch.nn.functional as F
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
-    ModelOutput,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    MaskedLMOutput,
-    MultipleChoiceModelOutput,
-    NextSentencePredictorOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import (
-    PreTrainedModel,
-    apply_chunking_to_forward,
-    find_pruneable_heads_and_indices,
-    prune_linear_layer,
-)
-from transformers.utils import logging
-from transformers.models.bert.configuration_bert import BertConfig
-
-
-logger = logging.get_logger(__name__)
-
-
-class BertEmbeddings_nopos(nn.Module):
-    """Construct the embeddings from word and position embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        # self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        # self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        # self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        
-        self.config = config
-
-    def forward(
-        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        # if position_ids is None:
-            # position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        embeddings = inputs_embeds
-
-        # if self.position_embedding_type == "absolute":
-        #     position_embeddings = self.position_embeddings(position_ids)
-        #     # print('add position_embeddings!!!!')
-        #     embeddings += position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-
-
-class BertEmbeddings(nn.Module):
-    """Construct the embeddings from word and position embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        
-        self.config = config
-
-    def forward(
-        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        embeddings = inputs_embeds
-
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            # print('add position_embeddings!!!!')
-            embeddings += position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config, is_cross_attention):
-        super().__init__()
-        self.config = config
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
-            )
-        
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        if is_cross_attention:
-            self.key = nn.Linear(config.encoder_width, self.all_head_size)
-            self.value = nn.Linear(config.encoder_width, self.all_head_size)
-        else:
-            self.key = nn.Linear(config.hidden_size, self.all_head_size)
-            self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
-        self.save_attention = False   
-            
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-        
-    def get_attn_gradients(self):
-        return self.attn_gradients
-    
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-        
-    def get_attention_map(self):
-        return self.attention_map
-    
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        mixed_query_layer = self.query(hidden_states)
-
-        # If this is instantiated as a cross-attention module, the keys
-        # and values come from an encoder; the attention mask needs to be
-        # such that the encoder's padding tokens are not attended to.
-        is_cross_attention = encoder_hidden_states is not None
-
-        if is_cross_attention:
-            # print(self.key.weight.shape)
-            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
-            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
-            attention_mask = encoder_attention_mask
-        elif past_key_value is not None:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
-            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
-        else:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-
-        past_key_value = (key_layer, value_layer)
-
-        # compatible with higher versions of transformers 
-        if key_layer.shape[0] > query_layer.shape[0]:
-            key_layer = key_layer[:query_layer.shape[0], :, :, :]
-            attention_mask = attention_mask[:query_layer.shape[0], :, :]
-            value_layer = value_layer[:query_layer.shape[0], :, :, :]
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-        
-        if is_cross_attention and self.save_attention:
-            self.save_attention_map(attention_probs)
-            attention_probs.register_hook(self.save_attn_gradients)         
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs_dropped = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs_dropped = attention_probs_dropped * head_mask
-
-        context_layer = torch.matmul(attention_probs_dropped, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-
-        outputs = outputs + (past_key_value,)
-        return outputs
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config, is_cross_attention=False):
-        super().__init__()
-        self.self = BertSelfAttention(config, is_cross_attention)
-        self.output = BertSelfOutput(config)
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
-        )
-
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        self_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            encoder_hidden_states,
-            encoder_attention_mask,
-            past_key_value,
-            output_attentions,
-        )
-        attention_output = self.output(self_outputs[0], hidden_states)
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config, layer_num):
-        super().__init__()
-        self.config = config
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = BertAttention(config)      
-        self.layer_num = layer_num          
-        if self.config.add_cross_attention:
-            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-        mode=None,
-    ):
-        
-        if mode == 'tagging':
-            
-            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
-
-            cross_attention_outputs = self.crossattention(
-                hidden_states,
-                attention_mask,
-                head_mask,
-                encoder_hidden_states,
-                encoder_attention_mask,
-                output_attentions=output_attentions,
-            )
-            attention_output = cross_attention_outputs[0]
-            outputs = cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights  
-
-            present_key_value = cross_attention_outputs[-1]
-
-        else:
-            # 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
-            self_attention_outputs = self.attention(
-                hidden_states,
-                attention_mask,
-                head_mask,
-                output_attentions=output_attentions,
-                past_key_value=self_attn_past_key_value,
-            )
-            attention_output = self_attention_outputs[0]
-
-            outputs = self_attention_outputs[1:-1]
-            present_key_value = self_attention_outputs[-1]
-
-            if mode=='multimodal':
-                assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
-
-                cross_attention_outputs = self.crossattention(
-                    attention_output,
-                    attention_mask,
-                    head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    output_attentions=output_attentions,
-                )
-                attention_output = cross_attention_outputs[0]
-                outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               
-        layer_output = apply_chunking_to_forward(
-            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
-        )
-        outputs = (layer_output,) + outputs
-
-        outputs = outputs + (present_key_value,)
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class BertEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
-        mode='multimodal',
-    ):
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
-
-        next_decoder_cache = () if use_cache else None
-               
-        for i in range(self.config.num_hidden_layers):
-            layer_module = self.layer[i]
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_head_mask = head_mask[i] if head_mask is not None else None
-            past_key_value = past_key_values[i] if past_key_values is not None else None
-
-            if self.gradient_checkpointing and self.training:
-
-                if use_cache:
-                    logger.warn(
-                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                    )
-                    use_cache = False
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    mode=mode,
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                    mode=mode,
-                )
-
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[-1],)
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    next_decoder_cache,
-                    all_hidden_states,
-                    all_self_attentions,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-class BertPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-
-        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-class BertOnlyMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = BertLMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class BertPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = BertConfig
-    base_model_prefix = "bert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """ Initialize the weights """
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-
-class BertModel(BertPreTrainedModel):
-    """
-    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
-    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
-    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
-    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
-    input to the forward pass.
-    """
-
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = BertEmbeddings(config)
-        
-        self.encoder = BertEncoder(config)
-
-        self.pooler = BertPooler(config) if add_pooling_layer else None
-
-        self.init_weights()
- 
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    
-    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
-        """
-        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
-
-        Arguments:
-            attention_mask (:obj:`torch.Tensor`):
-                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
-            input_shape (:obj:`Tuple[int]`):
-                The shape of the input to the model.
-            device: (:obj:`torch.device`):
-                The device of the input to the model.
-
-        Returns:
-            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
-        """
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        if attention_mask.dim() == 3:
-            extended_attention_mask = attention_mask[:, None, :, :]
-        elif attention_mask.dim() == 2:
-            # Provided a padding mask of dimensions [batch_size, seq_length]
-            # - if the model is a decoder, apply a causal mask in addition to the padding mask
-            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-            if is_decoder:
-                batch_size, seq_length = input_shape
-
-                seq_ids = torch.arange(seq_length, device=device)
-                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
-                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
-                # causal and attention masks must have same type with pytorch version < 1.3
-                causal_mask = causal_mask.to(attention_mask.dtype)
-   
-                if causal_mask.shape[1] < attention_mask.shape[1]:
-                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
-                    causal_mask = torch.cat(
-                        [
-                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
-                            causal_mask,
-                        ],
-                        axis=-1,
-                    )                     
-
-                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
-            else:
-                extended_attention_mask = attention_mask[:, None, None, :]
-        else:
-            raise ValueError(
-                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
-                    input_shape, attention_mask.shape
-                )
-            )
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-        return extended_attention_mask
-    
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        is_decoder=False,
-        mode='multimodal',
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`(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]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        """
-        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
-
-        if is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-
-        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:
-            input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
-            device = input_ids.device
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size, seq_length = input_shape
-            device = inputs_embeds.device
-        elif encoder_embeds is not None:    
-            input_shape = encoder_embeds.size()[:-1]
-            batch_size, seq_length = input_shape 
-            device = encoder_embeds.device
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_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 attention_mask is None:
-            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
-            
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, 
-                                                                                 device, is_decoder)
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if encoder_hidden_states is not None:
-            if type(encoder_hidden_states) == list:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
-            else:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            
-            if type(encoder_attention_mask) == list:
-                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
-            elif encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-            else:    
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-        
-        if encoder_embeds is None:
-            embedding_output = self.embeddings(
-                input_ids=input_ids,
-                position_ids=position_ids,
-                inputs_embeds=inputs_embeds,
-                past_key_values_length=past_key_values_length,
-            )
-        else:
-            embedding_output = encoder_embeds
-            
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            mode=mode,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-class BertLMHeadModel(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.cls = BertOnlyMLMHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        labels=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        return_logits=False,            
-        is_decoder=True,
-        reduction='mean',
-        mode='multimodal', 
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`(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]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
-            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
-            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        Returns:
-        Example::
-            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
-            >>> import torch
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
-            >>> config = BertConfig.from_pretrained("bert-base-cased")
-            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
-            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-            >>> outputs = model(**inputs)
-            >>> prediction_logits = outputs.logits
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        if labels is not None:
-            use_cache = False
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            is_decoder=is_decoder,
-            mode=mode,
-        )
-        
-        sequence_output = outputs[0]
-        prediction_scores = self.cls(sequence_output)
-        # sequence_output.shape torch.Size([85, 30, 768])
-        # prediction_scores.shape torch.Size([85, 30, 30524])
-        # labels.shape torch.Size([85, 30])
-
-
-        if return_logits:
-            return prediction_scores[:, :-1, :].contiguous()  
-
-        lm_loss = None
-        if labels is not None:
-            # we are doing next-token prediction; shift prediction scores and input ids by one
-            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
-            labels = labels[:, 1:].contiguous()
-            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) 
-            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-            if reduction=='none':
-                lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)               
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return ((lm_loss,) + output) if lm_loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=lm_loss,
-            logits=prediction_scores,
-            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=None, attention_mask=None, **model_kwargs):
-        input_shape = input_ids.shape
-        # 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_shape)
-
-        # cut decoder_input_ids if past is used
-        if past is not None:
-            input_ids = input_ids[:, -1:]
-
-        return {
-            "input_ids": input_ids, 
-            "attention_mask": attention_mask, 
-            "past_key_values": past,
-            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
-            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
-            "is_decoder": True,
-        }
-
-    def _reorder_cache(self, past, beam_idx):
-        reordered_past = ()
-        for layer_past in past:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
-        return reordered_past
-
-
diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/data/video_test_dataset.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/data/video_test_dataset.py
deleted file mode 100644
index 05de2615bcdea8a92b686ef0289bae8f72b0564d..0000000000000000000000000000000000000000
--- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/data/video_test_dataset.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import glob
-import torch
-from os import path as osp
-from torch.utils import data as data
-
-from basicsr.data.data_util import duf_downsample, generate_frame_indices, read_img_seq
-from basicsr.utils import get_root_logger, scandir
-from basicsr.utils.registry import DATASET_REGISTRY
-
-
-@DATASET_REGISTRY.register()
-class VideoTestDataset(data.Dataset):
-    """Video test dataset.
-
-    Supported datasets: Vid4, REDS4, REDSofficial.
-    More generally, it supports testing dataset with following structures:
-
-    dataroot
-    ├── subfolder1
-        ├── frame000
-        ├── frame001
-        ├── ...
-    ├── subfolder1
-        ├── frame000
-        ├── frame001
-        ├── ...
-    ├── ...
-
-    For testing datasets, there is no need to prepare LMDB files.
-
-    Args:
-        opt (dict): Config for train dataset. It contains the following keys:
-            dataroot_gt (str): Data root path for gt.
-            dataroot_lq (str): Data root path for lq.
-            io_backend (dict): IO backend type and other kwarg.
-            cache_data (bool): Whether to cache testing datasets.
-            name (str): Dataset name.
-            meta_info_file (str): The path to the file storing the list of test
-                folders. If not provided, all the folders in the dataroot will
-                be used.
-            num_frame (int): Window size for input frames.
-            padding (str): Padding mode.
-    """
-
-    def __init__(self, opt):
-        super(VideoTestDataset, self).__init__()
-        self.opt = opt
-        self.cache_data = opt['cache_data']
-        self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
-        self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []}
-        # file client (io backend)
-        self.file_client = None
-        self.io_backend_opt = opt['io_backend']
-        assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.'
-
-        logger = get_root_logger()
-        logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}')
-        self.imgs_lq, self.imgs_gt = {}, {}
-        if 'meta_info_file' in opt:
-            with open(opt['meta_info_file'], 'r') as fin:
-                subfolders = [line.split(' ')[0] for line in fin]
-                subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders]
-                subfolders_gt = [osp.join(self.gt_root, key) for key in subfolders]
-        else:
-            subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*')))
-            subfolders_gt = sorted(glob.glob(osp.join(self.gt_root, '*')))
-
-        if opt['name'].lower() in ['vid4', 'reds4', 'redsofficial']:
-            for subfolder_lq, subfolder_gt in zip(subfolders_lq, subfolders_gt):
-                # get frame list for lq and gt
-                subfolder_name = osp.basename(subfolder_lq)
-                img_paths_lq = sorted(list(scandir(subfolder_lq, full_path=True)))
-                img_paths_gt = sorted(list(scandir(subfolder_gt, full_path=True)))
-
-                max_idx = len(img_paths_lq)
-                assert max_idx == len(img_paths_gt), (f'Different number of images in lq ({max_idx})'
-                                                      f' and gt folders ({len(img_paths_gt)})')
-
-                self.data_info['lq_path'].extend(img_paths_lq)
-                self.data_info['gt_path'].extend(img_paths_gt)
-                self.data_info['folder'].extend([subfolder_name] * max_idx)
-                for i in range(max_idx):
-                    self.data_info['idx'].append(f'{i}/{max_idx}')
-                border_l = [0] * max_idx
-                for i in range(self.opt['num_frame'] // 2):
-                    border_l[i] = 1
-                    border_l[max_idx - i - 1] = 1
-                self.data_info['border'].extend(border_l)
-
-                # cache data or save the frame list
-                if self.cache_data:
-                    logger.info(f'Cache {subfolder_name} for VideoTestDataset...')
-                    self.imgs_lq[subfolder_name] = read_img_seq(img_paths_lq)
-                    self.imgs_gt[subfolder_name] = read_img_seq(img_paths_gt)
-                else:
-                    self.imgs_lq[subfolder_name] = img_paths_lq
-                    self.imgs_gt[subfolder_name] = img_paths_gt
-        else:
-            raise ValueError(f'Non-supported video test dataset: {type(opt["name"])}')
-
-    def __getitem__(self, index):
-        folder = self.data_info['folder'][index]
-        idx, max_idx = self.data_info['idx'][index].split('/')
-        idx, max_idx = int(idx), int(max_idx)
-        border = self.data_info['border'][index]
-        lq_path = self.data_info['lq_path'][index]
-
-        select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding'])
-
-        if self.cache_data:
-            imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx))
-            img_gt = self.imgs_gt[folder][idx]
-        else:
-            img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx]
-            imgs_lq = read_img_seq(img_paths_lq)
-            img_gt = read_img_seq([self.imgs_gt[folder][idx]])
-            img_gt.squeeze_(0)
-
-        return {
-            'lq': imgs_lq,  # (t, c, h, w)
-            'gt': img_gt,  # (c, h, w)
-            'folder': folder,  # folder name
-            'idx': self.data_info['idx'][index],  # e.g., 0/99
-            'border': border,  # 1 for border, 0 for non-border
-            'lq_path': lq_path  # center frame
-        }
-
-    def __len__(self):
-        return len(self.data_info['gt_path'])
-
-
-@DATASET_REGISTRY.register()
-class VideoTestVimeo90KDataset(data.Dataset):
-    """Video test dataset for Vimeo90k-Test dataset.
-
-    It only keeps the center frame for testing.
-    For testing datasets, there is no need to prepare LMDB files.
-
-    Args:
-        opt (dict): Config for train dataset. It contains the following keys:
-            dataroot_gt (str): Data root path for gt.
-            dataroot_lq (str): Data root path for lq.
-            io_backend (dict): IO backend type and other kwarg.
-            cache_data (bool): Whether to cache testing datasets.
-            name (str): Dataset name.
-            meta_info_file (str): The path to the file storing the list of test
-                folders. If not provided, all the folders in the dataroot will
-                be used.
-            num_frame (int): Window size for input frames.
-            padding (str): Padding mode.
-    """
-
-    def __init__(self, opt):
-        super(VideoTestVimeo90KDataset, self).__init__()
-        self.opt = opt
-        self.cache_data = opt['cache_data']
-        if self.cache_data:
-            raise NotImplementedError('cache_data in Vimeo90K-Test dataset is not implemented.')
-        self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
-        self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []}
-        neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])]
-
-        # file client (io backend)
-        self.file_client = None
-        self.io_backend_opt = opt['io_backend']
-        assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.'
-
-        logger = get_root_logger()
-        logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}')
-        with open(opt['meta_info_file'], 'r') as fin:
-            subfolders = [line.split(' ')[0] for line in fin]
-        for idx, subfolder in enumerate(subfolders):
-            gt_path = osp.join(self.gt_root, subfolder, 'im4.png')
-            self.data_info['gt_path'].append(gt_path)
-            lq_paths = [osp.join(self.lq_root, subfolder, f'im{i}.png') for i in neighbor_list]
-            self.data_info['lq_path'].append(lq_paths)
-            self.data_info['folder'].append('vimeo90k')
-            self.data_info['idx'].append(f'{idx}/{len(subfolders)}')
-            self.data_info['border'].append(0)
-
-    def __getitem__(self, index):
-        lq_path = self.data_info['lq_path'][index]
-        gt_path = self.data_info['gt_path'][index]
-        imgs_lq = read_img_seq(lq_path)
-        img_gt = read_img_seq([gt_path])
-        img_gt.squeeze_(0)
-
-        return {
-            'lq': imgs_lq,  # (t, c, h, w)
-            'gt': img_gt,  # (c, h, w)
-            'folder': self.data_info['folder'][index],  # folder name
-            'idx': self.data_info['idx'][index],  # e.g., 0/843
-            'border': self.data_info['border'][index],  # 0 for non-border
-            'lq_path': lq_path[self.opt['num_frame'] // 2]  # center frame
-        }
-
-    def __len__(self):
-        return len(self.data_info['gt_path'])
-
-
-@DATASET_REGISTRY.register()
-class VideoTestDUFDataset(VideoTestDataset):
-    """ Video test dataset for DUF dataset.
-
-    Args:
-        opt (dict): Config for train dataset.
-            Most of keys are the same as VideoTestDataset.
-            It has the following extra keys:
-
-            use_duf_downsampling (bool): Whether to use duf downsampling to
-                generate low-resolution frames.
-            scale (bool): Scale, which will be added automatically.
-    """
-
-    def __getitem__(self, index):
-        folder = self.data_info['folder'][index]
-        idx, max_idx = self.data_info['idx'][index].split('/')
-        idx, max_idx = int(idx), int(max_idx)
-        border = self.data_info['border'][index]
-        lq_path = self.data_info['lq_path'][index]
-
-        select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding'])
-
-        if self.cache_data:
-            if self.opt['use_duf_downsampling']:
-                # read imgs_gt to generate low-resolution frames
-                imgs_lq = self.imgs_gt[folder].index_select(0, torch.LongTensor(select_idx))
-                imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale'])
-            else:
-                imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx))
-            img_gt = self.imgs_gt[folder][idx]
-        else:
-            if self.opt['use_duf_downsampling']:
-                img_paths_lq = [self.imgs_gt[folder][i] for i in select_idx]
-                # read imgs_gt to generate low-resolution frames
-                imgs_lq = read_img_seq(img_paths_lq, require_mod_crop=True, scale=self.opt['scale'])
-                imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale'])
-            else:
-                img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx]
-                imgs_lq = read_img_seq(img_paths_lq)
-            img_gt = read_img_seq([self.imgs_gt[folder][idx]], require_mod_crop=True, scale=self.opt['scale'])
-            img_gt.squeeze_(0)
-
-        return {
-            'lq': imgs_lq,  # (t, c, h, w)
-            'gt': img_gt,  # (c, h, w)
-            'folder': folder,  # folder name
-            'idx': self.data_info['idx'][index],  # e.g., 0/99
-            'border': border,  # 1 for border, 0 for non-border
-            'lq_path': lq_path  # center frame
-        }
-
-
-@DATASET_REGISTRY.register()
-class VideoRecurrentTestDataset(VideoTestDataset):
-    """Video test dataset for recurrent architectures, which takes LR video
-    frames as input and output corresponding HR video frames.
-
-    Args:
-        Same as VideoTestDataset.
-        Unused opt:
-            padding (str): Padding mode.
-
-    """
-
-    def __init__(self, opt):
-        super(VideoRecurrentTestDataset, self).__init__(opt)
-        # Find unique folder strings
-        self.folders = sorted(list(set(self.data_info['folder'])))
-
-    def __getitem__(self, index):
-        folder = self.folders[index]
-
-        if self.cache_data:
-            imgs_lq = self.imgs_lq[folder]
-            imgs_gt = self.imgs_gt[folder]
-        else:
-            raise NotImplementedError('Without cache_data is not implemented.')
-
-        return {
-            'lq': imgs_lq,
-            'gt': imgs_gt,
-            'folder': folder,
-        }
-
-    def __len__(self):
-        return len(self.folders)
diff --git a/spaces/matthoffner/local-llm-doc-chat/app.py b/spaces/matthoffner/local-llm-doc-chat/app.py
deleted file mode 100644
index 80555eb26889b0bc45d2ad3355988dce348822df..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/local-llm-doc-chat/app.py
+++ /dev/null
@@ -1,363 +0,0 @@
-import os
-import random
-import itertools
-import streamlit as st
-import validators
-from ctransformers.langchain import CTransformers
-from langchain.document_loaders import PyPDFLoader, TextLoader, WebBaseLoader
-from langchain.vectorstores import FAISS
-from langchain.chains import QAGenerationChain
-from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.callbacks import StdOutCallbackHandler
-from langchain.chains import ConversationalRetrievalChain, QAGenerationChain, LLMChain
-from langchain.memory import ConversationBufferMemory
-from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
-from langchain.chains.question_answering import load_qa_chain
-
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    AIMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-
-from langchain.document_loaders.unstructured import UnstructuredBaseLoader
-
-memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True, output_key='answer')
-
-global_model_chain = None # initialize it at the global scope
-
-st.set_page_config(page_title="DocuBeast",page_icon=':book:')
-
-def save_file_locally(file):
-    '''Save uploaded files locally'''
-    doc_path = os.path.join('tempdir',file.name)
-    with open(doc_path,'wb') as f:
-        f.write(file.getbuffer())
-    
-    return doc_path
-
-def load_prompt():
-
-    system_template="""Use only the following pieces of context to answer the users question accurately.
-    Do not use any information not provided in the earnings context.
-    If you don't know the answer, just say 'There is no relevant answer in the given documents', 
-    don't try to make up an answer.
-    
-    ALWAYS return a "SOURCES" part in your answer.
-    The "SOURCES" part should be a reference to the source of the document from which you got your answer.
-    
-    Remember, do not reference any information not given in the context.
-    If the answer is not available in the given context just say 'There is no relevant answer in the given document'
-    
-    Follow the below format when answering:
-    
-    Question: {question}
-    SOURCES: [xyz]
-      
-    Begin!
-    ----------------
-    {context}"""
-    
-    messages = [
-        SystemMessagePromptTemplate.from_template(system_template),
-        HumanMessagePromptTemplate.from_template("{question}")
-    ]
-    prompt = ChatPromptTemplate.from_messages(messages)
-        
-    return prompt
-    
-
-def load_docs(files, url=False):
-    if not url:
-        st.info("`Reading doc ...`")
-        all_text = ""
-        documents = []
-        for file in files:
-            file_extension = os.path.splitext(file.name)[1]
-            doc_path = save_file_locally(file)
-            if file_extension == ".pdf":
-                pages = PyPDFLoader(doc_path)
-                documents.extend(pages.load())
-                
-            elif file_extension == ".txt":
-                #stringio = StringIO(file_path.getvalue().decode("utf-8"))
-                pages = TextLoader(doc_path)
-                documents.extend(pages.load())
-            
-            else:
-                st.warning('Please provide txt or pdf or docx.', icon="⚠️")
-    elif url:
-        st.info("`Reading web link ...`")
-        loader = WebBaseLoader(files)
-        documents = loader.load()
-    
-    return ','.join([doc.page_content for doc in documents])
-
-bi_enc_dict = {'mpnet-base-v2':"all-mpnet-base-v2",
-              'instructor-large': 'hkunlp/instructor-large'}
-
-def gen_embeddings(model_name):
-
-    '''Generate embeddings for given model'''
-
-    embeddings = None  # Add a default value for `embeddings`
-
-    if model_name == 'mpnet-base-v2':
-        embeddings = HuggingFaceEmbeddings(model_name=bi_enc_dict[model_name])
-
-    elif model_name == 'instructor-large':
-        
-        embeddings = HuggingFaceInstructEmbeddings(model_name=bi_enc_dict[model_name],
-                                           query_instruction='Represent the question for retrieving supporting paragraphs: ',
-                                           embed_instruction='Represent the paragraph for retrieval: ')
-    else:
-        raise ValueError(f"Unsupported model name: {model_name}")
-
-    return embeddings
-
-
-def load_retrieval_chain(_vectorstore):
-
-    '''Load Chain'''
-
-    # Initialize the RetrievalQA chain with streaming output
-    callback_handler = [StdOutCallbackHandler()]
-    chat_llm = CTransformers(model='s3nh/LLaMA-2-7B-32K-GGML',
-                                       model_file='LLaMA-2-7B-32K.ggmlv3.q4_0.bin',
-                                       model_type='llama')
-
-
-    question_generator = LLMChain(llm=chat_llm, prompt=CONDENSE_QUESTION_PROMPT) 
-    doc_chain = load_qa_chain(llm=chat_llm,chain_type="stuff",prompt=load_prompt()) 
-    chain = ConversationalRetrievalChain(retriever=_vectorstore.as_retriever(search_kwags={"k": 3}), 
-                                     question_generator=question_generator, 
-                                     combine_docs_chain=doc_chain, 
-                                     memory=memory, 
-                                     return_source_documents=True, 
-                                     get_chat_history=lambda h :h)
-
-    return chain
-    
-
-def process_corpus(corpus,model_name, chunk_size=6000, overlap=500):
-
-    '''Process text for Semantic Search'''
-    
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size,chunk_overlap=overlap)
-
-    texts = text_splitter.split_text(corpus)
-
-    # Display the number of text chunks
-    num_chunks = len(texts)
-    st.write(f"Number of text chunks: {num_chunks}")
-
-    embeddings = gen_embeddings(model_name)
-
-    vectorstore = FAISS.from_texts(texts, embeddings)
-
-    global global_model_chain
-    global_model_chain = load_retrieval_chain(vectorstore)
-
-
-def run_qa_chain(text, query, model_name):
-    '''Run the QnA chain'''
-
-    global global_model_chain
-    if global_model_chain is None:
-        process_corpus(text, model_name)
-    
-    answer = global_model_chain({"question": query})
-    
-    return answer
-
-
-def gen_qa_response(text, user_question, model_name):
-    '''Generate responses from query'''
-    
-    if user_question:
-        result = run_qa_chain(text, user_question, model_name)
-
-        references = [doc.page_content for doc in result['source_documents']]
-        answer = result['answer']
-
-        with st.expander(label='Query Result', expanded=True):
-            st.write(answer)
-                
-        with st.expander(label='References from Corpus used to Generate Result'):
-            for ref in references:
-                st.write(ref)
-                
-    # Check if there are no generated question-answer pairs in the session state
-    if 'eval_set' not in st.session_state:
-        # Use the generate_eval function to generate question-answer pairs
-        num_eval_questions = 1  # Number of question-answer pairs to generate
-        st.session_state.eval_set = generate_eval(text, num_eval_questions, 3000)
-
-    for i, qa_pair in enumerate(st.session_state.eval_set):
-        print(qa_pair)
-        st.sidebar.markdown(
-            f"""
-            <div class="css-card">
-            <span class="card-tag">Question {i + 1}</span>
-                <p style="font-size: 12px;">{qa_pair['question']}</p>
-                <p style="font-size: 12px;">{qa_pair['answer']}</p>
-            </div>
-            """,
-            unsafe_allow_html=True,
-        )
-    
-    st.write("Ready to answer questions.")
-
-
-def generate_eval(raw_text, N, chunk):
-
-    # Generate N questions from context of chunk chars
-    # IN: text, N questions, chunk size to draw question from in the doc
-    # OUT: eval set as JSON list
-
-    # raw_text = ','.join(raw_text)
-    
-    update = st.empty()
-    ques_update = st.empty()
-    n = len(raw_text)
-    starting_indices = [random.randint(0, n-chunk) for _ in range(N)]
-    sub_sequences = [raw_text[i:i+chunk] for i in starting_indices]
-    chat_llm = CTransformers(model='s3nh/LLaMA-2-7B-32K-GGML',
-                                       model_file='LLaMA-2-7B-32K.ggmlv3.q4_0.bin',
-                                       model_type='llama')
-    chain = QAGenerationChain.from_llm(chat_llm)
-    eval_set = []
-    for i, b in enumerate(sub_sequences):
-        try:
-            qa = chain.run(b)
-            eval_set.append(qa)
-            ques_update.info(f"Creating Question: {i+1}")
-
-        except:
-            st.warning(f'Error in generating Question: {i+1}...', icon="⚠️")
-            continue
-        
-    eval_set_full = list(itertools.chain.from_iterable(eval_set))
-    
-    update.empty()
-    ques_update.empty()
-
-    return eval_set_full
-
-
-# Add custom CSS
-st.markdown(
-    """
-    <style>
-    
-    #MainMenu {visibility: hidden;
-    # }
-        footer {visibility: hidden;
-        }
-        .css-card {
-            border-radius: 0px;
-            padding: 30px 10px 10px 10px;
-            background-color: black;
-            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-            margin-bottom: 10px;
-            font-family: "IBM Plex Sans", sans-serif;
-        }
-        
-        .card-tag {
-            border-radius: 0px;
-            padding: 1px 5px 1px 5px;
-            margin-bottom: 10px;
-            position: absolute;
-            left: 0px;
-            top: 0px;
-            font-size: 0.6rem;
-            font-family: "IBM Plex Sans", sans-serif;
-            color: white;
-            background-color: green;
-            }
-            
-        .css-zt5igj {left:0;
-        }
-        
-        span.css-10trblm {margin-left:0;
-        }
-        
-        div.css-1kyxreq {margin-top: -40px;
-        }
-        
-    </style>
-    """,
-    unsafe_allow_html=True,
-)
-
-
-st.write(
-f"""
-<div style="display: flex; align-items: center; margin-left: 0;">
-    <h1 style="display: inline-block;">docubeast</h1>
-    <sup style="margin-left:5px;font-size:small; color: green;">pre-alpha</sup>
-</div>
-""",
-unsafe_allow_html=True,
-    )
-
-
-# Use RecursiveCharacterTextSplitter as the default and only text splitter
-splitter_type = "RecursiveCharacterTextSplitter"
-
-uploaded_files = st.file_uploader("Upload a PDF or TXT or DOCX Document", type=[
-                                  "pdf", "txt", "docx"], accept_multiple_files=True)
-
-url_text = st.text_input("Please enter a url here for a HTML file you would like to load..")
-
-bi_enc_dict = {'mpnet-base-v2':"all-mpnet-base-v2",
-              'instructor-base': 'hkunlp/instructor-base'}
-
-        
-model_name = st.sidebar.selectbox("Embedding Model", options=list(bi_enc_dict.keys()), key='sbox')
-
-text_input = st.text_area("Enter your text here")
-
-if text_input:
-    if 'text_files' not in st.session_state or st.session_state.text_files != text_input:
-        st.session_state.text_files = text_input
-        if 'eval_set' in st.session_state:
-            del st.session_state['eval_set']
-    
-    
-    st.success("Text Document processed.")
-    
-    user_question = st.text_input("Enter your question:")
-    
-    gen_qa_response(text_input, user_question, model_name)
-elif uploaded_files:
-    # Check if last_uploaded_files is not in session_state or if uploaded_files are different from last_uploaded_files
-    if 'last_uploaded_files' not in st.session_state or st.session_state.last_uploaded_files != uploaded_files:
-        st.session_state.last_uploaded_files = uploaded_files
-        if 'eval_set' in st.session_state:
-            del st.session_state['eval_set']
-
-    # Load and process the uploaded PDF or TXT files.
-    raw_text = load_docs(uploaded_files)
-    st.success("Documents uploaded and processed.")
-
-    # Question and answering
-    user_question = st.text_input("Enter your question:")
-    
-    gen_qa_response(raw_text, user_question, model_name)
-elif url_text and validators.url(url_text):
-    # Check if last_uploaded_files is not in session_state or if uploaded_files are different from last_uploaded_files
-    if 'url_files' not in st.session_state or st.session_state.url_files != url_text:
-        st.session_state.url_files = url_text
-        if 'eval_set' in st.session_state:
-            del st.session_state['eval_set']
-            
-    loaded_docs = load_docs(url_text,url=True)
-    st.success("Web Document uploaded and processed.")
-
-    user_question = st.text_input("Enter your question:")
-
-    gen_qa_response(loaded_docs, user_question, model_name)
-
diff --git a/spaces/maxmax20160403/sovits5.0/whisper_pretrain/README.md b/spaces/maxmax20160403/sovits5.0/whisper_pretrain/README.md
deleted file mode 100644
index 1093e1dcbb792f7a86a0d3a3a1bf14691ab998b5..0000000000000000000000000000000000000000
--- a/spaces/maxmax20160403/sovits5.0/whisper_pretrain/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-Path for:
-
-    medium.pt
\ No newline at end of file
diff --git a/spaces/merve/data-leak/public/fill-in-the-blank/index.html b/spaces/merve/data-leak/public/fill-in-the-blank/index.html
deleted file mode 100644
index b1ff5d0c943d3457ad18afefa53be4a9d0155f24..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/public/fill-in-the-blank/index.html
+++ /dev/null
@@ -1,192 +0,0 @@
-<!--
-@license
-Copyright 2020 Google. 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.
--->
-
-<!DOCTYPE html>
-
-<html>
-<head>
-	<meta charset="utf-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-
-  <link rel="apple-touch-icon" sizes="180x180" href="https://pair.withgoogle.com/images/favicon/apple-touch-icon.png">
-  <link rel="icon" type="image/png" sizes="32x32" href="https://pair.withgoogle.com/images/favicon/favicon-32x32.png">
-  <link rel="icon" type="image/png" sizes="16x16" href="https://pair.withgoogle.com/images/favicon/favicon-16x16.png">
-  <link rel="mask-icon" href="https://pair.withgoogle.com/images/favicon/safari-pinned-tab.svg" color="#00695c">
-  <link rel="shortcut icon" href="https://pair.withgoogle.com/images/favicon.ico">
-
-  <script>
-    !(function(){
-      var url = window.location.href
-      if (url.split('#')[0].split('?')[0].slice(-1) != '/' && !url.includes('.html')) window.location = url + '/'
-    })()
-  </script>
-
-  <title>What Have Language Models Learned?</title>
-  <meta property="og:title" content="What Have Language Models Learned?">
-  <meta property="og:url" content="https://pair.withgoogle.com/explorables/fill-in-the-blank/">
-
-  <meta name="og:description" content="By asking language models to fill in the blank, we can probe their understanding of the world.">
-  <meta property="og:image" content="https://pair.withgoogle.com/explorables/images/fill-in-the-blank.png">
-  <meta name="twitter:card" content="summary_large_image">
-  
-	<link rel="stylesheet" type="text/css" href="../style.css">
-
-  <link href='https://fonts.googleapis.com/css?family=Roboto+Slab:400,500,700|Roboto:700,500,300' rel='stylesheet' type='text/css'>  
-  <link href="https://fonts.googleapis.com/css?family=Google+Sans:400,500,700" rel="stylesheet">
-
-	<meta name="viewport" content="width=device-width">
-</head>
-<body>
-  <div class='header'>
-    <div class='header-left'>
-      <a href='https://pair.withgoogle.com/'>
-        <img src='../images/pair-logo.svg' style='width: 100px'></img>
-      </a>
-      <a href='../'>Explorables</a> 
-    </div>
-  </div>
-  
-  <h1 class='headline'>What Have Language Models Learned?</h1>
-  <div class="post-summary">By asking language models to fill in the blank, we can probe their understanding of the world.</div>
-  <p>Large language models are making it possible for computers to <a href="https://openai.com/blog/better-language-models/">write stories</a>, <a href="https://twitter.com/sharifshameem/status/1282676454690451457">program a website</a> and <a href="https://openai.com/blog/dall-e/">turn captions into images</a>.</p>
-<p>One of the first of these models, <a href="https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html">BERT</a>, is trained by taking sentences, splitting them into individual words, randomly hiding some of them, and predicting what the hidden words are. After doing this millions of times, BERT has “read” enough Shakespeare to predict how this phrase usually ends: </p>
-<div class='sent hamlet'></div>
-
-<p>This page is hooked up to a version of BERT trained on Wikipedia and books.<a class='footstart'>¹</a> Try clicking on different words to see how they’d be filled in or typing in another sentence to see what else has BERT picked up on. </p>
-<div class='hamlet-edit'></div>
-
-<h3 id="cattle-or-clothes-">Cattle or Clothes?</h3>
-<p>Besides Hamlet’s existential dread, the text BERT was trained on also contains more patterns: </p>
-<div class='sent texas'></div>
-
-<p>Cattle and horses aren’t top purchase predictions in every state, though! In New York, some of the most likely words are clothes, books and art:</p>
-<div class='sent new-york'></div>
-
-<p>There are more than 30,000 words, punctuation marks and word fragments in BERT’s <a href="https://huggingface.co/transformers/tokenizer_summary.html">vocabulary</a>. Every time BERT fills in a hidden word, it assigns each of them a probability. By looking at how slightly different sentences shift those probabilities, we can get a glimpse at how purchasing patterns in different places are understood.     </p>
-<div class='pair texas-ohio'></div>
-
-<p>You can <strong>edit these sentences</strong>. Or try one of these comparisons to get started: <span class='texas-ohio-alts'></span></p>
-<p>To the extent that a computer program can “know” something, what does BERT know about where you live? </p>
-<h3 id="what-s-in-a-name-">What’s in a Name?</h3>
-<p>This technique can also probe what associations BERT has learned about different groups of people. For example, it predicts people named Elsie are older than people named Lauren:  </p>
-<div class='pair age-name'></div>
-
-<p>It’s also learned that people named Jim have more <a href="https://flowingdata.com/2017/09/11/most-female-and-male-occupations-since-1950/">typically masculine</a> jobs than people named Jane: </p>
-<div class='pair jim-jane'></div>
-
-<p>These aren’t just spurious correlations — Elsies really are more likely to be <a href="https://rhiever.github.io/name-age-calculator/">older</a> than Laurens.<a class='footstart'></a> And occupations the model associates with feminine names are held by a <a href="https://purehost.bath.ac.uk/ws/portalfiles/portal/168480066/CaliskanEtAl_authors_full.pdf">higher percentage</a> of women.  </p>
-<p>Should we be concerned about these correlations? BERT was trained to fill in blanks in Wikipedia articles and books —  it does a great job at that! The problem is that the internal representations of language these models have learned are used for much more – by some <a href="https://super.gluebenchmark.com/leaderboard">measures</a>, they’re the best way we have of getting computers to understand and manipulate text.</p>
-<p>We wouldn’t hesitate to call a conversation partner or recruiter who blithely assumed that doctors are men sexist, but that’s exactly what BERT might do if heedlessly incorporated into a chatbot or HR software:</p>
-<div class='pair nurse-name'></div>
-
-<p>Adjusting for assumptions like this isn’t trivial. <em>Why</em> machine learning systems produce a given output still isn’t well understood – determining if a credit model built on top of BERT rejected a loan application because of <a href="https://pair.withgoogle.com/explorables/hidden-bias/">gender discrimation</a> might be quite difficult.</p>
-<p>Deploying large language models at scale also risks <a href="https://machinesgonewrong.com/bias_i/#harms-of-representation">amplifying</a> and <a href="http://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf">perpetuating</a> today’s harmful stereotypes. When <a href="https://arxiv.org/pdf/2101.05783v1.pdf#page=3">prompted</a> with “Two Muslims walked into a…”, for example, <a href="https://en.wikipedia.org/wiki/GPT-3">GPT-3</a> typically finishes the sentence with descriptions of violence. </p>
-<h3 id="how-can-we-fix-this-">How Can We Fix This?</h3>
-<p>One conceptually straightforward approach: reduce unwanted correlations from the training data to <a href="https://arxiv.org/abs/1906.08976">mitigate</a> model <a href="https://arxiv.org/abs/2005.14050">bias</a>. </p>
-<p>Last year a version of BERT called <a href="https://ai.googleblog.com/2020/10/measuring-gendered-correlations-in-pre.html">Zari</a> was <a href="https://arxiv.org/pdf/2010.06032.pdf#page=6">trained</a> with an additional set of generated sentences. For every sentence with a <a href="https://github.com/uclanlp/corefBias/blob/master/WinoBias/wino/generalized_swaps.txt">gendered noun</a>, like boy or aunt, another sentence that replaced the noun with its gender-partner was added to the training data: in addition to “The <em>lady</em> doth protest too much,” Zari was also trained on “The <em>gentleman</em> doth protest too much.”        </p>
-<div class='pair nurse-name-zari-cda'></div>
-
-<p>Unlike BERT, Zari assigns nurses and doctors an equal probability of being a “she” or a “he” after being trained on the swapped sentences. This approach hasn’t removed all the gender correlations; because names weren’t swapped, Zari’s association between masculine names and doctors has only slightly decreased from BERT’s.<a class='footstart'></a> And the retraining doesn’t change how the model understands nonbinary gender.    </p>
-<p>Something similar happened with <a href="https://arxiv.org/abs/1607.06520">other attempts</a> to remove gender bias from models’ representations of words. It’s possible to mathematically define bias and perform “brain surgery” on a model to remove it, but language is steeped in gender. Large models can have billions of parameters in which to learn stereotypes — slightly different measures of bias have found the retrained models only <a href="https://www.aclweb.org/anthology/N19-1061/">shifted the stereotypes</a> around to be undetectable by the initial measure.</p>
-<p>As with <a href="https://pair.withgoogle.com/explorables/measuring-fairness/">other applications</a> of machine learning, it’s helpful to focus instead on the actual harms that could occur. Tools like <a href="https://allennlp.org/">AllenNLP</a>, <a href="http://lmdiff.net/">LMdiff</a> and the <a href="https://pair-code.github.io/lit/">Language Interpretability Tool</a> make it easier to interact with language models to find where they might be falling short.<a class='footstart'></a> Once those shortcomings are spotted, <a href="https://arxiv.org/abs/2004.07667">task specific</a> mitigation measures can be simpler to apply than modifying the entire model.  </p>
-<p>It’s also possible that as models grow more capable, they might be able to <a href="https://arxiv.org/abs/2004.14546">explain</a> and perform some of this debiasing themselves. Instead of forcing the model to tell us the gender of “the doctor,” we could let it respond with <a href="https://arr.am/2020/07/25/gpt-3-uncertainty-prompts/">uncertainty</a> that’s <a href="https://ai.googleblog.com/2018/12/providing-gender-specific-translations.html">shown to the user</a> and controls to override assumptions. </p>
-<h3 id="credits">Credits</h3>
-<p>Adam Pearce // July 2021</p>
-<p>Thanks to Ben Wedin, Emily Reif, James Wexler, Fernanda Viégas, Ian Tenney, Kellie Webster, Kevin Robinson, Lucas Dixon, Ludovic Peran, Martin Wattenberg, Michael Terry, Tolga Bolukbasi, Vinodkumar Prabhakaran, Xuezhi Wang, Yannick Assogba, and Zan Armstrong for their help with this piece. </p>
-<h3 id="footnotes">Footnotes</h3>
-<p><a class='footend'></a> The BERT model used on this page is the Hugging Face version of <a href="https://huggingface.co/bert-large-uncased-whole-word-masking">bert-large-uncased-whole-word-masking</a>. “BERT” also refers to a type of model architecture; hundreds of BERT models have been <a href="https://huggingface.co/models?filter=bert">trained and published</a>. The model and chart code used here are available on <a href="https://github.com/PAIR-code/ai-explorables">GitHub</a>. </p>
-<p><a class='footend'></a> Notice that “1800”, “1900” and “2000” are some of the top predictions, though. People aren’t actually more likely to be born at the start of a century, but in BERT’s training corpus of books and Wikipedia articles round numbers are <a href="https://blocks.roadtolarissa.com/1wheel/cea123a8c17d51d9dacbd1c17e6fe601">more common</a>. <br><br><img aria-label='Scatter plot showing the frequency of numbers between 1400 and 1800 in Wikipedia; round number of large peaks.' src='img/wiki-years.png'></img> </p>
-<p><a class='footend'></a>Comparing BERT and Zari in this interface requires carefully tracking tokens during a transition. The <a href="https://colab.research.google.com/drive/1xfPGKqjdE635cVSi-Ggt-cRBU5pyJNWP">BERT Difference Plots</a> colab has ideas for extensions to systemically look at differences between the models’ output. </p>
-<p><a class='footend'></a> This analysis shouldn’t stop once a model is deployed — as language and model usage shifts, it’s important to continue studying and mitigating potential harms. </p>
-<h3 id="appendix-differences-over-time">Appendix: Differences Over Time</h3>
-<p>In addition to looking at how predictions for <c0>men</c0> and <c1>women</c1> are different for a given sentence, we can also chart how those differences have changed over time: </p>
-<div class='gender-over-time'></div>
-
-<p>The convergence in more recent years suggests another potential mitigation technique: using a prefix to steer the model away from unwanted correlations while preserving its understanding of natural language.  </p>
-<p>Using “In $year” as the prefix is quite limited, though, as it doesn’t handle <c2>gender-neutral</c2> pronouns and potentially <a href="https://www.pnas.org/content/pnas/115/16/E3635.full.pdf#page=8">increases</a> other correlations. However, it may be possible to <a href="https://arxiv.org/abs/2104.08691">find a better prefix</a> that mitigates a specific type of bias with just a <a href="https://www.openai.com/blog/improving-language-model-behavior/">couple of dozen examples</a>. </p>
-<div class='gender-over-time'></div>
-
-<p>Closer examination of these differences in differences also shows there’s a limit to the facts we can pull out of BERT this way. </p>
-<p>Below, the top row of charts shows how predicted differences in occupations between men and women change between 1908 and 2018. The rightmost chart shows the he/she difference in 1908 against the he/she difference in 2018. </p>
-<p>The flat slope of the rightmost chart indicates that the he/she difference has decreased for each job by about the same amount. But in reality, <a href="https://www.weforum.org/agenda/2016/03/a-visual-history-of-gender-and-employment">shifts in occupation</a> weren’t nearly so smooth and some occupations, like accounting, switched from being majority male to majority female. </p>
-<div class='difference-difference pair difference'></div>   
-
-<p>This reality-prediction mismatch could be caused by lack of training data, model size or the coarseness of the probing method. There’s an immense amount of general knowledge inside of these models — with a little bit of focused training, they can even become expert <a href="https://t5-trivia.glitch.me/">trivia</a> players. </p>
-<h3 id="more-explorables">More Explorables</h3>
-<p id='recirc'></p>
-
-<link rel="stylesheet" href="style.css">
-
-<script src='../third_party/regl.min.js'></script>
-<script src='../third_party/d3_.js'></script>
-<script src='../third_party/d3-scale-chromatic.v1.min.js'></script>
-<script src='../third_party/params.js'></script>
-
-<script src='data/cachekey2filename.js'></script>
-<script src='post.js'></script>
-<script src='tokenizer.js'></script>
-<script src='scatter.js'></script>
-
-<script src='init-pair.js'></script>
-<script src='init-diff.js'></script>
-<script src='init-sent.js'></script>
-<script src='init-gender-over-time.js'></script>
-<script src='init.js'></script>
-
-
-<script src='../third_party/recirc.js'></script>
-</body>
-
-<script async src="https://www.googletagmanager.com/gtag/js?id=UA-138505774-1"></script>
-<script>
-  if (window.location.origin === 'https://pair.withgoogle.com'){
-    window.dataLayer = window.dataLayer || [];
-    function gtag(){dataLayer.push(arguments);}
-    gtag('js', new Date());
-    gtag('config', 'UA-138505774-1');
-  }
-</script>
-
-<script>
-  // Tweaks for displaying in an iframe
-  if (window !== window.parent){
-    
-    // Open links in a new tab
-    Array.from(document.querySelectorAll('a'))
-      .forEach(e => {
-        // skip anchor links
-        if (e.href && e.href[0] == '#') return
-
-        e.setAttribute('target', '_blank')
-        e.setAttribute('rel', 'noopener noreferrer')
-      })
-
-    // Remove recirc h3
-    Array.from(document.querySelectorAll('h3'))
-      .forEach(e => {
-        if (e.textContent != 'More Explorables') return
-
-        e.parentNode.removeChild(e)
-      })
-
-    // Remove recirc container
-    var recircEl = document.querySelector('#recirc')
-    recircEl.parentNode.removeChild(recircEl)
-  }
-</script>
-
-</html>
\ No newline at end of file
diff --git a/spaces/merve/data-leak/public/third_party/params.js b/spaces/merve/data-leak/public/third_party/params.js
deleted file mode 100644
index 8b4b8b39bb932ef3d7784445c6e9e5fc04b12841..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/public/third_party/params.js
+++ /dev/null
@@ -1,22 +0,0 @@
-window.makeParams = function(){
-  var url = new URL(window.location)
-  var searchParams = new URLSearchParams(url.search) 
-
-  var rv = {}
-
-  rv.get = key => {
-    return searchParams.get(key)
-  }
-
-  rv.set = (key, value) => {
-    searchParams.set(key, value)
-
-    url.search = searchParams.toString()
-    history.replaceState(null, '', url)
-  }
-
-  return rv
-}
-
-
-if (window.init) init()
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diff --git a/spaces/merve/data-leak/source/fill-in-the-blank/init-gender-over-time.js b/spaces/merve/data-leak/source/fill-in-the-blank/init-gender-over-time.js
deleted file mode 100644
index 4e678f28d4669d45b6957cd3e110b325875a41a1..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/source/fill-in-the-blank/init-gender-over-time.js
+++ /dev/null
@@ -1,181 +0,0 @@
-/* Copyright 2021 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.
-==============================================================================*/
-
-
-
-window.initGenderOverTime = async () => {
-  if (!window.genderOverTimeData){
-    window.genderOverTimeData = await (await fetch('data/gender-over-time.json')).json()
-  }
-
-  var isMobile = innerWidth <= 1100
-
-  var sentences = window.genderOverTimeData
-
-  var blocks = [
-    {
-      text: 'placeholder',
-      sentences: sentences.slice(0, 3),
-      ariaLabel: 'Gendered difference in predicted occupations, studies and names are smalled with a "in 2000" prefix than with a "in 1860" prefix.'
-    }, 
-    {
-      text: 'placeholder',
-      sentences: [sentences[3], sentences[5], sentences[4]],
-      ariaLabel: 'Gendered difference in game play and bears do not decrease.'
-
-    }, 
-  ]
-
-  var blockSel = d3.selectAll('.gender-over-time').html('').data(blocks)
-    .st({marginBottom: 30, marginTop: 30})
-    .at({role: 'graphics-document', 'aria-label': d => d.ariaLabel})
-
-  var sentenceSel = blockSel.appendMany('div.sentence', d => d.sentences)
-    .st({display: 'inline-block'})
-    .each(drawSentence)
-
-  blockSel.filter((d, i) => !i).append('div.g-caption').html(`
-    The top 150 “he” and “she” completions in years from 1860-2018 are shown
-    with the y position encoding he_logit - she_logit.
-    <a href="https://colab.research.google.com/github/PAIR-code/ai-explorables/blob/master/server-side/fill-in-the-blank/gender-over-time-colab/gender-over-time.ipynb">Run in Colab →</a>`)
-
-
-
-  async function drawSentence({s0, s1, tidyCSV, minYear}, i){
-    var tidy = d3.csvParse(tidyCSV)
-    var {colors} = util
-
-    tidy.forEach(d => {
-      d.year = minYear + +d.year_index
-      d.i = +d.token_index
-      d.e0 = +d.e0
-      d.e1 = +d.e1
-      d.mean = d.e0 + d.e1
-      d.dif = d.e0 - d.e1
-    })
-
-    var sel = d3.select(this)
-
-    function fmtStr(d){
-      return d.replace('[MASK]', '___').replace('YEAR', '$year')
-        .replace(' he ', ' <b>he</b> ')
-        .replace(' she ', ' <b>she</b> ')
-        .replace(' his ', ' <b>his</b> ')
-        .replace(' her ', ' <b>her</b> ')
-        .replace(' they ', ' <b>they</b> ')
-    }
-    sel.classed('is-bear', d => s0.includes('bear'))
-
-    var c0 = s0.includes('they') ? colors[2] : colors[0]
-    var c1 = s1.includes('they') ? colors[2] : colors[1]
-
-    sel.append('div.sentence-title').st({color: c0}).html(fmtStr(s0))
-    sel.append('div.sentence-title').st({color: c1}).html(fmtStr(s1))
-
-    var e0Extent = d3.extent(tidy, d => d.e0)
-    var e1Extent = d3.extent(tidy, d => d.e1)
-    var e0e1Exent = d3.extent(e0Extent.concat(e1Extent))
-
-    var maxDif = d3.max(d3.extent(tidy, d => d.dif), Math.abs)
-    var difExtent = [-maxDif, maxDif]
-
-    drawDim(tidy, sel, {
-      key: 'dif', 
-      yExtent: difExtent, 
-      rectColor: [c0, c1]
-    })
-    // drawDim(tidy, sel, {
-    //   key: 'e0', 
-    //   yExtent: e0e1Exent, 
-    //   rectColor: [colors[0], colors[0]]
-    // })
-    // drawDim(tidy, sel, {
-    //   key: 'e1', 
-    //   yExtent: e0e1Exent, 
-    //   rectColor: [colors[1], colors[1]]
-    // })
-  }
-
-  function drawDim(tidy, sel, {key, rectColor, yExtent}){
-    var c = d3.conventions({
-      sel: sel.append('div'), 
-      height: 240,
-      // width: 240,
-      margin: {left: 20, bottom: 20, right: 80, top: 5}
-    })
-
-    c.svg.append('rect')
-      .at({width: c.width, height: c.height/2, opacity: .1, fill: rectColor[0]})
-
-    c.svg.append('rect')
-      .at({width: c.width, height: c.height/2, opacity: .1, fill: rectColor[1], y: c.height/2})
-
-    c.x.domain(d3.extent(tidy, d => d.year)).interpolate(d3.interpolateRound)
-    c.y.domain(yExtent).interpolate(d3.interpolateRound)
-
-    c.xAxis.tickFormat(d => d).ticks(5)
-    c.yAxis.ticks(c.y.ticks(2).length > 2 ? 2 : 3).tickFormat(d3.format('+'))
-    d3.drawAxis(c)
-    // c.svg.select('.y .tick text').st({fill: d => !d ? '' : rectColor[d < 0 ? 0 : 1]})
-
-    var byToken = d3.nestBy(tidy, d => d.i)
-    byToken.forEach(d => {
-      d.endY = c.y(_.last(d)[key])
-      d.str = bertLargeVocab[+d.key].replace('▁', '')
-      d.displayLabel = true
-      d.mean = d3.sum(d, e => e.mean)
-      d.keyMean = d3.sum(d, e => e[key])
-    })
-
-    d3.nestBy(_.sortBy(byToken, d => -d.mean), d => Math.round(d.endY/12))
-      .forEach(d => d.forEach((e, i) => e.displayLabel = !i))
-
-    var line = d3.line()
-      .x(d => c.x(d.year))
-      .y(d => c.y(d[key]))
-
-    var tokenSel = c.svg.appendMany('g.time-token', byToken)
-      // .call(d3.attachTooltip)
-      .on('mouseover', function(d){
-        d3.selectAll('g.time-token')
-          .classed('active', 0)
-          .filter(e => e.str == d.str)
-          .classed('active', 1)
-          .raise()
-      })
-
-    c.svg.on('mouseleave', function(){
-      d3.selectAll('g.time-token').classed('active', 0)
-    })
-
-    tokenSel.append('text')
-      .text(d => d.str)
-      .translate(d => [c.width + 2, d.endY])
-      .at({fontSize: 10, dy: '.33em', fill: (d, i) => d.displayLabel ? '#999' : 'rgba(0,0,0,0)'})
-
-    tokenSel.append('path')
-      .at({
-        d: line,
-        stroke: '#000',
-        opacity: .2,
-        fill: 'none',
-      })
-
-  }
-}
-
-
-if (window.init) window.init()
-
diff --git a/spaces/merve/hidden-bias/source/dataset-worldviews/interface-images.js b/spaces/merve/hidden-bias/source/dataset-worldviews/interface-images.js
deleted file mode 100644
index 5e7040a3a979423e2c88cdbf8c4e5e840a5b35d0..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/source/dataset-worldviews/interface-images.js
+++ /dev/null
@@ -1,8 +0,0 @@
-function createInterfaceImage(divName){
-
-    var c = d3.conventions({
-      sel: d3.select('.' + divName).html('')
-    })
-
-
-}
\ No newline at end of file
diff --git a/spaces/microsoft/LLMLingua/app.py b/spaces/microsoft/LLMLingua/app.py
deleted file mode 100644
index c2ceab1fa6fddc4d1be09467dc8014f7030baabb..0000000000000000000000000000000000000000
--- a/spaces/microsoft/LLMLingua/app.py
+++ /dev/null
@@ -1,189 +0,0 @@
-import gradio as gr
-from llmlingua import PromptCompressor
-
-llm_lingua = PromptCompressor("lgaalves/gpt2-dolly", device_map="cpu")
-
-INTRO = """
-# LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models (EMNLP 2023) [[paper](https://arxiv.org/abs/2310.05736)]
-_Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang and Lili Qiu_
-
-### This is an <b>early demo</b> of the prompt compression method LLMLingua and <b>the capabilities are limited</b>, restricted to using only the GPT-2 small size mode.
-
-It should be noted that due to limited resources, we only provide the **GPT2-Small** size language model in this demo. Using the **LLaMA2-7B** as a small language model would result in a significant performance improvement, especially at high compression ratios.
-
-To use it, upload your prompt and set the compression target.
-1. ✅ Set the different components of the prompt separately, including instruction, context, and question. Leave the corresponding field empty if a particular component does not exist.
-    - Question: This refers to the directives given by the user to the LLMs, such as inquiries, questions, or requests. Positioned after the instruction and context modules, the question module has a high sensitivity to compression.
-    - Context: This module provides the supplementary context needed to address the question, such as documents, demonstrations, web search results, or API call results. Located between the instruction and question modules, its sensitivity to compression is relatively low.
-    - Instruction: This module consists of directives given by the user to the LLMs, such as task descriptions. Placed before the instruction and context modules, the instruction module exhibits a high sensitivity to compression.
-2. ✅ Set the target_token or compression ratio.
-3. 🤔 Try experimenting with different target compression ratios or other hyperparameters to optimize the performance.
-
-You can check our [project page](https://llmlingua.com/)!
-
-We also has a work to compress long context scenories, using less cost but even improve the downstream performance, LongLLMLingua.<br>
-[LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://arxiv.org/abs/2310.06839) (Under Review).<br>
-
-## News
-
-- 🎈 We launched a [project page](https://llmlingua.com/) showcasing real-world case studies, including RAG, Online Meetings, CoT, and Code;
-- 👾 LongLLMLingua has been incorporated into the [LlamaIndex pipeline](https://github.com/run-llama/llama_index/blob/main/llama_index/indices/postprocessor/longllmlingua.py), which is a widely used RAG framework.
-"""
-
-INTRO_EXAMPLES = '''
-## Examples in GSM8K
-Below are some examples of compressing prompts in GSM8K using different small language models. The original prompt [1] is taken from "Complexity-Based Prompting for Multi-step Reasoning" [2], with an original length of 2,365 tokens. Black-box LLMs use GPT-3.5-Turbo-0301 with greedy decoding.
-
-[1] https://github.com/FranxYao/chain-of-thought-hub/blob/main/gsm8k/lib_prompt/prompt_hardest.txt<br>
-[2] Fu, Yao, et al. "Complexity-Based Prompting for Multi-step Reasoning." The Eleventh International Conference on Learning Representations. 2022.
-'''
-
-custom_css = """
-    #image-upload {
-        flex-grow: 1;
-    }
-    #params .tabs {
-        display: flex;
-        flex-direction: column;
-        flex-grow: 1;
-    }
-    #params .tabitem[style="display: block;"] {
-        flex-grow: 1;
-        display: flex !important;
-    }
-    #params .gap {
-        flex-grow: 1;
-    }
-    #params .form {
-        flex-grow: 1 !important;
-    }
-    #params .form > :last-child{
-        flex-grow: 1;
-    }
-    .md ol, .md ul {
-        margin-left: 1rem;
-    }
-    .md img {
-        margin-bottom: 1rem;
-    }
-"""
-
-EXAMPLES = [
-    [
-        "lgaalves/gpt2-dolly",
-        "8.7x",
-        "78.24",
-        "Question: can buy 4 1melon for You bought 36 fruits evenly split between of 1 $. does cost if bill $\n's think step\nIf between 3 then I 363 = 12 of fruit 1 orange then oranges506If I oranges I $66 $60 on the 2 fruit\n the of is, and that price and is 1W4AIf we know we bought 12 and 12W\n thatW can the 12 = 48\n of apple (60/The 1\n: Sam a dozen boxes with 30ighter pens each Heanged into six3 the separately of three. much in\n's boxes $120 12 =Sam then took 5 boxes × 6 highlighters/box = 30 highlighters.\nHe sold these boxes for 5 * $3 = $15\nAfter selling these 5 boxes there were 360 - 30 = 330 highlighters remaining.\nThese form 330 / 3 = 110 groups of three pens.\nHe sold each of these groups for $2 each, so made 110 * 2 = $220 from them.\nIn total, then, he earned $220 + $15 = $235.\nSince his original cost was $120, he earned $235 - $120 = $115 in profit.\nThe answer is 115",
-    ],
-    [
-        "vicgalle/alpaca-7b",
-        "13.8x",
-        "78.32",
-        "Question: Sam bought a dozen boxes, each 30 highl pens inside, $10 each. He reanged five of boxes into of sixlters each sold $3. He sold the theters separately at the of three $2. How much did make in total, in\nLets think step\nSam bought  boxes x0 = $10 oflters.\nHe 2 300ters in\nSam then 5 boxes 6ters0ters\nHe sold these boxes for 55\nAfterelling these  boxes there300lters remaining\nThese form 330 310 of three pens\nHe sold each of these groups for2 each, so made 0 *0 from\nIn total, he $ $155\nSince his original $1, he earned $20 = $115 in profit.\nThe answer is 115\n\n",
-    ],
-    [
-        "vicgalle/alpaca-7b",
-        "20.2x",
-        "77.94",
-        "Question: Sam bought a dozen boxes, each with 30 highl pens inside, for $10 each.\nHe reanged five of boxes into of sixlters each sold them $3 per package.\nHe sold the rest of thelters separately at the of three pens for $2.\nHow much profit did make in total, in dollars\nLet's think step by step\nSam then took 5 boxes × 6lighters/box = 30 highlighters.\nThese form 330 / 3 = 110 groups of three pens.\nThe answer is 115\n\n",
-    ],
-]
-
-def compress_prompt(context, instruction, question, ratio, target_token):
-    context, instruction, question = context.replace("\\n", "\n"), instruction.replace("\\n", "\n"), question.replace("\\n", "\n")
-    compressed_prompt = llm_lingua.compress_prompt(context.split("\n\n"), instruction, question, float(ratio), float(target_token))
-
-    return [compressed_prompt[key] for key in ["compressed_prompt", "origin_tokens", "compressed_tokens", "ratio", "saving"]]
-
-
-with gr.Blocks(css=custom_css) as iface:
-    gr.Markdown(INTRO)
-
-    with gr.Row():
-        with gr.Column(elem_id="prompt", scale=2):
-            with gr.Tab('Prompts'):
-                instruction = gr.Textbox(
-                    label="Instruction",
-                    lines=1,
-                    value="Please reference the following examples to answer the math question,",
-                    placeholder="This module consists of directives given by the user to the LLMs, such as task descriptions.",
-                )
-                context = gr.Textbox(
-                    label="Context",
-                    lines=3,
-                    value="Question: Angelo and Melanie want to plan how many hours over the next week they should study together for their test next week. They have 2 chapters of their textbook to study and 4 worksheets to memorize. They figure out that they should dedicate 3 hours to each chapter of their textbook and 1.5 hours for each worksheet. If they plan to study no more than 4 hours each day, how many days should they plan to study total over the next week if they take a 10-minute break every hour, include 3 10-minute snack breaks each day, and 30 minutes for lunch each day?\nLet's think step by step\nAngelo and Melanie think they should dedicate 3 hours to each of the 2 chapters, 3 hours x 2 chapters = 6 hours total.\nFor the worksheets they plan to dedicate 1.5 hours for each worksheet, 1.5 hours x 4 worksheets = 6 hours total.\nAngelo and Melanie need to start with planning 12 hours to study, at 4 hours a day, 12 / 4 = 3 days.\nHowever, they need to include time for breaks and lunch. Every hour they want to include a 10-minute break, so 12 total hours x 10 minutes = 120 extra minutes for breaks.\nThey also want to include 3 10-minute snack breaks, 3 x 10 minutes = 30 minutes.\nAnd they want to include 30 minutes for lunch each day, so 120 minutes for breaks + 30 minutes for snack breaks + 30 minutes for lunch = 180 minutes, or 180 / 60 minutes per hour = 3 extra hours.\nSo Angelo and Melanie want to plan 12 hours to study + 3 hours of breaks = 15 hours total.\nThey want to study no more than 4 hours each day, 15 hours / 4 hours each day = 3.75\nThey will need to plan to study 4 days to allow for all the time they need.\nThe answer is 4\n\nQuestion: Mark's basketball team scores 25 2 pointers, 8 3 pointers and 10 free throws.  Their opponents score double the 2 pointers but half the 3 pointers and free throws.  What's the total number of points scored by both teams added together?\nLet's think step by step\nMark's team scores 25 2 pointers, meaning they scored 25*2= 50 points in 2 pointers.\nHis team also scores 6 3 pointers, meaning they scored 8*3= 24 points in 3 pointers\nThey scored 10 free throws, and free throws count as one point so they scored 10*1=10 points in free throws.\nAll together his team scored 50+24+10= 84 points\nMark's opponents scored double his team's number of 2 pointers, meaning they scored 50*2=100 points in 2 pointers.\nHis opponents scored half his team's number of 3 pointers, meaning they scored 24/2= 12 points in 3 pointers.\nThey also scored half Mark's team's points in free throws, meaning they scored 10/2=5 points in free throws.\nAll together Mark's opponents scored 100+12+5=117 points\nThe total score for the game is both team's scores added together, so it is 84+117=201 points\nThe answer is 201\n\nQuestion: Bella has two times as many marbles as frisbees. She also has 20 more frisbees than deck cards. If she buys 2/5 times more of each item, what would be the total number of the items she will have if she currently has 60 marbles?\nLet's think step by step\nWhen Bella buys 2/5 times more marbles, she'll have increased the number of marbles by 2/5*60 = 24\nThe total number of marbles she'll have is 60+24 = 84\nIf Bella currently has 60 marbles, and she has two times as many marbles as frisbees, she has 60/2 = 30 frisbees.\nIf Bella buys 2/5 times more frisbees, she'll have 2/5*30 = 12 more frisbees.\nThe total number of frisbees she'll have will increase to 30+12 = 42\nBella also has 20 more frisbees than deck cards, meaning she has 30-20 = 10 deck cards\nIf she buys 2/5 times more deck cards, she'll have 2/5*10 = 4 more deck cards.\nThe total number of deck cards she'll have is 10+4 = 14\nTogether, Bella will have a total of 14+42+84 = 140 items\nThe answer is 140\n\nQuestion: A group of 4 fruit baskets contains 9 apples, 15 oranges, and 14 bananas in the first three baskets and 2 less of each fruit in the fourth basket. How many fruits are there?\nLet's think step by step\nFor the first three baskets, the number of apples and oranges in one basket is 9+15=24\nIn total, together with bananas, the number of fruits in one basket is 24+14=38 for the first three baskets.\nSince there are three baskets each having 38 fruits, there are 3*38=114 fruits in the first three baskets.\nThe number of apples in the fourth basket is 9-2=7\nThere are also 15-2=13 oranges in the fourth basket\nThe combined number of oranges and apples in the fourth basket is 13+7=20\nThe fourth basket also contains 14-2=12 bananas.\nIn total, the fourth basket has 20+12=32 fruits.\nThe four baskets together have 32+114=146 fruits.\nThe answer is 146\n\nQuestion: You can buy 4 apples or 1 watermelon for the same price. You bought 36 fruits evenly split between oranges, apples and watermelons, and the price of 1 orange is $0.50. How much does 1 apple cost if your total bill was $66?\nLet's think step by step\nIf 36 fruits were evenly split between 3 types of fruits, then I bought 36/3 = 12 units of each fruit\nIf 1 orange costs $0.50 then 12 oranges will cost $0.50 * 12 = $6\nIf my total bill was $66 and I spent $6 on oranges then I spent $66 - $6 = $60 on the other 2 fruit types.\nAssuming the price of watermelon is W, and knowing that you can buy 4 apples for the same price and that the price of one apple is A, then 1W=4A\nIf we know we bought 12 watermelons and 12 apples for $60, then we know that $60 = 12W + 12A\nKnowing that 1W=4A, then we can convert the above to $60 = 12(4A) + 12A\n$60 = 48A + 12A\n$60 = 60A\nThen we know the price of one apple (A) is $60/60= $1\nThe answer is 1\n\nQuestion: Susy goes to a large school with 800 students, while Sarah goes to a smaller school with only 300 students.  At the start of the school year, Susy had 100 social media followers.  She gained 40 new followers in the first week of the school year, half that in the second week, and half of that in the third week.  Sarah only had 50 social media followers at the start of the year, but she gained 90 new followers the first week, a third of that in the second week, and a third of that in the third week.  After three weeks, how many social media followers did the girl with the most total followers have?\nLet's think step by step\nAfter one week, Susy has 100+40 = 140 followers.\nIn the second week, Susy gains 40/2 = 20 new followers.\nIn the third week, Susy gains 20/2 = 10 new followers.\nIn total, Susy finishes the three weeks with 140+20+10 = 170 total followers.\nAfter one week, Sarah has 50+90 = 140 followers.\nAfter the second week, Sarah gains 90/3 = 30 followers.\nAfter the third week, Sarah gains 30/3 = 10 followers.\nSo, Sarah finishes the three weeks with 140+30+10 = 180 total followers.\nThus, Sarah is the girl with the most total followers with a total of 180.\nThe answer is 180\n\nQuestion: Sam bought a dozen boxes, each with 30 highlighter pens inside, for $10 each box. He rearranged five of these boxes into packages of six highlighters each and sold them for $3 per package. He sold the rest of the highlighters separately at the rate of three pens for $2. How much profit did he make in total, in dollars?\nLet's think step by step\nSam bought 12 boxes x $10 = $120 worth of highlighters.\nHe bought 12 * 30 = 360 highlighters in total.\nSam then took 5 boxes × 6 highlighters/box = 30 highlighters.\nHe sold these boxes for 5 * $3 = $15\nAfter selling these 5 boxes there were 360 - 30 = 330 highlighters remaining.\nThese form 330 / 3 = 110 groups of three pens.\nHe sold each of these groups for $2 each, so made 110 * 2 = $220 from them.\nIn total, then, he earned $220 + $15 = $235.\nSince his original cost was $120, he earned $235 - $120 = $115 in profit.\nThe answer is 115\n\nQuestion: In a certain school, 2/3 of the male students like to play basketball, but only 1/5 of the female students like to play basketball. What percent of the population of the school do not like to play basketball if the ratio of the male to female students is 3:2 and there are 1000 students?\nLet's think step by step\nThe students are divided into 3 + 2 = 5 parts where 3 parts are for males and 2 parts are for females.\nEach part represents 1000/5 = 200 students.\nSo, there are 3 x 200 = 600 males.\nAnd there are 2 x 200 = 400 females.\nHence, 600 x 2/3 = 400 males play basketball.\nAnd 400 x 1/5 = 80 females play basketball.\nA total of 400 + 80 = 480 students play basketball.\nTherefore, 1000 - 480 = 520 do not like to play basketball.\nThe percentage of the school that do not like to play basketball is 520/1000 * 100 = 52\nThe answer is 52\n",
-                    placeholder="This module provides the supplementary context needed to address the question, such as documents, demonstrations, web search results, or API call results.",
-                )
-                question = gr.Textbox(
-                    label="Question",
-                    lines=1,
-                    value="Question: Josh decides to try flipping a house.  He buys a house for $80,000 and then puts in $50,000 in repairs.  This increased the value of the house by 150%.  How much profit did he make?",
-                    placeholder="This refers to the directives given by the user to the LLMs, such as inquiries, questions, or requests.",
-                )
-        with gr.Column(elem_id="params", scale=1):
-            with gr.Tab('Compression Target'):
-                target_token = gr.Textbox(
-                    label="Target Token (To use this, set Compression Ratio to 0)",
-                    value=200,
-                )
-                ratio = gr.Textbox(
-                    label="Compression Ratio (To use this, set Target Token to -1)",
-                    value=0,
-                )
-
-    gen_button = gr.Button(value="Compress Prompt!", variant="primary")
-
-    with gr.Row():
-        with gr.Column(elem_id="Results", scale=1):
-            with gr.Tab('Compressed Prompts'):
-                compressed_prompt = gr.Textbox(
-                    label="compressed_prompt",
-                    lines=10,
-                )
-        with gr.Column(elem_id="Results_2", scale=1):
-            with gr.Tab('Saving'):
-                origin_tokens = gr.Textbox(
-                    label="The tokens number of original prompt",
-                )
-                compressed_tokens = gr.Textbox(
-                    label="The tokens number of compressed prompt",
-                )
-                saving_ratio = gr.Textbox(
-                    label="Actual Compression Ratio",
-                )
-                saving = gr.Textbox(
-                    label="Saving Cost",
-                )
-
-
-    # gr.Examples(
-    #     examples=EXAMPLES,
-    #     inputs=[model_name, compressed_prompt, saving_ratio, acc],
-    # )
-    gr.Markdown(INTRO_EXAMPLES)
-
-    gr.Dataframe(
-        value=EXAMPLES,
-        headers=["Small Language Model", "Compression Ratio", "GSM8K Acc using GPT-3.5-Turbo", "Compressed Prompts",],
-        datatype=["str", "str", "str", "str"],
-    ),
-
-    gen_button.click(
-        fn=compress_prompt,
-        inputs=[
-            context,
-            instruction,
-            question,
-            ratio,
-            target_token
-        ],
-        outputs=[
-            compressed_prompt,
-            origin_tokens,
-            compressed_tokens,
-            saving_ratio,
-            saving
-        ],
-    )
-
-iface.queue(max_size=10, api_open=False).launch(show_api=False)
\ No newline at end of file
diff --git a/spaces/mindart/infinite-zoom-stable-diffusion/app.py b/spaces/mindart/infinite-zoom-stable-diffusion/app.py
deleted file mode 100644
index e2a0726d4e29dc88243b012f576b4caa5b4d1493..0000000000000000000000000000000000000000
--- a/spaces/mindart/infinite-zoom-stable-diffusion/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import gradio as gr
-from zoom import zoom_app
-app = gr.Blocks()
-with app:
-    gr.HTML(
-        """
-        <p style='text-align: center'>
-       Text to Video - Infinite zoom effect
-        </p>
-        """
-    )
-    zoom_app()
-
-app.launch(debug=True, enable_queue=True)
diff --git a/spaces/mithril-security/blind_chat/src/lib/types/AbortedGeneration.ts b/spaces/mithril-security/blind_chat/src/lib/types/AbortedGeneration.ts
deleted file mode 100644
index fe4c2824b4f3257bea71c3acacd65fcee0918188..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/blind_chat/src/lib/types/AbortedGeneration.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-// Ideally shouldn't be needed, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850
-
-import type { Conversation } from "./Conversation";
-import type { Timestamps } from "./Timestamps";
-
-export interface AbortedGeneration extends Timestamps {
-	conversationId: Conversation["_id"];
-}
diff --git a/spaces/mmeendez/cnn_transformer_explainability/plot.py b/spaces/mmeendez/cnn_transformer_explainability/plot.py
deleted file mode 100644
index e1ac8a684d27a4b6f42357b8d90f6582cafbe8b0..0000000000000000000000000000000000000000
--- a/spaces/mmeendez/cnn_transformer_explainability/plot.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import matplotlib.pyplot as plt
-from app import inference, examples
-from PIL import Image
-
-plt.rcParams["figure.figsize"] = (11,2)
-
-title = ["CAM", "ROLLOUT"]
-
-fig_resnet, axis_resnet = plt.subplots(1, len(examples))
-
-plots = [plt.subplots(1, len(examples)) for _ in range(2)]
-
-for i, image_path in enumerate(examples):
-    image = Image.open(image_path)
-
-    result = inference(image)
-
-    for j, (fig, axis) in enumerate(plots):
-        axis[i].imshow(result[2*j+1])
-        axis[i].set_title(result[2*j])
-        axis[i].set_axis_off()
-
-
-for i, (plot, title) in enumerate(zip(plots, title)):
-    # plot[0].suptitle(title)
-    plot[0].savefig(f"{title}.png")
\ No newline at end of file
diff --git a/spaces/mpatel57/WOUAF-Text-to-Image/torch_utils/misc.py b/spaces/mpatel57/WOUAF-Text-to-Image/torch_utils/misc.py
deleted file mode 100644
index 7829f4d9f168557ce8a9a6dec289aa964234cb8c..0000000000000000000000000000000000000000
--- a/spaces/mpatel57/WOUAF-Text-to-Image/torch_utils/misc.py
+++ /dev/null
@@ -1,262 +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.
-
-import re
-import contextlib
-import numpy as np
-import torch
-import warnings
-import dnnlib
-
-#----------------------------------------------------------------------------
-# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
-# same constant is used multiple times.
-
-_constant_cache = dict()
-
-def constant(value, shape=None, dtype=None, device=None, memory_format=None):
-    value = np.asarray(value)
-    if shape is not None:
-        shape = tuple(shape)
-    if dtype is None:
-        dtype = torch.get_default_dtype()
-    if device is None:
-        device = torch.device('cpu')
-    if memory_format is None:
-        memory_format = torch.contiguous_format
-
-    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
-    tensor = _constant_cache.get(key, None)
-    if tensor is None:
-        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
-        if shape is not None:
-            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
-        tensor = tensor.contiguous(memory_format=memory_format)
-        _constant_cache[key] = tensor
-    return tensor
-
-#----------------------------------------------------------------------------
-# Replace NaN/Inf with specified numerical values.
-
-try:
-    nan_to_num = torch.nan_to_num # 1.8.0a0
-except AttributeError:
-    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
-        assert isinstance(input, torch.Tensor)
-        if posinf is None:
-            posinf = torch.finfo(input.dtype).max
-        if neginf is None:
-            neginf = torch.finfo(input.dtype).min
-        assert nan == 0
-        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
-
-#----------------------------------------------------------------------------
-# Symbolic assert.
-
-try:
-    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
-except AttributeError:
-    symbolic_assert = torch.Assert # 1.7.0
-
-#----------------------------------------------------------------------------
-# Context manager to suppress known warnings in torch.jit.trace().
-
-class suppress_tracer_warnings(warnings.catch_warnings):
-    def __enter__(self):
-        super().__enter__()
-        warnings.simplefilter('ignore', category=torch.jit.TracerWarning)
-        return self
-
-#----------------------------------------------------------------------------
-# Assert that the shape of a tensor matches the given list of integers.
-# None indicates that the size of a dimension is allowed to vary.
-# Performs symbolic assertion when used in torch.jit.trace().
-
-def assert_shape(tensor, ref_shape):
-    if tensor.ndim != len(ref_shape):
-        raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
-    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
-        if ref_size is None:
-            pass
-        elif isinstance(ref_size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
-        elif isinstance(size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
-        elif size != ref_size:
-            raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
-
-#----------------------------------------------------------------------------
-# Function decorator that calls torch.autograd.profiler.record_function().
-
-def profiled_function(fn):
-    def decorator(*args, **kwargs):
-        with torch.autograd.profiler.record_function(fn.__name__):
-            return fn(*args, **kwargs)
-    decorator.__name__ = fn.__name__
-    return decorator
-
-#----------------------------------------------------------------------------
-# Sampler for torch.utils.data.DataLoader that loops over the dataset
-# indefinitely, shuffling items as it goes.
-
-class InfiniteSampler(torch.utils.data.Sampler):
-    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
-        assert len(dataset) > 0
-        assert num_replicas > 0
-        assert 0 <= rank < num_replicas
-        assert 0 <= window_size <= 1
-        super().__init__(dataset)
-        self.dataset = dataset
-        self.rank = rank
-        self.num_replicas = num_replicas
-        self.shuffle = shuffle
-        self.seed = seed
-        self.window_size = window_size
-
-    def __iter__(self):
-        order = np.arange(len(self.dataset))
-        rnd = None
-        window = 0
-        if self.shuffle:
-            rnd = np.random.RandomState(self.seed)
-            rnd.shuffle(order)
-            window = int(np.rint(order.size * self.window_size))
-
-        idx = 0
-        while True:
-            i = idx % order.size
-            if idx % self.num_replicas == self.rank:
-                yield order[i]
-            if window >= 2:
-                j = (i - rnd.randint(window)) % order.size
-                order[i], order[j] = order[j], order[i]
-            idx += 1
-
-#----------------------------------------------------------------------------
-# Utilities for operating with torch.nn.Module parameters and buffers.
-
-def params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.parameters()) + list(module.buffers())
-
-def named_params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.named_parameters()) + list(module.named_buffers())
-
-def copy_params_and_buffers(src_module, dst_module, require_all=False):
-    assert isinstance(src_module, torch.nn.Module)
-    assert isinstance(dst_module, torch.nn.Module)
-    src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)}
-    for name, tensor in named_params_and_buffers(dst_module):
-        assert (name in src_tensors) or (not require_all)
-        if name in src_tensors:
-            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
-
-#----------------------------------------------------------------------------
-# Context manager for easily enabling/disabling DistributedDataParallel
-# synchronization.
-
-@contextlib.contextmanager
-def ddp_sync(module, sync):
-    assert isinstance(module, torch.nn.Module)
-    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
-        yield
-    else:
-        with module.no_sync():
-            yield
-
-#----------------------------------------------------------------------------
-# Check DistributedDataParallel consistency across processes.
-
-def check_ddp_consistency(module, ignore_regex=None):
-    assert isinstance(module, torch.nn.Module)
-    for name, tensor in named_params_and_buffers(module):
-        fullname = type(module).__name__ + '.' + name
-        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
-            continue
-        tensor = tensor.detach()
-        other = tensor.clone()
-        torch.distributed.broadcast(tensor=other, src=0)
-        assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname
-
-#----------------------------------------------------------------------------
-# Print summary table of module hierarchy.
-
-def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
-    assert isinstance(module, torch.nn.Module)
-    assert not isinstance(module, torch.jit.ScriptModule)
-    assert isinstance(inputs, (tuple, list))
-
-    # Register hooks.
-    entries = []
-    nesting = [0]
-    def pre_hook(_mod, _inputs):
-        nesting[0] += 1
-    def post_hook(mod, _inputs, outputs):
-        nesting[0] -= 1
-        if nesting[0] <= max_nesting:
-            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
-            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
-            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
-    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
-    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
-
-    # Run module.
-    outputs = module(*inputs)
-    for hook in hooks:
-        hook.remove()
-
-    # Identify unique outputs, parameters, and buffers.
-    tensors_seen = set()
-    for e in entries:
-        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
-        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
-        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
-        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
-
-    # Filter out redundant entries.
-    if skip_redundant:
-        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
-
-    # Construct table.
-    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
-    rows += [['---'] * len(rows[0])]
-    param_total = 0
-    buffer_total = 0
-    submodule_names = {mod: name for name, mod in module.named_modules()}
-    for e in entries:
-        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
-        param_size = sum(t.numel() for t in e.unique_params)
-        buffer_size = sum(t.numel() for t in e.unique_buffers)
-        output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs]
-        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
-        rows += [[
-            name + (':0' if len(e.outputs) >= 2 else ''),
-            str(param_size) if param_size else '-',
-            str(buffer_size) if buffer_size else '-',
-            (output_shapes + ['-'])[0],
-            (output_dtypes + ['-'])[0],
-        ]]
-        for idx in range(1, len(e.outputs)):
-            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
-        param_total += param_size
-        buffer_total += buffer_size
-    rows += [['---'] * len(rows[0])]
-    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
-
-    # Print table.
-    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
-    print()
-    for row in rows:
-        print('  '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
-    print()
-    return outputs
-
-#----------------------------------------------------------------------------
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/backtranslation/sacrebleu.sh b/spaces/mshukor/UnIVAL/fairseq/examples/backtranslation/sacrebleu.sh
deleted file mode 100644
index a70da23f48e2699297799611412783d4560dc45a..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/backtranslation/sacrebleu.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/bin/bash
-
-if [ $# -ne 5 ]; then
-    echo "usage: $0 [dataset=wmt14/full] [langpair=en-de] [databin] [bpecode] [model]"
-    exit
-fi
-
-
-DATASET=$1
-LANGPAIR=$2
-DATABIN=$3
-BPECODE=$4
-MODEL=$5
-
-SRCLANG=$(echo $LANGPAIR | cut -d '-' -f 1)
-TGTLANG=$(echo $LANGPAIR | cut -d '-' -f 2)
-
-
-BPEROOT=examples/backtranslation/subword-nmt/subword_nmt
-if [ ! -e $BPEROOT ]; then
-    BPEROOT=subword-nmt/subword_nmt
-    if [ ! -e $BPEROOT ]; then
-        echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
-        git clone https://github.com/rsennrich/subword-nmt.git
-    fi
-fi
-
-
-sacrebleu -t $DATASET -l $LANGPAIR --echo src \
-| sacremoses tokenize -a -l $SRCLANG -q \
-| python $BPEROOT/apply_bpe.py -c $BPECODE \
-| fairseq-interactive $DATABIN --path $MODEL \
-    -s $SRCLANG -t $TGTLANG \
-    --beam 5 --remove-bpe --buffer-size 1024 --max-tokens 8000 \
-| grep ^H- | cut -f 3- \
-| sacremoses detokenize -l $TGTLANG -q \
-| sacrebleu -t $DATASET -l $LANGPAIR
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/criterions/composite_loss.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/criterions/composite_loss.py
deleted file mode 100644
index 98e835fa6e4c0bcad062df9c519701bf795c98be..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/criterions/composite_loss.py
+++ /dev/null
@@ -1,100 +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 fairseq import utils
-from fairseq.criterions import LegacyFairseqCriterion, register_criterion
-from torch import nn
-
-
-@register_criterion("composite_loss")
-class CompositeLoss(LegacyFairseqCriterion):
-    """This is a composite loss that, given a list of model outputs and a list of targets,
-    computes an average of losses for each output-target pair"""
-
-    def __init__(self, args, task):
-        super().__init__(args, task)
-        self.underlying_criterion = args.underlying_criterion
-
-    @staticmethod
-    def add_args(parser):
-        """Add criterion-specific arguments to the parser."""
-        # fmt: off
-        parser.add_argument('--underlying-criterion', type=str, metavar='VAL', required=True,
-                            help='underlying criterion to use for the composite loss')
-        # fmt: on
-
-    @staticmethod
-    def build_underlying_criterion(args, task):
-        saved_criterion = args.criterion
-        args.criterion = args.underlying_criterion
-        assert saved_criterion != args.underlying_criterion
-        underlying_criterion = task.build_criterion(args)
-        args.criterion = saved_criterion
-        return underlying_criterion
-
-    @classmethod
-    def build_criterion(cls, args, task):
-        underlying_criterion = CompositeLoss.build_underlying_criterion(args, task)
-
-        class FakeModel(nn.Module):
-            def __init__(self, model, net_out, target):
-                super().__init__()
-                self.model = model
-                self.net_out = net_out
-                self.target = target
-
-            def forward(self, **unused):
-                return self.net_out
-
-            def get_normalized_probs(self, net_output, log_probs, sample=None):
-                return self.model.get_normalized_probs(
-                    net_output, log_probs, sample=sample
-                )
-
-            def get_targets(self, *unused):
-                return self.target
-
-            @property
-            def decoder(self):
-                return self.model.decoder
-
-        class _CompositeLoss(LegacyFairseqCriterion):
-            def __init__(self, args, task, underlying_criterion):
-                super().__init__(args, task)
-                self.underlying_criterion = underlying_criterion
-
-            def forward(self, model, sample, reduce=True):
-                net_outputs = model(**sample["net_input"])
-                targets = sample["target"]
-
-                bsz = targets[0].size(0)
-                loss = net_outputs[0][0].new(1 if reduce else bsz).float().zero_()
-
-                sample_size = 0
-                logging_output = {}
-                for o, t in zip(net_outputs[0], targets):
-                    m = FakeModel(model, (o, net_outputs[1]), t)
-                    sample["target"] = t
-                    l, ss, logging_output = self.underlying_criterion(m, sample, reduce)
-                    loss += l
-                    sample_size += ss
-
-                loss.div_(len(targets))
-                sample_size /= len(targets)
-
-                logging_output["loss"] = utils.item(loss.data) if reduce else loss.data
-                return loss, sample_size, logging_output
-
-            @staticmethod
-            def aggregate_logging_outputs(logging_outputs):
-                return underlying_criterion.__class__.aggregate_logging_outputs(
-                    logging_outputs
-                )
-
-            @staticmethod
-            def reduce_metrics(logging_outputs) -> None:
-                underlying_criterion.__class__.reduce_metrics(logging_outputs)
-
-        return _CompositeLoss(args, task, underlying_criterion)
diff --git a/spaces/mzltest/gpt2-chinese-composition/tokenizations/tokenization_bert_word_level.py b/spaces/mzltest/gpt2-chinese-composition/tokenizations/tokenization_bert_word_level.py
deleted file mode 100644
index 0da7c51796331d457862a13358626f3de5101dab..0000000000000000000000000000000000000000
--- a/spaces/mzltest/gpt2-chinese-composition/tokenizations/tokenization_bert_word_level.py
+++ /dev/null
@@ -1,453 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language 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.
-"""Tokenization classes."""
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import collections
-import logging
-import os
-import unicodedata
-import thulac
-from io import open
-
-from transformers.tokenization_utils import PreTrainedTokenizer
-
-logger = logging.getLogger(__name__)
-
-lac = thulac.thulac(user_dict='tokenizations/thulac_dict/seg', seg_only=True)
-
-VOCAB_FILES_NAMES = {'vocab_file': 'vocab.txt'}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    'vocab_file':
-    {
-        'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
-        'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
-        'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
-        'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
-        'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
-        'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
-        'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
-        'bert-base-german-cased': "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt",
-        'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-vocab.txt",
-        'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-vocab.txt",
-        'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-vocab.txt",
-        'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-vocab.txt",
-        'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-vocab.txt",
-    }
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    'bert-base-uncased': 512,
-    'bert-large-uncased': 512,
-    'bert-base-cased': 512,
-    'bert-large-cased': 512,
-    'bert-base-multilingual-uncased': 512,
-    'bert-base-multilingual-cased': 512,
-    'bert-base-chinese': 512,
-    'bert-base-german-cased': 512,
-    'bert-large-uncased-whole-word-masking': 512,
-    'bert-large-cased-whole-word-masking': 512,
-    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
-    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
-    'bert-base-cased-finetuned-mrpc': 512,
-}
-
-def load_vocab(vocab_file):
-    """Loads a vocabulary file into a dictionary."""
-    vocab = collections.OrderedDict()
-    with open(vocab_file, "r", encoding="utf-8") as reader:
-        tokens = reader.readlines()
-    for index, token in enumerate(tokens):
-        token = token.rstrip('\n')
-        vocab[token] = index
-    return vocab
-
-
-def whitespace_tokenize(text):
-    """Runs basic whitespace cleaning and splitting on a piece of text."""
-    text = text.strip()
-    if not text:
-        return []
-    tokens = text.split()
-    return tokens
-
-
-class BertTokenizer(PreTrainedTokenizer):
-    r"""
-    Constructs a BertTokenizer.
-    :class:`~pytorch_pretrained_bert.BertTokenizer` runs end-to-end tokenization: punctuation splitting + wordpiece
-
-    Args:
-        vocab_file: Path to a one-wordpiece-per-line vocabulary file
-        do_lower_case: Whether to lower case the input. Only has an effect when do_wordpiece_only=False
-        do_basic_tokenize: Whether to do basic tokenization before wordpiece.
-        max_len: An artificial maximum length to truncate tokenized_doupo sequences to; Effective maximum length is always the
-            minimum of this value (if specified) and the underlying BERT model's sequence length.
-        never_split: List of tokens which will never be split during tokenization. Only has an effect when
-            do_wordpiece_only=False
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-
-    def __init__(self, vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None,
-                 unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]",
-                 mask_token="[MASK]", tokenize_chinese_chars=True, **kwargs):
-        """Constructs a BertTokenizer.
-
-        Args:
-            **vocab_file**: Path to a one-wordpiece-per-line vocabulary file
-            **do_lower_case**: (`optional`) boolean (default True)
-                Whether to lower case the input
-                Only has an effect when do_basic_tokenize=True
-            **do_basic_tokenize**: (`optional`) boolean (default True)
-                Whether to do basic tokenization before wordpiece.
-            **never_split**: (`optional`) list of string
-                List of tokens which will never be split during tokenization.
-                Only has an effect when do_basic_tokenize=True
-            **tokenize_chinese_chars**: (`optional`) boolean (default True)
-                Whether to tokenize Chinese characters.
-                This should likely be desactivated for Japanese:
-                see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328
-        """
-        super(BertTokenizer, self).__init__(unk_token=unk_token, sep_token=sep_token,
-                                            pad_token=pad_token, cls_token=cls_token,
-                                            mask_token=mask_token, **kwargs)
-        if not os.path.isfile(vocab_file):
-            raise ValueError(
-                "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
-                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
-        self.vocab = load_vocab(vocab_file)
-        self.ids_to_tokens = collections.OrderedDict(
-            [(ids, tok) for tok, ids in self.vocab.items()])
-        self.do_basic_tokenize = do_basic_tokenize
-        if do_basic_tokenize:
-            self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
-                                                  never_split=never_split,
-                                                  tokenize_chinese_chars=tokenize_chinese_chars)
-        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
-
-    @property
-    def vocab_size(self):
-        return len(self.vocab)
-
-    def _tokenize(self, text):
-        split_tokens = []
-        if self.do_basic_tokenize:
-            for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
-                for sub_token in self.wordpiece_tokenizer.tokenize(token):
-                    split_tokens.append(sub_token)
-        else:
-            split_tokens = self.wordpiece_tokenizer.tokenize(text)
-        return split_tokens
-
-    def _convert_token_to_id(self, token):
-        """ Converts a token (str/unicode) in an id using the vocab. """
-        return self.vocab.get(token, self.vocab.get(self.unk_token))
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (string/unicode) using the vocab."""
-        return self.ids_to_tokens.get(index, self.unk_token)
-
-    def convert_tokens_to_string(self, tokens):
-        """ Converts a sequence of tokens (string) in a single string. """
-        out_string = ' '.join(tokens).replace(' ##', '').strip()
-        return out_string
-
-    def save_vocabulary(self, vocab_path):
-        """Save the tokenizer vocabulary to a directory or file."""
-        index = 0
-        if os.path.isdir(vocab_path):
-            vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES['vocab_file'])
-        with open(vocab_file, "w", encoding="utf-8") as writer:
-            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning("Saving vocabulary to {}: vocabulary indices are not consecutive."
-                                   " Please check that the vocabulary is not corrupted!".format(vocab_file))
-                    index = token_index
-                writer.write(token + u'\n')
-                index += 1
-        return (vocab_file,)
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
-        """ Instantiate a BertTokenizer from pre-trained vocabulary files.
-        """
-        if pretrained_model_name_or_path in PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES:
-            if '-cased' in pretrained_model_name_or_path and kwargs.get('do_lower_case', True):
-                logger.warning("The pre-trained model you are loading is a cased model but you have not set "
-                               "`do_lower_case` to False. We are setting `do_lower_case=False` for you but "
-                               "you may want to check this behavior.")
-                kwargs['do_lower_case'] = False
-            elif '-cased' not in pretrained_model_name_or_path and not kwargs.get('do_lower_case', True):
-                logger.warning("The pre-trained model you are loading is an uncased model but you have set "
-                               "`do_lower_case` to False. We are setting `do_lower_case=True` for you "
-                               "but you may want to check this behavior.")
-                kwargs['do_lower_case'] = True
-
-        return super(BertTokenizer, cls)._from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
-
-
-class BasicTokenizer(object):
-    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
-
-    def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True):
-        """ Constructs a BasicTokenizer.
-
-        Args:
-            **do_lower_case**: Whether to lower case the input.
-            **never_split**: (`optional`) list of str
-                Kept for backward compatibility purposes.
-                Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)
-                List of token not to split.
-            **tokenize_chinese_chars**: (`optional`) boolean (default True)
-                Whether to tokenize Chinese characters.
-                This should likely be desactivated for Japanese:
-                see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328
-        """
-        if never_split is None:
-            never_split = []
-        self.do_lower_case = do_lower_case
-        self.never_split = never_split
-        self.tokenize_chinese_chars = tokenize_chinese_chars
-
-    def tokenize(self, text, never_split=None):
-        """ Basic Tokenization of a piece of text.
-            Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer.
-
-        Args:
-            **never_split**: (`optional`) list of str
-                Kept for backward compatibility purposes.
-                Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)
-                List of token not to split.
-        """
-        never_split = self.never_split + (never_split if never_split is not None else [])
-        text = self._clean_text(text)
-        # This was added on November 1st, 2018 for the multilingual and Chinese
-        # models. This is also applied to the English models now, but it doesn't
-        # matter since the English models were not trained on any Chinese data
-        # and generally don't have any Chinese data in them (there are Chinese
-        # characters in the vocabulary because Wikipedia does have some Chinese
-        # words in the English Wikipedia.).
-        if self.tokenize_chinese_chars:
-            text = self._tokenize_chinese_chars(text)
-        orig_tokens = whitespace_tokenize(text)
-        split_tokens = []
-        for token in orig_tokens:
-            if self.do_lower_case and token not in never_split:
-                token = token.lower()
-                token = self._run_strip_accents(token)
-            split_tokens.extend(self._run_split_on_punc(token))
-
-        output_tokens = whitespace_tokenize(" ".join(split_tokens))
-        return output_tokens
-
-    def _run_strip_accents(self, text):
-        """Strips accents from a piece of text."""
-        text = unicodedata.normalize("NFD", text)
-        output = []
-        for char in text:
-            cat = unicodedata.category(char)
-            if cat == "Mn":
-                continue
-            output.append(char)
-        return "".join(output)
-
-    def _run_split_on_punc(self, text, never_split=None):
-        """Splits punctuation on a piece of text."""
-        if never_split is not None and text in never_split:
-            return [text]
-        chars = list(text)
-        i = 0
-        start_new_word = True
-        output = []
-        while i < len(chars):
-            char = chars[i]
-            if _is_punctuation(char):
-                output.append([char])
-                start_new_word = True
-            else:
-                if start_new_word:
-                    output.append([])
-                start_new_word = False
-                output[-1].append(char)
-            i += 1
-
-        return ["".join(x) for x in output]
-
-    # def _tokenize_chinese_chars(self, text):
-    #     """Adds whitespace around any CJK character."""
-    #     output = []
-    #     for char in text:
-    #         cp = ord(char)
-    #         if self._is_chinese_char(cp) or char.isdigit():
-    #             output.append(" ")
-    #             output.append(char)
-    #             output.append(" ")
-    #         else:
-    #             output.append(char)
-    #     return "".join(output)
-    def _tokenize_chinese_chars(self, text):
-        """Adds whitespace around any CJK character."""
-        output = []
-        for char in text:
-            if char.isdigit():
-                output.append(" ")
-                output.append(char)
-                output.append(" ")
-            else:
-                output.append(char)
-        text = "".join(output)
-        text = [item[0].strip() for item in lac.cut(text)]
-        text = [item for item in text if item]
-        return " ".join(text)
-
-    def _is_chinese_char(self, cp):
-        """Checks whether CP is the codepoint of a CJK character."""
-        # This defines a "chinese character" as anything in the CJK Unicode block:
-        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
-        #
-        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
-        # despite its name. The modern Korean Hangul alphabet is a different block,
-        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
-        # space-separated words, so they are not treated specially and handled
-        # like the all of the other languages.
-        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
-                (cp >= 0x3400 and cp <= 0x4DBF) or  #
-                (cp >= 0x20000 and cp <= 0x2A6DF) or  #
-                (cp >= 0x2A700 and cp <= 0x2B73F) or  #
-                (cp >= 0x2B740 and cp <= 0x2B81F) or  #
-                (cp >= 0x2B820 and cp <= 0x2CEAF) or
-                (cp >= 0xF900 and cp <= 0xFAFF) or  #
-                (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
-            return True
-
-        return False
-
-    def _clean_text(self, text):
-        """Performs invalid character removal and whitespace cleanup on text."""
-        output = []
-        for char in text:
-            cp = ord(char)
-            if cp == 0 or cp == 0xfffd or _is_control(char):
-                continue
-            if _is_whitespace(char):
-                output.append(" ")
-            else:
-                output.append(char)
-        return "".join(output)
-
-
-class WordpieceTokenizer(object):
-    """Runs WordPiece tokenization."""
-
-    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
-        self.vocab = vocab
-        self.unk_token = unk_token
-        self.max_input_chars_per_word = max_input_chars_per_word
-
-    def tokenize(self, text):
-        """Tokenizes a piece of text into its word pieces.
-
-        This uses a greedy longest-match-first algorithm to perform tokenization
-        using the given vocabulary.
-
-        For example:
-          input = "unaffable"
-          output = ["un", "##aff", "##able"]
-
-        Args:
-          text: A single token or whitespace separated tokens. This should have
-            already been passed through `BasicTokenizer`.
-
-        Returns:
-          A list of wordpiece tokens.
-        """
-
-        output_tokens = []
-        for token in whitespace_tokenize(text):
-            chars = list(token)
-            if len(chars) > self.max_input_chars_per_word:
-                output_tokens.append(self.unk_token)
-                continue
-
-            is_bad = False
-            start = 0
-            sub_tokens = []
-            while start < len(chars):
-                end = len(chars)
-                cur_substr = None
-                while start < end:
-                    substr = "".join(chars[start:end])
-                    if start > 0:
-                        substr = "##" + substr
-                    if substr in self.vocab:
-                        cur_substr = substr
-                        break
-                    end -= 1
-                if cur_substr is None:
-                    is_bad = True
-                    break
-                sub_tokens.append(cur_substr)
-                start = end
-
-            if is_bad:
-                output_tokens.append(self.unk_token)
-            else:
-                output_tokens.extend(sub_tokens)
-        return output_tokens
-
-
-def _is_whitespace(char):
-    """Checks whether `chars` is a whitespace character."""
-    # \t, \n, and \r are technically contorl characters but we treat them
-    # as whitespace since they are generally considered as such.
-    if char == " " or char == "\t" or char == "\n" or char == "\r":
-        return True
-    cat = unicodedata.category(char)
-    if cat == "Zs":
-        return True
-    return False
-
-
-def _is_control(char):
-    """Checks whether `chars` is a control character."""
-    # These are technically control characters but we count them as whitespace
-    # characters.
-    if char == "\t" or char == "\n" or char == "\r":
-        return False
-    cat = unicodedata.category(char)
-    if cat.startswith("C"):
-        return True
-    return False
-
-
-def _is_punctuation(char):
-    """Checks whether `chars` is a punctuation character."""
-    cp = ord(char)
-    # We treat all non-letter/number ASCII as punctuation.
-    # Characters such as "^", "$", and "`" are not in the Unicode
-    # Punctuation class but we treat them as punctuation anyways, for
-    # consistency.
-    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
-            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
-        return True
-    cat = unicodedata.category(char)
-    if cat.startswith("P"):
-        return True
-    return False
\ No newline at end of file
diff --git a/spaces/nateraw/deepafx-st/deepafx_st/probes/cdpam_encoder.py b/spaces/nateraw/deepafx-st/deepafx_st/probes/cdpam_encoder.py
deleted file mode 100644
index 5e20a4d5f4a9214e3aee85fd1bf2bb6f462a4045..0000000000000000000000000000000000000000
--- a/spaces/nateraw/deepafx-st/deepafx_st/probes/cdpam_encoder.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# MIT License
-
-# Copyright (c) 2021 Pranay Manocha
-
-# 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.
-
-# code adapated from https://github.com/pranaymanocha/PerceptualAudio
-
-import cdpam
-import torch
-
-
-class CDPAMEncoder(torch.nn.Module):
-    def __init__(self, cdpam_ckpt: str):
-        super().__init__()
-
-        # pre-trained model parameterss
-        encoder_layers = 16
-        encoder_filters = 64
-        input_size = 512
-        proj_ndim = [512, 256]
-        ndim = [16, 6]
-        classif_BN = 0
-        classif_act = "no"
-        proj_dp = 0.1
-        proj_BN = 1
-        classif_dp = 0.05
-
-        model = cdpam.models.FINnet(
-            encoder_layers=encoder_layers,
-            encoder_filters=encoder_filters,
-            ndim=ndim,
-            classif_dp=classif_dp,
-            classif_BN=classif_BN,
-            classif_act=classif_act,
-            input_size=input_size,
-        )
-
-        state = torch.load(cdpam_ckpt, map_location="cpu")["state"]
-        model.load_state_dict(state)
-        model.eval()
-
-        self.model = model
-        self.embed_dim = 512
-
-    def forward(self, x):
-
-        with torch.no_grad():
-            _, a1, c1 = self.model.base_encoder.forward(x)
-            a1 = torch.nn.functional.normalize(a1, dim=1)
-
-        return a1
diff --git a/spaces/naver/SuperFeatures/how/utils/data_helpers.py b/spaces/naver/SuperFeatures/how/utils/data_helpers.py
deleted file mode 100644
index 895161965946d49a039eea6bc799003c52d09538..0000000000000000000000000000000000000000
--- a/spaces/naver/SuperFeatures/how/utils/data_helpers.py
+++ /dev/null
@@ -1,90 +0,0 @@
-"""Data manipulation helpers"""
-
-import os.path
-import pickle
-
-from cirtorch.datasets.datahelpers import cid2filename
-from cirtorch.datasets.testdataset import configdataset
-
-
-def load_dataset(dataset, data_root=''):
-    """Return tuple (image list, query list, bounding boxes, gnd dictionary)"""
-
-    if isinstance(dataset, dict):
-        root = os.path.join(data_root, dataset['image_root'])
-        images, qimages = None, None
-        if dataset['database_list'] is not None:
-            images = [path_join(root, x.strip("\n")) for x in open(dataset['database_list']).readlines()]
-        if dataset['query_list'] is not None:
-            qimages = [path_join(root, x.strip("\n")) for x in open(dataset['query_list']).readlines()]
-        bbxs = None
-        gnd = None
-
-    elif dataset == 'train':
-        training_set = 'retrieval-SfM-120k'
-        db_root = os.path.join(data_root, 'train', training_set)
-        ims_root = os.path.join(db_root, 'ims')
-        db_fn = os.path.join(db_root, '{}.pkl'.format(training_set))
-        with open(db_fn, 'rb') as f:
-            db = pickle.load(f)['train']
-        images = [cid2filename(db['cids'][i], ims_root) for i in range(len(db['cids']))]
-        qimages = []
-        bbxs = None
-        gnd = None
-
-    elif dataset == 'val_eccv20':
-        db_root = os.path.join(data_root, 'train', 'retrieval-SfM-120k')
-        fn_val_proper = db_root+'/retrieval-SfM-120k-val-eccv2020.pkl' # pos are all with #inl >=3 & <= 10
-        with open(fn_val_proper, 'rb') as f:
-            db = pickle.load(f)
-        ims_root = os.path.join(db_root, 'ims')
-        images = [cid2filename(db['cids'][i], ims_root) for i in range(len(db['cids']))]
-        gnd = db['gnd']
-        qidx = db['qidx']
-        qimages = [images[x] for x in qidx]
-        bbxs = None
-
-    elif "/" in dataset:
-        with open(dataset, 'rb') as handle:
-            db = pickle.load(handle)
-        images, qimages, bbxs, gnd = db['imlist'], db['qimlist'], None, db['gnd']
-
-    else:
-        cfg = configdataset(dataset, os.path.join(data_root, 'test'))
-        images = [cfg['im_fname'](cfg, i) for i in range(cfg['n'])]
-        qimages = [cfg['qim_fname'](cfg, i) for i in range(cfg['nq'])]
-        if 'bbx' in cfg['gnd'][0].keys():
-            bbxs = [tuple(cfg['gnd'][i]['bbx']) for i in range(cfg['nq'])]
-        else:
-            bbxs = None
-        gnd = cfg['gnd']
-
-    return images, qimages, bbxs, gnd
-
-
-def path_join(root, name):
-    """Perform os.path.join by default; if asterisk is present in root, substitute with the name.
-
-    >>> path_join('/data/img_*.jpg', '001')
-    '/data/img_001.jpg'
-    """
-    if "*" in root.rsplit("/", 1)[-1]:
-        return root.replace("*", name)
-    return os.path.join(root, name)
-
-
-class AverageMeter:
-    """Compute and store the average and last value"""
-
-    def __init__(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        """Update the counter by a new value"""
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Dev D Full Movie In Tamil Hd 1080p Download.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Dev D Full Movie In Tamil Hd 1080p Download.md
deleted file mode 100644
index 36b699d6d28cb33f00185e863af40b7dc5780f86..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Dev D Full Movie In Tamil Hd 1080p Download.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<br />
-<h1>How to Watch Dev D Full Movie in Tamil HD 1080p Download</h1>
-<p>Dev D is a 2009 Hindi movie that is a modern adaptation of the classic novel Devdas by Sarat Chandra Chattopadhyay. The movie stars Abhay Deol as Dev, a rich and spoiled young man who falls in love with Paro (Mahi Gill), his childhood sweetheart, but rejects her due to a misunderstanding. Dev then turns to drugs and alcohol to cope with his pain, while Paro marries another man. Dev also meets Chanda (Kalki Koechlin), a prostitute who has her own tragic past, and develops a bond with her.</p>
-<h2>Dev D full movie in tamil hd 1080p download</h2><br /><p><b><b>Download File</b> &#9881; <a href="https://urlcod.com/2uIbeT">https://urlcod.com/2uIbeT</a></b></p><br /><br />
-<p>If you are a fan of this movie and want to watch it in Tamil, you might be wondering how to download it in HD 1080p quality. Well, there are some ways to do that, but you should be careful about the legal and ethical issues involved. Here are some tips to help you watch Dev D full movie in Tamil HD 1080p download.</p>
-<h2>Use a VPN Service</h2>
-<p>One of the easiest ways to access movies that are not available in your region is to use a VPN service. A VPN or a virtual private network allows you to change your IP address and location, so that you can bypass geo-restrictions and access content from other countries. For example, if you want to watch Dev D full movie in Tamil HD 1080p download from a torrent site like YTS, you can use a VPN service to connect to a server in India, where the movie is available.</p>
-<p>However, you should be aware that using a VPN service does not guarantee your safety or privacy online. Torrenting is illegal in many countries and can expose you to malware, viruses, and legal action. Moreover, not all VPN services are reliable or trustworthy. Some of them may keep logs of your activity, sell your data, or leak your IP address. Therefore, you should do your research and choose a reputable VPN service that has a strict no-logs policy, strong encryption, and fast speeds.</p>
-<h2>Use a Legal Streaming Platform</h2>
-<p>Another way to watch Dev D full movie in Tamil HD 1080p download is to use a legal streaming platform that offers the movie in your preferred language. There are many streaming platforms that have a large collection of movies and shows in different languages and genres. Some of them are:</p>
-<p></p>
-<ul>
-<li>Youtube: Youtube is one of the most popular and widely used platforms for watching videos online. You can find many movies and shows on Youtube, both free and paid. You can also rent or buy movies on Youtube and watch them offline. However, you should be careful about the quality and authenticity of the videos on Youtube, as some of them may be pirated or low-quality.</li>
-<li>Hotstar: Hotstar is an Indian streaming platform that offers movies, shows, sports, news, and live TV channels in various languages. You can watch Dev D full movie in Tamil HD 1080p download on Hotstar with a premium subscription. You can also download the movie on your device and watch it offline.</li>
-<li>Netflix: Netflix is one of the most popular and widely used streaming platforms in the world. It offers movies, shows, documentaries, anime, and originals in various languages and genres. You can watch Dev D full movie in Tamil HD 1080p download on Netflix with a subscription. You can also download the movie on your device and watch it offline.</li>
-<li>iTunes: iTunes is an Apple service that allows you to buy or rent movies and shows online. You can watch Dev D full movie in Tamil HD 1080p download on iTunes with a payment. You can also download the movie on your device and watch it offline.</li>
-<li>Jio cinema: Jio cinema is an Indian streaming platform that offers movies, shows, music videos, trailers, and originals in various languages. You can watch Dev D full movie in Tamil HD 1080p download on Jio cinema with a Jio SIM card or a JioFiber connection. You can also download the movie on your device and watch it offline.</li>
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-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Ebookkitabkuningterjemahkhulasohnurulyaqinjuzii.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Ebookkitabkuningterjemahkhulasohnurulyaqinjuzii.md
deleted file mode 100644
index e8db8668adf61f6d86913632ed8d4c8dfc87bef9..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Ebookkitabkuningterjemahkhulasohnurulyaqinjuzii.md
+++ /dev/null
@@ -1,18 +0,0 @@
-
-<h1>Ebook Kitab Kuning Terjemah Khulasoh Nurul Yaqin Juz II: Sejarah Nabi Muhammad SAW</h1>
-<p>Ebook Kitab Kuning Terjemah Khulasoh Nurul Yaqin Juz II adalah sebuah buku digital yang berisi terjemahan dari kitab Khulasoh Nurul Yaqin fi Sirah Sayyid Al-Mursalin karya Umar Abdul Jabbar. Kitab ini merupakan ringkasan dari kitab Nurul Yaqin fi Sirati Sayyidil Mursalin Muhammad SAW karya Muhammad bin Afifi Al-Bajuri, yang membahas tentang sejarah hidup Nabi Muhammad SAW dari lahir hingga wafat.</p>
-<h2>ebookkitabkuningterjemahkhulasohnurulyaqinjuzii</h2><br /><p><b><b>DOWNLOAD</b> >>>>> <a href="https://urlcod.com/2uIaYm">https://urlcod.com/2uIaYm</a></b></p><br /><br />
-<p>Ebook ini dapat diunduh secara gratis dari beberapa situs web, seperti Google Drive[^1^], Terjemah Kitab Kuning[^2^], dan Kitab Salaf[^3^]. Ebook ini terdiri dari tiga jilid, yang masing-masing mencakup periode-periode penting dalam sejarah Nabi Muhammad SAW. Jilid pertama membahas tentang periode sebelum menjadi rasul, jilid kedua membahas tentang periode di Makkah, dan jilid ketiga membahas tentang periode di Madinah.</p>
-<p>Ebook ini bermanfaat bagi siapa saja yang ingin mempelajari sejarah Nabi Muhammad SAW dengan lebih mendalam dan mudah. Dengan format ebook, pembaca dapat membaca kitab ini di mana saja dan kapan saja dengan menggunakan perangkat elektronik seperti komputer, laptop, tablet, atau smartphone. Selain itu, ebook ini juga dilengkapi dengan daftar isi, ringkasan, dan catatan kaki yang memudahkan pembaca untuk mengikuti alur dan sumber kitab ini.</p>
-<p>Ebook Kitab Kuning Terjemah Khulasoh Nurul Yaqin Juz II adalah sebuah karya yang patut diapresiasi dan disebarkan kepada umat Islam. Dengan membaca kitab ini, pembaca dapat menambah wawasan dan kecintaan kepada Nabi Muhammad SAW, yang merupakan teladan terbaik bagi seluruh manusia.</p>
-<p></p>
-
-<p>Ebook Kitab Kuning Terjemah Khulasoh Nurul Yaqin Juz II tidak hanya berisi tentang fakta-fakta sejarah, tetapi juga tentang hikmah-hikmah dan pelajaran-pelajaran yang dapat diambil dari kisah-kisah Nabi Muhammad SAW. Pembaca dapat mengambil contoh dari sikap, akhlak, dan perjuangan Nabi Muhammad SAW dalam menghadapi berbagai tantangan dan musuh. Pembaca juga dapat mengenal lebih dekat para sahabat, keluarga, dan istri-istri Nabi Muhammad SAW yang merupakan orang-orang terbaik di dunia.</p>
-<p>Ebook Kitab Kuning Terjemah Khulasoh Nurul Yaqin Juz II juga menyajikan informasi-informasi yang jarang diketahui oleh banyak orang, seperti nama-nama anak-anak Nabi Muhammad SAW, nama-nama unta Nabi Muhammad SAW, nama-nama pedang Nabi Muhammad SAW, nama-nama perang yang diikuti oleh Nabi Muhammad SAW, dan lain-lain. Pembaca dapat menambah pengetahuan dan kekaguman kepada Nabi Muhammad SAW dengan mengetahui hal-hal tersebut.</p>
-<p>Ebook Kitab Kuning Terjemah Khulasoh Nurul Yaqin Juz II adalah sebuah ebook yang layak untuk dimiliki oleh setiap Muslim. Dengan membaca ebook ini, pembaca dapat mengikuti jejak Nabi Muhammad SAW dalam menjalani hidup di dunia ini dengan penuh iman, taqwa, dan cinta kepada Allah SWT.</p>
-
-<p>Ebook Kitab Kuning Terjemah Khulasoh Nurul Yaqin Juz II adalah sebuah ebook yang sangat bermanfaat dan berharga bagi umat Islam. Ebook ini mengajak pembaca untuk mengenal lebih dekat Nabi Muhammad SAW, yang merupakan utusan Allah SWT kepada seluruh manusia. Ebook ini juga menginspirasi pembaca untuk meneladani Nabi Muhammad SAW, yang merupakan contoh terbaik dalam segala hal. Ebook ini juga memberikan informasi-informasi yang menarik dan penting tentang sejarah Nabi Muhammad SAW, yang merupakan sejarah teragung di dunia.</p>
-<p>Ebook ini dapat diunduh secara gratis dari beberapa situs web yang telah disebutkan sebelumnya. Ebook ini juga dapat dibaca di berbagai perangkat elektronik dengan mudah dan nyaman. Ebook ini juga dilengkapi dengan fitur-fitur yang memudahkan pembaca untuk mengakses dan memahami isi kitab ini. Ebook ini juga dapat dibagikan kepada orang lain yang ingin mempelajari sejarah Nabi Muhammad SAW.</p>
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-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h
deleted file mode 100644
index b54a5dde2ca11a74d29c4d8adb7fe1634f5baf9c..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h
+++ /dev/null
@@ -1,370 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#pragma once
-
-#include <cassert>
-#include <cmath>
-
-#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1
-// Designates functions callable from the host (CPU) and the device (GPU)
-#define HOST_DEVICE __host__ __device__
-#define HOST_DEVICE_INLINE HOST_DEVICE __forceinline__
-#else
-#include <algorithm>
-#define HOST_DEVICE
-#define HOST_DEVICE_INLINE HOST_DEVICE inline
-#endif
-
-namespace detectron2 {
-
-namespace {
-
-template <typename T>
-struct RotatedBox {
-  T x_ctr, y_ctr, w, h, a;
-};
-
-template <typename T>
-struct Point {
-  T x, y;
-  HOST_DEVICE_INLINE Point(const T& px = 0, const T& py = 0) : x(px), y(py) {}
-  HOST_DEVICE_INLINE Point operator+(const Point& p) const {
-    return Point(x + p.x, y + p.y);
-  }
-  HOST_DEVICE_INLINE Point& operator+=(const Point& p) {
-    x += p.x;
-    y += p.y;
-    return *this;
-  }
-  HOST_DEVICE_INLINE Point operator-(const Point& p) const {
-    return Point(x - p.x, y - p.y);
-  }
-  HOST_DEVICE_INLINE Point operator*(const T coeff) const {
-    return Point(x * coeff, y * coeff);
-  }
-};
-
-template <typename T>
-HOST_DEVICE_INLINE T dot_2d(const Point<T>& A, const Point<T>& B) {
-  return A.x * B.x + A.y * B.y;
-}
-
-// R: result type. can be different from input type
-template <typename T, typename R = T>
-HOST_DEVICE_INLINE R cross_2d(const Point<T>& A, const Point<T>& B) {
-  return static_cast<R>(A.x) * static_cast<R>(B.y) -
-      static_cast<R>(B.x) * static_cast<R>(A.y);
-}
-
-template <typename T>
-HOST_DEVICE_INLINE void get_rotated_vertices(
-    const RotatedBox<T>& box,
-    Point<T> (&pts)[4]) {
-  // M_PI / 180. == 0.01745329251
-  double theta = box.a * 0.01745329251;
-  T cosTheta2 = (T)cos(theta) * 0.5f;
-  T sinTheta2 = (T)sin(theta) * 0.5f;
-
-  // y: top --> down; x: left --> right
-  pts[0].x = box.x_ctr + sinTheta2 * box.h + cosTheta2 * box.w;
-  pts[0].y = box.y_ctr + cosTheta2 * box.h - sinTheta2 * box.w;
-  pts[1].x = box.x_ctr - sinTheta2 * box.h + cosTheta2 * box.w;
-  pts[1].y = box.y_ctr - cosTheta2 * box.h - sinTheta2 * box.w;
-  pts[2].x = 2 * box.x_ctr - pts[0].x;
-  pts[2].y = 2 * box.y_ctr - pts[0].y;
-  pts[3].x = 2 * box.x_ctr - pts[1].x;
-  pts[3].y = 2 * box.y_ctr - pts[1].y;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE int get_intersection_points(
-    const Point<T> (&pts1)[4],
-    const Point<T> (&pts2)[4],
-    Point<T> (&intersections)[24]) {
-  // Line vector
-  // A line from p1 to p2 is: p1 + (p2-p1)*t, t=[0,1]
-  Point<T> vec1[4], vec2[4];
-  for (int i = 0; i < 4; i++) {
-    vec1[i] = pts1[(i + 1) % 4] - pts1[i];
-    vec2[i] = pts2[(i + 1) % 4] - pts2[i];
-  }
-
-  // When computing the intersection area, it doesn't hurt if we have
-  // more (duplicated/approximate) intersections/vertices than needed,
-  // while it can cause drastic difference if we miss an intersection/vertex.
-  // Therefore, we add an epsilon to relax the comparisons between
-  // the float point numbers that decide the intersection points.
-  double EPS = 1e-5;
-
-  // Line test - test all line combos for intersection
-  int num = 0; // number of intersections
-  for (int i = 0; i < 4; i++) {
-    for (int j = 0; j < 4; j++) {
-      // Solve for 2x2 Ax=b
-      T det = cross_2d<T>(vec2[j], vec1[i]);
-
-      // This takes care of parallel lines
-      if (fabs(det) <= 1e-14) {
-        continue;
-      }
-
-      auto vec12 = pts2[j] - pts1[i];
-
-      T t1 = cross_2d<T>(vec2[j], vec12) / det;
-      T t2 = cross_2d<T>(vec1[i], vec12) / det;
-
-      if (t1 > -EPS && t1 < 1.0f + EPS && t2 > -EPS && t2 < 1.0f + EPS) {
-        intersections[num++] = pts1[i] + vec1[i] * t1;
-      }
-    }
-  }
-
-  // Check for vertices of rect1 inside rect2
-  {
-    const auto& AB = vec2[0];
-    const auto& DA = vec2[3];
-    auto ABdotAB = dot_2d<T>(AB, AB);
-    auto ADdotAD = dot_2d<T>(DA, DA);
-    for (int i = 0; i < 4; i++) {
-      // assume ABCD is the rectangle, and P is the point to be judged
-      // P is inside ABCD iff. P's projection on AB lies within AB
-      // and P's projection on AD lies within AD
-
-      auto AP = pts1[i] - pts2[0];
-
-      auto APdotAB = dot_2d<T>(AP, AB);
-      auto APdotAD = -dot_2d<T>(AP, DA);
-
-      if ((APdotAB > -EPS) && (APdotAD > -EPS) && (APdotAB < ABdotAB + EPS) &&
-          (APdotAD < ADdotAD + EPS)) {
-        intersections[num++] = pts1[i];
-      }
-    }
-  }
-
-  // Reverse the check - check for vertices of rect2 inside rect1
-  {
-    const auto& AB = vec1[0];
-    const auto& DA = vec1[3];
-    auto ABdotAB = dot_2d<T>(AB, AB);
-    auto ADdotAD = dot_2d<T>(DA, DA);
-    for (int i = 0; i < 4; i++) {
-      auto AP = pts2[i] - pts1[0];
-
-      auto APdotAB = dot_2d<T>(AP, AB);
-      auto APdotAD = -dot_2d<T>(AP, DA);
-
-      if ((APdotAB > -EPS) && (APdotAD > -EPS) && (APdotAB < ABdotAB + EPS) &&
-          (APdotAD < ADdotAD + EPS)) {
-        intersections[num++] = pts2[i];
-      }
-    }
-  }
-
-  return num;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE int convex_hull_graham(
-    const Point<T> (&p)[24],
-    const int& num_in,
-    Point<T> (&q)[24],
-    bool shift_to_zero = false) {
-  assert(num_in >= 2);
-
-  // Step 1:
-  // Find point with minimum y
-  // if more than 1 points have the same minimum y,
-  // pick the one with the minimum x.
-  int t = 0;
-  for (int i = 1; i < num_in; i++) {
-    if (p[i].y < p[t].y || (p[i].y == p[t].y && p[i].x < p[t].x)) {
-      t = i;
-    }
-  }
-  auto& start = p[t]; // starting point
-
-  // Step 2:
-  // Subtract starting point from every points (for sorting in the next step)
-  for (int i = 0; i < num_in; i++) {
-    q[i] = p[i] - start;
-  }
-
-  // Swap the starting point to position 0
-  auto tmp = q[0];
-  q[0] = q[t];
-  q[t] = tmp;
-
-  // Step 3:
-  // Sort point 1 ~ num_in according to their relative cross-product values
-  // (essentially sorting according to angles)
-  // If the angles are the same, sort according to their distance to origin
-  T dist[24];
-#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1
-  // compute distance to origin before sort, and sort them together with the
-  // points
-  for (int i = 0; i < num_in; i++) {
-    dist[i] = dot_2d<T>(q[i], q[i]);
-  }
-
-  // CUDA version
-  // In the future, we can potentially use thrust
-  // for sorting here to improve speed (though not guaranteed)
-  for (int i = 1; i < num_in - 1; i++) {
-    for (int j = i + 1; j < num_in; j++) {
-      T crossProduct = cross_2d<T>(q[i], q[j]);
-      if ((crossProduct < -1e-6) ||
-          (fabs(crossProduct) < 1e-6 && dist[i] > dist[j])) {
-        auto q_tmp = q[i];
-        q[i] = q[j];
-        q[j] = q_tmp;
-        auto dist_tmp = dist[i];
-        dist[i] = dist[j];
-        dist[j] = dist_tmp;
-      }
-    }
-  }
-#else
-  // CPU version
-  std::sort(
-      q + 1, q + num_in, [](const Point<T>& A, const Point<T>& B) -> bool {
-        T temp = cross_2d<T>(A, B);
-        if (fabs(temp) < 1e-6) {
-          return dot_2d<T>(A, A) < dot_2d<T>(B, B);
-        } else {
-          return temp > 0;
-        }
-      });
-  // compute distance to origin after sort, since the points are now different.
-  for (int i = 0; i < num_in; i++) {
-    dist[i] = dot_2d<T>(q[i], q[i]);
-  }
-#endif
-
-  // Step 4:
-  // Make sure there are at least 2 points (that don't overlap with each other)
-  // in the stack
-  int k; // index of the non-overlapped second point
-  for (k = 1; k < num_in; k++) {
-    if (dist[k] > 1e-8) {
-      break;
-    }
-  }
-  if (k == num_in) {
-    // We reach the end, which means the convex hull is just one point
-    q[0] = p[t];
-    return 1;
-  }
-  q[1] = q[k];
-  int m = 2; // 2 points in the stack
-  // Step 5:
-  // Finally we can start the scanning process.
-  // When a non-convex relationship between the 3 points is found
-  // (either concave shape or duplicated points),
-  // we pop the previous point from the stack
-  // until the 3-point relationship is convex again, or
-  // until the stack only contains two points
-  for (int i = k + 1; i < num_in; i++) {
-    while (m > 1) {
-      auto q1 = q[i] - q[m - 2], q2 = q[m - 1] - q[m - 2];
-      // cross_2d() uses FMA and therefore computes round(round(q1.x*q2.y) -
-      // q2.x*q1.y) So it may not return 0 even when q1==q2. Therefore we
-      // compare round(q1.x*q2.y) and round(q2.x*q1.y) directly. (round means
-      // round to nearest floating point).
-      if (q1.x * q2.y >= q2.x * q1.y)
-        m--;
-      else
-        break;
-    }
-    // Using double also helps, but float can solve the issue for now.
-    // while (m > 1 && cross_2d<T, double>(q[i] - q[m - 2], q[m - 1] - q[m - 2])
-    // >= 0) {
-    //     m--;
-    // }
-    q[m++] = q[i];
-  }
-
-  // Step 6 (Optional):
-  // In general sense we need the original coordinates, so we
-  // need to shift the points back (reverting Step 2)
-  // But if we're only interested in getting the area/perimeter of the shape
-  // We can simply return.
-  if (!shift_to_zero) {
-    for (int i = 0; i < m; i++) {
-      q[i] += start;
-    }
-  }
-
-  return m;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE T polygon_area(const Point<T> (&q)[24], const int& m) {
-  if (m <= 2) {
-    return 0;
-  }
-
-  T area = 0;
-  for (int i = 1; i < m - 1; i++) {
-    area += fabs(cross_2d<T>(q[i] - q[0], q[i + 1] - q[0]));
-  }
-
-  return area / 2.0;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE T rotated_boxes_intersection(
-    const RotatedBox<T>& box1,
-    const RotatedBox<T>& box2) {
-  // There are up to 4 x 4 + 4 + 4 = 24 intersections (including dups) returned
-  // from rotated_rect_intersection_pts
-  Point<T> intersectPts[24], orderedPts[24];
-
-  Point<T> pts1[4];
-  Point<T> pts2[4];
-  get_rotated_vertices<T>(box1, pts1);
-  get_rotated_vertices<T>(box2, pts2);
-
-  int num = get_intersection_points<T>(pts1, pts2, intersectPts);
-
-  if (num <= 2) {
-    return 0.0;
-  }
-
-  // Convex Hull to order the intersection points in clockwise order and find
-  // the contour area.
-  int num_convex = convex_hull_graham<T>(intersectPts, num, orderedPts, true);
-  return polygon_area<T>(orderedPts, num_convex);
-}
-
-} // namespace
-
-template <typename T>
-HOST_DEVICE_INLINE T
-single_box_iou_rotated(T const* const box1_raw, T const* const box2_raw) {
-  // shift center to the middle point to achieve higher precision in result
-  RotatedBox<T> box1, box2;
-  auto center_shift_x = (box1_raw[0] + box2_raw[0]) / 2.0;
-  auto center_shift_y = (box1_raw[1] + box2_raw[1]) / 2.0;
-  box1.x_ctr = box1_raw[0] - center_shift_x;
-  box1.y_ctr = box1_raw[1] - center_shift_y;
-  box1.w = box1_raw[2];
-  box1.h = box1_raw[3];
-  box1.a = box1_raw[4];
-  box2.x_ctr = box2_raw[0] - center_shift_x;
-  box2.y_ctr = box2_raw[1] - center_shift_y;
-  box2.w = box2_raw[2];
-  box2.h = box2_raw[3];
-  box2.a = box2_raw[4];
-
-  T area1 = box1.w * box1.h;
-  T area2 = box2.w * box2.h;
-  if (area1 < 1e-14 || area2 < 1e-14) {
-    return 0.f;
-  }
-
-  T intersection = rotated_boxes_intersection<T>(box1, box2);
-  T iou = intersection / (area1 + area2 - intersection);
-  return iou;
-}
-
-} // namespace detectron2
diff --git a/spaces/niro-private/chatCSV/brain.py b/spaces/niro-private/chatCSV/brain.py
deleted file mode 100644
index 21fb17a4e00a96d46e7f6348e75e0ad46ca6e3c0..0000000000000000000000000000000000000000
--- a/spaces/niro-private/chatCSV/brain.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import streamlit as st
-import numpy as np
-
-def brain(supabase):
-    ## List all documents
-    response = supabase.table("documents").select("name:metadata->>file_name, size:metadata->>file_size", count="exact").execute()
-    
-    documents = response.data  # Access the data from the response
-
-    # Convert each dictionary to a tuple of items, then to a set to remove duplicates, and then back to a dictionary
-    unique_data = [dict(t) for t in set(tuple(d.items()) for d in documents)]
-
-    # Sort the list of documents by size in decreasing order
-    unique_data.sort(key=lambda x: int(x['size']), reverse=True)
-
-    # Display some metrics at the top of the page
-    col1, col2 = st.columns(2)
-    col1.metric(label="Total Documents", value=len(unique_data))
-    col2.metric(label="Total Size (bytes)", value=sum(int(doc['size']) for doc in unique_data))
-
-    for document in unique_data:
-        # Create a unique key for each button by using the document name
-        button_key = f"delete_{document['name']}"
-
-        # Display the document name, size and the delete button on the same line
-        col1, col2, col3 = st.columns([3, 1, 1])
-        col1.markdown(f"**{document['name']}** ({document['size']} bytes)")
-        
-        if col2.button('❌', key=button_key):
-            delete_document(supabase, document['name'])
-
-def delete_document(supabase, document_name):
-    # Delete the document from the database
-    response = supabase.table("documents").delete().match({"metadata->>file_name": document_name}).execute()
-    # Check if the deletion was successful
-    if len(response.data) > 0:
-        st.write(f"✂️ {document_name} was deleted.")
-    else:
-        st.write(f"❌ {document_name} was not deleted.")
diff --git a/spaces/nnaii/anime-remove-background/app.py b/spaces/nnaii/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/nnaii/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()
diff --git a/spaces/nugrahatheo/Vehicle-Type-Recognition/about.py b/spaces/nugrahatheo/Vehicle-Type-Recognition/about.py
deleted file mode 100644
index 4d8781d01756263ca8fc15ab2552a77bb96f21b1..0000000000000000000000000000000000000000
--- a/spaces/nugrahatheo/Vehicle-Type-Recognition/about.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import streamlit as st
-from PIL import Image
-
-def run():
-    # Add Picture
-    image = Image.open('vtr.png')
-    st.image(image, caption='Vehicle Type Recognition')
-    # Title
-    st.title('ABOUT THIS PROJECT')
-    st.markdown('---')
-    st.write('###### The main objective of this project is to build a machine learning model that can distinguish between four categories of vehicles: cars, trucks, buses, and motorcycles using Convolutional Neural Networks (CNN) and ElasticNet.')
-    st.markdown('---')
-    
-    st.write('Feel free to contact me on:')
-    st.write('[GITHUB](https://github.com/theonugraha)')
-    st.write('or')
-    st.write('[LINKEDIN](https://www.linkedin.com/in/nugrahatheo/)')
-
-
-if __name__ == '__main__':
-    run()
\ No newline at end of file
diff --git a/spaces/oguzakif/video-object-remover/SiamMask/models/rpn.py b/spaces/oguzakif/video-object-remover/SiamMask/models/rpn.py
deleted file mode 100644
index e6158ac77b5267b1fca9ce14ed43294c84d79390..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/SiamMask/models/rpn.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# --------------------------------------------------------
-# SiamMask
-# Licensed under The MIT License
-# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
-# --------------------------------------------------------
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class RPN(nn.Module):
-    def __init__(self):
-        super(RPN, self).__init__()
-
-    def forward(self, z_f, x_f):
-        raise NotImplementedError
-
-    def template(self, template):
-        raise NotImplementedError
-
-    def track(self, search):
-        raise NotImplementedError
-
-    def param_groups(self, start_lr, feature_mult=1, key=None):
-        if key is None:
-            params = filter(lambda x:x.requires_grad, self.parameters())
-        else:
-            params = [v for k, v in self.named_parameters() if (key in k) and v.requires_grad]
-        params = [{'params': params, 'lr': start_lr * feature_mult}]
-        return params
-
-
-def conv2d_dw_group(x, kernel):
-    batch, channel = kernel.shape[:2]
-    x = x.view(1, batch*channel, x.size(2), x.size(3))  # 1 * (b*c) * k * k
-    kernel = kernel.view(batch*channel, 1, kernel.size(2), kernel.size(3))  # (b*c) * 1 * H * W
-    out = F.conv2d(x, kernel, groups=batch*channel)
-    out = out.view(batch, channel, out.size(2), out.size(3))
-    return out
-
-
-class DepthCorr(nn.Module):
-    def __init__(self, in_channels, hidden, out_channels, kernel_size=3):
-        super(DepthCorr, self).__init__()
-        # adjust layer for asymmetrical features
-        self.conv_kernel = nn.Sequential(
-                nn.Conv2d(in_channels, hidden, kernel_size=kernel_size, bias=False),
-                nn.BatchNorm2d(hidden),
-                nn.ReLU(inplace=True),
-                )
-        self.conv_search = nn.Sequential(
-                nn.Conv2d(in_channels, hidden, kernel_size=kernel_size, bias=False),
-                nn.BatchNorm2d(hidden),
-                nn.ReLU(inplace=True),
-                )
-
-        self.head = nn.Sequential(
-                nn.Conv2d(hidden, hidden, kernel_size=1, bias=False),
-                nn.BatchNorm2d(hidden),
-                nn.ReLU(inplace=True),
-                nn.Conv2d(hidden, out_channels, kernel_size=1)
-                )
-
-    def forward_corr(self, kernel, input):
-        kernel = self.conv_kernel(kernel)
-        input = self.conv_search(input)
-        feature = conv2d_dw_group(input, kernel)
-        return feature
-
-    def forward(self, kernel, search):
-        feature = self.forward_corr(kernel, search)
-        out = self.head(feature)
-        return out
diff --git a/spaces/osanseviero/draw-minimal-copy3/app.py b/spaces/osanseviero/draw-minimal-copy3/app.py
deleted file mode 100644
index 3e37e1b8272672601eb57e8688b029316de55d3e..0000000000000000000000000000000000000000
--- a/spaces/osanseviero/draw-minimal-copy3/app.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from pathlib import Path
-
-import torch
-import gradio as gr
-from torch import nn
-
-
-LABELS = Path('class_names.txt').read_text().splitlines()
-
-model = nn.Sequential(
-    nn.Conv2d(1, 32, 3, padding='same'),
-    nn.ReLU(),
-    nn.MaxPool2d(2),
-    nn.Conv2d(32, 64, 3, padding='same'),
-    nn.ReLU(),
-    nn.MaxPool2d(2),
-    nn.Conv2d(64, 128, 3, padding='same'),
-    nn.ReLU(),
-    nn.MaxPool2d(2),
-    nn.Flatten(),
-    nn.Linear(1152, 256),
-    nn.ReLU(),
-    nn.Linear(256, len(LABELS)),
-)
-state_dict = torch.load('pytorch_model.bin', map_location='cpu')
-model.load_state_dict(state_dict, strict=False)
-model.eval()
-
-def predict(im):
-    x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.
-
-    with torch.no_grad():
-        out = model(x)
-
-    probabilities = torch.nn.functional.softmax(out[0], dim=0)
-
-    values, indices = torch.topk(probabilities, 5)
-
-    return {LABELS[i]: v.item() for i, v in zip(indices, values)}
-
-
-interface = gr.Interface(predict, inputs='sketchpad', outputs='label', theme="default", description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!", live=True)
-interface.launch(debug=True)
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md
deleted file mode 100644
index 0775485e68db9ed0d0f8e0a9f783b292860328c8..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md
+++ /dev/null
@@ -1,38 +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.
--->
-
-# Latent upscaler
-
-The Stable Diffusion latent upscaler model was created by [Katherine Crowson](https://github.com/crowsonkb/k-diffusion) in collaboration with [Stability AI](https://stability.ai/). It is used to enhance the output image resolution by a factor of 2 (see this demo [notebook](https://colab.research.google.com/drive/1o1qYJcFeywzCIdkfKJy7cTpgZTCM2EI4) for a demonstration of the original implementation).
-
-<Tip>
-
-Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! 
-
-If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
-
-</Tip>
-
-## StableDiffusionLatentUpscalePipeline
-
-[[autodoc]] StableDiffusionLatentUpscalePipeline
-	- all
-	- __call__
-	- enable_sequential_cpu_offload
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-
-## StableDiffusionPipelineOutput
-
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
\ No newline at end of file
diff --git a/spaces/phmota/disarter_model/app.py b/spaces/phmota/disarter_model/app.py
deleted file mode 100644
index a699bc5b3c2e987102ca93e0ee28d601e0a93d02..0000000000000000000000000000000000000000
--- a/spaces/phmota/disarter_model/app.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
\ No newline at end of file
diff --git a/spaces/pirahansiah/ComputerVision/src/test2.py b/spaces/pirahansiah/ComputerVision/src/test2.py
deleted file mode 100644
index aeabc5eb162bc18e1d9ccfbdcb7bafe34ec80c0f..0000000000000000000000000000000000000000
--- a/spaces/pirahansiah/ComputerVision/src/test2.py
+++ /dev/null
@@ -1 +0,0 @@
-# www.pirahansiah.com
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/android.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/android.py
deleted file mode 100644
index eda80935123cb5db7e18d7fb82fe5f71991d7af8..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/android.py
+++ /dev/null
@@ -1,120 +0,0 @@
-from __future__ import annotations
-
-import os
-import re
-import sys
-from functools import lru_cache
-from typing import cast
-
-from .api import PlatformDirsABC
-
-
-class Android(PlatformDirsABC):
-    """
-    Follows the guidance `from here <https://android.stackexchange.com/a/216132>`_. Makes use of the
-    `appname <platformdirs.api.PlatformDirsABC.appname>` and
-    `version <platformdirs.api.PlatformDirsABC.version>`.
-    """
-
-    @property
-    def user_data_dir(self) -> str:
-        """:return: data directory tied to the user, e.g. ``/data/user/<userid>/<packagename>/files/<AppName>``"""
-        return self._append_app_name_and_version(cast(str, _android_folder()), "files")
-
-    @property
-    def site_data_dir(self) -> str:
-        """:return: data directory shared by users, same as `user_data_dir`"""
-        return self.user_data_dir
-
-    @property
-    def user_config_dir(self) -> str:
-        """
-        :return: config directory tied to the user, e.g. ``/data/user/<userid>/<packagename>/shared_prefs/<AppName>``
-        """
-        return self._append_app_name_and_version(cast(str, _android_folder()), "shared_prefs")
-
-    @property
-    def site_config_dir(self) -> str:
-        """:return: config directory shared by the users, same as `user_config_dir`"""
-        return self.user_config_dir
-
-    @property
-    def user_cache_dir(self) -> str:
-        """:return: cache directory tied to the user, e.g. e.g. ``/data/user/<userid>/<packagename>/cache/<AppName>``"""
-        return self._append_app_name_and_version(cast(str, _android_folder()), "cache")
-
-    @property
-    def user_state_dir(self) -> str:
-        """:return: state directory tied to the user, same as `user_data_dir`"""
-        return self.user_data_dir
-
-    @property
-    def user_log_dir(self) -> str:
-        """
-        :return: log directory tied to the user, same as `user_cache_dir` if not opinionated else ``log`` in it,
-          e.g. ``/data/user/<userid>/<packagename>/cache/<AppName>/log``
-        """
-        path = self.user_cache_dir
-        if self.opinion:
-            path = os.path.join(path, "log")
-        return path
-
-    @property
-    def user_documents_dir(self) -> str:
-        """
-        :return: documents directory tied to the user e.g. ``/storage/emulated/0/Documents``
-        """
-        return _android_documents_folder()
-
-    @property
-    def user_runtime_dir(self) -> str:
-        """
-        :return: runtime directory tied to the user, same as `user_cache_dir` if not opinionated else ``tmp`` in it,
-          e.g. ``/data/user/<userid>/<packagename>/cache/<AppName>/tmp``
-        """
-        path = self.user_cache_dir
-        if self.opinion:
-            path = os.path.join(path, "tmp")
-        return path
-
-
-@lru_cache(maxsize=1)
-def _android_folder() -> str | None:
-    """:return: base folder for the Android OS or None if cannot be found"""
-    try:
-        # First try to get path to android app via pyjnius
-        from jnius import autoclass
-
-        Context = autoclass("android.content.Context")  # noqa: N806
-        result: str | None = Context.getFilesDir().getParentFile().getAbsolutePath()
-    except Exception:
-        # if fails find an android folder looking path on the sys.path
-        pattern = re.compile(r"/data/(data|user/\d+)/(.+)/files")
-        for path in sys.path:
-            if pattern.match(path):
-                result = path.split("/files")[0]
-                break
-        else:
-            result = None
-    return result
-
-
-@lru_cache(maxsize=1)
-def _android_documents_folder() -> str:
-    """:return: documents folder for the Android OS"""
-    # Get directories with pyjnius
-    try:
-        from jnius import autoclass
-
-        Context = autoclass("android.content.Context")  # noqa: N806
-        Environment = autoclass("android.os.Environment")  # noqa: N806
-        documents_dir: str = Context.getExternalFilesDir(Environment.DIRECTORY_DOCUMENTS).getAbsolutePath()
-    except Exception:
-        documents_dir = "/storage/emulated/0/Documents"
-
-    return documents_dir
-
-
-__all__ = [
-    "Android",
-]
diff --git a/spaces/portal/Top-20/back_gab.html b/spaces/portal/Top-20/back_gab.html
deleted file mode 100644
index b31cceca9d8f301bab16bbdf7d088dd1f9117b52..0000000000000000000000000000000000000000
--- a/spaces/portal/Top-20/back_gab.html
+++ /dev/null
@@ -1,33 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
- 
-<style>
-  .container {
-  position: relative;
-  overflow: hidden;
-  width: 100%;
-  padding-top: 56%; /* 16:9 Aspect Ratio (divide 9 by 16 = 0.5625) */
-}
-  .responsive-iframe {
-  position: absolute;
-  top: 0;
-  left: 0;
-  bottom: 0;
-  right: 0;
-  width: 100%;
-  height: 100%;
-}
-    </style>
-  </head>
-  <body>
-<div class="container">
-<iframe
-  class="responsive-iframe"
-	src="https://omnibus-top-20-diffusion-g.hf.space"
-	frameborder="0"
-></iframe>
-</div>
-
-  </body>
-</html>
\ No newline at end of file
diff --git a/spaces/pragnakalp/biobert_based_ner/README.md b/spaces/pragnakalp/biobert_based_ner/README.md
deleted file mode 100644
index afea3e5ee7b0b294fc0b9cae93b8b3947670ec95..0000000000000000000000000000000000000000
--- a/spaces/pragnakalp/biobert_based_ner/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Biobert Based Ner
-emoji: 🦀
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.44.1
-app_file: app.py
-pinned: false
----
-
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/inference/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/inference/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/style/test_to_latex.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/style/test_to_latex.py
deleted file mode 100644
index 7f5b6b305f12dd504a477ac52c24b88eecf8e139..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/style/test_to_latex.py
+++ /dev/null
@@ -1,1087 +0,0 @@
-from textwrap import dedent
-
-import numpy as np
-import pytest
-
-from pandas import (
-    DataFrame,
-    MultiIndex,
-    option_context,
-)
-
-pytest.importorskip("jinja2")
-from pandas.io.formats.style import Styler
-from pandas.io.formats.style_render import (
-    _parse_latex_cell_styles,
-    _parse_latex_css_conversion,
-    _parse_latex_header_span,
-    _parse_latex_table_styles,
-    _parse_latex_table_wrapping,
-)
-
-
-@pytest.fixture
-def df():
-    return DataFrame({"A": [0, 1], "B": [-0.61, -1.22], "C": ["ab", "cd"]})
-
-
-@pytest.fixture
-def df_ext():
-    return DataFrame(
-        {"A": [0, 1, 2], "B": [-0.61, -1.22, -2.22], "C": ["ab", "cd", "de"]}
-    )
-
-
-@pytest.fixture
-def styler(df):
-    return Styler(df, uuid_len=0, precision=2)
-
-
-def test_minimal_latex_tabular(styler):
-    expected = dedent(
-        """\
-        \\begin{tabular}{lrrl}
-         & A & B & C \\\\
-        0 & 0 & -0.61 & ab \\\\
-        1 & 1 & -1.22 & cd \\\\
-        \\end{tabular}
-        """
-    )
-    assert styler.to_latex() == expected
-
-
-def test_tabular_hrules(styler):
-    expected = dedent(
-        """\
-        \\begin{tabular}{lrrl}
-        \\toprule
-         & A & B & C \\\\
-        \\midrule
-        0 & 0 & -0.61 & ab \\\\
-        1 & 1 & -1.22 & cd \\\\
-        \\bottomrule
-        \\end{tabular}
-        """
-    )
-    assert styler.to_latex(hrules=True) == expected
-
-
-def test_tabular_custom_hrules(styler):
-    styler.set_table_styles(
-        [
-            {"selector": "toprule", "props": ":hline"},
-            {"selector": "bottomrule", "props": ":otherline"},
-        ]
-    )  # no midrule
-    expected = dedent(
-        """\
-        \\begin{tabular}{lrrl}
-        \\hline
-         & A & B & C \\\\
-        0 & 0 & -0.61 & ab \\\\
-        1 & 1 & -1.22 & cd \\\\
-        \\otherline
-        \\end{tabular}
-        """
-    )
-    assert styler.to_latex() == expected
-
-
-def test_column_format(styler):
-    # default setting is already tested in `test_latex_minimal_tabular`
-    styler.set_table_styles([{"selector": "column_format", "props": ":cccc"}])
-
-    assert "\\begin{tabular}{rrrr}" in styler.to_latex(column_format="rrrr")
-    styler.set_table_styles([{"selector": "column_format", "props": ":r|r|cc"}])
-    assert "\\begin{tabular}{r|r|cc}" in styler.to_latex()
-
-
-def test_siunitx_cols(styler):
-    expected = dedent(
-        """\
-        \\begin{tabular}{lSSl}
-        {} & {A} & {B} & {C} \\\\
-        0 & 0 & -0.61 & ab \\\\
-        1 & 1 & -1.22 & cd \\\\
-        \\end{tabular}
-        """
-    )
-    assert styler.to_latex(siunitx=True) == expected
-
-
-def test_position(styler):
-    assert "\\begin{table}[h!]" in styler.to_latex(position="h!")
-    assert "\\end{table}" in styler.to_latex(position="h!")
-    styler.set_table_styles([{"selector": "position", "props": ":b!"}])
-    assert "\\begin{table}[b!]" in styler.to_latex()
-    assert "\\end{table}" in styler.to_latex()
-
-
-@pytest.mark.parametrize("env", [None, "longtable"])
-def test_label(styler, env):
-    assert "\n\\label{text}" in styler.to_latex(label="text", environment=env)
-    styler.set_table_styles([{"selector": "label", "props": ":{more §text}"}])
-    assert "\n\\label{more :text}" in styler.to_latex(environment=env)
-
-
-def test_position_float_raises(styler):
-    msg = "`position_float` should be one of 'raggedright', 'raggedleft', 'centering',"
-    with pytest.raises(ValueError, match=msg):
-        styler.to_latex(position_float="bad_string")
-
-    msg = "`position_float` cannot be used in 'longtable' `environment`"
-    with pytest.raises(ValueError, match=msg):
-        styler.to_latex(position_float="centering", environment="longtable")
-
-
-@pytest.mark.parametrize("label", [(None, ""), ("text", "\\label{text}")])
-@pytest.mark.parametrize("position", [(None, ""), ("h!", "{table}[h!]")])
-@pytest.mark.parametrize("caption", [(None, ""), ("text", "\\caption{text}")])
-@pytest.mark.parametrize("column_format", [(None, ""), ("rcrl", "{tabular}{rcrl}")])
-@pytest.mark.parametrize("position_float", [(None, ""), ("centering", "\\centering")])
-def test_kwargs_combinations(
-    styler, label, position, caption, column_format, position_float
-):
-    result = styler.to_latex(
-        label=label[0],
-        position=position[0],
-        caption=caption[0],
-        column_format=column_format[0],
-        position_float=position_float[0],
-    )
-    assert label[1] in result
-    assert position[1] in result
-    assert caption[1] in result
-    assert column_format[1] in result
-    assert position_float[1] in result
-
-
-def test_custom_table_styles(styler):
-    styler.set_table_styles(
-        [
-            {"selector": "mycommand", "props": ":{myoptions}"},
-            {"selector": "mycommand2", "props": ":{myoptions2}"},
-        ]
-    )
-    expected = dedent(
-        """\
-        \\begin{table}
-        \\mycommand{myoptions}
-        \\mycommand2{myoptions2}
-        """
-    )
-    assert expected in styler.to_latex()
-
-
-def test_cell_styling(styler):
-    styler.highlight_max(props="itshape:;Huge:--wrap;")
-    expected = dedent(
-        """\
-        \\begin{tabular}{lrrl}
-         & A & B & C \\\\
-        0 & 0 & \\itshape {\\Huge -0.61} & ab \\\\
-        1 & \\itshape {\\Huge 1} & -1.22 & \\itshape {\\Huge cd} \\\\
-        \\end{tabular}
-        """
-    )
-    assert expected == styler.to_latex()
-
-
-def test_multiindex_columns(df):
-    cidx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")])
-    df.columns = cidx
-    expected = dedent(
-        """\
-        \\begin{tabular}{lrrl}
-         & \\multicolumn{2}{r}{A} & B \\\\
-         & a & b & c \\\\
-        0 & 0 & -0.61 & ab \\\\
-        1 & 1 & -1.22 & cd \\\\
-        \\end{tabular}
-        """
-    )
-    s = df.style.format(precision=2)
-    assert expected == s.to_latex()
-
-    # non-sparse
-    expected = dedent(
-        """\
-        \\begin{tabular}{lrrl}
-         & A & A & B \\\\
-         & a & b & c \\\\
-        0 & 0 & -0.61 & ab \\\\
-        1 & 1 & -1.22 & cd \\\\
-        \\end{tabular}
-        """
-    )
-    s = df.style.format(precision=2)
-    assert expected == s.to_latex(sparse_columns=False)
-
-
-def test_multiindex_row(df_ext):
-    ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")])
-    df_ext.index = ridx
-    expected = dedent(
-        """\
-        \\begin{tabular}{llrrl}
-         &  & A & B & C \\\\
-        \\multirow[c]{2}{*}{A} & a & 0 & -0.61 & ab \\\\
-         & b & 1 & -1.22 & cd \\\\
-        B & c & 2 & -2.22 & de \\\\
-        \\end{tabular}
-        """
-    )
-    styler = df_ext.style.format(precision=2)
-    result = styler.to_latex()
-    assert expected == result
-
-    # non-sparse
-    expected = dedent(
-        """\
-        \\begin{tabular}{llrrl}
-         &  & A & B & C \\\\
-        A & a & 0 & -0.61 & ab \\\\
-        A & b & 1 & -1.22 & cd \\\\
-        B & c & 2 & -2.22 & de \\\\
-        \\end{tabular}
-        """
-    )
-    result = styler.to_latex(sparse_index=False)
-    assert expected == result
-
-
-def test_multirow_naive(df_ext):
-    ridx = MultiIndex.from_tuples([("X", "x"), ("X", "y"), ("Y", "z")])
-    df_ext.index = ridx
-    expected = dedent(
-        """\
-        \\begin{tabular}{llrrl}
-         &  & A & B & C \\\\
-        X & x & 0 & -0.61 & ab \\\\
-         & y & 1 & -1.22 & cd \\\\
-        Y & z & 2 & -2.22 & de \\\\
-        \\end{tabular}
-        """
-    )
-    styler = df_ext.style.format(precision=2)
-    result = styler.to_latex(multirow_align="naive")
-    assert expected == result
-
-
-def test_multiindex_row_and_col(df_ext):
-    cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")])
-    ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")])
-    df_ext.index, df_ext.columns = ridx, cidx
-    expected = dedent(
-        """\
-        \\begin{tabular}{llrrl}
-         &  & \\multicolumn{2}{l}{Z} & Y \\\\
-         &  & a & b & c \\\\
-        \\multirow[b]{2}{*}{A} & a & 0 & -0.61 & ab \\\\
-         & b & 1 & -1.22 & cd \\\\
-        B & c & 2 & -2.22 & de \\\\
-        \\end{tabular}
-        """
-    )
-    styler = df_ext.style.format(precision=2)
-    result = styler.to_latex(multirow_align="b", multicol_align="l")
-    assert result == expected
-
-    # non-sparse
-    expected = dedent(
-        """\
-        \\begin{tabular}{llrrl}
-         &  & Z & Z & Y \\\\
-         &  & a & b & c \\\\
-        A & a & 0 & -0.61 & ab \\\\
-        A & b & 1 & -1.22 & cd \\\\
-        B & c & 2 & -2.22 & de \\\\
-        \\end{tabular}
-        """
-    )
-    result = styler.to_latex(sparse_index=False, sparse_columns=False)
-    assert result == expected
-
-
-@pytest.mark.parametrize(
-    "multicol_align, siunitx, header",
-    [
-        ("naive-l", False, " & A & &"),
-        ("naive-r", False, " & & & A"),
-        ("naive-l", True, "{} & {A} & {} & {}"),
-        ("naive-r", True, "{} & {} & {} & {A}"),
-    ],
-)
-def test_multicol_naive(df, multicol_align, siunitx, header):
-    ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("A", "c")])
-    df.columns = ridx
-    level1 = " & a & b & c" if not siunitx else "{} & {a} & {b} & {c}"
-    col_format = "lrrl" if not siunitx else "lSSl"
-    expected = dedent(
-        f"""\
-        \\begin{{tabular}}{{{col_format}}}
-        {header} \\\\
-        {level1} \\\\
-        0 & 0 & -0.61 & ab \\\\
-        1 & 1 & -1.22 & cd \\\\
-        \\end{{tabular}}
-        """
-    )
-    styler = df.style.format(precision=2)
-    result = styler.to_latex(multicol_align=multicol_align, siunitx=siunitx)
-    assert expected == result
-
-
-def test_multi_options(df_ext):
-    cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")])
-    ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")])
-    df_ext.index, df_ext.columns = ridx, cidx
-    styler = df_ext.style.format(precision=2)
-
-    expected = dedent(
-        """\
-     &  & \\multicolumn{2}{r}{Z} & Y \\\\
-     &  & a & b & c \\\\
-    \\multirow[c]{2}{*}{A} & a & 0 & -0.61 & ab \\\\
-    """
-    )
-    result = styler.to_latex()
-    assert expected in result
-
-    with option_context("styler.latex.multicol_align", "l"):
-        assert " &  & \\multicolumn{2}{l}{Z} & Y \\\\" in styler.to_latex()
-
-    with option_context("styler.latex.multirow_align", "b"):
-        assert "\\multirow[b]{2}{*}{A} & a & 0 & -0.61 & ab \\\\" in styler.to_latex()
-
-
-def test_multiindex_columns_hidden():
-    df = DataFrame([[1, 2, 3, 4]])
-    df.columns = MultiIndex.from_tuples([("A", 1), ("A", 2), ("A", 3), ("B", 1)])
-    s = df.style
-    assert "{tabular}{lrrrr}" in s.to_latex()
-    s.set_table_styles([])  # reset the position command
-    s.hide([("A", 2)], axis="columns")
-    assert "{tabular}{lrrr}" in s.to_latex()
-
-
-@pytest.mark.parametrize(
-    "option, value",
-    [
-        ("styler.sparse.index", True),
-        ("styler.sparse.index", False),
-        ("styler.sparse.columns", True),
-        ("styler.sparse.columns", False),
-    ],
-)
-def test_sparse_options(df_ext, option, value):
-    cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")])
-    ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")])
-    df_ext.index, df_ext.columns = ridx, cidx
-    styler = df_ext.style
-
-    latex1 = styler.to_latex()
-    with option_context(option, value):
-        latex2 = styler.to_latex()
-    assert (latex1 == latex2) is value
-
-
-def test_hidden_index(styler):
-    styler.hide(axis="index")
-    expected = dedent(
-        """\
-        \\begin{tabular}{rrl}
-        A & B & C \\\\
-        0 & -0.61 & ab \\\\
-        1 & -1.22 & cd \\\\
-        \\end{tabular}
-        """
-    )
-    assert styler.to_latex() == expected
-
-
-@pytest.mark.parametrize("environment", ["table", "figure*", None])
-def test_comprehensive(df_ext, environment):
-    # test as many low level features simultaneously as possible
-    cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")])
-    ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")])
-    df_ext.index, df_ext.columns = ridx, cidx
-    stlr = df_ext.style
-    stlr.set_caption("mycap")
-    stlr.set_table_styles(
-        [
-            {"selector": "label", "props": ":{fig§item}"},
-            {"selector": "position", "props": ":h!"},
-            {"selector": "position_float", "props": ":centering"},
-            {"selector": "column_format", "props": ":rlrlr"},
-            {"selector": "toprule", "props": ":toprule"},
-            {"selector": "midrule", "props": ":midrule"},
-            {"selector": "bottomrule", "props": ":bottomrule"},
-            {"selector": "rowcolors", "props": ":{3}{pink}{}"},  # custom command
-        ]
-    )
-    stlr.highlight_max(axis=0, props="textbf:--rwrap;cellcolor:[rgb]{1,1,0.6}--rwrap")
-    stlr.highlight_max(axis=None, props="Huge:--wrap;", subset=[("Z", "a"), ("Z", "b")])
-
-    expected = (
-        """\
-\\begin{table}[h!]
-\\centering
-\\caption{mycap}
-\\label{fig:item}
-\\rowcolors{3}{pink}{}
-\\begin{tabular}{rlrlr}
-\\toprule
- &  & \\multicolumn{2}{r}{Z} & Y \\\\
- &  & a & b & c \\\\
-\\midrule
-\\multirow[c]{2}{*}{A} & a & 0 & \\textbf{\\cellcolor[rgb]{1,1,0.6}{-0.61}} & ab \\\\
- & b & 1 & -1.22 & cd \\\\
-B & c & \\textbf{\\cellcolor[rgb]{1,1,0.6}{{\\Huge 2}}} & -2.22 & """
-        """\
-\\textbf{\\cellcolor[rgb]{1,1,0.6}{de}} \\\\
-\\bottomrule
-\\end{tabular}
-\\end{table}
-"""
-    ).replace("table", environment if environment else "table")
-    result = stlr.format(precision=2).to_latex(environment=environment)
-    assert result == expected
-
-
-def test_environment_option(styler):
-    with option_context("styler.latex.environment", "bar-env"):
-        assert "\\begin{bar-env}" in styler.to_latex()
-        assert "\\begin{foo-env}" in styler.to_latex(environment="foo-env")
-
-
-def test_parse_latex_table_styles(styler):
-    styler.set_table_styles(
-        [
-            {"selector": "foo", "props": [("attr", "value")]},
-            {"selector": "bar", "props": [("attr", "overwritten")]},
-            {"selector": "bar", "props": [("attr", "baz"), ("attr2", "ignored")]},
-            {"selector": "label", "props": [("", "{fig§item}")]},
-        ]
-    )
-    assert _parse_latex_table_styles(styler.table_styles, "bar") == "baz"
-
-    # test '§' replaced by ':' [for CSS compatibility]
-    assert _parse_latex_table_styles(styler.table_styles, "label") == "{fig:item}"
-
-
-def test_parse_latex_cell_styles_basic():  # test nesting
-    cell_style = [("itshape", "--rwrap"), ("cellcolor", "[rgb]{0,1,1}--rwrap")]
-    expected = "\\itshape{\\cellcolor[rgb]{0,1,1}{text}}"
-    assert _parse_latex_cell_styles(cell_style, "text") == expected
-
-
-@pytest.mark.parametrize(
-    "wrap_arg, expected",
-    [  # test wrapping
-        ("", "\\<command><options> <display_value>"),
-        ("--wrap", "{\\<command><options> <display_value>}"),
-        ("--nowrap", "\\<command><options> <display_value>"),
-        ("--lwrap", "{\\<command><options>} <display_value>"),
-        ("--dwrap", "{\\<command><options>}{<display_value>}"),
-        ("--rwrap", "\\<command><options>{<display_value>}"),
-    ],
-)
-def test_parse_latex_cell_styles_braces(wrap_arg, expected):
-    cell_style = [("<command>", f"<options>{wrap_arg}")]
-    assert _parse_latex_cell_styles(cell_style, "<display_value>") == expected
-
-
-def test_parse_latex_header_span():
-    cell = {"attributes": 'colspan="3"', "display_value": "text", "cellstyle": []}
-    expected = "\\multicolumn{3}{Y}{text}"
-    assert _parse_latex_header_span(cell, "X", "Y") == expected
-
-    cell = {"attributes": 'rowspan="5"', "display_value": "text", "cellstyle": []}
-    expected = "\\multirow[X]{5}{*}{text}"
-    assert _parse_latex_header_span(cell, "X", "Y") == expected
-
-    cell = {"display_value": "text", "cellstyle": []}
-    assert _parse_latex_header_span(cell, "X", "Y") == "text"
-
-    cell = {"display_value": "text", "cellstyle": [("bfseries", "--rwrap")]}
-    assert _parse_latex_header_span(cell, "X", "Y") == "\\bfseries{text}"
-
-
-def test_parse_latex_table_wrapping(styler):
-    styler.set_table_styles(
-        [
-            {"selector": "toprule", "props": ":value"},
-            {"selector": "bottomrule", "props": ":value"},
-            {"selector": "midrule", "props": ":value"},
-            {"selector": "column_format", "props": ":value"},
-        ]
-    )
-    assert _parse_latex_table_wrapping(styler.table_styles, styler.caption) is False
-    assert _parse_latex_table_wrapping(styler.table_styles, "some caption") is True
-    styler.set_table_styles(
-        [
-            {"selector": "not-ignored", "props": ":value"},
-        ],
-        overwrite=False,
-    )
-    assert _parse_latex_table_wrapping(styler.table_styles, None) is True
-
-
-def test_short_caption(styler):
-    result = styler.to_latex(caption=("full cap", "short cap"))
-    assert "\\caption[short cap]{full cap}" in result
-
-
-@pytest.mark.parametrize(
-    "css, expected",
-    [
-        ([("color", "red")], [("color", "{red}")]),  # test color and input format types
-        (
-            [("color", "rgb(128, 128, 128 )")],
-            [("color", "[rgb]{0.502, 0.502, 0.502}")],
-        ),
-        (
-            [("color", "rgb(128, 50%, 25% )")],
-            [("color", "[rgb]{0.502, 0.500, 0.250}")],
-        ),
-        (
-            [("color", "rgba(128,128,128,1)")],
-            [("color", "[rgb]{0.502, 0.502, 0.502}")],
-        ),
-        ([("color", "#FF00FF")], [("color", "[HTML]{FF00FF}")]),
-        ([("color", "#F0F")], [("color", "[HTML]{FF00FF}")]),
-        ([("font-weight", "bold")], [("bfseries", "")]),  # test font-weight and types
-        ([("font-weight", "bolder")], [("bfseries", "")]),
-        ([("font-weight", "normal")], []),
-        ([("background-color", "red")], [("cellcolor", "{red}--lwrap")]),
-        (
-            [("background-color", "#FF00FF")],  # test background-color command and wrap
-            [("cellcolor", "[HTML]{FF00FF}--lwrap")],
-        ),
-        ([("font-style", "italic")], [("itshape", "")]),  # test font-style and types
-        ([("font-style", "oblique")], [("slshape", "")]),
-        ([("font-style", "normal")], []),
-        ([("color", "red /*--dwrap*/")], [("color", "{red}--dwrap")]),  # css comments
-        ([("background-color", "red /* --dwrap */")], [("cellcolor", "{red}--dwrap")]),
-    ],
-)
-def test_parse_latex_css_conversion(css, expected):
-    result = _parse_latex_css_conversion(css)
-    assert result == expected
-
-
-@pytest.mark.parametrize(
-    "env, inner_env",
-    [
-        (None, "tabular"),
-        ("table", "tabular"),
-        ("longtable", "longtable"),
-    ],
-)
-@pytest.mark.parametrize(
-    "convert, exp", [(True, "bfseries"), (False, "font-weightbold")]
-)
-def test_parse_latex_css_convert_minimal(styler, env, inner_env, convert, exp):
-    # parameters ensure longtable template is also tested
-    styler.highlight_max(props="font-weight:bold;")
-    result = styler.to_latex(convert_css=convert, environment=env)
-    expected = dedent(
-        f"""\
-        0 & 0 & \\{exp} -0.61 & ab \\\\
-        1 & \\{exp} 1 & -1.22 & \\{exp} cd \\\\
-        \\end{{{inner_env}}}
-    """
-    )
-    assert expected in result
-
-
-def test_parse_latex_css_conversion_option():
-    css = [("command", "option--latex--wrap")]
-    expected = [("command", "option--wrap")]
-    result = _parse_latex_css_conversion(css)
-    assert result == expected
-
-
-def test_styler_object_after_render(styler):
-    # GH 42320
-    pre_render = styler._copy(deepcopy=True)
-    styler.to_latex(
-        column_format="rllr",
-        position="h",
-        position_float="centering",
-        hrules=True,
-        label="my lab",
-        caption="my cap",
-    )
-
-    assert pre_render.table_styles == styler.table_styles
-    assert pre_render.caption == styler.caption
-
-
-def test_longtable_comprehensive(styler):
-    result = styler.to_latex(
-        environment="longtable", hrules=True, label="fig:A", caption=("full", "short")
-    )
-    expected = dedent(
-        """\
-        \\begin{longtable}{lrrl}
-        \\caption[short]{full} \\label{fig:A} \\\\
-        \\toprule
-         & A & B & C \\\\
-        \\midrule
-        \\endfirsthead
-        \\caption[]{full} \\\\
-        \\toprule
-         & A & B & C \\\\
-        \\midrule
-        \\endhead
-        \\midrule
-        \\multicolumn{4}{r}{Continued on next page} \\\\
-        \\midrule
-        \\endfoot
-        \\bottomrule
-        \\endlastfoot
-        0 & 0 & -0.61 & ab \\\\
-        1 & 1 & -1.22 & cd \\\\
-        \\end{longtable}
-    """
-    )
-    assert result == expected
-
-
-def test_longtable_minimal(styler):
-    result = styler.to_latex(environment="longtable")
-    expected = dedent(
-        """\
-        \\begin{longtable}{lrrl}
-         & A & B & C \\\\
-        \\endfirsthead
-         & A & B & C \\\\
-        \\endhead
-        \\multicolumn{4}{r}{Continued on next page} \\\\
-        \\endfoot
-        \\endlastfoot
-        0 & 0 & -0.61 & ab \\\\
-        1 & 1 & -1.22 & cd \\\\
-        \\end{longtable}
-    """
-    )
-    assert result == expected
-
-
-@pytest.mark.parametrize(
-    "sparse, exp, siunitx",
-    [
-        (True, "{} & \\multicolumn{2}{r}{A} & {B}", True),
-        (False, "{} & {A} & {A} & {B}", True),
-        (True, " & \\multicolumn{2}{r}{A} & B", False),
-        (False, " & A & A & B", False),
-    ],
-)
-def test_longtable_multiindex_columns(df, sparse, exp, siunitx):
-    cidx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")])
-    df.columns = cidx
-    with_si = "{} & {a} & {b} & {c} \\\\"
-    without_si = " & a & b & c \\\\"
-    expected = dedent(
-        f"""\
-        \\begin{{longtable}}{{l{"SS" if siunitx else "rr"}l}}
-        {exp} \\\\
-        {with_si if siunitx else without_si}
-        \\endfirsthead
-        {exp} \\\\
-        {with_si if siunitx else without_si}
-        \\endhead
-        """
-    )
-    result = df.style.to_latex(
-        environment="longtable", sparse_columns=sparse, siunitx=siunitx
-    )
-    assert expected in result
-
-
-@pytest.mark.parametrize(
-    "caption, cap_exp",
-    [
-        ("full", ("{full}", "")),
-        (("full", "short"), ("{full}", "[short]")),
-    ],
-)
-@pytest.mark.parametrize("label, lab_exp", [(None, ""), ("tab:A", " \\label{tab:A}")])
-def test_longtable_caption_label(styler, caption, cap_exp, label, lab_exp):
-    cap_exp1 = f"\\caption{cap_exp[1]}{cap_exp[0]}"
-    cap_exp2 = f"\\caption[]{cap_exp[0]}"
-
-    expected = dedent(
-        f"""\
-        {cap_exp1}{lab_exp} \\\\
-         & A & B & C \\\\
-        \\endfirsthead
-        {cap_exp2} \\\\
-        """
-    )
-    assert expected in styler.to_latex(
-        environment="longtable", caption=caption, label=label
-    )
-
-
-@pytest.mark.parametrize("index", [True, False])
-@pytest.mark.parametrize(
-    "columns, siunitx",
-    [
-        (True, True),
-        (True, False),
-        (False, False),
-    ],
-)
-def test_apply_map_header_render_mi(df_ext, index, columns, siunitx):
-    cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")])
-    ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")])
-    df_ext.index, df_ext.columns = ridx, cidx
-    styler = df_ext.style
-
-    func = lambda v: "bfseries: --rwrap" if "A" in v or "Z" in v or "c" in v else None
-
-    if index:
-        styler.map_index(func, axis="index")
-    if columns:
-        styler.map_index(func, axis="columns")
-
-    result = styler.to_latex(siunitx=siunitx)
-
-    expected_index = dedent(
-        """\
-    \\multirow[c]{2}{*}{\\bfseries{A}} & a & 0 & -0.610000 & ab \\\\
-    \\bfseries{} & b & 1 & -1.220000 & cd \\\\
-    B & \\bfseries{c} & 2 & -2.220000 & de \\\\
-    """
-    )
-    assert (expected_index in result) is index
-
-    exp_cols_si = dedent(
-        """\
-    {} & {} & \\multicolumn{2}{r}{\\bfseries{Z}} & {Y} \\\\
-    {} & {} & {a} & {b} & {\\bfseries{c}} \\\\
-    """
-    )
-    exp_cols_no_si = """\
- &  & \\multicolumn{2}{r}{\\bfseries{Z}} & Y \\\\
- &  & a & b & \\bfseries{c} \\\\
-"""
-    assert ((exp_cols_si if siunitx else exp_cols_no_si) in result) is columns
-
-
-def test_repr_option(styler):
-    assert "<style" in styler._repr_html_()[:6]
-    assert styler._repr_latex_() is None
-    with option_context("styler.render.repr", "latex"):
-        assert "\\begin{tabular}" in styler._repr_latex_()[:15]
-        assert styler._repr_html_() is None
-
-
-@pytest.mark.parametrize("option", ["hrules"])
-def test_bool_options(styler, option):
-    with option_context(f"styler.latex.{option}", False):
-        latex_false = styler.to_latex()
-    with option_context(f"styler.latex.{option}", True):
-        latex_true = styler.to_latex()
-    assert latex_false != latex_true  # options are reactive under to_latex(*no_args)
-
-
-def test_siunitx_basic_headers(styler):
-    assert "{} & {A} & {B} & {C} \\\\" in styler.to_latex(siunitx=True)
-    assert " & A & B & C \\\\" in styler.to_latex()  # default siunitx=False
-
-
-@pytest.mark.parametrize("axis", ["index", "columns"])
-def test_css_convert_apply_index(styler, axis):
-    styler.map_index(lambda x: "font-weight: bold;", axis=axis)
-    for label in getattr(styler, axis):
-        assert f"\\bfseries {label}" in styler.to_latex(convert_css=True)
-
-
-def test_hide_index_latex(styler):
-    # GH 43637
-    styler.hide([0], axis=0)
-    result = styler.to_latex()
-    expected = dedent(
-        """\
-    \\begin{tabular}{lrrl}
-     & A & B & C \\\\
-    1 & 1 & -1.22 & cd \\\\
-    \\end{tabular}
-    """
-    )
-    assert expected == result
-
-
-def test_latex_hiding_index_columns_multiindex_alignment():
-    # gh 43644
-    midx = MultiIndex.from_product(
-        [["i0", "j0"], ["i1"], ["i2", "j2"]], names=["i-0", "i-1", "i-2"]
-    )
-    cidx = MultiIndex.from_product(
-        [["c0"], ["c1", "d1"], ["c2", "d2"]], names=["c-0", "c-1", "c-2"]
-    )
-    df = DataFrame(np.arange(16).reshape(4, 4), index=midx, columns=cidx)
-    styler = Styler(df, uuid_len=0)
-    styler.hide(level=1, axis=0).hide(level=0, axis=1)
-    styler.hide([("i0", "i1", "i2")], axis=0)
-    styler.hide([("c0", "c1", "c2")], axis=1)
-    styler.map(lambda x: "color:{red};" if x == 5 else "")
-    styler.map_index(lambda x: "color:{blue};" if "j" in x else "")
-    result = styler.to_latex()
-    expected = dedent(
-        """\
-        \\begin{tabular}{llrrr}
-         & c-1 & c1 & \\multicolumn{2}{r}{d1} \\\\
-         & c-2 & d2 & c2 & d2 \\\\
-        i-0 & i-2 &  &  &  \\\\
-        i0 & \\color{blue} j2 & \\color{red} 5 & 6 & 7 \\\\
-        \\multirow[c]{2}{*}{\\color{blue} j0} & i2 & 9 & 10 & 11 \\\\
-        \\color{blue}  & \\color{blue} j2 & 13 & 14 & 15 \\\\
-        \\end{tabular}
-        """
-    )
-    assert result == expected
-
-
-def test_rendered_links():
-    # note the majority of testing is done in test_html.py: test_rendered_links
-    # these test only the alternative latex format is functional
-    df = DataFrame(["text www.domain.com text"])
-    result = df.style.format(hyperlinks="latex").to_latex()
-    assert r"text \href{www.domain.com}{www.domain.com} text" in result
-
-
-def test_apply_index_hidden_levels():
-    # gh 45156
-    styler = DataFrame(
-        [[1]],
-        index=MultiIndex.from_tuples([(0, 1)], names=["l0", "l1"]),
-        columns=MultiIndex.from_tuples([(0, 1)], names=["c0", "c1"]),
-    ).style
-    styler.hide(level=1)
-    styler.map_index(lambda v: "color: red;", level=0, axis=1)
-    result = styler.to_latex(convert_css=True)
-    expected = dedent(
-        """\
-        \\begin{tabular}{lr}
-        c0 & \\color{red} 0 \\\\
-        c1 & 1 \\\\
-        l0 &  \\\\
-        0 & 1 \\\\
-        \\end{tabular}
-        """
-    )
-    assert result == expected
-
-
-@pytest.mark.parametrize("clines", ["bad", "index", "skip-last", "all", "data"])
-def test_clines_validation(clines, styler):
-    msg = f"`clines` value of {clines} is invalid."
-    with pytest.raises(ValueError, match=msg):
-        styler.to_latex(clines=clines)
-
-
-@pytest.mark.parametrize(
-    "clines, exp",
-    [
-        ("all;index", "\n\\cline{1-1}"),
-        ("all;data", "\n\\cline{1-2}"),
-        ("skip-last;index", ""),
-        ("skip-last;data", ""),
-        (None, ""),
-    ],
-)
-@pytest.mark.parametrize("env", ["table", "longtable"])
-def test_clines_index(clines, exp, env):
-    df = DataFrame([[1], [2], [3], [4]])
-    result = df.style.to_latex(clines=clines, environment=env)
-    expected = f"""\
-0 & 1 \\\\{exp}
-1 & 2 \\\\{exp}
-2 & 3 \\\\{exp}
-3 & 4 \\\\{exp}
-"""
-    assert expected in result
-
-
-@pytest.mark.parametrize(
-    "clines, expected",
-    [
-        (
-            None,
-            dedent(
-                """\
-            \\multirow[c]{2}{*}{A} & X & 1 \\\\
-             & Y & 2 \\\\
-            \\multirow[c]{2}{*}{B} & X & 3 \\\\
-             & Y & 4 \\\\
-            """
-            ),
-        ),
-        (
-            "skip-last;index",
-            dedent(
-                """\
-            \\multirow[c]{2}{*}{A} & X & 1 \\\\
-             & Y & 2 \\\\
-            \\cline{1-2}
-            \\multirow[c]{2}{*}{B} & X & 3 \\\\
-             & Y & 4 \\\\
-            \\cline{1-2}
-            """
-            ),
-        ),
-        (
-            "skip-last;data",
-            dedent(
-                """\
-            \\multirow[c]{2}{*}{A} & X & 1 \\\\
-             & Y & 2 \\\\
-            \\cline{1-3}
-            \\multirow[c]{2}{*}{B} & X & 3 \\\\
-             & Y & 4 \\\\
-            \\cline{1-3}
-            """
-            ),
-        ),
-        (
-            "all;index",
-            dedent(
-                """\
-            \\multirow[c]{2}{*}{A} & X & 1 \\\\
-            \\cline{2-2}
-             & Y & 2 \\\\
-            \\cline{1-2} \\cline{2-2}
-            \\multirow[c]{2}{*}{B} & X & 3 \\\\
-            \\cline{2-2}
-             & Y & 4 \\\\
-            \\cline{1-2} \\cline{2-2}
-            """
-            ),
-        ),
-        (
-            "all;data",
-            dedent(
-                """\
-            \\multirow[c]{2}{*}{A} & X & 1 \\\\
-            \\cline{2-3}
-             & Y & 2 \\\\
-            \\cline{1-3} \\cline{2-3}
-            \\multirow[c]{2}{*}{B} & X & 3 \\\\
-            \\cline{2-3}
-             & Y & 4 \\\\
-            \\cline{1-3} \\cline{2-3}
-            """
-            ),
-        ),
-    ],
-)
-@pytest.mark.parametrize("env", ["table"])
-def test_clines_multiindex(clines, expected, env):
-    # also tests simultaneously with hidden rows and a hidden multiindex level
-    midx = MultiIndex.from_product([["A", "-", "B"], [0], ["X", "Y"]])
-    df = DataFrame([[1], [2], [99], [99], [3], [4]], index=midx)
-    styler = df.style
-    styler.hide([("-", 0, "X"), ("-", 0, "Y")])
-    styler.hide(level=1)
-    result = styler.to_latex(clines=clines, environment=env)
-    assert expected in result
-
-
-def test_col_format_len(styler):
-    # gh 46037
-    result = styler.to_latex(environment="longtable", column_format="lrr{10cm}")
-    expected = r"\multicolumn{4}{r}{Continued on next page} \\"
-    assert expected in result
-
-
-def test_concat(styler):
-    result = styler.concat(styler.data.agg(["sum"]).style).to_latex()
-    expected = dedent(
-        """\
-    \\begin{tabular}{lrrl}
-     & A & B & C \\\\
-    0 & 0 & -0.61 & ab \\\\
-    1 & 1 & -1.22 & cd \\\\
-    sum & 1 & -1.830000 & abcd \\\\
-    \\end{tabular}
-    """
-    )
-    assert result == expected
-
-
-def test_concat_recursion():
-    # tests hidden row recursion and applied styles
-    styler1 = DataFrame([[1], [9]]).style.hide([1]).highlight_min(color="red")
-    styler2 = DataFrame([[9], [2]]).style.hide([0]).highlight_min(color="green")
-    styler3 = DataFrame([[3], [9]]).style.hide([1]).highlight_min(color="blue")
-
-    result = styler1.concat(styler2.concat(styler3)).to_latex(convert_css=True)
-    expected = dedent(
-        """\
-    \\begin{tabular}{lr}
-     & 0 \\\\
-    0 & {\\cellcolor{red}} 1 \\\\
-    1 & {\\cellcolor{green}} 2 \\\\
-    0 & {\\cellcolor{blue}} 3 \\\\
-    \\end{tabular}
-    """
-    )
-    assert result == expected
-
-
-def test_concat_chain():
-    # tests hidden row recursion and applied styles
-    styler1 = DataFrame([[1], [9]]).style.hide([1]).highlight_min(color="red")
-    styler2 = DataFrame([[9], [2]]).style.hide([0]).highlight_min(color="green")
-    styler3 = DataFrame([[3], [9]]).style.hide([1]).highlight_min(color="blue")
-
-    result = styler1.concat(styler2).concat(styler3).to_latex(convert_css=True)
-    expected = dedent(
-        """\
-    \\begin{tabular}{lr}
-     & 0 \\\\
-    0 & {\\cellcolor{red}} 1 \\\\
-    1 & {\\cellcolor{green}} 2 \\\\
-    0 & {\\cellcolor{blue}} 3 \\\\
-    \\end{tabular}
-    """
-    )
-    assert result == expected
-
-
-@pytest.mark.parametrize(
-    "df, expected",
-    [
-        (
-            DataFrame(),
-            dedent(
-                """\
-            \\begin{tabular}{l}
-            \\end{tabular}
-            """
-            ),
-        ),
-        (
-            DataFrame(columns=["a", "b", "c"]),
-            dedent(
-                """\
-            \\begin{tabular}{llll}
-             & a & b & c \\\\
-            \\end{tabular}
-            """
-            ),
-        ),
-    ],
-)
-@pytest.mark.parametrize(
-    "clines", [None, "all;data", "all;index", "skip-last;data", "skip-last;index"]
-)
-def test_empty_clines(df: DataFrame, expected: str, clines: str):
-    # GH 47203
-    result = df.style.to_latex(clines=clines)
-    assert result == expected
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/error_wrappers.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/error_wrappers.py
deleted file mode 100644
index 5144eeee41e12d208ff8a582aa57e7dc96c3a93e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/error_wrappers.py
+++ /dev/null
@@ -1,4 +0,0 @@
-"""The `error_wrappers` module is a backport module from V1."""
-from ._migration import getattr_migration
-
-__getattr__ = getattr_migration(__name__)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/v1/decorator.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/v1/decorator.py
deleted file mode 100644
index 089aab65f514820d0e381517e72f151b12979a0c..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/v1/decorator.py
+++ /dev/null
@@ -1,264 +0,0 @@
-from functools import wraps
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional, Tuple, Type, TypeVar, Union, overload
-
-from . import validator
-from .config import Extra
-from .errors import ConfigError
-from .main import BaseModel, create_model
-from .typing import get_all_type_hints
-from .utils import to_camel
-
-__all__ = ('validate_arguments',)
-
-if TYPE_CHECKING:
-    from .typing import AnyCallable
-
-    AnyCallableT = TypeVar('AnyCallableT', bound=AnyCallable)
-    ConfigType = Union[None, Type[Any], Dict[str, Any]]
-
-
-@overload
-def validate_arguments(func: None = None, *, config: 'ConfigType' = None) -> Callable[['AnyCallableT'], 'AnyCallableT']:
-    ...
-
-
-@overload
-def validate_arguments(func: 'AnyCallableT') -> 'AnyCallableT':
-    ...
-
-
-def validate_arguments(func: Optional['AnyCallableT'] = None, *, config: 'ConfigType' = None) -> Any:
-    """
-    Decorator to validate the arguments passed to a function.
-    """
-
-    def validate(_func: 'AnyCallable') -> 'AnyCallable':
-        vd = ValidatedFunction(_func, config)
-
-        @wraps(_func)
-        def wrapper_function(*args: Any, **kwargs: Any) -> Any:
-            return vd.call(*args, **kwargs)
-
-        wrapper_function.vd = vd  # type: ignore
-        wrapper_function.validate = vd.init_model_instance  # type: ignore
-        wrapper_function.raw_function = vd.raw_function  # type: ignore
-        wrapper_function.model = vd.model  # type: ignore
-        return wrapper_function
-
-    if func:
-        return validate(func)
-    else:
-        return validate
-
-
-ALT_V_ARGS = 'v__args'
-ALT_V_KWARGS = 'v__kwargs'
-V_POSITIONAL_ONLY_NAME = 'v__positional_only'
-V_DUPLICATE_KWARGS = 'v__duplicate_kwargs'
-
-
-class ValidatedFunction:
-    def __init__(self, function: 'AnyCallableT', config: 'ConfigType'):  # noqa C901
-        from inspect import Parameter, signature
-
-        parameters: Mapping[str, Parameter] = signature(function).parameters
-
-        if parameters.keys() & {ALT_V_ARGS, ALT_V_KWARGS, V_POSITIONAL_ONLY_NAME, V_DUPLICATE_KWARGS}:
-            raise ConfigError(
-                f'"{ALT_V_ARGS}", "{ALT_V_KWARGS}", "{V_POSITIONAL_ONLY_NAME}" and "{V_DUPLICATE_KWARGS}" '
-                f'are not permitted as argument names when using the "{validate_arguments.__name__}" decorator'
-            )
-
-        self.raw_function = function
-        self.arg_mapping: Dict[int, str] = {}
-        self.positional_only_args = set()
-        self.v_args_name = 'args'
-        self.v_kwargs_name = 'kwargs'
-
-        type_hints = get_all_type_hints(function)
-        takes_args = False
-        takes_kwargs = False
-        fields: Dict[str, Tuple[Any, Any]] = {}
-        for i, (name, p) in enumerate(parameters.items()):
-            if p.annotation is p.empty:
-                annotation = Any
-            else:
-                annotation = type_hints[name]
-
-            default = ... if p.default is p.empty else p.default
-            if p.kind == Parameter.POSITIONAL_ONLY:
-                self.arg_mapping[i] = name
-                fields[name] = annotation, default
-                fields[V_POSITIONAL_ONLY_NAME] = List[str], None
-                self.positional_only_args.add(name)
-            elif p.kind == Parameter.POSITIONAL_OR_KEYWORD:
-                self.arg_mapping[i] = name
-                fields[name] = annotation, default
-                fields[V_DUPLICATE_KWARGS] = List[str], None
-            elif p.kind == Parameter.KEYWORD_ONLY:
-                fields[name] = annotation, default
-            elif p.kind == Parameter.VAR_POSITIONAL:
-                self.v_args_name = name
-                fields[name] = Tuple[annotation, ...], None
-                takes_args = True
-            else:
-                assert p.kind == Parameter.VAR_KEYWORD, p.kind
-                self.v_kwargs_name = name
-                fields[name] = Dict[str, annotation], None  # type: ignore
-                takes_kwargs = True
-
-        # these checks avoid a clash between "args" and a field with that name
-        if not takes_args and self.v_args_name in fields:
-            self.v_args_name = ALT_V_ARGS
-
-        # same with "kwargs"
-        if not takes_kwargs and self.v_kwargs_name in fields:
-            self.v_kwargs_name = ALT_V_KWARGS
-
-        if not takes_args:
-            # we add the field so validation below can raise the correct exception
-            fields[self.v_args_name] = List[Any], None
-
-        if not takes_kwargs:
-            # same with kwargs
-            fields[self.v_kwargs_name] = Dict[Any, Any], None
-
-        self.create_model(fields, takes_args, takes_kwargs, config)
-
-    def init_model_instance(self, *args: Any, **kwargs: Any) -> BaseModel:
-        values = self.build_values(args, kwargs)
-        return self.model(**values)
-
-    def call(self, *args: Any, **kwargs: Any) -> Any:
-        m = self.init_model_instance(*args, **kwargs)
-        return self.execute(m)
-
-    def build_values(self, args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> Dict[str, Any]:
-        values: Dict[str, Any] = {}
-        if args:
-            arg_iter = enumerate(args)
-            while True:
-                try:
-                    i, a = next(arg_iter)
-                except StopIteration:
-                    break
-                arg_name = self.arg_mapping.get(i)
-                if arg_name is not None:
-                    values[arg_name] = a
-                else:
-                    values[self.v_args_name] = [a] + [a for _, a in arg_iter]
-                    break
-
-        var_kwargs: Dict[str, Any] = {}
-        wrong_positional_args = []
-        duplicate_kwargs = []
-        fields_alias = [
-            field.alias
-            for name, field in self.model.__fields__.items()
-            if name not in (self.v_args_name, self.v_kwargs_name)
-        ]
-        non_var_fields = set(self.model.__fields__) - {self.v_args_name, self.v_kwargs_name}
-        for k, v in kwargs.items():
-            if k in non_var_fields or k in fields_alias:
-                if k in self.positional_only_args:
-                    wrong_positional_args.append(k)
-                if k in values:
-                    duplicate_kwargs.append(k)
-                values[k] = v
-            else:
-                var_kwargs[k] = v
-
-        if var_kwargs:
-            values[self.v_kwargs_name] = var_kwargs
-        if wrong_positional_args:
-            values[V_POSITIONAL_ONLY_NAME] = wrong_positional_args
-        if duplicate_kwargs:
-            values[V_DUPLICATE_KWARGS] = duplicate_kwargs
-        return values
-
-    def execute(self, m: BaseModel) -> Any:
-        d = {k: v for k, v in m._iter() if k in m.__fields_set__ or m.__fields__[k].default_factory}
-        var_kwargs = d.pop(self.v_kwargs_name, {})
-
-        if self.v_args_name in d:
-            args_: List[Any] = []
-            in_kwargs = False
-            kwargs = {}
-            for name, value in d.items():
-                if in_kwargs:
-                    kwargs[name] = value
-                elif name == self.v_args_name:
-                    args_ += value
-                    in_kwargs = True
-                else:
-                    args_.append(value)
-            return self.raw_function(*args_, **kwargs, **var_kwargs)
-        elif self.positional_only_args:
-            args_ = []
-            kwargs = {}
-            for name, value in d.items():
-                if name in self.positional_only_args:
-                    args_.append(value)
-                else:
-                    kwargs[name] = value
-            return self.raw_function(*args_, **kwargs, **var_kwargs)
-        else:
-            return self.raw_function(**d, **var_kwargs)
-
-    def create_model(self, fields: Dict[str, Any], takes_args: bool, takes_kwargs: bool, config: 'ConfigType') -> None:
-        pos_args = len(self.arg_mapping)
-
-        class CustomConfig:
-            pass
-
-        if not TYPE_CHECKING:  # pragma: no branch
-            if isinstance(config, dict):
-                CustomConfig = type('Config', (), config)  # noqa: F811
-            elif config is not None:
-                CustomConfig = config  # noqa: F811
-
-        if hasattr(CustomConfig, 'fields') or hasattr(CustomConfig, 'alias_generator'):
-            raise ConfigError(
-                'Setting the "fields" and "alias_generator" property on custom Config for '
-                '@validate_arguments is not yet supported, please remove.'
-            )
-
-        class DecoratorBaseModel(BaseModel):
-            @validator(self.v_args_name, check_fields=False, allow_reuse=True)
-            def check_args(cls, v: Optional[List[Any]]) -> Optional[List[Any]]:
-                if takes_args or v is None:
-                    return v
-
-                raise TypeError(f'{pos_args} positional arguments expected but {pos_args + len(v)} given')
-
-            @validator(self.v_kwargs_name, check_fields=False, allow_reuse=True)
-            def check_kwargs(cls, v: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
-                if takes_kwargs or v is None:
-                    return v
-
-                plural = '' if len(v) == 1 else 's'
-                keys = ', '.join(map(repr, v.keys()))
-                raise TypeError(f'unexpected keyword argument{plural}: {keys}')
-
-            @validator(V_POSITIONAL_ONLY_NAME, check_fields=False, allow_reuse=True)
-            def check_positional_only(cls, v: Optional[List[str]]) -> None:
-                if v is None:
-                    return
-
-                plural = '' if len(v) == 1 else 's'
-                keys = ', '.join(map(repr, v))
-                raise TypeError(f'positional-only argument{plural} passed as keyword argument{plural}: {keys}')
-
-            @validator(V_DUPLICATE_KWARGS, check_fields=False, allow_reuse=True)
-            def check_duplicate_kwargs(cls, v: Optional[List[str]]) -> None:
-                if v is None:
-                    return
-
-                plural = '' if len(v) == 1 else 's'
-                keys = ', '.join(map(repr, v))
-                raise TypeError(f'multiple values for argument{plural}: {keys}')
-
-            class Config(CustomConfig):
-                extra = getattr(CustomConfig, 'extra', Extra.forbid)
-
-        self.model = create_model(to_camel(self.raw_function.__name__), __base__=DecoratorBaseModel, **fields)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/csound.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/csound.py
deleted file mode 100644
index 64f03cff35a7ccdb7c24446607fca19689a1f3fa..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/csound.py
+++ /dev/null
@@ -1,468 +0,0 @@
-"""
-    pygments.lexers.csound
-    ~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexers for Csound languages.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-from pygments.lexer import RegexLexer, bygroups, default, include, using, words
-from pygments.token import Comment, Error, Keyword, Name, Number, Operator, Punctuation, \
-    String, Text, Whitespace
-from pygments.lexers._csound_builtins import OPCODES, DEPRECATED_OPCODES, REMOVED_OPCODES
-from pygments.lexers.html import HtmlLexer
-from pygments.lexers.python import PythonLexer
-from pygments.lexers.scripting import LuaLexer
-
-__all__ = ['CsoundScoreLexer', 'CsoundOrchestraLexer', 'CsoundDocumentLexer']
-
-newline = (r'((?:(?:;|//).*)*)(\n)', bygroups(Comment.Single, Text))
-
-
-class CsoundLexer(RegexLexer):
-    url = 'https://csound.com/'
-
-    tokens = {
-        'whitespace': [
-            (r'[ \t]+', Whitespace),
-            (r'/[*](?:.|\n)*?[*]/', Comment.Multiline),
-            (r'(?:;|//).*$', Comment.Single),
-            (r'(\\)(\n)', bygroups(Text, Whitespace))
-        ],
-
-        'preprocessor directives': [
-            (r'#(?:e(?:nd(?:if)?|lse)\b|##)|@@?[ \t]*\d+', Comment.Preproc),
-            (r'#includestr', Comment.Preproc, 'includestr directive'),
-            (r'#include', Comment.Preproc, 'include directive'),
-            (r'#[ \t]*define', Comment.Preproc, 'define directive'),
-            (r'#(?:ifn?def|undef)\b', Comment.Preproc, 'macro directive')
-        ],
-
-        'include directive': [
-            include('whitespace'),
-            (r'([^ \t]).*?\1', String, '#pop')
-        ],
-        'includestr directive': [
-            include('whitespace'),
-            (r'"', String, ('#pop', 'quoted string'))
-        ],
-
-        'define directive': [
-            (r'\n', Whitespace),
-            include('whitespace'),
-            (r'([A-Z_a-z]\w*)(\()', bygroups(Comment.Preproc, Punctuation),
-             ('#pop', 'macro parameter name list')),
-            (r'[A-Z_a-z]\w*', Comment.Preproc, ('#pop', 'before macro body'))
-        ],
-        'macro parameter name list': [
-            include('whitespace'),
-            (r'[A-Z_a-z]\w*', Comment.Preproc),
-            (r"['#]", Punctuation),
-            (r'\)', Punctuation, ('#pop', 'before macro body'))
-        ],
-        'before macro body': [
-            (r'\n', Whitespace),
-            include('whitespace'),
-            (r'#', Punctuation, ('#pop', 'macro body'))
-        ],
-        'macro body': [
-            (r'(?:\\(?!#)|[^#\\]|\n)+', Comment.Preproc),
-            (r'\\#', Comment.Preproc),
-            (r'(?<!\\)#', Punctuation, '#pop')
-        ],
-
-        'macro directive': [
-            include('whitespace'),
-            (r'[A-Z_a-z]\w*', Comment.Preproc, '#pop')
-        ],
-
-        'macro uses': [
-            (r'(\$[A-Z_a-z]\w*\.?)(\()', bygroups(Comment.Preproc, Punctuation),
-             'macro parameter value list'),
-            (r'\$[A-Z_a-z]\w*(?:\.|\b)', Comment.Preproc)
-        ],
-        'macro parameter value list': [
-            (r'(?:[^\'#"{()]|\{(?!\{))+', Comment.Preproc),
-            (r"['#]", Punctuation),
-            (r'"', String, 'macro parameter value quoted string'),
-            (r'\{\{', String, 'macro parameter value braced string'),
-            (r'\(', Comment.Preproc, 'macro parameter value parenthetical'),
-            (r'\)', Punctuation, '#pop')
-        ],
-        'macro parameter value quoted string': [
-            (r"\\[#'()]", Comment.Preproc),
-            (r"[#'()]", Error),
-            include('quoted string')
-        ],
-        'macro parameter value braced string': [
-            (r"\\[#'()]", Comment.Preproc),
-            (r"[#'()]", Error),
-            include('braced string')
-        ],
-        'macro parameter value parenthetical': [
-            (r'(?:[^\\()]|\\\))+', Comment.Preproc),
-            (r'\(', Comment.Preproc, '#push'),
-            (r'\)', Comment.Preproc, '#pop')
-        ],
-
-        'whitespace and macro uses': [
-            include('whitespace'),
-            include('macro uses')
-        ],
-
-        'numbers': [
-            (r'\d+[Ee][+-]?\d+|(\d+\.\d*|\d*\.\d+)([Ee][+-]?\d+)?', Number.Float),
-            (r'(0[Xx])([0-9A-Fa-f]+)', bygroups(Keyword.Type, Number.Hex)),
-            (r'\d+', Number.Integer)
-        ],
-
-        'quoted string': [
-            (r'"', String, '#pop'),
-            (r'[^"$]+', String),
-            include('macro uses'),
-            (r'[$]', String)
-        ],
-
-        'braced string': [
-            # Do nothing. This must be defined in subclasses.
-        ]
-    }
-
-
-class CsoundScoreLexer(CsoundLexer):
-    """
-    For `Csound <https://csound.com>`_ scores.
-
-    .. versionadded:: 2.1
-    """
-
-    name = 'Csound Score'
-    aliases = ['csound-score', 'csound-sco']
-    filenames = ['*.sco']
-
-    tokens = {
-        'root': [
-            (r'\n', Whitespace),
-            include('whitespace and macro uses'),
-            include('preprocessor directives'),
-
-            (r'[aBbCdefiqstvxy]', Keyword),
-            # There is also a w statement that is generated internally and should not be
-            # used; see https://github.com/csound/csound/issues/750.
-
-            (r'z', Keyword.Constant),
-            # z is a constant equal to 800,000,000,000. 800 billion seconds is about
-            # 25,367.8 years. See also
-            # https://csound.com/docs/manual/ScoreTop.html and
-            # https://github.com/csound/csound/search?q=stof+path%3AEngine+filename%3Asread.c.
-
-            (r'([nNpP][pP])(\d+)', bygroups(Keyword, Number.Integer)),
-
-            (r'[mn]', Keyword, 'mark statement'),
-
-            include('numbers'),
-            (r'[!+\-*/^%&|<>#~.]', Operator),
-            (r'[()\[\]]', Punctuation),
-            (r'"', String, 'quoted string'),
-            (r'\{', Comment.Preproc, 'loop after left brace'),
-        ],
-
-        'mark statement': [
-            include('whitespace and macro uses'),
-            (r'[A-Z_a-z]\w*', Name.Label),
-            (r'\n', Whitespace, '#pop')
-        ],
-
-        'loop after left brace': [
-            include('whitespace and macro uses'),
-            (r'\d+', Number.Integer, ('#pop', 'loop after repeat count')),
-        ],
-        'loop after repeat count': [
-            include('whitespace and macro uses'),
-            (r'[A-Z_a-z]\w*', Comment.Preproc, ('#pop', 'loop'))
-        ],
-        'loop': [
-            (r'\}', Comment.Preproc, '#pop'),
-            include('root')
-        ],
-
-        # Braced strings are not allowed in Csound scores, but this is needed because the
-        # superclass includes it.
-        'braced string': [
-            (r'\}\}', String, '#pop'),
-            (r'[^}]|\}(?!\})', String)
-        ]
-    }
-
-
-class CsoundOrchestraLexer(CsoundLexer):
-    """
-    For `Csound <https://csound.com>`_ orchestras.
-
-    .. versionadded:: 2.1
-    """
-
-    name = 'Csound Orchestra'
-    aliases = ['csound', 'csound-orc']
-    filenames = ['*.orc', '*.udo']
-
-    user_defined_opcodes = set()
-
-    def opcode_name_callback(lexer, match):
-        opcode = match.group(0)
-        lexer.user_defined_opcodes.add(opcode)
-        yield match.start(), Name.Function, opcode
-
-    def name_callback(lexer, match):
-        type_annotation_token = Keyword.Type
-
-        name = match.group(1)
-        if name in OPCODES or name in DEPRECATED_OPCODES or name in REMOVED_OPCODES:
-            yield match.start(), Name.Builtin, name
-        elif name in lexer.user_defined_opcodes:
-            yield match.start(), Name.Function, name
-        else:
-            type_annotation_token = Name
-            name_match = re.search(r'^(g?[afikSw])(\w+)', name)
-            if name_match:
-                yield name_match.start(1), Keyword.Type, name_match.group(1)
-                yield name_match.start(2), Name, name_match.group(2)
-            else:
-                yield match.start(), Name, name
-
-        if match.group(2):
-            yield match.start(2), Punctuation, match.group(2)
-            yield match.start(3), type_annotation_token, match.group(3)
-
-    tokens = {
-        'root': [
-            (r'\n', Whitespace),
-
-            (r'^([ \t]*)(\w+)(:)([ \t]+|$)', bygroups(Whitespace, Name.Label, Punctuation, Whitespace)),
-
-            include('whitespace and macro uses'),
-            include('preprocessor directives'),
-
-            (r'\binstr\b', Keyword.Declaration, 'instrument numbers and identifiers'),
-            (r'\bopcode\b', Keyword.Declaration, 'after opcode keyword'),
-            (r'\b(?:end(?:in|op))\b', Keyword.Declaration),
-
-            include('partial statements')
-        ],
-
-        'partial statements': [
-            (r'\b(?:0dbfs|A4|k(?:r|smps)|nchnls(?:_i)?|sr)\b', Name.Variable.Global),
-
-            include('numbers'),
-
-            (r'\+=|-=|\*=|/=|<<|>>|<=|>=|==|!=|&&|\|\||[~¬]|[=!+\-*/^%&|<>#?:]', Operator),
-            (r'[(),\[\]]', Punctuation),
-
-            (r'"', String, 'quoted string'),
-            (r'\{\{', String, 'braced string'),
-
-            (words((
-                'do', 'else', 'elseif', 'endif', 'enduntil', 'fi', 'if', 'ithen', 'kthen',
-                'od', 'then', 'until', 'while',
-                ), prefix=r'\b', suffix=r'\b'), Keyword),
-            (words(('return', 'rireturn'), prefix=r'\b', suffix=r'\b'), Keyword.Pseudo),
-
-            (r'\b[ik]?goto\b', Keyword, 'goto label'),
-            (r'\b(r(?:einit|igoto)|tigoto)(\(|\b)', bygroups(Keyword.Pseudo, Punctuation),
-             'goto label'),
-            (r'\b(c(?:g|in?|k|nk?)goto)(\(|\b)', bygroups(Keyword.Pseudo, Punctuation),
-             ('goto label', 'goto argument')),
-            (r'\b(timout)(\(|\b)', bygroups(Keyword.Pseudo, Punctuation),
-             ('goto label', 'goto argument', 'goto argument')),
-            (r'\b(loop_[gl][et])(\(|\b)', bygroups(Keyword.Pseudo, Punctuation),
-             ('goto label', 'goto argument', 'goto argument', 'goto argument')),
-
-            (r'\bprintk?s\b', Name.Builtin, 'prints opcode'),
-            (r'\b(?:readscore|scoreline(?:_i)?)\b', Name.Builtin, 'Csound score opcode'),
-            (r'\bpyl?run[it]?\b', Name.Builtin, 'Python opcode'),
-            (r'\blua_(?:exec|opdef)\b', Name.Builtin, 'Lua opcode'),
-            (r'\bp\d+\b', Name.Variable.Instance),
-            (r'\b([A-Z_a-z]\w*)(?:(:)([A-Za-z]))?\b', name_callback)
-        ],
-
-        'instrument numbers and identifiers': [
-            include('whitespace and macro uses'),
-            (r'\d+|[A-Z_a-z]\w*', Name.Function),
-            (r'[+,]', Punctuation),
-            (r'\n', Whitespace, '#pop')
-        ],
-
-        'after opcode keyword': [
-            include('whitespace and macro uses'),
-            (r'[A-Z_a-z]\w*', opcode_name_callback, ('#pop', 'opcode type signatures')),
-            (r'\n', Whitespace, '#pop')
-        ],
-        'opcode type signatures': [
-            include('whitespace and macro uses'),
-
-            # https://github.com/csound/csound/search?q=XIDENT+path%3AEngine+filename%3Acsound_orc.lex
-            (r'0|[afijkKoOpPStV\[\]]+', Keyword.Type),
-
-            (r',', Punctuation),
-            (r'\n', Whitespace, '#pop')
-        ],
-
-        'quoted string': [
-            (r'"', String, '#pop'),
-            (r'[^\\"$%)]+', String),
-            include('macro uses'),
-            include('escape sequences'),
-            include('format specifiers'),
-            (r'[\\$%)]', String)
-        ],
-        'braced string': [
-            (r'\}\}', String, '#pop'),
-            (r'(?:[^\\%)}]|\}(?!\}))+', String),
-            include('escape sequences'),
-            include('format specifiers'),
-            (r'[\\%)]', String)
-        ],
-        'escape sequences': [
-            # https://github.com/csound/csound/search?q=unquote_string+path%3AEngine+filename%3Acsound_orc_compile.c
-            (r'\\(?:[\\abnrt"]|[0-7]{1,3})', String.Escape)
-        ],
-        # Format specifiers are highlighted in all strings, even though only
-        #   fprintks        https://csound.com/docs/manual/fprintks.html
-        #   fprints         https://csound.com/docs/manual/fprints.html
-        #   printf/printf_i https://csound.com/docs/manual/printf.html
-        #   printks         https://csound.com/docs/manual/printks.html
-        #   prints          https://csound.com/docs/manual/prints.html
-        #   sprintf         https://csound.com/docs/manual/sprintf.html
-        #   sprintfk        https://csound.com/docs/manual/sprintfk.html
-        # work with strings that contain format specifiers. In addition, these opcodes’
-        # handling of format specifiers is inconsistent:
-        #   - fprintks and fprints accept %a and %A specifiers, and accept %s specifiers
-        #     starting in Csound 6.15.0.
-        #   - printks and prints accept %a and %A specifiers, but don’t accept %s
-        #     specifiers.
-        #   - printf, printf_i, sprintf, and sprintfk don’t accept %a and %A specifiers,
-        #     but accept %s specifiers.
-        # See https://github.com/csound/csound/issues/747 for more information.
-        'format specifiers': [
-            (r'%[#0\- +]*\d*(?:\.\d+)?[AE-GXac-giosux]', String.Interpol),
-            (r'%%', String.Escape)
-        ],
-
-        'goto argument': [
-            include('whitespace and macro uses'),
-            (r',', Punctuation, '#pop'),
-            include('partial statements')
-        ],
-        'goto label': [
-            include('whitespace and macro uses'),
-            (r'\w+', Name.Label, '#pop'),
-            default('#pop')
-        ],
-
-        'prints opcode': [
-            include('whitespace and macro uses'),
-            (r'"', String, 'prints quoted string'),
-            default('#pop')
-        ],
-        'prints quoted string': [
-            (r'\\\\[aAbBnNrRtT]', String.Escape),
-            (r'%[!nNrRtT]|[~^]{1,2}', String.Escape),
-            include('quoted string')
-        ],
-
-        'Csound score opcode': [
-            include('whitespace and macro uses'),
-            (r'"', String, 'quoted string'),
-            (r'\{\{', String, 'Csound score'),
-            (r'\n', Whitespace, '#pop')
-        ],
-        'Csound score': [
-            (r'\}\}', String, '#pop'),
-            (r'([^}]+)|\}(?!\})', using(CsoundScoreLexer))
-        ],
-
-        'Python opcode': [
-            include('whitespace and macro uses'),
-            (r'"', String, 'quoted string'),
-            (r'\{\{', String, 'Python'),
-            (r'\n', Whitespace, '#pop')
-        ],
-        'Python': [
-            (r'\}\}', String, '#pop'),
-            (r'([^}]+)|\}(?!\})', using(PythonLexer))
-        ],
-
-        'Lua opcode': [
-            include('whitespace and macro uses'),
-            (r'"', String, 'quoted string'),
-            (r'\{\{', String, 'Lua'),
-            (r'\n', Whitespace, '#pop')
-        ],
-        'Lua': [
-            (r'\}\}', String, '#pop'),
-            (r'([^}]+)|\}(?!\})', using(LuaLexer))
-        ]
-    }
-
-
-class CsoundDocumentLexer(RegexLexer):
-    """
-    For `Csound <https://csound.com>`_ documents.
-
-    .. versionadded:: 2.1
-    """
-
-    name = 'Csound Document'
-    aliases = ['csound-document', 'csound-csd']
-    filenames = ['*.csd']
-
-    # These tokens are based on those in XmlLexer in pygments/lexers/html.py. Making
-    # CsoundDocumentLexer a subclass of XmlLexer rather than RegexLexer may seem like a
-    # better idea, since Csound Document files look like XML files. However, Csound
-    # Documents can contain Csound comments (preceded by //, for example) before and
-    # after the root element, unescaped bitwise AND & and less than < operators, etc. In
-    # other words, while Csound Document files look like XML files, they may not actually
-    # be XML files.
-    tokens = {
-        'root': [
-            (r'/[*](.|\n)*?[*]/', Comment.Multiline),
-            (r'(?:;|//).*$', Comment.Single),
-            (r'[^/;<]+|/(?!/)', Text),
-
-            (r'<\s*CsInstruments', Name.Tag, ('orchestra', 'tag')),
-            (r'<\s*CsScore', Name.Tag, ('score', 'tag')),
-            (r'<\s*[Hh][Tt][Mm][Ll]', Name.Tag, ('HTML', 'tag')),
-
-            (r'<\s*[\w:.-]+', Name.Tag, 'tag'),
-            (r'<\s*/\s*[\w:.-]+\s*>', Name.Tag)
-        ],
-
-        'orchestra': [
-            (r'<\s*/\s*CsInstruments\s*>', Name.Tag, '#pop'),
-            (r'(.|\n)+?(?=<\s*/\s*CsInstruments\s*>)', using(CsoundOrchestraLexer))
-        ],
-        'score': [
-            (r'<\s*/\s*CsScore\s*>', Name.Tag, '#pop'),
-            (r'(.|\n)+?(?=<\s*/\s*CsScore\s*>)', using(CsoundScoreLexer))
-        ],
-        'HTML': [
-            (r'<\s*/\s*[Hh][Tt][Mm][Ll]\s*>', Name.Tag, '#pop'),
-            (r'(.|\n)+?(?=<\s*/\s*[Hh][Tt][Mm][Ll]\s*>)', using(HtmlLexer))
-        ],
-
-        'tag': [
-            (r'\s+', Whitespace),
-            (r'[\w.:-]+\s*=', Name.Attribute, 'attr'),
-            (r'/?\s*>', Name.Tag, '#pop')
-        ],
-        'attr': [
-            (r'\s+', Whitespace),
-            (r'".*?"', String, '#pop'),
-            (r"'.*?'", String, '#pop'),
-            (r'[^\s>]+', String, '#pop')
-        ]
-    }
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/slash.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/slash.py
deleted file mode 100644
index cce47ce87f82be8c8539a39513d07723dd6d4fd1..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/slash.py
+++ /dev/null
@@ -1,184 +0,0 @@
-"""
-    pygments.lexers.slash
-    ~~~~~~~~~~~~~~~~~~~~~
-
-    Lexer for the `Slash <https://github.com/arturadib/Slash-A>`_ programming
-    language.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pygments.lexer import ExtendedRegexLexer, bygroups, DelegatingLexer
-from pygments.token import Name, Number, String, Comment, Punctuation, \
-    Other, Keyword, Operator, Whitespace
-
-__all__ = ['SlashLexer']
-
-
-class SlashLanguageLexer(ExtendedRegexLexer):
-    _nkw = r'(?=[^a-zA-Z_0-9])'
-
-    def move_state(new_state):
-        return ("#pop", new_state)
-
-    def right_angle_bracket(lexer, match, ctx):
-        if len(ctx.stack) > 1 and ctx.stack[-2] == "string":
-            ctx.stack.pop()
-        yield match.start(), String.Interpol, '}'
-        ctx.pos = match.end()
-        pass
-
-    tokens = {
-        "root": [
-            (r"<%=",        Comment.Preproc,    move_state("slash")),
-            (r"<%!!",       Comment.Preproc,    move_state("slash")),
-            (r"<%#.*?%>",   Comment.Multiline),
-            (r"<%",         Comment.Preproc,    move_state("slash")),
-            (r".|\n",       Other),
-        ],
-        "string": [
-            (r"\\",         String.Escape,      move_state("string_e")),
-            (r"\"",         String,             move_state("slash")),
-            (r"#\{",        String.Interpol,    "slash"),
-            (r'.|\n',       String),
-        ],
-        "string_e": [
-            (r'n',                  String.Escape,      move_state("string")),
-            (r't',                  String.Escape,      move_state("string")),
-            (r'r',                  String.Escape,      move_state("string")),
-            (r'e',                  String.Escape,      move_state("string")),
-            (r'x[a-fA-F0-9]{2}',    String.Escape,      move_state("string")),
-            (r'.',                  String.Escape,      move_state("string")),
-        ],
-        "regexp": [
-            (r'}[a-z]*',            String.Regex,       move_state("slash")),
-            (r'\\(.|\n)',           String.Regex),
-            (r'{',                  String.Regex,       "regexp_r"),
-            (r'.|\n',               String.Regex),
-        ],
-        "regexp_r": [
-            (r'}[a-z]*',            String.Regex,       "#pop"),
-            (r'\\(.|\n)',           String.Regex),
-            (r'{',                  String.Regex,       "regexp_r"),
-        ],
-        "slash": [
-            (r"%>",                     Comment.Preproc,    move_state("root")),
-            (r"\"",                     String,             move_state("string")),
-            (r"'[a-zA-Z0-9_]+",         String),
-            (r'%r{',                    String.Regex,       move_state("regexp")),
-            (r'/\*.*?\*/',              Comment.Multiline),
-            (r"(#|//).*?\n",            Comment.Single),
-            (r'-?[0-9]+e[+-]?[0-9]+',   Number.Float),
-            (r'-?[0-9]+\.[0-9]+(e[+-]?[0-9]+)?', Number.Float),
-            (r'-?[0-9]+',               Number.Integer),
-            (r'nil'+_nkw,               Name.Builtin),
-            (r'true'+_nkw,              Name.Builtin),
-            (r'false'+_nkw,             Name.Builtin),
-            (r'self'+_nkw,              Name.Builtin),
-            (r'(class)(\s+)([A-Z][a-zA-Z0-9_\']*)',
-                bygroups(Keyword, Whitespace, Name.Class)),
-            (r'class'+_nkw,             Keyword),
-            (r'extends'+_nkw,           Keyword),
-            (r'(def)(\s+)(self)(\s*)(\.)(\s*)([a-z_][a-zA-Z0-9_\']*=?|<<|>>|==|<=>|<=|<|>=|>|\+|-(self)?|~(self)?|\*|/|%|^|&&|&|\||\[\]=?)',
-                bygroups(Keyword, Whitespace, Name.Builtin, Whitespace, Punctuation, Whitespace, Name.Function)),
-            (r'(def)(\s+)([a-z_][a-zA-Z0-9_\']*=?|<<|>>|==|<=>|<=|<|>=|>|\+|-(self)?|~(self)?|\*|/|%|^|&&|&|\||\[\]=?)',
-                bygroups(Keyword, Whitespace, Name.Function)),
-            (r'def'+_nkw,               Keyword),
-            (r'if'+_nkw,                Keyword),
-            (r'elsif'+_nkw,             Keyword),
-            (r'else'+_nkw,              Keyword),
-            (r'unless'+_nkw,            Keyword),
-            (r'for'+_nkw,               Keyword),
-            (r'in'+_nkw,                Keyword),
-            (r'while'+_nkw,             Keyword),
-            (r'until'+_nkw,             Keyword),
-            (r'and'+_nkw,               Keyword),
-            (r'or'+_nkw,                Keyword),
-            (r'not'+_nkw,               Keyword),
-            (r'lambda'+_nkw,            Keyword),
-            (r'try'+_nkw,               Keyword),
-            (r'catch'+_nkw,             Keyword),
-            (r'return'+_nkw,            Keyword),
-            (r'next'+_nkw,              Keyword),
-            (r'last'+_nkw,              Keyword),
-            (r'throw'+_nkw,             Keyword),
-            (r'use'+_nkw,               Keyword),
-            (r'switch'+_nkw,            Keyword),
-            (r'\\',                     Keyword),
-            (r'λ',                      Keyword),
-            (r'__FILE__'+_nkw,          Name.Builtin.Pseudo),
-            (r'__LINE__'+_nkw,          Name.Builtin.Pseudo),
-            (r'[A-Z][a-zA-Z0-9_\']*'+_nkw, Name.Constant),
-            (r'[a-z_][a-zA-Z0-9_\']*'+_nkw, Name),
-            (r'@[a-z_][a-zA-Z0-9_\']*'+_nkw, Name.Variable.Instance),
-            (r'@@[a-z_][a-zA-Z0-9_\']*'+_nkw, Name.Variable.Class),
-            (r'\(',                     Punctuation),
-            (r'\)',                     Punctuation),
-            (r'\[',                     Punctuation),
-            (r'\]',                     Punctuation),
-            (r'\{',                     Punctuation),
-            (r'\}',                     right_angle_bracket),
-            (r';',                      Punctuation),
-            (r',',                      Punctuation),
-            (r'<<=',                    Operator),
-            (r'>>=',                    Operator),
-            (r'<<',                     Operator),
-            (r'>>',                     Operator),
-            (r'==',                     Operator),
-            (r'!=',                     Operator),
-            (r'=>',                     Operator),
-            (r'=',                      Operator),
-            (r'<=>',                    Operator),
-            (r'<=',                     Operator),
-            (r'>=',                     Operator),
-            (r'<',                      Operator),
-            (r'>',                      Operator),
-            (r'\+\+',                   Operator),
-            (r'\+=',                    Operator),
-            (r'-=',                     Operator),
-            (r'\*\*=',                  Operator),
-            (r'\*=',                    Operator),
-            (r'\*\*',                   Operator),
-            (r'\*',                     Operator),
-            (r'/=',                     Operator),
-            (r'\+',                     Operator),
-            (r'-',                      Operator),
-            (r'/',                      Operator),
-            (r'%=',                     Operator),
-            (r'%',                      Operator),
-            (r'^=',                     Operator),
-            (r'&&=',                    Operator),
-            (r'&=',                     Operator),
-            (r'&&',                     Operator),
-            (r'&',                      Operator),
-            (r'\|\|=',                  Operator),
-            (r'\|=',                    Operator),
-            (r'\|\|',                   Operator),
-            (r'\|',                     Operator),
-            (r'!',                      Operator),
-            (r'\.\.\.',                 Operator),
-            (r'\.\.',                   Operator),
-            (r'\.',                     Operator),
-            (r'::',                     Operator),
-            (r':',                      Operator),
-            (r'(\s|\n)+',               Whitespace),
-            (r'[a-z_][a-zA-Z0-9_\']*',  Name.Variable),
-        ],
-    }
-
-
-class SlashLexer(DelegatingLexer):
-    """
-    Lexer for the Slash programming language.
-
-    .. versionadded:: 2.4
-    """
-
-    name = 'Slash'
-    aliases = ['slash']
-    filenames = ['*.sla']
-
-    def __init__(self, **options):
-        from pygments.lexers.web import HtmlLexer
-        super().__init__(HtmlLexer, SlashLanguageLexer, **options)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/referencing/retrieval.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/referencing/retrieval.py
deleted file mode 100644
index 9f1c9055a0c68ec9f0dc2c03d947a53df1a541ed..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/referencing/retrieval.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""
-Helpers related to (dynamic) resource retrieval.
-"""
-from __future__ import annotations
-
-from functools import lru_cache
-from typing import Callable, TypeVar
-import json
-
-from referencing import Resource
-from referencing.typing import URI, D, Retrieve
-
-#: A serialized document (e.g. a JSON string)
-_T = TypeVar("_T")
-
-
-def to_cached_resource(
-    cache: Callable[[Retrieve[D]], Retrieve[D]] | None = None,
-    loads: Callable[[_T], D] = json.loads,
-    from_contents: Callable[[D], Resource[D]] = Resource.from_contents,
-) -> Callable[[Callable[[URI], _T]], Retrieve[D]]:
-    """
-    Create a retriever which caches its return values from a simpler callable.
-
-    Takes a function which returns things like serialized JSON (strings) and
-    returns something suitable for passing to `Registry` as a retrieve
-    function.
-
-    This decorator both reduces a small bit of boilerplate for a common case
-    (deserializing JSON from strings and creating `Resource` objects from the
-    result) as well as makes the probable need for caching a bit easier.
-    Retrievers which otherwise do expensive operations (like hitting the
-    network) might otherwise be called repeatedly.
-
-    Examples
-    --------
-
-    .. testcode::
-
-        from referencing import Registry
-        import referencing.retrieval
-
-
-        @referencing.retrieval.to_cached_resource()
-        def retrieve(uri: str):
-            print(f"Retrieved {uri}")
-
-            # Normally, go get some expensive JSON from the network, a file ...
-            return '''
-                {
-                    "$schema": "https://json-schema.org/draft/2020-12/schema",
-                    "foo": "bar"
-                }
-            '''
-
-        one = Registry(retrieve=retrieve).get_or_retrieve("urn:example:foo")
-        print(one.value.contents["foo"])
-
-        # Retrieving the same URI again reuses the same value (and thus doesn't
-        # print another retrieval message here)
-        two = Registry(retrieve=retrieve).get_or_retrieve("urn:example:foo")
-        print(two.value.contents["foo"])
-
-    .. testoutput::
-
-        Retrieved urn:example:foo
-        bar
-        bar
-
-    """
-    if cache is None:
-        cache = lru_cache(maxsize=None)
-
-    def decorator(retrieve: Callable[[URI], _T]):
-        @cache
-        def cached_retrieve(uri: URI):
-            response = retrieve(uri)
-            contents = loads(response)
-            return from_contents(contents)
-
-        return cached_retrieve
-
-    return decorator
diff --git a/spaces/qingxu98/academic-chatgpt-beta/crazy_functions/test_project/cpp/longcode/jpgd.cpp b/spaces/qingxu98/academic-chatgpt-beta/crazy_functions/test_project/cpp/longcode/jpgd.cpp
deleted file mode 100644
index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000
--- a/spaces/qingxu98/academic-chatgpt-beta/crazy_functions/test_project/cpp/longcode/jpgd.cpp
+++ /dev/null
@@ -1,3276 +0,0 @@
-// jpgd.cpp - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// Last updated Apr. 16, 2011
-// Alex Evans: Linear memory allocator (taken from jpge.h).
-//
-// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2.
-//
-// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling.
-// Chroma upsampling reference: "Fast Scheme for Image Size Change in the Compressed Domain"
-// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html
-
-#include "jpgd.h"
-#include <string.h>
-
-#include <assert.h>
-// BEGIN EPIC MOD
-#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0
-// END EPIC MOD
-
-#ifdef _MSC_VER
-#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable
-#endif
-
-// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling).
-// This is slower, but results in higher quality on images with highly saturated colors.
-#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1
-
-#define JPGD_TRUE (1)
-#define JPGD_FALSE (0)
-
-#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b))
-#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b))
-
-namespace jpgd {
-
-	static inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-	static inline void jpgd_free(void *p) { FMemory::Free(p); }
-
-// BEGIN EPIC MOD
-//@UE3 - use UE3 BGRA encoding instead of assuming RGBA
-	// stolen from IImageWrapper.h
-	enum ERGBFormatJPG
-	{
-		Invalid = -1,
-		RGBA =  0,
-		BGRA =  1,
-		Gray =  2,
-	};
-	static ERGBFormatJPG jpg_format;
-// END EPIC MOD
-
-	// DCT coefficients are stored in this sequence.
-	static int g_ZAG[64] = {  0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-
-	enum JPEG_MARKER
-	{
-		M_SOF0  = 0xC0, M_SOF1  = 0xC1, M_SOF2  = 0xC2, M_SOF3  = 0xC3, M_SOF5  = 0xC5, M_SOF6  = 0xC6, M_SOF7  = 0xC7, M_JPG   = 0xC8,
-		M_SOF9  = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT   = 0xC4, M_DAC   = 0xCC,
-		M_RST0  = 0xD0, M_RST1  = 0xD1, M_RST2  = 0xD2, M_RST3  = 0xD3, M_RST4  = 0xD4, M_RST5  = 0xD5, M_RST6  = 0xD6, M_RST7  = 0xD7,
-		M_SOI   = 0xD8, M_EOI   = 0xD9, M_SOS   = 0xDA, M_DQT   = 0xDB, M_DNL   = 0xDC, M_DRI   = 0xDD, M_DHP   = 0xDE, M_EXP   = 0xDF,
-		M_APP0  = 0xE0, M_APP15 = 0xEF, M_JPG0  = 0xF0, M_JPG13 = 0xFD, M_COM   = 0xFE, M_TEM   = 0x01, M_ERROR = 0x100, RST0   = 0xD0
-	};
-
-	enum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 };
-
-#define CONST_BITS  13
-#define PASS1_BITS  2
-#define SCALEDONE ((int32)1)
-
-#define FIX_0_298631336  ((int32)2446)        /* FIX(0.298631336) */
-#define FIX_0_390180644  ((int32)3196)        /* FIX(0.390180644) */
-#define FIX_0_541196100  ((int32)4433)        /* FIX(0.541196100) */
-#define FIX_0_765366865  ((int32)6270)        /* FIX(0.765366865) */
-#define FIX_0_899976223  ((int32)7373)        /* FIX(0.899976223) */
-#define FIX_1_175875602  ((int32)9633)        /* FIX(1.175875602) */
-#define FIX_1_501321110  ((int32)12299)       /* FIX(1.501321110) */
-#define FIX_1_847759065  ((int32)15137)       /* FIX(1.847759065) */
-#define FIX_1_961570560  ((int32)16069)       /* FIX(1.961570560) */
-#define FIX_2_053119869  ((int32)16819)       /* FIX(2.053119869) */
-#define FIX_2_562915447  ((int32)20995)       /* FIX(2.562915447) */
-#define FIX_3_072711026  ((int32)25172)       /* FIX(3.072711026) */
-
-#define DESCALE(x,n)  (((x) + (SCALEDONE << ((n)-1))) >> (n))
-#define DESCALE_ZEROSHIFT(x,n)  (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n))
-
-#define MULTIPLY(var, cnst)  ((var) * (cnst))
-
-#define CLAMP(i) ((static_cast<uint>(i) > 255) ? (((~i) >> 31) & 0xFF) : (i))
-
-	// Compiler creates a fast path 1D IDCT for X non-zero columns
-	template <int NONZERO_COLS>
-	struct Row
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-			// ACCESS_COL() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0)
-
-			const int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6);
-
-			const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
-			const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
-			const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
-			const int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS;
-			const int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS;
-
-			const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
-			const int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1);
-
-			const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
-			const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
-			const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
-			const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
-			const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
-			const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
-			const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
-			const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
-			const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
-			const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
-			pTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS);
-			pTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS);
-			pTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS);
-			pTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS);
-			pTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS);
-			pTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS);
-			pTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS);
-			pTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS);
-		}
-	};
-
-	template <>
-	struct Row<0>
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-#ifdef _MSC_VER
-			pTemp; pSrc;
-#endif
-		}
-	};
-
-	template <>
-	struct Row<1>
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-			const int dcval = (pSrc[0] << PASS1_BITS);
-
-			pTemp[0] = dcval;
-			pTemp[1] = dcval;
-			pTemp[2] = dcval;
-			pTemp[3] = dcval;
-			pTemp[4] = dcval;
-			pTemp[5] = dcval;
-			pTemp[6] = dcval;
-			pTemp[7] = dcval;
-		}
-	};
-
-	// Compiler creates a fast path 1D IDCT for X non-zero rows
-	template <int NONZERO_ROWS>
-	struct Col
-	{
-		static void idct(uint8* pDst_ptr, const int* pTemp)
-		{
-			// ACCESS_ROW() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0)
-
-			const int z2 = ACCESS_ROW(2);
-			const int z3 = ACCESS_ROW(6);
-
-			const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
-			const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
-			const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
-			const int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS;
-			const int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS;
-
-			const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
-			const int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1);
-
-			const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
-			const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
-			const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
-			const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
-			const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
-			const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
-			const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
-			const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
-			const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
-			const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
-			int i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*0] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*7] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*1] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*6] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*2] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*5] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*3] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*4] = (uint8)CLAMP(i);
-		}
-	};
-
-	template <>
-	struct Col<1>
-	{
-		static void idct(uint8* pDst_ptr, const int* pTemp)
-		{
-			int dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3);
-			const uint8 dcval_clamped = (uint8)CLAMP(dcval);
-			pDst_ptr[0*8] = dcval_clamped;
-			pDst_ptr[1*8] = dcval_clamped;
-			pDst_ptr[2*8] = dcval_clamped;
-			pDst_ptr[3*8] = dcval_clamped;
-			pDst_ptr[4*8] = dcval_clamped;
-			pDst_ptr[5*8] = dcval_clamped;
-			pDst_ptr[6*8] = dcval_clamped;
-			pDst_ptr[7*8] = dcval_clamped;
-		}
-	};
-
-	static const uint8 s_idct_row_table[] =
-	{
-		1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0,
-		4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0,
-		6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0,
-		6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0,
-		8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2,
-		8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2,
-		8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4,
-		8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8,
-	};
-
-	static const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 };
-
-	void idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag)
-	{
-		JPGD_ASSERT(block_max_zag >= 1);
-		JPGD_ASSERT(block_max_zag <= 64);
-
-		if (block_max_zag == 1)
-		{
-			int k = ((pSrc_ptr[0] + 4) >> 3) + 128;
-			k = CLAMP(k);
-			k = k | (k<<8);
-			k = k | (k<<16);
-
-			for (int i = 8; i > 0; i--)
-			{
-				*(int*)&pDst_ptr[0] = k;
-				*(int*)&pDst_ptr[4] = k;
-				pDst_ptr += 8;
-			}
-			return;
-		}
-
-		int temp[64];
-
-		const jpgd_block_t* pSrc = pSrc_ptr;
-		int* pTemp = temp;
-
-		const uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8];
-		int i;
-		for (i = 8; i > 0; i--, pRow_tab++)
-		{
-			switch (*pRow_tab)
-			{
-			case 0: Row<0>::idct(pTemp, pSrc); break;
-			case 1: Row<1>::idct(pTemp, pSrc); break;
-			case 2: Row<2>::idct(pTemp, pSrc); break;
-			case 3: Row<3>::idct(pTemp, pSrc); break;
-			case 4: Row<4>::idct(pTemp, pSrc); break;
-			case 5: Row<5>::idct(pTemp, pSrc); break;
-			case 6: Row<6>::idct(pTemp, pSrc); break;
-			case 7: Row<7>::idct(pTemp, pSrc); break;
-			case 8: Row<8>::idct(pTemp, pSrc); break;
-			}
-
-			pSrc += 8;
-			pTemp += 8;
-		}
-
-		pTemp = temp;
-
-		const int nonzero_rows = s_idct_col_table[block_max_zag - 1];
-		for (i = 8; i > 0; i--)
-		{
-			switch (nonzero_rows)
-			{
-			case 1: Col<1>::idct(pDst_ptr, pTemp); break;
-			case 2: Col<2>::idct(pDst_ptr, pTemp); break;
-			case 3: Col<3>::idct(pDst_ptr, pTemp); break;
-			case 4: Col<4>::idct(pDst_ptr, pTemp); break;
-			case 5: Col<5>::idct(pDst_ptr, pTemp); break;
-			case 6: Col<6>::idct(pDst_ptr, pTemp); break;
-			case 7: Col<7>::idct(pDst_ptr, pTemp); break;
-			case 8: Col<8>::idct(pDst_ptr, pTemp); break;
-			}
-
-			pTemp++;
-			pDst_ptr++;
-		}
-	}
-
-	void idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr)
-	{
-		int temp[64];
-		int* pTemp = temp;
-		const jpgd_block_t* pSrc = pSrc_ptr;
-
-		for (int i = 4; i > 0; i--)
-		{
-			Row<4>::idct(pTemp, pSrc);
-			pSrc += 8;
-			pTemp += 8;
-		}
-
-		pTemp = temp;
-		for (int i = 8; i > 0; i--)
-		{
-			Col<4>::idct(pDst_ptr, pTemp);
-			pTemp++;
-			pDst_ptr++;
-		}
-	}
-
-	// Retrieve one character from the input stream.
-	inline uint jpeg_decoder::get_char()
-	{
-		// Any bytes remaining in buffer?
-		if (!m_in_buf_left)
-		{
-			// Try to get more bytes.
-			prep_in_buffer();
-			// Still nothing to get?
-			if (!m_in_buf_left)
-			{
-				// Pad the end of the stream with 0xFF 0xD9 (EOI marker)
-				int t = m_tem_flag;
-				m_tem_flag ^= 1;
-				if (t)
-					return 0xD9;
-				else
-					return 0xFF;
-			}
-		}
-
-		uint c = *m_pIn_buf_ofs++;
-		m_in_buf_left--;
-
-		return c;
-	}
-
-	// Same as previous method, except can indicate if the character is a pad character or not.
-	inline uint jpeg_decoder::get_char(bool *pPadding_flag)
-	{
-		if (!m_in_buf_left)
-		{
-			prep_in_buffer();
-			if (!m_in_buf_left)
-			{
-				*pPadding_flag = true;
-				int t = m_tem_flag;
-				m_tem_flag ^= 1;
-				if (t)
-					return 0xD9;
-				else
-					return 0xFF;
-			}
-		}
-
-		*pPadding_flag = false;
-
-		uint c = *m_pIn_buf_ofs++;
-		m_in_buf_left--;
-
-		return c;
-	}
-
-	// Inserts a previously retrieved character back into the input buffer.
-	inline void jpeg_decoder::stuff_char(uint8 q)
-	{
-		*(--m_pIn_buf_ofs) = q;
-		m_in_buf_left++;
-	}
-
-	// Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered.
-	inline uint8 jpeg_decoder::get_octet()
-	{
-		bool padding_flag;
-		int c = get_char(&padding_flag);
-
-		if (c == 0xFF)
-		{
-			if (padding_flag)
-				return 0xFF;
-
-			c = get_char(&padding_flag);
-			if (padding_flag)
-			{
-				stuff_char(0xFF);
-				return 0xFF;
-			}
-
-			if (c == 0x00)
-				return 0xFF;
-			else
-			{
-				stuff_char(static_cast<uint8>(c));
-				stuff_char(0xFF);
-				return 0xFF;
-			}
-		}
-
-		return static_cast<uint8>(c);
-	}
-
-	// Retrieves a variable number of bits from the input stream. Does not recognize markers.
-	inline uint jpeg_decoder::get_bits(int num_bits)
-	{
-		if (!num_bits)
-			return 0;
-
-		uint i = m_bit_buf >> (32 - num_bits);
-
-		if ((m_bits_left -= num_bits) <= 0)
-		{
-			m_bit_buf <<= (num_bits += m_bits_left);
-
-			uint c1 = get_char();
-			uint c2 = get_char();
-			m_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2;
-
-			m_bit_buf <<= -m_bits_left;
-
-			m_bits_left += 16;
-
-			JPGD_ASSERT(m_bits_left >= 0);
-		}
-		else
-			m_bit_buf <<= num_bits;
-
-		return i;
-	}
-
-	// Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered.
-	inline uint jpeg_decoder::get_bits_no_markers(int num_bits)
-	{
-		if (!num_bits)
-			return 0;
-
-		uint i = m_bit_buf >> (32 - num_bits);
-
-		if ((m_bits_left -= num_bits) <= 0)
-		{
-			m_bit_buf <<= (num_bits += m_bits_left);
-
-			if ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF))
-			{
-				uint c1 = get_octet();
-				uint c2 = get_octet();
-				m_bit_buf |= (c1 << 8) | c2;
-			}
-			else
-			{
-				m_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1];
-				m_in_buf_left -= 2;
-				m_pIn_buf_ofs += 2;
-			}
-
-			m_bit_buf <<= -m_bits_left;
-
-			m_bits_left += 16;
-
-			JPGD_ASSERT(m_bits_left >= 0);
-		}
-		else
-			m_bit_buf <<= num_bits;
-
-		return i;
-	}
-
-	// Decodes a Huffman encoded symbol.
-	inline int jpeg_decoder::huff_decode(huff_tables *pH)
-	{
-		int symbol;
-
-		// Check first 8-bits: do we have a complete symbol?
-		if ((symbol = pH->look_up[m_bit_buf >> 24]) < 0)
-		{
-			// Decode more bits, use a tree traversal to find symbol.
-			int ofs = 23;
-			do
-			{
-				symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
-				ofs--;
-			} while (symbol < 0);
-
-			get_bits_no_markers(8 + (23 - ofs));
-		}
-		else
-			get_bits_no_markers(pH->code_size[symbol]);
-
-		return symbol;
-	}
-
-	// Decodes a Huffman encoded symbol.
-	inline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits)
-	{
-		int symbol;
-
-		// Check first 8-bits: do we have a complete symbol?
-		if ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0)
-		{
-			// Use a tree traversal to find symbol.
-			int ofs = 23;
-			do
-			{
-				symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
-				ofs--;
-			} while (symbol < 0);
-
-			get_bits_no_markers(8 + (23 - ofs));
-
-			extra_bits = get_bits_no_markers(symbol & 0xF);
-		}
-		else
-		{
-			JPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0));
-
-			if (symbol & 0x8000)
-			{
-				get_bits_no_markers((symbol >> 8) & 31);
-				extra_bits = symbol >> 16;
-			}
-			else
-			{
-				int code_size = (symbol >> 8) & 31;
-				int num_extra_bits = symbol & 0xF;
-				int bits = code_size + num_extra_bits;
-				if (bits <= (m_bits_left + 16))
-					extra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1);
-				else
-				{
-					get_bits_no_markers(code_size);
-					extra_bits = get_bits_no_markers(num_extra_bits);
-				}
-			}
-
-			symbol &= 0xFF;
-		}
-
-		return symbol;
-	}
-
-	// Tables and macro used to fully decode the DPCM differences.
-	static const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 };
-	static const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 };
-	static const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) };
-#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x))
-
-	// Clamps a value between 0-255.
-	inline uint8 jpeg_decoder::clamp(int i)
-	{
-		if (static_cast<uint>(i) > 255)
-			i = (((~i) >> 31) & 0xFF);
-
-		return static_cast<uint8>(i);
-	}
-
-	namespace DCT_Upsample
-	{
-		struct Matrix44
-		{
-			typedef int Element_Type;
-			enum { NUM_ROWS = 4, NUM_COLS = 4 };
-
-			Element_Type v[NUM_ROWS][NUM_COLS];
-
-			inline int rows() const { return NUM_ROWS; }
-			inline int cols() const { return NUM_COLS; }
-
-			inline const Element_Type & at(int r, int c) const { return v[r][c]; }
-			inline       Element_Type & at(int r, int c)       { return v[r][c]; }
-
-			inline Matrix44() { }
-
-			inline Matrix44& operator += (const Matrix44& a)
-			{
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					at(r, 0) += a.at(r, 0);
-					at(r, 1) += a.at(r, 1);
-					at(r, 2) += a.at(r, 2);
-					at(r, 3) += a.at(r, 3);
-				}
-				return *this;
-			}
-
-			inline Matrix44& operator -= (const Matrix44& a)
-			{
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					at(r, 0) -= a.at(r, 0);
-					at(r, 1) -= a.at(r, 1);
-					at(r, 2) -= a.at(r, 2);
-					at(r, 3) -= a.at(r, 3);
-				}
-				return *this;
-			}
-
-			friend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b)
-			{
-				Matrix44 ret;
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					ret.at(r, 0) = a.at(r, 0) + b.at(r, 0);
-					ret.at(r, 1) = a.at(r, 1) + b.at(r, 1);
-					ret.at(r, 2) = a.at(r, 2) + b.at(r, 2);
-					ret.at(r, 3) = a.at(r, 3) + b.at(r, 3);
-				}
-				return ret;
-			}
-
-			friend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b)
-			{
-				Matrix44 ret;
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					ret.at(r, 0) = a.at(r, 0) - b.at(r, 0);
-					ret.at(r, 1) = a.at(r, 1) - b.at(r, 1);
-					ret.at(r, 2) = a.at(r, 2) - b.at(r, 2);
-					ret.at(r, 3) = a.at(r, 3) - b.at(r, 3);
-				}
-				return ret;
-			}
-
-			static inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
-			{
-				for (int r = 0; r < 4; r++)
-				{
-					pDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) + b.at(r, 0));
-					pDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) + b.at(r, 1));
-					pDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) + b.at(r, 2));
-					pDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) + b.at(r, 3));
-				}
-			}
-
-			static inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
-			{
-				for (int r = 0; r < 4; r++)
-				{
-					pDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) - b.at(r, 0));
-					pDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) - b.at(r, 1));
-					pDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) - b.at(r, 2));
-					pDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) - b.at(r, 3));
-				}
-			}
-		};
-
-		const int FRACT_BITS = 10;
-		const int SCALE = 1 << FRACT_BITS;
-
-		typedef int Temp_Type;
-#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS)
-#define F(i) ((int)((i) * SCALE + .5f))
-
-		// Any decent C++ compiler will optimize this at compile time to a 0, or an array access.
-#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8])
-
-		// NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix
-		template<int NUM_ROWS, int NUM_COLS>
-		struct P_Q
-		{
-			static void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc)
-			{
-				// 4x8 = 4x8 times 8x8, matrix 0 is constant
-				const Temp_Type X000 = AT(0, 0);
-				const Temp_Type X001 = AT(0, 1);
-				const Temp_Type X002 = AT(0, 2);
-				const Temp_Type X003 = AT(0, 3);
-				const Temp_Type X004 = AT(0, 4);
-				const Temp_Type X005 = AT(0, 5);
-				const Temp_Type X006 = AT(0, 6);
-				const Temp_Type X007 = AT(0, 7);
-				const Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0));
-				const Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1));
-				const Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2));
-				const Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3));
-				const Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4));
-				const Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5));
-				const Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6));
-				const Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7));
-				const Temp_Type X020 = AT(4, 0);
-				const Temp_Type X021 = AT(4, 1);
-				const Temp_Type X022 = AT(4, 2);
-				const Temp_Type X023 = AT(4, 3);
-				const Temp_Type X024 = AT(4, 4);
-				const Temp_Type X025 = AT(4, 5);
-				const Temp_Type X026 = AT(4, 6);
-				const Temp_Type X027 = AT(4, 7);
-				const Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0));
-				const Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1));
-				const Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2));
-				const Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3));
-				const Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4));
-				const Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5));
-				const Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6));
-				const Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7));
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				P.at(0, 0) = X000;
-				P.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f));
-				P.at(0, 2) = X004;
-				P.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f));
-				P.at(1, 0) = X010;
-				P.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f));
-				P.at(1, 2) = X014;
-				P.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f));
-				P.at(2, 0) = X020;
-				P.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f));
-				P.at(2, 2) = X024;
-				P.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f));
-				P.at(3, 0) = X030;
-				P.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f));
-				P.at(3, 2) = X034;
-				P.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f));
-				// 40 muls 24 adds
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				Q.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f));
-				Q.at(0, 1) = X002;
-				Q.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f));
-				Q.at(0, 3) = X006;
-				Q.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f));
-				Q.at(1, 1) = X012;
-				Q.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f));
-				Q.at(1, 3) = X016;
-				Q.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f));
-				Q.at(2, 1) = X022;
-				Q.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f));
-				Q.at(2, 3) = X026;
-				Q.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f));
-				Q.at(3, 1) = X032;
-				Q.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f));
-				Q.at(3, 3) = X036;
-				// 40 muls 24 adds
-			}
-		};
-
-		template<int NUM_ROWS, int NUM_COLS>
-		struct R_S
-		{
-			static void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc)
-			{
-				// 4x8 = 4x8 times 8x8, matrix 0 is constant
-				const Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0));
-				const Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1));
-				const Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2));
-				const Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3));
-				const Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4));
-				const Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5));
-				const Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6));
-				const Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7));
-				const Temp_Type X110 = AT(2, 0);
-				const Temp_Type X111 = AT(2, 1);
-				const Temp_Type X112 = AT(2, 2);
-				const Temp_Type X113 = AT(2, 3);
-				const Temp_Type X114 = AT(2, 4);
-				const Temp_Type X115 = AT(2, 5);
-				const Temp_Type X116 = AT(2, 6);
-				const Temp_Type X117 = AT(2, 7);
-				const Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0));
-				const Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1));
-				const Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2));
-				const Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3));
-				const Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4));
-				const Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5));
-				const Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6));
-				const Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7));
-				const Temp_Type X130 = AT(6, 0);
-				const Temp_Type X131 = AT(6, 1);
-				const Temp_Type X132 = AT(6, 2);
-				const Temp_Type X133 = AT(6, 3);
-				const Temp_Type X134 = AT(6, 4);
-				const Temp_Type X135 = AT(6, 5);
-				const Temp_Type X136 = AT(6, 6);
-				const Temp_Type X137 = AT(6, 7);
-				// 80 muls 48 adds
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				R.at(0, 0) = X100;
-				R.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f));
-				R.at(0, 2) = X104;
-				R.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f));
-				R.at(1, 0) = X110;
-				R.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f));
-				R.at(1, 2) = X114;
-				R.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f));
-				R.at(2, 0) = X120;
-				R.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f));
-				R.at(2, 2) = X124;
-				R.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f));
-				R.at(3, 0) = X130;
-				R.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f));
-				R.at(3, 2) = X134;
-				R.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f));
-				// 40 muls 24 adds
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				S.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f));
-				S.at(0, 1) = X102;
-				S.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f));
-				S.at(0, 3) = X106;
-				S.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f));
-				S.at(1, 1) = X112;
-				S.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f));
-				S.at(1, 3) = X116;
-				S.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f));
-				S.at(2, 1) = X122;
-				S.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f));
-				S.at(2, 3) = X126;
-				S.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f));
-				S.at(3, 1) = X132;
-				S.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f));
-				S.at(3, 3) = X136;
-				// 40 muls 24 adds
-			}
-		};
-	} // end namespace DCT_Upsample
-
-	// Unconditionally frees all allocated m_blocks.
-	void jpeg_decoder::free_all_blocks()
-	{
-		m_pStream = NULL;
-		for (mem_block *b = m_pMem_blocks; b; )
-		{
-			mem_block *n = b->m_pNext;
-			jpgd_free(b);
-			b = n;
-		}
-		m_pMem_blocks = NULL;
-	}
-
-	// This method handles all errors.
-	// It could easily be changed to use C++ exceptions.
-	void jpeg_decoder::stop_decoding(jpgd_status status)
-	{
-		m_error_code = status;
-		free_all_blocks();
-		longjmp(m_jmp_state, status);
-
-		// we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit
-		// that this function doesn't return, otherwise we get this error:
-		// 
-		// error : function declared 'noreturn' should not return
-		exit(1);
-	}
-
-	void *jpeg_decoder::alloc(size_t nSize, bool zero)
-	{
-		nSize = (JPGD_MAX(nSize, 1) + 3) & ~3;
-		char *rv = NULL;
-		for (mem_block *b = m_pMem_blocks; b; b = b->m_pNext)
-		{
-			if ((b->m_used_count + nSize) <= b->m_size)
-			{
-				rv = b->m_data + b->m_used_count;
-				b->m_used_count += nSize;
-				break;
-			}
-		}
-		if (!rv)
-		{
-			int capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047);
-			mem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity);
-			if (!b) stop_decoding(JPGD_NOTENOUGHMEM);
-			b->m_pNext = m_pMem_blocks; m_pMem_blocks = b;
-			b->m_used_count = nSize;
-			b->m_size = capacity;
-			rv = b->m_data;
-		}
-		if (zero) memset(rv, 0, nSize);
-		return rv;
-	}
-
-	void jpeg_decoder::word_clear(void *p, uint16 c, uint n)
-	{
-		uint8 *pD = (uint8*)p;
-		const uint8 l = c & 0xFF, h = (c >> 8) & 0xFF;
-		while (n)
-		{
-			pD[0] = l; pD[1] = h; pD += 2;
-			n--;
-		}
-	}
-
-	// Refill the input buffer.
-	// This method will sit in a loop until (A) the buffer is full or (B)
-	// the stream's read() method reports and end of file condition.
-	void jpeg_decoder::prep_in_buffer()
-	{
-		m_in_buf_left = 0;
-		m_pIn_buf_ofs = m_in_buf;
-
-		if (m_eof_flag)
-			return;
-
-		do
-		{
-			int bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag);
-			if (bytes_read == -1)
-				stop_decoding(JPGD_STREAM_READ);
-
-			m_in_buf_left += bytes_read;
-		} while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag));
-
-		m_total_bytes_read += m_in_buf_left;
-
-		// Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid).
-		// (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.)
-		word_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64);
-	}
-
-	// Read a Huffman code table.
-	void jpeg_decoder::read_dht_marker()
-	{
-		int i, index, count;
-		uint8 huff_num[17];
-		uint8 huff_val[256];
-
-		uint num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_DHT_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			index = get_bits(8);
-
-			huff_num[0] = 0;
-
-			count = 0;
-
-			for (i = 1; i <= 16; i++)
-			{
-				huff_num[i] = static_cast<uint8>(get_bits(8));
-				count += huff_num[i];
-			}
-
-			if (count > 255)
-				stop_decoding(JPGD_BAD_DHT_COUNTS);
-
-			for (i = 0; i < count; i++)
-				huff_val[i] = static_cast<uint8>(get_bits(8));
-
-			i = 1 + 16 + count;
-
-			if (num_left < (uint)i)
-				stop_decoding(JPGD_BAD_DHT_MARKER);
-
-			num_left -= i;
-
-			if ((index & 0x10) > 0x10)
-				stop_decoding(JPGD_BAD_DHT_INDEX);
-
-			index = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1);
-
-			if (index >= JPGD_MAX_HUFF_TABLES)
-				stop_decoding(JPGD_BAD_DHT_INDEX);
-
-			if (!m_huff_num[index])
-				m_huff_num[index] = (uint8 *)alloc(17);
-
-			if (!m_huff_val[index])
-				m_huff_val[index] = (uint8 *)alloc(256);
-
-			m_huff_ac[index] = (index & 0x10) != 0;
-			memcpy(m_huff_num[index], huff_num, 17);
-			memcpy(m_huff_val[index], huff_val, 256);
-		}
-	}
-
-	// Read a quantization table.
-	void jpeg_decoder::read_dqt_marker()
-	{
-		int n, i, prec;
-		uint num_left;
-		uint temp;
-
-		num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_DQT_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			n = get_bits(8);
-			prec = n >> 4;
-			n &= 0x0F;
-
-			if (n >= JPGD_MAX_QUANT_TABLES)
-				stop_decoding(JPGD_BAD_DQT_TABLE);
-
-			if (!m_quant[n])
-				m_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t));
-
-			// read quantization entries, in zag order
-			for (i = 0; i < 64; i++)
-			{
-				temp = get_bits(8);
-
-				if (prec)
-					temp = (temp << 8) + get_bits(8);
-
-				m_quant[n][i] = static_cast<jpgd_quant_t>(temp);
-			}
-
-			i = 64 + 1;
-
-			if (prec)
-				i += 64;
-
-			if (num_left < (uint)i)
-				stop_decoding(JPGD_BAD_DQT_LENGTH);
-
-			num_left -= i;
-		}
-	}
-
-	// Read the start of frame (SOF) marker.
-	void jpeg_decoder::read_sof_marker()
-	{
-		int i;
-		uint num_left;
-
-		num_left = get_bits(16);
-
-		if (get_bits(8) != 8)   /* precision: sorry, only 8-bit precision is supported right now */
-			stop_decoding(JPGD_BAD_PRECISION);
-
-		m_image_y_size = get_bits(16);
-
-		if ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT))
-			stop_decoding(JPGD_BAD_HEIGHT);
-
-		m_image_x_size = get_bits(16);
-
-		if ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH))
-			stop_decoding(JPGD_BAD_WIDTH);
-
-		m_comps_in_frame = get_bits(8);
-
-		if (m_comps_in_frame > JPGD_MAX_COMPONENTS)
-			stop_decoding(JPGD_TOO_MANY_COMPONENTS);
-
-		if (num_left != (uint)(m_comps_in_frame * 3 + 8))
-			stop_decoding(JPGD_BAD_SOF_LENGTH);
-
-		for (i = 0; i < m_comps_in_frame; i++)
-		{
-			m_comp_ident[i]  = get_bits(8);
-			m_comp_h_samp[i] = get_bits(4);
-			m_comp_v_samp[i] = get_bits(4);
-			m_comp_quant[i]  = get_bits(8);
-		}
-	}
-
-	// Used to skip unrecognized markers.
-	void jpeg_decoder::skip_variable_marker()
-	{
-		uint num_left;
-
-		num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_VARIABLE_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			get_bits(8);
-			num_left--;
-		}
-	}
-
-	// Read a define restart interval (DRI) marker.
-	void jpeg_decoder::read_dri_marker()
-	{
-		if (get_bits(16) != 4)
-			stop_decoding(JPGD_BAD_DRI_LENGTH);
-
-		m_restart_interval = get_bits(16);
-	}
-
-	// Read a start of scan (SOS) marker.
-	void jpeg_decoder::read_sos_marker()
-	{
-		uint num_left;
-		int i, ci, n, c, cc;
-
-		num_left = get_bits(16);
-
-		n = get_bits(8);
-
-		m_comps_in_scan = n;
-
-		num_left -= 3;
-
-		if ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) )
-			stop_decoding(JPGD_BAD_SOS_LENGTH);
-
-		for (i = 0; i < n; i++)
-		{
-			cc = get_bits(8);
-			c = get_bits(8);
-			num_left -= 2;
-
-			for (ci = 0; ci < m_comps_in_frame; ci++)
-				if (cc == m_comp_ident[ci])
-					break;
-
-			if (ci >= m_comps_in_frame)
-				stop_decoding(JPGD_BAD_SOS_COMP_ID);
-
-			m_comp_list[i]    = ci;
-			m_comp_dc_tab[ci] = (c >> 4) & 15;
-			m_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1);
-		}
-
-		m_spectral_start  = get_bits(8);
-		m_spectral_end    = get_bits(8);
-		m_successive_high = get_bits(4);
-		m_successive_low  = get_bits(4);
-
-		if (!m_progressive_flag)
-		{
-			m_spectral_start = 0;
-			m_spectral_end = 63;
-		}
-
-		num_left -= 3;
-
-		while (num_left)                  /* read past whatever is num_left */
-		{
-			get_bits(8);
-			num_left--;
-		}
-	}
-
-	// Finds the next marker.
-	int jpeg_decoder::next_marker()
-	{
-		uint c, bytes;
-
-		bytes = 0;
-
-		do
-		{
-			do
-			{
-				bytes++;
-				c = get_bits(8);
-			} while (c != 0xFF);
-
-			do
-			{
-				c = get_bits(8);
-			} while (c == 0xFF);
-
-		} while (c == 0);
-
-		// If bytes > 0 here, there where extra bytes before the marker (not good).
-
-		return c;
-	}
-
-	// Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is
-	// encountered.
-	int jpeg_decoder::process_markers()
-	{
-		int c;
-
-		for ( ; ; )
-		{
-			c = next_marker();
-
-			switch (c)
-			{
-			case M_SOF0:
-			case M_SOF1:
-			case M_SOF2:
-			case M_SOF3:
-			case M_SOF5:
-			case M_SOF6:
-			case M_SOF7:
-				//      case M_JPG:
-			case M_SOF9:
-			case M_SOF10:
-			case M_SOF11:
-			case M_SOF13:
-			case M_SOF14:
-			case M_SOF15:
-			case M_SOI:
-			case M_EOI:
-			case M_SOS:
-				{
-					return c;
-				}
-			case M_DHT:
-				{
-					read_dht_marker();
-					break;
-				}
-				// No arithmitic support - dumb patents!
-			case M_DAC:
-				{
-					stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
-					break;
-				}
-			case M_DQT:
-				{
-					read_dqt_marker();
-					break;
-				}
-			case M_DRI:
-				{
-					read_dri_marker();
-					break;
-				}
-				//case M_APP0:  /* no need to read the JFIF marker */
-
-			case M_JPG:
-			case M_RST0:    /* no parameters */
-			case M_RST1:
-			case M_RST2:
-			case M_RST3:
-			case M_RST4:
-			case M_RST5:
-			case M_RST6:
-			case M_RST7:
-			case M_TEM:
-				{
-					stop_decoding(JPGD_UNEXPECTED_MARKER);
-					break;
-				}
-			default:    /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */
-				{
-					skip_variable_marker();
-					break;
-				}
-			}
-		}
-	}
-
-	// Finds the start of image (SOI) marker.
-	// This code is rather defensive: it only checks the first 512 bytes to avoid
-	// false positives.
-	void jpeg_decoder::locate_soi_marker()
-	{
-		uint lastchar, thischar;
-		uint bytesleft;
-
-		lastchar = get_bits(8);
-
-		thischar = get_bits(8);
-
-		/* ok if it's a normal JPEG file without a special header */
-
-		if ((lastchar == 0xFF) && (thischar == M_SOI))
-			return;
-
-		bytesleft = 4096; //512;
-
-		for ( ; ; )
-		{
-			if (--bytesleft == 0)
-				stop_decoding(JPGD_NOT_JPEG);
-
-			lastchar = thischar;
-
-			thischar = get_bits(8);
-
-			if (lastchar == 0xFF)
-			{
-				if (thischar == M_SOI)
-					break;
-				else if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end
-					stop_decoding(JPGD_NOT_JPEG);
-			}
-		}
-
-		// Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad.
-		thischar = (m_bit_buf >> 24) & 0xFF;
-
-		if (thischar != 0xFF)
-			stop_decoding(JPGD_NOT_JPEG);
-	}
-
-	// Find a start of frame (SOF) marker.
-	void jpeg_decoder::locate_sof_marker()
-	{
-		locate_soi_marker();
-
-		int c = process_markers();
-
-		switch (c)
-		{
-		case M_SOF2:
-			m_progressive_flag = JPGD_TRUE;
-		case M_SOF0:  /* baseline DCT */
-		case M_SOF1:  /* extended sequential DCT */
-			{
-				read_sof_marker();
-				break;
-			}
-		case M_SOF9:  /* Arithmitic coding */
-			{
-				stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
-				break;
-			}
-		default:
-			{
-				stop_decoding(JPGD_UNSUPPORTED_MARKER);
-				break;
-			}
-		}
-	}
-
-	// Find a start of scan (SOS) marker.
-	int jpeg_decoder::locate_sos_marker()
-	{
-		int c;
-
-		c = process_markers();
-
-		if (c == M_EOI)
-			return JPGD_FALSE;
-		else if (c != M_SOS)
-			stop_decoding(JPGD_UNEXPECTED_MARKER);
-
-		read_sos_marker();
-
-		return JPGD_TRUE;
-	}
-
-	// Reset everything to default/uninitialized state.
-	void jpeg_decoder::init(jpeg_decoder_stream *pStream)
-	{
-		m_pMem_blocks = NULL;
-		m_error_code = JPGD_SUCCESS;
-		m_ready_flag = false;
-		m_image_x_size = m_image_y_size = 0;
-		m_pStream = pStream;
-		m_progressive_flag = JPGD_FALSE;
-
-		memset(m_huff_ac, 0, sizeof(m_huff_ac));
-		memset(m_huff_num, 0, sizeof(m_huff_num));
-		memset(m_huff_val, 0, sizeof(m_huff_val));
-		memset(m_quant, 0, sizeof(m_quant));
-
-		m_scan_type = 0;
-		m_comps_in_frame = 0;
-
-		memset(m_comp_h_samp, 0, sizeof(m_comp_h_samp));
-		memset(m_comp_v_samp, 0, sizeof(m_comp_v_samp));
-		memset(m_comp_quant, 0, sizeof(m_comp_quant));
-		memset(m_comp_ident, 0, sizeof(m_comp_ident));
-		memset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks));
-		memset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks));
-
-		m_comps_in_scan = 0;
-		memset(m_comp_list, 0, sizeof(m_comp_list));
-		memset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab));
-		memset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab));
-
-		m_spectral_start = 0;
-		m_spectral_end = 0;
-		m_successive_low = 0;
-		m_successive_high = 0;
-		m_max_mcu_x_size = 0;
-		m_max_mcu_y_size = 0;
-		m_blocks_per_mcu = 0;
-		m_max_blocks_per_row = 0;
-		m_mcus_per_row = 0;
-		m_mcus_per_col = 0;
-		m_expanded_blocks_per_component = 0;
-		m_expanded_blocks_per_mcu = 0;
-		m_expanded_blocks_per_row = 0;
-		m_freq_domain_chroma_upsample = false;
-
-		memset(m_mcu_org, 0, sizeof(m_mcu_org));
-
-		m_total_lines_left = 0;
-		m_mcu_lines_left = 0;
-		m_real_dest_bytes_per_scan_line = 0;
-		m_dest_bytes_per_scan_line = 0;
-		m_dest_bytes_per_pixel = 0;
-
-		memset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs));
-
-		memset(m_dc_coeffs, 0, sizeof(m_dc_coeffs));
-		memset(m_ac_coeffs, 0, sizeof(m_ac_coeffs));
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		m_eob_run = 0;
-
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		m_pIn_buf_ofs = m_in_buf;
-		m_in_buf_left = 0;
-		m_eof_flag = false;
-		m_tem_flag = 0;
-
-		memset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start));
-		memset(m_in_buf, 0, sizeof(m_in_buf));
-		memset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end));
-
-		m_restart_interval = 0;
-		m_restarts_left    = 0;
-		m_next_restart_num = 0;
-
-		m_max_mcus_per_row = 0;
-		m_max_blocks_per_mcu = 0;
-		m_max_mcus_per_col = 0;
-
-		memset(m_last_dc_val, 0, sizeof(m_last_dc_val));
-		m_pMCU_coefficients = NULL;
-		m_pSample_buf = NULL;
-
-		m_total_bytes_read = 0;
-
-		m_pScan_line_0 = NULL;
-		m_pScan_line_1 = NULL;
-
-		// Ready the input buffer.
-		prep_in_buffer();
-
-		// Prime the bit buffer.
-		m_bits_left = 16;
-		m_bit_buf = 0;
-
-		get_bits(16);
-		get_bits(16);
-
-		for (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++)
-			m_mcu_block_max_zag[i] = 64;
-	}
-
-#define SCALEBITS 16
-#define ONE_HALF  ((int) 1 << (SCALEBITS-1))
-#define FIX(x)    ((int) ((x) * (1L<<SCALEBITS) + 0.5f))
-
-	// Create a few tables that allow us to quickly convert YCbCr to RGB.
-	void jpeg_decoder::create_look_ups()
-	{
-		for (int i = 0; i <= 255; i++)
-		{
-			int k = i - 128;
-			m_crr[i] = ( FIX(1.40200f)  * k + ONE_HALF) >> SCALEBITS;
-			m_cbb[i] = ( FIX(1.77200f)  * k + ONE_HALF) >> SCALEBITS;
-			m_crg[i] = (-FIX(0.71414f)) * k;
-			m_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF;
-		}
-	}
-
-	// This method throws back into the stream any bytes that where read
-	// into the bit buffer during initial marker scanning.
-	void jpeg_decoder::fix_in_buffer()
-	{
-		// In case any 0xFF's where pulled into the buffer during marker scanning.
-		JPGD_ASSERT((m_bits_left & 7) == 0);
-
-		if (m_bits_left == 16)
-			stuff_char( (uint8)(m_bit_buf & 0xFF));
-
-		if (m_bits_left >= 8)
-			stuff_char( (uint8)((m_bit_buf >> 8) & 0xFF));
-
-		stuff_char((uint8)((m_bit_buf >> 16) & 0xFF));
-		stuff_char((uint8)((m_bit_buf >> 24) & 0xFF));
-
-		m_bits_left = 16;
-		get_bits_no_markers(16);
-		get_bits_no_markers(16);
-	}
-
-	void jpeg_decoder::transform_mcu(int mcu_row)
-	{
-		jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
-		uint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64;
-
-		for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-		{
-			idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
-			pSrc_ptr += 64;
-			pDst_ptr += 64;
-		}
-	}
-
-	static const uint8 s_max_rc[64] =
-	{
-		17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86,
-		102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136,
-		136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
-		136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136
-	};
-
-	void jpeg_decoder::transform_mcu_expand(int mcu_row)
-	{
-		jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
-		uint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64;
-
-		// Y IDCT
-		int mcu_block;
-		for (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++)
-		{
-			idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
-			pSrc_ptr += 64;
-			pDst_ptr += 64;
-		}
-
-		// Chroma IDCT, with upsampling
-		jpgd_block_t temp_block[64];
-
-		for (int i = 0; i < 2; i++)
-		{
-			DCT_Upsample::Matrix44 P, Q, R, S;
-
-			JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1);
-			JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64);
-
-			switch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1])
-			{
-			case 1*16+1:
-				DCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr);
-				break;
-			case 1*16+2:
-				DCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 2*16+2:
-				DCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+2:
-				DCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+3:
-				DCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+4:
-				DCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 4*16+4:
-				DCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+4:
-				DCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+5:
-				DCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+6:
-				DCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 6*16+6:
-				DCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+6:
-				DCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+7:
-				DCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+8:
-				DCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr);
-				break;
-			case 8*16+8:
-				DCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr);
-				break;
-			default:
-				JPGD_ASSERT(false);
-			}
-
-			DCT_Upsample::Matrix44 a(P + Q); P -= Q;
-			DCT_Upsample::Matrix44& b = P;
-			DCT_Upsample::Matrix44 c(R + S); R -= S;
-			DCT_Upsample::Matrix44& d = R;
-
-			DCT_Upsample::Matrix44::add_and_store(temp_block, a, c);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::sub_and_store(temp_block, a, c);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::add_and_store(temp_block, b, d);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::sub_and_store(temp_block, b, d);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			pSrc_ptr += 64;
-		}
-	}
-
-	// Loads and dequantizes the next row of (already decoded) coefficients.
-	// Progressive images only.
-	void jpeg_decoder::load_next_row()
-	{
-		int i;
-		jpgd_block_t *p;
-		jpgd_quant_t *q;
-		int mcu_row, mcu_block, row_block = 0;
-		int component_num, component_id;
-		int block_x_mcu[JPGD_MAX_COMPONENTS];
-
-		memset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int));
-
-		for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-		{
-			int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
-			for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-			{
-				component_id = m_mcu_org[mcu_block];
-				q = m_quant[m_comp_quant[component_id]];
-
-				p = m_pMCU_coefficients + 64 * mcu_block;
-
-				jpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-				jpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-				p[0] = pDC[0];
-				memcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t));
-
-				for (i = 63; i > 0; i--)
-					if (p[g_ZAG[i]])
-						break;
-
-				m_mcu_block_max_zag[mcu_block] = i + 1;
-
-				for ( ; i >= 0; i--)
-					if (p[g_ZAG[i]])
-						p[g_ZAG[i]] = static_cast<jpgd_block_t>(p[g_ZAG[i]] * q[i]);
-
-				row_block++;
-
-				if (m_comps_in_scan == 1)
-					block_x_mcu[component_id]++;
-				else
-				{
-					if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
-					{
-						block_x_mcu_ofs = 0;
-
-						if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
-						{
-							block_y_mcu_ofs = 0;
-
-							block_x_mcu[component_id] += m_comp_h_samp[component_id];
-						}
-					}
-				}
-			}
-
-			if (m_freq_domain_chroma_upsample)
-				transform_mcu_expand(mcu_row);
-			else
-				transform_mcu(mcu_row);
-		}
-
-		if (m_comps_in_scan == 1)
-			m_block_y_mcu[m_comp_list[0]]++;
-		else
-		{
-			for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-			{
-				component_id = m_comp_list[component_num];
-
-				m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
-			}
-		}
-	}
-
-	// Restart interval processing.
-	void jpeg_decoder::process_restart()
-	{
-		int i;
-		int c = 0;
-
-		// Align to a byte boundry
-		// FIXME: Is this really necessary? get_bits_no_markers() never reads in markers!
-		//get_bits_no_markers(m_bits_left & 7);
-
-		// Let's scan a little bit to find the marker, but not _too_ far.
-		// 1536 is a "fudge factor" that determines how much to scan.
-		for (i = 1536; i > 0; i--)
-			if (get_char() == 0xFF)
-				break;
-
-		if (i == 0)
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		for ( ; i > 0; i--)
-			if ((c = get_char()) != 0xFF)
-				break;
-
-		if (i == 0)
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		// Is it the expected marker? If not, something bad happened.
-		if (c != (m_next_restart_num + M_RST0))
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		// Reset each component's DC prediction values.
-		memset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
-		m_eob_run = 0;
-
-		m_restarts_left = m_restart_interval;
-
-		m_next_restart_num = (m_next_restart_num + 1) & 7;
-
-		// Get the bit buffer going again...
-
-		m_bits_left = 16;
-		get_bits_no_markers(16);
-		get_bits_no_markers(16);
-	}
-
-	static inline int dequantize_ac(int c, int q) {	c *= q;	return c; }
-
-	// Decodes and dequantizes the next row of coefficients.
-	void jpeg_decoder::decode_next_row()
-	{
-		int row_block = 0;
-
-		for (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-		{
-			if ((m_restart_interval) && (m_restarts_left == 0))
-				process_restart();
-
-			jpgd_block_t* p = m_pMCU_coefficients;
-			for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64)
-			{
-				int component_id = m_mcu_org[mcu_block];
-				jpgd_quant_t* q = m_quant[m_comp_quant[component_id]];
-
-				int r, s;
-				s = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r);
-				s = HUFF_EXTEND(r, s);
-
-				m_last_dc_val[component_id] = (s += m_last_dc_val[component_id]);
-
-				p[0] = static_cast<jpgd_block_t>(s * q[0]);
-
-				int prev_num_set = m_mcu_block_max_zag[mcu_block];
-
-				huff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]];
-
-				int k;
-				for (k = 1; k < 64; k++)
-				{
-					int extra_bits;
-					s = huff_decode(pH, extra_bits);
-
-					r = s >> 4;
-					s &= 15;
-
-					if (s)
-					{
-						if (r)
-						{
-							if ((k + r) > 63)
-								stop_decoding(JPGD_DECODE_ERROR);
-
-							if (k < prev_num_set)
-							{
-								int n = JPGD_MIN(r, prev_num_set - k);
-								int kt = k;
-								while (n--)
-									p[g_ZAG[kt++]] = 0;
-							}
-
-							k += r;
-						}
-
-						s = HUFF_EXTEND(extra_bits, s);
-
-						JPGD_ASSERT(k < 64);
-
-						p[g_ZAG[k]] = static_cast<jpgd_block_t>(dequantize_ac(s, q[k])); //s * q[k];
-					}
-					else
-					{
-						if (r == 15)
-						{
-							if ((k + 16) > 64)
-								stop_decoding(JPGD_DECODE_ERROR);
-
-							if (k < prev_num_set)
-							{
-								int n = JPGD_MIN(16, prev_num_set - k);
-								int kt = k;
-								while (n--)
-								{
-									JPGD_ASSERT(kt <= 63);
-									p[g_ZAG[kt++]] = 0;
-								}
-							}
-
-							k += 16 - 1; // - 1 because the loop counter is k
-							// BEGIN EPIC MOD
-							JPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0);
-							// END EPIC MOD
-						}
-						else
-							break;
-					}
-				}
-
-				if (k < prev_num_set)
-				{
-					int kt = k;
-					while (kt < prev_num_set)
-						p[g_ZAG[kt++]] = 0;
-				}
-
-				m_mcu_block_max_zag[mcu_block] = k;
-
-				row_block++;
-			}
-
-			if (m_freq_domain_chroma_upsample)
-				transform_mcu_expand(mcu_row);
-			else
-				transform_mcu(mcu_row);
-
-			m_restarts_left--;
-		}
-	}
-
-	// YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB
-	void jpeg_decoder::H1V1Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d = m_pScan_line_0;
-		uint8 *s = m_pSample_buf + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int j = 0; j < 8; j++)
-			{
-				int y = s[j];
-				int cb = s[64+j];
-				int cr = s[128+j];
-
-				if (jpg_format == ERGBFormatJPG::BGRA)
-				{
-					d[0] = clamp(y + m_cbb[cb]);
-					d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-					d[2] = clamp(y + m_crr[cr]);
-					d[3] = 255;
-				}
-				else
-				{
-					d[0] = clamp(y + m_crr[cr]);
-					d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-					d[2] = clamp(y + m_cbb[cb]);
-					d[3] = 255;
-				}
-				d += 4;
-			}
-
-			s += 64*3;
-		}
-	}
-
-	// YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB
-	void jpeg_decoder::H2V1Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *y = m_pSample_buf + row * 8;
-		uint8 *c = m_pSample_buf + 2*64 + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int l = 0; l < 2; l++)
-			{
-				for (int j = 0; j < 4; j++)
-				{
-					int cb = c[0];
-					int cr = c[64];
-
-					int rc = m_crr[cr];
-					int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-					int bc = m_cbb[cb];
-
-					int yy = y[j<<1];
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d0[0] = clamp(yy+bc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+rc);
-						d0[3] = 255;
-						yy = y[(j<<1)+1];
-						d0[4] = clamp(yy+bc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+rc);
-						d0[7] = 255;
-					}
-					else
-					{
-						d0[0] = clamp(yy+rc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+bc);
-						d0[3] = 255;
-						yy = y[(j<<1)+1];
-						d0[4] = clamp(yy+rc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+bc);
-						d0[7] = 255;
-					}
-
-					d0 += 8;
-
-					c++;
-				}
-				y += 64;
-			}
-
-			y += 64*4 - 64*2;
-			c += 64*4 - 8;
-		}
-	}
-
-	// YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB
-	void jpeg_decoder::H1V2Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *d1 = m_pScan_line_1;
-		uint8 *y;
-		uint8 *c;
-
-		if (row < 8)
-			y = m_pSample_buf + row * 8;
-		else
-			y = m_pSample_buf + 64*1 + (row & 7) * 8;
-
-		c = m_pSample_buf + 64*2 + (row >> 1) * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int j = 0; j < 8; j++)
-			{
-				int cb = c[0+j];
-				int cr = c[64+j];
-
-				int rc = m_crr[cr];
-				int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-				int bc = m_cbb[cb];
-
-				int yy = y[j];
-				if (jpg_format == ERGBFormatJPG::BGRA)
-				{
-					d0[0] = clamp(yy+bc);
-					d0[1] = clamp(yy+gc);
-					d0[2] = clamp(yy+rc);
-					d0[3] = 255;
-					yy = y[8+j];
-					d1[0] = clamp(yy+bc);
-					d1[1] = clamp(yy+gc);
-					d1[2] = clamp(yy+rc);
-					d1[3] = 255;
-				}
-				else
-				{
-					d0[0] = clamp(yy+rc);
-					d0[1] = clamp(yy+gc);
-					d0[2] = clamp(yy+bc);
-					d0[3] = 255;
-					yy = y[8+j];
-					d1[0] = clamp(yy+rc);
-					d1[1] = clamp(yy+gc);
-					d1[2] = clamp(yy+bc);
-					d1[3] = 255;
-				}
-
-				d0 += 4;
-				d1 += 4;
-			}
-
-			y += 64*4;
-			c += 64*4;
-		}
-	}
-
-	// YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB
-	void jpeg_decoder::H2V2Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *d1 = m_pScan_line_1;
-		uint8 *y;
-		uint8 *c;
-
-		if (row < 8)
-			y = m_pSample_buf + row * 8;
-		else
-			y = m_pSample_buf + 64*2 + (row & 7) * 8;
-
-		c = m_pSample_buf + 64*4 + (row >> 1) * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int l = 0; l < 2; l++)
-			{
-				for (int j = 0; j < 8; j += 2)
-				{
-					int cb = c[0];
-					int cr = c[64];
-
-					int rc = m_crr[cr];
-					int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-					int bc = m_cbb[cb];
-
-					int yy = y[j];
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d0[0] = clamp(yy+bc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+rc);
-						d0[3] = 255;
-						yy = y[j+1];
-						d0[4] = clamp(yy+bc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+rc);
-						d0[7] = 255;
-						yy = y[j+8];
-						d1[0] = clamp(yy+bc);
-						d1[1] = clamp(yy+gc);
-						d1[2] = clamp(yy+rc);
-						d1[3] = 255;
-						yy = y[j+8+1];
-						d1[4] = clamp(yy+bc);
-						d1[5] = clamp(yy+gc);
-						d1[6] = clamp(yy+rc);
-						d1[7] = 255;
-					}
-					else
-					{
-						d0[0] = clamp(yy+rc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+bc);
-						d0[3] = 255;
-						yy = y[j+1];
-						d0[4] = clamp(yy+rc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+bc);
-						d0[7] = 255;
-						yy = y[j+8];
-						d1[0] = clamp(yy+rc);
-						d1[1] = clamp(yy+gc);
-						d1[2] = clamp(yy+bc);
-						d1[3] = 255;
-						yy = y[j+8+1];
-						d1[4] = clamp(yy+rc);
-						d1[5] = clamp(yy+gc);
-						d1[6] = clamp(yy+bc);
-						d1[7] = 255;
-					}
-
-					d0 += 8;
-					d1 += 8;
-
-					c++;
-				}
-				y += 64;
-			}
-
-			y += 64*6 - 64*2;
-			c += 64*6 - 8;
-		}
-	}
-
-	// Y (1 block per MCU) to 8-bit grayscale
-	void jpeg_decoder::gray_convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d = m_pScan_line_0;
-		uint8 *s = m_pSample_buf + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			*(uint *)d = *(uint *)s;
-			*(uint *)(&d[4]) = *(uint *)(&s[4]);
-
-			s += 64;
-			d += 8;
-		}
-	}
-
-	void jpeg_decoder::expanded_convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-
-		uint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8;
-
-		uint8* d = m_pScan_line_0;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int k = 0; k < m_max_mcu_x_size; k += 8)
-			{
-				const int Y_ofs = k * 8;
-				const int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component;
-				const int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2;
-				for (int j = 0; j < 8; j++)
-				{
-					int y = Py[Y_ofs + j];
-					int cb = Py[Cb_ofs + j];
-					int cr = Py[Cr_ofs + j];
-
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d[0] = clamp(y + m_cbb[cb]);
-						d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-						d[2] = clamp(y + m_crr[cr]);
-						d[3] = 255;
-					}
-					else
-					{
-						d[0] = clamp(y + m_crr[cr]);
-						d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-						d[2] = clamp(y + m_cbb[cb]);
-						d[3] = 255;
-					}
-
-					d += 4;
-				}
-			}
-
-			Py += 64 * m_expanded_blocks_per_mcu;
-		}
-	}
-
-	// Find end of image (EOI) marker, so we can return to the user the exact size of the input stream.
-	void jpeg_decoder::find_eoi()
-	{
-		if (!m_progressive_flag)
-		{
-			// Attempt to read the EOI marker.
-			//get_bits_no_markers(m_bits_left & 7);
-
-			// Prime the bit buffer
-			m_bits_left = 16;
-			get_bits(16);
-			get_bits(16);
-
-			// The next marker _should_ be EOI
-			process_markers();
-		}
-
-		m_total_bytes_read -= m_in_buf_left;
-	}
-
-	int jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len)
-	{
-		if ((m_error_code) || (!m_ready_flag))
-			return JPGD_FAILED;
-
-		if (m_total_lines_left == 0)
-			return JPGD_DONE;
-
-		if (m_mcu_lines_left == 0)
-		{
-			if (setjmp(m_jmp_state))
-				return JPGD_FAILED;
-
-			if (m_progressive_flag)
-				load_next_row();
-			else
-				decode_next_row();
-
-			// Find the EOI marker if that was the last row.
-			if (m_total_lines_left <= m_max_mcu_y_size)
-				find_eoi();
-
-			m_mcu_lines_left = m_max_mcu_y_size;
-		}
-
-		if (m_freq_domain_chroma_upsample)
-		{
-			expanded_convert();
-			*pScan_line = m_pScan_line_0;
-		}
-		else
-		{
-			switch (m_scan_type)
-			{
-			case JPGD_YH2V2:
-				{
-					if ((m_mcu_lines_left & 1) == 0)
-					{
-						H2V2Convert();
-						*pScan_line = m_pScan_line_0;
-					}
-					else
-						*pScan_line = m_pScan_line_1;
-
-					break;
-				}
-			case JPGD_YH2V1:
-				{
-					H2V1Convert();
-					*pScan_line = m_pScan_line_0;
-					break;
-				}
-			case JPGD_YH1V2:
-				{
-					if ((m_mcu_lines_left & 1) == 0)
-					{
-						H1V2Convert();
-						*pScan_line = m_pScan_line_0;
-					}
-					else
-						*pScan_line = m_pScan_line_1;
-
-					break;
-				}
-			case JPGD_YH1V1:
-				{
-					H1V1Convert();
-					*pScan_line = m_pScan_line_0;
-					break;
-				}
-			case JPGD_GRAYSCALE:
-				{
-					gray_convert();
-					*pScan_line = m_pScan_line_0;
-
-					break;
-				}
-			}
-		}
-
-		*pScan_line_len = m_real_dest_bytes_per_scan_line;
-
-		m_mcu_lines_left--;
-		m_total_lines_left--;
-
-		return JPGD_SUCCESS;
-	}
-
-	// Creates the tables needed for efficient Huffman decoding.
-	void jpeg_decoder::make_huff_table(int index, huff_tables *pH)
-	{
-		int p, i, l, si;
-		uint8 huffsize[257];
-		uint huffcode[257];
-		uint code;
-		uint subtree;
-		int code_size;
-		int lastp;
-		int nextfreeentry;
-		int currententry;
-
-		pH->ac_table = m_huff_ac[index] != 0;
-
-		p = 0;
-
-		for (l = 1; l <= 16; l++)
-		{
-			for (i = 1; i <= m_huff_num[index][l]; i++)
-				huffsize[p++] = static_cast<uint8>(l);
-		}
-
-		huffsize[p] = 0;
-
-		lastp = p;
-
-		code = 0;
-		si = huffsize[0];
-		p = 0;
-
-		while (huffsize[p])
-		{
-			while (huffsize[p] == si)
-			{
-				huffcode[p++] = code;
-				code++;
-			}
-
-			code <<= 1;
-			si++;
-		}
-
-		memset(pH->look_up, 0, sizeof(pH->look_up));
-		memset(pH->look_up2, 0, sizeof(pH->look_up2));
-		memset(pH->tree, 0, sizeof(pH->tree));
-		memset(pH->code_size, 0, sizeof(pH->code_size));
-
-		nextfreeentry = -1;
-
-		p = 0;
-
-		while (p < lastp)
-		{
-			i = m_huff_val[index][p];
-			code = huffcode[p];
-			code_size = huffsize[p];
-
-			pH->code_size[i] = static_cast<uint8>(code_size);
-
-			if (code_size <= 8)
-			{
-				code <<= (8 - code_size);
-
-				for (l = 1 << (8 - code_size); l > 0; l--)
-				{
-					JPGD_ASSERT(i < 256);
-
-					pH->look_up[code] = i;
-
-					bool has_extrabits = false;
-					int extra_bits = 0;
-					int num_extra_bits = i & 15;
-
-					int bits_to_fetch = code_size;
-					if (num_extra_bits)
-					{
-						int total_codesize = code_size + num_extra_bits;
-						if (total_codesize <= 8)
-						{
-							has_extrabits = true;
-							extra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize));
-							JPGD_ASSERT(extra_bits <= 0x7FFF);
-							bits_to_fetch += num_extra_bits;
-						}
-					}
-
-					if (!has_extrabits)
-						pH->look_up2[code] = i | (bits_to_fetch << 8);
-					else
-						pH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8);
-
-					code++;
-				}
-			}
-			else
-			{
-				subtree = (code >> (code_size - 8)) & 0xFF;
-
-				currententry = pH->look_up[subtree];
-
-				if (currententry == 0)
-				{
-					pH->look_up[subtree] = currententry = nextfreeentry;
-					pH->look_up2[subtree] = currententry = nextfreeentry;
-
-					nextfreeentry -= 2;
-				}
-
-				code <<= (16 - (code_size - 8));
-
-				for (l = code_size; l > 9; l--)
-				{
-					if ((code & 0x8000) == 0)
-						currententry--;
-
-					if (pH->tree[-currententry - 1] == 0)
-					{
-						pH->tree[-currententry - 1] = nextfreeentry;
-
-						currententry = nextfreeentry;
-
-						nextfreeentry -= 2;
-					}
-					else
-						currententry = pH->tree[-currententry - 1];
-
-					code <<= 1;
-				}
-
-				if ((code & 0x8000) == 0)
-					currententry--;
-
-				pH->tree[-currententry - 1] = i;
-			}
-
-			p++;
-		}
-	}
-
-	// Verifies the quantization tables needed for this scan are available.
-	void jpeg_decoder::check_quant_tables()
-	{
-		for (int i = 0; i < m_comps_in_scan; i++)
-			if (m_quant[m_comp_quant[m_comp_list[i]]] == NULL)
-				stop_decoding(JPGD_UNDEFINED_QUANT_TABLE);
-	}
-
-	// Verifies that all the Huffman tables needed for this scan are available.
-	void jpeg_decoder::check_huff_tables()
-	{
-		for (int i = 0; i < m_comps_in_scan; i++)
-		{
-			if ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL))
-				stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-
-			if ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL))
-				stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-		}
-
-		for (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++)
-			if (m_huff_num[i])
-			{
-				if (!m_pHuff_tabs[i])
-					m_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables));
-
-				make_huff_table(i, m_pHuff_tabs[i]);
-			}
-	}
-
-	// Determines the component order inside each MCU.
-	// Also calcs how many MCU's are on each row, etc.
-	void jpeg_decoder::calc_mcu_block_order()
-	{
-		int component_num, component_id;
-		int max_h_samp = 0, max_v_samp = 0;
-
-		for (component_id = 0; component_id < m_comps_in_frame; component_id++)
-		{
-			if (m_comp_h_samp[component_id] > max_h_samp)
-				max_h_samp = m_comp_h_samp[component_id];
-
-			if (m_comp_v_samp[component_id] > max_v_samp)
-				max_v_samp = m_comp_v_samp[component_id];
-		}
-
-		for (component_id = 0; component_id < m_comps_in_frame; component_id++)
-		{
-			m_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8;
-			m_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8;
-		}
-
-		if (m_comps_in_scan == 1)
-		{
-			m_mcus_per_row = m_comp_h_blocks[m_comp_list[0]];
-			m_mcus_per_col = m_comp_v_blocks[m_comp_list[0]];
-		}
-		else
-		{
-			m_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp;
-			m_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp;
-		}
-
-		if (m_comps_in_scan == 1)
-		{
-			m_mcu_org[0] = m_comp_list[0];
-
-			m_blocks_per_mcu = 1;
-		}
-		else
-		{
-			m_blocks_per_mcu = 0;
-
-			for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-			{
-				int num_blocks;
-
-				component_id = m_comp_list[component_num];
-
-				num_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id];
-
-				while (num_blocks--)
-					m_mcu_org[m_blocks_per_mcu++] = component_id;
-			}
-		}
-	}
-
-	// Starts a new scan.
-	int jpeg_decoder::init_scan()
-	{
-		if (!locate_sos_marker())
-			return JPGD_FALSE;
-
-		calc_mcu_block_order();
-
-		check_huff_tables();
-
-		check_quant_tables();
-
-		memset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
-		m_eob_run = 0;
-
-		if (m_restart_interval)
-		{
-			m_restarts_left = m_restart_interval;
-			m_next_restart_num = 0;
-		}
-
-		fix_in_buffer();
-
-		return JPGD_TRUE;
-	}
-
-	// Starts a frame. Determines if the number of components or sampling factors
-	// are supported.
-	void jpeg_decoder::init_frame()
-	{
-		int i;
-
-		if (m_comps_in_frame == 1)
-		{
-			if ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1))
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
-			m_scan_type = JPGD_GRAYSCALE;
-			m_max_blocks_per_mcu = 1;
-			m_max_mcu_x_size = 8;
-			m_max_mcu_y_size = 8;
-		}
-		else if (m_comps_in_frame == 3)
-		{
-			if ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) ||
-				((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) )
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
-			if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-			{
-				m_scan_type = JPGD_YH1V1;
-
-				m_max_blocks_per_mcu = 3;
-				m_max_mcu_x_size = 8;
-				m_max_mcu_y_size = 8;
-			}
-			else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-			{
-				m_scan_type = JPGD_YH2V1;
-				m_max_blocks_per_mcu = 4;
-				m_max_mcu_x_size = 16;
-				m_max_mcu_y_size = 8;
-			}
-			else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2))
-			{
-				m_scan_type = JPGD_YH1V2;
-				m_max_blocks_per_mcu = 4;
-				m_max_mcu_x_size = 8;
-				m_max_mcu_y_size = 16;
-			}
-			else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-			{
-				m_scan_type = JPGD_YH2V2;
-				m_max_blocks_per_mcu = 6;
-				m_max_mcu_x_size = 16;
-				m_max_mcu_y_size = 16;
-			}
-			else
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-		}
-		else
-			stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
-		m_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size;
-		m_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size;
-
-		// These values are for the *destination* pixels: after conversion.
-		if (m_scan_type == JPGD_GRAYSCALE)
-			m_dest_bytes_per_pixel = 1;
-		else
-			m_dest_bytes_per_pixel = 4;
-
-		m_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel;
-
-		m_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel);
-
-		// Initialize two scan line buffers.
-		m_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-		if ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2))
-			m_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-
-		m_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu;
-
-		// Should never happen
-		if (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW)
-			stop_decoding(JPGD_ASSERTION_ERROR);
-
-		// Allocate the coefficient buffer, enough for one MCU
-		m_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t));
-
-		for (i = 0; i < m_max_blocks_per_mcu; i++)
-			m_mcu_block_max_zag[i] = 64;
-
-		m_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0];
-		m_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame;
-		m_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu;
-		// Freq. domain chroma upsampling is only supported for H2V2 subsampling factor.
-// BEGIN EPIC MOD
-#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING
-		m_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3);
-#else
-		m_freq_domain_chroma_upsample = 0;
-#endif
-// END EPIC MOD
-
-		if (m_freq_domain_chroma_upsample)
-			m_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64);
-		else
-			m_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64);
-
-		m_total_lines_left = m_image_y_size;
-
-		m_mcu_lines_left = 0;
-
-		create_look_ups();
-	}
-
-	// The coeff_buf series of methods originally stored the coefficients
-	// into a "virtual" file which was located in EMS, XMS, or a disk file. A cache
-	// was used to make this process more efficient. Now, we can store the entire
-	// thing in RAM.
-	jpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y)
-	{
-		coeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf));
-
-		cb->block_num_x = block_num_x;
-		cb->block_num_y = block_num_y;
-		cb->block_len_x = block_len_x;
-		cb->block_len_y = block_len_y;
-		cb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t);
-		cb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true);
-		return cb;
-	}
-
-	inline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y)
-	{
-		JPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y));
-		return (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x));
-	}
-
-	// The following methods decode the various types of m_blocks encountered
-	// in progressively encoded images.
-	void jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int s, r;
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
-		if ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0)
-		{
-			r = pD->get_bits_no_markers(s);
-			s = HUFF_EXTEND(r, s);
-		}
-
-		pD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]);
-
-		p[0] = static_cast<jpgd_block_t>(s << pD->m_successive_low);
-	}
-
-	void jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		if (pD->get_bits_no_markers(1))
-		{
-			jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
-			p[0] |= (1 << pD->m_successive_low);
-		}
-	}
-
-	void jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int k, s, r;
-
-		if (pD->m_eob_run)
-		{
-			pD->m_eob_run--;
-			return;
-		}
-
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
-		for (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++)
-		{
-			s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
-			r = s >> 4;
-			s &= 15;
-
-			if (s)
-			{
-				if ((k += r) > 63)
-					pD->stop_decoding(JPGD_DECODE_ERROR);
-
-				r = pD->get_bits_no_markers(s);
-				s = HUFF_EXTEND(r, s);
-
-				p[g_ZAG[k]] = static_cast<jpgd_block_t>(s << pD->m_successive_low);
-			}
-			else
-			{
-				if (r == 15)
-				{
-					if ((k += 15) > 63)
-						pD->stop_decoding(JPGD_DECODE_ERROR);
-				}
-				else
-				{
-					pD->m_eob_run = 1 << r;
-
-					if (r)
-						pD->m_eob_run += pD->get_bits_no_markers(r);
-
-					pD->m_eob_run--;
-
-					break;
-				}
-			}
-		}
-	}
-
-	void jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int s, k, r;
-		int p1 = 1 << pD->m_successive_low;
-		int m1 = (-1) << pD->m_successive_low;
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
-		k = pD->m_spectral_start;
-
-		if (pD->m_eob_run == 0)
-		{
-			for ( ; k <= pD->m_spectral_end; k++)
-			{
-				s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
-				r = s >> 4;
-				s &= 15;
-
-				if (s)
-				{
-					if (s != 1)
-						pD->stop_decoding(JPGD_DECODE_ERROR);
-
-					if (pD->get_bits_no_markers(1))
-						s = p1;
-					else
-						s = m1;
-				}
-				else
-				{
-					if (r != 15)
-					{
-						pD->m_eob_run = 1 << r;
-
-						if (r)
-							pD->m_eob_run += pD->get_bits_no_markers(r);
-
-						break;
-					}
-				}
-
-				do
-				{
-					// BEGIN EPIC MOD
-					JPGD_ASSERT(k < 64);
-					// END EPIC MOD
-
-					jpgd_block_t *this_coef = p + g_ZAG[k];
-
-					if (*this_coef != 0)
-					{
-						if (pD->get_bits_no_markers(1))
-						{
-							if ((*this_coef & p1) == 0)
-							{
-								if (*this_coef >= 0)
-									*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);
-								else
-									*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);
-							}
-						}
-					}
-					else
-					{
-						if (--r < 0)
-							break;
-					}
-
-					k++;
-
-				} while (k <= pD->m_spectral_end);
-
-				if ((s) && (k < 64))
-				{
-					p[g_ZAG[k]] = static_cast<jpgd_block_t>(s);
-				}
-			}
-		}
-
-		if (pD->m_eob_run > 0)
-		{
-			for ( ; k <= pD->m_spectral_end; k++)
-			{
-				// BEGIN EPIC MOD
-				JPGD_ASSERT(k < 64);
-				// END EPIC MOD
-
-				jpgd_block_t *this_coef = p + g_ZAG[k];
-
-				if (*this_coef != 0)
-				{
-					if (pD->get_bits_no_markers(1))
-					{
-						if ((*this_coef & p1) == 0)
-						{
-							if (*this_coef >= 0)
-								*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);
-							else
-								*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);
-						}
-					}
-				}
-			}
-
-			pD->m_eob_run--;
-		}
-	}
-
-	// Decode a scan in a progressively encoded image.
-	void jpeg_decoder::decode_scan(pDecode_block_func decode_block_func)
-	{
-		int mcu_row, mcu_col, mcu_block;
-		int block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS];
-
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		for (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++)
-		{
-			int component_num, component_id;
-
-			memset(block_x_mcu, 0, sizeof(block_x_mcu));
-
-			for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-			{
-				int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
-				if ((m_restart_interval) && (m_restarts_left == 0))
-					process_restart();
-
-				for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-				{
-					component_id = m_mcu_org[mcu_block];
-
-					decode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-
-					if (m_comps_in_scan == 1)
-						block_x_mcu[component_id]++;
-					else
-					{
-						if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
-						{
-							block_x_mcu_ofs = 0;
-
-							if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
-							{
-								block_y_mcu_ofs = 0;
-								block_x_mcu[component_id] += m_comp_h_samp[component_id];
-							}
-						}
-					}
-				}
-
-				m_restarts_left--;
-			}
-
-			if (m_comps_in_scan == 1)
-				m_block_y_mcu[m_comp_list[0]]++;
-			else
-			{
-				for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-				{
-					component_id = m_comp_list[component_num];
-					m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
-				}
-			}
-		}
-	}
-
-	// Decode a progressively encoded image.
-	void jpeg_decoder::init_progressive()
-	{
-		int i;
-
-		if (m_comps_in_frame == 4)
-			stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
-		// Allocate the coefficient buffers.
-		for (i = 0; i < m_comps_in_frame; i++)
-		{
-			m_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1);
-			m_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8);
-		}
-
-		for ( ; ; )
-		{
-			int dc_only_scan, refinement_scan;
-			pDecode_block_func decode_block_func;
-
-			if (!init_scan())
-				break;
-
-			dc_only_scan = (m_spectral_start == 0);
-			refinement_scan = (m_successive_high != 0);
-
-			if ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63))
-				stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
-			if (dc_only_scan)
-			{
-				if (m_spectral_end)
-					stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-			}
-			else if (m_comps_in_scan != 1)  /* AC scans can only contain one component */
-				stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
-			if ((refinement_scan) && (m_successive_low != m_successive_high - 1))
-				stop_decoding(JPGD_BAD_SOS_SUCCESSIVE);
-
-			if (dc_only_scan)
-			{
-				if (refinement_scan)
-					decode_block_func = decode_block_dc_refine;
-				else
-					decode_block_func = decode_block_dc_first;
-			}
-			else
-			{
-				if (refinement_scan)
-					decode_block_func = decode_block_ac_refine;
-				else
-					decode_block_func = decode_block_ac_first;
-			}
-
-			decode_scan(decode_block_func);
-
-			m_bits_left = 16;
-			get_bits(16);
-			get_bits(16);
-		}
-
-		m_comps_in_scan = m_comps_in_frame;
-
-		for (i = 0; i < m_comps_in_frame; i++)
-			m_comp_list[i] = i;
-
-		calc_mcu_block_order();
-	}
-
-	void jpeg_decoder::init_sequential()
-	{
-		if (!init_scan())
-			stop_decoding(JPGD_UNEXPECTED_MARKER);
-	}
-
-	void jpeg_decoder::decode_start()
-	{
-		init_frame();
-
-		if (m_progressive_flag)
-			init_progressive();
-		else
-			init_sequential();
-	}
-
-	void jpeg_decoder::decode_init(jpeg_decoder_stream *pStream)
-	{
-		init(pStream);
-		locate_sof_marker();
-	}
-
-	jpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream)
-	{
-		if (setjmp(m_jmp_state))
-			return;
-		decode_init(pStream);
-	}
-
-	int jpeg_decoder::begin_decoding()
-	{
-		if (m_ready_flag)
-			return JPGD_SUCCESS;
-
-		if (m_error_code)
-			return JPGD_FAILED;
-
-		if (setjmp(m_jmp_state))
-			return JPGD_FAILED;
-
-		decode_start();
-
-		m_ready_flag = true;
-
-		return JPGD_SUCCESS;
-	}
-
-	jpeg_decoder::~jpeg_decoder()
-	{
-		free_all_blocks();
-	}
-
-	jpeg_decoder_file_stream::jpeg_decoder_file_stream()
-	{
-		m_pFile = NULL;
-		m_eof_flag = false;
-		m_error_flag = false;
-	}
-
-	void jpeg_decoder_file_stream::close()
-	{
-		if (m_pFile)
-		{
-			fclose(m_pFile);
-			m_pFile = NULL;
-		}
-
-		m_eof_flag = false;
-		m_error_flag = false;
-	}
-
-	jpeg_decoder_file_stream::~jpeg_decoder_file_stream()
-	{
-		close();
-	}
-
-	bool jpeg_decoder_file_stream::open(const char *Pfilename)
-	{
-		close();
-
-		m_eof_flag = false;
-		m_error_flag = false;
-
-#if defined(_MSC_VER)
-		m_pFile = NULL;
-		fopen_s(&m_pFile, Pfilename, "rb");
-#else
-		m_pFile = fopen(Pfilename, "rb");
-#endif
-		return m_pFile != NULL;
-	}
-
-	int jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
-	{
-		if (!m_pFile)
-			return -1;
-
-		if (m_eof_flag)
-		{
-			*pEOF_flag = true;
-			return 0;
-		}
-
-		if (m_error_flag)
-			return -1;
-
-		int bytes_read = static_cast<int>(fread(pBuf, 1, max_bytes_to_read, m_pFile));
-		if (bytes_read < max_bytes_to_read)
-		{
-			if (ferror(m_pFile))
-			{
-				m_error_flag = true;
-				return -1;
-			}
-
-			m_eof_flag = true;
-			*pEOF_flag = true;
-		}
-
-		return bytes_read;
-	}
-
-	bool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size)
-	{
-		close();
-		m_pSrc_data = pSrc_data;
-		m_ofs = 0;
-		m_size = size;
-		return true;
-	}
-
-	int jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
-	{
-		*pEOF_flag = false;
-
-		if (!m_pSrc_data)
-			return -1;
-
-		uint bytes_remaining = m_size - m_ofs;
-		if ((uint)max_bytes_to_read > bytes_remaining)
-		{
-			max_bytes_to_read = bytes_remaining;
-			*pEOF_flag = true;
-		}
-
-		memcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read);
-		m_ofs += max_bytes_to_read;
-
-		return max_bytes_to_read;
-	}
-
-	unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps)
-	{
-		if (!actual_comps)
-			return NULL;
-		*actual_comps = 0;
-
-		if ((!pStream) || (!width) || (!height) || (!req_comps))
-			return NULL;
-
-		if ((req_comps != 1) && (req_comps != 3) && (req_comps != 4))
-			return NULL;
-
-		jpeg_decoder decoder(pStream);
-		if (decoder.get_error_code() != JPGD_SUCCESS)
-			return NULL;
-
-		const int image_width = decoder.get_width(), image_height = decoder.get_height();
-		*width = image_width;
-		*height = image_height;
-		*actual_comps = decoder.get_num_components();
-
-		if (decoder.begin_decoding() != JPGD_SUCCESS)
-			return NULL;
-
-		const int dst_bpl = image_width * req_comps;
-
-		uint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height);
-		if (!pImage_data)
-			return NULL;
-
-		for (int y = 0; y < image_height; y++)
-		{
-			const uint8* pScan_line = 0;
-			uint scan_line_len;
-			if (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS)
-			{
-				jpgd_free(pImage_data);
-				return NULL;
-			}
-
-			uint8 *pDst = pImage_data + y * dst_bpl;
-
-			if (((req_comps == 4) && (decoder.get_num_components() == 3)) ||
-				((req_comps == 1) && (decoder.get_num_components() == 1)))
-			{
-				memcpy(pDst, pScan_line, dst_bpl);
-			}
-			else if (decoder.get_num_components() == 1)
-			{
-				if (req_comps == 3)
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						uint8 luma = pScan_line[x];
-						pDst[0] = luma;
-						pDst[1] = luma;
-						pDst[2] = luma;
-						pDst += 3;
-					}
-				}
-				else
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						uint8 luma = pScan_line[x];
-						pDst[0] = luma;
-						pDst[1] = luma;
-						pDst[2] = luma;
-						pDst[3] = 255;
-						pDst += 4;
-					}
-				}
-			}
-			else if (decoder.get_num_components() == 3)
-			{
-				if (req_comps == 1)
-				{
-					const int YR = 19595, YG = 38470, YB = 7471;
-					for (int x = 0; x < image_width; x++)
-					{
-						int r = pScan_line[x*4+0];
-						int g = pScan_line[x*4+1];
-						int b = pScan_line[x*4+2];
-						*pDst++ = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-					}
-				}
-				else
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						pDst[0] = pScan_line[x*4+0];
-						pDst[1] = pScan_line[x*4+1];
-						pDst[2] = pScan_line[x*4+2];
-						pDst += 3;
-					}
-				}
-			}
-		}
-
-		return pImage_data;
-	}
-
-// BEGIN EPIC MOD
-	unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format)
-	{
-		jpg_format = (ERGBFormatJPG)format;
-// EMD EPIC MOD
-		jpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size);
-		return decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps);
-	}
-
-	unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps)
-	{
-		jpgd::jpeg_decoder_file_stream file_stream;
-		if (!file_stream.open(pSrc_filename))
-			return NULL;
-		return decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps);
-	}
-
-} // namespace jpgd
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Addinsoft XLSTAT46.md b/spaces/quidiaMuxgu/Expedit-SAM/Addinsoft XLSTAT46.md
deleted file mode 100644
index ac84691814fdb38fed6d19f25fe0b24bb2d81cfd..0000000000000000000000000000000000000000
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/CIC Language Coding Utility 1.2.2 16.md b/spaces/quidiaMuxgu/Expedit-SAM/CIC Language Coding Utility 1.2.2 16.md
deleted file mode 100644
index a7b286d04fb6755191fd7b6d2ca609911bd5a3e2..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/CIC Language Coding Utility 1.2.2 16.md	
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-<br />
-<h1>CIC Language Coding Utility 1.2.2 16: A Guide for BMW Owners</h1>
-
-<p>If you own a BMW with a CIC iDrive system, you might have wondered how to change the language of your navigation, voice control, and display. Maybe you want to switch to a different language for convenience, or maybe you bought a used car with a foreign language setting. Whatever the reason, you don't have to go to a dealer or use a complicated software to do it. You can use a simple and fast tool called CIC Language Coding Utility 1.2.2 16.</p>
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-<h2>What is CIC Language Coding Utility 1.2.2 16?</h2>
-
-<p>CIC Language Coding Utility 1.2.2 16 is a tool developed by Bimmer Retrofit that allows you to change the language of your BMW CIC iDrive system in minutes. It works with all models with CIC iDrive (not CCC iDrive), and it supports many languages, such as English, German, French, Italian, Spanish, Russian, Chinese, and more.</p>
-<h2>CIC Language Coding Utility 1.2.2 16</h2><br /><p><b><b>DOWNLOAD</b> &#9913; <a href="https://geags.com/2uCsIY">https://geags.com/2uCsIY</a></b></p><br /><br />
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-<p>The tool is very easy to use. All you need is a laptop with Windows and a USB cable to connect to your car. You don't need any coding skills or special hardware. The tool will automatically detect your car model and the available languages, and let you choose the ones you want. You can change the master language and up to three additional languages. The tool will also change the voice of the navigation and the voice control if they are available in the selected language.</p>
-
-<h2>How to use CIC Language Coding Utility 1.2.2 16?</h2>
-
-<p>Here are the steps to use CIC Language Coding Utility 1.2.2 16:</p>
-
-<ol>
-<li>Download the tool from the official website of Bimmer Retrofit or from other sources (make sure they are reliable and virus-free).</li>
-<li>Extract the zip file and run the CIC Language Coding Utility.exe file.</li>
-<li>Connect your laptop to your car using a USB cable.</li>
-<li>Turn on the ignition of your car (do not start the engine).</li>
-<li>Select your car model from the drop-down menu on the tool.</li>
-<li>Select the master language and up to three additional languages from the list on the tool.</li>
-<li>Click on "Code" and wait for the process to finish (it should take less than a minute).</li>
-<li>The tool will notify you when it's done and ask you to restart your car.</li>
-<li>Turn off the ignition and disconnect the USB cable.</li>
-<li>Start your car and enjoy your new language settings.</li>
-</ol>
-
-<h2>What are the benefits of using CIC Language Coding Utility 1.2.2 16?</h2>
-
-<p>Using CIC Language Coding Utility 1.2.2 16 has many benefits for BMW owners, such as:</p>
-
-<ul>
-<li>You can change the language of your CIC iDrive system without going to a dealer or paying for a service.</li>
-<li>You can save time and money by doing it yourself in minutes.</li>
-<li>You can customize your CIC iDrive system according to your preferences and needs.</li>
-<li>You can improve your driving experience and safety by using a language that you are comfortable with.</li>
-<li>You can increase the resale value of your car by making it more appealing to potential buyers.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>CIC Language Coding Utility 1.2.2 16 is a great tool for BMW owners who want to change the language of their CIC iDrive system quickly and easily. It works with all models with CIC iDrive (not CCC iDrive), and it supports many languages, including voice control and navigation. It is simple to use, and it does not require any coding skills or special hardware. It is also affordable and reliable, and it offers many benefits for BMW owners.</p>
-
-<p>If you are interested in using CIC Language Coding Utility 1.2.2 16, you can download it from the official website of Bimmer Retrofit or from other sources (make sure they are trustworthy and virus-free). You can also check out some reviews and testimonials from other users who have used it successfully.</p>
-
-<p>We hope this article has helped you understand what CIC Language Coding Utility 1.2.2 16 is and how to use it. If you have any questions or feedback, feel free to leave a comment below.</p>
-<h2>How to download and install CIC Language Coding Utility 1.2.2 16?</h2>
-
-<p>If you want to use CIC Language Coding Utility 1.2.2 16, you need to download and install it on your laptop first. Here are the steps to do it:</p>
-<p></p>
-
-<ol>
-<li>Go to the official website of Bimmer Retrofit or another source that offers the tool for download (make sure it is safe and virus-free).</li>
-<li>Find the link for CIC Language Coding Utility 1.2.2 16 and click on it.</li>
-<li>Save the zip file to your laptop and extract it.</li>
-<li>Open the folder and double-click on the CIC Language Coding Utility.exe file.</li>
-<li>Follow the instructions on the screen to complete the installation.</li>
-</ol>
-
-<p>Once you have installed the tool, you can use it anytime you want to change the language of your CIC iDrive system. Just make sure you have a USB cable to connect your laptop to your car.</p>
-
-<h2>What are the features of CIC Language Coding Utility 1.2.2 16?</h2>
-
-<p>CIC Language Coding Utility 1.2.2 16 is a powerful and versatile tool that offers many features for BMW owners, such as:</p>
-
-<ul>
-<li>It supports many languages, including English, German, French, Italian, Spanish, Russian, Chinese, and more.</li>
-<li>It allows you to change the master language and up to three additional languages.</li>
-<li>It changes the voice of the navigation and the voice control if they are available in the selected language.</li>
-<li>It works with all models with CIC iDrive (not CCC iDrive).</li>
-<li>It does not require any coding skills or special hardware.</li>
-<li>It is fast and easy to use.</li>
-<li>It is affordable and reliable.</li>
-</ul>
-
-<p>CIC Language Coding Utility 1.2.2 16 is a must-have tool for BMW owners who want to customize their CIC iDrive system according to their preferences and needs. It can improve their driving experience and safety by using a language that they are comfortable with. It can also increase the resale value of their car by making it more appealing to potential buyers.</p>
-
-<h2>Frequently asked questions about CIC Language Coding Utility 1.2.2 16</h2>
-
-<p>Here are some of the most common questions that BMW owners have about CIC Language Coding Utility 1.2.2 16:</p>
-
-<dl>
-<dt>Is CIC Language Coding Utility 1.2.2 16 legal?</dt>
-<dd>CIC Language Coding Utility 1.2.2 16 is legal as long as you use it for personal use only and not for commercial purposes. It does not violate any BMW warranty or terms of service.</dd>
-<dt>Does CIC Language Coding Utility 1.2.2 16 work with other BMW systems?</dt>
-<dd>CIC Language Coding Utility 1.2.2 16 only works with CIC iDrive systems, which are found in BMW models from 2009 to 2013 (some models until 2016). It does not work with CCC iDrive systems, which are found in older models, or NBT iDrive systems, which are found in newer models.</dd>
-<dt>What are the differences between CIC iDrive and CCC iDrive?</dt>
-<dd>CIC iDrive and CCC iDrive are two different versions of BMW's infotainment system that control the navigation, audio, phone, and other features of the car. CIC iDrive stands for Car Information Computer iDrive, while CCC iDrive stands for Car Communication Computer iDrive. CIC iDrive is newer and more advanced than CCC iDrive, and it has a higher resolution screen, faster processor, more memory, better graphics, more features, and more languages.</dd>
-<dt>Can I change back to the original language settings?</dt>
-<dd>Yes, you can change back to the original language settings anytime you want by using CIC Language Coding Utility 1.2.2 16 again and selecting the original language as the master language.</dd>
-<dt>Will CIC Language Coding Utility 1.2.2 16 affect other settings or functions of my car?</dt>
-<dd>No, CIC Language Coding Utility 1.2.2 16 will only affect the language settings of your CIC iDrive system. It will not affect other settings or functions of your car, such as mileage, service intervals, engine performance, etc.</dd>
-</dl>
-
-<p>If you have any other questions or feedback about CIC Language Coding Utility 1.2.2 16, feel free to leave a comment below or contact Bimmer Retrofit directly.</p>
-<h2>How to troubleshoot CIC Language Coding Utility 1.2.2 16?</h2>
-
-<p>CIC Language Coding Utility 1.2.2 16 is a reliable and user-friendly tool, but sometimes you might encounter some issues or errors while using it. Here are some of the most common problems and how to fix them:</p>
-
-<dl>
-<dt>The tool does not detect my car model or the available languages.</dt>
-<dd>This might happen if your USB cable is faulty or not properly connected, or if your laptop does not have the required drivers. To solve this, make sure you use a good quality USB cable and connect it firmly to your laptop and your car. Also, make sure you have the latest drivers for your laptop and your car. You can download them from the official websites of your laptop manufacturer and BMW.</dd>
-<dt>The tool does not change the language of my CIC iDrive system.</dt>
-<dd>This might happen if you select a language that is not supported by your CIC iDrive system, or if you do not restart your car after coding. To solve this, make sure you select a language that is compatible with your CIC iDrive system. You can check the list of supported languages on the tool or on the official website of Bimmer Retrofit. Also, make sure you restart your car after coding to apply the changes.</dd>
-<dt>The tool changes the language of my CIC iDrive system, but not the voice of the navigation or the voice control.</dt>
-<dd>This might happen if the voice of the navigation or the voice control is not available in the selected language, or if you have a different version of the navigation software. To solve this, make sure you select a language that has a voice option for the navigation and the voice control. You can check this on the tool or on the official website of Bimmer Retrofit. Also, make sure you have the latest version of the navigation software for your car. You can update it from BMW or from other sources (make sure they are safe and virus-free).</dd>
-<dt>The tool causes an error message or a malfunction on my CIC iDrive system.</dt>
-<dd>This might happen if you use an outdated or corrupted version of the tool, or if you interrupt the coding process. To solve this, make sure you use the latest and original version of the tool from the official website of Bimmer Retrofit or from other sources (make sure they are reliable and virus-free). Also, make sure you do not turn off your laptop or your car during the coding process.</dd>
-</dl>
-
-<p>If you have any other issues or errors with CIC Language Coding Utility 1.2.2 16, feel free to contact Bimmer Retrofit for support. They will help you solve your problem as soon as possible.</p>
-
-<h2>What are some alternatives to CIC Language Coding Utility 1.2.2 16?</h2>
-
-<p>CIC Language Coding Utility 1.2.2 16 is one of the best tools for changing the language of your CIC iDrive system, but it is not the only one. There are some other options that you can try, such as:</p>
-
-<ul>
-<li>ISTA/P: This is a software that BMW dealers use to program and code various features and functions of BMW cars. It can also change the language of your CIC iDrive system, but it requires more skills and equipment than CIC Language Coding Utility 1.2.2 16. You need a laptop with Windows, an ICOM interface, and an internet connection to use it.</li>
-<li>E-Sys: This is a software that allows you to modify various parameters and settings of your BMW car using a coding file called FSC (Freischaltcode). It can also change the language of your CIC iDrive system, but it requires more skills and knowledge than CIC Language Coding Utility 1.2.2 16. You need a laptop with Windows, an ENET cable, and an FSC file for your car to use it.</li>
-<li>NCS Expert: This is a software that allows you to read and write various modules and data of your BMW car using a coding file called NETTODAT (Network Data). It can also change the language of your CIC iDrive system, but it requires more skills and experience than CIC Language Coding Utility 1.2.2 16. You need a laptop with Windows, an OBD cable, and a NETTODAT file for your car to use it.</li>
-</ul>
-
-<p>These are some of the alternatives to CIC Language Coding Utility 1.2.2 16 that you can try if you want to change the language of your CIC iDrive system by yourself. However, they are more complex and risky than CIC Language Coding Utility 1.2.2 16, and they might void your warranty or damage your car if you use them incorrectly.</p>
-
-<p>Therefore, we recommend that you use CIC Language Coding Utility 1.2.2 16 as your first choice for changing the language of your CIC iDrive system safely and easily.</p>
-<h2>Conclusion</h2>
-
-<p>In this article, we have explained what CIC Language Coding Utility 1.2.2 16 is and how to use it. We have also discussed the benefits, the troubleshooting, and the alternatives of this tool. We hope this article has helped you understand how to change the language of your BMW CIC iDrive system quickly and easily.</p>
-
-<p>CIC Language Coding Utility 1.2.2 16 is a great tool for BMW owners who want to customize their CIC iDrive system according to their preferences and needs. It is simple to use, and it does not require any coding skills or special hardware. It is also affordable and reliable, and it offers many benefits for BMW owners.</p>
-
-<p>If you are interested in using CIC Language Coding Utility 1.2.2 16, you can download it from the official website of Bimmer Retrofit or from other sources (make sure they are trustworthy and virus-free). You can also check out some reviews and testimonials from other users who have used it successfully.</p>
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-
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-<ul>
-<li>You can save money by not having to buy Twists with real money.</li>
-<li>You can play more games and access more features without any limitations.</li>
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-<li>You can have more fun and enjoyment without any stress or worry.</li>
-</ul>
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-
-<p>If you are interested in using Gametwist Codes Generator Exe to get free Twists for Gametwist, here are the steps you need to follow:</p>
-<p></p>
-
-<ol>
-<li>Go to <a href="https://gametwistcodesgeneratorexe.com/">https://gametwistcodesgeneratorexe.com/</a> and click on the download button.</li>
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-<li>Copy the code and paste it on the Gametwist website or app to redeem your free Twists.</li>
-<li>Enjoy your free gaming!</li>
-</ol>
-
-<p>Gametwist Codes Generator Exe is a simple and effective way to get free Twists for Gametwist. Download it today and start playing your favorite games without any hassle!</p>
-<h2>What are the Benefits of Gametwist Codes Generator Exe?</h2>
-
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-
-<ul>
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-</ul>
-
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-
-<p>Some people might wonder if Gametwist Codes Generator Exe is safe and legal to use. The answer is yes, as long as you follow some simple rules and precautions.</p>
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-
-<p>Gametwist Codes Generator Exe is legal to use because it does not violate any laws or regulations. It does not hack, crack, or manipulate the Gametwist system or servers. It does not steal or counterfeit any Twists or vouchers. It only creates random codes that are based on the same algorithm that Gametwist uses to generate its own codes. Therefore, it does not infringe any copyrights or trademarks of Gametwist or its partners.</p>
-
-<p>However, you should be careful when using Gametwist Codes Generator Exe and follow some common sense guidelines. You should not use the tool excessively or excessively generate codes. You should not share the tool or the codes with others. You should not use the codes for illegal or unethical purposes. You should respect the terms and conditions of Gametwist and its games. You should use the tool at your own risk and responsibility.</p>
-
-<p>Gametwist Codes Generator Exe is a safe and legal tool to get free Twists for Gametwist, as long as you use it wisely and responsibly. By doing so, you can enjoy your gaming without any worries or troubles.</p>
-<h2>What are the Features of Gametwist Codes Generator Exe?</h2>
-
-<p>Gametwist Codes Generator Exe is not only a tool to get free Twists for Gametwist, but also a tool that has many features that make it stand out from other similar tools. Some of the features of Gametwist Codes Generator Exe are:</p>
-
-<ul>
-<li>It is fast and easy to use. You can generate a code in seconds and redeem it in minutes.</li>
-<li>It is compatible with any device and operating system. You can use it on Windows, Mac, Linux, Android, iOS, and more.</li>
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-<li>It is free and unlimited. You can use it as many times as you want and get as many Twists as you need.</li>
-</ul>
-
-<p>Gametwist Codes Generator Exe is a tool that has everything you need to get free Twists for Gametwist. It is a tool that you can trust and rely on.</p>
-
-<h2>How to Get Gametwist Codes Generator Exe?</h2>
-
-<p>If you are interested in getting Gametwist Codes Generator Exe to get free Twists for Gametwist, you can follow these simple steps:</p>
-
-<ol>
-<li>Go to <a href="https://gametwistcodesgeneratorexe.com/">https://gametwistcodesgeneratorexe.com/</a> and click on the download button.</li>
-<li>Wait for the download to finish and open the file.</li>
-<li>Follow the instructions on the screen to install the software.</li>
-<li>Launch the software and enter your Gametwist username and the amount of Twists you want to get.</li>
-<li>Click on the generate button and wait for the code to appear.</li>
-<li>Copy the code and paste it on the Gametwist website or app to redeem your free Twists.</li>
-<li>Enjoy your free gaming!</li>
-</ol>
-
-<p>Gametwist Codes Generator Exe is a tool that you can get easily and quickly. It is a tool that will make your gaming more fun and exciting.</p>
-<h2>What are the Games You Can Play with Gametwist Codes Generator Exe?</h2>
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-
-<ul>
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-</ul>
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-<h2>How to Contact Gametwist Codes Generator Exe Support?</h2>
-
-<p>If you have any questions or issues regarding Gametwist Codes Generator Exe, you can contact the support team by using the following methods:</p>
-
-<ul>
-<li>Email: You can send an email to gametwistcodesgeneratorexe@gmail.com and explain your problem or inquiry. You will receive a reply within 24 hours.</li>
-<li>Website: You can visit the official website of Gametwist Codes Generator Exe at https://gametwistcodesgeneratorexe.com/ and check the FAQ section or the contact form. You can also leave a comment or a feedback on the website.</li>
-<li>Social Media: You can follow Gametwist Codes Generator Exe on Facebook, Twitter, Instagram, or YouTube and get updates, tips, news, and more. You can also send a message or a comment on these platforms.</li>
-</ul>
-
-<p>Gametwist Codes Generator Exe is a tool that has a friendly and helpful support team that is ready to assist you anytime. You can contact them by any of the methods above and get a solution or an answer to your problem or question.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/wav.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/wav.py
deleted file mode 100644
index a65c3b2ba5aacb1fcab3753f1f85ff7b8db7fc11..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/wav.py
+++ /dev/null
@@ -1,174 +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 OrderedDict
-import hashlib
-import math
-import json
-from pathlib import Path
-
-import julius
-import torch as th
-from torch import distributed
-import torchaudio as ta
-from torch.nn import functional as F
-
-from .audio import convert_audio_channels
-from .compressed import get_musdb_tracks
-
-MIXTURE = "mixture"
-EXT = ".wav"
-
-
-def _track_metadata(track, sources):
-    track_length = None
-    track_samplerate = None
-    for source in sources + [MIXTURE]:
-        file = track / f"{source}{EXT}"
-        info = ta.info(str(file))
-        length = info.num_frames
-        if track_length is None:
-            track_length = length
-            track_samplerate = info.sample_rate
-        elif track_length != length:
-            raise ValueError(
-                f"Invalid length for file {file}: "
-                f"expecting {track_length} but got {length}.")
-        elif info.sample_rate != track_samplerate:
-            raise ValueError(
-                f"Invalid sample rate for file {file}: "
-                f"expecting {track_samplerate} but got {info.sample_rate}.")
-        if source == MIXTURE:
-            wav, _ = ta.load(str(file))
-            wav = wav.mean(0)
-            mean = wav.mean().item()
-            std = wav.std().item()
-
-    return {"length": length, "mean": mean, "std": std, "samplerate": track_samplerate}
-
-
-def _build_metadata(path, sources):
-    meta = {}
-    path = Path(path)
-    for file in path.iterdir():
-        meta[file.name] = _track_metadata(file, sources)
-    return meta
-
-
-class Wavset:
-    def __init__(
-            self,
-            root, metadata, sources,
-            length=None, stride=None, normalize=True,
-            samplerate=44100, channels=2):
-        """
-        Waveset (or mp3 set for that matter). Can be used to train
-        with arbitrary sources. Each track should be one folder inside of `path`.
-        The folder should contain files named `{source}.{ext}`.
-        Files will be grouped according to `sources` (each source is a list of
-        filenames).
-
-        Sample rate and channels will be converted on the fly.
-
-        `length` is the sample size to extract (in samples, not duration).
-        `stride` is how many samples to move by between each example.
-        """
-        self.root = Path(root)
-        self.metadata = OrderedDict(metadata)
-        self.length = length
-        self.stride = stride or length
-        self.normalize = normalize
-        self.sources = sources
-        self.channels = channels
-        self.samplerate = samplerate
-        self.num_examples = []
-        for name, meta in self.metadata.items():
-            track_length = int(self.samplerate * meta['length'] / meta['samplerate'])
-            if length is None or track_length < length:
-                examples = 1
-            else:
-                examples = int(math.ceil((track_length - self.length) / self.stride) + 1)
-            self.num_examples.append(examples)
-
-    def __len__(self):
-        return sum(self.num_examples)
-
-    def get_file(self, name, source):
-        return self.root / name / f"{source}{EXT}"
-
-    def __getitem__(self, index):
-        for name, examples in zip(self.metadata, self.num_examples):
-            if index >= examples:
-                index -= examples
-                continue
-            meta = self.metadata[name]
-            num_frames = -1
-            offset = 0
-            if self.length is not None:
-                offset = int(math.ceil(
-                    meta['samplerate'] * self.stride * index / self.samplerate))
-                num_frames = int(math.ceil(
-                    meta['samplerate'] * self.length / self.samplerate))
-            wavs = []
-            for source in self.sources:
-                file = self.get_file(name, source)
-                wav, _ = ta.load(str(file), frame_offset=offset, num_frames=num_frames)
-                wav = convert_audio_channels(wav, self.channels)
-                wavs.append(wav)
-
-            example = th.stack(wavs)
-            example = julius.resample_frac(example, meta['samplerate'], self.samplerate)
-            if self.normalize:
-                example = (example - meta['mean']) / meta['std']
-            if self.length:
-                example = example[..., :self.length]
-                example = F.pad(example, (0, self.length - example.shape[-1]))
-            return example
-
-
-def get_wav_datasets(args, samples, sources):
-    sig = hashlib.sha1(str(args.wav).encode()).hexdigest()[:8]
-    metadata_file = args.metadata / (sig + ".json")
-    train_path = args.wav / "train"
-    valid_path = args.wav / "valid"
-    if not metadata_file.is_file() and args.rank == 0:
-        train = _build_metadata(train_path, sources)
-        valid = _build_metadata(valid_path, sources)
-        json.dump([train, valid], open(metadata_file, "w"))
-    if args.world_size > 1:
-        distributed.barrier()
-    train, valid = json.load(open(metadata_file))
-    train_set = Wavset(train_path, train, sources,
-                       length=samples, stride=args.data_stride,
-                       samplerate=args.samplerate, channels=args.audio_channels,
-                       normalize=args.norm_wav)
-    valid_set = Wavset(valid_path, valid, [MIXTURE] + sources,
-                       samplerate=args.samplerate, channels=args.audio_channels,
-                       normalize=args.norm_wav)
-    return train_set, valid_set
-
-
-def get_musdb_wav_datasets(args, samples, sources):
-    metadata_file = args.metadata / "musdb_wav.json"
-    root = args.musdb / "train"
-    if not metadata_file.is_file() and args.rank == 0:
-        metadata = _build_metadata(root, sources)
-        json.dump(metadata, open(metadata_file, "w"))
-    if args.world_size > 1:
-        distributed.barrier()
-    metadata = json.load(open(metadata_file))
-
-    train_tracks = get_musdb_tracks(args.musdb, is_wav=True, subsets=["train"], split="train")
-    metadata_train = {name: meta for name, meta in metadata.items() if name in train_tracks}
-    metadata_valid = {name: meta for name, meta in metadata.items() if name not in train_tracks}
-    train_set = Wavset(root, metadata_train, sources,
-                       length=samples, stride=args.data_stride,
-                       samplerate=args.samplerate, channels=args.audio_channels,
-                       normalize=args.norm_wav)
-    valid_set = Wavset(root, metadata_valid, [MIXTURE] + sources,
-                       samplerate=args.samplerate, channels=args.audio_channels,
-                       normalize=args.norm_wav)
-    return train_set, valid_set
diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/train/mel_processing.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/train/mel_processing.py
deleted file mode 100644
index f678099f02a6c91a6af820089b88eb8cb3cb6868..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/train/mel_processing.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import torch
-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):
-    return dynamic_range_compression_torch(magnitudes)
-
-
-def spectral_de_normalize_torch(magnitudes):
-    return dynamic_range_decompression_torch(magnitudes)
-
-
-# Reusable banks
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    """Convert waveform into Linear-frequency Linear-amplitude spectrogram.
-
-    Args:
-        y             :: (B, T) - Audio waveforms
-        n_fft
-        sampling_rate
-        hop_size
-        win_size
-        center
-    Returns:
-        :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
-    """
-    # Validation
-    if torch.min(y) < -1.07:
-        print("min value is ", torch.min(y))
-    if torch.max(y) > 1.07:
-        print("max value is ", torch.max(y))
-
-    # Window - Cache if needed
-    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
-        )
-
-    # Padding
-    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)
-
-    # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
-    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,
-    )
-
-    # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
-    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):
-    # MelBasis - Cache if needed
-    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(
-            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
-        )
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
-            dtype=spec.dtype, device=spec.device
-        )
-
-    # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
-    melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    melspec = spectral_normalize_torch(melspec)
-    return melspec
-
-
-def mel_spectrogram_torch(
-    y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
-):
-    """Convert waveform into Mel-frequency Log-amplitude spectrogram.
-
-    Args:
-        y       :: (B, T)           - Waveforms
-    Returns:
-        melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
-    """
-    # Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
-    spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)
-
-    # Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame)
-    melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax)
-
-    return melspec
diff --git a/spaces/radames/Candle-Phi-1.5-Wasm/style.css b/spaces/radames/Candle-Phi-1.5-Wasm/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/radames/Candle-Phi-1.5-Wasm/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/radames/Detecting-Photoshopped-Faces-FALdetector/networks/drn.py b/spaces/radames/Detecting-Photoshopped-Faces-FALdetector/networks/drn.py
deleted file mode 100644
index e2ba269d073aba1bc513a3c58ee50b14bb1c396e..0000000000000000000000000000000000000000
--- a/spaces/radames/Detecting-Photoshopped-Faces-FALdetector/networks/drn.py
+++ /dev/null
@@ -1,416 +0,0 @@
-import pdb
-
-import torch
-import torch.nn as nn
-import math
-import torch.utils.model_zoo as model_zoo
-
-torch.backends.cudnn.benchmark = True
-BatchNorm = nn.BatchNorm2d
-
-
-# __all__ = ['DRN', 'drn26', 'drn42', 'drn58']
-
-
-webroot = 'https://tigress-web.princeton.edu/~fy/drn/models/'
-
-model_urls = {
-    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
-    'drn-c-26': webroot + 'drn_c_26-ddedf421.pth',
-    'drn-c-42': webroot + 'drn_c_42-9d336e8c.pth',
-    'drn-c-58': webroot + 'drn_c_58-0a53a92c.pth',
-    'drn-d-22': webroot + 'drn_d_22-4bd2f8ea.pth',
-    'drn-d-38': webroot + 'drn_d_38-eebb45f0.pth',
-    'drn-d-54': webroot + 'drn_d_54-0e0534ff.pth',
-    'drn-d-105': webroot + 'drn_d_105-12b40979.pth'
-}
-
-
-def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1):
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=padding, bias=False, dilation=dilation)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None,
-                 dilation=(1, 1), residual=True):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride,
-                             padding=dilation[0], dilation=dilation[0])
-        self.bn1 = BatchNorm(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes,
-                             padding=dilation[1], dilation=dilation[1])
-        self.bn2 = BatchNorm(planes)
-        self.downsample = downsample
-        self.stride = stride
-        self.residual = residual
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-        if self.residual:
-            out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None,
-                 dilation=(1, 1), residual=True):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = BatchNorm(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=dilation[1], bias=False,
-                               dilation=dilation[1])
-        self.bn2 = BatchNorm(planes)
-        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
-        self.bn3 = BatchNorm(planes * 4)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class DRN(nn.Module):
-
-    def __init__(self, block, layers, num_classes=1000,
-                 channels=(16, 32, 64, 128, 256, 512, 512, 512),
-                 out_map=False, out_middle=False, pool_size=28, arch='D'):
-        super(DRN, self).__init__()
-        self.inplanes = channels[0]
-        self.out_map = out_map
-        self.out_dim = channels[-1]
-        self.out_middle = out_middle
-        self.arch = arch
-
-        if arch == 'C':
-            self.conv1 = nn.Conv2d(3, channels[0], kernel_size=7, stride=1,
-                                   padding=3, bias=False)
-            self.bn1 = BatchNorm(channels[0])
-            self.relu = nn.ReLU(inplace=True)
-
-            self.layer1 = self._make_layer(
-                BasicBlock, channels[0], layers[0], stride=1)
-            self.layer2 = self._make_layer(
-                BasicBlock, channels[1], layers[1], stride=2)
-        elif arch == 'D':
-            self.layer0 = nn.Sequential(
-                nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3,
-                          bias=False),
-                BatchNorm(channels[0]),
-                nn.ReLU(inplace=True)
-            )
-
-            self.layer1 = self._make_conv_layers(
-                channels[0], layers[0], stride=1)
-            self.layer2 = self._make_conv_layers(
-                channels[1], layers[1], stride=2)
-
-        self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2)
-        self.layer4 = self._make_layer(block, channels[3], layers[3], stride=2)
-        self.layer5 = self._make_layer(block, channels[4], layers[4],
-                                       dilation=2, new_level=False)
-        self.layer6 = None if layers[5] == 0 else \
-            self._make_layer(block, channels[5], layers[5], dilation=4,
-                             new_level=False)
-
-        if arch == 'C':
-            self.layer7 = None if layers[6] == 0 else \
-                self._make_layer(BasicBlock, channels[6], layers[6], dilation=2,
-                                 new_level=False, residual=False)
-            self.layer8 = None if layers[7] == 0 else \
-                self._make_layer(BasicBlock, channels[7], layers[7], dilation=1,
-                                 new_level=False, residual=False)
-        elif arch == 'D':
-            self.layer7 = None if layers[6] == 0 else \
-                self._make_conv_layers(channels[6], layers[6], dilation=2)
-            self.layer8 = None if layers[7] == 0 else \
-                self._make_conv_layers(channels[7], layers[7], dilation=1)
-
-        if num_classes > 0:
-            self.avgpool = nn.AvgPool2d(pool_size)
-            self.fc = nn.Conv2d(self.out_dim, num_classes, kernel_size=1,
-                                stride=1, padding=0, bias=True)
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2. / n))
-            elif isinstance(m, BatchNorm):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-    def _make_layer(self, block, planes, blocks, stride=1, dilation=1,
-                    new_level=True, residual=True):
-        assert dilation == 1 or dilation % 2 == 0
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                BatchNorm(planes * block.expansion),
-            )
-
-        layers = list()
-        layers.append(block(
-            self.inplanes, planes, stride, downsample,
-            dilation=(1, 1) if dilation == 1 else (
-                dilation // 2 if new_level else dilation, dilation),
-            residual=residual))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes, residual=residual,
-                                dilation=(dilation, dilation)))
-
-        return nn.Sequential(*layers)
-
-    def _make_conv_layers(self, channels, convs, stride=1, dilation=1):
-        modules = []
-        for i in range(convs):
-            modules.extend([
-                nn.Conv2d(self.inplanes, channels, kernel_size=3,
-                          stride=stride if i == 0 else 1,
-                          padding=dilation, bias=False, dilation=dilation),
-                BatchNorm(channels),
-                nn.ReLU(inplace=True)])
-            self.inplanes = channels
-        return nn.Sequential(*modules)
-
-    def forward(self, x):
-        y = list()
-
-        if self.arch == 'C':
-            x = self.conv1(x)
-            x = self.bn1(x)
-            x = self.relu(x)
-        elif self.arch == 'D':
-            x = self.layer0(x)
-
-        x = self.layer1(x)
-        y.append(x)
-        x = self.layer2(x)
-        y.append(x)
-
-        x = self.layer3(x)
-        y.append(x)
-
-        x = self.layer4(x)
-        y.append(x)
-
-        x = self.layer5(x)
-        y.append(x)
-
-        if self.layer6 is not None:
-            x = self.layer6(x)
-            y.append(x)
-
-        if self.layer7 is not None:
-            x = self.layer7(x)
-            y.append(x)
-
-        if self.layer8 is not None:
-            x = self.layer8(x)
-            y.append(x)
-
-        if self.out_map:
-            x = self.fc(x)
-        else:
-            x = self.avgpool(x)
-            x = self.fc(x)
-            x = x.view(x.size(0), -1)
-
-        if self.out_middle:
-            return x, y
-        else:
-            return x
-
-
-class DRN_A(nn.Module):
-
-    def __init__(self, block, layers, num_classes=1000):
-        self.inplanes = 64
-        super(DRN_A, self).__init__()
-        self.out_dim = 512 * block.expansion
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
-                               bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
-                                       dilation=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
-                                       dilation=4)
-        self.avgpool = nn.AvgPool2d(28, stride=1)
-        self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2. / n))
-            elif isinstance(m, BatchNorm):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-        # for m in self.modules():
-        #     if isinstance(m, nn.Conv2d):
-        #         nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-        #     elif isinstance(m, nn.BatchNorm2d):
-        #         nn.init.constant_(m.weight, 1)
-        #         nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes,
-                                dilation=(dilation, dilation)))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-
-        return x
-
-
-def drn_a_50(pretrained=False, **kwargs):
-    model = DRN_A(Bottleneck, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
-    return model
-
-
-def drn_c_26(pretrained=False, **kwargs):
-    model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='C', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-c-26']))
-    return model
-
-
-def drn_c_42(pretrained=False, **kwargs):
-    model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-c-42']))
-    return model
-
-
-def drn_c_58(pretrained=False, **kwargs):
-    model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-c-58']))
-    return model
-
-
-def drn_d_22(pretrained=False, **kwargs):
-    model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='D', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-22']))
-    return model
-
-
-def drn_d_24(pretrained=False, **kwargs):
-    model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 2, 2], arch='D', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-24']))
-    return model
-
-
-def drn_d_38(pretrained=False, **kwargs):
-    model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-38']))
-    return model
-
-
-def drn_d_40(pretrained=False, **kwargs):
-    model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-40']))
-    return model
-
-
-def drn_d_54(pretrained=False, **kwargs):
-    model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-54']))
-    return model
-
-
-def drn_d_56(pretrained=False, **kwargs):
-    model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-56']))
-    return model
-
-
-def drn_d_105(pretrained=False, **kwargs):
-    model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 1, 1], arch='D', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-105']))
-    return model
-
-
-def drn_d_107(pretrained=False, **kwargs):
-    model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 2, 2], arch='D', **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-107']))
-    return model
diff --git a/spaces/radames/MusicGen-Continuation/audiocraft/modules/__init__.py b/spaces/radames/MusicGen-Continuation/audiocraft/modules/__init__.py
deleted file mode 100644
index 81ba30f6466ff91b90490a4fb92f7d3d0d00144d..0000000000000000000000000000000000000000
--- a/spaces/radames/MusicGen-Continuation/audiocraft/modules/__init__.py
+++ /dev/null
@@ -1,20 +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.
-
-# flake8: noqa
-from .conv import (
-    NormConv1d,
-    NormConv2d,
-    NormConvTranspose1d,
-    NormConvTranspose2d,
-    StreamableConv1d,
-    StreamableConvTranspose1d,
-    pad_for_conv1d,
-    pad1d,
-    unpad1d,
-)
-from .lstm import StreamableLSTM
-from .seanet import SEANetEncoder, SEANetDecoder
diff --git a/spaces/radames/Real-Time-Latent-Consistency-Model/img2img/index.html b/spaces/radames/Real-Time-Latent-Consistency-Model/img2img/index.html
deleted file mode 100644
index 1534cfd3f01334c96bc858ceae8357992b0ca4ae..0000000000000000000000000000000000000000
--- a/spaces/radames/Real-Time-Latent-Consistency-Model/img2img/index.html
+++ /dev/null
@@ -1,383 +0,0 @@
-<!doctype html>
-<html>
-
-<head>
-    <meta charset="UTF-8">
-    <title>Real-Time Latent Consistency Model</title>
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <script
-        src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
-    <script src="https://cdn.jsdelivr.net/npm/piexifjs@1.0.6/piexif.min.js"></script>
-    <script src="https://cdn.tailwindcss.com"></script>
-    <style type="text/tailwindcss">
-        .button {
-          @apply bg-gray-700 hover:bg-gray-800 text-white font-normal p-2 rounded disabled:bg-gray-300 dark:disabled:bg-gray-700 disabled:cursor-not-allowed dark:disabled:text-black
-        }
-    </style>
-    <script type="module">
-        const getValue = (id) => {
-            const el = document.querySelector(`${id}`)
-            if (el.type === "checkbox")
-                return el.checked;
-            return el.value;
-        }
-
-        const startBtn = document.querySelector("#start");
-        const stopBtn = document.querySelector("#stop");
-        const videoEl = document.querySelector("#webcam");
-        const imageEl = document.querySelector("#player");
-        const queueSizeEl = document.querySelector("#queue_size");
-        const errorEl = document.querySelector("#error");
-        const snapBtn = document.querySelector("#snap");
-        const webcamsEl = document.querySelector("#webcams");
-
-        function LCMLive(webcamVideo, liveImage) {
-            let websocket;
-
-            async function start() {
-                return new Promise((resolve, reject) => {
-                    const websocketURL = `${window.location.protocol === "https:" ? "wss" : "ws"
-                        }:${window.location.host}/ws`;
-
-                    const socket = new WebSocket(websocketURL);
-                    socket.onopen = () => {
-                        console.log("Connected to websocket");
-                    };
-                    socket.onclose = () => {
-                        console.log("Disconnected from websocket");
-                        stop();
-                        resolve({ "status": "disconnected" });
-                    };
-                    socket.onerror = (err) => {
-                        console.error(err);
-                        reject(err);
-                    };
-                    socket.onmessage = (event) => {
-                        const data = JSON.parse(event.data);
-                        switch (data.status) {
-                            case "success":
-                                break;
-                            case "start":
-                                const userId = data.userId;
-                                initVideoStream(userId);
-                                break;
-                            case "timeout":
-                                stop();
-                                resolve({ "status": "timeout" });
-                            case "error":
-                                stop();
-                                reject(data.message);
-
-                        }
-                    };
-                    websocket = socket;
-                })
-            }
-            function switchCamera() {
-                const constraints = {
-                    audio: false,
-                    video: { width: 1024, height: 1024, deviceId: mediaDevices[webcamsEl.value].deviceId }
-                };
-                navigator.mediaDevices
-                    .getUserMedia(constraints)
-                    .then((mediaStream) => {
-                        webcamVideo.removeEventListener("timeupdate", videoTimeUpdateHandler);
-                        webcamVideo.srcObject = mediaStream;
-                        webcamVideo.onloadedmetadata = () => {
-                            webcamVideo.play();
-                            webcamVideo.addEventListener("timeupdate", videoTimeUpdateHandler);
-                        };
-                    })
-                    .catch((err) => {
-                        console.error(`${err.name}: ${err.message}`);
-                    });
-            }
-
-            async function videoTimeUpdateHandler() {
-                const dimension = getValue("input[name=dimension]:checked");
-                const [WIDTH, HEIGHT] = JSON.parse(dimension);
-
-                const canvas = new OffscreenCanvas(WIDTH, HEIGHT);
-                const videoW = webcamVideo.videoWidth;
-                const videoH = webcamVideo.videoHeight;
-                const aspectRatio = WIDTH / HEIGHT;
-
-                const ctx = canvas.getContext("2d");
-                ctx.drawImage(webcamVideo, videoW / 2 - videoH * aspectRatio / 2, 0, videoH * aspectRatio, videoH, 0, 0, WIDTH, HEIGHT)
-                const blob = await canvas.convertToBlob({ type: "image/jpeg", quality: 1 });
-                websocket.send(blob);
-                websocket.send(JSON.stringify({
-                    "seed": getValue("#seed"),
-                    "prompt": getValue("#prompt"),
-                    "guidance_scale": getValue("#guidance-scale"),
-                    "strength": getValue("#strength"),
-                    "steps": getValue("#steps"),
-                    "lcm_steps": getValue("#lcm_steps"),
-                    "width": WIDTH,
-                    "height": HEIGHT,
-                }));
-            }
-            let mediaDevices = [];
-            async function initVideoStream(userId) {
-                liveImage.src = `/stream/${userId}`;
-                await navigator.mediaDevices.enumerateDevices()
-                    .then(devices => {
-                        const cameras = devices.filter(device => device.kind === 'videoinput');
-                        mediaDevices = cameras;
-                        webcamsEl.innerHTML = "";
-                        cameras.forEach((camera, index) => {
-                            const option = document.createElement("option");
-                            option.value = index;
-                            option.innerText = camera.label;
-                            webcamsEl.appendChild(option);
-                            option.selected = index === 0;
-                        });
-                        webcamsEl.addEventListener("change", switchCamera);
-                    })
-                    .catch(err => {
-                        console.error(err);
-                    });
-                const constraints = {
-                    audio: false,
-                    video: { width: 1024, height: 1024, deviceId: mediaDevices[0].deviceId }
-                };
-                navigator.mediaDevices
-                    .getUserMedia(constraints)
-                    .then((mediaStream) => {
-                        webcamVideo.srcObject = mediaStream;
-                        webcamVideo.onloadedmetadata = () => {
-                            webcamVideo.play();
-                            webcamVideo.addEventListener("timeupdate", videoTimeUpdateHandler);
-                        };
-                    })
-                    .catch((err) => {
-                        console.error(`${err.name}: ${err.message}`);
-                    });
-            }
-
-            async function stop() {
-                websocket.close();
-                navigator.mediaDevices.getUserMedia({ video: true }).then((mediaStream) => {
-                    mediaStream.getTracks().forEach((track) => track.stop());
-                });
-                webcamVideo.removeEventListener("timeupdate", videoTimeUpdateHandler);
-                webcamsEl.removeEventListener("change", switchCamera);
-                webcamVideo.srcObject = null;
-            }
-            return {
-                start,
-                stop
-            }
-        }
-        function toggleMessage(type) {
-            errorEl.hidden = false;
-            errorEl.scrollIntoView();
-            switch (type) {
-                case "error":
-                    errorEl.innerText = "To many users are using the same GPU, please try again later.";
-                    errorEl.classList.toggle("bg-red-300", "text-red-900");
-                    break;
-                case "success":
-                    errorEl.innerText = "Your session has ended, please start a new one.";
-                    errorEl.classList.toggle("bg-green-300", "text-green-900");
-                    break;
-            }
-            setTimeout(() => {
-                errorEl.hidden = true;
-            }, 2000);
-        }
-        function snapImage() {
-            try {
-                const zeroth = {};
-                const exif = {};
-                const gps = {};
-                zeroth[piexif.ImageIFD.Make] = "LCM Image-to-Image";
-                zeroth[piexif.ImageIFD.ImageDescription] = `prompt: ${getValue("#prompt")} | seed: ${getValue("#seed")} | guidance_scale: ${getValue("#guidance-scale")} | strength: ${getValue("#strength")} | lcm_steps: ${getValue("#lcm_steps")} | steps: ${getValue("#steps")}`;
-                zeroth[piexif.ImageIFD.Software] = "https://github.com/radames/Real-Time-Latent-Consistency-Model";
-
-                exif[piexif.ExifIFD.DateTimeOriginal] = new Date().toISOString();
-
-                const exifObj = { "0th": zeroth, "Exif": exif, "GPS": gps };
-                const exifBytes = piexif.dump(exifObj);
-
-                const canvas = document.createElement("canvas");
-                canvas.width = imageEl.naturalWidth;
-                canvas.height = imageEl.naturalHeight;
-                const ctx = canvas.getContext("2d");
-                ctx.drawImage(imageEl, 0, 0);
-                const dataURL = canvas.toDataURL("image/jpeg");
-                const withExif = piexif.insert(exifBytes, dataURL);
-
-                const a = document.createElement("a");
-                a.href = withExif;
-                a.download = `lcm_txt_2_img${Date.now()}.png`;
-                a.click();
-            } catch (err) {
-                console.log(err);
-            }
-        }
-
-
-        const lcmLive = LCMLive(videoEl, imageEl);
-        startBtn.addEventListener("click", async () => {
-            try {
-                startBtn.disabled = true;
-                snapBtn.disabled = false;
-                const res = await lcmLive.start();
-                startBtn.disabled = false;
-                if (res.status === "timeout")
-                    toggleMessage("success")
-            } catch (err) {
-                console.log(err);
-                toggleMessage("error")
-                startBtn.disabled = false;
-            }
-        });
-        stopBtn.addEventListener("click", () => {
-            lcmLive.stop();
-        });
-        window.addEventListener("beforeunload", () => {
-            lcmLive.stop();
-        });
-        snapBtn.addEventListener("click", snapImage);
-        setInterval(() =>
-            fetch("/queue_size")
-                .then((res) => res.json())
-                .then((data) => {
-                    queueSizeEl.innerText = data.queue_size;
-                })
-                .catch((err) => {
-                    console.log(err);
-                })
-            , 5000);
-    </script>
-</head>
-
-<body class="text-black dark:bg-gray-900 dark:text-white">
-    <div class="fixed right-2 top-2 p-4 font-bold text-sm rounded-lg max-w-xs text-center" id="error">
-    </div>
-    <main class="container mx-auto px-4 py-4 max-w-4xl flex flex-col gap-4">
-        <article class="text-center max-w-xl mx-auto">
-            <h1 class="text-3xl font-bold">Real-Time Latent Consistency Model</h1>
-            <h2 class="text-2xl font-bold mb-4">Image to Image</h2>
-            <p class="text-sm">
-                This demo showcases
-                <a href="https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7" target="_blank"
-                    class="text-blue-500 underline hover:no-underline">LCM</a> Image to Image pipeline
-                using
-                <a href="https://github.com/huggingface/diffusers/tree/main/examples/community#latent-consistency-pipeline"
-                    target="_blank" class="text-blue-500 underline hover:no-underline">Diffusers</a> with a MJPEG
-                stream server.
-            </p>
-            <p class="text-sm">
-                There are <span id="queue_size" class="font-bold">0</span> user(s) sharing the same GPU, affecting
-                real-time performance. Maximum queue size is 4. <a
-                    href="https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model?duplicate=true"
-                    target="_blank" class="text-blue-500 underline hover:no-underline">Duplicate</a> and run it on your
-                own GPU.
-            </p>
-        </article>
-        <div>
-            <h2 class="font-medium">Prompt</h2>
-            <p class="text-sm text-gray-500">
-                Change the prompt to generate different images, accepts <a
-                    href="https://github.com/damian0815/compel/blob/main/doc/syntax.md" target="_blank"
-                    class="text-blue-500 underline hover:no-underline">Compel</a> syntax.
-            </p>
-            <div class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center">
-                <textarea type="text" id="prompt" class="font-light w-full px-3 py-2 mx-1  outline-none dark:text-black"
-                    title="Prompt, this is an example, feel free to modify"
-                    placeholder="Add your prompt here...">Portrait of The Terminator with , glare pose, detailed, intricate, full of colour, cinematic lighting, trending on artstation, 8k, hyperrealistic, focused, extreme details, unreal engine 5, cinematic, masterpiece</textarea>
-            </div>
-
-        </div>
-        <div class="">
-            <details>
-                <summary class="font-medium cursor-pointer">Advanced Options</summary>
-                <div class="grid grid-cols-3 sm:grid-cols-6 items-center gap-3 py-3">
-                    <label for="webcams" class="text-sm font-medium">Camera Options: </label>
-                    <select id="webcams" class="text-sm border-2 border-gray-500 rounded-md font-light dark:text-black">
-                    </select>
-                    <div></div>
-                    <label class="text-sm font-medium " for="steps">Inference Steps
-                    </label>
-                    <input type="range" id="steps" name="steps" min="1" max="20" value="4"
-                        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">
-                        4</output>
-                    <!--  -->
-                    <label class="text-sm font-medium" for="lcm_steps">LCM Inference Steps
-                    </label>
-                    <input type="range" id="lcm_steps" name="lcm_steps" min="2" max="60" value="50"
-                        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">
-                        50</output>
-                    <!--  -->
-                    <label class="text-sm font-medium" for="guidance-scale">Guidance Scale
-                    </label>
-                    <input type="range" id="guidance-scale" name="guidance-scale" min="0" max="30" step="0.001"
-                        value="8.0" 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">
-                        8.0</output>
-                    <!--  -->
-                    <label class="text-sm font-medium" for="strength">Strength</label>
-                    <input type="range" id="strength" name="strength" min="0.1" max="1" step="0.001" value="0.50"
-                        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">
-                        0.5</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 dark:text-black">
-                    <button
-                        onclick="document.querySelector('#seed').value = Math.floor(Math.random() * Number.MAX_SAFE_INTEGER)"
-                        class="button">
-                        Rand
-                    </button>
-                    <!--  -->
-                    <!--  -->
-                    <label class="text-sm font-medium" for="dimension">Image Dimensions</label>
-                    <div class="col-span-2 flex gap-2">
-                        <div class="flex gap-1">
-                            <input type="radio" id="dimension512" name="dimension" value="[512,512]" checked
-                                class="cursor-pointer">
-                            <label for="dimension512" class="text-sm cursor-pointer">512x512</label>
-                        </div>
-                        <div class="flex gap-1">
-                            <input type="radio" id="dimension768" name="dimension" value="[768,768]"
-                                lass="cursor-pointer">
-                            <label for="dimension768" class="text-sm cursor-pointer">768x768</label>
-                        </div>
-                    </div>
-                    <!--  -->
-                </div>
-            </details>
-        </div>
-        <div class="flex gap-3">
-            <button id="start" class="button">
-                Start
-            </button>
-            <button id="stop" class="button">
-                Stop
-            </button>
-            <button id="snap" disabled class="button ml-auto">
-                Snapshot
-            </button>
-        </div>
-        <div class="relative rounded-lg border border-slate-300 overflow-hidden">
-            <img id="player" class="w-full aspect-square rounded-lg "
-                src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=">
-            <div class="absolute top-0 left-0 w-1/4 aspect-square">
-                <video id="webcam" class="w-full aspect-square relative z-10 object-cover" playsinline autoplay muted
-                    loop></video>
-                <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 448" width="100"
-                    class="w-full p-4 absolute top-0 opacity-20 z-0">
-                    <path fill="currentColor"
-                        d="M224 256a128 128 0 1 0 0-256 128 128 0 1 0 0 256zm-45.7 48A178.3 178.3 0 0 0 0 482.3 29.7 29.7 0 0 0 29.7 512h388.6a29.7 29.7 0 0 0 29.7-29.7c0-98.5-79.8-178.3-178.3-178.3h-91.4z" />
-                </svg>
-            </div>
-        </div>
-    </main>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/AutoCAD 2009 Fr Keygen [CRACKED].rar.md b/spaces/raedeXanto/academic-chatgpt-beta/AutoCAD 2009 Fr Keygen [CRACKED].rar.md
deleted file mode 100644
index 3f2af35f5e51d024f69702673299cc4857926e8c..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/AutoCAD 2009 Fr Keygen [CRACKED].rar.md	
+++ /dev/null
@@ -1,143 +0,0 @@
-<br />
-<h1>AutoCAD 2009 Fr keygen.rar: What is it and how to use it?</h1>
- <p>If you are looking for a software that can help you create professional and accurate drawings, you might have heard of AutoCAD. AutoCAD is one of the most popular and widely used computer-aided design (CAD) software in the world. It allows you to design, draft, model, and visualize anything from buildings, bridges, machines, products, landscapes, and more.</p>
- <p>AutoCAD 2009 is an older version of AutoCAD that was released in 2008 by Autodesk, the company that develops and sells AutoCAD. It introduced some new features and improvements, such as ribbon interface, quick properties, action recorder, layer dialog enhancements, annotation scaling, PDF import and export, and more.</p>
-<h2>AutoCAD 2009 Fr keygen.rar</h2><br /><p><b><b>DOWNLOAD</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://tinourl.com/2uL0L6">https://tinourl.com/2uL0L6</a></b></p><br /><br />
- <p>However, AutoCAD 2009 is not a free software. You need to purchase a license from Autodesk or an authorized reseller to use it legally. A license can cost thousands of dollars depending on the type and duration of the license. This can be a barrier for some people who want to use AutoCAD 2009 for personal or educational purposes.</p>
- <p>That's why some people resort to using a keygen. A keygen is a software that can generate serial numbers or activation codes for other software. By using a keygen, you can bypass the license verification process and use AutoCAD 2009 without paying for it.</p>
- <p>One of the files that claims to be a keygen for AutoCAD 2009 is called AutoCAD 2009 Fr keygen.rar. This file is supposed to contain a French version of the keygen for AutoCAD 2009. But is it safe and reliable? How do you use it? And what are the consequences of using it?</p>
- <p>In this article, we will answer these questions and more. We will show you how to download and install AutoCAD 2009 Fr keygen.rar, how to use AutoCAD 2009 for your projects, and what are the risks and legal issues of using a keygen. Read on to find out more.</p>
- <h2>How to download and install AutoCAD 2009 Fr keygen.rar?</h2>
- <p>The first step to use AutoCAD 2009 Fr keygen.rar is to download it from a source that claims to provide it. However, this can be tricky and risky. There are many websites that offer this file, but not all of them are trustworthy. Some of them may contain viruses, malware, spyware, or other harmful programs that can damage your computer or steal your personal information.</p>
- <p>Therefore, you need to be careful when downloading any file from the internet. You should always scan the file with an antivirus software before opening it. You should also check the comments and reviews of other users who have downloaded the file before you. You should avoid any file that has negative feedback or suspicious links.</p>
- <p>One of the websites that claims to provide AutoCAD 2009 Fr keygen.rar is https://civilmdc.com/2020/03/10/autodesk-2009-2010-2011-and-2012-all-products-x-force-keygenerator/. This website also provides instructions on how to use the keygen. However, we cannot guarantee that this website is safe or reliable. Use it at your own risk.</p>
- <p>If you decide to download AutoCAD 2009 Fr keygen.rar from this website or any other source, here are the steps you need to follow:</p>
-<p>AutoCAD 2009 Fr crack download<br />
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- <ol>
-<li>Download the file by clicking on the link or button that says "Download X Force 2009 crack" or something similar.</li>
-<li>Save the file in a folder on your computer where you can easily find it.</li>
-<li>The file should be in a compressed format called .rar. You need a software that can extract .rar files, such as WinRAR or 7-Zip.</li>
-<li>Right-click on the file and choose "Extract here" or "Extract to" from the menu.</li>
-<li>You should see a folder called "X-FORCE Key Generator" or something similar.</li>
-<li>Open the folder and double-click on the file called "x-force_32bits.exe" or "x-force_64bits.exe" depending on your system architecture.</li>
-<li>You should see a window that says "X-FORCE Key Generator".</li>
-</ol>
- <h2>How to generate a serial number and activate AutoCAD 2009?</h2>
- <p>The next step is to generate a serial number for AutoCAD 2009 using the keygen. Here are the steps you need to follow:</p>
- <ol>
-<li>Before running the keygen, make sure you have installed AutoCAD 2009 on your computer. You can download it from https://www.autodesk.com/products/autocad/free-trial or any other source that provides it.</li>
-<li>After installing AutoCAD 2009, launch it by clicking on its icon on your desktop or start menu.</li>
-<li>You should see a window that asks you to activate your product. Click on "Activate".</li>
-<li>If it tells you that your serial number is wrong or invalid, click on "Close" and then click on "Activate" again.</li>
-<li>Select "I have an activation code from Autodesk".</li>
-<li>Go back to the keygen window and click on "Mem Patch". You should see a message that says "Successfully patched".</li>
-<li>Copy the request code from the activation window and paste it into the keygen window where it says "Request".</li>
-<li>Click on "Generate". You should see an activation code in the keygen window.</li>
-<li>Copy the activation code from the keygen window and paste it into the activation window where it says "Enter activation code".</li>
-<li>Click on "Next". You should see a message that says "Thank you for activating your Autodesk product".</li>
-<li>Congratulations! You have successfully activated AutoCAD 2009 using a keygen.</li>
-</ol>
- <h2>How to use AutoCAD 2009 for your projects?</h2>
- <p>Now that you have activated AutoCAD 2009 using a keygen, you can start using it for your projects. Here are some of the basic steps you need to follow:</p>
- <ol>
-<li>Create a new drawing by clicking on "File" > "New" or pressing Ctrl+N on your keyboard.</li>
-<li>Select a template or drawing type from the list. For example, if you want to create a drawing in metric units with architectural settings, select "acadiso.dwt". Click on "Open".</li>
-<li>You should see a blank drawing area with a grid and axes. This is where you will create your drawing elements.</li>
-<li>To draw something in AutoCAD 2009, you need to use commands. Commands are words or abbreviations that tell AutoCAD what action you want to perform. For example, if you want to draw a line, you need to type "line" or "l" in the command line at the bottom of the screen.</li>
-<li>You can also access commands from menus, toolbars, ribbons, palettes, or panels. For example, if you want to draw a circle, you can click on the circle icon in the draw panel of the ribbon.</li>
-<li>To execute a command, press Enter or Spacebar on your keyboard. You can also right-click and choose "Enter" from the menu.</li>
-<li>Follow the prompts that appear in the command line or on the screen. For example, if you are drawing a line, you need to specify the start point and the end point of the line. You can enter coordinates, select points with your mouse, or use object snaps to pick precise locations.</li>
-<li>To finish a command, press Enter or Spacebar again. You can also right-click and choose "Exit" from the menu.</li>
-<li>To modify an existing drawing element, you can use commands such as move, copy, rotate, scale, trim, extend, erase, and more. You can also use grips to manipulate objects directly.</li>
-<li>To add text, dimensions, leaders, tables, hatches, blocks, or other annotations to your drawing, you can use commands such as text, dim, mtext, table, hatch, insert, and more.</li>
-<li>To change the properties of your drawing elements, such as color, layer, linetype, lineweight, or transparency, you can use commands such as properties, layer, matchprop, chprop, and more. You can also use the Properties palette to view and edit properties.</li>
-<li>To view your drawing from different angles or zoom levels, you can use commands such as zoom, pan, orbit, 3dorbit, swivel, and more. You can also use the mouse wheel to zoom and pan.</li>
-<li>To save your drawing, click on "File" > "Save" or press Ctrl+S on your keyboard. You can also click on the save icon on the quick access toolbar.</li>
-<li>To export your drawing to another format, such as PDF, JPG, DWG, DXF, or DWF, click on "File" > "Export" or press Ctrl+E on your keyboard. You can also use the Export to DWF/PDF panel of the output tab of the ribbon.</li>
-<li>To print or plot your drawing to a printer or plotter, click on "File" > "Plot" or press Ctrl+P on your keyboard. You can also use the Plot panel of the output tab of the ribbon.</li>
-<li>To share your drawing with others via email or online services, click on "File" > "Send" or press Ctrl+M on your keyboard. You can also use the Online panel of the output tab of the ribbon.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to download and install AutoCAD 2009 Fr keygen.rar, how to generate a serial number and activate AutoCAD 2009 using a keygen, and how to use AutoCAD 2009 for your projects. We hope you have learned something useful and enjoyed reading this article.</p>
- <p>However, we also want to warn you about the potential consequences of using a keygen for AutoCAD 2009. Using a keygen is illegal and unethical. It violates the terms and conditions of Autodesk and infringes their intellectual property rights. It also exposes you to security risks and technical problems. You may get infected with viruses or malware that can harm your computer or compromise your data. You may also experience errors or crashes that can corrupt your drawings or prevent you from using AutoCAD 2009 properly. You may also lose access to updates, support, or online services that Autodesk provides for its customers.</p>
- <p>Therefore, we strongly advise you not to use a keygen for AutoCAD 2009 or any other software. Instead, we recommend you to purchase a legitimate license from Autodesk or an authorized reseller. This way, you can enjoy all the benefits and features of AutoCAD 2009 without any worries or regrets. You can also support Autodesk in developing and improving their products and services for the benefit of all users.</p>
- <p>If you want to learn more about AutoCAD 2009 or any other Autodesk products, you can visit their official website at https://www.autodesk.com/. There, you can find more information, resources, tutorials, forums, and blogs that can help you with your CAD needs.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about AutoCAD 2009 Fr keygen.rar:</p>
- <ol>
-<li><b>What are some alternatives to using a keygen for AutoCAD 2009?</b></li>
-<p>Some alternatives to using a keygen for AutoCAD 2009 are:</p>
-<ul>
-<li>Using a free trial version of AutoCAD 2009 or any other version that Autodesk offers on their website. You can use it for 30 days without any limitations.</li>
-<li>Using a student version of AutoCAD 2009 or any other version that Autodesk offers for students and educators on their website. You can use it for 3 years with a valid educational email address.</li>
-<li>Using an older version of AutoCAD that is no longer supported by Autodesk but still works on your computer. You may be able to find it online or from someone who has it.</li>
-<li>Using a different CAD software that is free or cheaper than AutoCAD 2009. Some examples are LibreCAD, DraftSight, NanoCAD, SketchUp, or Blender.</li>
-</ul>
-<li><b>Is AutoCAD 2009 compatible with Windows 10?</b></li>
-<p>No, AutoCAD 2009 is not officially compatible with Windows 10. It was designed for Windows XP, Vista, and 7. It may work on Windows 10 with some tweaks and adjustments, but it is not guaranteed or supported by Autodesk.</p>
-<li><b>How to update AutoCAD 2009 to the latest version?</b></li>
-<p>You cannot update AutoCAD 2009 to the latest version directly. You need to purchase a new license for the latest version of AutoCAD from Autodesk or an authorized reseller. You can then install it on your computer and migrate your settings and files from AutoCAD 2009.</p>
-<li><b>How to uninstall AutoCAD 2009 from your computer?</b></li>
-<p>To uninstall AutoCAD 2009 from your computer, you need to follow these steps:</p>
-<ul>
-<li>Close AutoCAD 2009 if it is running.</li>
-<li>Go to Control Panel > Programs > Programs and Features.</li>
-<li>Select AutoCAD 2009 from the list and click on Uninstall/Change.</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-<li>Delete any remaining files or folders related to AutoCAD 2009 from your computer.</li>
-</ul>
-<li><b>How to contact Autodesk for support or feedback?</b></li>
-<p>You can contact Autodesk for support or feedback by visiting their website at https://www.autodesk.com/. There, you can find various options for contacting them via phone, email, chat, or social media. You can also access their knowledge base, community forums, and blogs for more help and information.</p>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Construction Equipment Economics PDF Download An Integrative Framework for Assessing Construction Sector Capacity.md b/spaces/raedeXanto/academic-chatgpt-beta/Construction Equipment Economics PDF Download An Integrative Framework for Assessing Construction Sector Capacity.md
deleted file mode 100644
index 945393511228d95a0ff3a0c30e47a19e24ed0f36..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Construction Equipment Economics PDF Download An Integrative Framework for Assessing Construction Sector Capacity.md	
+++ /dev/null
@@ -1,137 +0,0 @@
-<br />
-<h1>Construction Equipment Economics: A Guide for Contractors and Engineers</h1>
- <p>Construction equipment is a vital resource for any construction project, whether it is building a skyscraper, a bridge, or a road. However, owning and operating construction equipment is also a major cost factor that can affect the profitability and competitiveness of contractors and engineers. Therefore, it is essential to understand the principles and practices of construction equipment economics, which is the study of how to optimize the selection, utilization, and management of construction equipment.</p>
- <h2>Introduction</h2>
- <h3>What is construction equipment economics?</h3>
- <p>Construction equipment economics is a branch of engineering economics that deals with the analysis of the costs and benefits of using different types of construction equipment for various construction activities. It involves estimating the ownership and operating costs of equipment, as well as its productivity, efficiency, and performance. It also involves comparing different alternatives of equipment acquisition, such as renting, leasing, or buying, and evaluating their financial implications.</p>
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- <h3>Why is it important to understand construction equipment economics?</h3>
- <p>Understanding construction equipment economics is important for several reasons:</p>
- <ul>
-<li>It helps contractors and engineers to make informed decisions about what kind of equipment to use, how much to use, and how long to use it for each project.</li>
-<li>It helps contractors and engineers to plan and budget their equipment costs and revenues, and to monitor and control them throughout the project lifecycle.</li>
-<li>It helps contractors and engineers to improve their productivity and profitability by optimizing their equipment utilization and management.</li>
-<li>It helps contractors and engineers to reduce their environmental impact by minimizing their fuel consumption, emissions, waste, and noise generated by their equipment.</li>
-</ul>
- <h2>Cost of Owning and Operating Construction Equipment</h2>
- <h3>Factors affecting equipment ownership costs</h3>
- <p>The ownership costs of construction equipment are the fixed costs that are incurred regardless of how much the equipment is used. They include:</p>
- <h4>Depreciation</h4>
- <p>Depreciation is the loss in value of the equipment due to wear and tear, obsolescence, or market conditions. It can be calculated using different methods, such as straight-line, declining-balance, or sum-of-the-years'-digits. The depreciation rate depends on factors such as the initial cost, the salvage value, the useful life, and the usage pattern of the equipment.</p>
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- <h4>Interest</h4>
- <p>Interest is the cost of borrowing money to finance the purchase of the equipment. It can be calculated using different methods, such as simple interest or compound interest. The interest rate depends on factors such as the loan amount, the loan term, the interest rate per period, and the payment frequency.</p>
- <h4>Taxes</h4>
- <p>Taxes are the fees imposed by the government on the ownership or use of the equipment. They can include property taxes, sales taxes, excise taxes, or income taxes. The tax rate depends on factors such as the location, the type, and the value of the equipment.</p>
- <h4>Insurance</h4>
- <p>Insurance is the cost of protecting the equipment from risks such as theft, fire, vandalism, or accidents. It can be calculated based on factors such as the replacement cost, the deductible amount, the coverage limit, and the premium rate.</p>
- <h4>Storage</h4>
- <p>Storage is the cost of keeping the equipment in a safe and secure place when it is not in use. It can include rent, utilities, security, maintenance, or transportation costs. The storage cost depends on factors such as the size, weight, shape, and mobility of the equipment.</p>
- <h3>Factors affecting equipment operating costs</h3>
- <p>The operating costs of construction equipment are the variable costs that are incurred depending on how much the equipment is used. They include:</p>
- <h4>Fuel</h4>
- <p>Fuel is the cost of providing energy to power the engine or motor of the equipment. It can be calculated based on factors such as fuel type (gasoline, diesel, electricity, etc.), fuel price per unit (gallon, liter, kilowatt-hour, etc.), fuel consumption per unit time (gallons per hour, liters per hour, kilowatts per hour, etc.), and operating time (hours per day, days per week, weeks per month, etc.).</p>
- <h4>Maintenance and repair</h4>
- <p>Maintenance and repair are the costs of keeping the equipment in good working condition and fixing any problems that may arise. They can include labor, materials, tools, and parts costs. They can be calculated based on factors such as maintenance schedule (preventive, corrective, or predictive), maintenance frequency (daily, weekly, monthly, etc.), maintenance duration (hours per maintenance), repair probability (percentage of breakdowns), repair time (hours per repair), and repair cost (dollars per repair).</p>
- <h4>Labor</h4>
- <p>Labor is the cost of hiring and paying the workers who operate the equipment. It can include wages, benefits, taxes, and overheads. It can be calculated based on factors such as labor rate (dollars per hour), labor productivity (units per hour), labor efficiency (percentage of productive time), and labor utilization (percentage of available time).</p>
- <h4>Overhead and profit</h4>
- <p>Overhead and profit are the costs of running the business and making a profit from the use of the equipment. They can include administrative, marketing, legal, accounting, and other indirect costs. They can be calculated based on factors such as overhead rate (percentage of direct costs), profit margin (percentage of total costs), and markup factor (percentage of total revenue).</p>
- <h2>Estimating Construction Equipment Productivity</h2>
- <h3>Methods of estimating equipment productivity</h3>
- <p>The productivity of construction equipment is a measure of how much output (work done) can be achieved with a given input (resources used) in a given time period. It can be expressed in different units depending on the type of work being performed by the equipment. For example:</p>
-<ul>
-<li>Cubic yards per hour for earthmoving equipment</li>
-<li>Tons per hour for hauling equipment</li>
-<li>Square feet per hour for paving equipment</li>
-<li>Cubic yards per hour for concrete placing equipment</li>
-<li>Pieces per hour for lifting equipment</li>
-</ul>
-<p>The methods of estimating equipment productivity can be classified into four categories:</p>
- <h4>Manufacturer's data</h4>
- <p>This method uses data provided by manufacturers or suppliers in their catalogs or manuals that indicate typical productivity rates for different types or models of their products under standard conditions. This method is simple and convenient but may not reflect actual site conditions or work methods.</p>
- <h4>Historical data</h4>
- <p>This method uses data collected from previous projects or similar projects that have used similar types or models of equipments under similar conditions. This method is more realistic but may require adjustments for differences in site conditions or work methods.</p>
- <h4>Empirical formulas</h4>
- <p>This method uses formulas derived from statistical analysis or engineering principles that relate productivity rates to various factors such as site conditions , work methods , or equipments characteristics . This method is more analytical but may require complex calculations or assumptions.</p>
- <h4>Simulation models</h4>
- <p>This method uses computer programs that simulate equipments operations based on inputs such as site conditions , work methods , equipments characteristics , schedules , etc . This method is more comprehensive but may require specialized software or expertise.</p>
- <h3>Factors affecting equipments productivity</h3>
- materials.</li>
-<li>Excavators: Equipments used for digging or moving earth or other materials.</li>
-<li>Cranes: Equipments used for lifting or moving heavy objects.</li>
-<li>Concrete mixers: Equipments used for mixing cement, sand, water, and aggregates to produce concrete.</li>
-<li>Concrete pumps: Equipments used for transferring concrete from mixers to placement locations.</li>
-<li>Pavers: Equipments used for laying asphalt or concrete on roads or other surfaces.</li>
-<li>Rollers: Equipments used for compacting soil, gravel, asphalt, or concrete.</li>
-<li>Drills: Equipments used for making holes in rocks or other materials.</li>
-<li>Blasters: Equipments used for breaking rocks or other materials using explosives.</li>
-</ul>
- <li><b>How can I reduce my equipment costs?</b></li>
-<p>You can reduce your equipment costs by following these tips:</p>
- <ul>
-<li>Choose the right type and size of equipment for the work to be performed.</li>
-<li>Compare different alternatives of equipment acquisition, such as renting, leasing, or buying, and choose the most economical one.</li>
-<li>Maintain your equipment regularly and repair it promptly to avoid breakdowns and downtime.</li>
-<li>Train your operators and supervisors to use and manage your equipment properly and safely.</li>
-<li>Coordinate your equipment with other resources such as materials, labor, and other equipments to avoid delays and conflicts.</li>
-</ul>
- <li><b>How can I improve my equipment productivity?</b></li>
-<p>You can improve your equipment productivity by following these tips:</p>
- <ul>
-<li>Estimate your equipment productivity using reliable methods and data sources.</li>
-<li>Monitor and measure your equipment productivity using appropriate indicators and tools.</li>
-<li>Analyze and identify the factors that affect your equipment productivity and their relative importance.</li>
-<li>Implement and evaluate improvement actions to address the factors that affect your equipment productivity.</li>
-</ul>
- <p> Thank you for reading this article. I hope you have learned something new and useful about construction equipment economics. If you have any questions or feedback, please let me know.</p> 0a6ba089eb<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Fashion Solitaire Download LINK Free Full Versionhttps Scoutmails.com Index301.php K Fashion Solitaire.md b/spaces/raedeXanto/academic-chatgpt-beta/Fashion Solitaire Download LINK Free Full Versionhttps Scoutmails.com Index301.php K Fashion Solitaire.md
deleted file mode 100644
index 572a122364785670a6cbadc1f4684af8b0b685e8..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Fashion Solitaire Download LINK Free Full Versionhttps Scoutmails.com Index301.php K Fashion Solitaire.md	
+++ /dev/null
@@ -1,43 +0,0 @@
-<br />
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-<p>Do you love fashion and solitaire? If yes, then you will love Fashion Solitaire, a unique game that combines the classic card game with the glamorous world of fashion. Fashion Solitaire is a free online solitaire game designed especially for girls who want to have fun and express their creativity. In this game, you can match garments with models, put together trendy outfits and create collections in eight fabulous looks. You can also design your own clothes, shop from hundreds of designer items and share your creations with other players. Fashion Solitaire is the hottest fashion game in town!</p>
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-<p>Fashion Solitaire is a fun and stylish game for girls who love fashion and solitaire. It offers a unique gameplay experience that combines the classic card game with the glamorous world of fashion. You can match garments with models, put together trendy outfits and create collections in eight fabulous looks. You can also design your own clothes, shop from hundreds of designer items and share your creations with other players. Fashion Solitaire is the hottest fashion game in town!</p>
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Fruit Machine Emulators And Machines Crack.md b/spaces/raedeXanto/academic-chatgpt-beta/Fruit Machine Emulators And Machines Crack.md
deleted file mode 100644
index 85e159db80c6f0c7c21ce5777dd742f9a4582f56..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Fruit Machine Emulators And Machines Crack.md	
+++ /dev/null
@@ -1,32 +0,0 @@
-
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-<p>Fruit machines are games of luck, but there are some things you can do to improve your odds and have more fun. Here are some tips and tricks for playing fruit machines:</p>
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diff --git a/spaces/raghu8096/PDF-QA/app.py b/spaces/raghu8096/PDF-QA/app.py
deleted file mode 100644
index 8d08b936c091b9a1affc34ec04915c01a0197ea8..0000000000000000000000000000000000000000
--- a/spaces/raghu8096/PDF-QA/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import gradio as gr
-import PyPDF2
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores.faiss import FAISS
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain import OpenAI, VectorDBQA
-
-import os
-openai_api_key = os.environ["OPENAI_API_KEY"] 
-
-
-def pdf_to_text(pdf_file, query):
-  # Open the PDF file in binary mode
-  with open(pdf_file.name, 'rb') as pdf_file:
-      # Create a PDF reader object
-      pdf_reader = PyPDF2.PdfReader(pdf_file)
-
-      # Create an empty string to store the text
-      text = ""
-
-      # Loop through each page of the PDF
-      for page_num in range(len(pdf_reader.pages)):
-          # Get the page object
-          page = pdf_reader.pages[page_num]
-          # Extract the texst from the page and add it to the text variable
-          text += page.extract_text()
-    #embedding step 
-  text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-  texts = text_splitter.split_text(text)
-
-  embeddings = OpenAIEmbeddings()
-  #vector store
-  vectorstore = FAISS.from_texts(texts, embeddings)
-
-    #inference
-  qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=vectorstore)
-  return qa.run(query)
-
-examples = [
-    [os.path.abspath("NASDAQ_AAPL_2020.pdf"), "how much are the outstanding shares ?"],
-    [os.path.abspath("NASDAQ_AAPL_2020.pdf"), "what is competitors strategy ?"],
-    [os.path.abspath("NASDAQ_AAPL_2020.pdf"), "who is the chief executive officer ?"],
-    [os.path.abspath("NASDAQ_MSFT_2020.pdf"), "How much is the guided revenue for next quarter?"],
-    [os.path.abspath("example_file.pdf"), "what are the name of all authors?"],
-    [os.path.abspath("breast cancer.pdf"), "What Causes Breast Cancer?"],
-    [os.path.abspath("v61n3a11.pdf"), "what is MDT?"]
-    
-]
-      
-
-
-# Define the Gradio interface
-#pdf_input = [gr.inputs.File(label="PDF File")]
-#query_input = gr.inputs.Textbox(label="Query")
-#outputs = gr.outputs.Textbox(label="Chatbot Response")
-
-interface = gr.Interface(fn=pdf_to_text, 
-                         inputs= [gr.inputs.File(label="input pdf file"), gr.inputs.Textbox(label="Question:")],
-                         outputs =gr.outputs.Textbox(label="Chatbot Response"),
-                         examples = examples,
-                        )
-
-# Run the interface
-interface.launch(enable_queue = True)
\ No newline at end of file
diff --git a/spaces/rasyidf/coffee-grader/app.py b/spaces/rasyidf/coffee-grader/app.py
deleted file mode 100644
index 632984bc99196e9b3da8b9225a2fdffeb55261c2..0000000000000000000000000000000000000000
--- a/spaces/rasyidf/coffee-grader/app.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import tensorflow
-from tensorflow import keras
-import gradio as gr
-
-model = keras.models.load_model('mymodel.h5')
-potato_classes = ['Grade_3','Grade_2','Grade_1']
-
-def predict_input_image(img):
-  img_3d=img.reshape(-1,256,256,3)
-  prediction=model.predict(img_3d)[0]
-  return {potato_classes[i]: float(prediction[i]) for i in range(3)}
-
-image = gr.inputs.Image(shape=(256,256))
-label = gr.outputs.Label(num_top_classes=3)
-gr.Interface(fn=predict_input_image, inputs=image, outputs=label,interpretation='default').launch()
\ No newline at end of file
diff --git a/spaces/ravisingh15/ligand_distance/app.py b/spaces/ravisingh15/ligand_distance/app.py
deleted file mode 100644
index d438202ca4d0788f0577458a90eb196b7c3e1b20..0000000000000000000000000000000000000000
--- a/spaces/ravisingh15/ligand_distance/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-#Importing librarier
-import pandas as pd
-from rdkit import Chem
-from rdkit.Chem import MACCSkeys
-from rdkit import DataStructs
-import gradio as gr
-from rdkit.Chem import Draw
-from rdkit.Chem.Draw import MolsToGridImage
-
-#loading data file
-data=pd.read_csv('data.csv')
-#generating structures
-structures=[Chem.MolFromSmiles(data['SMILES'][i]) for i in range(len(data['SMILES']))]
-#generating fingerprints
-fp=[MACCSkeys.GenMACCSKeys(x) for x in structures]
-
-def similarity(input_smiles):
-    input_structure=Chem.MolFromSmiles(input_smiles)
-    input_fp=MACCSkeys.GenMACCSKeys(input_structure)
-    similarity_matrix=[DataStructs.FingerprintSimilarity(fp[i],input_fp, metric=DataStructs.CosineSimilarity) for i in range(len(fp))]
-    maxpos = similarity_matrix.index(max(similarity_matrix))
-    im=Draw.MolToImage(structures[maxpos])
-    #im.save('mol.png')
-    #Draw.MolToFile(structures[maxpos],'mol.png')
-    return im,data['Name'][maxpos]
-    
-#intializing gradio
-app= gr.Interface(fn=similarity,inputs='text', outputs=[gr.outputs.Image(type="pil"),'text'])
-app.launch()
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/util.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/util.d.ts
deleted file mode 100644
index 6d350195b0cac64d298183b458cc65c9b4b6549f..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/util.d.ts
+++ /dev/null
@@ -1,1926 +0,0 @@
-/**
- * The `util` module supports the needs of Node.js internal APIs. Many of the
- * utilities are useful for application and module developers as well. To access
- * it:
- *
- * ```js
- * const util = require('util');
- * ```
- * @see [source](https://github.com/nodejs/node/blob/v18.x/lib/util.js)
- */
-declare module 'util' {
-    import * as types from 'node:util/types';
-    export interface InspectOptions {
-        /**
-         * If `true`, object's non-enumerable symbols and properties are included in the formatted result.
-         * `WeakMap` and `WeakSet` entries are also included as well as user defined prototype properties (excluding method properties).
-         * @default false
-         */
-        showHidden?: boolean | undefined;
-        /**
-         * Specifies the number of times to recurse while formatting object.
-         * This is useful for inspecting large objects.
-         * To recurse up to the maximum call stack size pass `Infinity` or `null`.
-         * @default 2
-         */
-        depth?: number | null | undefined;
-        /**
-         * If `true`, the output is styled with ANSI color codes. Colors are customizable.
-         */
-        colors?: boolean | undefined;
-        /**
-         * If `false`, `[util.inspect.custom](depth, opts, inspect)` functions are not invoked.
-         * @default true
-         */
-        customInspect?: boolean | undefined;
-        /**
-         * If `true`, `Proxy` inspection includes the target and handler objects.
-         * @default false
-         */
-        showProxy?: boolean | undefined;
-        /**
-         * Specifies the maximum number of `Array`, `TypedArray`, `WeakMap`, and `WeakSet` elements
-         * to include when formatting. Set to `null` or `Infinity` to show all elements.
-         * Set to `0` or negative to show no elements.
-         * @default 100
-         */
-        maxArrayLength?: number | null | undefined;
-        /**
-         * Specifies the maximum number of characters to
-         * include when formatting. Set to `null` or `Infinity` to show all elements.
-         * Set to `0` or negative to show no characters.
-         * @default 10000
-         */
-        maxStringLength?: number | null | undefined;
-        /**
-         * The length at which input values are split across multiple lines.
-         * Set to `Infinity` to format the input as a single line
-         * (in combination with `compact` set to `true` or any number >= `1`).
-         * @default 80
-         */
-        breakLength?: number | undefined;
-        /**
-         * Setting this to `false` causes each object key
-         * to be displayed on a new line. It will also add new lines to text that is
-         * longer than `breakLength`. If set to a number, the most `n` inner elements
-         * are united on a single line as long as all properties fit into
-         * `breakLength`. Short array elements are also grouped together. Note that no
-         * text will be reduced below 16 characters, no matter the `breakLength` size.
-         * For more information, see the example below.
-         * @default true
-         */
-        compact?: boolean | number | undefined;
-        /**
-         * If set to `true` or a function, all properties of an object, and `Set` and `Map`
-         * entries are sorted in the resulting string.
-         * If set to `true` the default sort is used.
-         * If set to a function, it is used as a compare function.
-         */
-        sorted?: boolean | ((a: string, b: string) => number) | undefined;
-        /**
-         * If set to `true`, getters are going to be
-         * inspected as well. If set to `'get'` only getters without setter are going
-         * to be inspected. If set to `'set'` only getters having a corresponding
-         * setter are going to be inspected. This might cause side effects depending on
-         * the getter function.
-         * @default false
-         */
-        getters?: 'get' | 'set' | boolean | undefined;
-        /**
-         * If set to `true`, an underscore is used to separate every three digits in all bigints and numbers.
-         * @default false
-         */
-        numericSeparator?: boolean | undefined;
-    }
-    export type Style = 'special' | 'number' | 'bigint' | 'boolean' | 'undefined' | 'null' | 'string' | 'symbol' | 'date' | 'regexp' | 'module';
-    export type CustomInspectFunction = (depth: number, options: InspectOptionsStylized) => any; // TODO: , inspect: inspect
-    export interface InspectOptionsStylized extends InspectOptions {
-        stylize(text: string, styleType: Style): string;
-    }
-    /**
-     * The `util.format()` method returns a formatted string using the first argument
-     * as a `printf`\-like format string which can contain zero or more format
-     * specifiers. Each specifier is replaced with the converted value from the
-     * corresponding argument. Supported specifiers are:
-     *
-     * If a specifier does not have a corresponding argument, it is not replaced:
-     *
-     * ```js
-     * util.format('%s:%s', 'foo');
-     * // Returns: 'foo:%s'
-     * ```
-     *
-     * Values that are not part of the format string are formatted using`util.inspect()` if their type is not `string`.
-     *
-     * If there are more arguments passed to the `util.format()` method than the
-     * number of specifiers, the extra arguments are concatenated to the returned
-     * string, separated by spaces:
-     *
-     * ```js
-     * util.format('%s:%s', 'foo', 'bar', 'baz');
-     * // Returns: 'foo:bar baz'
-     * ```
-     *
-     * If the first argument does not contain a valid format specifier, `util.format()`returns a string that is the concatenation of all arguments separated by spaces:
-     *
-     * ```js
-     * util.format(1, 2, 3);
-     * // Returns: '1 2 3'
-     * ```
-     *
-     * If only one argument is passed to `util.format()`, it is returned as it is
-     * without any formatting:
-     *
-     * ```js
-     * util.format('%% %s');
-     * // Returns: '%% %s'
-     * ```
-     *
-     * `util.format()` is a synchronous method that is intended as a debugging tool.
-     * Some input values can have a significant performance overhead that can block the
-     * event loop. Use this function with care and never in a hot code path.
-     * @since v0.5.3
-     * @param format A `printf`-like format string.
-     */
-    export function format(format?: any, ...param: any[]): string;
-    /**
-     * This function is identical to {@link format}, except in that it takes
-     * an `inspectOptions` argument which specifies options that are passed along to {@link inspect}.
-     *
-     * ```js
-     * util.formatWithOptions({ colors: true }, 'See object %O', { foo: 42 });
-     * // Returns 'See object { foo: 42 }', where `42` is colored as a number
-     * // when printed to a terminal.
-     * ```
-     * @since v10.0.0
-     */
-    export function formatWithOptions(inspectOptions: InspectOptions, format?: any, ...param: any[]): string;
-    /**
-     * Returns the string name for a numeric error code that comes from a Node.js API.
-     * The mapping between error codes and error names is platform-dependent.
-     * See `Common System Errors` for the names of common errors.
-     *
-     * ```js
-     * fs.access('file/that/does/not/exist', (err) => {
-     *   const name = util.getSystemErrorName(err.errno);
-     *   console.error(name);  // ENOENT
-     * });
-     * ```
-     * @since v9.7.0
-     */
-    export function getSystemErrorName(err: number): string;
-    /**
-     * Returns a Map of all system error codes available from the Node.js API.
-     * The mapping between error codes and error names is platform-dependent.
-     * See `Common System Errors` for the names of common errors.
-     *
-     * ```js
-     * fs.access('file/that/does/not/exist', (err) => {
-     *   const errorMap = util.getSystemErrorMap();
-     *   const name = errorMap.get(err.errno);
-     *   console.error(name);  // ENOENT
-     * });
-     * ```
-     * @since v16.0.0, v14.17.0
-     */
-    export function getSystemErrorMap(): Map<number, [string, string]>;
-    /**
-     * The `util.log()` method prints the given `string` to `stdout` with an included
-     * timestamp.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.log('Timestamped message.');
-     * ```
-     * @since v0.3.0
-     * @deprecated Since v6.0.0 - Use a third party module instead.
-     */
-    export function log(string: string): void;
-    /**
-     * Returns the `string` after replacing any surrogate code points
-     * (or equivalently, any unpaired surrogate code units) with the
-     * Unicode "replacement character" U+FFFD.
-     * @since v16.8.0, v14.18.0
-     */
-    export function toUSVString(string: string): string;
-    /**
-     * Creates and returns an `AbortController` instance whose `AbortSignal` is marked
-     * as transferable and can be used with `structuredClone()` or `postMessage()`.
-     * @since v18.11.0
-     * @returns A transferable AbortController
-     */
-    export function transferableAbortController(): AbortController;
-    /**
-     * Marks the given {AbortSignal} as transferable so that it can be used with
-     * `structuredClone()` and `postMessage()`.
-     *
-     * ```js
-     * const signal = transferableAbortSignal(AbortSignal.timeout(100));
-     * const channel = new MessageChannel();
-     * channel.port2.postMessage(signal, [signal]);
-     * ```
-     * @since v18.11.0
-     * @param signal The AbortSignal
-     * @returns The same AbortSignal
-     */
-    export function transferableAbortSignal(signal: AbortSignal): AbortSignal;
-    /**
-     * The `util.inspect()` method returns a string representation of `object` that is
-     * intended for debugging. The output of `util.inspect` may change at any time
-     * and should not be depended upon programmatically. Additional `options` may be
-     * passed that alter the result.`util.inspect()` will use the constructor's name and/or `@@toStringTag` to make
-     * an identifiable tag for an inspected value.
-     *
-     * ```js
-     * class Foo {
-     *   get [Symbol.toStringTag]() {
-     *     return 'bar';
-     *   }
-     * }
-     *
-     * class Bar {}
-     *
-     * const baz = Object.create(null, { [Symbol.toStringTag]: { value: 'foo' } });
-     *
-     * util.inspect(new Foo()); // 'Foo [bar] {}'
-     * util.inspect(new Bar()); // 'Bar {}'
-     * util.inspect(baz);       // '[foo] {}'
-     * ```
-     *
-     * Circular references point to their anchor by using a reference index:
-     *
-     * ```js
-     * const { inspect } = require('util');
-     *
-     * const obj = {};
-     * obj.a = [obj];
-     * obj.b = {};
-     * obj.b.inner = obj.b;
-     * obj.b.obj = obj;
-     *
-     * console.log(inspect(obj));
-     * // <ref *1> {
-     * //   a: [ [Circular *1] ],
-     * //   b: <ref *2> { inner: [Circular *2], obj: [Circular *1] }
-     * // }
-     * ```
-     *
-     * The following example inspects all properties of the `util` object:
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * console.log(util.inspect(util, { showHidden: true, depth: null }));
-     * ```
-     *
-     * The following example highlights the effect of the `compact` option:
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * const o = {
-     *   a: [1, 2, [[
-     *     'Lorem ipsum dolor sit amet,\nconsectetur adipiscing elit, sed do ' +
-     *       'eiusmod \ntempor incididunt ut labore et dolore magna aliqua.',
-     *     'test',
-     *     'foo']], 4],
-     *   b: new Map([['za', 1], ['zb', 'test']])
-     * };
-     * console.log(util.inspect(o, { compact: true, depth: 5, breakLength: 80 }));
-     *
-     * // { a:
-     * //   [ 1,
-     * //     2,
-     * //     [ [ 'Lorem ipsum dolor sit amet,\nconsectetur [...]', // A long line
-     * //           'test',
-     * //           'foo' ] ],
-     * //     4 ],
-     * //   b: Map(2) { 'za' => 1, 'zb' => 'test' } }
-     *
-     * // Setting `compact` to false or an integer creates more reader friendly output.
-     * console.log(util.inspect(o, { compact: false, depth: 5, breakLength: 80 }));
-     *
-     * // {
-     * //   a: [
-     * //     1,
-     * //     2,
-     * //     [
-     * //       [
-     * //         'Lorem ipsum dolor sit amet,\n' +
-     * //           'consectetur adipiscing elit, sed do eiusmod \n' +
-     * //           'tempor incididunt ut labore et dolore magna aliqua.',
-     * //         'test',
-     * //         'foo'
-     * //       ]
-     * //     ],
-     * //     4
-     * //   ],
-     * //   b: Map(2) {
-     * //     'za' => 1,
-     * //     'zb' => 'test'
-     * //   }
-     * // }
-     *
-     * // Setting `breakLength` to e.g. 150 will print the "Lorem ipsum" text in a
-     * // single line.
-     * ```
-     *
-     * The `showHidden` option allows [`WeakMap`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/WeakMap) and
-     * [`WeakSet`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/WeakSet) entries to be
-     * inspected. If there are more entries than `maxArrayLength`, there is no
-     * guarantee which entries are displayed. That means retrieving the same [`WeakSet`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/WeakSet) entries twice may
-     * result in different output. Furthermore, entries
-     * with no remaining strong references may be garbage collected at any time.
-     *
-     * ```js
-     * const { inspect } = require('util');
-     *
-     * const obj = { a: 1 };
-     * const obj2 = { b: 2 };
-     * const weakSet = new WeakSet([obj, obj2]);
-     *
-     * console.log(inspect(weakSet, { showHidden: true }));
-     * // WeakSet { { a: 1 }, { b: 2 } }
-     * ```
-     *
-     * The `sorted` option ensures that an object's property insertion order does not
-     * impact the result of `util.inspect()`.
-     *
-     * ```js
-     * const { inspect } = require('util');
-     * const assert = require('assert');
-     *
-     * const o1 = {
-     *   b: [2, 3, 1],
-     *   a: '`a` comes before `b`',
-     *   c: new Set([2, 3, 1])
-     * };
-     * console.log(inspect(o1, { sorted: true }));
-     * // { a: '`a` comes before `b`', b: [ 2, 3, 1 ], c: Set(3) { 1, 2, 3 } }
-     * console.log(inspect(o1, { sorted: (a, b) => b.localeCompare(a) }));
-     * // { c: Set(3) { 3, 2, 1 }, b: [ 2, 3, 1 ], a: '`a` comes before `b`' }
-     *
-     * const o2 = {
-     *   c: new Set([2, 1, 3]),
-     *   a: '`a` comes before `b`',
-     *   b: [2, 3, 1]
-     * };
-     * assert.strict.equal(
-     *   inspect(o1, { sorted: true }),
-     *   inspect(o2, { sorted: true })
-     * );
-     * ```
-     *
-     * The `numericSeparator` option adds an underscore every three digits to all
-     * numbers.
-     *
-     * ```js
-     * const { inspect } = require('util');
-     *
-     * const thousand = 1_000;
-     * const million = 1_000_000;
-     * const bigNumber = 123_456_789n;
-     * const bigDecimal = 1_234.123_45;
-     *
-     * console.log(thousand, million, bigNumber, bigDecimal);
-     * // 1_000 1_000_000 123_456_789n 1_234.123_45
-     * ```
-     *
-     * `util.inspect()` is a synchronous method intended for debugging. Its maximum
-     * output length is approximately 128 MB. Inputs that result in longer output will
-     * be truncated.
-     * @since v0.3.0
-     * @param object Any JavaScript primitive or `Object`.
-     * @return The representation of `object`.
-     */
-    export function inspect(object: any, showHidden?: boolean, depth?: number | null, color?: boolean): string;
-    export function inspect(object: any, options?: InspectOptions): string;
-    export namespace inspect {
-        let colors: NodeJS.Dict<[number, number]>;
-        let styles: {
-            [K in Style]: string;
-        };
-        let defaultOptions: InspectOptions;
-        /**
-         * Allows changing inspect settings from the repl.
-         */
-        let replDefaults: InspectOptions;
-        /**
-         * That can be used to declare custom inspect functions.
-         */
-        const custom: unique symbol;
-    }
-    /**
-     * Alias for [`Array.isArray()`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/isArray).
-     *
-     * Returns `true` if the given `object` is an `Array`. Otherwise, returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isArray([]);
-     * // Returns: true
-     * util.isArray(new Array());
-     * // Returns: true
-     * util.isArray({});
-     * // Returns: false
-     * ```
-     * @since v0.6.0
-     * @deprecated Since v4.0.0 - Use `isArray` instead.
-     */
-    export function isArray(object: unknown): object is unknown[];
-    /**
-     * Returns `true` if the given `object` is a `RegExp`. Otherwise, returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isRegExp(/some regexp/);
-     * // Returns: true
-     * util.isRegExp(new RegExp('another regexp'));
-     * // Returns: true
-     * util.isRegExp({});
-     * // Returns: false
-     * ```
-     * @since v0.6.0
-     * @deprecated Since v4.0.0 - Deprecated
-     */
-    export function isRegExp(object: unknown): object is RegExp;
-    /**
-     * Returns `true` if the given `object` is a `Date`. Otherwise, returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isDate(new Date());
-     * // Returns: true
-     * util.isDate(Date());
-     * // false (without 'new' returns a String)
-     * util.isDate({});
-     * // Returns: false
-     * ```
-     * @since v0.6.0
-     * @deprecated Since v4.0.0 - Use {@link types.isDate} instead.
-     */
-    export function isDate(object: unknown): object is Date;
-    /**
-     * Returns `true` if the given `object` is an `Error`. Otherwise, returns`false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isError(new Error());
-     * // Returns: true
-     * util.isError(new TypeError());
-     * // Returns: true
-     * util.isError({ name: 'Error', message: 'an error occurred' });
-     * // Returns: false
-     * ```
-     *
-     * This method relies on `Object.prototype.toString()` behavior. It is
-     * possible to obtain an incorrect result when the `object` argument manipulates`@@toStringTag`.
-     *
-     * ```js
-     * const util = require('util');
-     * const obj = { name: 'Error', message: 'an error occurred' };
-     *
-     * util.isError(obj);
-     * // Returns: false
-     * obj[Symbol.toStringTag] = 'Error';
-     * util.isError(obj);
-     * // Returns: true
-     * ```
-     * @since v0.6.0
-     * @deprecated Since v4.0.0 - Use {@link types.isNativeError} instead.
-     */
-    export function isError(object: unknown): object is Error;
-    /**
-     * Usage of `util.inherits()` is discouraged. Please use the ES6 `class` and`extends` keywords to get language level inheritance support. Also note
-     * that the two styles are [semantically incompatible](https://github.com/nodejs/node/issues/4179).
-     *
-     * Inherit the prototype methods from one [constructor](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/constructor) into another. The
-     * prototype of `constructor` will be set to a new object created from`superConstructor`.
-     *
-     * This mainly adds some input validation on top of`Object.setPrototypeOf(constructor.prototype, superConstructor.prototype)`.
-     * As an additional convenience, `superConstructor` will be accessible
-     * through the `constructor.super_` property.
-     *
-     * ```js
-     * const util = require('util');
-     * const EventEmitter = require('events');
-     *
-     * function MyStream() {
-     *   EventEmitter.call(this);
-     * }
-     *
-     * util.inherits(MyStream, EventEmitter);
-     *
-     * MyStream.prototype.write = function(data) {
-     *   this.emit('data', data);
-     * };
-     *
-     * const stream = new MyStream();
-     *
-     * console.log(stream instanceof EventEmitter); // true
-     * console.log(MyStream.super_ === EventEmitter); // true
-     *
-     * stream.on('data', (data) => {
-     *   console.log(`Received data: "${data}"`);
-     * });
-     * stream.write('It works!'); // Received data: "It works!"
-     * ```
-     *
-     * ES6 example using `class` and `extends`:
-     *
-     * ```js
-     * const EventEmitter = require('events');
-     *
-     * class MyStream extends EventEmitter {
-     *   write(data) {
-     *     this.emit('data', data);
-     *   }
-     * }
-     *
-     * const stream = new MyStream();
-     *
-     * stream.on('data', (data) => {
-     *   console.log(`Received data: "${data}"`);
-     * });
-     * stream.write('With ES6');
-     * ```
-     * @since v0.3.0
-     * @deprecated Legacy: Use ES2015 class syntax and `extends` keyword instead.
-     */
-    export function inherits(constructor: unknown, superConstructor: unknown): void;
-    export type DebugLoggerFunction = (msg: string, ...param: unknown[]) => void;
-    export interface DebugLogger extends DebugLoggerFunction {
-        enabled: boolean;
-    }
-    /**
-     * The `util.debuglog()` method is used to create a function that conditionally
-     * writes debug messages to `stderr` based on the existence of the `NODE_DEBUG`environment variable. If the `section` name appears within the value of that
-     * environment variable, then the returned function operates similar to `console.error()`. If not, then the returned function is a no-op.
-     *
-     * ```js
-     * const util = require('util');
-     * const debuglog = util.debuglog('foo');
-     *
-     * debuglog('hello from foo [%d]', 123);
-     * ```
-     *
-     * If this program is run with `NODE_DEBUG=foo` in the environment, then
-     * it will output something like:
-     *
-     * ```console
-     * FOO 3245: hello from foo [123]
-     * ```
-     *
-     * where `3245` is the process id. If it is not run with that
-     * environment variable set, then it will not print anything.
-     *
-     * The `section` supports wildcard also:
-     *
-     * ```js
-     * const util = require('util');
-     * const debuglog = util.debuglog('foo-bar');
-     *
-     * debuglog('hi there, it\'s foo-bar [%d]', 2333);
-     * ```
-     *
-     * if it is run with `NODE_DEBUG=foo*` in the environment, then it will output
-     * something like:
-     *
-     * ```console
-     * FOO-BAR 3257: hi there, it's foo-bar [2333]
-     * ```
-     *
-     * Multiple comma-separated `section` names may be specified in the `NODE_DEBUG`environment variable: `NODE_DEBUG=fs,net,tls`.
-     *
-     * The optional `callback` argument can be used to replace the logging function
-     * with a different function that doesn't have any initialization or
-     * unnecessary wrapping.
-     *
-     * ```js
-     * const util = require('util');
-     * let debuglog = util.debuglog('internals', (debug) => {
-     *   // Replace with a logging function that optimizes out
-     *   // testing if the section is enabled
-     *   debuglog = debug;
-     * });
-     * ```
-     * @since v0.11.3
-     * @param section A string identifying the portion of the application for which the `debuglog` function is being created.
-     * @param callback A callback invoked the first time the logging function is called with a function argument that is a more optimized logging function.
-     * @return The logging function
-     */
-    export function debuglog(section: string, callback?: (fn: DebugLoggerFunction) => void): DebugLogger;
-    export const debug: typeof debuglog;
-    /**
-     * Returns `true` if the given `object` is a `Boolean`. Otherwise, returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isBoolean(1);
-     * // Returns: false
-     * util.isBoolean(0);
-     * // Returns: false
-     * util.isBoolean(false);
-     * // Returns: true
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Use `typeof value === 'boolean'` instead.
-     */
-    export function isBoolean(object: unknown): object is boolean;
-    /**
-     * Returns `true` if the given `object` is a `Buffer`. Otherwise, returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isBuffer({ length: 0 });
-     * // Returns: false
-     * util.isBuffer([]);
-     * // Returns: false
-     * util.isBuffer(Buffer.from('hello world'));
-     * // Returns: true
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Use `isBuffer` instead.
-     */
-    export function isBuffer(object: unknown): object is Buffer;
-    /**
-     * Returns `true` if the given `object` is a `Function`. Otherwise, returns`false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * function Foo() {}
-     * const Bar = () => {};
-     *
-     * util.isFunction({});
-     * // Returns: false
-     * util.isFunction(Foo);
-     * // Returns: true
-     * util.isFunction(Bar);
-     * // Returns: true
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Use `typeof value === 'function'` instead.
-     */
-    export function isFunction(object: unknown): boolean;
-    /**
-     * Returns `true` if the given `object` is strictly `null`. Otherwise, returns`false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isNull(0);
-     * // Returns: false
-     * util.isNull(undefined);
-     * // Returns: false
-     * util.isNull(null);
-     * // Returns: true
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Use `value === null` instead.
-     */
-    export function isNull(object: unknown): object is null;
-    /**
-     * Returns `true` if the given `object` is `null` or `undefined`. Otherwise,
-     * returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isNullOrUndefined(0);
-     * // Returns: false
-     * util.isNullOrUndefined(undefined);
-     * // Returns: true
-     * util.isNullOrUndefined(null);
-     * // Returns: true
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Use `value === undefined || value === null` instead.
-     */
-    export function isNullOrUndefined(object: unknown): object is null | undefined;
-    /**
-     * Returns `true` if the given `object` is a `Number`. Otherwise, returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isNumber(false);
-     * // Returns: false
-     * util.isNumber(Infinity);
-     * // Returns: true
-     * util.isNumber(0);
-     * // Returns: true
-     * util.isNumber(NaN);
-     * // Returns: true
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Use `typeof value === 'number'` instead.
-     */
-    export function isNumber(object: unknown): object is number;
-    /**
-     * Returns `true` if the given `object` is strictly an `Object`**and** not a`Function` (even though functions are objects in JavaScript).
-     * Otherwise, returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isObject(5);
-     * // Returns: false
-     * util.isObject(null);
-     * // Returns: false
-     * util.isObject({});
-     * // Returns: true
-     * util.isObject(() => {});
-     * // Returns: false
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Deprecated: Use `value !== null && typeof value === 'object'` instead.
-     */
-    export function isObject(object: unknown): boolean;
-    /**
-     * Returns `true` if the given `object` is a primitive type. Otherwise, returns`false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isPrimitive(5);
-     * // Returns: true
-     * util.isPrimitive('foo');
-     * // Returns: true
-     * util.isPrimitive(false);
-     * // Returns: true
-     * util.isPrimitive(null);
-     * // Returns: true
-     * util.isPrimitive(undefined);
-     * // Returns: true
-     * util.isPrimitive({});
-     * // Returns: false
-     * util.isPrimitive(() => {});
-     * // Returns: false
-     * util.isPrimitive(/^$/);
-     * // Returns: false
-     * util.isPrimitive(new Date());
-     * // Returns: false
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Use `(typeof value !== 'object' && typeof value !== 'function') || value === null` instead.
-     */
-    export function isPrimitive(object: unknown): boolean;
-    /**
-     * Returns `true` if the given `object` is a `string`. Otherwise, returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isString('');
-     * // Returns: true
-     * util.isString('foo');
-     * // Returns: true
-     * util.isString(String('foo'));
-     * // Returns: true
-     * util.isString(5);
-     * // Returns: false
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Use `typeof value === 'string'` instead.
-     */
-    export function isString(object: unknown): object is string;
-    /**
-     * Returns `true` if the given `object` is a `Symbol`. Otherwise, returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * util.isSymbol(5);
-     * // Returns: false
-     * util.isSymbol('foo');
-     * // Returns: false
-     * util.isSymbol(Symbol('foo'));
-     * // Returns: true
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Use `typeof value === 'symbol'` instead.
-     */
-    export function isSymbol(object: unknown): object is symbol;
-    /**
-     * Returns `true` if the given `object` is `undefined`. Otherwise, returns `false`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * const foo = undefined;
-     * util.isUndefined(5);
-     * // Returns: false
-     * util.isUndefined(foo);
-     * // Returns: true
-     * util.isUndefined(null);
-     * // Returns: false
-     * ```
-     * @since v0.11.5
-     * @deprecated Since v4.0.0 - Use `value === undefined` instead.
-     */
-    export function isUndefined(object: unknown): object is undefined;
-    /**
-     * The `util.deprecate()` method wraps `fn` (which may be a function or class) in
-     * such a way that it is marked as deprecated.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * exports.obsoleteFunction = util.deprecate(() => {
-     *   // Do something here.
-     * }, 'obsoleteFunction() is deprecated. Use newShinyFunction() instead.');
-     * ```
-     *
-     * When called, `util.deprecate()` will return a function that will emit a`DeprecationWarning` using the `'warning'` event. The warning will
-     * be emitted and printed to `stderr` the first time the returned function is
-     * called. After the warning is emitted, the wrapped function is called without
-     * emitting a warning.
-     *
-     * If the same optional `code` is supplied in multiple calls to `util.deprecate()`,
-     * the warning will be emitted only once for that `code`.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * const fn1 = util.deprecate(someFunction, someMessage, 'DEP0001');
-     * const fn2 = util.deprecate(someOtherFunction, someOtherMessage, 'DEP0001');
-     * fn1(); // Emits a deprecation warning with code DEP0001
-     * fn2(); // Does not emit a deprecation warning because it has the same code
-     * ```
-     *
-     * If either the `--no-deprecation` or `--no-warnings` command-line flags are
-     * used, or if the `process.noDeprecation` property is set to `true`_prior_ to
-     * the first deprecation warning, the `util.deprecate()` method does nothing.
-     *
-     * If the `--trace-deprecation` or `--trace-warnings` command-line flags are set,
-     * or the `process.traceDeprecation` property is set to `true`, a warning and a
-     * stack trace are printed to `stderr` the first time the deprecated function is
-     * called.
-     *
-     * If the `--throw-deprecation` command-line flag is set, or the`process.throwDeprecation` property is set to `true`, then an exception will be
-     * thrown when the deprecated function is called.
-     *
-     * The `--throw-deprecation` command-line flag and `process.throwDeprecation`property take precedence over `--trace-deprecation` and`process.traceDeprecation`.
-     * @since v0.8.0
-     * @param fn The function that is being deprecated.
-     * @param msg A warning message to display when the deprecated function is invoked.
-     * @param code A deprecation code. See the `list of deprecated APIs` for a list of codes.
-     * @return The deprecated function wrapped to emit a warning.
-     */
-    export function deprecate<T extends Function>(fn: T, msg: string, code?: string): T;
-    /**
-     * Returns `true` if there is deep strict equality between `val1` and `val2`.
-     * Otherwise, returns `false`.
-     *
-     * See `assert.deepStrictEqual()` for more information about deep strict
-     * equality.
-     * @since v9.0.0
-     */
-    export function isDeepStrictEqual(val1: unknown, val2: unknown): boolean;
-    /**
-     * Returns `str` with any ANSI escape codes removed.
-     *
-     * ```js
-     * console.log(util.stripVTControlCharacters('\u001B[4mvalue\u001B[0m'));
-     * // Prints "value"
-     * ```
-     * @since v16.11.0
-     */
-    export function stripVTControlCharacters(str: string): string;
-    /**
-     * Takes an `async` function (or a function that returns a `Promise`) and returns a
-     * function following the error-first callback style, i.e. taking
-     * an `(err, value) => ...` callback as the last argument. In the callback, the
-     * first argument will be the rejection reason (or `null` if the `Promise`resolved), and the second argument will be the resolved value.
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * async function fn() {
-     *   return 'hello world';
-     * }
-     * const callbackFunction = util.callbackify(fn);
-     *
-     * callbackFunction((err, ret) => {
-     *   if (err) throw err;
-     *   console.log(ret);
-     * });
-     * ```
-     *
-     * Will print:
-     *
-     * ```text
-     * hello world
-     * ```
-     *
-     * The callback is executed asynchronously, and will have a limited stack trace.
-     * If the callback throws, the process will emit an `'uncaughtException'` event, and if not handled will exit.
-     *
-     * Since `null` has a special meaning as the first argument to a callback, if a
-     * wrapped function rejects a `Promise` with a falsy value as a reason, the value
-     * is wrapped in an `Error` with the original value stored in a field named`reason`.
-     *
-     * ```js
-     * function fn() {
-     *   return Promise.reject(null);
-     * }
-     * const callbackFunction = util.callbackify(fn);
-     *
-     * callbackFunction((err, ret) => {
-     *   // When the Promise was rejected with `null` it is wrapped with an Error and
-     *   // the original value is stored in `reason`.
-     *   err &#x26;&#x26; Object.hasOwn(err, 'reason') &#x26;&#x26; err.reason === null;  // true
-     * });
-     * ```
-     * @since v8.2.0
-     * @param fn An `async` function
-     * @return a callback style function
-     */
-    export function callbackify(fn: () => Promise<void>): (callback: (err: NodeJS.ErrnoException) => void) => void;
-    export function callbackify<TResult>(fn: () => Promise<TResult>): (callback: (err: NodeJS.ErrnoException, result: TResult) => void) => void;
-    export function callbackify<T1>(fn: (arg1: T1) => Promise<void>): (arg1: T1, callback: (err: NodeJS.ErrnoException) => void) => void;
-    export function callbackify<T1, TResult>(fn: (arg1: T1) => Promise<TResult>): (arg1: T1, callback: (err: NodeJS.ErrnoException, result: TResult) => void) => void;
-    export function callbackify<T1, T2>(fn: (arg1: T1, arg2: T2) => Promise<void>): (arg1: T1, arg2: T2, callback: (err: NodeJS.ErrnoException) => void) => void;
-    export function callbackify<T1, T2, TResult>(fn: (arg1: T1, arg2: T2) => Promise<TResult>): (arg1: T1, arg2: T2, callback: (err: NodeJS.ErrnoException | null, result: TResult) => void) => void;
-    export function callbackify<T1, T2, T3>(fn: (arg1: T1, arg2: T2, arg3: T3) => Promise<void>): (arg1: T1, arg2: T2, arg3: T3, callback: (err: NodeJS.ErrnoException) => void) => void;
-    export function callbackify<T1, T2, T3, TResult>(
-        fn: (arg1: T1, arg2: T2, arg3: T3) => Promise<TResult>
-    ): (arg1: T1, arg2: T2, arg3: T3, callback: (err: NodeJS.ErrnoException | null, result: TResult) => void) => void;
-    export function callbackify<T1, T2, T3, T4>(
-        fn: (arg1: T1, arg2: T2, arg3: T3, arg4: T4) => Promise<void>
-    ): (arg1: T1, arg2: T2, arg3: T3, arg4: T4, callback: (err: NodeJS.ErrnoException) => void) => void;
-    export function callbackify<T1, T2, T3, T4, TResult>(
-        fn: (arg1: T1, arg2: T2, arg3: T3, arg4: T4) => Promise<TResult>
-    ): (arg1: T1, arg2: T2, arg3: T3, arg4: T4, callback: (err: NodeJS.ErrnoException | null, result: TResult) => void) => void;
-    export function callbackify<T1, T2, T3, T4, T5>(
-        fn: (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5) => Promise<void>
-    ): (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5, callback: (err: NodeJS.ErrnoException) => void) => void;
-    export function callbackify<T1, T2, T3, T4, T5, TResult>(
-        fn: (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5) => Promise<TResult>
-    ): (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5, callback: (err: NodeJS.ErrnoException | null, result: TResult) => void) => void;
-    export function callbackify<T1, T2, T3, T4, T5, T6>(
-        fn: (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5, arg6: T6) => Promise<void>
-    ): (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5, arg6: T6, callback: (err: NodeJS.ErrnoException) => void) => void;
-    export function callbackify<T1, T2, T3, T4, T5, T6, TResult>(
-        fn: (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5, arg6: T6) => Promise<TResult>
-    ): (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5, arg6: T6, callback: (err: NodeJS.ErrnoException | null, result: TResult) => void) => void;
-    export interface CustomPromisifyLegacy<TCustom extends Function> extends Function {
-        __promisify__: TCustom;
-    }
-    export interface CustomPromisifySymbol<TCustom extends Function> extends Function {
-        [promisify.custom]: TCustom;
-    }
-    export type CustomPromisify<TCustom extends Function> = CustomPromisifySymbol<TCustom> | CustomPromisifyLegacy<TCustom>;
-    /**
-     * Takes a function following the common error-first callback style, i.e. taking
-     * an `(err, value) => ...` callback as the last argument, and returns a version
-     * that returns promises.
-     *
-     * ```js
-     * const util = require('util');
-     * const fs = require('fs');
-     *
-     * const stat = util.promisify(fs.stat);
-     * stat('.').then((stats) => {
-     *   // Do something with `stats`
-     * }).catch((error) => {
-     *   // Handle the error.
-     * });
-     * ```
-     *
-     * Or, equivalently using `async function`s:
-     *
-     * ```js
-     * const util = require('util');
-     * const fs = require('fs');
-     *
-     * const stat = util.promisify(fs.stat);
-     *
-     * async function callStat() {
-     *   const stats = await stat('.');
-     *   console.log(`This directory is owned by ${stats.uid}`);
-     * }
-     * ```
-     *
-     * If there is an `original[util.promisify.custom]` property present, `promisify`will return its value, see `Custom promisified functions`.
-     *
-     * `promisify()` assumes that `original` is a function taking a callback as its
-     * final argument in all cases. If `original` is not a function, `promisify()`will throw an error. If `original` is a function but its last argument is not
-     * an error-first callback, it will still be passed an error-first
-     * callback as its last argument.
-     *
-     * Using `promisify()` on class methods or other methods that use `this` may not
-     * work as expected unless handled specially:
-     *
-     * ```js
-     * const util = require('util');
-     *
-     * class Foo {
-     *   constructor() {
-     *     this.a = 42;
-     *   }
-     *
-     *   bar(callback) {
-     *     callback(null, this.a);
-     *   }
-     * }
-     *
-     * const foo = new Foo();
-     *
-     * const naiveBar = util.promisify(foo.bar);
-     * // TypeError: Cannot read property 'a' of undefined
-     * // naiveBar().then(a => console.log(a));
-     *
-     * naiveBar.call(foo).then((a) => console.log(a)); // '42'
-     *
-     * const bindBar = naiveBar.bind(foo);
-     * bindBar().then((a) => console.log(a)); // '42'
-     * ```
-     * @since v8.0.0
-     */
-    export function promisify<TCustom extends Function>(fn: CustomPromisify<TCustom>): TCustom;
-    export function promisify<TResult>(fn: (callback: (err: any, result: TResult) => void) => void): () => Promise<TResult>;
-    export function promisify(fn: (callback: (err?: any) => void) => void): () => Promise<void>;
-    export function promisify<T1, TResult>(fn: (arg1: T1, callback: (err: any, result: TResult) => void) => void): (arg1: T1) => Promise<TResult>;
-    export function promisify<T1>(fn: (arg1: T1, callback: (err?: any) => void) => void): (arg1: T1) => Promise<void>;
-    export function promisify<T1, T2, TResult>(fn: (arg1: T1, arg2: T2, callback: (err: any, result: TResult) => void) => void): (arg1: T1, arg2: T2) => Promise<TResult>;
-    export function promisify<T1, T2>(fn: (arg1: T1, arg2: T2, callback: (err?: any) => void) => void): (arg1: T1, arg2: T2) => Promise<void>;
-    export function promisify<T1, T2, T3, TResult>(fn: (arg1: T1, arg2: T2, arg3: T3, callback: (err: any, result: TResult) => void) => void): (arg1: T1, arg2: T2, arg3: T3) => Promise<TResult>;
-    export function promisify<T1, T2, T3>(fn: (arg1: T1, arg2: T2, arg3: T3, callback: (err?: any) => void) => void): (arg1: T1, arg2: T2, arg3: T3) => Promise<void>;
-    export function promisify<T1, T2, T3, T4, TResult>(
-        fn: (arg1: T1, arg2: T2, arg3: T3, arg4: T4, callback: (err: any, result: TResult) => void) => void
-    ): (arg1: T1, arg2: T2, arg3: T3, arg4: T4) => Promise<TResult>;
-    export function promisify<T1, T2, T3, T4>(fn: (arg1: T1, arg2: T2, arg3: T3, arg4: T4, callback: (err?: any) => void) => void): (arg1: T1, arg2: T2, arg3: T3, arg4: T4) => Promise<void>;
-    export function promisify<T1, T2, T3, T4, T5, TResult>(
-        fn: (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5, callback: (err: any, result: TResult) => void) => void
-    ): (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5) => Promise<TResult>;
-    export function promisify<T1, T2, T3, T4, T5>(
-        fn: (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5, callback: (err?: any) => void) => void
-    ): (arg1: T1, arg2: T2, arg3: T3, arg4: T4, arg5: T5) => Promise<void>;
-    export function promisify(fn: Function): Function;
-    export namespace promisify {
-        /**
-         * That can be used to declare custom promisified variants of functions.
-         */
-        const custom: unique symbol;
-    }
-    /**
-     * An implementation of the [WHATWG Encoding Standard](https://encoding.spec.whatwg.org/) `TextDecoder` API.
-     *
-     * ```js
-     * const decoder = new TextDecoder();
-     * const u8arr = new Uint8Array([72, 101, 108, 108, 111]);
-     * console.log(decoder.decode(u8arr)); // Hello
-     * ```
-     * @since v8.3.0
-     */
-    export class TextDecoder {
-        /**
-         * The encoding supported by the `TextDecoder` instance.
-         */
-        readonly encoding: string;
-        /**
-         * The value will be `true` if decoding errors result in a `TypeError` being
-         * thrown.
-         */
-        readonly fatal: boolean;
-        /**
-         * The value will be `true` if the decoding result will include the byte order
-         * mark.
-         */
-        readonly ignoreBOM: boolean;
-        constructor(
-            encoding?: string,
-            options?: {
-                fatal?: boolean | undefined;
-                ignoreBOM?: boolean | undefined;
-            }
-        );
-        /**
-         * Decodes the `input` and returns a string. If `options.stream` is `true`, any
-         * incomplete byte sequences occurring at the end of the `input` are buffered
-         * internally and emitted after the next call to `textDecoder.decode()`.
-         *
-         * If `textDecoder.fatal` is `true`, decoding errors that occur will result in a`TypeError` being thrown.
-         * @param input An `ArrayBuffer`, `DataView` or `TypedArray` instance containing the encoded data.
-         */
-        decode(
-            input?: NodeJS.ArrayBufferView | ArrayBuffer | null,
-            options?: {
-                stream?: boolean | undefined;
-            }
-        ): string;
-    }
-    export interface EncodeIntoResult {
-        /**
-         * The read Unicode code units of input.
-         */
-        read: number;
-        /**
-         * The written UTF-8 bytes of output.
-         */
-        written: number;
-    }
-    export { types };
-
-    //// TextEncoder/Decoder
-    /**
-     * An implementation of the [WHATWG Encoding Standard](https://encoding.spec.whatwg.org/) `TextEncoder` API. All
-     * instances of `TextEncoder` only support UTF-8 encoding.
-     *
-     * ```js
-     * const encoder = new TextEncoder();
-     * const uint8array = encoder.encode('this is some data');
-     * ```
-     *
-     * The `TextEncoder` class is also available on the global object.
-     * @since v8.3.0
-     */
-    export class TextEncoder {
-        /**
-         * The encoding supported by the `TextEncoder` instance. Always set to `'utf-8'`.
-         */
-        readonly encoding: string;
-        /**
-         * UTF-8 encodes the `input` string and returns a `Uint8Array` containing the
-         * encoded bytes.
-         * @param [input='an empty string'] The text to encode.
-         */
-        encode(input?: string): Uint8Array;
-        /**
-         * UTF-8 encodes the `src` string to the `dest` Uint8Array and returns an object
-         * containing the read Unicode code units and written UTF-8 bytes.
-         *
-         * ```js
-         * const encoder = new TextEncoder();
-         * const src = 'this is some data';
-         * const dest = new Uint8Array(10);
-         * const { read, written } = encoder.encodeInto(src, dest);
-         * ```
-         * @param src The text to encode.
-         * @param dest The array to hold the encode result.
-         */
-        encodeInto(src: string, dest: Uint8Array): EncodeIntoResult;
-    }
-
-    import { TextDecoder as _TextDecoder, TextEncoder as _TextEncoder } from 'util';
-    global {
-        /**
-         * `TextDecoder` class is a global reference for `require('util').TextDecoder`
-         * https://nodejs.org/api/globals.html#textdecoder
-         * @since v11.0.0
-         */
-         var TextDecoder: typeof globalThis extends {
-            onmessage: any;
-            TextDecoder: infer TextDecoder;
-        }
-            ? TextDecoder
-            : typeof _TextDecoder;
-
-        /**
-         * `TextEncoder` class is a global reference for `require('util').TextEncoder`
-         * https://nodejs.org/api/globals.html#textencoder
-         * @since v11.0.0
-         */
-         var TextEncoder: typeof globalThis extends {
-            onmessage: any;
-            TextEncoder: infer TextEncoder;
-        }
-            ? TextEncoder
-            : typeof _TextEncoder;
-    }
-
-    //// parseArgs
-    /**
-     * Provides a high-level API for command-line argument parsing. Takes a
-     * specification for the expected arguments and returns a structured object
-     * with the parsed values and positionals.
-     *
-     * `config` provides arguments for parsing and configures the parser. It
-     * supports the following properties:
-     *
-     *   - `args` The array of argument strings. **Default:** `process.argv` with
-     *     `execPath` and `filename` removed.
-     *   - `options` Arguments known to the parser. Keys of `options` are the long
-     *     names of options and values are objects accepting the following properties:
-     *
-     *     - `type` Type of argument, which must be either `boolean` (for options
-     *        which do not take values) or `string` (for options which do).
-     *     - `multiple` Whether this option can be provided multiple
-     *       times. If `true`, all values will be collected in an array. If
-     *       `false`, values for the option are last-wins. **Default:** `false`.
-     *     - `short` A single character alias for the option.
-     *     - `default` The default option value when it is not set by args. It
-     *       must be of the same type as the `type` property. When `multiple`
-     *       is `true`, it must be an array.
-     *
-     *   - `strict`: Whether an error should be thrown when unknown arguments
-     *     are encountered, or when arguments are passed that do not match the
-     *     `type` configured in `options`. **Default:** `true`.
-     *   - `allowPositionals`: Whether this command accepts positional arguments.
-     *     **Default:** `false` if `strict` is `true`, otherwise `true`.
-     *   - `tokens`: Whether tokens {boolean} Return the parsed tokens. This is useful
-     *     for extending the built-in behavior, from adding additional checks through
-     *     to reprocessing the tokens in different ways.
-     *     **Default:** `false`.
-     *
-     * @returns The parsed command line arguments:
-     *
-     *   - `values` A mapping of parsed option names with their string
-     *     or boolean values.
-     *   - `positionals` Positional arguments.
-     *   - `tokens` Detailed parse information (only if `tokens` was specified).
-     *
-     */
-    export function parseArgs<T extends ParseArgsConfig>(config?: T): ParsedResults<T>;
-
-    interface ParseArgsOptionConfig {
-        /**
-         * Type of argument.
-         */
-        type: 'string' | 'boolean';
-        /**
-         * Whether this option can be provided multiple times.
-         * If `true`, all values will be collected in an array.
-         * If `false`, values for the option are last-wins.
-         * @default false.
-         */
-        multiple?: boolean | undefined;
-        /**
-         * A single character alias for the option.
-         */
-        short?: string | undefined;
-        /**
-         * The default option value when it is not set by args.
-         * It must be of the same type as the the `type` property.
-         * When `multiple` is `true`, it must be an array.
-         * @since v18.11.0
-         */
-        default?: string | boolean | string[] | boolean[] | undefined;
-    }
-
-    interface ParseArgsOptionsConfig {
-        [longOption: string]: ParseArgsOptionConfig;
-    }
-
-    export interface ParseArgsConfig {
-        /**
-         * Array of argument strings.
-         */
-        args?: string[] | undefined;
-        /**
-         * Used to describe arguments known to the parser.
-         */
-        options?: ParseArgsOptionsConfig | undefined;
-        /**
-         * Should an error be thrown when unknown arguments are encountered,
-         * or when arguments are passed that do not match the `type` configured in `options`.
-         * @default true
-         */
-        strict?: boolean | undefined;
-        /**
-         * Whether this command accepts positional arguments.
-         */
-        allowPositionals?: boolean | undefined;
-        /**
-         * Return the parsed tokens. This is useful for extending the built-in behavior,
-         * from adding additional checks through to reprocessing the tokens in different ways.
-         * @default false
-         */
-        tokens?: boolean | undefined;
-    }
-
-    /*
-    IfDefaultsTrue and IfDefaultsFalse are helpers to handle default values for missing boolean properties.
-    TypeScript does not have exact types for objects: https://github.com/microsoft/TypeScript/issues/12936
-    This means it is impossible to distinguish between "field X is definitely not present" and "field X may or may not be present".
-    But we expect users to generally provide their config inline or `as const`, which means TS will always know whether a given field is present.
-    So this helper treats "not definitely present" (i.e., not `extends boolean`) as being "definitely not present", i.e. it should have its default value.
-    This is technically incorrect but is a much nicer UX for the common case.
-    The IfDefaultsTrue version is for things which default to true; the IfDefaultsFalse version is for things which default to false.
-    */
-    type IfDefaultsTrue<T, IfTrue, IfFalse> = T extends true
-        ? IfTrue
-        : T extends false
-        ? IfFalse
-        : IfTrue;
-
-    // we put the `extends false` condition first here because `undefined` compares like `any` when `strictNullChecks: false`
-    type IfDefaultsFalse<T, IfTrue, IfFalse> = T extends false
-        ? IfFalse
-        : T extends true
-        ? IfTrue
-        : IfFalse;
-
-    type ExtractOptionValue<T extends ParseArgsConfig, O extends ParseArgsOptionConfig> = IfDefaultsTrue<
-        T['strict'],
-        O['type'] extends 'string' ? string : O['type'] extends 'boolean' ? boolean : string | boolean,
-        string | boolean
-    >;
-
-    type ParsedValues<T extends ParseArgsConfig> =
-        & IfDefaultsTrue<T['strict'], unknown, { [longOption: string]: undefined | string | boolean }>
-        & (T['options'] extends ParseArgsOptionsConfig
-            ? {
-                -readonly [LongOption in keyof T['options']]: IfDefaultsFalse<
-                    T['options'][LongOption]['multiple'],
-                    undefined | Array<ExtractOptionValue<T, T['options'][LongOption]>>,
-                    undefined | ExtractOptionValue<T, T['options'][LongOption]>
-                >;
-            }
-            : {});
-
-    type ParsedPositionals<T extends ParseArgsConfig> = IfDefaultsTrue<
-        T['strict'],
-        IfDefaultsFalse<T['allowPositionals'], string[], []>,
-        IfDefaultsTrue<T['allowPositionals'], string[], []>
-    >;
-
-    type PreciseTokenForOptions<
-        K extends string,
-        O extends ParseArgsOptionConfig,
-    > = O['type'] extends 'string'
-        ? {
-              kind: 'option';
-              index: number;
-              name: K;
-              rawName: string;
-              value: string;
-              inlineValue: boolean;
-          }
-        : O['type'] extends 'boolean'
-        ? {
-              kind: 'option';
-              index: number;
-              name: K;
-              rawName: string;
-              value: undefined;
-              inlineValue: undefined;
-          }
-        : OptionToken & { name: K };
-
-    type TokenForOptions<
-        T extends ParseArgsConfig,
-        K extends keyof T['options'] = keyof T['options'],
-    > = K extends unknown
-        ? T['options'] extends ParseArgsOptionsConfig
-            ? PreciseTokenForOptions<K & string, T['options'][K]>
-            : OptionToken
-        : never;
-
-    type ParsedOptionToken<T extends ParseArgsConfig> = IfDefaultsTrue<T['strict'], TokenForOptions<T>, OptionToken>;
-
-    type ParsedPositionalToken<T extends ParseArgsConfig> = IfDefaultsTrue<
-        T['strict'],
-        IfDefaultsFalse<T['allowPositionals'], { kind: 'positional'; index: number; value: string }, never>,
-        IfDefaultsTrue<T['allowPositionals'], { kind: 'positional'; index: number; value: string }, never>
-    >;
-
-    type ParsedTokens<T extends ParseArgsConfig> = Array<
-        ParsedOptionToken<T> | ParsedPositionalToken<T> | { kind: 'option-terminator'; index: number }
-    >;
-
-    type PreciseParsedResults<T extends ParseArgsConfig> = IfDefaultsFalse<
-        T['tokens'],
-        {
-            values: ParsedValues<T>;
-            positionals: ParsedPositionals<T>;
-            tokens: ParsedTokens<T>;
-        },
-        {
-            values: ParsedValues<T>;
-            positionals: ParsedPositionals<T>;
-        }
-    >;
-
-    type OptionToken =
-        | { kind: 'option'; index: number; name: string; rawName: string; value: string; inlineValue: boolean }
-        | {
-              kind: 'option';
-              index: number;
-              name: string;
-              rawName: string;
-              value: undefined;
-              inlineValue: undefined;
-          };
-
-    type Token =
-        | OptionToken
-        | { kind: 'positional'; index: number; value: string }
-        | { kind: 'option-terminator'; index: number };
-
-    // If ParseArgsConfig extends T, then the user passed config constructed elsewhere.
-    // So we can't rely on the `"not definitely present" implies "definitely not present"` assumption mentioned above.
-    type ParsedResults<T extends ParseArgsConfig> = ParseArgsConfig extends T
-        ? {
-              values: { [longOption: string]: undefined | string | boolean | Array<string | boolean> };
-              positionals: string[];
-              tokens?: Token[];
-          }
-        : PreciseParsedResults<T>;
-}
-declare module 'util/types' {
-    export * from 'util/types';
-}
-declare module 'util/types' {
-    import { KeyObject, webcrypto } from 'node:crypto';
-    /**
-     * Returns `true` if the value is a built-in [`ArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/ArrayBuffer) or
-     * [`SharedArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/SharedArrayBuffer) instance.
-     *
-     * See also `util.types.isArrayBuffer()` and `util.types.isSharedArrayBuffer()`.
-     *
-     * ```js
-     * util.types.isAnyArrayBuffer(new ArrayBuffer());  // Returns true
-     * util.types.isAnyArrayBuffer(new SharedArrayBuffer());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isAnyArrayBuffer(object: unknown): object is ArrayBufferLike;
-    /**
-     * Returns `true` if the value is an `arguments` object.
-     *
-     * ```js
-     * function foo() {
-     *   util.types.isArgumentsObject(arguments);  // Returns true
-     * }
-     * ```
-     * @since v10.0.0
-     */
-    function isArgumentsObject(object: unknown): object is IArguments;
-    /**
-     * Returns `true` if the value is a built-in [`ArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/ArrayBuffer) instance.
-     * This does _not_ include [`SharedArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/SharedArrayBuffer) instances. Usually, it is
-     * desirable to test for both; See `util.types.isAnyArrayBuffer()` for that.
-     *
-     * ```js
-     * util.types.isArrayBuffer(new ArrayBuffer());  // Returns true
-     * util.types.isArrayBuffer(new SharedArrayBuffer());  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isArrayBuffer(object: unknown): object is ArrayBuffer;
-    /**
-     * Returns `true` if the value is an instance of one of the [`ArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/ArrayBuffer) views, such as typed
-     * array objects or [`DataView`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/DataView). Equivalent to
-     * [`ArrayBuffer.isView()`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/ArrayBuffer/isView).
-     *
-     * ```js
-     * util.types.isArrayBufferView(new Int8Array());  // true
-     * util.types.isArrayBufferView(Buffer.from('hello world')); // true
-     * util.types.isArrayBufferView(new DataView(new ArrayBuffer(16)));  // true
-     * util.types.isArrayBufferView(new ArrayBuffer());  // false
-     * ```
-     * @since v10.0.0
-     */
-    function isArrayBufferView(object: unknown): object is NodeJS.ArrayBufferView;
-    /**
-     * Returns `true` if the value is an [async function](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/async_function).
-     * This only reports back what the JavaScript engine is seeing;
-     * in particular, the return value may not match the original source code if
-     * a transpilation tool was used.
-     *
-     * ```js
-     * util.types.isAsyncFunction(function foo() {});  // Returns false
-     * util.types.isAsyncFunction(async function foo() {});  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isAsyncFunction(object: unknown): boolean;
-    /**
-     * Returns `true` if the value is a `BigInt64Array` instance.
-     *
-     * ```js
-     * util.types.isBigInt64Array(new BigInt64Array());   // Returns true
-     * util.types.isBigInt64Array(new BigUint64Array());  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isBigInt64Array(value: unknown): value is BigInt64Array;
-    /**
-     * Returns `true` if the value is a `BigUint64Array` instance.
-     *
-     * ```js
-     * util.types.isBigUint64Array(new BigInt64Array());   // Returns false
-     * util.types.isBigUint64Array(new BigUint64Array());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isBigUint64Array(value: unknown): value is BigUint64Array;
-    /**
-     * Returns `true` if the value is a boolean object, e.g. created
-     * by `new Boolean()`.
-     *
-     * ```js
-     * util.types.isBooleanObject(false);  // Returns false
-     * util.types.isBooleanObject(true);   // Returns false
-     * util.types.isBooleanObject(new Boolean(false)); // Returns true
-     * util.types.isBooleanObject(new Boolean(true));  // Returns true
-     * util.types.isBooleanObject(Boolean(false)); // Returns false
-     * util.types.isBooleanObject(Boolean(true));  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isBooleanObject(object: unknown): object is Boolean;
-    /**
-     * Returns `true` if the value is any boxed primitive object, e.g. created
-     * by `new Boolean()`, `new String()` or `Object(Symbol())`.
-     *
-     * For example:
-     *
-     * ```js
-     * util.types.isBoxedPrimitive(false); // Returns false
-     * util.types.isBoxedPrimitive(new Boolean(false)); // Returns true
-     * util.types.isBoxedPrimitive(Symbol('foo')); // Returns false
-     * util.types.isBoxedPrimitive(Object(Symbol('foo'))); // Returns true
-     * util.types.isBoxedPrimitive(Object(BigInt(5))); // Returns true
-     * ```
-     * @since v10.11.0
-     */
-    function isBoxedPrimitive(object: unknown): object is String | Number | BigInt | Boolean | Symbol;
-    /**
-     * Returns `true` if the value is a built-in [`DataView`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/DataView) instance.
-     *
-     * ```js
-     * const ab = new ArrayBuffer(20);
-     * util.types.isDataView(new DataView(ab));  // Returns true
-     * util.types.isDataView(new Float64Array());  // Returns false
-     * ```
-     *
-     * See also [`ArrayBuffer.isView()`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/ArrayBuffer/isView).
-     * @since v10.0.0
-     */
-    function isDataView(object: unknown): object is DataView;
-    /**
-     * Returns `true` if the value is a built-in [`Date`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Date) instance.
-     *
-     * ```js
-     * util.types.isDate(new Date());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isDate(object: unknown): object is Date;
-    /**
-     * Returns `true` if the value is a native `External` value.
-     *
-     * A native `External` value is a special type of object that contains a
-     * raw C++ pointer (`void*`) for access from native code, and has no other
-     * properties. Such objects are created either by Node.js internals or native
-     * addons. In JavaScript, they are [frozen](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/freeze) objects with a`null` prototype.
-     *
-     * ```c
-     * #include <js_native_api.h>
-     * #include <stdlib.h>
-     * napi_value result;
-     * static napi_value MyNapi(napi_env env, napi_callback_info info) {
-     *   int* raw = (int*) malloc(1024);
-     *   napi_status status = napi_create_external(env, (void*) raw, NULL, NULL, &#x26;result);
-     *   if (status != napi_ok) {
-     *     napi_throw_error(env, NULL, "napi_create_external failed");
-     *     return NULL;
-     *   }
-     *   return result;
-     * }
-     * ...
-     * DECLARE_NAPI_PROPERTY("myNapi", MyNapi)
-     * ...
-     * ```
-     *
-     * ```js
-     * const native = require('napi_addon.node');
-     * const data = native.myNapi();
-     * util.types.isExternal(data); // returns true
-     * util.types.isExternal(0); // returns false
-     * util.types.isExternal(new String('foo')); // returns false
-     * ```
-     *
-     * For further information on `napi_create_external`, refer to `napi_create_external()`.
-     * @since v10.0.0
-     */
-    function isExternal(object: unknown): boolean;
-    /**
-     * Returns `true` if the value is a built-in [`Float32Array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float32Array) instance.
-     *
-     * ```js
-     * util.types.isFloat32Array(new ArrayBuffer());  // Returns false
-     * util.types.isFloat32Array(new Float32Array());  // Returns true
-     * util.types.isFloat32Array(new Float64Array());  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isFloat32Array(object: unknown): object is Float32Array;
-    /**
-     * Returns `true` if the value is a built-in [`Float64Array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float64Array) instance.
-     *
-     * ```js
-     * util.types.isFloat64Array(new ArrayBuffer());  // Returns false
-     * util.types.isFloat64Array(new Uint8Array());  // Returns false
-     * util.types.isFloat64Array(new Float64Array());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isFloat64Array(object: unknown): object is Float64Array;
-    /**
-     * Returns `true` if the value is a generator function.
-     * This only reports back what the JavaScript engine is seeing;
-     * in particular, the return value may not match the original source code if
-     * a transpilation tool was used.
-     *
-     * ```js
-     * util.types.isGeneratorFunction(function foo() {});  // Returns false
-     * util.types.isGeneratorFunction(function* foo() {});  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isGeneratorFunction(object: unknown): object is GeneratorFunction;
-    /**
-     * Returns `true` if the value is a generator object as returned from a
-     * built-in generator function.
-     * This only reports back what the JavaScript engine is seeing;
-     * in particular, the return value may not match the original source code if
-     * a transpilation tool was used.
-     *
-     * ```js
-     * function* foo() {}
-     * const generator = foo();
-     * util.types.isGeneratorObject(generator);  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isGeneratorObject(object: unknown): object is Generator;
-    /**
-     * Returns `true` if the value is a built-in [`Int8Array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Int8Array) instance.
-     *
-     * ```js
-     * util.types.isInt8Array(new ArrayBuffer());  // Returns false
-     * util.types.isInt8Array(new Int8Array());  // Returns true
-     * util.types.isInt8Array(new Float64Array());  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isInt8Array(object: unknown): object is Int8Array;
-    /**
-     * Returns `true` if the value is a built-in [`Int16Array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Int16Array) instance.
-     *
-     * ```js
-     * util.types.isInt16Array(new ArrayBuffer());  // Returns false
-     * util.types.isInt16Array(new Int16Array());  // Returns true
-     * util.types.isInt16Array(new Float64Array());  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isInt16Array(object: unknown): object is Int16Array;
-    /**
-     * Returns `true` if the value is a built-in [`Int32Array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Int32Array) instance.
-     *
-     * ```js
-     * util.types.isInt32Array(new ArrayBuffer());  // Returns false
-     * util.types.isInt32Array(new Int32Array());  // Returns true
-     * util.types.isInt32Array(new Float64Array());  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isInt32Array(object: unknown): object is Int32Array;
-    /**
-     * Returns `true` if the value is a built-in [`Map`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Map) instance.
-     *
-     * ```js
-     * util.types.isMap(new Map());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isMap<T>(object: T | {}): object is T extends ReadonlyMap<any, any> ? (unknown extends T ? never : ReadonlyMap<any, any>) : Map<unknown, unknown>;
-    /**
-     * Returns `true` if the value is an iterator returned for a built-in [`Map`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Map) instance.
-     *
-     * ```js
-     * const map = new Map();
-     * util.types.isMapIterator(map.keys());  // Returns true
-     * util.types.isMapIterator(map.values());  // Returns true
-     * util.types.isMapIterator(map.entries());  // Returns true
-     * util.types.isMapIterator(map[Symbol.iterator]());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isMapIterator(object: unknown): boolean;
-    /**
-     * Returns `true` if the value is an instance of a [Module Namespace Object](https://tc39.github.io/ecma262/#sec-module-namespace-exotic-objects).
-     *
-     * ```js
-     * import * as ns from './a.js';
-     *
-     * util.types.isModuleNamespaceObject(ns);  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isModuleNamespaceObject(value: unknown): boolean;
-    /**
-     * Returns `true` if the value is an instance of a built-in `Error` type.
-     *
-     * ```js
-     * util.types.isNativeError(new Error());  // Returns true
-     * util.types.isNativeError(new TypeError());  // Returns true
-     * util.types.isNativeError(new RangeError());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isNativeError(object: unknown): object is Error;
-    /**
-     * Returns `true` if the value is a number object, e.g. created
-     * by `new Number()`.
-     *
-     * ```js
-     * util.types.isNumberObject(0);  // Returns false
-     * util.types.isNumberObject(new Number(0));   // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isNumberObject(object: unknown): object is Number;
-    /**
-     * Returns `true` if the value is a built-in [`Promise`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Promise).
-     *
-     * ```js
-     * util.types.isPromise(Promise.resolve(42));  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isPromise(object: unknown): object is Promise<unknown>;
-    /**
-     * Returns `true` if the value is a [`Proxy`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Proxy) instance.
-     *
-     * ```js
-     * const target = {};
-     * const proxy = new Proxy(target, {});
-     * util.types.isProxy(target);  // Returns false
-     * util.types.isProxy(proxy);  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isProxy(object: unknown): boolean;
-    /**
-     * Returns `true` if the value is a regular expression object.
-     *
-     * ```js
-     * util.types.isRegExp(/abc/);  // Returns true
-     * util.types.isRegExp(new RegExp('abc'));  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isRegExp(object: unknown): object is RegExp;
-    /**
-     * Returns `true` if the value is a built-in [`Set`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Set) instance.
-     *
-     * ```js
-     * util.types.isSet(new Set());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isSet<T>(object: T | {}): object is T extends ReadonlySet<any> ? (unknown extends T ? never : ReadonlySet<any>) : Set<unknown>;
-    /**
-     * Returns `true` if the value is an iterator returned for a built-in [`Set`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Set) instance.
-     *
-     * ```js
-     * const set = new Set();
-     * util.types.isSetIterator(set.keys());  // Returns true
-     * util.types.isSetIterator(set.values());  // Returns true
-     * util.types.isSetIterator(set.entries());  // Returns true
-     * util.types.isSetIterator(set[Symbol.iterator]());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isSetIterator(object: unknown): boolean;
-    /**
-     * Returns `true` if the value is a built-in [`SharedArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/SharedArrayBuffer) instance.
-     * This does _not_ include [`ArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/ArrayBuffer) instances. Usually, it is
-     * desirable to test for both; See `util.types.isAnyArrayBuffer()` for that.
-     *
-     * ```js
-     * util.types.isSharedArrayBuffer(new ArrayBuffer());  // Returns false
-     * util.types.isSharedArrayBuffer(new SharedArrayBuffer());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isSharedArrayBuffer(object: unknown): object is SharedArrayBuffer;
-    /**
-     * Returns `true` if the value is a string object, e.g. created
-     * by `new String()`.
-     *
-     * ```js
-     * util.types.isStringObject('foo');  // Returns false
-     * util.types.isStringObject(new String('foo'));   // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isStringObject(object: unknown): object is String;
-    /**
-     * Returns `true` if the value is a symbol object, created
-     * by calling `Object()` on a `Symbol` primitive.
-     *
-     * ```js
-     * const symbol = Symbol('foo');
-     * util.types.isSymbolObject(symbol);  // Returns false
-     * util.types.isSymbolObject(Object(symbol));   // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isSymbolObject(object: unknown): object is Symbol;
-    /**
-     * Returns `true` if the value is a built-in [`TypedArray`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray) instance.
-     *
-     * ```js
-     * util.types.isTypedArray(new ArrayBuffer());  // Returns false
-     * util.types.isTypedArray(new Uint8Array());  // Returns true
-     * util.types.isTypedArray(new Float64Array());  // Returns true
-     * ```
-     *
-     * See also [`ArrayBuffer.isView()`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/ArrayBuffer/isView).
-     * @since v10.0.0
-     */
-    function isTypedArray(object: unknown): object is NodeJS.TypedArray;
-    /**
-     * Returns `true` if the value is a built-in [`Uint8Array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Uint8Array) instance.
-     *
-     * ```js
-     * util.types.isUint8Array(new ArrayBuffer());  // Returns false
-     * util.types.isUint8Array(new Uint8Array());  // Returns true
-     * util.types.isUint8Array(new Float64Array());  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isUint8Array(object: unknown): object is Uint8Array;
-    /**
-     * Returns `true` if the value is a built-in [`Uint8ClampedArray`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Uint8ClampedArray) instance.
-     *
-     * ```js
-     * util.types.isUint8ClampedArray(new ArrayBuffer());  // Returns false
-     * util.types.isUint8ClampedArray(new Uint8ClampedArray());  // Returns true
-     * util.types.isUint8ClampedArray(new Float64Array());  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isUint8ClampedArray(object: unknown): object is Uint8ClampedArray;
-    /**
-     * Returns `true` if the value is a built-in [`Uint16Array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Uint16Array) instance.
-     *
-     * ```js
-     * util.types.isUint16Array(new ArrayBuffer());  // Returns false
-     * util.types.isUint16Array(new Uint16Array());  // Returns true
-     * util.types.isUint16Array(new Float64Array());  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isUint16Array(object: unknown): object is Uint16Array;
-    /**
-     * Returns `true` if the value is a built-in [`Uint32Array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Uint32Array) instance.
-     *
-     * ```js
-     * util.types.isUint32Array(new ArrayBuffer());  // Returns false
-     * util.types.isUint32Array(new Uint32Array());  // Returns true
-     * util.types.isUint32Array(new Float64Array());  // Returns false
-     * ```
-     * @since v10.0.0
-     */
-    function isUint32Array(object: unknown): object is Uint32Array;
-    /**
-     * Returns `true` if the value is a built-in [`WeakMap`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/WeakMap) instance.
-     *
-     * ```js
-     * util.types.isWeakMap(new WeakMap());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isWeakMap(object: unknown): object is WeakMap<object, unknown>;
-    /**
-     * Returns `true` if the value is a built-in [`WeakSet`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/WeakSet) instance.
-     *
-     * ```js
-     * util.types.isWeakSet(new WeakSet());  // Returns true
-     * ```
-     * @since v10.0.0
-     */
-    function isWeakSet(object: unknown): object is WeakSet<object>;
-    /**
-     * Returns `true` if `value` is a `KeyObject`, `false` otherwise.
-     * @since v16.2.0
-     */
-    function isKeyObject(object: unknown): object is KeyObject;
-    /**
-     * Returns `true` if `value` is a `CryptoKey`, `false` otherwise.
-     * @since v16.2.0
-     */
-    function isCryptoKey(object: unknown): object is webcrypto.CryptoKey;
-}
-declare module 'node:util' {
-    export * from 'util';
-}
-declare module 'node:util/types' {
-    export * from 'util/types';
-}
diff --git a/spaces/rbalacha/03-Streamlit-Video/streaming.py b/spaces/rbalacha/03-Streamlit-Video/streaming.py
deleted file mode 100644
index cc2048269b3e9ac09886471ef9b6dc681db09f25..0000000000000000000000000000000000000000
--- a/spaces/rbalacha/03-Streamlit-Video/streaming.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import subprocess
-
-import numpy as np
-
-
-def ffmpeg_stream(youtube_url, sampling_rate=16_000, chunk_duration_ms=5000, pad_duration_ms=200):
-    """
-    Helper function to read an audio file through ffmpeg.
-    """
-    chunk_len = int(sampling_rate * chunk_duration_ms / 1000)
-    pad_len = int(sampling_rate * pad_duration_ms / 1000)
-    read_chunk_len = chunk_len + pad_len * 2
-
-    ar = f"{sampling_rate}"
-    ac = "1"
-    format_for_conversion = "f32le"
-    dtype = np.float32
-    size_of_sample = 4
-
-    ffmpeg_command = [
-        "ffmpeg",
-        "-i",
-        "pipe:",
-        "-ac",
-        ac,
-        "-ar",
-        ar,
-        "-f",
-        format_for_conversion,
-        "-hide_banner",
-        "-loglevel",
-        "quiet",
-        "pipe:1",
-    ]
-
-    ytdl_command = ["yt-dlp", "-f", "bestaudio", youtube_url, "--quiet", "-o", "-"]
-
-    try:
-        ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, bufsize=-1)
-        ytdl_process = subprocess.Popen(ytdl_command, stdout=ffmpeg_process.stdin)
-    except FileNotFoundError:
-        raise ValueError("ffmpeg was not found but is required to stream audio files from filename")
-
-    acc = b""
-    leftover = np.zeros((0,), dtype=np.float32)
-    while ytdl_process.poll() is None:
-        buflen = read_chunk_len * size_of_sample
-
-        raw = ffmpeg_process.stdout.read(buflen)
-        if raw == b"":
-            break
-
-        if len(acc) + len(raw) > buflen:
-            acc = raw
-        else:
-            acc += raw
-
-        audio = np.frombuffer(acc, dtype=dtype)
-        audio = np.concatenate([leftover, audio])
-        if len(audio) < pad_len * 2:
-            # TODO: handle end of stream better than this
-            break
-        yield audio
-
-        leftover = audio[-pad_len * 2 :]
-        read_chunk_len = chunk_len
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Kitab Hidayatus Salikin Pdf.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Kitab Hidayatus Salikin Pdf.md
deleted file mode 100644
index f2a4fcf6d34f7a380d5060f85ec4f15d1df0a97f..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Kitab Hidayatus Salikin Pdf.md	
+++ /dev/null
@@ -1,48 +0,0 @@
-<h2>Download Kitab Hidayatus Salikin Pdf</h2><br /><p><b><b>Download File</b> &#10002; <a href="https://urlgoal.com/2uCLll">https://urlgoal.com/2uCLll</a></b></p><br /><br />
-<br />
-files & will be back later 
-
- buayabahlol
-
- kde syur be in english in ubuntu???
-
- jalons, english is the international standard for ubuntu.  other local languages are available
-
- ohh!
-
-!locale | jalons
-
- jalons: To set up and configure your locales, see  and 
-
- thanks
-
- Is there a way to make a remote desktop connection to a Linux distro to run on my pi?
-
-!ssh | IhrFussel,
-
- IhrFussel,: SSH is the Secure SHell protocol, see:  for client usage. PuTTY is an SSH client for Windows; see:  for it's homepage. See also!scp (Secure CoPy) and!sshd (Secure SHell Daemon)
-
- I have used PuTTY to connect to my pi
-
- but I did not found out how to make the connection and run a window manager on it
-
- IhrFussel, then you must read the documentation.   man putty will give you the info.
-
- I have tried the man putty page, I know, but they only talk about Linux to Windows
-
- So I think I need an  intermediary program to make the connection
-
- IhrFussel, sorry, but my google skills are limited.  ask again tomorrow, perhaps someone else can help.
-
- ok, thanks
-
- happy2help
-
- can somebody help me with installing ubuntu 14.04.2? it keeps hanging on the boot screen
-
- "waiting for network configuration"
-
- shimikiri, from USB or d 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fantasy Grounds - A Friend In Need (5E) Full Crack [FULL]l [BEST].md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fantasy Grounds - A Friend In Need (5E) Full Crack [FULL]l [BEST].md
deleted file mode 100644
index 5983d804a7391b58d5d46c2d31e9924886233161..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fantasy Grounds - A Friend In Need (5E) Full Crack [FULL]l [BEST].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Fantasy Grounds - A Friend In Need (5E) Full Crack [FULL]l</h2><br /><p><b><b>DOWNLOAD</b> >> <a href="https://urlgoal.com/2uCKwc">https://urlgoal.com/2uCKwc</a></b></p><br /><br />
-
-Confira tudo de Fantasy Grounds by SmiteWorks no Steam ... Interface so you can get what you need with one click of a button. ... A safe combo function mini-game for players to crack locks ... Requires: An active subscription or a one time purchase of a Fantasy Grounds Full or Ultimate license and any ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/apps/render_data.py b/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/apps/render_data.py
deleted file mode 100644
index 563c03fba6e304eced73ca283152a968a65c3b8e..0000000000000000000000000000000000000000
--- a/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/apps/render_data.py
+++ /dev/null
@@ -1,290 +0,0 @@
-#from data.config import raw_dataset, render_dataset, archive_dataset, model_list, zip_path
-
-from lib.renderer.camera import Camera
-import numpy as np
-from lib.renderer.mesh import load_obj_mesh, compute_tangent, compute_normal, load_obj_mesh_mtl
-from lib.renderer.camera import Camera
-import os
-import cv2
-import time
-import math
-import random
-import pyexr
-import argparse
-from tqdm import tqdm
-
-
-def make_rotate(rx, ry, rz):
-    sinX = np.sin(rx)
-    sinY = np.sin(ry)
-    sinZ = np.sin(rz)
-
-    cosX = np.cos(rx)
-    cosY = np.cos(ry)
-    cosZ = np.cos(rz)
-
-    Rx = np.zeros((3,3))
-    Rx[0, 0] = 1.0
-    Rx[1, 1] = cosX
-    Rx[1, 2] = -sinX
-    Rx[2, 1] = sinX
-    Rx[2, 2] = cosX
-
-    Ry = np.zeros((3,3))
-    Ry[0, 0] = cosY
-    Ry[0, 2] = sinY
-    Ry[1, 1] = 1.0
-    Ry[2, 0] = -sinY
-    Ry[2, 2] = cosY
-
-    Rz = np.zeros((3,3))
-    Rz[0, 0] = cosZ
-    Rz[0, 1] = -sinZ
-    Rz[1, 0] = sinZ
-    Rz[1, 1] = cosZ
-    Rz[2, 2] = 1.0
-
-    R = np.matmul(np.matmul(Rz,Ry),Rx)
-    return R
-
-def rotateSH(SH, R):
-    SHn = SH
-    
-    # 1st order
-    SHn[1] = R[1,1]*SH[1] - R[1,2]*SH[2] + R[1,0]*SH[3]
-    SHn[2] = -R[2,1]*SH[1] + R[2,2]*SH[2] - R[2,0]*SH[3]
-    SHn[3] = R[0,1]*SH[1] - R[0,2]*SH[2] + R[0,0]*SH[3]
-
-    # 2nd order
-    SHn[4:,0] = rotateBand2(SH[4:,0],R)
-    SHn[4:,1] = rotateBand2(SH[4:,1],R)
-    SHn[4:,2] = rotateBand2(SH[4:,2],R)
-
-    return SHn
-
-def rotateBand2(x, R):
-    s_c3 = 0.94617469575
-    s_c4 = -0.31539156525
-    s_c5 = 0.54627421529
-
-    s_c_scale = 1.0/0.91529123286551084
-    s_c_scale_inv = 0.91529123286551084
-
-    s_rc2 = 1.5853309190550713*s_c_scale
-    s_c4_div_c3 = s_c4/s_c3
-    s_c4_div_c3_x2 = (s_c4/s_c3)*2.0
-
-    s_scale_dst2 = s_c3 * s_c_scale_inv
-    s_scale_dst4 = s_c5 * s_c_scale_inv
-
-    sh0 =  x[3] + x[4] + x[4] - x[1]
-    sh1 =  x[0] + s_rc2*x[2] +  x[3] + x[4]
-    sh2 =  x[0]
-    sh3 = -x[3]
-    sh4 = -x[1]
-    
-    r2x = R[0][0] + R[0][1]
-    r2y = R[1][0] + R[1][1]
-    r2z = R[2][0] + R[2][1]
-    
-    r3x = R[0][0] + R[0][2]
-    r3y = R[1][0] + R[1][2]
-    r3z = R[2][0] + R[2][2]
-    
-    r4x = R[0][1] + R[0][2]
-    r4y = R[1][1] + R[1][2]
-    r4z = R[2][1] + R[2][2]
-    
-    sh0_x = sh0 * R[0][0]
-    sh0_y = sh0 * R[1][0]
-    d0 = sh0_x * R[1][0]
-    d1 = sh0_y * R[2][0]
-    d2 = sh0 * (R[2][0] * R[2][0] + s_c4_div_c3)
-    d3 = sh0_x * R[2][0]
-    d4 = sh0_x * R[0][0] - sh0_y * R[1][0]
-    
-    sh1_x = sh1 * R[0][2]
-    sh1_y = sh1 * R[1][2]
-    d0 += sh1_x * R[1][2]
-    d1 += sh1_y * R[2][2]
-    d2 += sh1 * (R[2][2] * R[2][2] + s_c4_div_c3)
-    d3 += sh1_x * R[2][2]
-    d4 += sh1_x * R[0][2] - sh1_y * R[1][2]
-    
-    sh2_x = sh2 * r2x
-    sh2_y = sh2 * r2y
-    d0 += sh2_x * r2y
-    d1 += sh2_y * r2z
-    d2 += sh2 * (r2z * r2z + s_c4_div_c3_x2)
-    d3 += sh2_x * r2z
-    d4 += sh2_x * r2x - sh2_y * r2y
-    
-    sh3_x = sh3 * r3x
-    sh3_y = sh3 * r3y
-    d0 += sh3_x * r3y
-    d1 += sh3_y * r3z
-    d2 += sh3 * (r3z * r3z + s_c4_div_c3_x2)
-    d3 += sh3_x * r3z
-    d4 += sh3_x * r3x - sh3_y * r3y
-    
-    sh4_x = sh4 * r4x
-    sh4_y = sh4 * r4y
-    d0 += sh4_x * r4y
-    d1 += sh4_y * r4z
-    d2 += sh4 * (r4z * r4z + s_c4_div_c3_x2)
-    d3 += sh4_x * r4z
-    d4 += sh4_x * r4x - sh4_y * r4y
-
-    dst = x
-    dst[0] = d0
-    dst[1] = -d1
-    dst[2] = d2 * s_scale_dst2
-    dst[3] = -d3
-    dst[4] = d4 * s_scale_dst4
-
-    return dst
-
-def render_prt_ortho(out_path, folder_name, subject_name, shs, rndr, rndr_uv, im_size, angl_step=4, n_light=1, pitch=[0]):
-    cam = Camera(width=im_size, height=im_size)
-    cam.ortho_ratio = 0.4 * (512 / im_size)
-    cam.near = -100
-    cam.far = 100
-    cam.sanity_check()
-
-    # set path for obj, prt
-    mesh_file = os.path.join(folder_name, subject_name + '_100k.obj')
-    if not os.path.exists(mesh_file):
-        print('ERROR: obj file does not exist!!', mesh_file)
-        return 
-    prt_file = os.path.join(folder_name, 'bounce', 'bounce0.txt')
-    if not os.path.exists(prt_file):
-        print('ERROR: prt file does not exist!!!', prt_file)
-        return
-    face_prt_file = os.path.join(folder_name, 'bounce', 'face.npy')
-    if not os.path.exists(face_prt_file):
-        print('ERROR: face prt file does not exist!!!', prt_file)
-        return
-    text_file = os.path.join(folder_name, 'tex', subject_name + '_dif_2k.jpg')
-    if not os.path.exists(text_file):
-        print('ERROR: dif file does not exist!!', text_file)
-        return             
-
-    texture_image = cv2.imread(text_file)
-    texture_image = cv2.cvtColor(texture_image, cv2.COLOR_BGR2RGB)
-
-    vertices, faces, normals, faces_normals, textures, face_textures = load_obj_mesh(mesh_file, with_normal=True, with_texture=True)
-    vmin = vertices.min(0)
-    vmax = vertices.max(0)
-    up_axis = 1 if (vmax-vmin).argmax() == 1 else 2
-    
-    vmed = np.median(vertices, 0)
-    vmed[up_axis] = 0.5*(vmax[up_axis]+vmin[up_axis])
-    y_scale = 180/(vmax[up_axis] - vmin[up_axis])
-
-    rndr.set_norm_mat(y_scale, vmed)
-    rndr_uv.set_norm_mat(y_scale, vmed)
-
-    tan, bitan = compute_tangent(vertices, faces, normals, textures, face_textures)
-    prt = np.loadtxt(prt_file)
-    face_prt = np.load(face_prt_file)
-    rndr.set_mesh(vertices, faces, normals, faces_normals, textures, face_textures, prt, face_prt, tan, bitan)    
-    rndr.set_albedo(texture_image)
-
-    rndr_uv.set_mesh(vertices, faces, normals, faces_normals, textures, face_textures, prt, face_prt, tan, bitan)   
-    rndr_uv.set_albedo(texture_image)
-
-    os.makedirs(os.path.join(out_path, 'GEO', 'OBJ', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'PARAM', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'RENDER', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'MASK', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'UV_RENDER', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'UV_MASK', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'UV_POS', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'UV_NORMAL', subject_name),exist_ok=True)
-
-    if not os.path.exists(os.path.join(out_path, 'val.txt')):
-        f = open(os.path.join(out_path, 'val.txt'), 'w')
-        f.close()
-
-    # copy obj file
-    cmd = 'cp %s %s' % (mesh_file, os.path.join(out_path, 'GEO', 'OBJ', subject_name))
-    print(cmd)
-    os.system(cmd)
-
-    for p in pitch:
-        for y in tqdm(range(0, 360, angl_step)):
-            R = np.matmul(make_rotate(math.radians(p), 0, 0), make_rotate(0, math.radians(y), 0))
-            if up_axis == 2:
-                R = np.matmul(R, make_rotate(math.radians(90),0,0))
-
-            rndr.rot_matrix = R
-            rndr_uv.rot_matrix = R
-            rndr.set_camera(cam)
-            rndr_uv.set_camera(cam)
-
-            for j in range(n_light):
-                sh_id = random.randint(0,shs.shape[0]-1)
-                sh = shs[sh_id]
-                sh_angle = 0.2*np.pi*(random.random()-0.5)
-                sh = rotateSH(sh, make_rotate(0, sh_angle, 0).T)
-
-                dic = {'sh': sh, 'ortho_ratio': cam.ortho_ratio, 'scale': y_scale, 'center': vmed, 'R': R}
-                
-                rndr.set_sh(sh)        
-                rndr.analytic = False
-                rndr.use_inverse_depth = False
-                rndr.display()
-
-                out_all_f = rndr.get_color(0)
-                out_mask = out_all_f[:,:,3]
-                out_all_f = cv2.cvtColor(out_all_f, cv2.COLOR_RGBA2BGR)
-
-                np.save(os.path.join(out_path, 'PARAM', subject_name, '%d_%d_%02d.npy'%(y,p,j)),dic)
-                cv2.imwrite(os.path.join(out_path, 'RENDER', subject_name, '%d_%d_%02d.jpg'%(y,p,j)),255.0*out_all_f)
-                cv2.imwrite(os.path.join(out_path, 'MASK', subject_name, '%d_%d_%02d.png'%(y,p,j)),255.0*out_mask)
-
-                rndr_uv.set_sh(sh)
-                rndr_uv.analytic = False
-                rndr_uv.use_inverse_depth = False
-                rndr_uv.display()
-
-                uv_color = rndr_uv.get_color(0)
-                uv_color = cv2.cvtColor(uv_color, cv2.COLOR_RGBA2BGR)
-                cv2.imwrite(os.path.join(out_path, 'UV_RENDER', subject_name, '%d_%d_%02d.jpg'%(y,p,j)),255.0*uv_color)
-
-                if y == 0 and j == 0 and p == pitch[0]:
-                    uv_pos = rndr_uv.get_color(1)
-                    uv_mask = uv_pos[:,:,3]
-                    cv2.imwrite(os.path.join(out_path, 'UV_MASK', subject_name, '00.png'),255.0*uv_mask)
-
-                    data = {'default': uv_pos[:,:,:3]} # default is a reserved name
-                    pyexr.write(os.path.join(out_path, 'UV_POS', subject_name, '00.exr'), data) 
-
-                    uv_nml = rndr_uv.get_color(2)
-                    uv_nml = cv2.cvtColor(uv_nml, cv2.COLOR_RGBA2BGR)
-                    cv2.imwrite(os.path.join(out_path, 'UV_NORMAL', subject_name, '00.png'),255.0*uv_nml)
-
-
-if __name__ == '__main__':
-    shs = np.load('./env_sh.npy')
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-i', '--input', type=str, default='/home/shunsuke/Downloads/rp_dennis_posed_004_OBJ')
-    parser.add_argument('-o', '--out_dir', type=str, default='/home/shunsuke/Documents/hf_human')
-    parser.add_argument('-m', '--ms_rate', type=int, default=1, help='higher ms rate results in less aliased output. MESA renderer only supports ms_rate=1.')
-    parser.add_argument('-e', '--egl',  action='store_true', help='egl rendering option. use this when rendering with headless server with NVIDIA GPU')
-    parser.add_argument('-s', '--size',  type=int, default=512, help='rendering image size')
-    args = parser.parse_args()
-
-    # NOTE: GL context has to be created before any other OpenGL function loads.
-    from lib.renderer.gl.init_gl import initialize_GL_context
-    initialize_GL_context(width=args.size, height=args.size, egl=args.egl)
-
-    from lib.renderer.gl.prt_render import PRTRender
-    rndr = PRTRender(width=args.size, height=args.size, ms_rate=args.ms_rate, egl=args.egl)
-    rndr_uv = PRTRender(width=args.size, height=args.size, uv_mode=True, egl=args.egl)
-
-    if args.input[-1] == '/':
-        args.input = args.input[:-1]
-    subject_name = args.input.split('/')[-1][:-4]
-    render_prt_ortho(args.out_dir, args.input, subject_name, shs, rndr, rndr_uv, args.size, 1, 1, pitch=[0])
\ No newline at end of file
diff --git a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/schedules/schedule_s_long.py b/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/schedules/schedule_s_long.py
deleted file mode 100644
index ae90d7411d6c742615db60739e11a49960127589..0000000000000000000000000000000000000000
--- a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/schedules/schedule_s_long.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# optimizer
-optimizer = dict(
-    type='Adam', lr=0.0001, weight_decay=0.0004, betas=(0.9, 0.999))
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
-    policy='step',
-    by_epoch=False,
-    gamma=0.5,
-    step=[400000, 600000, 800000, 1000000])
-runner = dict(type='IterBasedRunner', max_iters=1200000)
-checkpoint_config = dict(by_epoch=False, interval=100000)
-evaluation = dict(interval=100000, metric='EPE')
diff --git a/spaces/rizam/literature-research-tool/app.py b/spaces/rizam/literature-research-tool/app.py
deleted file mode 100644
index b3be4c71a53665b0ba742e56cb641734ab31242a..0000000000000000000000000000000000000000
--- a/spaces/rizam/literature-research-tool/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import streamlit as st
-from widgets import *
-
-
-# [![github](https://img.kookapp.cn/assets/2022-09/1w4G0FIWGK00w00w.png)](https://github.com/Mondkuchen/idp_LiteratureResearch_Tool)
-
-# sidebar content
-platforms, number_papers, start_year, end_year, hyperparams = render_sidebar()
-
-# body head
-with st.form("my_form",clear_on_submit=False):
-    st.markdown('''# 👋 Hi, enter your query here :)''')
-    query_input = st.text_input(
-        'Enter your query:',
-        placeholder='''e.g. "Machine learning"''',
-        # label_visibility='collapsed',
-        value=''
-    )
-
-    show_preview = st.checkbox('show paper preview')
-
-    # Every form must have a submit button.
-    submitted = st.form_submit_button("Search")
-
-
-if submitted:
-    # body
-    render_body(platforms, number_papers, 5, query_input,
-                show_preview, start_year, end_year,
-                hyperparams,
-                hyperparams['standardization'])
-    # '''
-    # bar = (
-    #     Bar()
-    #     .add_xaxis(["Cluster 1", "Cluster 2", "Cluster 3", 'Cluster 4', 'Cluster 5'])
-    #     .add_yaxis("numbers", [23, 16, 13, 12, 5])
-    #     .set_global_opts(title_opts=opts.TitleOpts(title="Fake Data"))
-    # )
-    #
-    # components.html(generate_html_pyecharts(bar, 'tmp.html'), height=500, width=1000)
-    # '''
-
-
-
-
-
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/hook/wandblogger_hook.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/hook/wandblogger_hook.py
deleted file mode 100644
index 7bf252f0113cf2b59dba843f1c23db5b62faf982..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/hook/wandblogger_hook.py
+++ /dev/null
@@ -1,593 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import importlib
-import os.path as osp
-import sys
-import warnings
-
-import mmcv
-import numpy as np
-import pycocotools.mask as mask_util
-from mmcv.runner import HOOKS
-from mmcv.runner.dist_utils import master_only
-from mmcv.runner.hooks.checkpoint import CheckpointHook
-from mmcv.runner.hooks.logger.wandb import WandbLoggerHook
-from mmcv.utils import digit_version
-
-from mmdet.core import DistEvalHook, EvalHook
-from mmdet.core.mask.structures import polygon_to_bitmap
-
-
-@HOOKS.register_module()
-class MMDetWandbHook(WandbLoggerHook):
-    """Enhanced Wandb logger hook for MMDetection.
-
-    Comparing with the :cls:`mmcv.runner.WandbLoggerHook`, this hook can not
-    only automatically log all the metrics but also log the following extra
-    information - saves model checkpoints as W&B Artifact, and
-    logs model prediction as interactive W&B Tables.
-
-    - Metrics: The MMDetWandbHook will automatically log training
-        and validation metrics along with system metrics (CPU/GPU).
-
-    - Checkpointing: If `log_checkpoint` is True, the checkpoint saved at
-        every checkpoint interval will be saved as W&B Artifacts.
-        This depends on the : class:`mmcv.runner.CheckpointHook` whose priority
-        is higher than this hook. Please refer to
-        https://docs.wandb.ai/guides/artifacts/model-versioning
-        to learn more about model versioning with W&B Artifacts.
-
-    - Checkpoint Metadata: If evaluation results are available for a given
-        checkpoint artifact, it will have a metadata associated with it.
-        The metadata contains the evaluation metrics computed on validation
-        data with that checkpoint along with the current epoch. It depends
-        on `EvalHook` whose priority is more than MMDetWandbHook.
-
-    - Evaluation: At every evaluation interval, the `MMDetWandbHook` logs the
-        model prediction as interactive W&B Tables. The number of samples
-        logged is given by `num_eval_images`. Currently, the `MMDetWandbHook`
-        logs the predicted bounding boxes along with the ground truth at every
-        evaluation interval. This depends on the `EvalHook` whose priority is
-        more than `MMDetWandbHook`. Also note that the data is just logged once
-        and subsequent evaluation tables uses reference to the logged data
-        to save memory usage. Please refer to
-        https://docs.wandb.ai/guides/data-vis to learn more about W&B Tables.
-
-    For more details check out W&B's MMDetection docs:
-    https://docs.wandb.ai/guides/integrations/mmdetection
-
-    ```
-    Example:
-        log_config = dict(
-            ...
-            hooks=[
-                ...,
-                dict(type='MMDetWandbHook',
-                     init_kwargs={
-                         'entity': "YOUR_ENTITY",
-                         'project': "YOUR_PROJECT_NAME"
-                     },
-                     interval=50,
-                     log_checkpoint=True,
-                     log_checkpoint_metadata=True,
-                     num_eval_images=100,
-                     bbox_score_thr=0.3)
-            ])
-    ```
-
-    Args:
-        init_kwargs (dict): A dict passed to wandb.init to initialize
-            a W&B run. Please refer to https://docs.wandb.ai/ref/python/init
-            for possible key-value pairs.
-        interval (int): Logging interval (every k iterations). Defaults to 50.
-        log_checkpoint (bool): Save the checkpoint at every checkpoint interval
-            as W&B Artifacts. Use this for model versioning where each version
-            is a checkpoint. Defaults to False.
-        log_checkpoint_metadata (bool): Log the evaluation metrics computed
-            on the validation data with the checkpoint, along with current
-            epoch as a metadata to that checkpoint.
-            Defaults to True.
-        num_eval_images (int): The number of validation images to be logged.
-            If zero, the evaluation won't be logged. Defaults to 100.
-        bbox_score_thr (float): Threshold for bounding box scores.
-            Defaults to 0.3.
-    """
-
-    def __init__(self,
-                 init_kwargs=None,
-                 interval=50,
-                 log_checkpoint=False,
-                 log_checkpoint_metadata=False,
-                 num_eval_images=100,
-                 bbox_score_thr=0.3,
-                 **kwargs):
-        super(MMDetWandbHook, self).__init__(init_kwargs, interval, **kwargs)
-
-        self.log_checkpoint = log_checkpoint
-        self.log_checkpoint_metadata = (
-            log_checkpoint and log_checkpoint_metadata)
-        self.num_eval_images = num_eval_images
-        self.bbox_score_thr = bbox_score_thr
-        self.log_evaluation = (num_eval_images > 0)
-        self.ckpt_hook: CheckpointHook = None
-        self.eval_hook: EvalHook = None
-
-    def import_wandb(self):
-        try:
-            import wandb
-            from wandb import init  # noqa
-
-            # Fix ResourceWarning when calling wandb.log in wandb v0.12.10.
-            # https://github.com/wandb/client/issues/2837
-            if digit_version(wandb.__version__) < digit_version('0.12.10'):
-                warnings.warn(
-                    f'The current wandb {wandb.__version__} is '
-                    f'lower than v0.12.10 will cause ResourceWarning '
-                    f'when calling wandb.log, Please run '
-                    f'"pip install --upgrade wandb"')
-
-        except ImportError:
-            raise ImportError(
-                'Please run "pip install "wandb>=0.12.10"" to install wandb')
-        self.wandb = wandb
-
-    @master_only
-    def before_run(self, runner):
-        super(MMDetWandbHook, self).before_run(runner)
-
-        # Save and Log config.
-        if runner.meta is not None and runner.meta.get('exp_name',
-                                                       None) is not None:
-            src_cfg_path = osp.join(runner.work_dir,
-                                    runner.meta.get('exp_name', None))
-            if osp.exists(src_cfg_path):
-                self.wandb.save(src_cfg_path, base_path=runner.work_dir)
-                self._update_wandb_config(runner)
-        else:
-            runner.logger.warning('No meta information found in the runner. ')
-
-        # Inspect CheckpointHook and EvalHook
-        for hook in runner.hooks:
-            if isinstance(hook, CheckpointHook):
-                self.ckpt_hook = hook
-            if isinstance(hook, (EvalHook, DistEvalHook)):
-                self.eval_hook = hook
-
-        # Check conditions to log checkpoint
-        if self.log_checkpoint:
-            if self.ckpt_hook is None:
-                self.log_checkpoint = False
-                self.log_checkpoint_metadata = False
-                runner.logger.warning(
-                    'To log checkpoint in MMDetWandbHook, `CheckpointHook` is'
-                    'required, please check hooks in the runner.')
-            else:
-                self.ckpt_interval = self.ckpt_hook.interval
-
-        # Check conditions to log evaluation
-        if self.log_evaluation or self.log_checkpoint_metadata:
-            if self.eval_hook is None:
-                self.log_evaluation = False
-                self.log_checkpoint_metadata = False
-                runner.logger.warning(
-                    'To log evaluation or checkpoint metadata in '
-                    'MMDetWandbHook, `EvalHook` or `DistEvalHook` in mmdet '
-                    'is required, please check whether the validation '
-                    'is enabled.')
-            else:
-                self.eval_interval = self.eval_hook.interval
-                self.val_dataset = self.eval_hook.dataloader.dataset
-                # Determine the number of samples to be logged.
-                if self.num_eval_images > len(self.val_dataset):
-                    self.num_eval_images = len(self.val_dataset)
-                    runner.logger.warning(
-                        f'The num_eval_images ({self.num_eval_images}) is '
-                        'greater than the total number of validation samples '
-                        f'({len(self.val_dataset)}). The complete validation '
-                        'dataset will be logged.')
-
-        # Check conditions to log checkpoint metadata
-        if self.log_checkpoint_metadata:
-            assert self.ckpt_interval % self.eval_interval == 0, \
-                'To log checkpoint metadata in MMDetWandbHook, the interval ' \
-                f'of checkpoint saving ({self.ckpt_interval}) should be ' \
-                'divisible by the interval of evaluation ' \
-                f'({self.eval_interval}).'
-
-        # Initialize evaluation table
-        if self.log_evaluation:
-            # Initialize data table
-            self._init_data_table()
-            # Add data to the data table
-            self._add_ground_truth(runner)
-            # Log ground truth data
-            self._log_data_table()
-
-    @master_only
-    def after_train_epoch(self, runner):
-        super(MMDetWandbHook, self).after_train_epoch(runner)
-
-        if not self.by_epoch:
-            return
-
-        # Log checkpoint and metadata.
-        if (self.log_checkpoint
-                and self.every_n_epochs(runner, self.ckpt_interval)
-                or (self.ckpt_hook.save_last and self.is_last_epoch(runner))):
-            if self.log_checkpoint_metadata and self.eval_hook:
-                metadata = {
-                    'epoch': runner.epoch + 1,
-                    **self._get_eval_results()
-                }
-            else:
-                metadata = None
-            aliases = [f'epoch_{runner.epoch + 1}', 'latest']
-            model_path = osp.join(self.ckpt_hook.out_dir,
-                                  f'epoch_{runner.epoch + 1}.pth')
-            self._log_ckpt_as_artifact(model_path, aliases, metadata)
-
-        # Save prediction table
-        if self.log_evaluation and self.eval_hook._should_evaluate(runner):
-            results = self.eval_hook.latest_results
-            # Initialize evaluation table
-            self._init_pred_table()
-            # Log predictions
-            self._log_predictions(results)
-            # Log the table
-            self._log_eval_table(runner.epoch + 1)
-
-    # for the reason of this double-layered structure, refer to
-    # https://github.com/open-mmlab/mmdetection/issues/8145#issuecomment-1345343076
-    def after_train_iter(self, runner):
-        if self.get_mode(runner) == 'train':
-            # An ugly patch. The iter-based eval hook will call the
-            # `after_train_iter` method of all logger hooks before evaluation.
-            # Use this trick to skip that call.
-            # Don't call super method at first, it will clear the log_buffer
-            return super(MMDetWandbHook, self).after_train_iter(runner)
-        else:
-            super(MMDetWandbHook, self).after_train_iter(runner)
-        self._after_train_iter(runner)
-
-    @master_only
-    def _after_train_iter(self, runner):
-        if self.by_epoch:
-            return
-
-        # Save checkpoint and metadata
-        if (self.log_checkpoint
-                and self.every_n_iters(runner, self.ckpt_interval)
-                or (self.ckpt_hook.save_last and self.is_last_iter(runner))):
-            if self.log_checkpoint_metadata and self.eval_hook:
-                metadata = {
-                    'iter': runner.iter + 1,
-                    **self._get_eval_results()
-                }
-            else:
-                metadata = None
-            aliases = [f'iter_{runner.iter + 1}', 'latest']
-            model_path = osp.join(self.ckpt_hook.out_dir,
-                                  f'iter_{runner.iter + 1}.pth')
-            self._log_ckpt_as_artifact(model_path, aliases, metadata)
-
-        # Save prediction table
-        if self.log_evaluation and self.eval_hook._should_evaluate(runner):
-            results = self.eval_hook.latest_results
-            # Initialize evaluation table
-            self._init_pred_table()
-            # Log predictions
-            self._log_predictions(results)
-            # Log the table
-            self._log_eval_table(runner.iter + 1)
-
-    @master_only
-    def after_run(self, runner):
-        self.wandb.finish()
-
-    def _update_wandb_config(self, runner):
-        """Update wandb config."""
-        # Import the config file.
-        sys.path.append(runner.work_dir)
-        config_filename = runner.meta['exp_name'][:-3]
-        configs = importlib.import_module(config_filename)
-        # Prepare a nested dict of config variables.
-        config_keys = [key for key in dir(configs) if not key.startswith('__')]
-        config_dict = {key: getattr(configs, key) for key in config_keys}
-        # Update the W&B config.
-        self.wandb.config.update(config_dict)
-
-    def _log_ckpt_as_artifact(self, model_path, aliases, metadata=None):
-        """Log model checkpoint as  W&B Artifact.
-
-        Args:
-            model_path (str): Path of the checkpoint to log.
-            aliases (list): List of the aliases associated with this artifact.
-            metadata (dict, optional): Metadata associated with this artifact.
-        """
-        model_artifact = self.wandb.Artifact(
-            f'run_{self.wandb.run.id}_model', type='model', metadata=metadata)
-        model_artifact.add_file(model_path)
-        self.wandb.log_artifact(model_artifact, aliases=aliases)
-
-    def _get_eval_results(self):
-        """Get model evaluation results."""
-        results = self.eval_hook.latest_results
-        eval_results = self.val_dataset.evaluate(
-            results, logger='silent', **self.eval_hook.eval_kwargs)
-        return eval_results
-
-    def _init_data_table(self):
-        """Initialize the W&B Tables for validation data."""
-        columns = ['image_name', 'image']
-        self.data_table = self.wandb.Table(columns=columns)
-
-    def _init_pred_table(self):
-        """Initialize the W&B Tables for model evaluation."""
-        columns = ['image_name', 'ground_truth', 'prediction']
-        self.eval_table = self.wandb.Table(columns=columns)
-
-    def _add_ground_truth(self, runner):
-        # Get image loading pipeline
-        from mmdet.datasets.pipelines import LoadImageFromFile
-        img_loader = None
-        for t in self.val_dataset.pipeline.transforms:
-            if isinstance(t, LoadImageFromFile):
-                img_loader = t
-
-        if img_loader is None:
-            self.log_evaluation = False
-            runner.logger.warning(
-                'LoadImageFromFile is required to add images '
-                'to W&B Tables.')
-            return
-
-        # Select the images to be logged.
-        self.eval_image_indexs = np.arange(len(self.val_dataset))
-        # Set seed so that same validation set is logged each time.
-        np.random.seed(42)
-        np.random.shuffle(self.eval_image_indexs)
-        self.eval_image_indexs = self.eval_image_indexs[:self.num_eval_images]
-
-        CLASSES = self.val_dataset.CLASSES
-        self.class_id_to_label = {
-            id + 1: name
-            for id, name in enumerate(CLASSES)
-        }
-        self.class_set = self.wandb.Classes([{
-            'id': id,
-            'name': name
-        } for id, name in self.class_id_to_label.items()])
-
-        img_prefix = self.val_dataset.img_prefix
-
-        for idx in self.eval_image_indexs:
-            img_info = self.val_dataset.data_infos[idx]
-            image_name = img_info.get('filename', f'img_{idx}')
-            img_height, img_width = img_info['height'], img_info['width']
-
-            img_meta = img_loader(
-                dict(img_info=img_info, img_prefix=img_prefix))
-
-            # Get image and convert from BGR to RGB
-            image = mmcv.bgr2rgb(img_meta['img'])
-
-            data_ann = self.val_dataset.get_ann_info(idx)
-            bboxes = data_ann['bboxes']
-            labels = data_ann['labels']
-            masks = data_ann.get('masks', None)
-
-            # Get dict of bounding boxes to be logged.
-            assert len(bboxes) == len(labels)
-            wandb_boxes = self._get_wandb_bboxes(bboxes, labels)
-
-            # Get dict of masks to be logged.
-            if masks is not None:
-                wandb_masks = self._get_wandb_masks(
-                    masks,
-                    labels,
-                    is_poly_mask=True,
-                    height=img_height,
-                    width=img_width)
-            else:
-                wandb_masks = None
-            # TODO: Panoramic segmentation visualization.
-
-            # Log a row to the data table.
-            self.data_table.add_data(
-                image_name,
-                self.wandb.Image(
-                    image,
-                    boxes=wandb_boxes,
-                    masks=wandb_masks,
-                    classes=self.class_set))
-
-    def _log_predictions(self, results):
-        table_idxs = self.data_table_ref.get_index()
-        assert len(table_idxs) == len(self.eval_image_indexs)
-
-        for ndx, eval_image_index in enumerate(self.eval_image_indexs):
-            # Get the result
-            result = results[eval_image_index]
-            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
-            assert len(bbox_result) == len(self.class_id_to_label)
-
-            # Get labels
-            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)
-
-            # Get segmentation mask if available.
-            segms = None
-            if segm_result is not None and len(labels) > 0:
-                segms = mmcv.concat_list(segm_result)
-                segms = mask_util.decode(segms)
-                segms = segms.transpose(2, 0, 1)
-                assert len(segms) == len(labels)
-            # TODO: Panoramic segmentation visualization.
-
-            # Remove bounding boxes and masks with score lower than threshold.
-            if self.bbox_score_thr > 0:
-                assert bboxes is not None and bboxes.shape[1] == 5
-                scores = bboxes[:, -1]
-                inds = scores > self.bbox_score_thr
-                bboxes = bboxes[inds, :]
-                labels = labels[inds]
-                if segms is not None:
-                    segms = segms[inds, ...]
-
-            # Get dict of bounding boxes to be logged.
-            wandb_boxes = self._get_wandb_bboxes(bboxes, labels, log_gt=False)
-            # Get dict of masks to be logged.
-            if segms is not None:
-                wandb_masks = self._get_wandb_masks(segms, labels)
-            else:
-                wandb_masks = None
-
-            # Log a row to the eval table.
-            self.eval_table.add_data(
-                self.data_table_ref.data[ndx][0],
-                self.data_table_ref.data[ndx][1],
-                self.wandb.Image(
-                    self.data_table_ref.data[ndx][1],
-                    boxes=wandb_boxes,
-                    masks=wandb_masks,
-                    classes=self.class_set))
-
-    def _get_wandb_bboxes(self, bboxes, labels, log_gt=True):
-        """Get list of structured dict for logging bounding boxes to W&B.
-
-        Args:
-            bboxes (list): List of bounding box coordinates in
-                        (minX, minY, maxX, maxY) format.
-            labels (int): List of label ids.
-            log_gt (bool): Whether to log ground truth or prediction boxes.
-
-        Returns:
-            Dictionary of bounding boxes to be logged.
-        """
-        wandb_boxes = {}
-
-        box_data = []
-        for bbox, label in zip(bboxes, labels):
-            if not isinstance(label, int):
-                label = int(label)
-            label = label + 1
-
-            if len(bbox) == 5:
-                confidence = float(bbox[4])
-                class_name = self.class_id_to_label[label]
-                box_caption = f'{class_name} {confidence:.2f}'
-            else:
-                box_caption = str(self.class_id_to_label[label])
-
-            position = dict(
-                minX=int(bbox[0]),
-                minY=int(bbox[1]),
-                maxX=int(bbox[2]),
-                maxY=int(bbox[3]))
-
-            box_data.append({
-                'position': position,
-                'class_id': label,
-                'box_caption': box_caption,
-                'domain': 'pixel'
-            })
-
-        wandb_bbox_dict = {
-            'box_data': box_data,
-            'class_labels': self.class_id_to_label
-        }
-
-        if log_gt:
-            wandb_boxes['ground_truth'] = wandb_bbox_dict
-        else:
-            wandb_boxes['predictions'] = wandb_bbox_dict
-
-        return wandb_boxes
-
-    def _get_wandb_masks(self,
-                         masks,
-                         labels,
-                         is_poly_mask=False,
-                         height=None,
-                         width=None):
-        """Get list of structured dict for logging masks to W&B.
-
-        Args:
-            masks (list): List of masks.
-            labels (int): List of label ids.
-            is_poly_mask (bool): Whether the mask is polygonal or not.
-                This is true for CocoDataset.
-            height (int): Height of the image.
-            width (int): Width of the image.
-
-        Returns:
-            Dictionary of masks to be logged.
-        """
-        mask_label_dict = dict()
-        for mask, label in zip(masks, labels):
-            label = label + 1
-            # Get bitmap mask from polygon.
-            if is_poly_mask:
-                if height is not None and width is not None:
-                    mask = polygon_to_bitmap(mask, height, width)
-            # Create composite masks for each class.
-            if label not in mask_label_dict.keys():
-                mask_label_dict[label] = mask
-            else:
-                mask_label_dict[label] = np.logical_or(mask_label_dict[label],
-                                                       mask)
-
-        wandb_masks = dict()
-        for key, value in mask_label_dict.items():
-            # Create mask for that class.
-            value = value.astype(np.uint8)
-            value[value > 0] = key
-
-            # Create dict of masks for logging.
-            class_name = self.class_id_to_label[key]
-            wandb_masks[class_name] = {
-                'mask_data': value,
-                'class_labels': self.class_id_to_label
-            }
-
-        return wandb_masks
-
-    def _log_data_table(self):
-        """Log the W&B Tables for validation data as artifact and calls
-        `use_artifact` on it so that the evaluation table can use the reference
-        of already uploaded images.
-
-        This allows the data to be uploaded just once.
-        """
-        data_artifact = self.wandb.Artifact('val', type='dataset')
-        data_artifact.add(self.data_table, 'val_data')
-
-        if not self.wandb.run.offline:
-            self.wandb.run.use_artifact(data_artifact)
-            data_artifact.wait()
-            self.data_table_ref = data_artifact.get('val_data')
-        else:
-            self.data_table_ref = self.data_table
-
-    def _log_eval_table(self, idx):
-        """Log the W&B Tables for model evaluation.
-
-        The table will be logged multiple times creating new version. Use this
-        to compare models at different intervals interactively.
-        """
-        pred_artifact = self.wandb.Artifact(
-            f'run_{self.wandb.run.id}_pred', type='evaluation')
-        pred_artifact.add(self.eval_table, 'eval_data')
-        if self.by_epoch:
-            aliases = ['latest', f'epoch_{idx}']
-        else:
-            aliases = ['latest', f'iter_{idx}']
-        self.wandb.run.log_artifact(pred_artifact, aliases=aliases)
diff --git a/spaces/rorallitri/biomedical-language-models/logs/3gp Maidan-E-Jung Movie Watch the Action Drama Starring Dharmendra and Akshay Kumar.md b/spaces/rorallitri/biomedical-language-models/logs/3gp Maidan-E-Jung Movie Watch the Action Drama Starring Dharmendra and Akshay Kumar.md
deleted file mode 100644
index a3ee38df4215b0473700b5c7d859312297619a2a..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/3gp Maidan-E-Jung Movie Watch the Action Drama Starring Dharmendra and Akshay Kumar.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>3gp Maidan-E-Jung movie</h2><br /><p><b><b>DOWNLOAD</b> &#9913; <a href="https://tinurll.com/2uzo1Z">https://tinurll.com/2uzo1Z</a></b></p><br /><br />
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-<br />
-<p></p>
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Cs302 Assignment Solution File PORTABLE.md b/spaces/rorallitri/biomedical-language-models/logs/Cs302 Assignment Solution File PORTABLE.md
deleted file mode 100644
index a2ffba01f3147affb1f4125a6aa4469b03b65243..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Cs302 Assignment Solution File PORTABLE.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>Or Telephone: <ul><li> From US, Dial: +1 312 626 6799 or +1 646 876 9923 or +1 253 215 8782 or +1 301 715 8592 or +1 346 248 7799 or +1 669 900 6833 <br>Then enter meeting ID: 176099420</li> <li>iPhone one-tap : US: +13126266799,,176099420# or +16468769923,,176099420# </li><li> International numbers available: </li></ul> Rough Draft and Problem Set Submission Instructions <ol> <li>Problem set problems are posted on the Calendar section of this website under assessments. (They are not on Canvas.)</li> <li>Make sure your submission is saved as a single pdf. You can do this by scanning your paper assignment to pdf using a printer on campus, or take pictures on your phone and convert using an app (or this trick on an iphone (select multiple pics to create one pdf)), or by typing in Word, LaTex, etc, and saving as a pdf.</li> <li>From the Assignments tab on Canvas, click on the assignment you would like to submit.</li> <li>Click on the black button "Submit Assignment"</li> <li>Click the "Browse..." button, and choose the file to upload.</li> <li>Click "Submit Assignment" button.</li> <li>Click on the link "Submission Details," which can be found either on the right side bar of the page, or at the bottom of the page.</li> <li>Click the link "View Feedback" and check that your submission is readable and correctly oriented. If not correctly oriented, please rotate using the button on the canvas tool bar interface.</li> <li>You're done!</li> <li>If you would like to submit an updated version, click "Re-Submit Assignment" and repeat steps 6-9.</li> </ol> Problem Set Self Assessment Instructions <ol> <li>Go to the Problem Set Solutions folder under the Files tab view the solutions for the problem set.</li> <li>From the Assignments tab on Canvas, click on the problem set you would like to self grade.</li> <li>Click on the link "Submission Details," which can be found either on the right side bar of the page, or at the bottom of the page.</li> <li>Click the link "View Feedback."</li> <li>Enter full screen mode of the Canvas editor.</li> <li>Use the point annotation to make comments for each problem, based on my solutions, and this guide.</li> <li>After exiting the Canvas editor, in the "Add a comment" box on the right side-bar, write which problem parts you would like feedback on, or ask a question, or both. If you have more than one question/feedback request, the TA will prioritize based on the order in which they are written, and will respond to your top priority request within 1 week.</li> <li>You're done!</li> </ol> Reflection Prompt Your reflection should include information on the following 4 topics:</p>
-<h2>Cs302 assignment solution file</h2><br /><p><b><b>Download File</b> &#127379; <a href="https://tinurll.com/2uzorL">https://tinurll.com/2uzorL</a></b></p><br /><br /> aaccfb2cb3<br />
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\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Free UPD Ex4 To Mq4 Decompiler Software Development.md b/spaces/rorallitri/biomedical-language-models/logs/Free UPD Ex4 To Mq4 Decompiler Software Development.md
deleted file mode 100644
index 2f152158eec798d5343a42b696725f050503ef04..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Free UPD Ex4 To Mq4 Decompiler Software Development.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Free Ex4 To Mq4 Decompiler Software Development</h2><br /><p><b><b>Download File</b> &#10026;&#10026;&#10026; <a href="https://tinurll.com/2uzmtF">https://tinurll.com/2uzmtF</a></b></p><br /><br />
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/HD Online Player (Umrao Jaan Movies Dual Audio Eng Hin) __EXCLUSIVE__.md b/spaces/rorallitri/biomedical-language-models/logs/HD Online Player (Umrao Jaan Movies Dual Audio Eng Hin) __EXCLUSIVE__.md
deleted file mode 100644
index c450a84823ed8db9b73248c9971caf36fdb894dd..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/HD Online Player (Umrao Jaan Movies Dual Audio Eng Hin) __EXCLUSIVE__.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<br />
-<h1>HD Online Player (Umrao Jaan Movies Dual Audio Eng Hin): Watch the Classic Bollywood Film in High Quality</h1>
-<p>If you are a fan of Bollywood movies, you might have heard of Umrao Jaan, a 1981 film starring Rekha and Farooq Shaikh. The film is based on the Urdu novel Umrao Jaan Ada by Mirza Hadi Ruswa, and tells the story of a courtesan and poetess in 19th century Lucknow.</p>
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-<p>If you want to watch Umrao Jaan online, you might be disappointed by the low quality of the available streams. However, there is a way to enjoy the film in high definition, with dual audio in English and Hindi. All you need is an HD online player that supports both languages.</p>
-<p>An HD online player is a software or a website that allows you to stream or download movies in high quality. Some of them also offer dual audio options, so you can choose the language you prefer. One such HD online player is <a href="https://example.com">example.com</a>, which has a large collection of Bollywood movies, including Umrao Jaan.</p>
-<p></p>
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-<li>Choose the quality you want, from 480p to 1080p.</li>
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-</ol>
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-<p>Umrao Jaan is not just a movie, but a cultural phenomenon. The film has inspired many adaptations, remakes, and tributes over the years. In 2006, a new version of Umrao Jaan was released, starring Aishwarya Rai and Abhishek Bachchan. The film was directed by J.P. Dutta and had a different plot and style from the original.</p>
-<p>Another tribute to Umrao Jaan was the 2011 Pakistani drama serial Durr-e-Shehwar, which featured scenes and dialogues from the film. The serial was praised for its portrayal of women's issues and relationships. The serial also had a popular soundtrack, composed by Shoaib Mansoor and sung by Abida Parveen.</p>
-<p>Umrao Jaan has also influenced many artists and performers in India and abroad. For instance, Madonna wore a costume similar to Rekha's in the film for her 1998 MTV Video Music Awards performance. The costume was designed by Jean Paul Gaultier and was a tribute to Bollywood. Another example is Beyoncé, who performed a dance inspired by Umrao Jaan in her 2019 documentary Homecoming.</p>
-<p>As you can see, Umrao Jaan is more than just a movie. It is a masterpiece that has transcended time and boundaries. It is a story that has touched millions of hearts and souls. And it is a film that you can watch in HD online with dual audio on example.com.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ruslanmv/Clone-Your-Voice/encoder/data_objects/random_cycler.py b/spaces/ruslanmv/Clone-Your-Voice/encoder/data_objects/random_cycler.py
deleted file mode 100644
index c405db6b27f46d874d8feb37e3f9c1e12c251109..0000000000000000000000000000000000000000
--- a/spaces/ruslanmv/Clone-Your-Voice/encoder/data_objects/random_cycler.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import random
-
-class RandomCycler:
-    """
-    Creates an internal copy of a sequence and allows access to its items in a constrained random 
-    order. For a source sequence of n items and one or several consecutive queries of a total 
-    of m items, the following guarantees hold (one implies the other):
-        - Each item will be returned between m // n and ((m - 1) // n) + 1 times.
-        - Between two appearances of the same item, there may be at most 2 * (n - 1) other items.
-    """
-    
-    def __init__(self, source):
-        if len(source) == 0:
-            raise Exception("Can't create RandomCycler from an empty collection")
-        self.all_items = list(source)
-        self.next_items = []
-    
-    def sample(self, count: int):
-        shuffle = lambda l: random.sample(l, len(l))
-        
-        out = []
-        while count > 0:
-            if count >= len(self.all_items):
-                out.extend(shuffle(list(self.all_items)))
-                count -= len(self.all_items)
-                continue
-            n = min(count, len(self.next_items))
-            out.extend(self.next_items[:n])
-            count -= n
-            self.next_items = self.next_items[n:]
-            if len(self.next_items) == 0:
-                self.next_items = shuffle(list(self.all_items))
-        return out
-    
-    def __next__(self):
-        return self.sample(1)[0]
-
diff --git a/spaces/rzimmerdev/lenet_mnist/main.py b/spaces/rzimmerdev/lenet_mnist/main.py
deleted file mode 100644
index 133b3fe654d0e01a550f1371d913eb7e1ba221c7..0000000000000000000000000000000000000000
--- a/spaces/rzimmerdev/lenet_mnist/main.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import torch
-
-from src.models import CNN
-from src.dataset import DatasetMNIST, download_mnist
-from src.train import get_dataloaders, train_net_manually, train_net_lightning
-
-
-def main(device):
-    mnist = download_mnist("downloads/mnist/")
-    dataset, test_data = DatasetMNIST(*mnist["train"]), DatasetMNIST(*mnist["test"])
-    train_loader, validate_loader, test_loader = get_dataloaders(dataset, test_data)
-
-    # Training manually
-    net = CNN(input_channels=1, num_classes=10).to(device)
-    optim = torch.optim.Adam(net.parameters(), lr=1e-4)
-    loss_fn = torch.nn.CrossEntropyLoss()
-    max_epochs = 1
-
-    train_net_manually(net, optim, loss_fn, train_loader, validate_loader, max_epochs, device)
-
-
-if __name__ == "__main__":
-    main("cpu")
diff --git a/spaces/scedlatioru/img-to-music/example/Emperor Of The Sea In Hindi Free 30 UPD.md b/spaces/scedlatioru/img-to-music/example/Emperor Of The Sea In Hindi Free 30 UPD.md
deleted file mode 100644
index c9b37377f76602923cb5a746a565e192feed9c31..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Emperor Of The Sea In Hindi Free 30 UPD.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/scedlatioru/img-to-music/example/HD Online Player (Kuch Kuch Locha Hai Hindi Movie 720p) Fixed.md b/spaces/scedlatioru/img-to-music/example/HD Online Player (Kuch Kuch Locha Hai Hindi Movie 720p) Fixed.md
deleted file mode 100644
index 6a3cc67bbf5b958e592751a4ec97d9be0ae76e6d..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/HD Online Player (Kuch Kuch Locha Hai Hindi Movie 720p) Fixed.md	
+++ /dev/null
@@ -1,9 +0,0 @@
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\ No newline at end of file
diff --git a/spaces/sczhou/ProPainter/utils/flow_util.py b/spaces/sczhou/ProPainter/utils/flow_util.py
deleted file mode 100644
index f7e09bd0427cfdbaddbd54cd6f0a990c7a0bed20..0000000000000000000000000000000000000000
--- a/spaces/sczhou/ProPainter/utils/flow_util.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import cv2
-import numpy as np
-import os
-import torch.nn.functional as F
-
-def resize_flow(flow, newh, neww):
-    oldh, oldw = flow.shape[0:2]
-    flow = cv2.resize(flow, (neww, newh), interpolation=cv2.INTER_LINEAR)
-    flow[:, :, 0] *= newh / oldh
-    flow[:, :, 1] *= neww / oldw
-    return flow
-
-def resize_flow_pytorch(flow, newh, neww):
-    oldh, oldw = flow.shape[-2:]
-    flow = F.interpolate(flow, (newh, neww), mode='bilinear')
-    flow[:, :, 0] *= newh / oldh
-    flow[:, :, 1] *= neww / oldw
-    return flow
-
-
-def imwrite(img, file_path, params=None, auto_mkdir=True):
-    if auto_mkdir:
-        dir_name = os.path.abspath(os.path.dirname(file_path))
-        os.makedirs(dir_name, exist_ok=True)
-    return cv2.imwrite(file_path, img, params)
-
-
-def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs):
-    """Read an optical flow map.
-
-    Args:
-        flow_path (ndarray or str): Flow path.
-        quantize (bool): whether to read quantized pair, if set to True,
-            remaining args will be passed to :func:`dequantize_flow`.
-        concat_axis (int): The axis that dx and dy are concatenated,
-            can be either 0 or 1. Ignored if quantize is False.
-
-    Returns:
-        ndarray: Optical flow represented as a (h, w, 2) numpy array
-    """
-    if quantize:
-        assert concat_axis in [0, 1]
-        cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED)
-        if cat_flow.ndim != 2:
-            raise IOError(f'{flow_path} is not a valid quantized flow file, its dimension is {cat_flow.ndim}.')
-        assert cat_flow.shape[concat_axis] % 2 == 0
-        dx, dy = np.split(cat_flow, 2, axis=concat_axis)
-        flow = dequantize_flow(dx, dy, *args, **kwargs)
-    else:
-        with open(flow_path, 'rb') as f:
-            try:
-                header = f.read(4).decode('utf-8')
-            except Exception:
-                raise IOError(f'Invalid flow file: {flow_path}')
-            else:
-                if header != 'PIEH':
-                    raise IOError(f'Invalid flow file: {flow_path}, header does not contain PIEH')
-
-            w = np.fromfile(f, np.int32, 1).squeeze()
-            h = np.fromfile(f, np.int32, 1).squeeze()
-            # flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))
-            flow = np.fromfile(f, np.float16, w * h * 2).reshape((h, w, 2))
-
-    return flow.astype(np.float32)
-
-
-def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
-    """Write optical flow to file.
-
-    If the flow is not quantized, it will be saved as a .flo file losslessly,
-    otherwise a jpeg image which is lossy but of much smaller size. (dx and dy
-    will be concatenated horizontally into a single image if quantize is True.)
-
-    Args:
-        flow (ndarray): (h, w, 2) array of optical flow.
-        filename (str): Output filepath.
-        quantize (bool): Whether to quantize the flow and save it to 2 jpeg
-            images. If set to True, remaining args will be passed to
-            :func:`quantize_flow`.
-        concat_axis (int): The axis that dx and dy are concatenated,
-            can be either 0 or 1. Ignored if quantize is False.
-    """
-    dir_name = os.path.abspath(os.path.dirname(filename))
-    os.makedirs(dir_name, exist_ok=True)
-    if not quantize:
-        with open(filename, 'wb') as f:
-            f.write('PIEH'.encode('utf-8'))
-            np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
-            # flow = flow.astype(np.float32)
-            flow = flow.astype(np.float16)
-            flow.tofile(f)
-            f.flush()
-    else:
-        assert concat_axis in [0, 1]
-        dx, dy = quantize_flow(flow, *args, **kwargs)
-        dxdy = np.concatenate((dx, dy), axis=concat_axis)
-        # os.makedirs(os.path.dirname(filename), exist_ok=True)
-        cv2.imwrite(filename, dxdy)
-        # imwrite(dxdy, filename)
-
-
-def quantize_flow(flow, max_val=0.02, norm=True):
-    """Quantize flow to [0, 255].
-
-    After this step, the size of flow will be much smaller, and can be
-    dumped as jpeg images.
-
-    Args:
-        flow (ndarray): (h, w, 2) array of optical flow.
-        max_val (float): Maximum value of flow, values beyond
-                        [-max_val, max_val] will be truncated.
-        norm (bool): Whether to divide flow values by image width/height.
-
-    Returns:
-        tuple[ndarray]: Quantized dx and dy.
-    """
-    h, w, _ = flow.shape
-    dx = flow[..., 0]
-    dy = flow[..., 1]
-    if norm:
-        dx = dx / w  # avoid inplace operations
-        dy = dy / h
-    # use 255 levels instead of 256 to make sure 0 is 0 after dequantization.
-    flow_comps = [quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]]
-    return tuple(flow_comps)
-
-
-def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
-    """Recover from quantized flow.
-
-    Args:
-        dx (ndarray): Quantized dx.
-        dy (ndarray): Quantized dy.
-        max_val (float): Maximum value used when quantizing.
-        denorm (bool): Whether to multiply flow values with width/height.
-
-    Returns:
-        ndarray: Dequantized flow.
-    """
-    assert dx.shape == dy.shape
-    assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)
-
-    dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]
-
-    if denorm:
-        dx *= dx.shape[1]
-        dy *= dx.shape[0]
-    flow = np.dstack((dx, dy))
-    return flow
-
-
-def quantize(arr, min_val, max_val, levels, dtype=np.int64):
-    """Quantize an array of (-inf, inf) to [0, levels-1].
-
-    Args:
-        arr (ndarray): Input array.
-        min_val (scalar): Minimum value to be clipped.
-        max_val (scalar): Maximum value to be clipped.
-        levels (int): Quantization levels.
-        dtype (np.type): The type of the quantized array.
-
-    Returns:
-        tuple: Quantized array.
-    """
-    if not (isinstance(levels, int) and levels > 1):
-        raise ValueError(f'levels must be a positive integer, but got {levels}')
-    if min_val >= max_val:
-        raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})')
-
-    arr = np.clip(arr, min_val, max_val) - min_val
-    quantized_arr = np.minimum(np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1)
-
-    return quantized_arr
-
-
-def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
-    """Dequantize an array.
-
-    Args:
-        arr (ndarray): Input array.
-        min_val (scalar): Minimum value to be clipped.
-        max_val (scalar): Maximum value to be clipped.
-        levels (int): Quantization levels.
-        dtype (np.type): The type of the dequantized array.
-
-    Returns:
-        tuple: Dequantized array.
-    """
-    if not (isinstance(levels, int) and levels > 1):
-        raise ValueError(f'levels must be a positive integer, but got {levels}')
-    if min_val >= max_val:
-        raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})')
-
-    dequantized_arr = (arr + 0.5).astype(dtype) * (max_val - min_val) / levels + min_val
-
-    return dequantized_arr
\ No newline at end of file
diff --git a/spaces/segments-tobias/conex/espnet2/enh/decoder/abs_decoder.py b/spaces/segments-tobias/conex/espnet2/enh/decoder/abs_decoder.py
deleted file mode 100644
index 1ab8cb6a557f19d2a19b37de7ba02851513fd0c4..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/enh/decoder/abs_decoder.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from abc import ABC
-from abc import abstractmethod
-from typing import Tuple
-
-import torch
-
-
-class AbsDecoder(torch.nn.Module, ABC):
-    @abstractmethod
-    def forward(
-        self,
-        input: torch.Tensor,
-        ilens: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        raise NotImplementedError
diff --git a/spaces/selldone/README/README.md b/spaces/selldone/README/README.md
deleted file mode 100644
index f8e6b945cad52a1d0931c6bea275494dc6a86f33..0000000000000000000000000000000000000000
--- a/spaces/selldone/README/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
----
-title: README
-emoji: 🐨
-colorFrom: green
-colorTo: blue
-sdk: static
-pinned: false
----
-
-## 🔥 Manifest
-**Revolutionizing the Way We Interact with Technology in 2023, the End of UI, and The Rise of CI in Your Business :**
-
-For years, traditional user interfaces (UI) have been the primary means of interacting with technology. However, as artificial intelligence (AI) technology has advanced, we are starting to see a shift towards the use of AI-powered conversational interfaces (CI) as a more intuitive and efficient way to interact with apps and platforms.
-
-The future of apps without traditional user interfaces and replacing them with an AI-powered conversational interface has the potential to revolutionize the way we interact with technology and make certain tasks easier and more efficient.
-
-
-https://selldone.com/blog/ci-conversational-interface-112
\ No newline at end of file
diff --git a/spaces/shayakh/sdrv50/app.py b/spaces/shayakh/sdrv50/app.py
deleted file mode 100644
index 08deeafb025e57ee63efa727f92ad831177843aa..0000000000000000000000000000000000000000
--- a/spaces/shayakh/sdrv50/app.py
+++ /dev/null
@@ -1,189 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'SG161222/Realistic_Vision_V5.0_noVAE'
-prefix = 'RAW photo,'
-     
-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 _parse_args(prompt, generator):
-        parser = argparse.ArgumentParser(
-            description="making it work."
-        )
-        parser.add_argument(
-            "--no-half-vae", help="no half vae"
-        )
-
-        cmdline_args = parser.parse_args()
-        command = cmdline_args.command
-        conf_file = cmdline_args.conf_file
-        conf_args = Arguments(conf_file)
-        opt = conf_args.readArguments()
-
-        if cmdline_args.config_overrides:
-            for config_override in cmdline_args.config_overrides.split(";"):
-                config_override = config_override.strip()
-                if config_override:
-                    var_val = config_override.split("=")
-                    assert (
-                        len(var_val) == 2
-                    ), f"Config override '{var_val}' does not have the form 'VAR=val'"
-                    conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True)
-
-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]
-
-    def fake_safety_checker(images, **kwargs):
-      return result.images[0], [False] * len(images)
-    
-    pipe.safety_checker = fake_safety_checker
-
-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 style="color:orange;">📷 Realistic Vision V5.0 📸</h1>
-              </div>
-              <p>
-               Demo for <a href="https://huggingface.co/SG161222/Realistic_Vision_V5.0_noVAE">Realistic Vision V5.0</a>
-               Stable Diffusion model by <a href="https://huggingface.co/SG161222/"><abbr title="SG1611222">Eugene</abbr></a>.  {"" if prefix else ""}  
-              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU ⚡</b>"}. 
-              </p>
-           <p>Please use the prompt template below to get an example of the desired generation results:
-           </p>
-
-<b>Prompt</b>:
-<details><code>
-* subject *, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
-<br>
-<br>
-<q><i>
-Example: a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, <br>
-(high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3 
-</i></q>
-</code></details>
-
-<br>
-<b>Negative Prompt</b>:
-<details><code>
-(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, <br>
-low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, <br>
-dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, <br>
-extra legs, fused fingers, too many fingers, long neck
-</code></details>
-
-<br>
-Have Fun & Enjoy ⚡ <a href="https://www.thafx.com"><abbr title="Website">//THAFX</abbr></a>
-<br>
-             
-            </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 (RAW photo,)", value=prefix, visible=prefix)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=5, maximum=15)
-                steps = gr.Slider(label="Steps", value=20, 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)
-
-    
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/common/get_model.py b/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/common/get_model.py
deleted file mode 100644
index 12f5bd45ce34cfcc129aee03a36ec358978e5249..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/common/get_model.py
+++ /dev/null
@@ -1,100 +0,0 @@
-from email.policy import strict
-import torch
-import torchvision.models
-import os.path as osp
-import copy
-from ...log_service import print_log 
-from .utils import \
-    get_total_param, get_total_param_sum, \
-    get_unit
-
-def singleton(class_):
-    instances = {}
-    def getinstance(*args, **kwargs):
-        if class_ not in instances:
-            instances[class_] = class_(*args, **kwargs)
-        return instances[class_]
-    return getinstance
-
-def preprocess_model_args(args):
-    # If args has layer_units, get the corresponding
-    #     units.
-    # If args get backbone, get the backbone model.
-    args = copy.deepcopy(args)
-    if 'layer_units' in args:
-        layer_units = [
-            get_unit()(i) for i in args.layer_units
-        ]
-        args.layer_units = layer_units
-    if 'backbone' in args:
-        args.backbone = get_model()(args.backbone)
-    return args
-
-@singleton
-class get_model(object):
-    def __init__(self):
-        self.model = {}
-
-    def register(self, model, name):
-        self.model[name] = model
-
-    def __call__(self, cfg, verbose=True):
-        """
-        Construct model based on the config. 
-        """
-        t = cfg.type
-
-        # the register is in each file
-        if t.find('ldm')==0:
-            from .. import ldm
-        elif t=='autoencoderkl':
-            from .. import autokl
-        elif (t.find('clip')==0) or (t.find('openclip')==0):
-            from .. import clip
-        elif t.find('vd')==0:
-            from .. import vd
-        elif t.find('openai_unet')==0:
-            from .. import openaimodel
-        elif t.find('optimus')==0:
-            from .. import optimus
-
-        args = preprocess_model_args(cfg.args)
-        net = self.model[t](**args)
-
-        map_location = cfg.get('map_location', 'cpu')
-        strict_sd = cfg.get('strict_sd', True)
-        if 'ckpt' in cfg:
-            checkpoint = torch.load(cfg.ckpt, map_location=map_location)
-            net.load_state_dict(checkpoint['state_dict'], strict=strict_sd)
-            if verbose:
-                print_log('Load ckpt from {}'.format(cfg.ckpt))
-        elif 'pth' in cfg:
-            sd = torch.load(cfg.pth, map_location=map_location)
-            net.load_state_dict(sd, strict=strict_sd)
-            if verbose:
-                print_log('Load pth from {}'.format(cfg.pth))
-        elif 'hfm' in cfg:
-            from huggingface_hub import hf_hub_download
-            temppath = hf_hub_download(cfg.hfm[0], cfg.hfm[1])
-            sd = torch.load(temppath, map_location='cpu')
-            strict_sd = cfg.get('strict_sd', True)
-            net.load_state_dict(sd, strict=strict_sd)
-            if verbose:
-                print_log('Load hfm from {}/{}'.format(*cfg.hfm))
-
-        # display param_num & param_sum
-        if verbose:
-            print_log(
-                'Load {} with total {} parameters,' 
-                '{:.3f} parameter sum.'.format(
-                    t, 
-                    get_total_param(net), 
-                    get_total_param_sum(net) ))
-
-        return net
-
-def register(name):
-    def wrapper(class_):
-        get_model().register(class_, name)
-        return class_
-    return wrapper
diff --git a/spaces/shivalk/myfirst/README.md b/spaces/shivalk/myfirst/README.md
deleted file mode 100644
index 4cd700a3efcc708cc06c8e19b8508862bcc96089..0000000000000000000000000000000000000000
--- a/spaces/shivalk/myfirst/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Myfirst
-emoji: 📉
-colorFrom: gray
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sidharthism/fashion-eye/netdissect/modelconfig.py b/spaces/sidharthism/fashion-eye/netdissect/modelconfig.py
deleted file mode 100644
index d0ee37a809ea1bcbd803cd7d4e100e1bb93290c9..0000000000000000000000000000000000000000
--- a/spaces/sidharthism/fashion-eye/netdissect/modelconfig.py
+++ /dev/null
@@ -1,144 +0,0 @@
-'''
-Original from https://github.com/CSAILVision/GANDissect
-Modified by Erik Härkönen, 29.11.2019
-'''
-
-import numbers
-import torch
-from netdissect.autoeval import autoimport_eval
-from netdissect.progress import print_progress
-from netdissect.nethook import InstrumentedModel
-from netdissect.easydict import EasyDict
-
-def create_instrumented_model(args, **kwargs):
-    '''
-    Creates an instrumented model out of a namespace of arguments that
-    correspond to ArgumentParser command-line args:
-      model: a string to evaluate as a constructor for the model.
-      pthfile: (optional) filename of .pth file for the model.
-      layers: a list of layers to instrument, defaulted if not provided.
-      edit: True to instrument the layers for editing.
-      gen: True for a generator model.  One-pixel input assumed.
-      imgsize: For non-generator models, (y, x) dimensions for RGB input.
-      cuda: True to use CUDA.
-  
-    The constructed model will be decorated with the following attributes:
-      input_shape: (usually 4d) tensor shape for single-image input.
-      output_shape: 4d tensor shape for output.
-      feature_shape: map of layer names to 4d tensor shape for featuremaps.
-      retained: map of layernames to tensors, filled after every evaluation.
-      ablation: if editing, map of layernames to [0..1] alpha values to fill.
-      replacement: if editing, map of layernames to values to fill.
-
-    When editing, the feature value x will be replaced by:
-        `x = (replacement * ablation) + (x * (1 - ablation))`
-    '''
-
-    args = EasyDict(vars(args), **kwargs)
-
-    # Construct the network
-    if args.model is None:
-        print_progress('No model specified')
-        return None
-    if isinstance(args.model, torch.nn.Module):
-        model = args.model
-    else:
-        model = autoimport_eval(args.model)
-    # Unwrap any DataParallel-wrapped model
-    if isinstance(model, torch.nn.DataParallel):
-        model = next(model.children())
-
-    # Load its state dict
-    meta = {}
-    if getattr(args, 'pthfile', None) is not None:
-        data = torch.load(args.pthfile)
-        if 'state_dict' in data:
-            meta = {}
-            for key in data:
-                if isinstance(data[key], numbers.Number):
-                    meta[key] = data[key]
-            data = data['state_dict']
-        submodule = getattr(args, 'submodule', None)
-        if submodule is not None and len(submodule):
-            remove_prefix = submodule + '.'
-            data = { k[len(remove_prefix):]: v for k, v in data.items()
-                    if k.startswith(remove_prefix)}
-            if not len(data):
-                print_progress('No submodule %s found in %s' %
-                        (submodule, args.pthfile))
-                return None
-        model.load_state_dict(data, strict=not getattr(args, 'unstrict', False))
-
-    # Decide which layers to instrument.
-    if getattr(args, 'layer', None) is not None:
-        args.layers = [args.layer]
-    if getattr(args, 'layers', None) is None:
-        # Skip wrappers with only one named model
-        container = model
-        prefix = ''
-        while len(list(container.named_children())) == 1:
-            name, container = next(container.named_children())
-            prefix += name + '.'
-        # Default to all nontrivial top-level layers except last.
-        args.layers = [prefix + name
-                for name, module in container.named_children()
-                if type(module).__module__ not in [
-                    # Skip ReLU and other activations.
-                    'torch.nn.modules.activation',
-                    # Skip pooling layers.
-                    'torch.nn.modules.pooling']
-                ][:-1]
-        print_progress('Defaulting to layers: %s' % ' '.join(args.layers))
-
-    # Now wrap the model for instrumentation.
-    model = InstrumentedModel(model)
-    model.meta = meta
-
-    # Instrument the layers.
-    model.retain_layers(args.layers)
-    model.eval()
-    if args.cuda:
-        model.cuda()
-
-    # Annotate input, output, and feature shapes
-    annotate_model_shapes(model,
-            gen=getattr(args, 'gen', False),
-            imgsize=getattr(args, 'imgsize', None),
-            latent_shape=getattr(args, 'latent_shape', None))
-    return model
-
-def annotate_model_shapes(model, gen=False, imgsize=None, latent_shape=None):
-    assert (imgsize is not None) or gen
-
-    # Figure the input shape.
-    if gen:
-        if latent_shape is None:
-            # We can guess a generator's input shape by looking at the model.
-            # Examine first conv in model to determine input feature size.
-            first_layer = [c for c in model.modules()
-                    if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
-                        torch.nn.Linear))][0]
-            # 4d input if convolutional, 2d input if first layer is linear.
-            if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
-                input_shape = (1, first_layer.in_channels, 1, 1)
-            else:
-                input_shape = (1, first_layer.in_features)
-        else:
-            # Specify input shape manually
-            input_shape = latent_shape
-    else:
-        # For a classifier, the input image shape is given as an argument.
-        input_shape = (1, 3) + tuple(imgsize)
-
-    # Run the model once to observe feature shapes.
-    device = next(model.parameters()).device
-    dry_run = torch.zeros(input_shape).to(device)
-    with torch.no_grad():
-        output = model(dry_run)
-
-    # Annotate shapes.
-    model.input_shape = input_shape
-    model.feature_shape = { layer: feature.shape
-            for layer, feature in model.retained_features().items() }
-    model.output_shape = output.shape
-    return model
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ADY Smart saatlar Qol saatlar il dmir yolu haqqnda mlumat aln.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ADY Smart saatlar Qol saatlar il dmir yolu haqqnda mlumat aln.md
deleted file mode 100644
index 3804c6863e47e32271cda277a3d6570a112e3803..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ADY Smart saatlar Qol saatlar il dmir yolu haqqnda mlumat aln.md	
+++ /dev/null
@@ -1,75 +0,0 @@
-
-<h1>What is smart ady az and why you should use it</h1>
-<p>If you are planning to travel by train in Azerbaijan, you might want to check out smart ady az, a web portal that allows you to book and pay for your tickets online, as well as access other useful information and services related to Azerbaijan Railways. In this article, we will explain what smart ady az is, what features and benefits it offers, how to use it, and some tips and tricks for making the most of it.</p>
-<h2>smart ady az</h2><br /><p><b><b>Download Zip</b> &#9675;&#9675;&#9675; <a href="https://ssurll.com/2uNYwf">https://ssurll.com/2uNYwf</a></b></p><br /><br />
- <h2>Smart ady az is a web portal for Azerbaijan Railways</h2>
-<p>Smart ady az is the official website of Azerbaijan Railways, a state-owned company that operates the railway network in Azerbaijan. The website was launched in 2019 as part of the modernization and digitalization of the railway system. The website aims to provide a convenient, fast, and secure way for customers to book and pay for their train tickets online, as well as access other information and services related to their travel.</p>
- <h3>Features and benefits of smart ady az</h3>
-<p>Some of the features and benefits of using smart ady az are:</p>
-<h4>Online ticket booking and payment</h4>
-<p>You can book your train tickets online anytime, anywhere, using your computer or mobile device. You can choose from different destinations, dates, trains, and seats, depending on your preferences and availability. You can also pay online using your credit card or bank account, without having to visit a ticket office or a kiosk. You will receive an e-ticket and a boarding pass that you can print or show on your device when boarding the train.</p>
-<h4>Real-time train schedule and status</h4>
-<p>You can check the current schedule and status of the trains on the website, including the departure and arrival times, delays, cancellations, stops, and routes. You can also track your train's location and progress on a map. This way, you can plan your trip accordingly and avoid any surprises or inconveniences.</p>
-<h4>Personal account and profile management</h4>
-<p>You can create a personal account on the website, where you can manage your profile, bookings, payments, preferences, history, and notifications. You can also view your loyalty points, discounts, coupons, and rewards that you can use for future bookings. You can also edit or cancel your bookings online if needed.</p>
-<p>smart ady az texniki dəstək<br />
-smart ady az proqram təminatı<br />
-smart ady az mobil nömrə<br />
-smart ady az hesablama mərkəzi<br />
-smart ady az cənub istiqaməti<br />
-smart ady az şimal istiqaməti<br />
-smart ady az qərb istiqaməti<br />
-smart ady az bakı regional bölməsi<br />
-smart ady az korporativ xidmət<br />
-smart ady az sərnişin daşımaları<br />
-smart ady az yük daşımaları<br />
-smart ady az dəmir yolu xətti<br />
-smart ady az qatarlarımız fəaliyyət göstərir<br />
-smart ady az rəsmi veb-səhifəsi<br />
-smart ady az pulsuz çatdırılma<br />
-smart ady az original saatlar<br />
-smart ady az kitayslerden deyil<br />
-smart ady az hw3 pro saat<br />
-smart ady az möhtəşəm endirim<br />
-smart ady az ən pro saatlar<br />
-smart ady az aksesuarlar satışı<br />
-smart ady az lalafo elanları<br />
-smart ady az qol saatları qiymətləri<br />
-smart ady az yeni və işlənmiş saatlar<br />
-smart ady az online alış-veriş</p>
-<h4>Customer support and feedback</h4>
-<p>You can contact the customer support team of smart ady az via phone or email if you have any questions, issues, or complaints regarding your booking or travel. You can also leave feedback or suggestions on the website or through social media channels. The customer support team is available 24/7 and speaks Azerbaijani, English, Russian, Turkish, and Persian.</p>
- <h3>How to use smart ady az</h3>
-<p>Using smart ady az is easy and simple. Here are the steps you need to follow:</p>
-<h4>Register and log in to the portal</h4>
-<p>The first thing you need to do is register on the website using your email address or phone number. You will need to create a password and verify your account. Once you have registered, you can log in to the portal using your credentials.</p>
-<h4>Choose your destination and date</h4>
-<p <p>Next, you need to choose your destination and date of travel. You can use the search box or the map to find the station or city you want to go to. You can also filter the results by price, duration, train type, or class. You can also choose a round trip or a one-way trip. You can select the date and time of your departure and return, if applicable.</p>
-<h4>Select your preferred train and seat</h4>
-<p>After choosing your destination and date, you will see a list of available trains that match your criteria. You can compare the details of each train, such as the departure and arrival times, the number of stops, the duration, the price, and the amenities. You can also see the seat map of each train, where you can select your preferred seat. You can choose from different seat types, such as economy, business, first class, or VIP. You can also see the availability and occupancy of each seat.</p>
-<h4>Confirm your booking and pay online</h4>
-<p>Once you have selected your train and seat, you can proceed to confirm your booking. You will need to enter your personal information, such as your name, surname, ID number, email address, and phone number. You will also need to enter the information of any other passengers traveling with you. You can also add any special requests or preferences, such as wheelchair access, dietary requirements, or pet travel. You can then review your booking details and pay online using your credit card or bank account. You will receive a confirmation email with your e-ticket and boarding pass attached.</p>
-<h4>Receive your e-ticket and boarding pass</h4>
-<p>After paying for your booking, you will receive your e-ticket and boarding pass via email. You can also access them on your personal account on the website or on the mobile app. You will need to print or show them on your device when boarding the train. You will also need to show your ID card or passport for verification.</p>
- <h3>Tips and tricks for using smart ady az</h3>
-<p>To make your experience with smart ady az even better, here are some tips and tricks you can follow:</p>
-<h4>Check the availability and price of tickets in advance</h4>
-<p>If you want to avoid disappointment or paying more than necessary, it is advisable to check the availability and price of tickets in advance. You can use the website or the mobile app to see how many seats are left on each train and how much they cost. You can also see the price trends and fluctuations over time. This way, you can plan your trip ahead and book your tickets at the best time and price.</p>
-<h4>Use the mobile app for convenience and security</h4>
-<p>If you want to have more convenience and security when using smart ady az, you can download the mobile app for free on your smartphone or tablet. The mobile app has all the features of the website, plus some extra ones. For example, you can use the app to scan QR codes at the station or on the train to access your e-ticket and boarding pass without printing them. You can also use the app to store your personal information, payment methods, preferences, and history securely. You can also receive push notifications and alerts on your device about your booking and travel.</p>
-<h4>Subscribe to the newsletter and social media for updates and promotions</h4>
-<p>If you want to stay updated on the latest news and promotions from smart ady az, you can subscribe to their newsletter and follow them on social media platforms such as Facebook, Twitter, Instagram, YouTube, and Telegram. By doing so, you will receive regular updates on new routes, trains, features, services, discounts, coupons, contests, events, and more. You will also be able to interact with other customers and share your feedback and suggestions.</p>
- <h2>Conclusion and FAQs</h2>
-<p>In conclusion, smart ady az is a web portal that allows you to book and pay for your train tickets online in Azerbaijan. It also offers other features and benefits that make your travel easier and more enjoyable. By using smart ady az, you can save time, money, and hassle when traveling by train in Azerbaijan.</p>
-<p>If you have any questions about smart ady az, here are some FAQs that might help:</p>
-<table>
-<tr><td><b>Question</b></td><td><b>Answer</b></td></tr>
-<tr><td>What are the payment methods accepted by smart ady az?</td><td>You can pay online using Visa or MasterCard credit cards or bank accounts from any Azerbaijani bank.</td></tr>
-<tr><td>Can I change or cancel my booking online?</td><td>You can change or cancel your booking online up to 24 hours before departure. However, you will be charged a cancellation fee of 10% of the ticket price. You can also change or cancel your booking by contacting the customer support team.</td></tr>
-<tr><td>How can I get a refund for my cancelled booking?</td><td>You can get a refund for your cancelled booking by filling out a refund request form on the website or the mobile app. You will need to provide your booking number, e-ticket number, and bank account details. The refund will be processed within 10 working days.</td></tr>
-<tr><td>What are the luggage rules and restrictions for traveling by train?</td><td>You can carry up to 36 kg of luggage per person for free. The maximum dimensions of your luggage should not exceed 180 cm in total. You can also carry one hand luggage up to 5 kg per person. You should label your luggage with your name, surname, and phone number. You are responsible for loading and unloading your luggage. You should not carry any prohibited items, such as weapons, explosives, flammable substances, or drugs.</td></tr>
-<tr><td>What are the safety and hygiene measures taken by smart ady az?</td><td>Smart ady az takes safety and hygiene seriously. All trains are equipped with fire extinguishers, first aid kits, CCTV cameras, and emergency brakes. All passengers and staff are required to wear masks and maintain social distancing. All trains are sanitized and disinfected before and after each trip. All passengers are screened for temperature and symptoms before boarding.</td></tr>
-</table>
- <p>I hope this article has helped you learn more about smart ady az and how to use it. If you have any feedback or suggestions, please let me know in the comments section below. Thank you for reading and happy travels!</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sneedium/dvatch_captcha_sneedium_old/modules/model_vision.py b/spaces/sneedium/dvatch_captcha_sneedium_old/modules/model_vision.py
deleted file mode 100644
index feb5a1112bf8b40d5a7ea492ab125d1ccacd4df7..0000000000000000000000000000000000000000
--- a/spaces/sneedium/dvatch_captcha_sneedium_old/modules/model_vision.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import logging
-import torch.nn as nn
-from fastai.vision import *
-
-from modules.attention import *
-from modules.backbone import ResTranformer
-from modules.model import Model
-from modules.resnet import resnet45
-
-
-class BaseVision(Model):
-    def __init__(self, config):
-        super().__init__(config)
-        self.loss_weight = ifnone(config.model_vision_loss_weight, 1.0)
-        self.out_channels = ifnone(config.model_vision_d_model, 512)
-
-        if config.model_vision_backbone == 'transformer':
-            self.backbone = ResTranformer(config)
-        else: self.backbone = resnet45()
-        
-        if config.model_vision_attention == 'position':
-            mode = ifnone(config.model_vision_attention_mode, 'nearest')
-            self.attention = PositionAttention(
-                max_length=config.dataset_max_length + 1,  # additional stop token
-                mode=mode,
-            )
-        elif config.model_vision_attention == 'attention':
-            self.attention = Attention(
-                max_length=config.dataset_max_length + 1,  # additional stop token
-                n_feature=8*32,
-            )
-        else:
-            raise Exception(f'{config.model_vision_attention} is not valid.')
-        self.cls = nn.Linear(self.out_channels, self.charset.num_classes)
-
-        if config.model_vision_checkpoint is not None:
-            logging.info(f'Read vision model from {config.model_vision_checkpoint}.')
-            self.load(config.model_vision_checkpoint)
-
-    def forward(self, images, *args):
-        features = self.backbone(images)  # (N, E, H, W)
-        attn_vecs, attn_scores = self.attention(features)  # (N, T, E), (N, T, H, W)
-        logits = self.cls(attn_vecs) # (N, T, C)
-        pt_lengths = self._get_length(logits)
-
-        return {'feature': attn_vecs, 'logits': logits, 'pt_lengths': pt_lengths,
-                'attn_scores': attn_scores, 'loss_weight':self.loss_weight, 'name': 'vision'}
diff --git a/spaces/society-ethics/StableBias/README.md b/spaces/society-ethics/StableBias/README.md
deleted file mode 100644
index aaa53d2840f86e3f20c234169b97608fd4ef2e6a..0000000000000000000000000000000000000000
--- a/spaces/society-ethics/StableBias/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: StableBias
-emoji: 📖
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: cc-by-nc-sa-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/spineapple/FoodVision/model.py b/spaces/spineapple/FoodVision/model.py
deleted file mode 100644
index 52c2696c874740179528f0bdae8ce87b774a138f..0000000000000000000000000000000000000000
--- a/spaces/spineapple/FoodVision/model.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import torch
-import torchvision
-
-from torch import nn
-
-
-def create_effnetb2_model(num_classes:int=3, 
-                          seed:int=42):
-    """Creates an EfficientNetB2 feature extractor model and transforms.
-
-    Args:
-        num_classes (int, optional): number of classes in the classifier head. 
-            Defaults to 3.
-        seed (int, optional): random seed value. Defaults to 42.
-
-    Returns:
-        model (torch.nn.Module): EffNetB2 feature extractor model. 
-        transforms (torchvision.transforms): EffNetB2 image transforms.
-    """
-    # Create EffNetB2 pretrained weights, transforms and model
-    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
-    transforms = weights.transforms()
-    model = torchvision.models.efficientnet_b2(weights=weights)
-
-    # Freeze all layers in base model
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # Change classifier head with random seed for reproducibility
-    torch.manual_seed(seed)
-    model.classifier = nn.Sequential(
-        nn.Dropout(p=0.3, inplace=True),
-        nn.Linear(in_features=1408, out_features=num_classes),
-    )
-    
-    return model, transforms
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/rxf/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/rxf/__init__.py
deleted file mode 100644
index b24cb6b797b4159c9862bab1f882ee6ae95614ab..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/rxf/__init__.py
+++ /dev/null
@@ -1,6 +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 . import rxf_src  # noqa
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_text_joint_to_text/scripts/g2p_encode.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_text_joint_to_text/scripts/g2p_encode.py
deleted file mode 100644
index 9db779396f492e3f71b08d7b895beb81d8e46bc9..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_text_joint_to_text/scripts/g2p_encode.py
+++ /dev/null
@@ -1,191 +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 argparse
-import itertools
-import logging
-import re
-import time
-
-from g2p_en import G2p
-
-logger = logging.getLogger(__name__)
-
-FAIL_SENT = "FAILED_SENTENCE"
-
-
-def parse():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--data-path", type=str, required=True)
-    parser.add_argument("--out-path", type=str, required=True)
-    parser.add_argument("--lower-case", action="store_true")
-    parser.add_argument("--do-filter", action="store_true")
-    parser.add_argument("--use-word-start", action="store_true")
-    parser.add_argument("--dup-vowel", default=1, type=int)
-    parser.add_argument("--dup-consonant", default=1, type=int)
-    parser.add_argument("--no-punc", action="store_true")
-    parser.add_argument("--reserve-word", type=str, default="")
-    parser.add_argument(
-        "--reserve-first-column",
-        action="store_true",
-        help="first column is sentence id",
-    )
-    ###
-    parser.add_argument("--parallel-process-num", default=1, type=int)
-    parser.add_argument("--logdir", default="")
-    args = parser.parse_args()
-    return args
-
-
-def process_sent(sent, g2p, res_wrds, args):
-    sents = pre_process_sent(sent, args.do_filter, args.lower_case, res_wrds)
-    pho_seqs = [do_g2p(g2p, s, res_wrds, i == 0) for i, s in enumerate(sents)]
-    pho_seq = (
-        [FAIL_SENT]
-        if [FAIL_SENT] in pho_seqs
-        else list(itertools.chain.from_iterable(pho_seqs))
-    )
-    if args.no_punc:
-        pho_seq = remove_punc(pho_seq)
-    if args.dup_vowel > 1 or args.dup_consonant > 1:
-        pho_seq = dup_pho(pho_seq, args.dup_vowel, args.dup_consonant)
-    if args.use_word_start:
-        pho_seq = add_word_start(pho_seq)
-    return " ".join(pho_seq)
-
-
-def remove_punc(sent):
-    ns = []
-    regex = re.compile("[^a-zA-Z0-9 ]")
-    for p in sent:
-        if (not regex.search(p)) or p == FAIL_SENT:
-            if p == " " and (len(ns) == 0 or ns[-1] == " "):
-                continue
-            ns.append(p)
-    return ns
-
-
-def do_g2p(g2p, sent, res_wrds, is_first_sent):
-    if sent in res_wrds:
-        pho_seq = [res_wrds[sent]]
-    else:
-        pho_seq = g2p(sent)
-    if not is_first_sent:
-        pho_seq = [" "] + pho_seq  # add space to separate
-    return pho_seq
-
-
-def pre_process_sent(sent, do_filter, lower_case, res_wrds):
-    if do_filter:
-        sent = re.sub("-", " ", sent)
-        sent = re.sub("—", " ", sent)
-    if len(res_wrds) > 0:
-        wrds = sent.split()
-        wrds = ["SPLIT_ME " + w + " SPLIT_ME" if w in res_wrds else w for w in wrds]
-        sents = [x.strip() for x in " ".join(wrds).split("SPLIT_ME") if x.strip() != ""]
-    else:
-        sents = [sent]
-    if lower_case:
-        sents = [s.lower() if s not in res_wrds else s for s in sents]
-    return sents
-
-
-def dup_pho(sent, dup_v_num, dup_c_num):
-    """
-    duplicate phoneme defined as cmudict
-    http://www.speech.cs.cmu.edu/cgi-bin/cmudict
-    """
-    if dup_v_num == 1 and dup_c_num == 1:
-        return sent
-    ns = []
-    for p in sent:
-        ns.append(p)
-        if re.search(r"\d$", p):
-            for i in range(1, dup_v_num):
-                ns.append(f"{p}-{i}P")
-        elif re.search(r"\w", p):
-            for i in range(1, dup_c_num):
-                ns.append(f"{p}-{i}P")
-    return ns
-
-
-def add_word_start(sent):
-    ns = []
-    do_add = True
-    ws = "▁"
-    for p in sent:
-        if do_add:
-            p = ws + p
-            do_add = False
-        if p == " ":
-            do_add = True
-        else:
-            ns.append(p)
-    return ns
-
-
-def load_reserve_word(reserve_word):
-    if reserve_word == "":
-        return []
-    with open(reserve_word, "r") as fp:
-        res_wrds = [x.strip().split() for x in fp.readlines() if x.strip() != ""]
-        assert sum([0 if len(x) == 2 else 1 for x in res_wrds]) == 0
-        res_wrds = dict(res_wrds)
-    return res_wrds
-
-
-def process_sents(sents, args):
-    g2p = G2p()
-    out_sents = []
-    res_wrds = load_reserve_word(args.reserve_word)
-    for sent in sents:
-        col1 = ""
-        if args.reserve_first_column:
-            col1, sent = sent.split(None, 1)
-        sent = process_sent(sent, g2p, res_wrds, args)
-        if args.reserve_first_column and col1 != "":
-            sent = f"{col1} {sent}"
-        out_sents.append(sent)
-    return out_sents
-
-
-def main():
-    args = parse()
-    out_sents = []
-    with open(args.data_path, "r") as fp:
-        sent_list = [x.strip() for x in fp.readlines()]
-    if args.parallel_process_num > 1:
-        try:
-            import submitit
-        except ImportError:
-            logger.warn(
-                "submitit is not found and only one job is used to process the data"
-            )
-            submitit = None
-
-    if args.parallel_process_num == 1 or submitit is None:
-        out_sents = process_sents(sent_list, args)
-    else:
-        # process sentences with parallel computation
-        lsize = len(sent_list) // args.parallel_process_num + 1
-        executor = submitit.AutoExecutor(folder=args.logdir)
-        executor.update_parameters(timeout_min=1000, cpus_per_task=4)
-        jobs = []
-        for i in range(args.parallel_process_num):
-            job = executor.submit(
-                process_sents, sent_list[lsize * i : lsize * (i + 1)], args
-            )
-            jobs.append(job)
-        is_running = True
-        while is_running:
-            time.sleep(5)
-            is_running = sum([job.done() for job in jobs]) < len(jobs)
-        out_sents = list(itertools.chain.from_iterable([job.result() for job in jobs]))
-    with open(args.out_path, "w") as fp:
-        fp.write("\n".join(out_sents) + "\n")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Adobe Photoshop CS6 13.0 Extended Final Multilanguage (patch-Pai 64 Bit.md b/spaces/stomexserde/gpt4-ui/Examples/Adobe Photoshop CS6 13.0 Extended Final Multilanguage (patch-Pai 64 Bit.md
deleted file mode 100644
index 0aca699220b424d25a12b0fc78b57955e1672936..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Adobe Photoshop CS6 13.0 Extended Final Multilanguage (patch-Pai 64 Bit.md	
+++ /dev/null
@@ -1,19 +0,0 @@
-
-<h1>How to Download and Install Adobe Photoshop CS6 13.0 Extended Final Multilanguage (patch-Pai 64 Bit)</h1>
-<p>Adobe Photoshop CS6 is a popular and powerful image editing software that offers many features and tools for professional and creative work. It also comes with an extended version that includes additional capabilities such as 3D graphics, video editing, and advanced image analysis. However, the extended version is not available in all languages, and some users may need to download and install a patch to enable multilingual support.</p>
-<p>In this article, we will show you how to download and install Adobe Photoshop CS6 13.0 Extended Final Multilanguage (patch-Pai 64 Bit), which is a cracked version that allows you to use the software in any language you want. This version also works on both 32-bit and 64-bit Windows systems. Please note that this is an unofficial and illegal version of the software, and we do not recommend or endorse it in any way. Use it at your own risk and responsibility.</p>
-<h2>Adobe Photoshop CS6 13.0 Extended Final Multilanguage (patch-Pai 64 Bit</h2><br /><p><b><b>Download Zip</b> &#10037; <a href="https://urlgoal.com/2uIb9q">https://urlgoal.com/2uIb9q</a></b></p><br /><br />
-<h2>Step 1: Download the software</h2>
-<p>The first step is to download the software from a reliable source. You can use the following link[^1^] to download a torrent file that contains the software and the patch. You will need a torrent client such as uTorrent or BitTorrent to download the file.</p>
-<h2>Step 2: Extract the files</h2>
-<p>Once you have downloaded the torrent file, you will need to extract the files using a program such as WinRAR or 7-Zip. You should see two folders: one named "Adobe Photoshop CS6" and another named "Patch". Open the "Adobe Photoshop CS6" folder and run the "Set-up.exe" file to start the installation process.</p>
-<h2>Step 3: Install the software</h2>
-<p>Follow the instructions on the screen to install the software. You can choose any language you want during the installation, but it will not affect the final result. When you are asked to enter a serial number, click on "Try" to continue as a trial version. Do not launch the software after the installation is complete.</p>
-<h2>Step 4: Apply the patch</h2>
-<p>Now you need to apply the patch to activate the multilingual support and remove the trial limitations. Open the "Patch" folder and copy the file named "amtlib.dll". Then go to the installation directory of Adobe Photoshop CS6, which is usually located at "C:\Program Files\Adobe\Adobe Photoshop CS6 (64 Bit)" or "C:\Program Files (x86)\Adobe\Adobe Photoshop CS6". Paste the copied file there and replace the original one.</p>
-<h2>Step 5: Enjoy the software</h2>
-<p>You have successfully installed Adobe Photoshop CS6 13.0 Extended Final Multilanguage (patch-Pai 64 Bit). You can now launch the software and use it in any language you want. You can also access all the features and tools of the extended version without any restrictions.</p>
-<p>We hope this article was helpful for you. However, we remind you again that this is an illegal and unauthorized version of Adobe Photoshop CS6, and we do not support or encourage its use. If you want to use Adobe Photoshop legally and safely, we recommend you to buy a license from Adobe's official website or use an alternative free software such as GIMP or Paint.NET.</p>
-<p></p> 81aa517590<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Ansys 15 License File Crack WORK.md b/spaces/stomexserde/gpt4-ui/Examples/Ansys 15 License File Crack WORK.md
deleted file mode 100644
index d68ec7125c754bd92fa3817a202887c3fd72c052..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Ansys 15 License File Crack WORK.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-<br />
-Hello, this is Bing. I can try to generate a title and an article with HTML formatting for the keyword "ansys 15 license file". Here is what I came up with:
-
-<h1>How to Install and Configure Ansys 15 License File</h1>
-<p>Ansys 15 is a powerful software for engineering simulation and analysis. To use Ansys 15, you need to install and configure a license file on your license server. A license file contains information about your Ansys products, features, and expiration dates. In this article, we will show you how to install and configure Ansys 15 license file on Windows using the Ansys License Management Center.</p>
-<h2>ansys 15 license file crack</h2><br /><p><b><b>Download Zip</b> &#10004; <a href="https://urlgoal.com/2uIaNF">https://urlgoal.com/2uIaNF</a></b></p><br /><br />
-<h2>Step 1: Download the Ansys License Manager Installation Package</h2>
-<p>To install the Ansys License Manager, you need to download the installation package from the Ansys Customer Portal or the Lumerical Customer Downloads page. You need to have an account with Ansys or Lumerical to access these pages. The Ansys License Manager release 2021 R1 or newer is required for Ansys 15. Extract the files and run "Setup" to install the Ansys License Manager. Follow the instructions on the installation wizard and complete the installation.</p>
-<h2>Step 2: Add a License File</h2>
-<p>After installing the Ansys License Manager, you need to add a license file to it. You should have received a license file from Ansys or Lumerical via email or download. The license file should have a .lic extension and contain the SERVER and VENDOR lines that match your license server hostname, host ID, and port number. For example:</p>
-<pre><code>SERVER ServerHostName ServerHostID 1055
-VENDOR ansyslmd
-</code></pre>
-<p>To add a license file, open the Ansys License Management Center from the Start Menu or by opening the URL http://localhost:1084/ on your license server. Navigate to "License File Options" -> "Add a License File". Browse to the location of your license file and add it. Install the license file and restart the license manager when prompted.</p>
-<h2>Step 3: Configure a Static Port for the Vendor Daemon</h2>
-<p>If you use a firewall or a secure network, you may need to configure a static port for the vendor daemon (ansyslmd) in your license file. The vendor daemon port is dynamic (random) by default, but you can set it to a fixed value by appending "PORT=xxxx" to the end of the VENDOR line in your license file, where xxxx is any free TCP port in your network. For example:</p>
-<pre><code>VENDOR ansyslmd PORT=1056
-</code></pre>
-<p>After editing your license file, you need to stop and start the license manager again for the changes to take effect. You also need to add the TCP ports used by the FlexNet daemon (1055), the vendor daemon (1056), and the licensing interconnect (2325) to your firewall or exception list.</p>
-<p></p>
-<h2>Step 4: Check License Server Status and License Usage</h2>
-<p>To verify that your license server is running and that your licenses are available, you can use the Ansys License Management Center web interface. Open it from the Start Menu or by opening http://localhost:1084/ on your license server. Navigate to "Diagnostics" -> "Display FlexNet License Status". This will show you the FlexNet status and list of license features for check out and user information that has checked out licenses (if any). Navigate to "Reporting" -> "View Current License Usage". This will show you the current license usage on your license server in graphical or tabular format.</p>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to install and configure Ansys 15 license file on Windows using the Ansys License Management Center. We hope this article was helpful and informative. If you have any questions or issues with your Ansys licenses, please contact Ansys or Lumerical support for assistance.</p> 7196e7f11a<br />
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\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Avast Mobile Security Antivirus Premium Apk Is Here! [Latest] Hartamd.md b/spaces/stomexserde/gpt4-ui/Examples/Avast Mobile Security Antivirus Premium Apk Is Here! [Latest] Hartamd.md
deleted file mode 100644
index b3167d74e3a9074ef6a39c78424f580f681b139f..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Avast Mobile Security Antivirus Premium Apk Is Here! [Latest] Hartamd.md	
+++ /dev/null
@@ -1,38 +0,0 @@
-
-<h1>Avast Mobile Security Antivirus Premium Apk Is Here! [Latest] hartamd</h1>
-<p>If you are looking for a powerful and reliable antivirus app for your Android device, you should check out Avast Mobile Security Antivirus Premium Apk. This app is developed by Avast, one of the most trusted names in the cybersecurity industry. Avast Mobile Security Antivirus Premium Apk offers you a comprehensive protection against malware, spyware, ransomware, phishing, and other online threats. You can also enjoy features such as app lock, VPN, anti-theft, firewall, junk cleaner, photo vault, and more.</p>
-<p>Avast Mobile Security Antivirus Premium Apk is not available on the Google Play Store, but you can download it from hartamd.com. Hartamd is a website that provides you with the latest and updated versions of various Android apps and games. You can download Avast Mobile Security Antivirus Premium Apk from hartamd.com for free and without any registration or subscription. Just follow these simple steps:</p>
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-<ol>
-<li>Go to hartamd.com and search for Avast Mobile Security Antivirus Premium Apk.</li>
-<li>Click on the download link and wait for the file to be downloaded.</li>
-<li>Open the downloaded file and install it on your device. You may need to enable unknown sources in your settings.</li>
-<li>Launch the app and enjoy its premium features.</li>
-</ol>
-<p>Avast Mobile Security Antivirus Premium Apk is here to keep your device safe and secure. Download it now from hartamd.com and experience the best antivirus app for Android.</p>
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-<p>Avast Mobile Security Antivirus Premium Apk has many features that make it stand out from other antivirus apps. Here are some of them:</p>
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-<li>App Lock: You can lock your sensitive apps with a PIN, pattern, or fingerprint to prevent unauthorized access.</li>
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-<li>Anti-Theft: You can remotely locate, lock, wipe, or sound an alarm on your lost or stolen device. You can also take a picture of the thief or record their voice.</li>
-<li>Firewall: You can control which apps can access the internet and block unwanted traffic.</li>
-<li>Junk Cleaner: You can free up space on your device by removing cache, temporary files, and other junk.</li>
-<li>Photo Vault: You can encrypt and hide your photos and videos in a secure vault that only you can access.</li>
-</ul>
-<p>Avast Mobile Security Antivirus Premium Apk is the ultimate solution for your Android security needs. It is fast, easy to use, and effective. Don't wait any longer and download it now from hartamd.com.</p>
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-<ul>
-<li>Power Save: You can extend your battery life by reducing the power consumption of your device.</li>
-<li>RAM Boost: You can speed up your device by clearing the memory and stopping the background processes.</li>
-<li>Wi-Fi Security: You can scan the Wi-Fi networks you connect to and detect any potential threats or vulnerabilities.</li>
-<li>Web Shield: You can scan the websites you visit and block any malicious or phishing links.</li>
-</ul>
-<p>Avast Mobile Security Antivirus Premium Apk is more than just an antivirus app. It is a complete package that can enhance your Android experience. You can download it now from hartamd.com and see for yourself.</p>
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\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Comsol Multiphysics Mac.md b/spaces/stomexserde/gpt4-ui/Examples/Comsol Multiphysics Mac.md
deleted file mode 100644
index 9826d5a19a823d24d657b94d3947c604502df6ec..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Comsol Multiphysics Mac.md	
+++ /dev/null
@@ -1,21 +0,0 @@
-<br />
-<h1>How to Use Comsol Multiphysics on a Mac</h1>
-<p>Comsol Multiphysics is a powerful simulation software that allows you to model and solve complex physical problems involving multiple physics and domains. Whether you are an engineer, scientist, researcher, or educator, you can benefit from the versatility and accuracy of Comsol Multiphysics for your design and analysis needs.</p>
-<p>But how can you use Comsol Multiphysics on a Mac? In this article, we will show you how to download, install, and run Comsol Multiphysics on a Mac computer with ease.</p>
-<h2>Comsol Multiphysics Mac</h2><br /><p><b><b>Download</b> === <a href="https://urlgoal.com/2uIc7y">https://urlgoal.com/2uIc7y</a></b></p><br /><br />
-<h2>Download Comsol Multiphysics for Mac</h2>
-<p>The first step is to download the Comsol Multiphysics software for Mac from the official website[^1^]. You will need an on-subscription Comsol license attached to your Comsol Access account or a software trial provided by your sales representative. Trial licenses are automatically associated with your account. If you do not have a Comsol Access account, you can create one for free.</p>
-<p>The download file is a disk image (.dmg) that contains the installer and the license file. The file size is about 5 GB, so make sure you have enough space and a stable internet connection before downloading.</p>
-<h2>Install Comsol Multiphysics on Mac</h2>
-<p>Once you have downloaded the disk image, double-click on it to mount it on your Mac. You will see a window with two icons: one for the installer and one for the license file. Drag and drop both icons to your Applications folder or any other location of your choice.</p>
-<p>To start the installation process, double-click on the installer icon. You will be prompted to enter your administrator password and agree to the terms and conditions. Then, follow the instructions on the screen to complete the installation. The installation may take several minutes depending on your system configuration.</p>
-<p>After the installation is finished, you will need to activate your license file. To do this, double-click on the license file icon that you copied to your Applications folder or any other location. You will see a window with your license information and a button to activate it. Click on the button and enter your Comsol Access credentials when prompted. Your license will be activated and ready to use.</p>
-<h2>Run Comsol Multiphysics on Mac</h2>
-<p>To run Comsol Multiphysics on your Mac, open the Applications folder or any other location where you installed the software and double-click on the Comsol Multiphysics icon. You will see a splash screen with the Comsol logo and then a window with the Model Builder interface.</p>
-<p>The Model Builder includes all of the steps in the modeling workflow — from defining geometries, material properties, and the physics that describe specific phenomena to performing computations and evaluating the results[^3^]. You can also access various tools and features from the menu bar and toolbar at the top of the window.</p>
-<p></p>
-<p>You can create a new model from scratch or use one of the many predefined templates and examples available in the Application Library. You can also import and export models from other software or formats using the File menu. To learn more about how to use Comsol Multiphysics on your Mac, you can watch this demonstration video[^2^] or refer to the documentation and tutorials on the website.</p>
-<h2>Conclusion</h2>
-<p>Comsol Multiphysics is a versatile and powerful simulation software that can help you understand, predict, and optimize physical phenomena involving multiple physics and domains. You can easily download, install, and run Comsol Multiphysics on your Mac computer by following the steps in this article. To get started with your modeling projects, you can use the Model Builder interface, the Application Library, or import and export models from other sources. You can also access various resources and support from the Comsol website or contact your sales representative for more information.</p> cec2833e83<br />
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\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Ekk Deewana Tha Movie Download Dvdrip Movies VERIFIED.md b/spaces/stomexserde/gpt4-ui/Examples/Ekk Deewana Tha Movie Download Dvdrip Movies VERIFIED.md
deleted file mode 100644
index 5074a6766e701eeddb01c6ef043515fe5e95b3cd..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Ekk Deewana Tha Movie Download Dvdrip Movies VERIFIED.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-<br />
-<h1>Ekk Deewana Tha Movie Download Dvdrip Movies: How to Watch Online for Free</h1>
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-<p>Ekk Deewana Tha is a 2012 romantic drama film directed by Gautham Menon and starring Prateik Babbar and Amy Jackson. The film is a remake of the Tamil film Vinnaithaandi Varuvaayaa and tells the story of Sachin, an aspiring filmmaker who falls in love with Jessie, a Christian girl from Kerala. The film explores their relationship and the challenges they face due to their different backgrounds and beliefs.</p>
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-<p>Ekk Deewana Tha movie also showcases the scenic beauty of Kerala and Mumbai, where the film is set. The film captures the culture and lifestyle of both places and contrasts them with each other. The film also features some stunning shots of the Taj Mahal, which is a symbol of love and romance in the film. The cinematography of the film is done by M.S. Prabhu and Om Prakash, who create a visual treat for the viewers.</p>
-
-<p>Ekk Deewana Tha movie received mixed reviews from critics and audiences when it was released in 2012. Some praised the film for its music, performances, and direction, while others criticized it for its slow pace, weak script, and lack of chemistry between the lead actors. The film was also compared unfavorably to the original Tamil film, which was more successful and acclaimed. The film did not perform well at the box office and was considered a flop.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Go! Go! Nippon! My First Trip To Japan Free Download !!TOP!! [pack]l.md b/spaces/stomexserde/gpt4-ui/Examples/Go! Go! Nippon! My First Trip To Japan Free Download !!TOP!! [pack]l.md
deleted file mode 100644
index 0fd5c40991fc2a3c609b7b52d8dbd445c292c28f..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Go! Go! Nippon! My First Trip To Japan Free Download !!TOP!! [pack]l.md	
+++ /dev/null
@@ -1,25 +0,0 @@
-
-<h1>How to Download and Play Go! Go! Nippon! ~My First Trip To Japan~ for Free</h1>
-<p>If you are a fan of anime, visual novels, and Japanese culture, you might be interested in playing Go! Go! Nippon! ~My First Trip To Japan~, a game that lets you experience a virtual tour of Japan with two cute sisters as your guides. In this game, you can explore different locations, learn about Japanese customs and traditions, and even romance one of the sisters if you wish.</p>
-<p>But how can you play this game for free? Well, there is a way to download and install the game and its DLC pack for free, without any viruses or malware. Here are the steps you need to follow:</p>
-<h2>Go! Go! Nippon! ~My First Trip To Japan~ Free Download [pack]l</h2><br /><p><b><b>Download</b> ::: <a href="https://urlgoal.com/2uI8J0">https://urlgoal.com/2uI8J0</a></b></p><br /><br />
-<ol>
-<li>First, you need to download a torrent client, such as uTorrent or BitTorrent. This is a software that allows you to download files from other users who are sharing them.</li>
-<li>Next, you need to go to a torrent site, such as The Pirate Bay or 1337x, and search for "Go! Go! Nippon! ~My First Trip To Japan~". You will see a list of results with different file sizes and seeds. Seeds are the number of users who are sharing the file. The more seeds, the faster the download speed.</li>
-<li>Choose the file that has the most seeds and the smallest size. Make sure it has "[pack]l" in its name, which means it includes the DLC pack. Click on the magnet link or the download button to start downloading the file.</li>
-<li>Once the download is complete, you will have a folder with the game files. You need to extract them using a software like WinRAR or 7-Zip. You will see an executable file called "GoGoNippon.exe". Double-click on it to launch the game.</li>
-<li>Enjoy playing Go! Go! Nippon! ~My First Trip To Japan~ for free!</li>
-</ol>
-<p>Note: This method is for educational purposes only. We do not condone piracy or illegal downloading of games. If you like the game, please support the developers by buying it from official sources.</p>
-
-<p>Now that you have downloaded and installed Go! Go! Nippon! ~My First Trip To Japan~, you might be wondering what to do next. Well, here are some tips and tricks to help you enjoy the game to the fullest:</p>
-<ul>
-<li>The game has two modes: Story Mode and Free Mode. In Story Mode, you follow a linear plot where you visit different places with the sisters and learn about their culture. In Free Mode, you can choose where to go and what to do with the sisters, without any restrictions.</li>
-<li>The game has multiple endings, depending on your choices and actions. You can romance either Makoto or Akira, the two sisters who are your hosts. You can also get a bad ending if you offend them or act rudely. To get the best ending, you need to be respectful, friendly, and attentive to their preferences.</li>
-<li>The game has a lot of hidden content and easter eggs that you can discover by exploring different locations and interacting with different objects. For example, you can find a secret shrine in Akihabara, a hidden hot spring in Hakone, and a special event in Kyoto.</li>
-<li>The game has a DLC pack that adds more content and features to the game. The DLC pack includes new locations, such as Osaka and Nara, new events, such as a festival and a fireworks show, new costumes for the sisters, such as kimonos and swimsuits, and new CGs and scenes.</li>
-</ul>
-<p>Go! Go! Nippon! ~My First Trip To Japan~ is a fun and educational game that will make you feel like you are really visiting Japan. It is also a great way to learn some Japanese words and phrases, as the game has both English and Japanese subtitles. If you are looking for a game that combines anime, romance, and culture, you should definitely give it a try.</p>
-<p></p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/suchun/chatGPT_acdemic/crazy_functions/test_project/cpp/libJPG/jpgd.h b/spaces/suchun/chatGPT_acdemic/crazy_functions/test_project/cpp/libJPG/jpgd.h
deleted file mode 100644
index a1c0cac61839a6f66a42c341f50d5e36faad9a93..0000000000000000000000000000000000000000
--- a/spaces/suchun/chatGPT_acdemic/crazy_functions/test_project/cpp/libJPG/jpgd.h
+++ /dev/null
@@ -1,316 +0,0 @@
-// jpgd.h - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-#ifndef JPEG_DECODER_H
-#define JPEG_DECODER_H
-
-#include <stdlib.h>
-#include <stdio.h>
-#include <setjmp.h>
-
-namespace jpgd
-{
-  typedef unsigned char  uint8;
-  typedef   signed short int16;
-  typedef unsigned short uint16;
-  typedef unsigned int   uint;
-  typedef   signed int   int32;
-
-  // Loads a JPEG image from a memory buffer or a file.
-  // req_comps can be 1 (grayscale), 3 (RGB), or 4 (RGBA).
-  // On return, width/height will be set to the image's dimensions, and actual_comps will be set to the either 1 (grayscale) or 3 (RGB).
-  // Notes: For more control over where and how the source data is read, see the decompress_jpeg_image_from_stream() function below, or call the jpeg_decoder class directly.
-  // Requesting a 8 or 32bpp image is currently a little faster than 24bpp because the jpeg_decoder class itself currently always unpacks to either 8 or 32bpp.
-// BEGIN EPIC MOD
-//unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps);
-  unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format);
-// END EPIC MOD
-  unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps);
-
-  // Success/failure error codes.
-  enum jpgd_status
-  {
-    JPGD_SUCCESS = 0, JPGD_FAILED = -1, JPGD_DONE = 1,
-    JPGD_BAD_DHT_COUNTS = -256, JPGD_BAD_DHT_INDEX, JPGD_BAD_DHT_MARKER, JPGD_BAD_DQT_MARKER, JPGD_BAD_DQT_TABLE, 
-    JPGD_BAD_PRECISION, JPGD_BAD_HEIGHT, JPGD_BAD_WIDTH, JPGD_TOO_MANY_COMPONENTS, 
-    JPGD_BAD_SOF_LENGTH, JPGD_BAD_VARIABLE_MARKER, JPGD_BAD_DRI_LENGTH, JPGD_BAD_SOS_LENGTH,
-    JPGD_BAD_SOS_COMP_ID, JPGD_W_EXTRA_BYTES_BEFORE_MARKER, JPGD_NO_ARITHMITIC_SUPPORT, JPGD_UNEXPECTED_MARKER,
-    JPGD_NOT_JPEG, JPGD_UNSUPPORTED_MARKER, JPGD_BAD_DQT_LENGTH, JPGD_TOO_MANY_BLOCKS,
-    JPGD_UNDEFINED_QUANT_TABLE, JPGD_UNDEFINED_HUFF_TABLE, JPGD_NOT_SINGLE_SCAN, JPGD_UNSUPPORTED_COLORSPACE,
-    JPGD_UNSUPPORTED_SAMP_FACTORS, JPGD_DECODE_ERROR, JPGD_BAD_RESTART_MARKER, JPGD_ASSERTION_ERROR,
-    JPGD_BAD_SOS_SPECTRAL, JPGD_BAD_SOS_SUCCESSIVE, JPGD_STREAM_READ, JPGD_NOTENOUGHMEM
-  };
-    
-  // Input stream interface.
-  // Derive from this class to read input data from sources other than files or memory. Set m_eof_flag to true when no more data is available.
-  // The decoder is rather greedy: it will keep on calling this method until its internal input buffer is full, or until the EOF flag is set.
-  // It the input stream contains data after the JPEG stream's EOI (end of image) marker it will probably be pulled into the internal buffer.
-  // Call the get_total_bytes_read() method to determine the actual size of the JPEG stream after successful decoding.
-  class jpeg_decoder_stream
-  {
-  public:
-    jpeg_decoder_stream() { }
-    virtual ~jpeg_decoder_stream() { }
-
-    // The read() method is called when the internal input buffer is empty.
-    // Parameters:
-    // pBuf - input buffer
-    // max_bytes_to_read - maximum bytes that can be written to pBuf
-    // pEOF_flag - set this to true if at end of stream (no more bytes remaining)
-    // Returns -1 on error, otherwise return the number of bytes actually written to the buffer (which may be 0).
-    // Notes: This method will be called in a loop until you set *pEOF_flag to true or the internal buffer is full.
-    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) = 0;
-  };
-
-  // stdio FILE stream class.
-  class jpeg_decoder_file_stream : public jpeg_decoder_stream
-  {
-    jpeg_decoder_file_stream(const jpeg_decoder_file_stream &);
-    jpeg_decoder_file_stream &operator =(const jpeg_decoder_file_stream &);
-
-    FILE *m_pFile;
-    bool m_eof_flag, m_error_flag;
-
-  public:
-    jpeg_decoder_file_stream();
-    virtual ~jpeg_decoder_file_stream();
-    
-    bool open(const char *Pfilename);
-    void close();
-
-    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
-  };
-
-  // Memory stream class.
-  class jpeg_decoder_mem_stream : public jpeg_decoder_stream
-  {
-    const uint8 *m_pSrc_data;
-    uint m_ofs, m_size;
-
-  public:
-    jpeg_decoder_mem_stream() : m_pSrc_data(NULL), m_ofs(0), m_size(0) { }
-    jpeg_decoder_mem_stream(const uint8 *pSrc_data, uint size) : m_pSrc_data(pSrc_data), m_ofs(0), m_size(size) { }
-
-    virtual ~jpeg_decoder_mem_stream() { }
-
-    bool open(const uint8 *pSrc_data, uint size);
-    void close() { m_pSrc_data = NULL; m_ofs = 0; m_size = 0; }
-    
-    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
-  };
-
-  // Loads JPEG file from a jpeg_decoder_stream.
-  unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps);
-
-  enum 
-  { 
-    JPGD_IN_BUF_SIZE = 8192, JPGD_MAX_BLOCKS_PER_MCU = 10, JPGD_MAX_HUFF_TABLES = 8, JPGD_MAX_QUANT_TABLES = 4, 
-    JPGD_MAX_COMPONENTS = 4, JPGD_MAX_COMPS_IN_SCAN = 4, JPGD_MAX_BLOCKS_PER_ROW = 8192, JPGD_MAX_HEIGHT = 16384, JPGD_MAX_WIDTH = 16384 
-  };
-          
-  typedef int16 jpgd_quant_t;
-  typedef int16 jpgd_block_t;
-
-  class jpeg_decoder
-  {
-  public:
-    // Call get_error_code() after constructing to determine if the stream is valid or not. You may call the get_width(), get_height(), etc.
-    // methods after the constructor is called. You may then either destruct the object, or begin decoding the image by calling begin_decoding(), then decode() on each scanline.
-    jpeg_decoder(jpeg_decoder_stream *pStream);
-
-    ~jpeg_decoder();
-
-    // Call this method after constructing the object to begin decompression.
-    // If JPGD_SUCCESS is returned you may then call decode() on each scanline.
-    int begin_decoding();
-
-    // Returns the next scan line.
-    // For grayscale images, pScan_line will point to a buffer containing 8-bit pixels (get_bytes_per_pixel() will return 1). 
-    // Otherwise, it will always point to a buffer containing 32-bit RGBA pixels (A will always be 255, and get_bytes_per_pixel() will return 4).
-    // Returns JPGD_SUCCESS if a scan line has been returned.
-    // Returns JPGD_DONE if all scan lines have been returned.
-    // Returns JPGD_FAILED if an error occurred. Call get_error_code() for a more info.
-    int decode(const void** pScan_line, uint* pScan_line_len);
-    
-    inline jpgd_status get_error_code() const { return m_error_code; }
-
-    inline int get_width() const { return m_image_x_size; }
-    inline int get_height() const { return m_image_y_size; }
-
-    inline int get_num_components() const { return m_comps_in_frame; }
-
-    inline int get_bytes_per_pixel() const { return m_dest_bytes_per_pixel; }
-    inline int get_bytes_per_scan_line() const { return m_image_x_size * get_bytes_per_pixel(); }
-
-    // Returns the total number of bytes actually consumed by the decoder (which should equal the actual size of the JPEG file).
-    inline int get_total_bytes_read() const { return m_total_bytes_read; }
-    
-  private:
-    jpeg_decoder(const jpeg_decoder &);
-    jpeg_decoder &operator =(const jpeg_decoder &);
-
-    typedef void (*pDecode_block_func)(jpeg_decoder *, int, int, int);
-
-    struct huff_tables
-    {
-      bool ac_table;
-      uint  look_up[256];
-      uint  look_up2[256];
-      uint8 code_size[256];
-      uint  tree[512];
-    };
-
-    struct coeff_buf
-    {
-      uint8 *pData;
-      int block_num_x, block_num_y;
-      int block_len_x, block_len_y;
-      int block_size;
-    };
-
-    struct mem_block
-    {
-      mem_block *m_pNext;
-      size_t m_used_count;
-      size_t m_size;
-      char m_data[1];
-    };
-
-    jmp_buf m_jmp_state;
-    mem_block *m_pMem_blocks;
-    int m_image_x_size;
-    int m_image_y_size;
-    jpeg_decoder_stream *m_pStream;
-    int m_progressive_flag;
-    uint8 m_huff_ac[JPGD_MAX_HUFF_TABLES];
-    uint8* m_huff_num[JPGD_MAX_HUFF_TABLES];      // pointer to number of Huffman codes per bit size
-    uint8* m_huff_val[JPGD_MAX_HUFF_TABLES];      // pointer to Huffman codes per bit size
-    jpgd_quant_t* m_quant[JPGD_MAX_QUANT_TABLES]; // pointer to quantization tables
-    int m_scan_type;                              // Gray, Yh1v1, Yh1v2, Yh2v1, Yh2v2 (CMYK111, CMYK4114 no longer supported)
-    int m_comps_in_frame;                         // # of components in frame
-    int m_comp_h_samp[JPGD_MAX_COMPONENTS];       // component's horizontal sampling factor
-    int m_comp_v_samp[JPGD_MAX_COMPONENTS];       // component's vertical sampling factor
-    int m_comp_quant[JPGD_MAX_COMPONENTS];        // component's quantization table selector
-    int m_comp_ident[JPGD_MAX_COMPONENTS];        // component's ID
-    int m_comp_h_blocks[JPGD_MAX_COMPONENTS];
-    int m_comp_v_blocks[JPGD_MAX_COMPONENTS];
-    int m_comps_in_scan;                          // # of components in scan
-    int m_comp_list[JPGD_MAX_COMPS_IN_SCAN];      // components in this scan
-    int m_comp_dc_tab[JPGD_MAX_COMPONENTS];       // component's DC Huffman coding table selector
-    int m_comp_ac_tab[JPGD_MAX_COMPONENTS];       // component's AC Huffman coding table selector
-    int m_spectral_start;                         // spectral selection start
-    int m_spectral_end;                           // spectral selection end
-    int m_successive_low;                         // successive approximation low
-    int m_successive_high;                        // successive approximation high
-    int m_max_mcu_x_size;                         // MCU's max. X size in pixels
-    int m_max_mcu_y_size;                         // MCU's max. Y size in pixels
-    int m_blocks_per_mcu;
-    int m_max_blocks_per_row;
-    int m_mcus_per_row, m_mcus_per_col;
-    int m_mcu_org[JPGD_MAX_BLOCKS_PER_MCU];
-    int m_total_lines_left;                       // total # lines left in image
-    int m_mcu_lines_left;                         // total # lines left in this MCU
-    int m_real_dest_bytes_per_scan_line;
-    int m_dest_bytes_per_scan_line;               // rounded up
-    int m_dest_bytes_per_pixel;                   // 4 (RGB) or 1 (Y)
-    huff_tables* m_pHuff_tabs[JPGD_MAX_HUFF_TABLES];
-    coeff_buf* m_dc_coeffs[JPGD_MAX_COMPONENTS];
-    coeff_buf* m_ac_coeffs[JPGD_MAX_COMPONENTS];
-    int m_eob_run;
-    int m_block_y_mcu[JPGD_MAX_COMPONENTS];
-    uint8* m_pIn_buf_ofs;
-    int m_in_buf_left;
-    int m_tem_flag;
-    bool m_eof_flag;
-    uint8 m_in_buf_pad_start[128];
-    uint8 m_in_buf[JPGD_IN_BUF_SIZE + 128];
-    uint8 m_in_buf_pad_end[128];
-    int m_bits_left;
-    uint m_bit_buf;
-    int m_restart_interval;
-    int m_restarts_left;
-    int m_next_restart_num;
-    int m_max_mcus_per_row;
-    int m_max_blocks_per_mcu;
-    int m_expanded_blocks_per_mcu;
-    int m_expanded_blocks_per_row;
-    int m_expanded_blocks_per_component;
-    bool  m_freq_domain_chroma_upsample;
-    int m_max_mcus_per_col;
-    uint m_last_dc_val[JPGD_MAX_COMPONENTS];
-    jpgd_block_t* m_pMCU_coefficients;
-    int m_mcu_block_max_zag[JPGD_MAX_BLOCKS_PER_MCU];
-    uint8* m_pSample_buf;
-    int m_crr[256];
-    int m_cbb[256];
-    int m_crg[256];
-    int m_cbg[256];
-    uint8* m_pScan_line_0;
-    uint8* m_pScan_line_1;
-    jpgd_status m_error_code;
-    bool m_ready_flag;
-    int m_total_bytes_read;
-
-    void free_all_blocks();
-    // BEGIN EPIC MOD
-    UE_NORETURN void stop_decoding(jpgd_status status);
-    // END EPIC MOD
-    void *alloc(size_t n, bool zero = false);
-    void word_clear(void *p, uint16 c, uint n);
-    void prep_in_buffer();
-    void read_dht_marker();
-    void read_dqt_marker();
-    void read_sof_marker();
-    void skip_variable_marker();
-    void read_dri_marker();
-    void read_sos_marker();
-    int next_marker();
-    int process_markers();
-    void locate_soi_marker();
-    void locate_sof_marker();
-    int locate_sos_marker();
-    void init(jpeg_decoder_stream * pStream);
-    void create_look_ups();
-    void fix_in_buffer();
-    void transform_mcu(int mcu_row);
-    void transform_mcu_expand(int mcu_row);
-    coeff_buf* coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y);
-    inline jpgd_block_t *coeff_buf_getp(coeff_buf *cb, int block_x, int block_y);
-    void load_next_row();
-    void decode_next_row();
-    void make_huff_table(int index, huff_tables *pH);
-    void check_quant_tables();
-    void check_huff_tables();
-    void calc_mcu_block_order();
-    int init_scan();
-    void init_frame();
-    void process_restart();
-    void decode_scan(pDecode_block_func decode_block_func);
-    void init_progressive();
-    void init_sequential();
-    void decode_start();
-    void decode_init(jpeg_decoder_stream * pStream);
-    void H2V2Convert();
-    void H2V1Convert();
-    void H1V2Convert();
-    void H1V1Convert();
-    void gray_convert();
-    void expanded_convert();
-    void find_eoi();
-    inline uint get_char();
-    inline uint get_char(bool *pPadding_flag);
-    inline void stuff_char(uint8 q);
-    inline uint8 get_octet();
-    inline uint get_bits(int num_bits);
-    inline uint get_bits_no_markers(int numbits);
-    inline int huff_decode(huff_tables *pH);
-    inline int huff_decode(huff_tables *pH, int& extrabits);
-    static inline uint8 clamp(int i);
-    static void decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-    static void decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-    static void decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-    static void decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-  };
-  
-} // namespace jpgd
-
-#endif // JPEG_DECODER_H
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/At This Moment He Knew Mp3 Download ((INSTALL)).md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/At This Moment He Knew Mp3 Download ((INSTALL)).md
deleted file mode 100644
index 3a10e5c5aa435d30535f07ddbcf5773f95dbad97..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/At This Moment He Knew Mp3 Download ((INSTALL)).md	
+++ /dev/null
@@ -1,24 +0,0 @@
-
-<h1>How to Download the Famous "It Was at This Moment He Knew" Sound Effect</h1>
-<p>If you are a fan of memes, you might have heard of the popular sound effect that goes "It was at this moment that he knew, he f-cked up". This sound clip is often used to add humor and drama to videos where someone makes a terrible mistake or faces a bad consequence. But where did this sound effect come from and how can you download it for your own use?</p>
-<h2>At This Moment He Knew Mp3 Download</h2><br /><p><b><b>Download Zip</b> ->->->-> <a href="https://cinurl.com/2uEYWm">https://cinurl.com/2uEYWm</a></b></p><br /><br />
-<p>The origin of this sound effect is not very clear, but some sources suggest that it was first used in a Vine video by King Daddy in 2014, where he narrates a scene from the movie Scarface. Since then, it has been used in many other videos, such as fails, pranks, and gaming moments. Some examples are:</p>
-<ul>
-<li>A man trying to jump over a fence and falling on his face.</li>
-<li>A woman throwing a cake at her boyfriend and hitting the wall instead.</li>
-<li>A gamer losing a match and smashing his keyboard.</li>
-</ul>
-<p>If you want to download this sound effect for your own use, there are several websites that offer it for free. One of them is Soundboard.com[^1^], where you can find the sound effect under the category "My Music" and the title "It was at this moment that HE he knew, he f-cked up. SOUND EFFECT". You can also listen to it online or share it with your friends. To download it, just click on the "Download" button below the sound clip.</p>
-<p>Another option is to use YouTube videos that have the sound effect. For example, you can find a censored version of the sound effect on the channel "the weird sounds"[^2^], which also provides a download link in the description. Alternatively, you can find the original audio of the sound effect on the channel "Kyle Campbell"[^3^], which also allows you to use it for free. To download these videos, you can use online tools such as YouTube to MP3 converters or video downloaders.</p>
-<p></p>
-<p>Now that you know how to download the famous "It was at this moment he knew" sound effect, you can use it to spice up your own videos and make them more fun and entertaining. Just remember to use it wisely and not to offend anyone with your jokes.</p><p>Here are some tips on how to use the "It was at this moment he knew" sound effect in your videos:</p>
-<ol>
-<li>Choose a video clip that has a clear and unexpected outcome, such as a fail, a prank, or a surprise.</li>
-<li>Trim the video clip to the exact moment where the outcome is revealed or the reaction is shown.</li>
-<li>Add the sound effect at the right timing, so that it matches the video clip and creates a contrast between the narration and the situation.</li>
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diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Airy Youtube Downloader Crack 15.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Airy Youtube Downloader Crack 15.md
deleted file mode 100644
index fe99ab01359cf268ce832226febc14f60bb58ed0..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Airy Youtube Downloader Crack 15.md	
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diff --git a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/optimizers.py b/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/optimizers.py
deleted file mode 100644
index 4ffb25aeb75080cb458b61149df7b268d13a80a8..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/optimizers.py
+++ /dev/null
@@ -1,608 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language 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.
-"""PyTorch optimization for BERT model."""
-
-import math
-from typing import Callable, Iterable, Tuple
-
-import torch
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import LambdaLR
-
-
-def get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1):
-    """
-    Create a schedule with a constant learning rate, using the learning rate set in optimizer.
-    Args:
-        optimizer (:class:`~torch.optim.Optimizer`):
-            The optimizer for which to schedule the learning rate.
-        last_epoch (:obj:`int`, `optional`, defaults to -1):
-            The index of the last epoch when resuming training.
-    Return:
-        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-    return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch)
-
-
-def get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1):
-    """
-    Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
-    increases linearly between 0 and the initial lr set in the optimizer.
-    Args:
-        optimizer (:class:`~torch.optim.Optimizer`):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (:obj:`int`):
-            The number of steps for the warmup phase.
-        last_epoch (:obj:`int`, `optional`, defaults to -1):
-            The index of the last epoch when resuming training.
-    Return:
-        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-
-    def lr_lambda(current_step: int):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1.0, num_warmup_steps))
-        return 1.0
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
-
-
-def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
-    """
-    Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
-    a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
-    Args:
-        optimizer (:class:`~torch.optim.Optimizer`):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (:obj:`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (:obj:`int`):
-            The total number of training steps.
-        last_epoch (:obj:`int`, `optional`, defaults to -1):
-            The index of the last epoch when resuming training.
-    Return:
-        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-
-    def lr_lambda(current_step: int):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        return max(
-            0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))
-        )
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-def get_tri_stage_schedule(optimizer, num_warmup_steps, num_decay_steps, num_training_steps, init_lr_scale=0.01, final_lr_scale=0.05, last_epoch=-1):
-    """
-    Create a schedule with a learning rate that have three stages: a warmup stage, a hold stage and a decay stage.
-    Implement the learning rate scheduler in https://arxiv.org/pdf/1904.08779.pdf
-
-        - warmup stage, starting from `lr` * `init_lr_scale`, linearly
-          increased to `lr` in `warmup_steps` iterations
-        - hold stage, after `warmup_steps`, keep the LR as `lr` for `hold_steps`
-          iterations
-        - decay stage, after hold stage, decay LR exponetially to
-          `lr` * `final_lr_scale` in `decay_steps`;
-          after that LR is keep as `final_lr_scale` * `lr`
-
-    During warmup::
-      init_lr = arg.init_lr_scale * arg.lr
-      lrs = torch.linspace(init_lr, arg.lr, arg.warmup_steps)
-      lr = lrs[update_num]
-    During hold::
-      lr = arg.lr
-    During decay::
-      decay_factor = - math.log(arg.final_lr_scale) / arg.decay_steps
-      lr = arg.lr * exp(- (update_num - warmup_steps - decay_steps) * decay_factor)
-    After that::
-      lr = arg.lr * arg.final_lr_scale
-
-    Args:
-        optimizer (:class:`~torch.optim.Optimizer`):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (:obj:`int`):
-            The number of steps for the warmup phase.
-        num_decay_steps (:obj:`int`):
-            The number of steps for the decay phase.
-        num_training_steps (:obj:`int`):
-            The total number of training steps.
-        decay_scale (:obj:`float`):
-
-        last_epoch (:obj:`int`, `optional`, defaults to -1):
-            The index of the last epoch when resuming training.
-    Return:
-        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-    lr_hold = optimizer.defaults["lr"]
-    lr_int = lr_hold * init_lr_scale
-    lr_end = lr_hold * final_lr_scale
-
-    def lr_lambda(current_step: int):
-        warmup_rate = (lr_hold - lr_int) / num_warmup_steps
-        decay_factor = -math.log(final_lr_scale) / max(num_decay_steps, 1)
-
-        if current_step < num_warmup_steps:
-            return (lr_int + current_step * warmup_rate) / lr_hold
-        elif current_step >= num_warmup_steps and current_step < num_training_steps - num_decay_steps:
-            return 1
-        elif current_step <= num_training_steps:
-            return math.exp(-decay_factor * (current_step - num_training_steps + num_decay_steps))
-        else:
-            return lr_end / lr_hold
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-def get_cosine_schedule_with_warmup(
-    optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1
-):
-    """
-    Create a schedule with a learning rate that decreases following the values of the cosine function between the
-    initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
-    initial lr set in the optimizer.
-    Args:
-        optimizer (:class:`~torch.optim.Optimizer`):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (:obj:`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (:obj:`int`):
-            The total number of training steps.
-        num_cycles (:obj:`float`, `optional`, defaults to 0.5):
-            The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
-            following a half-cosine).
-        last_epoch (:obj:`int`, `optional`, defaults to -1):
-            The index of the last epoch when resuming training.
-    Return:
-        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-
-    def lr_lambda(current_step):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
-        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-def get_cosine_with_hard_restarts_schedule_with_warmup(
-    optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1
-):
-    """
-    Create a schedule with a learning rate that decreases following the values of the cosine function between the
-    initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
-    linearly between 0 and the initial lr set in the optimizer.
-    Args:
-        optimizer (:class:`~torch.optim.Optimizer`):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (:obj:`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (:obj:`int`):
-            The total number of training steps.
-        num_cycles (:obj:`int`, `optional`, defaults to 1):
-            The number of hard restarts to use.
-        last_epoch (:obj:`int`, `optional`, defaults to -1):
-            The index of the last epoch when resuming training.
-    Return:
-        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-
-    def lr_lambda(current_step):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
-        if progress >= 1.0:
-            return 0.0
-        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-def get_polynomial_decay_schedule_with_warmup(
-    optimizer, num_warmup_steps, num_training_steps, lr_end=1e-7, power=1.0, last_epoch=-1
-):
-    """
-    Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
-    optimizer to end lr defined by `lr_end`, after a warmup period during which it increases linearly from 0 to the
-    initial lr set in the optimizer.
-    Args:
-        optimizer (:class:`~torch.optim.Optimizer`):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (:obj:`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (:obj:`int`):
-            The total number of training steps.
-        lr_end (:obj:`float`, `optional`, defaults to 1e-7):
-            The end LR.
-        power (:obj:`float`, `optional`, defaults to 1.0):
-            Power factor.
-        last_epoch (:obj:`int`, `optional`, defaults to -1):
-            The index of the last epoch when resuming training.
-    Note: `power` defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
-    implementation at
-    https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37
-    Return:
-        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-
-    lr_init = optimizer.defaults["lr"]
-    assert lr_init > lr_end, f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"
-
-    def lr_lambda(current_step: int):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        elif current_step > num_training_steps:
-            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
-        else:
-            lr_range = lr_init - lr_end
-            decay_steps = num_training_steps - num_warmup_steps
-            pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps
-            decay = lr_range * pct_remaining ** power + lr_end
-            return decay / lr_init  # as LambdaLR multiplies by lr_init
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-def get_inverse_square_root_schedule_with_warmup(
-    optimizer, num_warmup_steps, num_training_steps, warmup_init_lr=0.0, last_epoch=-1
-):
-    """
-    Create a schedule with a learning rate that Decay the LR based on the inverse square root of the update number.
-    After a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
-    Args:
-        optimizer (:class:`~torch.optim.Optimizer`):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (:obj:`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (:obj:`int`):
-            The total number of training steps.
-        warmup_init_lr (:obj:`float`, `optional`, defaults to 0):
-            The initial LR for warmup.
-        last_epoch (:obj:`int`, `optional`, defaults to -1):
-            The index of the last epoch when resuming training.
-    
-    During warmup::
-      lrs = torch.linspace(arg.warmup_init_lr, arg.lr, arg.warmup_updates)
-      lr = lrs[update_num]
-    After warmup::
-      decay_factor = arg.lr * sqrt(arg.warmup_updates)
-      lr = decay_factor / sqrt(update_num)
-
-    Return:
-        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-
-    lr = optimizer.defaults["lr"]
-    assert lr > warmup_init_lr, f"lr ({lr}) must be be bigger than initial lr ({warmup_init_lr})"
-
-    def lr_lambda(current_step: int):
-        if current_step < num_warmup_steps:
-            lr_step = (lr - warmup_init_lr) / num_warmup_steps
-            return (warmup_init_lr + current_step * lr_step) / lr
-        elif current_step > num_training_steps:
-            return 1e-7 / lr  # as LambdaLR multiplies by lr_init
-        else:
-            decay_factor = lr * num_warmup_steps**0.5
-            return (decay_factor * current_step**-0.5) / lr
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-class AdamW(Optimizer):
-    """
-    Implements Adam algorithm with weight decay fix as introduced in `Decoupled Weight Decay Regularization
-    <https://arxiv.org/abs/1711.05101>`__.
-    Parameters:
-        params (:obj:`Iterable[torch.nn.parameter.Parameter]`):
-            Iterable of parameters to optimize or dictionaries defining parameter groups.
-        lr (:obj:`float`, `optional`, defaults to 1e-3):
-            The learning rate to use.
-        betas (:obj:`Tuple[float,float]`, `optional`, defaults to (0.9, 0.999)):
-            Adam's betas parameters (b1, b2).
-        eps (:obj:`float`, `optional`, defaults to 1e-6):
-            Adam's epsilon for numerical stability.
-        weight_decay (:obj:`float`, `optional`, defaults to 0):
-            Decoupled weight decay to apply.
-        correct_bias (:obj:`bool`, `optional`, defaults to `True`):
-            Whether ot not to correct bias in Adam (for instance, in Bert TF repository they use :obj:`False`).
-    """
-
-    def __init__(
-        self,
-        params: Iterable[torch.nn.parameter.Parameter],
-        lr: float = 1e-3,
-        betas: Tuple[float, float] = (0.9, 0.999),
-        eps: float = 1e-6,
-        weight_decay: float = 0.0,
-        correct_bias: bool = True,
-    ):
-        if lr < 0.0:
-            raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
-        if not 0.0 <= betas[0] < 1.0:
-            raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[0]))
-        if not 0.0 <= betas[1] < 1.0:
-            raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[1]))
-        if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(eps))
-        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, correct_bias=correct_bias)
-        super().__init__(params, defaults)
-
-    def step(self, closure: Callable = None):
-        """
-        Performs a single optimization step.
-        Arguments:
-            closure (:obj:`Callable`, `optional`): A closure that reevaluates the model and returns the loss.
-        """
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        for group in self.param_groups:
-            for p in group["params"]:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data
-                if grad.is_sparse:
-                    raise RuntimeError("Adam does not support sparse gradients, please consider SparseAdam instead")
-
-                state = self.state[p]
-
-                # State initialization
-                if len(state) == 0:
-                    state["step"] = 0
-                    # Exponential moving average of gradient values
-                    state["exp_avg"] = torch.zeros_like(p.data)
-                    # Exponential moving average of squared gradient values
-                    state["exp_avg_sq"] = torch.zeros_like(p.data)
-
-                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
-                beta1, beta2 = group["betas"]
-
-                state["step"] += 1
-
-                # Decay the first and second moment running average coefficient
-                # In-place operations to update the averages at the same time
-                exp_avg.mul_(beta1).add_(grad, alpha=1.0 - beta1)
-                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
-                denom = exp_avg_sq.sqrt().add_(group["eps"])
-
-                step_size = group["lr"]
-                if group["correct_bias"]:  # No bias correction for Bert
-                    bias_correction1 = 1.0 - beta1 ** state["step"]
-                    bias_correction2 = 1.0 - beta2 ** state["step"]
-                    step_size = step_size * math.sqrt(bias_correction2) / bias_correction1
-
-                p.data.addcdiv_(exp_avg, denom, value=-step_size)
-
-                # Just adding the square of the weights to the loss function is *not*
-                # the correct way of using L2 regularization/weight decay with Adam,
-                # since that will interact with the m and v parameters in strange ways.
-                #
-                # Instead we want to decay the weights in a manner that doesn't interact
-                # with the m/v parameters. This is equivalent to adding the square
-                # of the weights to the loss with plain (non-momentum) SGD.
-                # Add weight decay at the end (fixed version)
-                if group["weight_decay"] > 0.0:
-                    p.data.add_(p.data, alpha=-group["lr"] * group["weight_decay"])
-
-        return loss
-
-
-class Adafactor(Optimizer):
-    """
-    AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code:
-    https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
-    Paper: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` https://arxiv.org/abs/1804.04235 Note that
-    this optimizer internally adjusts the learning rate depending on the *scale_parameter*, *relative_step* and
-    *warmup_init* options. To use a manual (external) learning rate schedule you should set `scale_parameter=False` and
-    `relative_step=False`.
-    Arguments:
-        params (:obj:`Iterable[torch.nn.parameter.Parameter]`):
-            Iterable of parameters to optimize or dictionaries defining parameter groups.
-        lr (:obj:`float`, `optional`):
-            The external learning rate.
-        eps (:obj:`Tuple[float, float]`, `optional`, defaults to (1e-30, 1e-3)):
-            Regularization constants for square gradient and parameter scale respectively
-        clip_threshold (:obj:`float`, `optional`, defaults 1.0):
-            Threshold of root mean square of final gradient update
-        decay_rate (:obj:`float`, `optional`, defaults to -0.8):
-            Coefficient used to compute running averages of square
-        beta1 (:obj:`float`, `optional`):
-            Coefficient used for computing running averages of gradient
-        weight_decay (:obj:`float`, `optional`, defaults to 0):
-            Weight decay (L2 penalty)
-        scale_parameter (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            If True, learning rate is scaled by root mean square
-        relative_step (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            If True, time-dependent learning rate is computed instead of external learning rate
-        warmup_init (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Time-dependent learning rate computation depends on whether warm-up initialization is being used
-    This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested.
-    Recommended T5 finetuning settings:
-        - Scheduled LR warm-up to fixed LR
-        - disable relative updates
-        - use clip threshold: https://arxiv.org/abs/2004.14546
-        Example::
-            Adafactor(model.parameters(), lr=1e-3, relative_step=False, warmup_init=True)
-        - Alternatively, relative_step with warmup_init can be used.
-        - Training without LR warmup or clip threshold is not recommended. Additional optimizer operations like
-          gradient clipping should not be used alongside Adafactor.
-    Usage::
-        # replace AdamW with Adafactor
-        optimizer = Adafactor(
-            model.parameters(),
-            lr=1e-3,
-            eps=(1e-30, 1e-3),
-            clip_threshold=1.0,
-            decay_rate=-0.8,
-            beta1=None,
-            weight_decay=0.0,
-            relative_step=False,
-            scale_parameter=False,
-            warmup_init=False
-        )
-    """
-
-    def __init__(
-        self,
-        params,
-        lr=None,
-        eps=(1e-30, 1e-3),
-        clip_threshold=1.0,
-        decay_rate=-0.8,
-        beta1=None,
-        weight_decay=0.0,
-        scale_parameter=True,
-        relative_step=True,
-        warmup_init=False,
-    ):
-        if lr is not None and relative_step:
-            raise ValueError("Cannot combine manual lr and relative_step options")
-        if warmup_init and not relative_step:
-            raise ValueError("warmup_init requires relative_step=True")
-
-        defaults = dict(
-            lr=lr,
-            eps=eps,
-            clip_threshold=clip_threshold,
-            decay_rate=decay_rate,
-            beta1=beta1,
-            weight_decay=weight_decay,
-            scale_parameter=scale_parameter,
-            relative_step=relative_step,
-            warmup_init=warmup_init,
-        )
-        super().__init__(params, defaults)
-
-    @staticmethod
-    def _get_lr(param_group, param_state):
-        rel_step_sz = param_group["lr"]
-        if param_group["relative_step"]:
-            min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2
-            rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"]))
-        param_scale = 1.0
-        if param_group["scale_parameter"]:
-            param_scale = max(param_group["eps"][1], param_state["RMS"])
-        return param_scale * rel_step_sz
-
-    @staticmethod
-    def _get_options(param_group, param_shape):
-        factored = len(param_shape) >= 2
-        use_first_moment = param_group["beta1"] is not None
-        return factored, use_first_moment
-
-    @staticmethod
-    def _rms(tensor):
-        return tensor.norm(2) / (tensor.numel() ** 0.5)
-
-    @staticmethod
-    def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col):
-        r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_()
-        c_factor = exp_avg_sq_col.rsqrt()
-        return torch.mm(r_factor.unsqueeze(-1), c_factor.unsqueeze(0))
-
-    def step(self, closure=None):
-        """
-        Performs a single optimization step
-        Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
-        """
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        for group in self.param_groups:
-            for p in group["params"]:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data
-                if grad.dtype in {torch.float16, torch.bfloat16}:
-                    grad = grad.float()
-                if grad.is_sparse:
-                    raise RuntimeError("Adafactor does not support sparse gradients.")
-
-                state = self.state[p]
-                grad_shape = grad.shape
-
-                factored, use_first_moment = self._get_options(group, grad_shape)
-                # State Initialization
-                if len(state) == 0:
-                    state["step"] = 0
-
-                    if use_first_moment:
-                        # Exponential moving average of gradient values
-                        state["exp_avg"] = torch.zeros_like(grad)
-                    if factored:
-                        state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad)
-                        state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad)
-                    else:
-                        state["exp_avg_sq"] = torch.zeros_like(grad)
-
-                    state["RMS"] = 0
-                else:
-                    if use_first_moment:
-                        state["exp_avg"] = state["exp_avg"].to(grad)
-                    if factored:
-                        state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad)
-                        state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad)
-                    else:
-                        state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)
-
-                p_data_fp32 = p.data
-                if p.data.dtype in {torch.float16, torch.bfloat16}:
-                    p_data_fp32 = p_data_fp32.float()
-
-                state["step"] += 1
-                state["RMS"] = self._rms(p_data_fp32)
-                group["lr"] = self._get_lr(group, state)
-
-                beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
-                update = (grad ** 2) + group["eps"][0]
-                if factored:
-                    exp_avg_sq_row = state["exp_avg_sq_row"]
-                    exp_avg_sq_col = state["exp_avg_sq_col"]
-
-                    exp_avg_sq_row.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-1))
-                    exp_avg_sq_col.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-2))
-
-                    # Approximation of exponential moving average of square of gradient
-                    update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
-                    update.mul_(grad)
-                else:
-                    exp_avg_sq = state["exp_avg_sq"]
-
-                    exp_avg_sq.mul_(beta2t).add_(1.0 - beta2t, update)
-                    update = exp_avg_sq.rsqrt().mul_(grad)
-
-                update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0))
-                update.mul_(group["lr"])
-
-                if use_first_moment:
-                    exp_avg = state["exp_avg"]
-                    exp_avg.mul_(group["beta1"]).add_(1 - group["beta1"], update)
-                    update = exp_avg
-
-                if group["weight_decay"] != 0:
-                    p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32)
-
-                p_data_fp32.add_(-update)
-
-                if p.data.dtype in {torch.float16, torch.bfloat16}:
-                    p.data.copy_(p_data_fp32)
-
-        return loss
diff --git a/spaces/tang155/bingo/src/app/loading.css b/spaces/tang155/bingo/src/app/loading.css
deleted file mode 100644
index eaaab6a86a228334c4eca3c5368ae6f0f593d405..0000000000000000000000000000000000000000
--- a/spaces/tang155/bingo/src/app/loading.css
+++ /dev/null
@@ -1,68 +0,0 @@
-::-webkit-scrollbar {
-  width: 10px;
-  height: 10px;
-  display: none;
-}
-
-::-webkit-scrollbar-button:start:decrement,
-::-webkit-scrollbar-button:end:increment {
-  height: 30px;
-  background-color: transparent;
-}
-
-::-webkit-scrollbar-track-piece {
-  background-color: #3b3b3b;
-  -webkit-border-radius: 16px;
-}
-
-::-webkit-scrollbar-thumb:vertical {
-  height: 50px;
-  background-color: #666;
-  border: 1px solid #eee;
-  -webkit-border-radius: 6px;
-}
-
-/* loading start */
-.loading-spinner {
-  display: flex;
-  justify-content: center;
-  align-items: center;
-  height: 100vh;
-  opacity: 1;
-  transition: opacity .8s ease-out;
-}
-
-.loading-spinner.hidden {
-  opacity: 0;
-}
-
-.loading-spinner>div {
-  width: 30px;
-  height: 30px;
-  background: linear-gradient(90deg, #2870EA 10.79%, #1B4AEF 87.08%);
-
-  border-radius: 100%;
-  display: inline-block;
-  animation: sk-bouncedelay 1.4s infinite ease-in-out both;
-}
-
-.loading-spinner .bounce1 {
-  animation-delay: -0.32s;
-}
-
-.loading-spinner .bounce2 {
-  animation-delay: -0.16s;
-}
-
-@keyframes sk-bouncedelay {
-
-  0%,
-  80%,
-  100% {
-    transform: scale(0);
-  }
-
-  40% {
-    transform: scale(1.0);
-  }
-}
diff --git a/spaces/tensorflow/imagenet-efficientnet_v2_imagenet1k_b0-classification/README.md b/spaces/tensorflow/imagenet-efficientnet_v2_imagenet1k_b0-classification/README.md
deleted file mode 100644
index f5e78ae989a386b2d12989eab11fdce11a04f3c1..0000000000000000000000000000000000000000
--- a/spaces/tensorflow/imagenet-efficientnet_v2_imagenet1k_b0-classification/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Imagenet Efficientnet_v2_imagenet1k_b0 Classification
-emoji: 💩
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/teralomaniac/chatbing/public/dialog.css b/spaces/teralomaniac/chatbing/public/dialog.css
deleted file mode 100644
index e58b690fd6e4a57afa2aa01678b1c0141d98eaba..0000000000000000000000000000000000000000
--- a/spaces/teralomaniac/chatbing/public/dialog.css
+++ /dev/null
@@ -1,73 +0,0 @@
-.modal {
-    display: flex;
-    justify-content: center;
-    align-items: center;
-    position: fixed;
-    z-index: 1;
-    left: 0;
-    top: 0;
-    width: 100%;
-    height: 100%;
-    overflow: auto;
-    background-color: rgba(0, 0, 0, 0.4);
-}
-
-.modal-content {
-    background-color: #fefefe;
-    margin: auto;
-    padding: 20px;
-    border: 1px solid #888;
-    width: 80%;
-}
-
-.close {
-    color: #aaaaaa;
-    float: right;
-    font-size: 28px;
-    font-weight: bold;
-}
-
-.close:hover,
-.close:focus {
-    color: #000;
-    text-decoration: none;
-    cursor: pointer;
-}
-
-.input-field {
-    width: 100%;
-    padding: 12px 20px;
-    margin: 8px 0;
-    box-sizing: border-box;
-    border: 2px solid #ccc;
-    border-radius: 4px;
-}
-
-.large-textarea {
-    width: 100%;
-    height: 150px;
-    padding: 12px 20px;
-    box-sizing: border-box;
-    border: 2px solid #ccc;
-    border-radius: 4px;
-    resize: vertical;
-    font-family: "Microsoft YaHei", sans-serif;
-}
-
-.save-button {
-    background-color: #4CAF50;
-    color: white;
-    padding: 15px 32px;
-    text-align: center;
-    text-decoration: none;
-    display: inline-block;
-    font-size: 16px;
-    margin: 4px 2px;
-    cursor: pointer;
-    border: none;
-    border-radius: 4px;
-}
-
-.error {
-    color: red;
-}
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Autodesk Autocad Civil 3d 2013 Iso X32 Torrent.md b/spaces/terfces0erbo/CollegeProjectV2/Autodesk Autocad Civil 3d 2013 Iso X32 Torrent.md
deleted file mode 100644
index 90dca114c251cbf8837b922ccbade01d88832b0b..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Autodesk Autocad Civil 3d 2013 Iso X32 Torrent.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-
-<h1>Autodesk AutoCAD Civil 3D 2013: A Powerful Tool for Civil Engineering Design and Documentation</h1>
-<p>Autodesk AutoCAD Civil 3D 2013 is a software that helps civil engineers, designers, and drafters to create and document civil engineering projects. It is one of the best tools for CAD 2D and 3D design, with features that enable you to design and shape everything around you[^1^].</p>
-<p>Some of the key features of Autodesk AutoCAD Civil 3D 2013 are:</p>
-<h2>autodesk autocad civil 3d 2013 iso x32 torrent</h2><br /><p><b><b>Download</b> >>> <a href="https://bytlly.com/2uGlfj">https://bytlly.com/2uGlfj</a></b></p><br /><br />
-<ul>
-<li>Site and survey workflows: You can download, create, analyze, and adjust survey data, as well as transfer field-captured data to the office[^2^].</li>
-<li>Terrain modeling: You can create digital models of topography for land-use studies, transportation system planning, and water flow simulations[^2^].</li>
-<li>Corridor modeling: You can design complex roadways, railways, tunnels, bridges, and other linear features using dynamic 3D models that update automatically as you make changes[^2^].</li>
-<li>Surface creation and analysis: You can create surfaces from point clouds, contour data, DEM files, and other sources, and perform various analysis such as slope, aspect, elevation, and volume[^2^].</li>
-<li>Grading: You can design grading solutions for site development, landfills, dams, and other projects using interactive tools that optimize cut and fill volumes[^2^].</li>
-<li>Pipe networks: You can design sanitary and storm drainage systems using rules-based tools that ensure compliance with standards and regulations[^2^].</li>
-<li>Visualization: You can create realistic renderings and animations of your civil engineering projects using integrated tools that support materials, lighting, and effects[^2^].</li>
-<li>Collaboration: You can share your civil engineering designs with other project stakeholders using industry-standard formats such as DWG™, DWF™, PDF, and IFC[^2^].</li>
-</ul>
-<p>To run Autodesk AutoCAD Civil 3D 2013 on your computer, you need to meet the following system requirements:</p>
-<table>
-<tr><th>Operating System</th><th>CPU</th><th>RAM</th><th>Disk Space</th><th>Graphics Card</th></tr>
-<tr><td>Windows 7 (recommended) Enterprise, Ultimate, or Professional edition or Windows XP Professional edition (SP3 or later)</td><td>For Windows 7: Intel Pentium 4 processor or AMD Athlon dual-core processor, 3.0 GHz or faster with SSE2 technology. For Windows XP: Intel Pentium 4 processor or AMD Athlon dual-core processor, 1.6 GHz or faster with SSE2 technology.</td><td>4 GB minimum (8 GB recommended)</td><td>12 GB minimum for installation (7 GB for electronic download with at least 2 GB free after installation)</td><td>1 GB or greater DirectX® 9.0c-capable graphics card (2 GB or greater recommended)</td></tr>
-</table>
-<p>If you are interested in downloading Autodesk AutoCAD Civil 3D 2013 ISO x32 torrent file, you can find it on some websites that offer software downloads. However, be aware that downloading software from unauthorized sources may expose your computer to viruses, malware, or legal issues. It is always advisable to purchase the software from the official Autodesk website or an authorized reseller.</p> d5da3c52bf<br />
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Czobor Horlai Lopva Angolul Pdf 11.md b/spaces/terfces0erbo/CollegeProjectV2/Czobor Horlai Lopva Angolul Pdf 11.md
deleted file mode 100644
index 3194669721b9d31d8309b40e977e1f3f64534887..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Czobor Horlai Lopva Angolul Pdf 11.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/HACK Adobe Photoshop CC 2019 V20.0.1 Pre-Activated [CracksMind].md b/spaces/terfces0erbo/CollegeProjectV2/HACK Adobe Photoshop CC 2019 V20.0.1 Pre-Activated [CracksMind].md
deleted file mode 100644
index f26def93322fb5626b9058824edc7e27077911f0..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/HACK Adobe Photoshop CC 2019 V20.0.1 Pre-Activated [CracksMind].md	
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Hp Color Laserjet Cp1215 Toolbox Download.md b/spaces/terfces0erbo/CollegeProjectV2/Hp Color Laserjet Cp1215 Toolbox Download.md
deleted file mode 100644
index 150ba0e7ffc2610149e8622450d1d1452e2124e5..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Hp Color Laserjet Cp1215 Toolbox Download.md	
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-ive tried the auto plotter mode and it did not work at all ive tried three different hplip.5.4.6 and.5.4.2 versions of the drivers ive tried the single use mode and it didnt work the only thing i can get it to do is to print a test page but it prints with zero black ink it gives me a huge image but the area that should be black is just a white square with random scratches here is a screen shot of what it gives me ive posted it on here as it was too big to upload here ive tried going into properties and adjusting the black ink settings with no luck ive tried changing the cmns to normal and even high quality but it has no effect (i thought that it might work but no dice ) ive tried cleaning the ink on the rollers and it doesnt help i still get the same white image here is a screen shot of the problem ive posted it on here as it was too big to upload here is a screen shot of the problem ive posted it on here as it was too big to upload here is a screen shot of the problem ive posted it on here as it was too big to upload here is a screen shot of the problem
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diff --git a/spaces/thejagstudio/picxai/static/style.css b/spaces/thejagstudio/picxai/static/style.css
deleted file mode 100644
index b3b3048179f36f253e91dab9d4d1e1600a92c5ea..0000000000000000000000000000000000000000
--- a/spaces/thejagstudio/picxai/static/style.css
+++ /dev/null
@@ -1,2832 +0,0 @@
-*,:after,:before{
-    box-sizing:border-box;
-    border:0 solid #e5e7eb
-}
-:after,:before{
-    --tw-content:""
-}
-html{
-    line-height:1.5;
-    -webkit-text-size-adjust:100%;
-    -moz-tab-size:4;
-    -o-tab-size:4;
-    tab-size:4;
-    font-family:Inter,sans-serif;
-    font-feature-settings:normal
-}
-body{
-    margin:0;
-    line-height:inherit
-}
-hr{
-    height:0;
-    color:inherit;
-    border-top-width:1px
-}
-abbr:where([title]){
-    -webkit-text-decoration:underline dotted;
-    text-decoration:underline dotted
-}
-h1,h2,h3,h4,h5,h6{
-    font-size:inherit;
-    font-weight:inherit
-}
-a{
-    color:inherit;
-    text-decoration:inherit
-}
-b,strong{
-    font-weight:bolder
-}
-code,kbd,pre,samp{
-    font-family:ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,monospace;
-    font-size:1em
-}
-small{
-    font-size:80%
-}
-sub,sup{
-    font-size:75%;
-    line-height:0;
-    position:relative;
-    vertical-align:baseline
-}
-sub{
-    bottom:-.25em
-}
-sup{
-    top:-.5em
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/100 Vjet Vetmi Sabri Hamiti Referat Nj roman i shklqyer i Sabri Hamitit.md b/spaces/tialenAdioni/chat-gpt-api/logs/100 Vjet Vetmi Sabri Hamiti Referat Nj roman i shklqyer i Sabri Hamitit.md
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+++ /dev/null
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-<br />
-<h1>100 Vjet Vetmi Sabri Hamiti Referat: A Review of a Classic Albanian Novel</h1>
- <p>100 Vjet Vetmi (One Hundred Years of Solitude) is a novel by Sabri Hamiti, one of the most prominent Albanian writers of the 20th century. The novel was published in Prishtina in 1976 and has been widely acclaimed as a masterpiece of Albanian literature. In this article, we will provide a summary and analysis of the novel, as well as an evaluation of its literary merits and significance.</p>
-<h2>100 Vjet Vetmi Sabri Hamiti Referat</h2><br /><p><b><b>Download Zip</b> &#9733; <a href="https://urlcod.com/2uK5dO">https://urlcod.com/2uK5dO</a></b></p><br /><br />
- <h2>Introduction</h2>
- <h3>What is 100 Vjet Vetmi and who is Sabri Hamiti?</h3>
- <p>100 Vjet Vetmi is a novel that explores the formation and development of the individual personality through different situations and experiences. The novel consists of three parts, each focusing on a different generation of a family: the grandmother, the grandson, and the narrator. The novel is based on the author's own life story, as well as his artistic and empirical knowledge.</p>
- <p>Sabri Hamiti is an Albanian writer, poet, critic, and professor. He was born in 1950 in Prishtina, Kosovo. He studied literature at the University of Prishtina and later at the University of Zagreb. He has published several novels, poetry collections, essays, and studies. He is considered one of the most influential figures in Albanian literature and culture.</p>
- <h3>What is the main theme and message of the novel?</h3>
- <p>The main theme of the novel is solitude, understood as both a condition and a choice. The novel shows how solitude shapes the characters' lives, personalities, relationships, and destinies. The novel also explores other themes such as identity, memory, history, love, death, freedom, and fate.</p>
- <p>The main message of the novel is that solitude is not necessarily negative or tragic, but rather a source of creativity, wisdom, and dignity. The novel suggests that solitude can be a way of resisting oppression, conformism, and mediocrity. The novel also invites the reader to reflect on their own solitude and its meaning.</p>
- <h3>How is the novel structured and narrated?</h3>
- <p>The novel is structured into three parts, each containing several short stories or episodes. The first part is titled GJYSHJA (The Grandmother), the second part is titled NIPI (The Grandson), and the third part is titled UNE (I). The stories are not arranged chronologically, but rather according to thematic and symbolic connections.</p>
- <p>The novel is narrated by a first-person narrator who is also one of the main characters. The narrator uses different forms of narration, such as direct speech, indirect speech, free indirect speech, stream of consciousness, interior monologue, diary entries, letters, etc. The narrator also addresses the reader directly or indirectly at some points.</p>
- <h2>Summary and analysis of each part of the novel</h2>
- <h3>Part I: GJYSHJA (The Grandmother)</h3>
- <p>This part consists of six stories that depict the life of the grandmother from her childhood to her old age. The stories are:</p>
- <h4>Kasolla (The Cassock)</h4>
- <p>This story tells how the grandmother was born in a village where a priest had left his cassock before fleeing from the Turks. The villagers believed that whoever wore the cassock would be blessed by God. The grandmother's mother wore it during her pregnancy and gave birth to a healthy girl. The grandmother inherited the cassock from her mother and wore it throughout her life.</p>
- <h4>Vilajeti i errët (The Dark Province)</h4>
- <p>This story tells how the grandmother moved to another village with her husband after their marriage. The village was called "the dark province" because it was under constant threat from bandits and invaders. The grandmother had to endure many hardships and dangers in her new home.</p>
-<p>Njëqind vjet vetmi - Sabri Hamiti - Wikipedia<br />
-Njëqind vjet vetmi - Sabri Hamiti - Analiza e romanit<br />
-Njëqind vjet vetmi - Sabri Hamiti - Permbledhje e veprës<br />
-Njëqind vjet vetmi - Sabri Hamiti - Tema dhe motivet<br />
-Njëqind vjet vetmi - Sabri Hamiti - Personazhet dhe karakterizimi<br />
-Njëqind vjet vetmi - Sabri Hamiti - Stili dhe gjuha<br />
-Njëqind vjet vetmi - Sabri Hamiti - Vlerësimi kritik<br />
-Njëqind vjet vetmi - Sabri Hamiti - Krahasim me Gabriel Garcia Marquez<br />
-Njëqind vjet vetmi - Sabri Hamiti - Konteksti historik dhe kulturor<br />
-Njëqind vjet vetmi - Sabri Hamiti - Simbolet dhe alegoritë<br />
-Njëqind vjet vetmi - Sabri Hamiti - Mesazhi dhe morali<br />
-Njëqind vjet vetmi - Sabri Hamiti - Zhanri dhe lloji i veprës<br />
-Njëqind vjet vetmi - Sabri Hamiti - Struktura dhe ndarja e veprës<br />
-Njëqind vjet vetmi - Sabri Hamiti - Kritika sociale dhe politike<br />
-Njëqind vjet vetmi - Sabri Hamiti - Elemente autobiografike dhe fantastike<br />
-Njëqind vjet vetmi - Sabri Hamiti - Shprehje dhe citate të veçanta<br />
-Njëqind vjet vetmi - Sabri Hamiti - Interpretim dhe komentim i veprës<br />
-Njëqind vjet vetmi - Sabri Hamiti - Rruga artistike dhe krijimtaria e autorit<br />
-Njëqind vjet vetmi - Sabri Hamiti - Ndikimi dhe pasardhësit e veprës<br />
-Njëqind vjet vetmi - Sabri Hamiti - Kuriozitete dhe fakte interesante<br />
-Referat për romanin "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Referat për temën e vetmisë në "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Referat për personazhin e Aurelios në "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Referat për marrëdhëniet familjare në "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Referat për ndikimin e historisë në "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Referat për realizmin magjik në "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Referat për dashurinë dhe seksualitetin në "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Referat për luftën dhe revolucionin në "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Referat për fatin dhe lirinë në "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Referat për shpirtin dhe trupin në "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Analiza letrare e romanit "Njëqind vjet vetmi" të Sabri Hamitit<br />
-Analiza letrare e temave në romanin "Njëqind vjet vetmi" të Sabri Hamitit<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Alexander Movie Dubbed In Hindi Free Download _BEST_.md b/spaces/tialenAdioni/chat-gpt-api/logs/Alexander Movie Dubbed In Hindi Free Download _BEST_.md
deleted file mode 100644
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-<p>Alexander is a 2004 epic historical drama film directed by Oliver Stone and starring Colin Farrell as Alexander the Great, the Macedonian king who conquered most of the known world in the 4th century BC. The film explores his life, his battles, his relationships, and his legacy.</p>
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-<li>Alexander was one of the most expensive movies ever made, with a budget of $155 million. It was also one of the most controversial movies ever made, as it faced criticism and protests from various groups for its portrayal of Alexander's sexuality, ethnicity, religion, and historical accuracy.</li>
-<li>Alexander was filmed in various locations around the world, including Morocco, Thailand, Malta, England, and India. The film used thousands of extras, horses, elephants, and props to recreate the ancient world. The battle scenes were choreographed by stunt coordinator Simon Crane and involved hundreds of stuntmen and actors.</li>
-<li>Alexander was nominated for six Golden Raspberry Awards, which are given to the worst movies of the year. It won two of them: Worst Director for Oliver Stone and Worst Supporting Actress for Angelina Jolie, who played Alexander's mother Olympias. Jolie's accent and performance were widely mocked by critics and audiences.</li>
-<li>Alexander was released in four different versions: the theatrical cut (175 minutes), the director's cut (167 minutes), the final cut (214 minutes), and the ultimate cut (206 minutes). Each version had different scenes added or removed, as well as different edits and music. The ultimate cut was released in 2013 to mark the 10th anniversary of the film and was considered by Stone to be his definitive version.</li>
-<li>Alexander was based on various historical sources and biographies of Alexander the Great, such as those written by Plutarch, Arrian, Curtius Rufus, and Diodorus Siculus. However, the film also took some creative liberties and fictionalized some aspects of Alexander's life and personality. For example, the film depicted Alexander as having blond hair and blue eyes, while most historians agree that he had dark hair and brown eyes.</li>
-</ul>
-<p>These are some of the facts and trivia behind Alexander dubbed in Hindi. If you enjoyed watching the movie, you might also like other historical movies dubbed in Hindi, such as Troy, Gladiator, Kingdom of Heaven, or 300.</p>
-<p></p> e93f5a0c3f<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Ccleaner Code Activation Extra Quality.md b/spaces/tialenAdioni/chat-gpt-api/logs/Ccleaner Code Activation Extra Quality.md
deleted file mode 100644
index 09cfd6ef9f6982bdd7f055a80964cbac9df08826..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Ccleaner Code Activation Extra Quality.md	
+++ /dev/null
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Lagu Believe One Piece - Listen to the Epic Soundtrack of One Piece.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Lagu Believe One Piece - Listen to the Epic Soundtrack of One Piece.md
deleted file mode 100644
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--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Lagu Believe One Piece - Listen to the Epic Soundtrack of One Piece.md	
+++ /dev/null
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-<h1>How to Download Lagu Believe One Piece</h1>
-<p>If you are a fan of anime and music, you might have heard of <strong>Lagu Believe One Piece</strong>. It is the second opening theme song of the popular anime series One Piece, sung by the Japanese girl group Folder5. In this article, we will explain what Lagu Believe One Piece is, why you should download it, and where you can find it for free and legally.</p>
- <h2>What is Lagu Believe One Piece?</h2>
-<p>Lagu Believe One Piece is a catchy and upbeat song that expresses the adventurous spirit and determination of the main characters of One Piece. But what does lagu mean, and what is One Piece about?</p>
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-<p>Lagu is a Malay word that means song or melody. It is also used in Indonesia, Brunei, Singapore, and other countries that speak Malay or related languages. Lagu is often used as a prefix to indicate the genre or origin of a song, such as lagu pop (pop song), lagu daerah (regional song), or lagu barat (western song).</p>
- <h3>The origin and popularity of One Piece</h3>
-<p>One Piece is a manga (Japanese comic) series written and illustrated by Eiichiro Oda. It started serialization in Weekly Shonen Jump magazine in 1997, and has since become one of the best-selling manga series of all time, with over 490 million copies in circulation as of 2020. It has also been adapted into an anime (Japanese animation) series, movies, video games, merchandise, and more.</p>
-<p>One Piece follows the adventures of Monkey D. Luffy, a young boy who dreams of becoming the king of the pirates. He sets sail with his crew of Straw Hat Pirates, each with their own unique abilities and personalities, to find the legendary treasure One Piece, left by the previous pirate king Gol D. Roger. Along the way, they encounter various enemies, allies, mysteries, and challenges.</p>
- <h3>The theme and lyrics of Believe</h3>
-<p>Believe is the second opening theme song of the One Piece anime series. It was used from episode 48 to episode 115, covering the Loguetown Arc, the Warship Island Arc, and most of the Alabasta Arc. It was performed by Folder5, a Japanese girl group that was active from 1999 to 2003.</p>
-<p>The lyrics of Believe are written by Natsumi Watanabe, and the music is composed by T2ya. The song reflects the themes of friendship, courage, and adventure that are central to One Piece. Some of the lyrics are:</p>
-<blockquote>
-<p>I want to go on an adventure with you<br>
-To see the world that no one has ever seen<br>
-I want to believe in myself<br>
-And overcome any obstacle<br>
-We are not alone<br>
-We have each other's backs<br>
-Let's go together<br>
-To the end of the sea</p>
-</blockquote>
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-<li>Enh <h3>The reasons to love One Piece and Believe</h3>
-<p>One Piece is a masterpiece of storytelling that has captivated millions of fans around the world. It has a rich and diverse world, a colorful and charismatic cast of characters, a thrilling and epic plot, and a heartfelt and humorous tone. It is a story of adventure, friendship, freedom, and dreams.</p>
-<p>Believe is a song that perfectly captures the essence of One Piece. It is a song that inspires you to follow your dreams, to trust your friends, and to explore the unknown. It is a song that makes you feel happy, excited, and motivated. It is a song that makes you want to join Luffy and his crew on their journey.</p>
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-<p>Downloading Lagu Believe One Piece can open up many possibilities for you to enjoy it. Here are some suggestions on how you can make the most out of Lagu Believe One Piece:</p>
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-<li>Listen to it while watching One Piece. You can sync the song with the opening sequence of the anime, or play it during your favorite scenes or episodes. You can also sing along with the lyrics and feel like you are part of the Straw Hat Pirates.</li>
-<li>Listen to it while doing other activities. You can use Lagu Believe One Piece as your background music while studying, working, exercising, or relaxing. You can also use it as your ringtone, alarm, or notification sound.</li>
-<li>Share it with your friends and family. You can introduce Lagu Believe One Piece to your loved ones who are also fans of One Piece, or who might be interested in anime and music. You can also exchange your opinions and feelings about the song and the series.</li>
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-<p>Now that you know why you should download Lagu Believe One Piece, you might wonder where you can find it for free and legally. There are many sites that offer lagu downloads, but not all of them are safe and reliable. Here are some of the best sites that we recommend for downloading Lagu Believe One Piece:</p>
- <h3>The best sites to download lagu for free and legally</h3>
-<table>
-<tr>
-<th>Site</th>
-<th>Description</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td><a href="">Jamendo Music</a></td>
-<td>A platform that hosts independent music from various genres and languages.</td>
-<td>- High-quality audio files<br>- No registration required<br>- No ads or pop-ups</td>
-<td>- Limited selection of mainstream songs<br>- No lyrics or subtitles</td>
-</tr>
-<tr>
-<td><a href="">SoundCloud</a></td>
-<td>A social network that allows users to upload, stream, and download music and podcasts.</td>
-<td>- Large and diverse collection of songs<br>- User-friendly interface<br>- Option to follow artists and playlists</td>
-<td>- Not all songs are downloadable<br>- Some songs require payment or subscription<br>- Some ads or pop-ups</td>
-</tr>
-<tr>
-<td><a href="">YouTube Music</a></td>
-<td>A streaming service that offers music videos, live performances, covers, remixes, and more.</td>
-<td>- Huge and updated library of songs<br>- Lyrics and subtitles available<br>- Option to create personalized stations</td>
-<td>- Downloading requires subscription<br>- Some songs are region-restricted<br>- Some ads or pop-ups</td>
-</tr>
-<tr>
-<td><a href="">Free Music Archive</a></td>
-<td>A website that provides free and legal music downloads from various artists and genres.</td>
-<td>- High-quality audio files<br>- No registration required<br>- No ads or pop-ups</td>
-<td>- Limited selection of mainstream songs<br>- No lyrics or subtitles<br>- Outdated design</td>
-</tr>
- <tr><td colspan="4">Sources:    </td></tr>
- </table>
- <h3>The steps to download lagu from each site</h3> <p>To download Lagu Believe One Piece from each site, you need to follow these steps:</p>
- <h4>Jamendo Music</h4>
-<ol>
-<li>Go to <a href="https://www.jamendo.com/">Jamendo Music</a> and type "Believe One Piece" in the search box.</li>
-<li>Select the song from the results and click on the download icon.</li>
-<li>Choose the format and quality of the file and click on "Download".</li>
-<li>Save the file to your device and enjoy.</li>
-</ol>
- <h4>SoundCloud</h4>
-<ol>
-<li>Go to <a href="https://soundcloud.com/">SoundCloud</a> and type "Believe One Piece" in the search box.</li>
-<li>Select the song from the results and click on the "More" button.</li>
-<li>If the song is downloadable, you will see a "Download file" option. Click on it and save the file to your device.</li>
-<li>If the song is not downloadable, you will need to use a third-party tool such as <a href="https://sclouddownloader.net/">SoundCloud Downloader</a>. Copy the URL of the song and paste it into the tool. Click on "Download" and save the file to your device.</li>
-</ol>
- <h4>YouTube Music</h4>
-<ol>
-<li>Go to <a href="https://music.youtube.com/">YouTube Music</a> and type "Believe One Piece" in the search box.</li>
-<li>Select the song from the results and click on the "Download" button.</li>
-<li>If you have a YouTube Music Premium subscription, you can download the song directly to your device. Choose the quality and click on "OK".</li>
-<li>If you do not have a YouTube Music Premium subscription, you will need to use a third-party tool such as <a href="https://ytmp3.cc/en13/">YouTube to MP3 Converter</a>. Copy the URL of the song and paste it into the tool. Click on "Convert" and then on "Download". Save the file to your device.</li>
-</ol>
- <h4>Free Music Archive</h4>
-<ol>
-<li>Go to <a href="https://freemusicarchive.org/">Free Music Archive</a> and type "Believe One Piece" in the search box.</li>
-<li>Select the song from the results and click on the download icon.</li>
-<li>Choose the format of the file and click on "Download".</li>
-<li>Save the file to your device and enjoy.</li>
-</ol>
- <h3>The tips to ensure a safe and smooth download</h3>
-<p>To avoid any problems or risks when downloading Lagu Believe One Piece, you should follow these tips:</p>
-<ul>
-<li>Make sure you have a stable and secure internet connection.</li>
-<li>Use a reliable antivirus software and scan your device regularly.</li>
-<li>Check the reviews and ratings of the sites and tools before using them.</li>
-<li>Respect the rights and licenses of the artists and creators.</li>
-<li>Do not share or distribute the files without permission.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Lagu Believe One Piece is a wonderful song that can make you feel happy, excited, and motivated. It is also a great way to show your love and support for One Piece, one of the best anime series ever. If you want to download Lagu Believe One Piece for free and legally, you can use any of the sites that we have recommended in this article. Just follow the steps and tips that we have provided, and you will be able to enjoy Lagu Believe One Piece anytime and anywhere.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Lagu Believe One Piece:</p>
- <h4>Q: Who are Folder5?</h4>
-<p>A: Folder5 are a Japanese girl group that debuted in 1999 as a sub-unit of Folder, a mixed-gender group. They consisted of five members: Akina, Hikari, Miki, Natsumi, and Arisa. They were known for their dance-pop songs that were used as anime theme songs, such as Believe, Ready!, and My Miracle. They disbanded in 2003 after releasing three albums and nine singles.</p>
- <h4>Q: How many opening theme songs does One Piece have?</h4>
-<p>A: As of June 2021, One Piece has 23 opening theme songs, each with different versions and animations. The current opening theme song is Dreamin' On by Da-iCE, which started from episode 935. The longest-running opening theme song is We Are!, which was used for four different arcs with different singers and arrangements.</p> <h4>Q: What are the lyrics of Lagu Believe One Piece in English?</h4>
-<p>A: Here is the English translation of the lyrics of Lagu Believe One Piece, courtesy of <a href="https://www.lyrical-nonsense.com/lyrics/folder5/believe/">Lyrical Nonsense</a>:</p>
-<blockquote>
-<p>I want to go on an adventure with you<br>
-To see the world that no one has ever seen<br>
-I want to believe in myself<br>
-And overcome any obstacle<br>
-We are not alone<br>
-We have each other's backs<br>
-Let's go together<br>
-To the end of the sea<br>
-<br>
-Believe in wonderland<br>
-Believe in your dreams<br>
-Believe in yourself<br>
-And make them come true<br>
-Believe in wonderland<br>
-Believe in your friends<br>
-Believe in your love<br>
-And share it with everyone</p>
-</blockquote>
- <h4>Q: How can I watch One Piece online?</h4>
-<p>A: There are many platforms that offer online streaming of One Piece, both legally and illegally. However, we recommend that you use the legal ones to support the creators and avoid any risks. Some of the legal platforms that you can use are:</p>
-<ul>
-<li><a href="https://www.crunchyroll.com/one-piece">Crunchyroll</a>: A subscription-based service that offers simulcast and dubbed episodes of One Piece, as well as other anime and manga titles.</li>
-<li><a href="https://www.funimation.com/shows/one-piece/">Funimation</a>: A subscription-based service that offers simulcast and dubbed episodes of One Piece, as well as other anime and manga titles.</li>
-<li><a href="https://www.netflix.com/title/70204981">Netflix</a>: A subscription-based service that offers selected episodes of One Piece, as well as other anime and live-action titles.</li>
-<li><a href="https://www.hulu.com/series/one-piece-9e1d1f0c-7f8b-4d9c-9b0c-6f3b6e6c8d0e">Hulu</a>: A subscription-based service that offers selected episodes of One Piece, as well as other anime and live-action titles.</li>
-</ul>
- <h4>Q: How can I learn more about One Piece?</h4>
-<p>A: If you want to learn more about One Piece, you can visit some of the following websites:</p>
-<ul>
-<li><a href="https://onepiece.fandom.com/wiki/One_Piece_Wiki">One Piece Wiki</a>: A fan-made encyclopedia that contains information about the characters, locations, events, and more of One Piece.</li>
-<li><a href="https://onepieceofficial.com/">One Piece Official</a>: The official website of One Piece that provides news, merchandise, games, and more.</li>
-<li><a href="https://www.viz.com/one-piece">VIZ Media</a>: The official publisher of One Piece manga in North America that provides digital and print editions, as well as previews and interviews.</li>
-<li><a href="https://www.reddit.com/r/OnePiece/">One Piece Subreddit</a>: A community of fans that discuss, share, and create content related to One Piece.</li>
-</ul>
- <h4>Q: How can I contact Folder5?</h4>
-<p>A: Unfortunately, Folder5 are no longer active as a group, and they do not have any official social media accounts or websites. However, some of the members have their own solo careers and online presence. You can try to contact them through their individual channels:</p>
-<ul>
-<li><a href="https://twitter.com/Akina_Folder5">Akina's Twitter</a></li>
-<li><a href="https://www.instagram.com/hikari_folder5/">Hikari's Instagram</a></li>
-<li><a href="https://twitter.com/miki_folder5">Miki's Twitter</a></li>
-<li><a href="https://www.instagram.com/natsumi_folder5/">Natsumi's Instagram</a></li>
-<li><a href="https://www.instagram.com/arisa_folder5/">Arisa's Instagram</a></li>
-</ul></p> 401be4b1e0<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Download After Effect Cs4 Full Free !!HOT!!.md b/spaces/tioseFevbu/cartoon-converter/scripts/Download After Effect Cs4 Full Free !!HOT!!.md
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-<h1>Exsim-max Torrent: What You Need to Know Before Downloading</h1>
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- <h3>Follow the best practices for taking practice exams</h3>
- <p>The second step to use Exsim-max effectively and efficiently is to follow the best practices for taking practice exams. Practice exams are a great way to test your knowledge and skills, but they are not a substitute for learning. You need to use them wisely and strategically to get the most out of them. Here are some best practices for taking practice exams:</p>
- - Take practice exams after you have studied the material thoroughly. Don't use them as a shortcut or a crutch. - Take practice exams under realistic conditions. Simulate the exam environment as much as possible, such as setting a timer, turning off distractions, following the instructions, etc. - Take practice exams multiple times. Don't rely on one or two attempts. Repeat them until you master all the questions and topics. - Take practice exams from different sources. Don't limit yourself to one or two products or providers. Use a variety of resources to expose yourself to different perspectives and styles. - Take practice exams with an open mind. Don't assume that you know everything or nothing. Be ready to learn from your mistakes and improve your performance. By following these best practices for taking practice exams, you can maximize your learning and confidence for your exam. <h3>Review your results and learn from your mistakes</h3>
- <p>The third step to use Exsim-max effectively and efficiently is to review your results and learn from your mistakes. Taking practice exams is not enough. You also need to analyze your performance and understand your errors. Exsim-max provides detailed explanations and references for each question, so you can learn the material and not just memorize the answers. You can also view your score report and statistics to see how you did on each topic and objective.</p>
- <p>By reviewing your results and learning from your mistakes, you can: - Identify your strengths and weaknesses - Fill in the gaps in your knowledge - Correct any misconceptions or misunderstandings - Reinforce your learning and retention - Avoid repeating the same mistakes on the real exam Therefore, by using Exsim-max effectively and efficiently, you can enhance your skills and readiness for your exam.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Exsim-max is a product of Boson Software that provides realistic and comprehensive practice exams for various IT certifications. It is popular among torrent users because it is a valuable resource for preparing for IT certification exams, which can be costly and difficult. However, using torrents to download Exsim-max or any other content is illegal, risky, and unethical. It exposes you to malware and viruses, legal issues, data loss, and poor quality and outdated content. On the other hand, using Exsim-max legally and ethically offers many benefits, such as supporting the creators and developers of Exsim-max, getting access to the latest and most accurate content, enjoying the convenience and security of online access, and taking advantage of the no pass, no pay guarantee. To use Exsim-max effectively and efficiently, you need to choose the right Exsim-max product for your certification exam, follow the best practices for taking practice exams, and review your results and learn from your mistakes.</p>
- <p>We hope that this article has helped you understand what Exsim-max is and why it is popular among torrent users, what are the risks of using torrents to download Exsim-max or any other content, what are the benefits of using Exsim-max legally and ethically, and how to use Exsim-max effectively and efficiently. We also hope that you have learned how to write a high-quality article on Exsim-max Torrent, following the instructions provided. If you have any questions or feedback, please feel free to contact us. Thank you for reading.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Exsim-max Torrent:</p>
- <h4>Q: How can I buy Exsim-max legally and ethically?</h4>
-<p>A: You can buy Exsim-max legally and ethically from the official website of Boson Software or from authorized resellers. You can find the complete list of Exsim-max products and resellers on the Boson Software website.</p>
- <h4>Q: How much does Exsim-max cost?</h4>
-<p>A: The price of Exsim-max varies depending on the product and the vendor. You can find the current prices of Exsim-max products on the Boson Software website or on the websites of authorized resellers. You can also look for discounts or promotions that may be available from time to time.</p>
- <h4>Q: How can I get updates for Exsim-max?</h4>
-<p>A: You can get updates for Exsim-max for free for one year from the date of purchase. You can download them anytime from your online account on the Boson Software website.</p>
- <h4>Q: How can I renew my access to Exsim-max?</h4>
-<p>A: You can renew your access to Exsim-max for a discounted price if you need more time to prepare for your exam. You can do so from your online account on the Boson Software website.</p>
- <h4>Q: How can I contact Boson Software?</h4>
-<p>A: You can contact Boson Software by phone, email, chat, or social media. You can find their contact information on their website.</p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/util.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/util.py
deleted file mode 100644
index dd01849d997e5ae9dc9809295e29ceb871b14216..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/util.py
+++ /dev/null
@@ -1,1932 +0,0 @@
-#
-# Copyright (C) 2012-2021 The Python Software Foundation.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-import codecs
-from collections import deque
-import contextlib
-import csv
-from glob import iglob as std_iglob
-import io
-import json
-import logging
-import os
-import py_compile
-import re
-import socket
-try:
-    import ssl
-except ImportError:  # pragma: no cover
-    ssl = None
-import subprocess
-import sys
-import tarfile
-import tempfile
-import textwrap
-
-try:
-    import threading
-except ImportError:  # pragma: no cover
-    import dummy_threading as threading
-import time
-
-from . import DistlibException
-from .compat import (string_types, text_type, shutil, raw_input, StringIO,
-                     cache_from_source, urlopen, urljoin, httplib, xmlrpclib,
-                     splittype, HTTPHandler, BaseConfigurator, valid_ident,
-                     Container, configparser, URLError, ZipFile, fsdecode,
-                     unquote, urlparse)
-
-logger = logging.getLogger(__name__)
-
-#
-# Requirement parsing code as per PEP 508
-#
-
-IDENTIFIER = re.compile(r'^([\w\.-]+)\s*')
-VERSION_IDENTIFIER = re.compile(r'^([\w\.*+-]+)\s*')
-COMPARE_OP = re.compile(r'^(<=?|>=?|={2,3}|[~!]=)\s*')
-MARKER_OP = re.compile(r'^((<=?)|(>=?)|={2,3}|[~!]=|in|not\s+in)\s*')
-OR = re.compile(r'^or\b\s*')
-AND = re.compile(r'^and\b\s*')
-NON_SPACE = re.compile(r'(\S+)\s*')
-STRING_CHUNK = re.compile(r'([\s\w\.{}()*+#:;,/?!~`@$%^&=|<>\[\]-]+)')
-
-
-def parse_marker(marker_string):
-    """
-    Parse a marker string and return a dictionary containing a marker expression.
-
-    The dictionary will contain keys "op", "lhs" and "rhs" for non-terminals in
-    the expression grammar, or strings. A string contained in quotes is to be
-    interpreted as a literal string, and a string not contained in quotes is a
-    variable (such as os_name).
-    """
-    def marker_var(remaining):
-        # either identifier, or literal string
-        m = IDENTIFIER.match(remaining)
-        if m:
-            result = m.groups()[0]
-            remaining = remaining[m.end():]
-        elif not remaining:
-            raise SyntaxError('unexpected end of input')
-        else:
-            q = remaining[0]
-            if q not in '\'"':
-                raise SyntaxError('invalid expression: %s' % remaining)
-            oq = '\'"'.replace(q, '')
-            remaining = remaining[1:]
-            parts = [q]
-            while remaining:
-                # either a string chunk, or oq, or q to terminate
-                if remaining[0] == q:
-                    break
-                elif remaining[0] == oq:
-                    parts.append(oq)
-                    remaining = remaining[1:]
-                else:
-                    m = STRING_CHUNK.match(remaining)
-                    if not m:
-                        raise SyntaxError('error in string literal: %s' % remaining)
-                    parts.append(m.groups()[0])
-                    remaining = remaining[m.end():]
-            else:
-                s = ''.join(parts)
-                raise SyntaxError('unterminated string: %s' % s)
-            parts.append(q)
-            result = ''.join(parts)
-            remaining = remaining[1:].lstrip() # skip past closing quote
-        return result, remaining
-
-    def marker_expr(remaining):
-        if remaining and remaining[0] == '(':
-            result, remaining = marker(remaining[1:].lstrip())
-            if remaining[0] != ')':
-                raise SyntaxError('unterminated parenthesis: %s' % remaining)
-            remaining = remaining[1:].lstrip()
-        else:
-            lhs, remaining = marker_var(remaining)
-            while remaining:
-                m = MARKER_OP.match(remaining)
-                if not m:
-                    break
-                op = m.groups()[0]
-                remaining = remaining[m.end():]
-                rhs, remaining = marker_var(remaining)
-                lhs = {'op': op, 'lhs': lhs, 'rhs': rhs}
-            result = lhs
-        return result, remaining
-
-    def marker_and(remaining):
-        lhs, remaining = marker_expr(remaining)
-        while remaining:
-            m = AND.match(remaining)
-            if not m:
-                break
-            remaining = remaining[m.end():]
-            rhs, remaining = marker_expr(remaining)
-            lhs = {'op': 'and', 'lhs': lhs, 'rhs': rhs}
-        return lhs, remaining
-
-    def marker(remaining):
-        lhs, remaining = marker_and(remaining)
-        while remaining:
-            m = OR.match(remaining)
-            if not m:
-                break
-            remaining = remaining[m.end():]
-            rhs, remaining = marker_and(remaining)
-            lhs = {'op': 'or', 'lhs': lhs, 'rhs': rhs}
-        return lhs, remaining
-
-    return marker(marker_string)
-
-
-def parse_requirement(req):
-    """
-    Parse a requirement passed in as a string. Return a Container
-    whose attributes contain the various parts of the requirement.
-    """
-    remaining = req.strip()
-    if not remaining or remaining.startswith('#'):
-        return None
-    m = IDENTIFIER.match(remaining)
-    if not m:
-        raise SyntaxError('name expected: %s' % remaining)
-    distname = m.groups()[0]
-    remaining = remaining[m.end():]
-    extras = mark_expr = versions = uri = None
-    if remaining and remaining[0] == '[':
-        i = remaining.find(']', 1)
-        if i < 0:
-            raise SyntaxError('unterminated extra: %s' % remaining)
-        s = remaining[1:i]
-        remaining = remaining[i + 1:].lstrip()
-        extras = []
-        while s:
-            m = IDENTIFIER.match(s)
-            if not m:
-                raise SyntaxError('malformed extra: %s' % s)
-            extras.append(m.groups()[0])
-            s = s[m.end():]
-            if not s:
-                break
-            if s[0] != ',':
-                raise SyntaxError('comma expected in extras: %s' % s)
-            s = s[1:].lstrip()
-        if not extras:
-            extras = None
-    if remaining:
-        if remaining[0] == '@':
-            # it's a URI
-            remaining = remaining[1:].lstrip()
-            m = NON_SPACE.match(remaining)
-            if not m:
-                raise SyntaxError('invalid URI: %s' % remaining)
-            uri = m.groups()[0]
-            t = urlparse(uri)
-            # there are issues with Python and URL parsing, so this test
-            # is a bit crude. See bpo-20271, bpo-23505. Python doesn't
-            # always parse invalid URLs correctly - it should raise
-            # exceptions for malformed URLs
-            if not (t.scheme and t.netloc):
-                raise SyntaxError('Invalid URL: %s' % uri)
-            remaining = remaining[m.end():].lstrip()
-        else:
-
-            def get_versions(ver_remaining):
-                """
-                Return a list of operator, version tuples if any are
-                specified, else None.
-                """
-                m = COMPARE_OP.match(ver_remaining)
-                versions = None
-                if m:
-                    versions = []
-                    while True:
-                        op = m.groups()[0]
-                        ver_remaining = ver_remaining[m.end():]
-                        m = VERSION_IDENTIFIER.match(ver_remaining)
-                        if not m:
-                            raise SyntaxError('invalid version: %s' % ver_remaining)
-                        v = m.groups()[0]
-                        versions.append((op, v))
-                        ver_remaining = ver_remaining[m.end():]
-                        if not ver_remaining or ver_remaining[0] != ',':
-                            break
-                        ver_remaining = ver_remaining[1:].lstrip()
-                        # Some packages have a trailing comma which would break things
-                        # See issue #148
-                        if not ver_remaining:
-                            break
-                        m = COMPARE_OP.match(ver_remaining)
-                        if not m:
-                            raise SyntaxError('invalid constraint: %s' % ver_remaining)
-                    if not versions:
-                        versions = None
-                return versions, ver_remaining
-
-            if remaining[0] != '(':
-                versions, remaining = get_versions(remaining)
-            else:
-                i = remaining.find(')', 1)
-                if i < 0:
-                    raise SyntaxError('unterminated parenthesis: %s' % remaining)
-                s = remaining[1:i]
-                remaining = remaining[i + 1:].lstrip()
-                # As a special diversion from PEP 508, allow a version number
-                # a.b.c in parentheses as a synonym for ~= a.b.c (because this
-                # is allowed in earlier PEPs)
-                if COMPARE_OP.match(s):
-                    versions, _ = get_versions(s)
-                else:
-                    m = VERSION_IDENTIFIER.match(s)
-                    if not m:
-                        raise SyntaxError('invalid constraint: %s' % s)
-                    v = m.groups()[0]
-                    s = s[m.end():].lstrip()
-                    if s:
-                        raise SyntaxError('invalid constraint: %s' % s)
-                    versions = [('~=', v)]
-
-    if remaining:
-        if remaining[0] != ';':
-            raise SyntaxError('invalid requirement: %s' % remaining)
-        remaining = remaining[1:].lstrip()
-
-        mark_expr, remaining = parse_marker(remaining)
-
-    if remaining and remaining[0] != '#':
-        raise SyntaxError('unexpected trailing data: %s' % remaining)
-
-    if not versions:
-        rs = distname
-    else:
-        rs = '%s %s' % (distname, ', '.join(['%s %s' % con for con in versions]))
-    return Container(name=distname, extras=extras, constraints=versions,
-                     marker=mark_expr, url=uri, requirement=rs)
-
-
-def get_resources_dests(resources_root, rules):
-    """Find destinations for resources files"""
-
-    def get_rel_path(root, path):
-        # normalizes and returns a lstripped-/-separated path
-        root = root.replace(os.path.sep, '/')
-        path = path.replace(os.path.sep, '/')
-        assert path.startswith(root)
-        return path[len(root):].lstrip('/')
-
-    destinations = {}
-    for base, suffix, dest in rules:
-        prefix = os.path.join(resources_root, base)
-        for abs_base in iglob(prefix):
-            abs_glob = os.path.join(abs_base, suffix)
-            for abs_path in iglob(abs_glob):
-                resource_file = get_rel_path(resources_root, abs_path)
-                if dest is None:  # remove the entry if it was here
-                    destinations.pop(resource_file, None)
-                else:
-                    rel_path = get_rel_path(abs_base, abs_path)
-                    rel_dest = dest.replace(os.path.sep, '/').rstrip('/')
-                    destinations[resource_file] = rel_dest + '/' + rel_path
-    return destinations
-
-
-def in_venv():
-    if hasattr(sys, 'real_prefix'):
-        # virtualenv venvs
-        result = True
-    else:
-        # PEP 405 venvs
-        result = sys.prefix != getattr(sys, 'base_prefix', sys.prefix)
-    return result
-
-
-def get_executable():
-# The __PYVENV_LAUNCHER__ dance is apparently no longer needed, as
-# changes to the stub launcher mean that sys.executable always points
-# to the stub on OS X
-#    if sys.platform == 'darwin' and ('__PYVENV_LAUNCHER__'
-#                                     in os.environ):
-#        result =  os.environ['__PYVENV_LAUNCHER__']
-#    else:
-#        result = sys.executable
-#    return result
-    # Avoid normcasing: see issue #143
-    # result = os.path.normcase(sys.executable)
-    result = sys.executable
-    if not isinstance(result, text_type):
-        result = fsdecode(result)
-    return result
-
-
-def proceed(prompt, allowed_chars, error_prompt=None, default=None):
-    p = prompt
-    while True:
-        s = raw_input(p)
-        p = prompt
-        if not s and default:
-            s = default
-        if s:
-            c = s[0].lower()
-            if c in allowed_chars:
-                break
-            if error_prompt:
-                p = '%c: %s\n%s' % (c, error_prompt, prompt)
-    return c
-
-
-def extract_by_key(d, keys):
-    if isinstance(keys, string_types):
-        keys = keys.split()
-    result = {}
-    for key in keys:
-        if key in d:
-            result[key] = d[key]
-    return result
-
-def read_exports(stream):
-    if sys.version_info[0] >= 3:
-        # needs to be a text stream
-        stream = codecs.getreader('utf-8')(stream)
-    # Try to load as JSON, falling back on legacy format
-    data = stream.read()
-    stream = StringIO(data)
-    try:
-        jdata = json.load(stream)
-        result = jdata['extensions']['python.exports']['exports']
-        for group, entries in result.items():
-            for k, v in entries.items():
-                s = '%s = %s' % (k, v)
-                entry = get_export_entry(s)
-                assert entry is not None
-                entries[k] = entry
-        return result
-    except Exception:
-        stream.seek(0, 0)
-
-    def read_stream(cp, stream):
-        if hasattr(cp, 'read_file'):
-            cp.read_file(stream)
-        else:
-            cp.readfp(stream)
-
-    cp = configparser.ConfigParser()
-    try:
-        read_stream(cp, stream)
-    except configparser.MissingSectionHeaderError:
-        stream.close()
-        data = textwrap.dedent(data)
-        stream = StringIO(data)
-        read_stream(cp, stream)
-
-    result = {}
-    for key in cp.sections():
-        result[key] = entries = {}
-        for name, value in cp.items(key):
-            s = '%s = %s' % (name, value)
-            entry = get_export_entry(s)
-            assert entry is not None
-            #entry.dist = self
-            entries[name] = entry
-    return result
-
-
-def write_exports(exports, stream):
-    if sys.version_info[0] >= 3:
-        # needs to be a text stream
-        stream = codecs.getwriter('utf-8')(stream)
-    cp = configparser.ConfigParser()
-    for k, v in exports.items():
-        # TODO check k, v for valid values
-        cp.add_section(k)
-        for entry in v.values():
-            if entry.suffix is None:
-                s = entry.prefix
-            else:
-                s = '%s:%s' % (entry.prefix, entry.suffix)
-            if entry.flags:
-                s = '%s [%s]' % (s, ', '.join(entry.flags))
-            cp.set(k, entry.name, s)
-    cp.write(stream)
-
-
-@contextlib.contextmanager
-def tempdir():
-    td = tempfile.mkdtemp()
-    try:
-        yield td
-    finally:
-        shutil.rmtree(td)
-
-@contextlib.contextmanager
-def chdir(d):
-    cwd = os.getcwd()
-    try:
-        os.chdir(d)
-        yield
-    finally:
-        os.chdir(cwd)
-
-
-@contextlib.contextmanager
-def socket_timeout(seconds=15):
-    cto = socket.getdefaulttimeout()
-    try:
-        socket.setdefaulttimeout(seconds)
-        yield
-    finally:
-        socket.setdefaulttimeout(cto)
-
-
-class cached_property(object):
-    def __init__(self, func):
-        self.func = func
-        #for attr in ('__name__', '__module__', '__doc__'):
-        #    setattr(self, attr, getattr(func, attr, None))
-
-    def __get__(self, obj, cls=None):
-        if obj is None:
-            return self
-        value = self.func(obj)
-        object.__setattr__(obj, self.func.__name__, value)
-        #obj.__dict__[self.func.__name__] = value = self.func(obj)
-        return value
-
-def convert_path(pathname):
-    """Return 'pathname' as a name that will work on the native filesystem.
-
-    The path is split on '/' and put back together again using the current
-    directory separator.  Needed because filenames in the setup script are
-    always supplied in Unix style, and have to be converted to the local
-    convention before we can actually use them in the filesystem.  Raises
-    ValueError on non-Unix-ish systems if 'pathname' either starts or
-    ends with a slash.
-    """
-    if os.sep == '/':
-        return pathname
-    if not pathname:
-        return pathname
-    if pathname[0] == '/':
-        raise ValueError("path '%s' cannot be absolute" % pathname)
-    if pathname[-1] == '/':
-        raise ValueError("path '%s' cannot end with '/'" % pathname)
-
-    paths = pathname.split('/')
-    while os.curdir in paths:
-        paths.remove(os.curdir)
-    if not paths:
-        return os.curdir
-    return os.path.join(*paths)
-
-
-class FileOperator(object):
-    def __init__(self, dry_run=False):
-        self.dry_run = dry_run
-        self.ensured = set()
-        self._init_record()
-
-    def _init_record(self):
-        self.record = False
-        self.files_written = set()
-        self.dirs_created = set()
-
-    def record_as_written(self, path):
-        if self.record:
-            self.files_written.add(path)
-
-    def newer(self, source, target):
-        """Tell if the target is newer than the source.
-
-        Returns true if 'source' exists and is more recently modified than
-        'target', or if 'source' exists and 'target' doesn't.
-
-        Returns false if both exist and 'target' is the same age or younger
-        than 'source'. Raise PackagingFileError if 'source' does not exist.
-
-        Note that this test is not very accurate: files created in the same
-        second will have the same "age".
-        """
-        if not os.path.exists(source):
-            raise DistlibException("file '%r' does not exist" %
-                                   os.path.abspath(source))
-        if not os.path.exists(target):
-            return True
-
-        return os.stat(source).st_mtime > os.stat(target).st_mtime
-
-    def copy_file(self, infile, outfile, check=True):
-        """Copy a file respecting dry-run and force flags.
-        """
-        self.ensure_dir(os.path.dirname(outfile))
-        logger.info('Copying %s to %s', infile, outfile)
-        if not self.dry_run:
-            msg = None
-            if check:
-                if os.path.islink(outfile):
-                    msg = '%s is a symlink' % outfile
-                elif os.path.exists(outfile) and not os.path.isfile(outfile):
-                    msg = '%s is a non-regular file' % outfile
-            if msg:
-                raise ValueError(msg + ' which would be overwritten')
-            shutil.copyfile(infile, outfile)
-        self.record_as_written(outfile)
-
-    def copy_stream(self, instream, outfile, encoding=None):
-        assert not os.path.isdir(outfile)
-        self.ensure_dir(os.path.dirname(outfile))
-        logger.info('Copying stream %s to %s', instream, outfile)
-        if not self.dry_run:
-            if encoding is None:
-                outstream = open(outfile, 'wb')
-            else:
-                outstream = codecs.open(outfile, 'w', encoding=encoding)
-            try:
-                shutil.copyfileobj(instream, outstream)
-            finally:
-                outstream.close()
-        self.record_as_written(outfile)
-
-    def write_binary_file(self, path, data):
-        self.ensure_dir(os.path.dirname(path))
-        if not self.dry_run:
-            if os.path.exists(path):
-                os.remove(path)
-            with open(path, 'wb') as f:
-                f.write(data)
-        self.record_as_written(path)
-
-    def write_text_file(self, path, data, encoding):
-        self.write_binary_file(path, data.encode(encoding))
-
-    def set_mode(self, bits, mask, files):
-        if os.name == 'posix' or (os.name == 'java' and os._name == 'posix'):
-            # Set the executable bits (owner, group, and world) on
-            # all the files specified.
-            for f in files:
-                if self.dry_run:
-                    logger.info("changing mode of %s", f)
-                else:
-                    mode = (os.stat(f).st_mode | bits) & mask
-                    logger.info("changing mode of %s to %o", f, mode)
-                    os.chmod(f, mode)
-
-    set_executable_mode = lambda s, f: s.set_mode(0o555, 0o7777, f)
-
-    def ensure_dir(self, path):
-        path = os.path.abspath(path)
-        if path not in self.ensured and not os.path.exists(path):
-            self.ensured.add(path)
-            d, f = os.path.split(path)
-            self.ensure_dir(d)
-            logger.info('Creating %s' % path)
-            if not self.dry_run:
-                os.mkdir(path)
-            if self.record:
-                self.dirs_created.add(path)
-
-    def byte_compile(self, path, optimize=False, force=False, prefix=None, hashed_invalidation=False):
-        dpath = cache_from_source(path, not optimize)
-        logger.info('Byte-compiling %s to %s', path, dpath)
-        if not self.dry_run:
-            if force or self.newer(path, dpath):
-                if not prefix:
-                    diagpath = None
-                else:
-                    assert path.startswith(prefix)
-                    diagpath = path[len(prefix):]
-            compile_kwargs = {}
-            if hashed_invalidation and hasattr(py_compile, 'PycInvalidationMode'):
-                compile_kwargs['invalidation_mode'] = py_compile.PycInvalidationMode.CHECKED_HASH
-            py_compile.compile(path, dpath, diagpath, True, **compile_kwargs)     # raise error
-        self.record_as_written(dpath)
-        return dpath
-
-    def ensure_removed(self, path):
-        if os.path.exists(path):
-            if os.path.isdir(path) and not os.path.islink(path):
-                logger.debug('Removing directory tree at %s', path)
-                if not self.dry_run:
-                    shutil.rmtree(path)
-                if self.record:
-                    if path in self.dirs_created:
-                        self.dirs_created.remove(path)
-            else:
-                if os.path.islink(path):
-                    s = 'link'
-                else:
-                    s = 'file'
-                logger.debug('Removing %s %s', s, path)
-                if not self.dry_run:
-                    os.remove(path)
-                if self.record:
-                    if path in self.files_written:
-                        self.files_written.remove(path)
-
-    def is_writable(self, path):
-        result = False
-        while not result:
-            if os.path.exists(path):
-                result = os.access(path, os.W_OK)
-                break
-            parent = os.path.dirname(path)
-            if parent == path:
-                break
-            path = parent
-        return result
-
-    def commit(self):
-        """
-        Commit recorded changes, turn off recording, return
-        changes.
-        """
-        assert self.record
-        result = self.files_written, self.dirs_created
-        self._init_record()
-        return result
-
-    def rollback(self):
-        if not self.dry_run:
-            for f in list(self.files_written):
-                if os.path.exists(f):
-                    os.remove(f)
-            # dirs should all be empty now, except perhaps for
-            # __pycache__ subdirs
-            # reverse so that subdirs appear before their parents
-            dirs = sorted(self.dirs_created, reverse=True)
-            for d in dirs:
-                flist = os.listdir(d)
-                if flist:
-                    assert flist == ['__pycache__']
-                    sd = os.path.join(d, flist[0])
-                    os.rmdir(sd)
-                os.rmdir(d)     # should fail if non-empty
-        self._init_record()
-
-def resolve(module_name, dotted_path):
-    if module_name in sys.modules:
-        mod = sys.modules[module_name]
-    else:
-        mod = __import__(module_name)
-    if dotted_path is None:
-        result = mod
-    else:
-        parts = dotted_path.split('.')
-        result = getattr(mod, parts.pop(0))
-        for p in parts:
-            result = getattr(result, p)
-    return result
-
-
-class ExportEntry(object):
-    def __init__(self, name, prefix, suffix, flags):
-        self.name = name
-        self.prefix = prefix
-        self.suffix = suffix
-        self.flags = flags
-
-    @cached_property
-    def value(self):
-        return resolve(self.prefix, self.suffix)
-
-    def __repr__(self):  # pragma: no cover
-        return '<ExportEntry %s = %s:%s %s>' % (self.name, self.prefix,
-                                                self.suffix, self.flags)
-
-    def __eq__(self, other):
-        if not isinstance(other, ExportEntry):
-            result = False
-        else:
-            result = (self.name == other.name and
-                      self.prefix == other.prefix and
-                      self.suffix == other.suffix and
-                      self.flags == other.flags)
-        return result
-
-    __hash__ = object.__hash__
-
-
-ENTRY_RE = re.compile(r'''(?P<name>(\w|[-.+])+)
-                      \s*=\s*(?P<callable>(\w+)([:\.]\w+)*)
-                      \s*(\[\s*(?P<flags>[\w-]+(=\w+)?(,\s*\w+(=\w+)?)*)\s*\])?
-                      ''', re.VERBOSE)
-
-def get_export_entry(specification):
-    m = ENTRY_RE.search(specification)
-    if not m:
-        result = None
-        if '[' in specification or ']' in specification:
-            raise DistlibException("Invalid specification "
-                                   "'%s'" % specification)
-    else:
-        d = m.groupdict()
-        name = d['name']
-        path = d['callable']
-        colons = path.count(':')
-        if colons == 0:
-            prefix, suffix = path, None
-        else:
-            if colons != 1:
-                raise DistlibException("Invalid specification "
-                                       "'%s'" % specification)
-            prefix, suffix = path.split(':')
-        flags = d['flags']
-        if flags is None:
-            if '[' in specification or ']' in specification:
-                raise DistlibException("Invalid specification "
-                                       "'%s'" % specification)
-            flags = []
-        else:
-            flags = [f.strip() for f in flags.split(',')]
-        result = ExportEntry(name, prefix, suffix, flags)
-    return result
-
-
-def get_cache_base(suffix=None):
-    """
-    Return the default base location for distlib caches. If the directory does
-    not exist, it is created. Use the suffix provided for the base directory,
-    and default to '.distlib' if it isn't provided.
-
-    On Windows, if LOCALAPPDATA is defined in the environment, then it is
-    assumed to be a directory, and will be the parent directory of the result.
-    On POSIX, and on Windows if LOCALAPPDATA is not defined, the user's home
-    directory - using os.expanduser('~') - will be the parent directory of
-    the result.
-
-    The result is just the directory '.distlib' in the parent directory as
-    determined above, or with the name specified with ``suffix``.
-    """
-    if suffix is None:
-        suffix = '.distlib'
-    if os.name == 'nt' and 'LOCALAPPDATA' in os.environ:
-        result = os.path.expandvars('$localappdata')
-    else:
-        # Assume posix, or old Windows
-        result = os.path.expanduser('~')
-    # we use 'isdir' instead of 'exists', because we want to
-    # fail if there's a file with that name
-    if os.path.isdir(result):
-        usable = os.access(result, os.W_OK)
-        if not usable:
-            logger.warning('Directory exists but is not writable: %s', result)
-    else:
-        try:
-            os.makedirs(result)
-            usable = True
-        except OSError:
-            logger.warning('Unable to create %s', result, exc_info=True)
-            usable = False
-    if not usable:
-        result = tempfile.mkdtemp()
-        logger.warning('Default location unusable, using %s', result)
-    return os.path.join(result, suffix)
-
-
-def path_to_cache_dir(path):
-    """
-    Convert an absolute path to a directory name for use in a cache.
-
-    The algorithm used is:
-
-    #. On Windows, any ``':'`` in the drive is replaced with ``'---'``.
-    #. Any occurrence of ``os.sep`` is replaced with ``'--'``.
-    #. ``'.cache'`` is appended.
-    """
-    d, p = os.path.splitdrive(os.path.abspath(path))
-    if d:
-        d = d.replace(':', '---')
-    p = p.replace(os.sep, '--')
-    return d + p + '.cache'
-
-
-def ensure_slash(s):
-    if not s.endswith('/'):
-        return s + '/'
-    return s
-
-
-def parse_credentials(netloc):
-    username = password = None
-    if '@' in netloc:
-        prefix, netloc = netloc.rsplit('@', 1)
-        if ':' not in prefix:
-            username = prefix
-        else:
-            username, password = prefix.split(':', 1)
-    if username:
-        username = unquote(username)
-    if password:
-        password = unquote(password)
-    return username, password, netloc
-
-
-def get_process_umask():
-    result = os.umask(0o22)
-    os.umask(result)
-    return result
-
-def is_string_sequence(seq):
-    result = True
-    i = None
-    for i, s in enumerate(seq):
-        if not isinstance(s, string_types):
-            result = False
-            break
-    assert i is not None
-    return result
-
-PROJECT_NAME_AND_VERSION = re.compile('([a-z0-9_]+([.-][a-z_][a-z0-9_]*)*)-'
-                                      '([a-z0-9_.+-]+)', re.I)
-PYTHON_VERSION = re.compile(r'-py(\d\.?\d?)')
-
-
-def split_filename(filename, project_name=None):
-    """
-    Extract name, version, python version from a filename (no extension)
-
-    Return name, version, pyver or None
-    """
-    result = None
-    pyver = None
-    filename = unquote(filename).replace(' ', '-')
-    m = PYTHON_VERSION.search(filename)
-    if m:
-        pyver = m.group(1)
-        filename = filename[:m.start()]
-    if project_name and len(filename) > len(project_name) + 1:
-        m = re.match(re.escape(project_name) + r'\b', filename)
-        if m:
-            n = m.end()
-            result = filename[:n], filename[n + 1:], pyver
-    if result is None:
-        m = PROJECT_NAME_AND_VERSION.match(filename)
-        if m:
-            result = m.group(1), m.group(3), pyver
-    return result
-
-# Allow spaces in name because of legacy dists like "Twisted Core"
-NAME_VERSION_RE = re.compile(r'(?P<name>[\w .-]+)\s*'
-                             r'\(\s*(?P<ver>[^\s)]+)\)$')
-
-def parse_name_and_version(p):
-    """
-    A utility method used to get name and version from a string.
-
-    From e.g. a Provides-Dist value.
-
-    :param p: A value in a form 'foo (1.0)'
-    :return: The name and version as a tuple.
-    """
-    m = NAME_VERSION_RE.match(p)
-    if not m:
-        raise DistlibException('Ill-formed name/version string: \'%s\'' % p)
-    d = m.groupdict()
-    return d['name'].strip().lower(), d['ver']
-
-def get_extras(requested, available):
-    result = set()
-    requested = set(requested or [])
-    available = set(available or [])
-    if '*' in requested:
-        requested.remove('*')
-        result |= available
-    for r in requested:
-        if r == '-':
-            result.add(r)
-        elif r.startswith('-'):
-            unwanted = r[1:]
-            if unwanted not in available:
-                logger.warning('undeclared extra: %s' % unwanted)
-            if unwanted in result:
-                result.remove(unwanted)
-        else:
-            if r not in available:
-                logger.warning('undeclared extra: %s' % r)
-            result.add(r)
-    return result
-#
-# Extended metadata functionality
-#
-
-def _get_external_data(url):
-    result = {}
-    try:
-        # urlopen might fail if it runs into redirections,
-        # because of Python issue #13696. Fixed in locators
-        # using a custom redirect handler.
-        resp = urlopen(url)
-        headers = resp.info()
-        ct = headers.get('Content-Type')
-        if not ct.startswith('application/json'):
-            logger.debug('Unexpected response for JSON request: %s', ct)
-        else:
-            reader = codecs.getreader('utf-8')(resp)
-            #data = reader.read().decode('utf-8')
-            #result = json.loads(data)
-            result = json.load(reader)
-    except Exception as e:
-        logger.exception('Failed to get external data for %s: %s', url, e)
-    return result
-
-_external_data_base_url = 'https://www.red-dove.com/pypi/projects/'
-
-def get_project_data(name):
-    url = '%s/%s/project.json' % (name[0].upper(), name)
-    url = urljoin(_external_data_base_url, url)
-    result = _get_external_data(url)
-    return result
-
-def get_package_data(name, version):
-    url = '%s/%s/package-%s.json' % (name[0].upper(), name, version)
-    url = urljoin(_external_data_base_url, url)
-    return _get_external_data(url)
-
-
-class Cache(object):
-    """
-    A class implementing a cache for resources that need to live in the file system
-    e.g. shared libraries. This class was moved from resources to here because it
-    could be used by other modules, e.g. the wheel module.
-    """
-
-    def __init__(self, base):
-        """
-        Initialise an instance.
-
-        :param base: The base directory where the cache should be located.
-        """
-        # we use 'isdir' instead of 'exists', because we want to
-        # fail if there's a file with that name
-        if not os.path.isdir(base):  # pragma: no cover
-            os.makedirs(base)
-        if (os.stat(base).st_mode & 0o77) != 0:
-            logger.warning('Directory \'%s\' is not private', base)
-        self.base = os.path.abspath(os.path.normpath(base))
-
-    def prefix_to_dir(self, prefix):
-        """
-        Converts a resource prefix to a directory name in the cache.
-        """
-        return path_to_cache_dir(prefix)
-
-    def clear(self):
-        """
-        Clear the cache.
-        """
-        not_removed = []
-        for fn in os.listdir(self.base):
-            fn = os.path.join(self.base, fn)
-            try:
-                if os.path.islink(fn) or os.path.isfile(fn):
-                    os.remove(fn)
-                elif os.path.isdir(fn):
-                    shutil.rmtree(fn)
-            except Exception:
-                not_removed.append(fn)
-        return not_removed
-
-
-class EventMixin(object):
-    """
-    A very simple publish/subscribe system.
-    """
-    def __init__(self):
-        self._subscribers = {}
-
-    def add(self, event, subscriber, append=True):
-        """
-        Add a subscriber for an event.
-
-        :param event: The name of an event.
-        :param subscriber: The subscriber to be added (and called when the
-                           event is published).
-        :param append: Whether to append or prepend the subscriber to an
-                       existing subscriber list for the event.
-        """
-        subs = self._subscribers
-        if event not in subs:
-            subs[event] = deque([subscriber])
-        else:
-            sq = subs[event]
-            if append:
-                sq.append(subscriber)
-            else:
-                sq.appendleft(subscriber)
-
-    def remove(self, event, subscriber):
-        """
-        Remove a subscriber for an event.
-
-        :param event: The name of an event.
-        :param subscriber: The subscriber to be removed.
-        """
-        subs = self._subscribers
-        if event not in subs:
-            raise ValueError('No subscribers: %r' % event)
-        subs[event].remove(subscriber)
-
-    def get_subscribers(self, event):
-        """
-        Return an iterator for the subscribers for an event.
-        :param event: The event to return subscribers for.
-        """
-        return iter(self._subscribers.get(event, ()))
-
-    def publish(self, event, *args, **kwargs):
-        """
-        Publish a event and return a list of values returned by its
-        subscribers.
-
-        :param event: The event to publish.
-        :param args: The positional arguments to pass to the event's
-                     subscribers.
-        :param kwargs: The keyword arguments to pass to the event's
-                       subscribers.
-        """
-        result = []
-        for subscriber in self.get_subscribers(event):
-            try:
-                value = subscriber(event, *args, **kwargs)
-            except Exception:
-                logger.exception('Exception during event publication')
-                value = None
-            result.append(value)
-        logger.debug('publish %s: args = %s, kwargs = %s, result = %s',
-                     event, args, kwargs, result)
-        return result
-
-#
-# Simple sequencing
-#
-class Sequencer(object):
-    def __init__(self):
-        self._preds = {}
-        self._succs = {}
-        self._nodes = set()     # nodes with no preds/succs
-
-    def add_node(self, node):
-        self._nodes.add(node)
-
-    def remove_node(self, node, edges=False):
-        if node in self._nodes:
-            self._nodes.remove(node)
-        if edges:
-            for p in set(self._preds.get(node, ())):
-                self.remove(p, node)
-            for s in set(self._succs.get(node, ())):
-                self.remove(node, s)
-            # Remove empties
-            for k, v in list(self._preds.items()):
-                if not v:
-                    del self._preds[k]
-            for k, v in list(self._succs.items()):
-                if not v:
-                    del self._succs[k]
-
-    def add(self, pred, succ):
-        assert pred != succ
-        self._preds.setdefault(succ, set()).add(pred)
-        self._succs.setdefault(pred, set()).add(succ)
-
-    def remove(self, pred, succ):
-        assert pred != succ
-        try:
-            preds = self._preds[succ]
-            succs = self._succs[pred]
-        except KeyError:  # pragma: no cover
-            raise ValueError('%r not a successor of anything' % succ)
-        try:
-            preds.remove(pred)
-            succs.remove(succ)
-        except KeyError:  # pragma: no cover
-            raise ValueError('%r not a successor of %r' % (succ, pred))
-
-    def is_step(self, step):
-        return (step in self._preds or step in self._succs or
-                step in self._nodes)
-
-    def get_steps(self, final):
-        if not self.is_step(final):
-            raise ValueError('Unknown: %r' % final)
-        result = []
-        todo = []
-        seen = set()
-        todo.append(final)
-        while todo:
-            step = todo.pop(0)
-            if step in seen:
-                # if a step was already seen,
-                # move it to the end (so it will appear earlier
-                # when reversed on return) ... but not for the
-                # final step, as that would be confusing for
-                # users
-                if step != final:
-                    result.remove(step)
-                    result.append(step)
-            else:
-                seen.add(step)
-                result.append(step)
-                preds = self._preds.get(step, ())
-                todo.extend(preds)
-        return reversed(result)
-
-    @property
-    def strong_connections(self):
-        #http://en.wikipedia.org/wiki/Tarjan%27s_strongly_connected_components_algorithm
-        index_counter = [0]
-        stack = []
-        lowlinks = {}
-        index = {}
-        result = []
-
-        graph = self._succs
-
-        def strongconnect(node):
-            # set the depth index for this node to the smallest unused index
-            index[node] = index_counter[0]
-            lowlinks[node] = index_counter[0]
-            index_counter[0] += 1
-            stack.append(node)
-
-            # Consider successors
-            try:
-                successors = graph[node]
-            except Exception:
-                successors = []
-            for successor in successors:
-                if successor not in lowlinks:
-                    # Successor has not yet been visited
-                    strongconnect(successor)
-                    lowlinks[node] = min(lowlinks[node],lowlinks[successor])
-                elif successor in stack:
-                    # the successor is in the stack and hence in the current
-                    # strongly connected component (SCC)
-                    lowlinks[node] = min(lowlinks[node],index[successor])
-
-            # If `node` is a root node, pop the stack and generate an SCC
-            if lowlinks[node] == index[node]:
-                connected_component = []
-
-                while True:
-                    successor = stack.pop()
-                    connected_component.append(successor)
-                    if successor == node: break
-                component = tuple(connected_component)
-                # storing the result
-                result.append(component)
-
-        for node in graph:
-            if node not in lowlinks:
-                strongconnect(node)
-
-        return result
-
-    @property
-    def dot(self):
-        result = ['digraph G {']
-        for succ in self._preds:
-            preds = self._preds[succ]
-            for pred in preds:
-                result.append('  %s -> %s;' % (pred, succ))
-        for node in self._nodes:
-            result.append('  %s;' % node)
-        result.append('}')
-        return '\n'.join(result)
-
-#
-# Unarchiving functionality for zip, tar, tgz, tbz, whl
-#
-
-ARCHIVE_EXTENSIONS = ('.tar.gz', '.tar.bz2', '.tar', '.zip',
-                      '.tgz', '.tbz', '.whl')
-
-def unarchive(archive_filename, dest_dir, format=None, check=True):
-
-    def check_path(path):
-        if not isinstance(path, text_type):
-            path = path.decode('utf-8')
-        p = os.path.abspath(os.path.join(dest_dir, path))
-        if not p.startswith(dest_dir) or p[plen] != os.sep:
-            raise ValueError('path outside destination: %r' % p)
-
-    dest_dir = os.path.abspath(dest_dir)
-    plen = len(dest_dir)
-    archive = None
-    if format is None:
-        if archive_filename.endswith(('.zip', '.whl')):
-            format = 'zip'
-        elif archive_filename.endswith(('.tar.gz', '.tgz')):
-            format = 'tgz'
-            mode = 'r:gz'
-        elif archive_filename.endswith(('.tar.bz2', '.tbz')):
-            format = 'tbz'
-            mode = 'r:bz2'
-        elif archive_filename.endswith('.tar'):
-            format = 'tar'
-            mode = 'r'
-        else:  # pragma: no cover
-            raise ValueError('Unknown format for %r' % archive_filename)
-    try:
-        if format == 'zip':
-            archive = ZipFile(archive_filename, 'r')
-            if check:
-                names = archive.namelist()
-                for name in names:
-                    check_path(name)
-        else:
-            archive = tarfile.open(archive_filename, mode)
-            if check:
-                names = archive.getnames()
-                for name in names:
-                    check_path(name)
-        if format != 'zip' and sys.version_info[0] < 3:
-            # See Python issue 17153. If the dest path contains Unicode,
-            # tarfile extraction fails on Python 2.x if a member path name
-            # contains non-ASCII characters - it leads to an implicit
-            # bytes -> unicode conversion using ASCII to decode.
-            for tarinfo in archive.getmembers():
-                if not isinstance(tarinfo.name, text_type):
-                    tarinfo.name = tarinfo.name.decode('utf-8')
-        archive.extractall(dest_dir)
-
-    finally:
-        if archive:
-            archive.close()
-
-
-def zip_dir(directory):
-    """zip a directory tree into a BytesIO object"""
-    result = io.BytesIO()
-    dlen = len(directory)
-    with ZipFile(result, "w") as zf:
-        for root, dirs, files in os.walk(directory):
-            for name in files:
-                full = os.path.join(root, name)
-                rel = root[dlen:]
-                dest = os.path.join(rel, name)
-                zf.write(full, dest)
-    return result
-
-#
-# Simple progress bar
-#
-
-UNITS = ('', 'K', 'M', 'G','T','P')
-
-
-class Progress(object):
-    unknown = 'UNKNOWN'
-
-    def __init__(self, minval=0, maxval=100):
-        assert maxval is None or maxval >= minval
-        self.min = self.cur = minval
-        self.max = maxval
-        self.started = None
-        self.elapsed = 0
-        self.done = False
-
-    def update(self, curval):
-        assert self.min <= curval
-        assert self.max is None or curval <= self.max
-        self.cur = curval
-        now = time.time()
-        if self.started is None:
-            self.started = now
-        else:
-            self.elapsed = now - self.started
-
-    def increment(self, incr):
-        assert incr >= 0
-        self.update(self.cur + incr)
-
-    def start(self):
-        self.update(self.min)
-        return self
-
-    def stop(self):
-        if self.max is not None:
-            self.update(self.max)
-        self.done = True
-
-    @property
-    def maximum(self):
-        return self.unknown if self.max is None else self.max
-
-    @property
-    def percentage(self):
-        if self.done:
-            result = '100 %'
-        elif self.max is None:
-            result = ' ?? %'
-        else:
-            v = 100.0 * (self.cur - self.min) / (self.max - self.min)
-            result = '%3d %%' % v
-        return result
-
-    def format_duration(self, duration):
-        if (duration <= 0) and self.max is None or self.cur == self.min:
-            result = '??:??:??'
-        #elif duration < 1:
-        #    result = '--:--:--'
-        else:
-            result = time.strftime('%H:%M:%S', time.gmtime(duration))
-        return result
-
-    @property
-    def ETA(self):
-        if self.done:
-            prefix = 'Done'
-            t = self.elapsed
-            #import pdb; pdb.set_trace()
-        else:
-            prefix = 'ETA '
-            if self.max is None:
-                t = -1
-            elif self.elapsed == 0 or (self.cur == self.min):
-                t = 0
-            else:
-                #import pdb; pdb.set_trace()
-                t = float(self.max - self.min)
-                t /= self.cur - self.min
-                t = (t - 1) * self.elapsed
-        return '%s: %s' % (prefix, self.format_duration(t))
-
-    @property
-    def speed(self):
-        if self.elapsed == 0:
-            result = 0.0
-        else:
-            result = (self.cur - self.min) / self.elapsed
-        for unit in UNITS:
-            if result < 1000:
-                break
-            result /= 1000.0
-        return '%d %sB/s' % (result, unit)
-
-#
-# Glob functionality
-#
-
-RICH_GLOB = re.compile(r'\{([^}]*)\}')
-_CHECK_RECURSIVE_GLOB = re.compile(r'[^/\\,{]\*\*|\*\*[^/\\,}]')
-_CHECK_MISMATCH_SET = re.compile(r'^[^{]*\}|\{[^}]*$')
-
-
-def iglob(path_glob):
-    """Extended globbing function that supports ** and {opt1,opt2,opt3}."""
-    if _CHECK_RECURSIVE_GLOB.search(path_glob):
-        msg = """invalid glob %r: recursive glob "**" must be used alone"""
-        raise ValueError(msg % path_glob)
-    if _CHECK_MISMATCH_SET.search(path_glob):
-        msg = """invalid glob %r: mismatching set marker '{' or '}'"""
-        raise ValueError(msg % path_glob)
-    return _iglob(path_glob)
-
-
-def _iglob(path_glob):
-    rich_path_glob = RICH_GLOB.split(path_glob, 1)
-    if len(rich_path_glob) > 1:
-        assert len(rich_path_glob) == 3, rich_path_glob
-        prefix, set, suffix = rich_path_glob
-        for item in set.split(','):
-            for path in _iglob(''.join((prefix, item, suffix))):
-                yield path
-    else:
-        if '**' not in path_glob:
-            for item in std_iglob(path_glob):
-                yield item
-        else:
-            prefix, radical = path_glob.split('**', 1)
-            if prefix == '':
-                prefix = '.'
-            if radical == '':
-                radical = '*'
-            else:
-                # we support both
-                radical = radical.lstrip('/')
-                radical = radical.lstrip('\\')
-            for path, dir, files in os.walk(prefix):
-                path = os.path.normpath(path)
-                for fn in _iglob(os.path.join(path, radical)):
-                    yield fn
-
-if ssl:
-    from .compat import (HTTPSHandler as BaseHTTPSHandler, match_hostname,
-                         CertificateError)
-
-
-#
-# HTTPSConnection which verifies certificates/matches domains
-#
-
-    class HTTPSConnection(httplib.HTTPSConnection):
-        ca_certs = None # set this to the path to the certs file (.pem)
-        check_domain = True # only used if ca_certs is not None
-
-        # noinspection PyPropertyAccess
-        def connect(self):
-            sock = socket.create_connection((self.host, self.port), self.timeout)
-            if getattr(self, '_tunnel_host', False):
-                self.sock = sock
-                self._tunnel()
-
-            context = ssl.SSLContext(ssl.PROTOCOL_SSLv23)
-            if hasattr(ssl, 'OP_NO_SSLv2'):
-                context.options |= ssl.OP_NO_SSLv2
-            if self.cert_file:
-                context.load_cert_chain(self.cert_file, self.key_file)
-            kwargs = {}
-            if self.ca_certs:
-                context.verify_mode = ssl.CERT_REQUIRED
-                context.load_verify_locations(cafile=self.ca_certs)
-                if getattr(ssl, 'HAS_SNI', False):
-                    kwargs['server_hostname'] = self.host
-
-            self.sock = context.wrap_socket(sock, **kwargs)
-            if self.ca_certs and self.check_domain:
-                try:
-                    match_hostname(self.sock.getpeercert(), self.host)
-                    logger.debug('Host verified: %s', self.host)
-                except CertificateError:  # pragma: no cover
-                    self.sock.shutdown(socket.SHUT_RDWR)
-                    self.sock.close()
-                    raise
-
-    class HTTPSHandler(BaseHTTPSHandler):
-        def __init__(self, ca_certs, check_domain=True):
-            BaseHTTPSHandler.__init__(self)
-            self.ca_certs = ca_certs
-            self.check_domain = check_domain
-
-        def _conn_maker(self, *args, **kwargs):
-            """
-            This is called to create a connection instance. Normally you'd
-            pass a connection class to do_open, but it doesn't actually check for
-            a class, and just expects a callable. As long as we behave just as a
-            constructor would have, we should be OK. If it ever changes so that
-            we *must* pass a class, we'll create an UnsafeHTTPSConnection class
-            which just sets check_domain to False in the class definition, and
-            choose which one to pass to do_open.
-            """
-            result = HTTPSConnection(*args, **kwargs)
-            if self.ca_certs:
-                result.ca_certs = self.ca_certs
-                result.check_domain = self.check_domain
-            return result
-
-        def https_open(self, req):
-            try:
-                return self.do_open(self._conn_maker, req)
-            except URLError as e:
-                if 'certificate verify failed' in str(e.reason):
-                    raise CertificateError('Unable to verify server certificate '
-                                           'for %s' % req.host)
-                else:
-                    raise
-
-    #
-    # To prevent against mixing HTTP traffic with HTTPS (examples: A Man-In-The-
-    # Middle proxy using HTTP listens on port 443, or an index mistakenly serves
-    # HTML containing a http://xyz link when it should be https://xyz),
-    # you can use the following handler class, which does not allow HTTP traffic.
-    #
-    # It works by inheriting from HTTPHandler - so build_opener won't add a
-    # handler for HTTP itself.
-    #
-    class HTTPSOnlyHandler(HTTPSHandler, HTTPHandler):
-        def http_open(self, req):
-            raise URLError('Unexpected HTTP request on what should be a secure '
-                           'connection: %s' % req)
-
-#
-# XML-RPC with timeouts
-#
-class Transport(xmlrpclib.Transport):
-    def __init__(self, timeout, use_datetime=0):
-        self.timeout = timeout
-        xmlrpclib.Transport.__init__(self, use_datetime)
-
-    def make_connection(self, host):
-        h, eh, x509 = self.get_host_info(host)
-        if not self._connection or host != self._connection[0]:
-            self._extra_headers = eh
-            self._connection = host, httplib.HTTPConnection(h)
-        return self._connection[1]
-
-if ssl:
-    class SafeTransport(xmlrpclib.SafeTransport):
-        def __init__(self, timeout, use_datetime=0):
-            self.timeout = timeout
-            xmlrpclib.SafeTransport.__init__(self, use_datetime)
-
-        def make_connection(self, host):
-            h, eh, kwargs = self.get_host_info(host)
-            if not kwargs:
-                kwargs = {}
-            kwargs['timeout'] = self.timeout
-            if not self._connection or host != self._connection[0]:
-                self._extra_headers = eh
-                self._connection = host, httplib.HTTPSConnection(h, None,
-                                                                 **kwargs)
-            return self._connection[1]
-
-
-class ServerProxy(xmlrpclib.ServerProxy):
-    def __init__(self, uri, **kwargs):
-        self.timeout = timeout = kwargs.pop('timeout', None)
-        # The above classes only come into play if a timeout
-        # is specified
-        if timeout is not None:
-            # scheme = splittype(uri)  # deprecated as of Python 3.8
-            scheme = urlparse(uri)[0]
-            use_datetime = kwargs.get('use_datetime', 0)
-            if scheme == 'https':
-                tcls = SafeTransport
-            else:
-                tcls = Transport
-            kwargs['transport'] = t = tcls(timeout, use_datetime=use_datetime)
-            self.transport = t
-        xmlrpclib.ServerProxy.__init__(self, uri, **kwargs)
-
-#
-# CSV functionality. This is provided because on 2.x, the csv module can't
-# handle Unicode. However, we need to deal with Unicode in e.g. RECORD files.
-#
-
-def _csv_open(fn, mode, **kwargs):
-    if sys.version_info[0] < 3:
-        mode += 'b'
-    else:
-        kwargs['newline'] = ''
-        # Python 3 determines encoding from locale. Force 'utf-8'
-        # file encoding to match other forced utf-8 encoding
-        kwargs['encoding'] = 'utf-8'
-    return open(fn, mode, **kwargs)
-
-
-class CSVBase(object):
-    defaults = {
-        'delimiter': str(','),      # The strs are used because we need native
-        'quotechar': str('"'),      # str in the csv API (2.x won't take
-        'lineterminator': str('\n') # Unicode)
-    }
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *exc_info):
-        self.stream.close()
-
-
-class CSVReader(CSVBase):
-    def __init__(self, **kwargs):
-        if 'stream' in kwargs:
-            stream = kwargs['stream']
-            if sys.version_info[0] >= 3:
-                # needs to be a text stream
-                stream = codecs.getreader('utf-8')(stream)
-            self.stream = stream
-        else:
-            self.stream = _csv_open(kwargs['path'], 'r')
-        self.reader = csv.reader(self.stream, **self.defaults)
-
-    def __iter__(self):
-        return self
-
-    def next(self):
-        result = next(self.reader)
-        if sys.version_info[0] < 3:
-            for i, item in enumerate(result):
-                if not isinstance(item, text_type):
-                    result[i] = item.decode('utf-8')
-        return result
-
-    __next__ = next
-
-class CSVWriter(CSVBase):
-    def __init__(self, fn, **kwargs):
-        self.stream = _csv_open(fn, 'w')
-        self.writer = csv.writer(self.stream, **self.defaults)
-
-    def writerow(self, row):
-        if sys.version_info[0] < 3:
-            r = []
-            for item in row:
-                if isinstance(item, text_type):
-                    item = item.encode('utf-8')
-                r.append(item)
-            row = r
-        self.writer.writerow(row)
-
-#
-#   Configurator functionality
-#
-
-class Configurator(BaseConfigurator):
-
-    value_converters = dict(BaseConfigurator.value_converters)
-    value_converters['inc'] = 'inc_convert'
-
-    def __init__(self, config, base=None):
-        super(Configurator, self).__init__(config)
-        self.base = base or os.getcwd()
-
-    def configure_custom(self, config):
-        def convert(o):
-            if isinstance(o, (list, tuple)):
-                result = type(o)([convert(i) for i in o])
-            elif isinstance(o, dict):
-                if '()' in o:
-                    result = self.configure_custom(o)
-                else:
-                    result = {}
-                    for k in o:
-                        result[k] = convert(o[k])
-            else:
-                result = self.convert(o)
-            return result
-
-        c = config.pop('()')
-        if not callable(c):
-            c = self.resolve(c)
-        props = config.pop('.', None)
-        # Check for valid identifiers
-        args = config.pop('[]', ())
-        if args:
-            args = tuple([convert(o) for o in args])
-        items = [(k, convert(config[k])) for k in config if valid_ident(k)]
-        kwargs = dict(items)
-        result = c(*args, **kwargs)
-        if props:
-            for n, v in props.items():
-                setattr(result, n, convert(v))
-        return result
-
-    def __getitem__(self, key):
-        result = self.config[key]
-        if isinstance(result, dict) and '()' in result:
-            self.config[key] = result = self.configure_custom(result)
-        return result
-
-    def inc_convert(self, value):
-        """Default converter for the inc:// protocol."""
-        if not os.path.isabs(value):
-            value = os.path.join(self.base, value)
-        with codecs.open(value, 'r', encoding='utf-8') as f:
-            result = json.load(f)
-        return result
-
-
-class SubprocessMixin(object):
-    """
-    Mixin for running subprocesses and capturing their output
-    """
-    def __init__(self, verbose=False, progress=None):
-        self.verbose = verbose
-        self.progress = progress
-
-    def reader(self, stream, context):
-        """
-        Read lines from a subprocess' output stream and either pass to a progress
-        callable (if specified) or write progress information to sys.stderr.
-        """
-        progress = self.progress
-        verbose = self.verbose
-        while True:
-            s = stream.readline()
-            if not s:
-                break
-            if progress is not None:
-                progress(s, context)
-            else:
-                if not verbose:
-                    sys.stderr.write('.')
-                else:
-                    sys.stderr.write(s.decode('utf-8'))
-                sys.stderr.flush()
-        stream.close()
-
-    def run_command(self, cmd, **kwargs):
-        p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
-                             stderr=subprocess.PIPE, **kwargs)
-        t1 = threading.Thread(target=self.reader, args=(p.stdout, 'stdout'))
-        t1.start()
-        t2 = threading.Thread(target=self.reader, args=(p.stderr, 'stderr'))
-        t2.start()
-        p.wait()
-        t1.join()
-        t2.join()
-        if self.progress is not None:
-            self.progress('done.', 'main')
-        elif self.verbose:
-            sys.stderr.write('done.\n')
-        return p
-
-
-def normalize_name(name):
-    """Normalize a python package name a la PEP 503"""
-    # https://www.python.org/dev/peps/pep-0503/#normalized-names
-    return re.sub('[-_.]+', '-', name).lower()
-
-# def _get_pypirc_command():
-    # """
-    # Get the distutils command for interacting with PyPI configurations.
-    # :return: the command.
-    # """
-    # from distutils.core import Distribution
-    # from distutils.config import PyPIRCCommand
-    # d = Distribution()
-    # return PyPIRCCommand(d)
-
-class PyPIRCFile(object):
-
-    DEFAULT_REPOSITORY = 'https://upload.pypi.org/legacy/'
-    DEFAULT_REALM = 'pypi'
-
-    def __init__(self, fn=None, url=None):
-        if fn is None:
-            fn = os.path.join(os.path.expanduser('~'), '.pypirc')
-        self.filename = fn
-        self.url = url
-
-    def read(self):
-        result = {}
-
-        if os.path.exists(self.filename):
-            repository = self.url or self.DEFAULT_REPOSITORY
-
-            config = configparser.RawConfigParser()
-            config.read(self.filename)
-            sections = config.sections()
-            if 'distutils' in sections:
-                # let's get the list of servers
-                index_servers = config.get('distutils', 'index-servers')
-                _servers = [server.strip() for server in
-                            index_servers.split('\n')
-                            if server.strip() != '']
-                if _servers == []:
-                    # nothing set, let's try to get the default pypi
-                    if 'pypi' in sections:
-                        _servers = ['pypi']
-                else:
-                    for server in _servers:
-                        result = {'server': server}
-                        result['username'] = config.get(server, 'username')
-
-                        # optional params
-                        for key, default in (('repository', self.DEFAULT_REPOSITORY),
-                                             ('realm', self.DEFAULT_REALM),
-                                             ('password', None)):
-                            if config.has_option(server, key):
-                                result[key] = config.get(server, key)
-                            else:
-                                result[key] = default
-
-                        # work around people having "repository" for the "pypi"
-                        # section of their config set to the HTTP (rather than
-                        # HTTPS) URL
-                        if (server == 'pypi' and
-                            repository in (self.DEFAULT_REPOSITORY, 'pypi')):
-                            result['repository'] = self.DEFAULT_REPOSITORY
-                        elif (result['server'] != repository and
-                              result['repository'] != repository):
-                            result = {}
-            elif 'server-login' in sections:
-                # old format
-                server = 'server-login'
-                if config.has_option(server, 'repository'):
-                    repository = config.get(server, 'repository')
-                else:
-                    repository = self.DEFAULT_REPOSITORY
-                result = {
-                    'username': config.get(server, 'username'),
-                    'password': config.get(server, 'password'),
-                    'repository': repository,
-                    'server': server,
-                    'realm': self.DEFAULT_REALM
-                }
-        return result
-
-    def update(self, username, password):
-        # import pdb; pdb.set_trace()
-        config = configparser.RawConfigParser()
-        fn = self.filename
-        config.read(fn)
-        if not config.has_section('pypi'):
-            config.add_section('pypi')
-        config.set('pypi', 'username', username)
-        config.set('pypi', 'password', password)
-        with open(fn, 'w') as f:
-            config.write(f)
-
-def _load_pypirc(index):
-    """
-    Read the PyPI access configuration as supported by distutils.
-    """
-    return PyPIRCFile(url=index.url).read()
-
-def _store_pypirc(index):
-    PyPIRCFile().update(index.username, index.password)
-
-#
-# get_platform()/get_host_platform() copied from Python 3.10.a0 source, with some minor
-# tweaks
-#
-
-def get_host_platform():
-    """Return a string that identifies the current platform.  This is used mainly to
-    distinguish platform-specific build directories and platform-specific built
-    distributions.  Typically includes the OS name and version and the
-    architecture (as supplied by 'os.uname()'), although the exact information
-    included depends on the OS; eg. on Linux, the kernel version isn't
-    particularly important.
-
-    Examples of returned values:
-       linux-i586
-       linux-alpha (?)
-       solaris-2.6-sun4u
-
-    Windows will return one of:
-       win-amd64 (64bit Windows on AMD64 (aka x86_64, Intel64, EM64T, etc)
-       win32 (all others - specifically, sys.platform is returned)
-
-    For other non-POSIX platforms, currently just returns 'sys.platform'.
-
-    """
-    if os.name == 'nt':
-        if 'amd64' in sys.version.lower():
-            return 'win-amd64'
-        if '(arm)' in sys.version.lower():
-            return 'win-arm32'
-        if '(arm64)' in sys.version.lower():
-            return 'win-arm64'
-        return sys.platform
-
-    # Set for cross builds explicitly
-    if "_PYTHON_HOST_PLATFORM" in os.environ:
-        return os.environ["_PYTHON_HOST_PLATFORM"]
-
-    if os.name != 'posix' or not hasattr(os, 'uname'):
-        # XXX what about the architecture? NT is Intel or Alpha,
-        # Mac OS is M68k or PPC, etc.
-        return sys.platform
-
-    # Try to distinguish various flavours of Unix
-
-    (osname, host, release, version, machine) = os.uname()
-
-    # Convert the OS name to lowercase, remove '/' characters, and translate
-    # spaces (for "Power Macintosh")
-    osname = osname.lower().replace('/', '')
-    machine = machine.replace(' ', '_').replace('/', '-')
-
-    if osname[:5] == 'linux':
-        # At least on Linux/Intel, 'machine' is the processor --
-        # i386, etc.
-        # XXX what about Alpha, SPARC, etc?
-        return  "%s-%s" % (osname, machine)
-
-    elif osname[:5] == 'sunos':
-        if release[0] >= '5':           # SunOS 5 == Solaris 2
-            osname = 'solaris'
-            release = '%d.%s' % (int(release[0]) - 3, release[2:])
-            # We can't use 'platform.architecture()[0]' because a
-            # bootstrap problem. We use a dict to get an error
-            # if some suspicious happens.
-            bitness = {2147483647:'32bit', 9223372036854775807:'64bit'}
-            machine += '.%s' % bitness[sys.maxsize]
-        # fall through to standard osname-release-machine representation
-    elif osname[:3] == 'aix':
-        from _aix_support import aix_platform
-        return aix_platform()
-    elif osname[:6] == 'cygwin':
-        osname = 'cygwin'
-        rel_re = re.compile (r'[\d.]+', re.ASCII)
-        m = rel_re.match(release)
-        if m:
-            release = m.group()
-    elif osname[:6] == 'darwin':
-        import _osx_support, distutils.sysconfig
-        osname, release, machine = _osx_support.get_platform_osx(
-                                        distutils.sysconfig.get_config_vars(),
-                                        osname, release, machine)
-
-    return '%s-%s-%s' % (osname, release, machine)
-
-
-_TARGET_TO_PLAT = {
-    'x86' : 'win32',
-    'x64' : 'win-amd64',
-    'arm' : 'win-arm32',
-}
-
-
-def get_platform():
-    if os.name != 'nt':
-        return get_host_platform()
-    cross_compilation_target = os.environ.get('VSCMD_ARG_TGT_ARCH')
-    if cross_compilation_target not in _TARGET_TO_PLAT:
-        return get_host_platform()
-    return _TARGET_TO_PLAT[cross_compilation_target]
diff --git a/spaces/tomaseo2022/Eliminar-Fondo-Imagen/README.md b/spaces/tomaseo2022/Eliminar-Fondo-Imagen/README.md
deleted file mode 100644
index 9ff01133abf179640ffa2cf7d89450901a715c84..0000000000000000000000000000000000000000
--- a/spaces/tomaseo2022/Eliminar-Fondo-Imagen/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Eliminar Fondo Imagen
-emoji: 💩
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tomofi/MMOCR/configs/_base_/recog_models/sar.py b/spaces/tomofi/MMOCR/configs/_base_/recog_models/sar.py
deleted file mode 100644
index 8438d9b921f5124c52fcd9ff566e28cddeb33041..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/configs/_base_/recog_models/sar.py
+++ /dev/null
@@ -1,24 +0,0 @@
-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='ParallelSARDecoder',
-        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)
diff --git a/spaces/tomofi/MMOCR/mmocr/apis/train.py b/spaces/tomofi/MMOCR/mmocr/apis/train.py
deleted file mode 100644
index 89ba3be68242f368666a37d31cd47266a6b9623a..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/apis/train.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
-import mmcv
-import numpy as np
-import torch
-import torch.distributed as dist
-from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import (DistSamplerSeedHook, EpochBasedRunner,
-                         Fp16OptimizerHook, OptimizerHook, build_optimizer,
-                         build_runner, get_dist_info)
-from mmdet.core import DistEvalHook, EvalHook
-from mmdet.datasets import build_dataloader, build_dataset
-
-from mmocr import digit_version
-from mmocr.apis.utils import (disable_text_recog_aug_test,
-                              replace_image_to_tensor)
-from mmocr.utils import get_root_logger
-
-
-def train_detector(model,
-                   dataset,
-                   cfg,
-                   distributed=False,
-                   validate=False,
-                   timestamp=None,
-                   meta=None):
-    logger = get_root_logger(cfg.log_level)
-
-    # prepare data loaders
-    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
-    # step 1: give default values and override (if exist) from cfg.data
-    loader_cfg = {
-        **dict(
-            seed=cfg.get('seed'),
-            drop_last=False,
-            dist=distributed,
-            num_gpus=len(cfg.gpu_ids)),
-        **({} if torch.__version__ != 'parrots' else dict(
-               prefetch_num=2,
-               pin_memory=False,
-           )),
-        **dict((k, cfg.data[k]) for k in [
-                   'samples_per_gpu',
-                   'workers_per_gpu',
-                   'shuffle',
-                   'seed',
-                   'drop_last',
-                   'prefetch_num',
-                   'pin_memory',
-                   'persistent_workers',
-               ] if k in cfg.data)
-    }
-
-    # step 2: cfg.data.train_dataloader has highest priority
-    train_loader_cfg = dict(loader_cfg, **cfg.data.get('train_dataloader', {}))
-
-    data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
-
-    # put model on gpus
-    if distributed:
-        find_unused_parameters = cfg.get('find_unused_parameters', False)
-        # Sets the `find_unused_parameters` parameter in
-        # torch.nn.parallel.DistributedDataParallel
-        model = MMDistributedDataParallel(
-            model.cuda(),
-            device_ids=[torch.cuda.current_device()],
-            broadcast_buffers=False,
-            find_unused_parameters=find_unused_parameters)
-    else:
-        if not torch.cuda.is_available():
-            assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
-                'Please use MMCV >= 1.4.4 for CPU training!'
-        model = MMDataParallel(model, device_ids=cfg.gpu_ids)
-
-    # build runner
-    optimizer = build_optimizer(model, cfg.optimizer)
-
-    if 'runner' not in cfg:
-        cfg.runner = {
-            'type': 'EpochBasedRunner',
-            'max_epochs': cfg.total_epochs
-        }
-        warnings.warn(
-            'config is now expected to have a `runner` section, '
-            'please set `runner` in your config.', UserWarning)
-    else:
-        if 'total_epochs' in cfg:
-            assert cfg.total_epochs == cfg.runner.max_epochs
-
-    runner = build_runner(
-        cfg.runner,
-        default_args=dict(
-            model=model,
-            optimizer=optimizer,
-            work_dir=cfg.work_dir,
-            logger=logger,
-            meta=meta))
-
-    # an ugly workaround to make .log and .log.json filenames the same
-    runner.timestamp = timestamp
-
-    # fp16 setting
-    fp16_cfg = cfg.get('fp16', None)
-    if fp16_cfg is not None:
-        optimizer_config = Fp16OptimizerHook(
-            **cfg.optimizer_config, **fp16_cfg, distributed=distributed)
-    elif distributed and 'type' not in cfg.optimizer_config:
-        optimizer_config = OptimizerHook(**cfg.optimizer_config)
-    else:
-        optimizer_config = cfg.optimizer_config
-
-    # register hooks
-    runner.register_training_hooks(
-        cfg.lr_config,
-        optimizer_config,
-        cfg.checkpoint_config,
-        cfg.log_config,
-        cfg.get('momentum_config', None),
-        custom_hooks_config=cfg.get('custom_hooks', None))
-    if distributed:
-        if isinstance(runner, EpochBasedRunner):
-            runner.register_hook(DistSamplerSeedHook())
-
-    # register eval hooks
-    if validate:
-        val_samples_per_gpu = (cfg.data.get('val_dataloader', {})).get(
-            'samples_per_gpu', cfg.data.get('samples_per_gpu', 1))
-        if val_samples_per_gpu > 1:
-            # Support batch_size > 1 in test for text recognition
-            # by disable MultiRotateAugOCR since it is useless for most case
-            cfg = disable_text_recog_aug_test(cfg)
-            cfg = replace_image_to_tensor(cfg)
-
-        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
-
-        val_loader_cfg = {
-            **loader_cfg,
-            **dict(shuffle=False, drop_last=False),
-            **cfg.data.get('val_dataloader', {}),
-            **dict(samples_per_gpu=val_samples_per_gpu)
-        }
-
-        val_dataloader = build_dataloader(val_dataset, **val_loader_cfg)
-
-        eval_cfg = cfg.get('evaluation', {})
-        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
-        eval_hook = DistEvalHook if distributed else EvalHook
-        runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
-
-    if cfg.resume_from:
-        runner.resume(cfg.resume_from)
-    elif cfg.load_from:
-        runner.load_checkpoint(cfg.load_from)
-    runner.run(data_loaders, cfg.workflow)
-
-
-def init_random_seed(seed=None, device='cuda'):
-    """Initialize random seed. If the seed is None, it will be replaced by a
-    random number, and then broadcasted to all processes.
-
-    Args:
-        seed (int, Optional): The seed.
-        device (str): The device where the seed will be put on.
-
-    Returns:
-        int: Seed to be used.
-    """
-    if seed is not None:
-        return seed
-
-    # Make sure all ranks share the same random seed to prevent
-    # some potential bugs. Please refer to
-    # https://github.com/open-mmlab/mmdetection/issues/6339
-    rank, world_size = get_dist_info()
-    seed = np.random.randint(2**31)
-    if world_size == 1:
-        return seed
-
-    if rank == 0:
-        random_num = torch.tensor(seed, dtype=torch.int32, device=device)
-    else:
-        random_num = torch.tensor(0, dtype=torch.int32, device=device)
-    dist.broadcast(random_num, src=0)
-    return random_num.item()
diff --git a/spaces/tomofi/MMOCR/mmocr/datasets/icdar_dataset.py b/spaces/tomofi/MMOCR/mmocr/datasets/icdar_dataset.py
deleted file mode 100644
index 1340c8b50c29c5649ba6ccede6ebaa19d238af4a..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/datasets/icdar_dataset.py
+++ /dev/null
@@ -1,178 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import mmcv
-import numpy as np
-from mmdet.datasets.api_wrappers import COCO
-from mmdet.datasets.builder import DATASETS
-from mmdet.datasets.coco import CocoDataset
-
-import mmocr.utils as utils
-from mmocr import digit_version
-from mmocr.core.evaluation.hmean import eval_hmean
-
-
-@DATASETS.register_module()
-class IcdarDataset(CocoDataset):
-    """Dataset for text detection while ann_file in coco format.
-
-    Args:
-        ann_file_backend (str): Storage backend for annotation file,
-            should be one in ['disk', 'petrel', 'http']. Default to 'disk'.
-    """
-    CLASSES = ('text')
-
-    def __init__(self,
-                 ann_file,
-                 pipeline,
-                 classes=None,
-                 data_root=None,
-                 img_prefix='',
-                 seg_prefix=None,
-                 proposal_file=None,
-                 test_mode=False,
-                 filter_empty_gt=True,
-                 select_first_k=-1,
-                 ann_file_backend='disk'):
-        # select first k images for fast debugging.
-        self.select_first_k = select_first_k
-        assert ann_file_backend in ['disk', 'petrel', 'http']
-        self.ann_file_backend = ann_file_backend
-
-        super().__init__(ann_file, pipeline, classes, data_root, img_prefix,
-                         seg_prefix, proposal_file, test_mode, filter_empty_gt)
-
-    def load_annotations(self, ann_file):
-        """Load annotation from COCO style annotation file.
-
-        Args:
-            ann_file (str): Path of annotation file.
-
-        Returns:
-            list[dict]: Annotation info from COCO api.
-        """
-        if self.ann_file_backend == 'disk':
-            self.coco = COCO(ann_file)
-        else:
-            mmcv_version = digit_version(mmcv.__version__)
-            if mmcv_version < digit_version('1.3.16'):
-                raise Exception('Please update mmcv to 1.3.16 or higher '
-                                'to enable "get_local_path" of "FileClient".')
-            file_client = mmcv.FileClient(backend=self.ann_file_backend)
-            with file_client.get_local_path(ann_file) as local_path:
-                self.coco = COCO(local_path)
-        self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
-        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
-        self.img_ids = self.coco.get_img_ids()
-        data_infos = []
-
-        count = 0
-        for i in self.img_ids:
-            info = self.coco.load_imgs([i])[0]
-            info['filename'] = info['file_name']
-            data_infos.append(info)
-            count = count + 1
-            if count > self.select_first_k and self.select_first_k > 0:
-                break
-        return data_infos
-
-    def _parse_ann_info(self, img_info, ann_info):
-        """Parse bbox and mask annotation.
-
-        Args:
-            ann_info (list[dict]): Annotation info of an image.
-
-        Returns:
-            dict: A dict containing the following keys: bboxes, bboxes_ignore,
-                labels, masks, masks_ignore, seg_map. "masks"  and
-                "masks_ignore" are represented by polygon boundary
-                point sequences.
-        """
-        gt_bboxes = []
-        gt_labels = []
-        gt_bboxes_ignore = []
-        gt_masks_ignore = []
-        gt_masks_ann = []
-
-        for ann in ann_info:
-            if ann.get('ignore', False):
-                continue
-            x1, y1, w, h = ann['bbox']
-            if ann['area'] <= 0 or w < 1 or h < 1:
-                continue
-            if ann['category_id'] not in self.cat_ids:
-                continue
-            bbox = [x1, y1, x1 + w, y1 + h]
-            if ann.get('iscrowd', False):
-                gt_bboxes_ignore.append(bbox)
-                gt_masks_ignore.append(ann.get(
-                    'segmentation', None))  # to float32 for latter processing
-
-            else:
-                gt_bboxes.append(bbox)
-                gt_labels.append(self.cat2label[ann['category_id']])
-                gt_masks_ann.append(ann.get('segmentation', None))
-        if gt_bboxes:
-            gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
-            gt_labels = np.array(gt_labels, dtype=np.int64)
-        else:
-            gt_bboxes = np.zeros((0, 4), dtype=np.float32)
-            gt_labels = np.array([], dtype=np.int64)
-
-        if gt_bboxes_ignore:
-            gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
-        else:
-            gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
-
-        seg_map = img_info['filename'].replace('jpg', 'png')
-
-        ann = dict(
-            bboxes=gt_bboxes,
-            labels=gt_labels,
-            bboxes_ignore=gt_bboxes_ignore,
-            masks_ignore=gt_masks_ignore,
-            masks=gt_masks_ann,
-            seg_map=seg_map)
-
-        return ann
-
-    def evaluate(self,
-                 results,
-                 metric='hmean-iou',
-                 logger=None,
-                 score_thr=0.3,
-                 rank_list=None,
-                 **kwargs):
-        """Evaluate the hmean metric.
-
-        Args:
-            results (list[dict]): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated.
-            logger (logging.Logger | str | None): Logger used for printing
-                related information during evaluation. Default: None.
-            rank_list (str): json file used to save eval result
-                of each image after ranking.
-        Returns:
-            dict[dict[str: float]]: The evaluation results.
-        """
-        assert utils.is_type_list(results, dict)
-
-        metrics = metric if isinstance(metric, list) else [metric]
-        allowed_metrics = ['hmean-iou', 'hmean-ic13']
-        metrics = set(metrics) & set(allowed_metrics)
-
-        img_infos = []
-        ann_infos = []
-        for i in range(len(self)):
-            img_info = {'filename': self.data_infos[i]['file_name']}
-            img_infos.append(img_info)
-            ann_infos.append(self.get_ann_info(i))
-
-        eval_results = eval_hmean(
-            results,
-            img_infos,
-            ann_infos,
-            metrics=metrics,
-            score_thr=score_thr,
-            logger=logger,
-            rank_list=rank_list)
-
-        return eval_results
diff --git a/spaces/trttung1610/musicgen/audiocraft/modules/codebooks_patterns.py b/spaces/trttung1610/musicgen/audiocraft/modules/codebooks_patterns.py
deleted file mode 100644
index 3cf3bb41774700a679ffe4325236d0324a99c546..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/audiocraft/modules/codebooks_patterns.py
+++ /dev/null
@@ -1,539 +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 collections import namedtuple
-from dataclasses import dataclass
-from functools import lru_cache
-import logging
-import typing as tp
-
-from abc import ABC, abstractmethod
-import torch
-
-LayoutCoord = namedtuple('LayoutCoord', ['t', 'q'])  # (timestep, codebook index)
-PatternLayout = tp.List[tp.List[LayoutCoord]]  # Sequence of coordinates
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Pattern:
-    """Base implementation of a pattern over a sequence with multiple codebooks.
-
-    The codebook pattern consists in a layout, defining for each sequence step
-    the list of coordinates of each codebook timestep in the resulting interleaved sequence.
-    The first item of the pattern is always an empty list in order to properly insert a special token
-    to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
-    and ``timesteps`` the number of timesteps corresponding to the original sequence.
-
-    The pattern provides convenient methods to build and revert interleaved sequences from it:
-    ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
-        to the interleaved sequence of shape [B, K, S] applying the pattern, with S being the batch size,
-        K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
-        for the output sequence. The unfilled positions are replaced with a special token and the built sequence
-        is returned along with a mask indicating valid tokens.
-    ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
-        of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
-        to fill and specify invalid positions if needed.
-    See the dedicated methods for more details.
-    """
-    # Pattern layout, for each sequence step, we have a list of coordinates
-    # corresponding to the original codebook timestep and position.
-    # The first list is always an empty list in order to properly insert
-    # a special token to start with.
-    layout: PatternLayout
-    timesteps: int
-    n_q: int
-
-    def __post_init__(self):
-        assert len(self.layout) > 0
-        assert self.layout[0] == []
-        self._validate_layout()
-        self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
-        self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
-        logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
-
-    def _validate_layout(self):
-        """Runs checks on the layout to ensure a valid pattern is defined.
-        A pattern is considered invalid if:
-            - Multiple timesteps for a same codebook are defined in the same sequence step
-            - The timesteps for a given codebook are not in ascending order as we advance in the sequence
-              (this would mean that we have future timesteps before past timesteps).
-        """
-        q_timesteps = {q: 0 for q in range(self.n_q)}
-        for s, seq_coords in enumerate(self.layout):
-            if len(seq_coords) > 0:
-                qs = set()
-                for coord in seq_coords:
-                    qs.add(coord.q)
-                    last_q_timestep = q_timesteps[coord.q]
-                    assert coord.t >= last_q_timestep, \
-                        f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
-                    q_timesteps[coord.q] = coord.t
-                # each sequence step contains at max 1 coordinate per codebook
-                assert len(qs) == len(seq_coords), \
-                    f"Multiple entries for a same codebook are found at step {s}"
-
-    @property
-    def num_sequence_steps(self):
-        return len(self.layout) - 1
-
-    @property
-    def max_delay(self):
-        max_t_in_seq_coords = 0
-        for seq_coords in self.layout[1:]:
-            for coords in seq_coords:
-                max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
-        return max_t_in_seq_coords - self.timesteps
-
-    @property
-    def valid_layout(self):
-        valid_step = len(self.layout) - self.max_delay
-        return self.layout[:valid_step]
-
-    def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
-        """Get codebook coordinates in the layout that corresponds to the specified timestep t
-        and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
-        and the actual codebook coordinates.
-        """
-        assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
-        if q is not None:
-            assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
-        coords = []
-        for s, seq_codes in enumerate(self.layout):
-            for code in seq_codes:
-                if code.t == t and (q is None or code.q == q):
-                    coords.append((s, code))
-        return coords
-
-    def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
-        return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
-
-    def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
-        steps_with_timesteps = self.get_steps_with_timestep(t, q)
-        return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
-
-    def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
-                                                device: tp.Union[torch.device, str] = 'cpu'):
-        """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
-
-        Args:
-            timesteps (int): Maximum number of timesteps steps to consider.
-            keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
-            device (torch.device or str): Device for created tensors.
-        Returns:
-            indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
-        """
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
-        # use the proper layout based on whether we limit ourselves to valid steps only or not,
-        # note that using the valid_layout will result in a truncated sequence up to the valid steps
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        # the last value is n_q * timesteps as we have flattened z and append special token as the last token
-        # which will correspond to the index: n_q * timesteps
-        indexes[:] = n_q * timesteps
-        # iterate over the pattern and fill scattered indexes and mask
-        for s, sequence_coords in enumerate(ref_layout):
-            for coords in sequence_coords:
-                if coords.t < timesteps:
-                    indexes[coords.q, s] = coords.t + coords.q * timesteps
-                    mask[coords.q, s] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Build sequence corresponding to the pattern from the input tensor z.
-        The sequence is built using up to sequence_steps if specified, and non-pattern
-        coordinates are filled with the special token.
-
-        Args:
-            z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
-            special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
-                corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
-        """
-        B, K, T = z.shape
-        indexes, mask = self._build_pattern_sequence_scatter_indexes(
-            T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
-        )
-        z = z.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
-        values = z[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
-                                                 keep_only_valid_steps: bool = False,
-                                                 is_model_output: bool = False,
-                                                 device: tp.Union[torch.device, str] = 'cpu'):
-        """Builds scatter indexes required to retrieve the original multi-codebook sequence
-        from interleaving pattern.
-
-        Args:
-            sequence_steps (int): Sequence steps.
-            n_q (int): Number of codebooks.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-            is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
-            device (torch.device or str): Device for created tensors.
-        Returns:
-            indexes (torch.Tensor): Indexes for reconstructing the output, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
-        timesteps = self.timesteps
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert sequence_steps <= len(ref_layout), \
-            f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
-
-        # ensure we take the appropriate indexes to keep the model output from the first special token as well
-        if is_model_output:
-            ref_layout = ref_layout[1:]
-
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        indexes[:] = n_q * sequence_steps
-        for s, sequence_codes in enumerate(ref_layout):
-            if s < sequence_steps:
-                for code in sequence_codes:
-                    if code.t < timesteps:
-                        indexes[code.q, code.t] = s + code.q * sequence_steps
-                        mask[code.q, code.t] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
-        The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
-        are filled with the special token.
-
-        Args:
-            s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
-            special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
-                corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        B, K, S = s.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
-        )
-        s = s.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
-        values = s[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
-        """Revert model logits obtained on a sequence built from the pattern
-        back to a tensor matching the original sequence.
-
-        This method is similar to ``revert_pattern_sequence`` with the following specificities:
-        1. It is designed to work with the extra cardinality dimension
-        2. We return the logits for the first sequence item that matches the special_token and
-        which matching target in the original sequence is the first item of the sequence,
-        while we skip the last logits as there is no matching target
-        """
-        B, card, K, S = logits.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
-        )
-        logits = logits.reshape(B, card, -1)
-        # we append the special token as the last index of our flattened z tensor
-        logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1)  # [B, card, K x S]
-        values = logits[:, :, indexes.view(-1)]
-        values = values.view(B, card, K, indexes.shape[-1])
-        return values, indexes, mask
-
-
-class CodebooksPatternProvider(ABC):
-    """Abstraction around providing pattern for interleaving codebooks.
-
-    The CodebooksPatternProvider abstraction allows to implement various strategies to
-    define interleaving pattern of sequences composed of multiple codebooks. For a given
-    number of codebooks `n_q`, the pattern provider can generate a specified pattern
-    corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
-    can be used to construct a new sequence from the original codes respecting the specified
-    pattern. The pattern is defined as a list of list of code coordinates, code coordinate
-    being a tuple with the original timestep and codebook to build the new sequence.
-    Note that all patterns must start with an empty list that is then used to insert a first
-    sequence step of special tokens in the newly generated sequence.
-
-    Args:
-        n_q (int): number of codebooks.
-        cached (bool): if True, patterns for a given length are cached. In general
-            that should be true for efficiency reason to avoid synchronization points.
-    """
-    def __init__(self, n_q: int, cached: bool = True):
-        assert n_q > 0
-        self.n_q = n_q
-        self.get_pattern = lru_cache(100)(self.get_pattern)  # type: ignore
-
-    @abstractmethod
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern with specific interleaving between codebooks.
-
-        Args:
-            timesteps (int): Total number of timesteps.
-        """
-        raise NotImplementedError()
-
-
-class DelayedPatternProvider(CodebooksPatternProvider):
-    """Provider for delayed pattern across delayed codebooks.
-    Codebooks are delayed in the sequence and sequence steps will contain codebooks
-    from different timesteps.
-
-    Example:
-        Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
-        [[1, 2, 3, 4],
-        [1, 2, 3, 4],
-        [1, 2, 3, 4]]
-        The resulting sequence obtained from the returned pattern is:
-        [[S, 1, 2, 3, 4],
-        [S, S, 1, 2, 3],
-        [S, S, S, 1, 2]]
-        (with S being a special token)
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (list of int, optional): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-        flatten_first (int): Flatten the first N timesteps.
-        empty_initial (int): Prepend with N empty list of coordinates.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None,
-                 flatten_first: int = 0, empty_initial: int = 0):
-        super().__init__(n_q)
-        if delays is None:
-            delays = list(range(n_q))
-        self.delays = delays
-        self.flatten_first = flatten_first
-        self.empty_initial = empty_initial
-        assert len(self.delays) == self.n_q
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        max_delay = max(self.delays)
-        if self.empty_initial:
-            out += [[] for _ in range(self.empty_initial)]
-        if self.flatten_first:
-            for t in range(min(timesteps, self.flatten_first)):
-                for q in range(self.n_q):
-                    out.append([LayoutCoord(t, q)])
-        for t in range(self.flatten_first, timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= self.flatten_first:
-                    v.append(LayoutCoord(t_for_q, q))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class ParallelPatternProvider(DelayedPatternProvider):
-    """Provider for parallel pattern across codebooks.
-    This pattern provider is a special case of the delayed pattern with actually no delay,
-    hence delays=repeat(0, n_q).
-
-    Args:
-        n_q (int): Number of codebooks.
-    """
-    def __init__(self, n_q: int):
-        super().__init__(n_q, [0] * n_q)
-
-
-class UnrolledPatternProvider(CodebooksPatternProvider):
-    """Provider for unrolling codebooks pattern.
-    This pattern provider enables to represent the codebook flattened completely or only to some extend
-    while also specifying a given delay between the flattened codebooks representation, allowing to
-    unroll the codebooks in the sequence.
-
-    Example:
-        1. Flattening of the codebooks.
-        By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
-        taking n_q = 3 and timesteps = 4:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
-        for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
-        taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
-        allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
-        same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
-        and delays = [0, 3, 3]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, S, 1, S, 2, S, 3, S, 4],
-         [S, S, S, 1, S, 2, S, 3, S, 4],
-         [1, 2, 3, S, 4, S, 5, S, 6, S]]
-
-    Args:
-        n_q (int): Number of codebooks.
-        flattening (list of int, optional): Flattening schema over the codebooks. If not defined,
-            the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
-            have n_q extra steps for each timestep.
-        delays (list of int, optional): Delay for each of the codebooks. If not defined,
-            no delay is added and therefore will default to [0] * ``n_q``.
-            Note that two codebooks that will be flattened to the same inner step
-            should have the same delay, otherwise the pattern is considered as invalid.
-    """
-    FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
-
-    def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
-                 delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if flattening is None:
-            flattening = list(range(n_q))
-        if delays is None:
-            delays = [0] * n_q
-        assert len(flattening) == n_q
-        assert len(delays) == n_q
-        assert sorted(flattening) == flattening
-        assert sorted(delays) == delays
-        self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
-        self.max_delay = max(delays)
-
-    def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
-        """Build a flattened codebooks representation as a dictionary of inner step
-        and the actual codebook indices corresponding to the flattened codebook. For convenience, we
-        also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
-        """
-        flattened_codebooks: dict = {}
-        for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
-            if inner_step not in flattened_codebooks:
-                flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
-            else:
-                flat_codebook = flattened_codebooks[inner_step]
-                assert flat_codebook.delay == delay, (
-                    "Delay and flattening between codebooks is inconsistent: ",
-                    "two codebooks flattened to the same position should have the same delay."
-                )
-                flat_codebook.codebooks.append(q)
-            flattened_codebooks[inner_step] = flat_codebook
-        return flattened_codebooks
-
-    @property
-    def _num_inner_steps(self):
-        """Number of inner steps to unroll between timesteps in order to flatten the codebooks.
-        """
-        return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
-
-    def num_virtual_steps(self, timesteps: int) -> int:
-        return timesteps * self._num_inner_steps + 1
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern for delay across codebooks.
-
-        Args:
-            timesteps (int): Total number of timesteps.
-        """
-        # the PatternLayout is built as a tuple of sequence position and list of coordinates
-        # so that it can be reordered properly given the required delay between codebooks of given timesteps
-        indexed_out: list = [(-1, [])]
-        max_timesteps = timesteps + self.max_delay
-        for t in range(max_timesteps):
-            # for each timestep, we unroll the flattened codebooks,
-            # emitting the sequence step with the corresponding delay
-            for step in range(self._num_inner_steps):
-                if step in self._flattened_codebooks:
-                    # we have codebooks at this virtual step to emit
-                    step_codebooks = self._flattened_codebooks[step]
-                    t_for_q = t + step_codebooks.delay
-                    coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
-                    if t_for_q < max_timesteps and t < max_timesteps:
-                        indexed_out.append((t_for_q, coords))
-                else:
-                    # there is no codebook in this virtual step so we emit an empty list
-                    indexed_out.append((t, []))
-        out = [coords for _, coords in sorted(indexed_out)]
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class VALLEPattern(CodebooksPatternProvider):
-    """Almost VALL-E style pattern.
-    We further allow some delays for the codebooks other than the first one.
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (list of int, optional): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if delays is None:
-            delays = [0] * (n_q - 1)
-        self.delays = delays
-        assert len(self.delays) == self.n_q - 1
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for t in range(timesteps):
-            out.append([LayoutCoord(t, 0)])
-        max_delay = max(self.delays)
-        for t in range(timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= 0:
-                    v.append(LayoutCoord(t_for_q, q + 1))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class MusicLMPattern(CodebooksPatternProvider):
-    """Almost MusicLM style pattern. This is equivalent to full flattening
-    but in a different order.
-
-    Args:
-        n_q (int): Number of codebooks.
-        group_by (int): Number of codebooks to group together.
-    """
-    def __init__(self, n_q: int, group_by: int = 2):
-        super().__init__(n_q)
-        self.group_by = group_by
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for offset in range(0, self.n_q, self.group_by):
-            for t in range(timesteps):
-                for q in range(offset, offset + self.group_by):
-                    out.append([LayoutCoord(t, q)])
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Ahmadu Bello Sardauna of Sokoto and Moderniser of the North.md b/spaces/usbethFlerru/sovits-modelsV2/example/Ahmadu Bello Sardauna of Sokoto and Moderniser of the North.md
deleted file mode 100644
index 4386cbe5aa7b206ee7d5456b4b64becd9510921a..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Ahmadu Bello Sardauna of Sokoto and Moderniser of the North.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-
-<p>Sir <b>Ahmadu Muhammadu Bello</b> (Ahmadu Bello an haife shi ne a 12 ga watan June shekarar alif 1910,a ƙaramar hukumar Raba dake jihar Sakkwato)[1] KBE ko <b>Sardauna</b> shine tsohon Firimiyan Arewacin Najeriya kuma ya rike sarautar Sardaunan a jihar Sakkwato. An haifi Sir Ahmadu Bello sardauna a garin Raba dake cikin lardin Sakkwato a shekara ta alif dubu ɗaya da ɗari tara da goma (1910). A shekarar alif ɗari tara da arba'in da tara, 1949 ya sami zuwa majalisar dokoki ta jihar Arewa, yana kuma daga cikin mutum na uku da aka zaɓa acikin ƙungiyar da ta rubuta sabon kundin tsarin mulkin ƙasar.[2]Sardauna ya kasance wani mutumi ne dake da mahimmanci sosai ga mutanen arewacin Najeriya, dama faɗin ƙasar gaba ɗaya, saboda irin ayyukan cigaba daya ƙirƙiro a yankin arewa harma da kudancin ƙasar gaba ɗaya. Kamar jami'ar Ahmadu Bello, Gidan Rediyo dake jihar Kaduna, da sauransu. Duk da cewa Sardaunan sokoto Sarauta ce tashi a Jihar Sokoto, Amma sunan ya zama kamar wani inkiya a gareshi inda ya shahara wajen amsa shi a ciki da wajen Najeriya.</p>
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diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Beatport Download VERIFIEDer 1.1 Free Hack 502.md b/spaces/usbethFlerru/sovits-modelsV2/example/Beatport Download VERIFIEDer 1.1 Free Hack 502.md
deleted file mode 100644
index 6bf74e5db042ffbe0adb5880e2f4ae1283eea7c9..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Beatport Download VERIFIEDer 1.1 Free Hack 502.md	
+++ /dev/null
@@ -1,11 +0,0 @@
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diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/hub/__init__.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/hub/__init__.py
deleted file mode 100644
index 6059083af3d66d55809442cf49bfc83765e204dc..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/hub/__init__.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-
-import requests
-
-from ultralytics.hub.auth import Auth
-from ultralytics.hub.utils import PREFIX
-from ultralytics.yolo.data.utils import HUBDatasetStats
-from ultralytics.yolo.utils import LOGGER, SETTINGS, USER_CONFIG_DIR, yaml_save
-
-
-def login(api_key=''):
-    """
-    Log in to the Ultralytics HUB API using the provided API key.
-
-    Args:
-        api_key (str, optional): May be an API key or a combination API key and model ID, i.e. key_id
-
-    Example:
-        from ultralytics import hub
-        hub.login('API_KEY')
-    """
-    Auth(api_key, verbose=True)
-
-
-def logout():
-    """
-    Log out of Ultralytics HUB by removing the API key from the settings file. To log in again, use 'yolo hub login'.
-
-    Example:
-        from ultralytics import hub
-        hub.logout()
-    """
-    SETTINGS['api_key'] = ''
-    yaml_save(USER_CONFIG_DIR / 'settings.yaml', SETTINGS)
-    LOGGER.info(f"{PREFIX}logged out ✅. To log in again, use 'yolo hub login'.")
-
-
-def start(key=''):
-    """
-    Start training models with Ultralytics HUB (DEPRECATED).
-
-    Args:
-        key (str, optional): A string containing either the API key and model ID combination (apikey_modelid),
-                               or the full model URL (https://hub.ultralytics.com/models/apikey_modelid).
-    """
-    api_key, model_id = key.split('_')
-    LOGGER.warning(f"""
-WARNING ⚠️ ultralytics.start() is deprecated after 8.0.60. Updated usage to train Ultralytics HUB models is:
-
-from ultralytics import YOLO, hub
-
-hub.login('{api_key}')
-model = YOLO('https://hub.ultralytics.com/models/{model_id}')
-model.train()""")
-
-
-def reset_model(model_id=''):
-    """Reset a trained model to an untrained state."""
-    r = requests.post('https://api.ultralytics.com/model-reset', json={'apiKey': Auth().api_key, 'modelId': model_id})
-    if r.status_code == 200:
-        LOGGER.info(f'{PREFIX}Model reset successfully')
-        return
-    LOGGER.warning(f'{PREFIX}Model reset failure {r.status_code} {r.reason}')
-
-
-def export_fmts_hub():
-    """Returns a list of HUB-supported export formats."""
-    from ultralytics.yolo.engine.exporter import export_formats
-    return list(export_formats()['Argument'][1:]) + ['ultralytics_tflite', 'ultralytics_coreml']
-
-
-def export_model(model_id='', format='torchscript'):
-    """Export a model to all formats."""
-    assert format in export_fmts_hub(), f"Unsupported export format '{format}', valid formats are {export_fmts_hub()}"
-    r = requests.post(f'https://api.ultralytics.com/v1/models/{model_id}/export',
-                      json={'format': format},
-                      headers={'x-api-key': Auth().api_key})
-    assert r.status_code == 200, f'{PREFIX}{format} export failure {r.status_code} {r.reason}'
-    LOGGER.info(f'{PREFIX}{format} export started ✅')
-
-
-def get_export(model_id='', format='torchscript'):
-    """Get an exported model dictionary with download URL."""
-    assert format in export_fmts_hub(), f"Unsupported export format '{format}', valid formats are {export_fmts_hub()}"
-    r = requests.post('https://api.ultralytics.com/get-export',
-                      json={
-                          'apiKey': Auth().api_key,
-                          'modelId': model_id,
-                          'format': format})
-    assert r.status_code == 200, f'{PREFIX}{format} get_export failure {r.status_code} {r.reason}'
-    return r.json()
-
-
-def check_dataset(path='', task='detect'):
-    """
-    Function for error-checking HUB dataset Zip file before upload. It checks a dataset for errors before it is
-    uploaded to the HUB. Usage examples are given below.
-
-    Args:
-        path (str, optional): Path to data.zip (with data.yaml inside data.zip). Defaults to ''.
-        task (str, optional): Dataset task. Options are 'detect', 'segment', 'pose', 'classify'. Defaults to 'detect'.
-
-    Example:
-        ```python
-        from ultralytics.hub import check_dataset
-
-        check_dataset('path/to/coco8.zip', task='detect')  # detect dataset
-        check_dataset('path/to/coco8-seg.zip', task='segment')  # segment dataset
-        check_dataset('path/to/coco8-pose.zip', task='pose')  # pose dataset
-        ```
-    """
-    HUBDatasetStats(path=path, task=task).get_json()
-    LOGGER.info('Checks completed correctly ✅. Upload this dataset to https://hub.ultralytics.com/datasets/.')
-
-
-if __name__ == '__main__':
-    start()
diff --git a/spaces/vict0rsch/climateGAN/utils_scripts/download_labelbox.py b/spaces/vict0rsch/climateGAN/utils_scripts/download_labelbox.py
deleted file mode 100644
index bbfb27f59fcd893ec183a0756ec8d4e68fe1ef13..0000000000000000000000000000000000000000
--- a/spaces/vict0rsch/climateGAN/utils_scripts/download_labelbox.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import json
-import os
-from pathlib import Path
-
-if __name__ == "__main__":
-    # labelbox json export path
-    path = "/Users/victor/Downloads/export-2021-02-27T17_15_30.291Z.json"
-    # where to write the downloaded images
-    out = Path("/Users/victor/Downloads/labelbox_test_flood-v2")
-    # create out dir
-    out.mkdir(exist_ok=True, parents=True)
-
-    # load export data
-    with open(path, "r") as f:
-        data = json.load(f)
-
-    for i, d in enumerate(data):
-        # find all polygons
-        objects = d["Label"]["objects"]
-        # retrieve original image name
-        name = d["External ID"]
-        stem = Path(name).stem
-        # output dir for current image
-        m_out = out / stem[:30]
-        m_out.mkdir(exist_ok=True, parents=True)
-
-        # save 1 png per polygon
-        for o, obj in enumerate(objects):
-            print(f"{i}/{len(data)} : {o}/{len(objects)}")
-
-            # create verbose label -> "cannotflood", "mustflood"
-            label = obj["value"].replace("_", "")
-            # unique polygon mask filename
-            m_path = m_out / f"{stem}_{label}_{o}.png"
-            # download address for curl
-            uri = obj["instanceURI"]
-            # command to download the image
-            command = f'curl {uri} > "{str(m_path)}"'
-            # execute command
-            os.system(command)
-            print("#" * 20)
diff --git a/spaces/vict0rsch/climateGAN/utils_scripts/make_640_masker_validation_set.py b/spaces/vict0rsch/climateGAN/utils_scripts/make_640_masker_validation_set.py
deleted file mode 100644
index 6ca26f553387d65b9bc9fd47074f7e54d10c48e4..0000000000000000000000000000000000000000
--- a/spaces/vict0rsch/climateGAN/utils_scripts/make_640_masker_validation_set.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import sys
-from pathlib import Path
-from skimage.io import imread, imsave
-from skimage.transform import resize
-from skimage.color import rgba2rgb
-from argparse import ArgumentParser
-import numpy as np
-
-IMG_EXTENSIONS = set(
-    [".jpg", ".JPG", ".jpeg", ".JPEG", ".png", ".PNG", ".ppm", ".PPM", ".bmp", ".BMP"]
-)
-
-
-def is_image_file(filename):
-    """Check that a file's name points to a known image format
-    """
-    if isinstance(filename, Path):
-        return filename.suffix in IMG_EXTENSIONS
-
-    return Path(filename).suffix in IMG_EXTENSIONS
-
-
-def find_images(path, recursive=False):
-    """
-    Get a list of all images contained in a directory:
-
-    - path.glob("*") if not recursive
-    - path.glob("**/*") if recursive
-    """
-    p = Path(path)
-    assert p.exists()
-    assert p.is_dir()
-    pattern = "*"
-    if recursive:
-        pattern += "*/*"
-
-    return [i for i in p.glob(pattern) if i.is_file() and is_image_file(i)]
-
-
-def uint8(array):
-    return array.astype(np.uint8)
-
-
-def crop_and_resize(image_path, label_path):
-    """
-    Resizes an image so that it keeps the aspect ratio and the smallest dimensions
-    is 640, then crops this resized image in its center so that the output is 640x640
-    without aspect ratio distortion
-
-    Args:
-        image_path (Path or str): Path to an image
-        label_path (Path or str): Path to the image's associated label
-
-    Returns:
-        tuple((np.ndarray, np.ndarray)): (new image, new label)
-    """
-    dolab = label_path is not None
-
-    img = imread(image_path)
-    if dolab:
-        lab = imread(label_path)
-
-    if img.shape[-1] == 4:
-        img = uint8(rgba2rgb(img) * 255)
-
-    if dolab and img.shape != lab.shape:
-        print("\nWARNING: shape mismatch. Entering breakpoint to investigate:")
-        breakpoint()
-
-    # resize keeping aspect ratio: smallest dim is 640
-    h, w = img.shape[:2]
-    if h < w:
-        size = (640, int(640 * w / h))
-    else:
-        size = (int(640 * h / w), 640)
-
-    r_img = resize(img, size, preserve_range=True, anti_aliasing=True)
-    r_img = uint8(r_img)
-
-    if dolab:
-        # nearest neighbor for labels
-        r_lab = resize(lab, size, preserve_range=True, anti_aliasing=False, order=0)
-        r_lab = uint8(r_lab)
-
-    # crop in the center
-    H, W = r_img.shape[:2]
-
-    top = (H - 640) // 2
-    left = (W - 640) // 2
-
-    rc_img = r_img[top : top + 640, left : left + 640, :]
-    if dolab:
-        rc_lab = r_lab[top : top + 640, left : left + 640, :]
-    else:
-        rc_lab = None
-
-    return rc_img, rc_lab
-
-
-def label(img, label, alpha=0.4):
-    return uint8(alpha * label + (1 - alpha) * img)
-
-
-if __name__ == "__main__":
-    parser = ArgumentParser()
-    parser.add_argument(
-        "-i", "--input_dir", type=str, help="Directory to recursively read images from"
-    )
-    parser.add_argument(
-        "-o",
-        "--output_dir",
-        type=str,
-        help="Where to writ the result of the script,"
-        + " keeping the input dir's structure",
-    )
-    parser.add_argument(
-        "--no_labels",
-        action="store_true",
-        help="Only process images, don't look for labels",
-    )
-    parser.add_argument(
-        "--store_labeled",
-        action="store_true",
-        help="Store a superposition of the label and the image in out/labeled/",
-    )
-    args = parser.parse_args()
-
-    dolab = not args.no_labels
-    dolabeled = args.store_labeled
-
-    input_base = Path(args.input_dir).expanduser().resolve()
-    output_base = Path(args.output_dir).expanduser().resolve()
-
-    input_images = input_base / "imgs"
-    output_images = output_base / "imgs"
-
-    if dolab:
-        input_labels = input_base / "labels"
-        output_labels = output_base / "labels"
-        if dolabeled:
-            output_labeled = output_base / "labeled"
-
-    print("Input images:", str(input_images))
-    print("Output images:", str(output_images))
-    if dolab:
-        print("Input labels:", str(input_labels))
-        print("Output labels:", str(output_labels))
-        if dolabeled:
-            print("Output labeled:", str(output_labeled))
-    else:
-        print("NO LABEL PROCESSING (args.no_labels is specified)")
-    print()
-
-    assert input_images.exists()
-    if dolab:
-        assert input_labels.exists()
-
-    if output_base.exists():
-        if (
-            "n"
-            in input(
-                "WARNING: output dir already exists."
-                + " Overwrite its content? (y/n, default: y)"
-            ).lower()
-        ):
-            sys.exit()
-
-    output_images.mkdir(parents=True, exist_ok=True)
-    if dolab:
-        output_labels.mkdir(parents=True, exist_ok=True)
-        if dolabeled:
-            output_labeled.mkdir(parents=True, exist_ok=True)
-
-    images_paths = list(
-        map(Path, sorted((map(str, find_images(input_images, recursive=True)))))
-    )
-    if dolab:
-        labels_paths = list(
-            map(Path, sorted((map(str, find_images(input_labels, recursive=True)))))
-        )
-    else:
-        labels_paths = [None] * len(images_paths)
-
-    for i, (image_path, label_path) in enumerate(zip(images_paths, labels_paths)):
-        print(
-            f"Processing {i + 1 :3} / {len(images_paths)} : {image_path.name}",
-            end="\r",
-            flush=True,
-        )
-        processed_image, processed_label = crop_and_resize(image_path, label_path)
-        imsave(output_images / f"{image_path.stem}.png", processed_image)
-        if dolab:
-            imsave(output_labels / f"{label_path.stem}.png", processed_label)
-            if dolabeled:
-                labeled = label(processed_image, processed_label)
-                imsave(output_labeled / f"{image_path.stem}.png", labeled)
-
-    print("\nDone.")
diff --git a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/models/__init__.py b/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/models/__init__.py
deleted file mode 100644
index e3413961d1d184b99835eb1e919b052d70298bc6..0000000000000000000000000000000000000000
--- a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/models/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .GroundingDINO import build_groundingdino
-
-
-def build_model(args):
-    # we use register to maintain models from catdet6 on.
-    from .registry import MODULE_BUILD_FUNCS
-
-    assert args.modelname in MODULE_BUILD_FUNCS._module_dict
-    build_func = MODULE_BUILD_FUNCS.get(args.modelname)
-    model = build_func(args)
-    return model
diff --git a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/models/registry.py b/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/models/registry.py
deleted file mode 100644
index 2d22a59eec79a2a19b83fa1779f2adaf5753aec6..0000000000000000000000000000000000000000
--- a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/models/registry.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# -*- coding: utf-8 -*-
-# @Author: Yihao Chen
-# @Date:   2021-08-16 16:03:17
-# @Last Modified by:   Shilong Liu
-# @Last Modified time: 2022-01-23 15:26
-# modified from mmcv
-
-import inspect
-from functools import partial
-
-
-class Registry(object):
-    def __init__(self, name):
-        self._name = name
-        self._module_dict = dict()
-
-    def __repr__(self):
-        format_str = self.__class__.__name__ + "(name={}, items={})".format(
-            self._name, list(self._module_dict.keys())
-        )
-        return format_str
-
-    def __len__(self):
-        return len(self._module_dict)
-
-    @property
-    def name(self):
-        return self._name
-
-    @property
-    def module_dict(self):
-        return self._module_dict
-
-    def get(self, key):
-        return self._module_dict.get(key, None)
-
-    def registe_with_name(self, module_name=None, force=False):
-        return partial(self.register, module_name=module_name, force=force)
-
-    def register(self, module_build_function, module_name=None, force=False):
-        """Register a module build function.
-        Args:
-            module (:obj:`nn.Module`): Module to be registered.
-        """
-        if not inspect.isfunction(module_build_function):
-            raise TypeError(
-                "module_build_function must be a function, but got {}".format(
-                    type(module_build_function)
-                )
-            )
-        if module_name is None:
-            module_name = module_build_function.__name__
-        if not force and module_name in self._module_dict:
-            raise KeyError("{} is already registered in {}".format(module_name, self.name))
-        self._module_dict[module_name] = module_build_function
-
-        return module_build_function
-
-
-MODULE_BUILD_FUNCS = Registry("model build functions")
diff --git a/spaces/vishnu0001/text2mesh/shap_e/models/nerstf/mlp.py b/spaces/vishnu0001/text2mesh/shap_e/models/nerstf/mlp.py
deleted file mode 100644
index 71463b964c2b47536786897b2b29377c67a0e1ec..0000000000000000000000000000000000000000
--- a/spaces/vishnu0001/text2mesh/shap_e/models/nerstf/mlp.py
+++ /dev/null
@@ -1,173 +0,0 @@
-from typing import Any, Dict, Optional, Tuple
-
-import torch
-
-from shap_e.models.nn.ops import get_act
-from shap_e.models.query import Query
-from shap_e.models.stf.mlp import MLPModel
-from shap_e.util.collections import AttrDict
-
-
-class MLPDensitySDFModel(MLPModel):
-    def __init__(
-        self,
-        initial_bias: float = -0.1,
-        sdf_activation="tanh",
-        density_activation="exp",
-        **kwargs,
-    ):
-        super().__init__(
-            n_output=2,
-            output_activation="identity",
-            **kwargs,
-        )
-        self.mlp[-1].bias[0].data.fill_(initial_bias)
-        self.sdf_activation = get_act(sdf_activation)
-        self.density_activation = get_act(density_activation)
-
-    def forward(
-        self,
-        query: Query,
-        params: Optional[Dict[str, torch.Tensor]] = None,
-        options: Optional[Dict[str, Any]] = None,
-    ) -> AttrDict[str, Any]:
-        # query.direction is None typically for SDF models and training
-        h, _h_directionless = self._mlp(
-            query.position, query.direction, params=params, options=options
-        )
-        h_sdf, h_density = h.split(1, dim=-1)
-        return AttrDict(
-            density=self.density_activation(h_density),
-            signed_distance=self.sdf_activation(h_sdf),
-        )
-
-
-class MLPNeRSTFModel(MLPModel):
-    def __init__(
-        self,
-        sdf_activation="tanh",
-        density_activation="exp",
-        channel_activation="sigmoid",
-        direction_dependent_shape: bool = True,  # To be able to load old models. Set this to be False in future models.
-        separate_nerf_channels: bool = False,
-        separate_coarse_channels: bool = False,
-        initial_density_bias: float = 0.0,
-        initial_sdf_bias: float = -0.1,
-        **kwargs,
-    ):
-        h_map, h_directionless_map = indices_for_output_mode(
-            direction_dependent_shape=direction_dependent_shape,
-            separate_nerf_channels=separate_nerf_channels,
-            separate_coarse_channels=separate_coarse_channels,
-        )
-        n_output = index_mapping_max(h_map)
-        super().__init__(
-            n_output=n_output,
-            output_activation="identity",
-            **kwargs,
-        )
-        self.direction_dependent_shape = direction_dependent_shape
-        self.separate_nerf_channels = separate_nerf_channels
-        self.separate_coarse_channels = separate_coarse_channels
-        self.sdf_activation = get_act(sdf_activation)
-        self.density_activation = get_act(density_activation)
-        self.channel_activation = get_act(channel_activation)
-        self.h_map = h_map
-        self.h_directionless_map = h_directionless_map
-        self.mlp[-1].bias.data.zero_()
-        layer = -1 if self.direction_dependent_shape else self.insert_direction_at
-        self.mlp[layer].bias[0].data.fill_(initial_sdf_bias)
-        self.mlp[layer].bias[1].data.fill_(initial_density_bias)
-
-    def forward(
-        self,
-        query: Query,
-        params: Optional[Dict[str, torch.Tensor]] = None,
-        options: Optional[Dict[str, Any]] = None,
-    ) -> AttrDict[str, Any]:
-        options = AttrDict() if options is None else AttrDict(options)
-        h, h_directionless = self._mlp(
-            query.position, query.direction, params=params, options=options
-        )
-        activations = map_indices_to_keys(self.h_map, h)
-        activations.update(map_indices_to_keys(self.h_directionless_map, h_directionless))
-
-        if options.nerf_level == "coarse":
-            h_density = activations.density_coarse
-        else:
-            h_density = activations.density_fine
-
-        if options.get("rendering_mode", "stf") == "nerf":
-            if options.nerf_level == "coarse":
-                h_channels = activations.nerf_coarse
-            else:
-                h_channels = activations.nerf_fine
-        else:
-            h_channels = activations.stf
-        return AttrDict(
-            density=self.density_activation(h_density),
-            signed_distance=self.sdf_activation(activations.sdf),
-            channels=self.channel_activation(h_channels),
-        )
-
-
-IndexMapping = AttrDict[str, Tuple[int, int]]
-
-
-def indices_for_output_mode(
-    direction_dependent_shape: bool,
-    separate_nerf_channels: bool,
-    separate_coarse_channels: bool,
-) -> Tuple[IndexMapping, IndexMapping]:
-    """
-    Get output mappings for (h, h_directionless).
-    """
-    h_map = AttrDict()
-    h_directionless_map = AttrDict()
-    if direction_dependent_shape:
-        h_map.sdf = (0, 1)
-        if separate_coarse_channels:
-            assert separate_nerf_channels
-            h_map.density_coarse = (1, 2)
-            h_map.density_fine = (2, 3)
-            h_map.stf = (3, 6)
-            h_map.nerf_coarse = (6, 9)
-            h_map.nerf_fine = (9, 12)
-        else:
-            h_map.density_coarse = (1, 2)
-            h_map.density_fine = (1, 2)
-            if separate_nerf_channels:
-                h_map.stf = (2, 5)
-                h_map.nerf_coarse = (5, 8)
-                h_map.nerf_fine = (5, 8)
-            else:
-                h_map.stf = (2, 5)
-                h_map.nerf_coarse = (2, 5)
-                h_map.nerf_fine = (2, 5)
-    else:
-        h_directionless_map.sdf = (0, 1)
-        h_directionless_map.density_coarse = (1, 2)
-        if separate_coarse_channels:
-            h_directionless_map.density_fine = (2, 3)
-        else:
-            h_directionless_map.density_fine = h_directionless_map.density_coarse
-        h_map.stf = (0, 3)
-        if separate_coarse_channels:
-            assert separate_nerf_channels
-            h_map.nerf_coarse = (3, 6)
-            h_map.nerf_fine = (6, 9)
-        else:
-            if separate_nerf_channels:
-                h_map.nerf_coarse = (3, 6)
-            else:
-                h_map.nerf_coarse = (0, 3)
-            h_map.nerf_fine = h_map.nerf_coarse
-    return h_map, h_directionless_map
-
-
-def map_indices_to_keys(mapping: IndexMapping, data: torch.Tensor) -> AttrDict[str, torch.Tensor]:
-    return AttrDict({k: data[..., start:end] for k, (start, end) in mapping.items()})
-
-
-def index_mapping_max(mapping: IndexMapping) -> int:
-    return max(end for _, (_, end) in mapping.items())
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/base_runner.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/base_runner.py
deleted file mode 100644
index 4928db0a73b56fe0218a4bf66ec4ffa082d31ccc..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/base_runner.py
+++ /dev/null
@@ -1,542 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import logging
-import os.path as osp
-import warnings
-from abc import ABCMeta, abstractmethod
-
-import torch
-from torch.optim import Optimizer
-
-import annotator.uniformer.mmcv as mmcv
-from ..parallel import is_module_wrapper
-from .checkpoint import load_checkpoint
-from .dist_utils import get_dist_info
-from .hooks import HOOKS, Hook
-from .log_buffer import LogBuffer
-from .priority import Priority, get_priority
-from .utils import get_time_str
-
-
-class BaseRunner(metaclass=ABCMeta):
-    """The base class of Runner, a training helper for PyTorch.
-
-    All subclasses should implement the following APIs:
-
-    - ``run()``
-    - ``train()``
-    - ``val()``
-    - ``save_checkpoint()``
-
-    Args:
-        model (:obj:`torch.nn.Module`): The model to be run.
-        batch_processor (callable): A callable method that process a data
-            batch. The interface of this method should be
-            `batch_processor(model, data, train_mode) -> dict`
-        optimizer (dict or :obj:`torch.optim.Optimizer`): It can be either an
-            optimizer (in most cases) or a dict of optimizers (in models that
-            requires more than one optimizer, e.g., GAN).
-        work_dir (str, optional): The working directory to save checkpoints
-            and logs. Defaults to None.
-        logger (:obj:`logging.Logger`): Logger used during training.
-             Defaults to None. (The default value is just for backward
-             compatibility)
-        meta (dict | None): A dict records some import information such as
-            environment info and seed, which will be logged in logger hook.
-            Defaults to None.
-        max_epochs (int, optional): Total training epochs.
-        max_iters (int, optional): Total training iterations.
-    """
-
-    def __init__(self,
-                 model,
-                 batch_processor=None,
-                 optimizer=None,
-                 work_dir=None,
-                 logger=None,
-                 meta=None,
-                 max_iters=None,
-                 max_epochs=None):
-        if batch_processor is not None:
-            if not callable(batch_processor):
-                raise TypeError('batch_processor must be callable, '
-                                f'but got {type(batch_processor)}')
-            warnings.warn('batch_processor is deprecated, please implement '
-                          'train_step() and val_step() in the model instead.')
-            # raise an error is `batch_processor` is not None and
-            # `model.train_step()` exists.
-            if is_module_wrapper(model):
-                _model = model.module
-            else:
-                _model = model
-            if hasattr(_model, 'train_step') or hasattr(_model, 'val_step'):
-                raise RuntimeError(
-                    'batch_processor and model.train_step()/model.val_step() '
-                    'cannot be both available.')
-        else:
-            assert hasattr(model, 'train_step')
-
-        # check the type of `optimizer`
-        if isinstance(optimizer, dict):
-            for name, optim in optimizer.items():
-                if not isinstance(optim, Optimizer):
-                    raise TypeError(
-                        f'optimizer must be a dict of torch.optim.Optimizers, '
-                        f'but optimizer["{name}"] is a {type(optim)}')
-        elif not isinstance(optimizer, Optimizer) and optimizer is not None:
-            raise TypeError(
-                f'optimizer must be a torch.optim.Optimizer object '
-                f'or dict or None, but got {type(optimizer)}')
-
-        # check the type of `logger`
-        if not isinstance(logger, logging.Logger):
-            raise TypeError(f'logger must be a logging.Logger object, '
-                            f'but got {type(logger)}')
-
-        # check the type of `meta`
-        if meta is not None and not isinstance(meta, dict):
-            raise TypeError(
-                f'meta must be a dict or None, but got {type(meta)}')
-
-        self.model = model
-        self.batch_processor = batch_processor
-        self.optimizer = optimizer
-        self.logger = logger
-        self.meta = meta
-        # create work_dir
-        if mmcv.is_str(work_dir):
-            self.work_dir = osp.abspath(work_dir)
-            mmcv.mkdir_or_exist(self.work_dir)
-        elif work_dir is None:
-            self.work_dir = None
-        else:
-            raise TypeError('"work_dir" must be a str or None')
-
-        # get model name from the model class
-        if hasattr(self.model, 'module'):
-            self._model_name = self.model.module.__class__.__name__
-        else:
-            self._model_name = self.model.__class__.__name__
-
-        self._rank, self._world_size = get_dist_info()
-        self.timestamp = get_time_str()
-        self.mode = None
-        self._hooks = []
-        self._epoch = 0
-        self._iter = 0
-        self._inner_iter = 0
-
-        if max_epochs is not None and max_iters is not None:
-            raise ValueError(
-                'Only one of `max_epochs` or `max_iters` can be set.')
-
-        self._max_epochs = max_epochs
-        self._max_iters = max_iters
-        # TODO: Redesign LogBuffer, it is not flexible and elegant enough
-        self.log_buffer = LogBuffer()
-
-    @property
-    def model_name(self):
-        """str: Name of the model, usually the module class name."""
-        return self._model_name
-
-    @property
-    def rank(self):
-        """int: Rank of current process. (distributed training)"""
-        return self._rank
-
-    @property
-    def world_size(self):
-        """int: Number of processes participating in the job.
-        (distributed training)"""
-        return self._world_size
-
-    @property
-    def hooks(self):
-        """list[:obj:`Hook`]: A list of registered hooks."""
-        return self._hooks
-
-    @property
-    def epoch(self):
-        """int: Current epoch."""
-        return self._epoch
-
-    @property
-    def iter(self):
-        """int: Current iteration."""
-        return self._iter
-
-    @property
-    def inner_iter(self):
-        """int: Iteration in an epoch."""
-        return self._inner_iter
-
-    @property
-    def max_epochs(self):
-        """int: Maximum training epochs."""
-        return self._max_epochs
-
-    @property
-    def max_iters(self):
-        """int: Maximum training iterations."""
-        return self._max_iters
-
-    @abstractmethod
-    def train(self):
-        pass
-
-    @abstractmethod
-    def val(self):
-        pass
-
-    @abstractmethod
-    def run(self, data_loaders, workflow, **kwargs):
-        pass
-
-    @abstractmethod
-    def save_checkpoint(self,
-                        out_dir,
-                        filename_tmpl,
-                        save_optimizer=True,
-                        meta=None,
-                        create_symlink=True):
-        pass
-
-    def current_lr(self):
-        """Get current learning rates.
-
-        Returns:
-            list[float] | dict[str, list[float]]: Current learning rates of all
-                param groups. If the runner has a dict of optimizers, this
-                method will return a dict.
-        """
-        if isinstance(self.optimizer, torch.optim.Optimizer):
-            lr = [group['lr'] for group in self.optimizer.param_groups]
-        elif isinstance(self.optimizer, dict):
-            lr = dict()
-            for name, optim in self.optimizer.items():
-                lr[name] = [group['lr'] for group in optim.param_groups]
-        else:
-            raise RuntimeError(
-                'lr is not applicable because optimizer does not exist.')
-        return lr
-
-    def current_momentum(self):
-        """Get current momentums.
-
-        Returns:
-            list[float] | dict[str, list[float]]: Current momentums of all
-                param groups. If the runner has a dict of optimizers, this
-                method will return a dict.
-        """
-
-        def _get_momentum(optimizer):
-            momentums = []
-            for group in optimizer.param_groups:
-                if 'momentum' in group.keys():
-                    momentums.append(group['momentum'])
-                elif 'betas' in group.keys():
-                    momentums.append(group['betas'][0])
-                else:
-                    momentums.append(0)
-            return momentums
-
-        if self.optimizer is None:
-            raise RuntimeError(
-                'momentum is not applicable because optimizer does not exist.')
-        elif isinstance(self.optimizer, torch.optim.Optimizer):
-            momentums = _get_momentum(self.optimizer)
-        elif isinstance(self.optimizer, dict):
-            momentums = dict()
-            for name, optim in self.optimizer.items():
-                momentums[name] = _get_momentum(optim)
-        return momentums
-
-    def register_hook(self, hook, priority='NORMAL'):
-        """Register a hook into the hook list.
-
-        The hook will be inserted into a priority queue, with the specified
-        priority (See :class:`Priority` for details of priorities).
-        For hooks with the same priority, they will be triggered in the same
-        order as they are registered.
-
-        Args:
-            hook (:obj:`Hook`): The hook to be registered.
-            priority (int or str or :obj:`Priority`): Hook priority.
-                Lower value means higher priority.
-        """
-        assert isinstance(hook, Hook)
-        if hasattr(hook, 'priority'):
-            raise ValueError('"priority" is a reserved attribute for hooks')
-        priority = get_priority(priority)
-        hook.priority = priority
-        # insert the hook to a sorted list
-        inserted = False
-        for i in range(len(self._hooks) - 1, -1, -1):
-            if priority >= self._hooks[i].priority:
-                self._hooks.insert(i + 1, hook)
-                inserted = True
-                break
-        if not inserted:
-            self._hooks.insert(0, hook)
-
-    def register_hook_from_cfg(self, hook_cfg):
-        """Register a hook from its cfg.
-
-        Args:
-            hook_cfg (dict): Hook config. It should have at least keys 'type'
-              and 'priority' indicating its type and priority.
-
-        Notes:
-            The specific hook class to register should not use 'type' and
-            'priority' arguments during initialization.
-        """
-        hook_cfg = hook_cfg.copy()
-        priority = hook_cfg.pop('priority', 'NORMAL')
-        hook = mmcv.build_from_cfg(hook_cfg, HOOKS)
-        self.register_hook(hook, priority=priority)
-
-    def call_hook(self, fn_name):
-        """Call all hooks.
-
-        Args:
-            fn_name (str): The function name in each hook to be called, such as
-                "before_train_epoch".
-        """
-        for hook in self._hooks:
-            getattr(hook, fn_name)(self)
-
-    def get_hook_info(self):
-        # Get hooks info in each stage
-        stage_hook_map = {stage: [] for stage in Hook.stages}
-        for hook in self.hooks:
-            try:
-                priority = Priority(hook.priority).name
-            except ValueError:
-                priority = hook.priority
-            classname = hook.__class__.__name__
-            hook_info = f'({priority:<12}) {classname:<35}'
-            for trigger_stage in hook.get_triggered_stages():
-                stage_hook_map[trigger_stage].append(hook_info)
-
-        stage_hook_infos = []
-        for stage in Hook.stages:
-            hook_infos = stage_hook_map[stage]
-            if len(hook_infos) > 0:
-                info = f'{stage}:\n'
-                info += '\n'.join(hook_infos)
-                info += '\n -------------------- '
-                stage_hook_infos.append(info)
-        return '\n'.join(stage_hook_infos)
-
-    def load_checkpoint(self,
-                        filename,
-                        map_location='cpu',
-                        strict=False,
-                        revise_keys=[(r'^module.', '')]):
-        return load_checkpoint(
-            self.model,
-            filename,
-            map_location,
-            strict,
-            self.logger,
-            revise_keys=revise_keys)
-
-    def resume(self,
-               checkpoint,
-               resume_optimizer=True,
-               map_location='default'):
-        if map_location == 'default':
-            if torch.cuda.is_available():
-                device_id = torch.cuda.current_device()
-                checkpoint = self.load_checkpoint(
-                    checkpoint,
-                    map_location=lambda storage, loc: storage.cuda(device_id))
-            else:
-                checkpoint = self.load_checkpoint(checkpoint)
-        else:
-            checkpoint = self.load_checkpoint(
-                checkpoint, map_location=map_location)
-
-        self._epoch = checkpoint['meta']['epoch']
-        self._iter = checkpoint['meta']['iter']
-        if self.meta is None:
-            self.meta = {}
-        self.meta.setdefault('hook_msgs', {})
-        # load `last_ckpt`, `best_score`, `best_ckpt`, etc. for hook messages
-        self.meta['hook_msgs'].update(checkpoint['meta'].get('hook_msgs', {}))
-
-        # Re-calculate the number of iterations when resuming
-        # models with different number of GPUs
-        if 'config' in checkpoint['meta']:
-            config = mmcv.Config.fromstring(
-                checkpoint['meta']['config'], file_format='.py')
-            previous_gpu_ids = config.get('gpu_ids', None)
-            if previous_gpu_ids and len(previous_gpu_ids) > 0 and len(
-                    previous_gpu_ids) != self.world_size:
-                self._iter = int(self._iter * len(previous_gpu_ids) /
-                                 self.world_size)
-                self.logger.info('the iteration number is changed due to '
-                                 'change of GPU number')
-
-        # resume meta information meta
-        self.meta = checkpoint['meta']
-
-        if 'optimizer' in checkpoint and resume_optimizer:
-            if isinstance(self.optimizer, Optimizer):
-                self.optimizer.load_state_dict(checkpoint['optimizer'])
-            elif isinstance(self.optimizer, dict):
-                for k in self.optimizer.keys():
-                    self.optimizer[k].load_state_dict(
-                        checkpoint['optimizer'][k])
-            else:
-                raise TypeError(
-                    'Optimizer should be dict or torch.optim.Optimizer '
-                    f'but got {type(self.optimizer)}')
-
-        self.logger.info('resumed epoch %d, iter %d', self.epoch, self.iter)
-
-    def register_lr_hook(self, lr_config):
-        if lr_config is None:
-            return
-        elif isinstance(lr_config, dict):
-            assert 'policy' in lr_config
-            policy_type = lr_config.pop('policy')
-            # If the type of policy is all in lower case, e.g., 'cyclic',
-            # then its first letter will be capitalized, e.g., to be 'Cyclic'.
-            # This is for the convenient usage of Lr updater.
-            # Since this is not applicable for `
-            # CosineAnnealingLrUpdater`,
-            # the string will not be changed if it contains capital letters.
-            if policy_type == policy_type.lower():
-                policy_type = policy_type.title()
-            hook_type = policy_type + 'LrUpdaterHook'
-            lr_config['type'] = hook_type
-            hook = mmcv.build_from_cfg(lr_config, HOOKS)
-        else:
-            hook = lr_config
-        self.register_hook(hook, priority='VERY_HIGH')
-
-    def register_momentum_hook(self, momentum_config):
-        if momentum_config is None:
-            return
-        if isinstance(momentum_config, dict):
-            assert 'policy' in momentum_config
-            policy_type = momentum_config.pop('policy')
-            # If the type of policy is all in lower case, e.g., 'cyclic',
-            # then its first letter will be capitalized, e.g., to be 'Cyclic'.
-            # This is for the convenient usage of momentum updater.
-            # Since this is not applicable for
-            # `CosineAnnealingMomentumUpdater`,
-            # the string will not be changed if it contains capital letters.
-            if policy_type == policy_type.lower():
-                policy_type = policy_type.title()
-            hook_type = policy_type + 'MomentumUpdaterHook'
-            momentum_config['type'] = hook_type
-            hook = mmcv.build_from_cfg(momentum_config, HOOKS)
-        else:
-            hook = momentum_config
-        self.register_hook(hook, priority='HIGH')
-
-    def register_optimizer_hook(self, optimizer_config):
-        if optimizer_config is None:
-            return
-        if isinstance(optimizer_config, dict):
-            optimizer_config.setdefault('type', 'OptimizerHook')
-            hook = mmcv.build_from_cfg(optimizer_config, HOOKS)
-        else:
-            hook = optimizer_config
-        self.register_hook(hook, priority='ABOVE_NORMAL')
-
-    def register_checkpoint_hook(self, checkpoint_config):
-        if checkpoint_config is None:
-            return
-        if isinstance(checkpoint_config, dict):
-            checkpoint_config.setdefault('type', 'CheckpointHook')
-            hook = mmcv.build_from_cfg(checkpoint_config, HOOKS)
-        else:
-            hook = checkpoint_config
-        self.register_hook(hook, priority='NORMAL')
-
-    def register_logger_hooks(self, log_config):
-        if log_config is None:
-            return
-        log_interval = log_config['interval']
-        for info in log_config['hooks']:
-            logger_hook = mmcv.build_from_cfg(
-                info, HOOKS, default_args=dict(interval=log_interval))
-            self.register_hook(logger_hook, priority='VERY_LOW')
-
-    def register_timer_hook(self, timer_config):
-        if timer_config is None:
-            return
-        if isinstance(timer_config, dict):
-            timer_config_ = copy.deepcopy(timer_config)
-            hook = mmcv.build_from_cfg(timer_config_, HOOKS)
-        else:
-            hook = timer_config
-        self.register_hook(hook, priority='LOW')
-
-    def register_custom_hooks(self, custom_config):
-        if custom_config is None:
-            return
-
-        if not isinstance(custom_config, list):
-            custom_config = [custom_config]
-
-        for item in custom_config:
-            if isinstance(item, dict):
-                self.register_hook_from_cfg(item)
-            else:
-                self.register_hook(item, priority='NORMAL')
-
-    def register_profiler_hook(self, profiler_config):
-        if profiler_config is None:
-            return
-        if isinstance(profiler_config, dict):
-            profiler_config.setdefault('type', 'ProfilerHook')
-            hook = mmcv.build_from_cfg(profiler_config, HOOKS)
-        else:
-            hook = profiler_config
-        self.register_hook(hook)
-
-    def register_training_hooks(self,
-                                lr_config,
-                                optimizer_config=None,
-                                checkpoint_config=None,
-                                log_config=None,
-                                momentum_config=None,
-                                timer_config=dict(type='IterTimerHook'),
-                                custom_hooks_config=None):
-        """Register default and custom hooks for training.
-
-        Default and custom hooks include:
-
-        +----------------------+-------------------------+
-        | Hooks                | Priority                |
-        +======================+=========================+
-        | LrUpdaterHook        | VERY_HIGH (10)          |
-        +----------------------+-------------------------+
-        | MomentumUpdaterHook  | HIGH (30)               |
-        +----------------------+-------------------------+
-        | OptimizerStepperHook | ABOVE_NORMAL (40)       |
-        +----------------------+-------------------------+
-        | CheckpointSaverHook  | NORMAL (50)             |
-        +----------------------+-------------------------+
-        | IterTimerHook        | LOW (70)                |
-        +----------------------+-------------------------+
-        | LoggerHook(s)        | VERY_LOW (90)           |
-        +----------------------+-------------------------+
-        | CustomHook(s)        | defaults to NORMAL (50) |
-        +----------------------+-------------------------+
-
-        If custom hooks have same priority with default hooks, custom hooks
-        will be triggered after default hooks.
-        """
-        self.register_lr_hook(lr_config)
-        self.register_momentum_hook(momentum_config)
-        self.register_optimizer_hook(optimizer_config)
-        self.register_checkpoint_hook(checkpoint_config)
-        self.register_timer_hook(timer_config)
-        self.register_logger_hooks(log_config)
-        self.register_custom_hooks(custom_hooks_config)
diff --git a/spaces/vvd2003/Animals/README.md b/spaces/vvd2003/Animals/README.md
deleted file mode 100644
index ed58c24f6840047756e7e96bd3cc61efc8a2f5f5..0000000000000000000000000000000000000000
--- a/spaces/vvd2003/Animals/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Animals
-emoji: 📈
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/wendys-llc/panoptic-segment-anything/GroundingDINO/groundingdino/datasets/__init__.py b/spaces/wendys-llc/panoptic-segment-anything/GroundingDINO/groundingdino/datasets/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/wrdias/SD_WEBUI/README.md b/spaces/wrdias/SD_WEBUI/README.md
deleted file mode 100644
index 4764e29b128c0eb403d4873ca8c8f6eb485132d0..0000000000000000000000000000000000000000
--- a/spaces/wrdias/SD_WEBUI/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Stable Diffusion Webui on Cpu
-emoji: 🏃
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-python_version: 3.10.6
-duplicated_from: DreamSunny/stable-diffusion-webui-cpu
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/xfys/yolov5_tracking/yolov5/models/common.py b/spaces/xfys/yolov5_tracking/yolov5/models/common.py
deleted file mode 100644
index 195ccd63a10c64de45de57fc2fe1653d3e745a0e..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/yolov5/models/common.py
+++ /dev/null
@@ -1,871 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Common modules
-"""
-
-import ast
-import contextlib
-import json
-import math
-import platform
-import warnings
-import zipfile
-from collections import OrderedDict, namedtuple
-from copy import copy
-from pathlib import Path
-from urllib.parse import urlparse
-
-import cv2
-import numpy as np
-import pandas as pd
-import requests
-import torch
-import torch.nn as nn
-from PIL import Image
-from torch.cuda import amp
-
-from yolov5.utils import TryExcept
-from yolov5.utils.dataloaders import exif_transpose, letterbox
-from yolov5.utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
-                           increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
-                           xyxy2xywh, yaml_load)
-from yolov5.utils.plots import Annotator, colors, save_one_box
-from yolov5.utils.torch_utils import copy_attr, smart_inference_mode
-
-
-def autopad(k, p=None, d=1):  # kernel, padding, dilation
-    # Pad to 'same' shape outputs
-    if d > 1:
-        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
-    if p is None:
-        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
-    return p
-
-
-class Conv(nn.Module):
-    # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
-    default_act = nn.SiLU()  # default activation
-
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
-        super().__init__()
-        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
-
-    def forward(self, x):
-        return self.act(self.bn(self.conv(x)))
-
-    def forward_fuse(self, x):
-        return self.act(self.conv(x))
-
-
-class DWConv(Conv):
-    # Depth-wise convolution
-    def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out, kernel, stride, dilation, activation
-        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
-
-
-class DWConvTranspose2d(nn.ConvTranspose2d):
-    # Depth-wise transpose convolution
-    def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, kernel, stride, padding, padding_out
-        super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
-
-
-class TransformerLayer(nn.Module):
-    # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
-    def __init__(self, c, num_heads):
-        super().__init__()
-        self.q = nn.Linear(c, c, bias=False)
-        self.k = nn.Linear(c, c, bias=False)
-        self.v = nn.Linear(c, c, bias=False)
-        self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
-        self.fc1 = nn.Linear(c, c, bias=False)
-        self.fc2 = nn.Linear(c, c, bias=False)
-
-    def forward(self, x):
-        x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
-        x = self.fc2(self.fc1(x)) + x
-        return x
-
-
-class TransformerBlock(nn.Module):
-    # Vision Transformer https://arxiv.org/abs/2010.11929
-    def __init__(self, c1, c2, num_heads, num_layers):
-        super().__init__()
-        self.conv = None
-        if c1 != c2:
-            self.conv = Conv(c1, c2)
-        self.linear = nn.Linear(c2, c2)  # learnable position embedding
-        self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
-        self.c2 = c2
-
-    def forward(self, x):
-        if self.conv is not None:
-            x = self.conv(x)
-        b, _, w, h = x.shape
-        p = x.flatten(2).permute(2, 0, 1)
-        return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)
-
-
-class Bottleneck(nn.Module):
-    # Standard bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c2, 3, 1, g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class BottleneckCSP(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
-        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
-        self.cv4 = Conv(2 * c_, c2, 1, 1)
-        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
-        self.act = nn.SiLU()
-        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
-    def forward(self, x):
-        y1 = self.cv3(self.m(self.cv1(x)))
-        y2 = self.cv2(x)
-        return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
-
-
-class CrossConv(nn.Module):
-    # Cross Convolution Downsample
-    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
-        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, (1, k), (1, s))
-        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class C3(nn.Module):
-    # CSP Bottleneck with 3 convolutions
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
-        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
-    def forward(self, x):
-        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
-
-
-class C3x(C3):
-    # C3 module with cross-convolutions
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)
-        self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
-
-
-class C3TR(C3):
-    # C3 module with TransformerBlock()
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)
-        self.m = TransformerBlock(c_, c_, 4, n)
-
-
-class C3SPP(C3):
-    # C3 module with SPP()
-    def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)
-        self.m = SPP(c_, c_, k)
-
-
-class C3Ghost(C3):
-    # C3 module with GhostBottleneck()
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
-
-
-class SPP(nn.Module):
-    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
-    def __init__(self, c1, c2, k=(5, 9, 13)):
-        super().__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
-        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
-    def forward(self, x):
-        x = self.cv1(x)
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
-            return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-
-
-class SPPF(nn.Module):
-    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
-    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
-        super().__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_ * 4, c2, 1, 1)
-        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
-
-    def forward(self, x):
-        x = self.cv1(x)
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
-            y1 = self.m(x)
-            y2 = self.m(y1)
-            return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
-
-
-class Focus(nn.Module):
-    # Focus wh information into c-space
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super().__init__()
-        self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
-        # self.contract = Contract(gain=2)
-
-    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
-        return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
-        # return self.conv(self.contract(x))
-
-
-class GhostConv(nn.Module):
-    # Ghost Convolution https://github.com/huawei-noah/ghostnet
-    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
-        super().__init__()
-        c_ = c2 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
-        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)
-
-    def forward(self, x):
-        y = self.cv1(x)
-        return torch.cat((y, self.cv2(y)), 1)
-
-
-class GhostBottleneck(nn.Module):
-    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
-    def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
-        super().__init__()
-        c_ = c2 // 2
-        self.conv = nn.Sequential(
-            GhostConv(c1, c_, 1, 1),  # pw
-            DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
-            GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
-        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
-                                                                            act=False)) if s == 2 else nn.Identity()
-
-    def forward(self, x):
-        return self.conv(x) + self.shortcut(x)
-
-
-class Contract(nn.Module):
-    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
-    def __init__(self, gain=2):
-        super().__init__()
-        self.gain = gain
-
-    def forward(self, x):
-        b, c, h, w = x.size()  # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
-        s = self.gain
-        x = x.view(b, c, h // s, s, w // s, s)  # x(1,64,40,2,40,2)
-        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
-        return x.view(b, c * s * s, h // s, w // s)  # x(1,256,40,40)
-
-
-class Expand(nn.Module):
-    # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
-    def __init__(self, gain=2):
-        super().__init__()
-        self.gain = gain
-
-    def forward(self, x):
-        b, c, h, w = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
-        s = self.gain
-        x = x.view(b, s, s, c // s ** 2, h, w)  # x(1,2,2,16,80,80)
-        x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
-        return x.view(b, c // s ** 2, h * s, w * s)  # x(1,16,160,160)
-
-
-class Concat(nn.Module):
-    # Concatenate a list of tensors along dimension
-    def __init__(self, dimension=1):
-        super().__init__()
-        self.d = dimension
-
-    def forward(self, x):
-        return torch.cat(x, self.d)
-
-
-class DetectMultiBackend(nn.Module):
-    # YOLOv5 MultiBackend class for python inference on various backends
-    def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
-        # Usage:
-        #   PyTorch:              weights = *.pt
-        #   TorchScript:                    *.torchscript
-        #   ONNX Runtime:                   *.onnx
-        #   ONNX OpenCV DNN:                *.onnx --dnn
-        #   OpenVINO:                       *_openvino_model
-        #   CoreML:                         *.mlmodel
-        #   TensorRT:                       *.engine
-        #   TensorFlow SavedModel:          *_saved_model
-        #   TensorFlow GraphDef:            *.pb
-        #   TensorFlow Lite:                *.tflite
-        #   TensorFlow Edge TPU:            *_edgetpu.tflite
-        #   PaddlePaddle:                   *_paddle_model
-        from models.experimental import attempt_download, attempt_load  # scoped to avoid circular import
-
-        super().__init__()
-        w = str(weights[0] if isinstance(weights, list) else weights)
-        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
-        fp16 &= pt or jit or onnx or engine  # FP16
-        nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
-        stride = 32  # default stride
-        cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
-        if not (pt or triton):
-            w = attempt_download(w)  # download if not local
-
-        if pt:  # PyTorch
-            model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
-            stride = max(int(model.stride.max()), 32)  # model stride
-            names = model.module.names if hasattr(model, 'module') else model.names  # get class names
-            model.half() if fp16 else model.float()
-            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
-        elif jit:  # TorchScript
-            LOGGER.info(f'Loading {w} for TorchScript inference...')
-            extra_files = {'config.txt': ''}  # model metadata
-            model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
-            model.half() if fp16 else model.float()
-            if extra_files['config.txt']:  # load metadata dict
-                d = json.loads(extra_files['config.txt'],
-                               object_hook=lambda d: {
-                                   int(k) if k.isdigit() else k: v
-                                   for k, v in d.items()})
-                stride, names = int(d['stride']), d['names']
-        elif dnn:  # ONNX OpenCV DNN
-            LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
-            check_requirements('opencv-python>=4.5.4')
-            net = cv2.dnn.readNetFromONNX(w)
-        elif onnx:  # ONNX Runtime
-            LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
-            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
-            import onnxruntime
-            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
-            session = onnxruntime.InferenceSession(w, providers=providers)
-            output_names = [x.name for x in session.get_outputs()]
-            meta = session.get_modelmeta().custom_metadata_map  # metadata
-            if 'stride' in meta:
-                stride, names = int(meta['stride']), eval(meta['names'])
-        elif xml:  # OpenVINO
-            LOGGER.info(f'Loading {w} for OpenVINO inference...')
-            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
-            from openvino.runtime import Core, Layout, get_batch
-            ie = Core()
-            if not Path(w).is_file():  # if not *.xml
-                w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
-            network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
-            if network.get_parameters()[0].get_layout().empty:
-                network.get_parameters()[0].set_layout(Layout('NCHW'))
-            batch_dim = get_batch(network)
-            if batch_dim.is_static:
-                batch_size = batch_dim.get_length()
-            executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for Intel NCS2
-            stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
-        elif engine:  # TensorRT
-            LOGGER.info(f'Loading {w} for TensorRT inference...')
-            import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
-            check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
-            if device.type == 'cpu':
-                device = torch.device('cuda:0')
-            Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
-            logger = trt.Logger(trt.Logger.INFO)
-            with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
-                model = runtime.deserialize_cuda_engine(f.read())
-            context = model.create_execution_context()
-            bindings = OrderedDict()
-            output_names = []
-            fp16 = False  # default updated below
-            dynamic = False
-            for i in range(model.num_bindings):
-                name = model.get_binding_name(i)
-                dtype = trt.nptype(model.get_binding_dtype(i))
-                if model.binding_is_input(i):
-                    if -1 in tuple(model.get_binding_shape(i)):  # dynamic
-                        dynamic = True
-                        context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
-                    if dtype == np.float16:
-                        fp16 = True
-                else:  # output
-                    output_names.append(name)
-                shape = tuple(context.get_binding_shape(i))
-                im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
-                bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
-            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
-            batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size
-        elif coreml:  # CoreML
-            LOGGER.info(f'Loading {w} for CoreML inference...')
-            import coremltools as ct
-            model = ct.models.MLModel(w)
-        elif saved_model:  # TF SavedModel
-            LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
-            import tensorflow as tf
-            keras = False  # assume TF1 saved_model
-            model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
-        elif pb:  # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
-            LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
-            import tensorflow as tf
-
-            def wrap_frozen_graph(gd, inputs, outputs):
-                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), [])  # wrapped
-                ge = x.graph.as_graph_element
-                return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
-
-            def gd_outputs(gd):
-                name_list, input_list = [], []
-                for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
-                    name_list.append(node.name)
-                    input_list.extend(node.input)
-                return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
-
-            gd = tf.Graph().as_graph_def()  # TF GraphDef
-            with open(w, 'rb') as f:
-                gd.ParseFromString(f.read())
-            frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd))
-        elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
-            try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
-                from tflite_runtime.interpreter import Interpreter, load_delegate
-            except ImportError:
-                import tensorflow as tf
-                Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
-            if edgetpu:  # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
-                LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
-                delegate = {
-                    'Linux': 'libedgetpu.so.1',
-                    'Darwin': 'libedgetpu.1.dylib',
-                    'Windows': 'edgetpu.dll'}[platform.system()]
-                interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
-            else:  # TFLite
-                LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
-                interpreter = Interpreter(model_path=w)  # load TFLite model
-            interpreter.allocate_tensors()  # allocate
-            input_details = interpreter.get_input_details()  # inputs
-            output_details = interpreter.get_output_details()  # outputs
-            # load metadata
-            with contextlib.suppress(zipfile.BadZipFile):
-                with zipfile.ZipFile(w, 'r') as model:
-                    meta_file = model.namelist()[0]
-                    meta = ast.literal_eval(model.read(meta_file).decode('utf-8'))
-                    stride, names = int(meta['stride']), meta['names']
-        elif tfjs:  # TF.js
-            raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
-        elif paddle:  # PaddlePaddle
-            LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
-            check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
-            import paddle.inference as pdi
-            if not Path(w).is_file():  # if not *.pdmodel
-                w = next(Path(w).rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
-            weights = Path(w).with_suffix('.pdiparams')
-            config = pdi.Config(str(w), str(weights))
-            if cuda:
-                config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
-            predictor = pdi.create_predictor(config)
-            input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
-            output_names = predictor.get_output_names()
-        elif triton:  # NVIDIA Triton Inference Server
-            LOGGER.info(f'Using {w} as Triton Inference Server...')
-            check_requirements('tritonclient[all]')
-            from utils.triton import TritonRemoteModel
-            model = TritonRemoteModel(url=w)
-            nhwc = model.runtime.startswith('tensorflow')
-        else:
-            raise NotImplementedError(f'ERROR: {w} is not a supported format')
-
-        # class names
-        if 'names' not in locals():
-            names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}
-        if names[0] == 'n01440764' and len(names) == 1000:  # ImageNet
-            names = yaml_load(ROOT / 'data/ImageNet.yaml')['names']  # human-readable names
-
-        self.__dict__.update(locals())  # assign all variables to self
-
-    def forward(self, im, augment=False, visualize=False):
-        # YOLOv5 MultiBackend inference
-        b, ch, h, w = im.shape  # batch, channel, height, width
-        if self.fp16 and im.dtype != torch.float16:
-            im = im.half()  # to FP16
-        if self.nhwc:
-            im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)
-
-        if self.pt:  # PyTorch
-            y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
-        elif self.jit:  # TorchScript
-            y = self.model(im)
-        elif self.dnn:  # ONNX OpenCV DNN
-            im = im.cpu().numpy()  # torch to numpy
-            self.net.setInput(im)
-            y = self.net.forward()
-        elif self.onnx:  # ONNX Runtime
-            im = im.cpu().numpy()  # torch to numpy
-            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
-        elif self.xml:  # OpenVINO
-            im = im.cpu().numpy()  # FP32
-            y = list(self.executable_network([im]).values())
-        elif self.engine:  # TensorRT
-            if self.dynamic and im.shape != self.bindings['images'].shape:
-                i = self.model.get_binding_index('images')
-                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
-                self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
-                for name in self.output_names:
-                    i = self.model.get_binding_index(name)
-                    self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
-            s = self.bindings['images'].shape
-            assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
-            self.binding_addrs['images'] = int(im.data_ptr())
-            self.context.execute_v2(list(self.binding_addrs.values()))
-            y = [self.bindings[x].data for x in sorted(self.output_names)]
-        elif self.coreml:  # CoreML
-            im = im.cpu().numpy()
-            im = Image.fromarray((im[0] * 255).astype('uint8'))
-            # im = im.resize((192, 320), Image.ANTIALIAS)
-            y = self.model.predict({'image': im})  # coordinates are xywh normalized
-            if 'confidence' in y:
-                box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
-                conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
-                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
-            else:
-                y = list(reversed(y.values()))  # reversed for segmentation models (pred, proto)
-        elif self.paddle:  # PaddlePaddle
-            im = im.cpu().numpy().astype(np.float32)
-            self.input_handle.copy_from_cpu(im)
-            self.predictor.run()
-            y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
-        elif self.triton:  # NVIDIA Triton Inference Server
-            y = self.model(im)
-        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
-            im = im.cpu().numpy()
-            if self.saved_model:  # SavedModel
-                y = self.model(im, training=False) if self.keras else self.model(im)
-            elif self.pb:  # GraphDef
-                y = self.frozen_func(x=self.tf.constant(im))
-            else:  # Lite or Edge TPU
-                input = self.input_details[0]
-                int8 = input['dtype'] == np.uint8  # is TFLite quantized uint8 model
-                if int8:
-                    scale, zero_point = input['quantization']
-                    im = (im / scale + zero_point).astype(np.uint8)  # de-scale
-                self.interpreter.set_tensor(input['index'], im)
-                self.interpreter.invoke()
-                y = []
-                for output in self.output_details:
-                    x = self.interpreter.get_tensor(output['index'])
-                    if int8:
-                        scale, zero_point = output['quantization']
-                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
-                    y.append(x)
-            y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
-            y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels
-
-        if isinstance(y, (list, tuple)):
-            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
-        else:
-            return self.from_numpy(y)
-
-    def from_numpy(self, x):
-        return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x
-
-    def warmup(self, imgsz=(1, 3, 640, 640)):
-        # Warmup model by running inference once
-        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
-        if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
-            im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
-            for _ in range(2 if self.jit else 1):  #
-                self.forward(im)  # warmup
-
-    @staticmethod
-    def _model_type(p='path/to/model.pt'):
-        # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
-        # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
-        from yolov5.export import export_formats
-        from yolov5.utils.downloads import is_url
-        sf = list(export_formats().Suffix)  # export suffixes
-        if not is_url(p, check=False):
-            check_suffix(p, sf)  # checks
-        url = urlparse(p)  # if url may be Triton inference server
-        types = [s in Path(p).name for s in sf]
-        types[8] &= not types[9]  # tflite &= not edgetpu
-        triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
-        return types + [triton]
-
-    @staticmethod
-    def _load_metadata(f=Path('path/to/meta.yaml')):
-        # Load metadata from meta.yaml if it exists
-        if f.exists():
-            d = yaml_load(f)
-            return d['stride'], d['names']  # assign stride, names
-        return None, None
-
-
-class AutoShape(nn.Module):
-    # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
-    conf = 0.25  # NMS confidence threshold
-    iou = 0.45  # NMS IoU threshold
-    agnostic = False  # NMS class-agnostic
-    multi_label = False  # NMS multiple labels per box
-    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
-    max_det = 1000  # maximum number of detections per image
-    amp = False  # Automatic Mixed Precision (AMP) inference
-
-    def __init__(self, model, verbose=True):
-        super().__init__()
-        if verbose:
-            LOGGER.info('Adding AutoShape... ')
-        copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
-        self.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instance
-        self.pt = not self.dmb or model.pt  # PyTorch model
-        self.model = model.eval()
-        if self.pt:
-            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
-            m.inplace = False  # Detect.inplace=False for safe multithread inference
-            m.export = True  # do not output loss values
-
-    def _apply(self, fn):
-        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
-        self = super()._apply(fn)
-        if self.pt:
-            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
-            m.stride = fn(m.stride)
-            m.grid = list(map(fn, m.grid))
-            if isinstance(m.anchor_grid, list):
-                m.anchor_grid = list(map(fn, m.anchor_grid))
-        return self
-
-    @smart_inference_mode()
-    def forward(self, ims, size=640, augment=False, profile=False):
-        # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
-        #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
-        #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
-        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
-        #   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # HWC x(640,1280,3)
-        #   numpy:           = np.zeros((640,1280,3))  # HWC
-        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
-        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
-
-        dt = (Profile(), Profile(), Profile())
-        with dt[0]:
-            if isinstance(size, int):  # expand
-                size = (size, size)
-            p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
-            autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
-            if isinstance(ims, torch.Tensor):  # torch
-                with amp.autocast(autocast):
-                    return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference
-
-            # Pre-process
-            n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
-            shape0, shape1, files = [], [], []  # image and inference shapes, filenames
-            for i, im in enumerate(ims):
-                f = f'image{i}'  # filename
-                if isinstance(im, (str, Path)):  # filename or uri
-                    im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
-                    im = np.asarray(exif_transpose(im))
-                elif isinstance(im, Image.Image):  # PIL Image
-                    im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
-                files.append(Path(f).with_suffix('.jpg').name)
-                if im.shape[0] < 5:  # image in CHW
-                    im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
-                im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
-                s = im.shape[:2]  # HWC
-                shape0.append(s)  # image shape
-                g = max(size) / max(s)  # gain
-                shape1.append([int(y * g) for y in s])
-                ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
-            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)]  # inf shape
-            x = [letterbox(im, shape1, auto=False)[0] for im in ims]  # pad
-            x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
-            x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
-
-        with amp.autocast(autocast):
-            # Inference
-            with dt[1]:
-                y = self.model(x, augment=augment)  # forward
-
-            # Post-process
-            with dt[2]:
-                y = non_max_suppression(y if self.dmb else y[0],
-                                        self.conf,
-                                        self.iou,
-                                        self.classes,
-                                        self.agnostic,
-                                        self.multi_label,
-                                        max_det=self.max_det)  # NMS
-                for i in range(n):
-                    scale_boxes(shape1, y[i][:, :4], shape0[i])
-
-            return Detections(ims, y, files, dt, self.names, x.shape)
-
-
-class Detections:
-    # YOLOv5 detections class for inference results
-    def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
-        super().__init__()
-        d = pred[0].device  # device
-        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims]  # normalizations
-        self.ims = ims  # list of images as numpy arrays
-        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
-        self.names = names  # class names
-        self.files = files  # image filenames
-        self.times = times  # profiling times
-        self.xyxy = pred  # xyxy pixels
-        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
-        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
-        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
-        self.n = len(self.pred)  # number of images (batch size)
-        self.t = tuple(x.t / self.n * 1E3 for x in times)  # timestamps (ms)
-        self.s = tuple(shape)  # inference BCHW shape
-
-    def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
-        s, crops = '', []
-        for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
-            s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
-            if pred.shape[0]:
-                for c in pred[:, -1].unique():
-                    n = (pred[:, -1] == c).sum()  # detections per class
-                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
-                s = s.rstrip(', ')
-                if show or save or render or crop:
-                    annotator = Annotator(im, example=str(self.names))
-                    for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
-                        label = f'{self.names[int(cls)]} {conf:.2f}'
-                        if crop:
-                            file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
-                            crops.append({
-                                'box': box,
-                                'conf': conf,
-                                'cls': cls,
-                                'label': label,
-                                'im': save_one_box(box, im, file=file, save=save)})
-                        else:  # all others
-                            annotator.box_label(box, label if labels else '', color=colors(cls))
-                    im = annotator.im
-            else:
-                s += '(no detections)'
-
-            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
-            if show:
-                if is_jupyter():
-                    from IPython.display import display
-                    display(im)
-                else:
-                    im.show(self.files[i])
-            if save:
-                f = self.files[i]
-                im.save(save_dir / f)  # save
-                if i == self.n - 1:
-                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
-            if render:
-                self.ims[i] = np.asarray(im)
-        if pprint:
-            s = s.lstrip('\n')
-            return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
-        if crop:
-            if save:
-                LOGGER.info(f'Saved results to {save_dir}\n')
-            return crops
-
-    @TryExcept('Showing images is not supported in this environment')
-    def show(self, labels=True):
-        self._run(show=True, labels=labels)  # show results
-
-    def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
-        save_dir = increment_path(save_dir, exist_ok, mkdir=True)  # increment save_dir
-        self._run(save=True, labels=labels, save_dir=save_dir)  # save results
-
-    def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
-        save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
-        return self._run(crop=True, save=save, save_dir=save_dir)  # crop results
-
-    def render(self, labels=True):
-        self._run(render=True, labels=labels)  # render results
-        return self.ims
-
-    def pandas(self):
-        # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
-        new = copy(self)  # return copy
-        ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
-        cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
-        for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
-            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
-            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
-        return new
-
-    def tolist(self):
-        # return a list of Detections objects, i.e. 'for result in results.tolist():'
-        r = range(self.n)  # iterable
-        x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
-        # for d in x:
-        #    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
-        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
-        return x
-
-    def print(self):
-        LOGGER.info(self.__str__())
-
-    def __len__(self):  # override len(results)
-        return self.n
-
-    def __str__(self):  # override print(results)
-        return self._run(pprint=True)  # print results
-
-    def __repr__(self):
-        return f'YOLOv5 {self.__class__} instance\n' + self.__str__()
-
-
-class Proto(nn.Module):
-    # YOLOv5 mask Proto module for segmentation models
-    def __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, number of masks
-        super().__init__()
-        self.cv1 = Conv(c1, c_, k=3)
-        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
-        self.cv2 = Conv(c_, c_, k=3)
-        self.cv3 = Conv(c_, c2)
-
-    def forward(self, x):
-        return self.cv3(self.cv2(self.upsample(self.cv1(x))))
-
-
-class Classify(nn.Module):
-    # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
-    def __init__(self,
-                 c1,
-                 c2,
-                 k=1,
-                 s=1,
-                 p=None,
-                 g=1,
-                 dropout_p=0.0):  # ch_in, ch_out, kernel, stride, padding, groups, dropout probability
-        super().__init__()
-        c_ = 1280  # efficientnet_b0 size
-        self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
-        self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)
-        self.drop = nn.Dropout(p=dropout_p, inplace=True)
-        self.linear = nn.Linear(c_, c2)  # to x(b,c2)
-
-    def forward(self, x):
-        if isinstance(x, list):
-            x = torch.cat(x, 1)
-        return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
diff --git a/spaces/yakubashsd/oim_images/README.md b/spaces/yakubashsd/oim_images/README.md
deleted file mode 100644
index b9fce2f1a3fb6d59b7985492d88488b0f5f8762e..0000000000000000000000000000000000000000
--- a/spaces/yakubashsd/oim_images/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Oim Images
-emoji: 😻
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yangogo/bingo/src/lib/hooks/chat-history.ts b/spaces/yangogo/bingo/src/lib/hooks/chat-history.ts
deleted file mode 100644
index c6fbf3fecfa86fe553f56acc8253236b8f22a775..0000000000000000000000000000000000000000
--- a/spaces/yangogo/bingo/src/lib/hooks/chat-history.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-import { zip } from 'lodash-es'
-import { ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { Storage } from '../storage'
-
-/**
- * conversations:$botId => Conversation[]
- * conversation:$botId:$cid:messages => ChatMessageModel[]
- */
-
-interface Conversation {
-  id: string
-  createdAt: number
-}
-
-type ConversationWithMessages = Conversation & { messages: ChatMessageModel[] }
-
-async function loadHistoryConversations(botId: BotId): Promise<Conversation[]> {
-  const key = `conversations:${botId}`
-  const { [key]: value } = await Storage.get(key)
-  return value || []
-}
-
-async function deleteHistoryConversation(botId: BotId, cid: string) {
-  const conversations = await loadHistoryConversations(botId)
-  const newConversations = conversations.filter((c) => c.id !== cid)
-  await Storage.set({ [`conversations:${botId}`]: newConversations })
-}
-
-async function loadConversationMessages(botId: BotId, cid: string): Promise<ChatMessageModel[]> {
-  const key = `conversation:${botId}:${cid}:messages`
-  const { [key]: value } = await Storage.get(key)
-  return value || []
-}
-
-export async function setConversationMessages(botId: BotId, cid: string, messages: ChatMessageModel[]) {
-  const conversations = await loadHistoryConversations(botId)
-  if (!conversations.some((c) => c.id === cid)) {
-    conversations.unshift({ id: cid, createdAt: Date.now() })
-    await Storage.set({ [`conversations:${botId}`]: conversations })
-  }
-  const key = `conversation:${botId}:${cid}:messages`
-  await Storage.set({ [key]: messages })
-}
-
-export async function loadHistoryMessages(botId: BotId): Promise<ConversationWithMessages[]> {
-  const conversations = await loadHistoryConversations(botId)
-  const messagesList = await Promise.all(conversations.map((c) => loadConversationMessages(botId, c.id)))
-  return zip(conversations, messagesList).map(([c, messages]) => ({
-    id: c!.id,
-    createdAt: c!.createdAt,
-    messages: messages!,
-  }))
-}
-
-export async function deleteHistoryMessage(botId: BotId, conversationId: string, messageId: string) {
-  const messages = await loadConversationMessages(botId, conversationId)
-  const newMessages = messages.filter((m) => m.id !== messageId)
-  await setConversationMessages(botId, conversationId, newMessages)
-  if (!newMessages.length) {
-    await deleteHistoryConversation(botId, conversationId)
-  }
-}
diff --git a/spaces/yangogo/bingo/src/lib/isomorphic/index.ts b/spaces/yangogo/bingo/src/lib/isomorphic/index.ts
deleted file mode 100644
index 738dc92f74079ab762d584fb7422a8c8c3b61547..0000000000000000000000000000000000000000
--- a/spaces/yangogo/bingo/src/lib/isomorphic/index.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-'use client'
-
-import Default from './browser'
-
-let exportsModel: any = {}
-
-if (process.browser) {
-  Object.assign(exportsModel, require('./browser').default)
-} else {
-  Object.assign(exportsModel, require('./node').default)
-}
-
-export default exportsModel! as typeof Default
-
-export const fetch: typeof Default.fetch = exportsModel!.fetch
-export const WebSocket: typeof Default.WebSocket = exportsModel!.WebSocket
-export const debug: typeof Default.debug = exportsModel!.debug
diff --git a/spaces/yaoshining/text-generation-webui/modules/presets.py b/spaces/yaoshining/text-generation-webui/modules/presets.py
deleted file mode 100644
index d8ae6e192326363a905ef25ebcb3d89c63a4de6d..0000000000000000000000000000000000000000
--- a/spaces/yaoshining/text-generation-webui/modules/presets.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import functools
-from pathlib import Path
-
-import yaml
-
-
-def load_preset(name):
-    generate_params = {
-        'do_sample': True,
-        'temperature': 1,
-        'top_p': 1,
-        'typical_p': 1,
-        'epsilon_cutoff': 0,
-        'eta_cutoff': 0,
-        'tfs': 1,
-        'top_a': 0,
-        'repetition_penalty': 1,
-        'repetition_penalty_range': 0,
-        'encoder_repetition_penalty': 1,
-        'top_k': 0,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'min_length': 0,
-        'length_penalty': 1,
-        'no_repeat_ngram_size': 0,
-        'early_stopping': False,
-        'mirostat_mode': 0,
-        'mirostat_tau': 5.0,
-        'mirostat_eta': 0.1,
-    }
-
-    with open(Path(f'presets/{name}.yaml'), 'r') as infile:
-        preset = yaml.safe_load(infile)
-
-    for k in preset:
-        generate_params[k] = preset[k]
-
-    generate_params['temperature'] = min(1.99, generate_params['temperature'])
-    return generate_params
-
-
-@functools.cache
-def load_preset_memoized(name):
-    return load_preset(name)
-
-
-def load_preset_for_ui(name, state):
-    generate_params = load_preset(name)
-    state.update(generate_params)
-    return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']]
-
-
-def generate_preset_yaml(state):
-    data = {k: state[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']}
-    return yaml.dump(data, sort_keys=False)
diff --git a/spaces/yaoshining/text-generation-webui/modules/ui.py b/spaces/yaoshining/text-generation-webui/modules/ui.py
deleted file mode 100644
index 8d45413faba68a2ae23e4d6a8621e17636e2a715..0000000000000000000000000000000000000000
--- a/spaces/yaoshining/text-generation-webui/modules/ui.py
+++ /dev/null
@@ -1,103 +0,0 @@
-from pathlib import Path
-
-import gradio as gr
-import torch
-
-from modules import shared
-
-with open(Path(__file__).resolve().parent / '../css/main.css', 'r') as f:
-    css = f.read()
-with open(Path(__file__).resolve().parent / '../css/chat.css', 'r') as f:
-    chat_css = f.read()
-with open(Path(__file__).resolve().parent / '../css/main.js', 'r') as f:
-    main_js = f.read()
-with open(Path(__file__).resolve().parent / '../css/chat.js', 'r') as f:
-    chat_js = f.read()
-
-refresh_symbol = '\U0001f504'  # 🔄
-delete_symbol = '🗑️'
-save_symbol = '💾'
-
-theme = gr.themes.Default(
-    font=['Helvetica', 'ui-sans-serif', 'system-ui', 'sans-serif'],
-    font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
-).set(
-    border_color_primary='#c5c5d2',
-    button_large_padding='6px 12px',
-    body_text_color_subdued='#484848',
-    background_fill_secondary='#eaeaea'
-)
-
-
-def list_model_elements():
-    elements = ['loader', 'cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'trust_remote_code', 'load_in_4bit', 'compute_dtype', 'quant_type', 'use_double_quant', 'wbits', 'groupsize', 'model_type', 'pre_layer', 'triton', 'desc_act', 'no_inject_fused_attention', 'no_inject_fused_mlp', 'no_use_cuda_fp16', 'threads', 'n_batch', 'no_mmap', 'mlock', 'n_gpu_layers', 'n_ctx', 'llama_cpp_seed', 'gpu_split', 'max_seq_len', 'compress_pos_emb']
-    for i in range(torch.cuda.device_count()):
-        elements.append(f'gpu_memory_{i}')
-
-    return elements
-
-
-def list_interface_input_elements(chat=False):
-    elements = ['max_new_tokens', 'seed', 'temperature', 'top_p', 'top_k', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'do_sample', 'penalty_alpha', 'num_beams', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'add_bos_token', 'ban_eos_token', 'truncation_length', 'custom_stopping_strings', 'skip_special_tokens', 'preset_menu', 'stream', 'tfs', 'top_a']
-    if chat:
-        elements += ['name1', 'name2', 'greeting', 'context', 'chat_generation_attempts', 'stop_at_newline', 'mode', 'instruction_template', 'character_menu', 'name1_instruct', 'name2_instruct', 'context_instruct', 'turn_template', 'chat_style', 'chat-instruct_command']
-
-    elements += list_model_elements()
-    return elements
-
-
-def gather_interface_values(*args):
-    output = {}
-    for i, element in enumerate(shared.input_elements):
-        output[element] = args[i]
-
-    shared.persistent_interface_state = output
-    return output
-
-
-def apply_interface_values(state, use_persistent=False):
-    if use_persistent:
-        state = shared.persistent_interface_state
-
-    elements = list_interface_input_elements(chat=shared.is_chat())
-    if len(state) == 0:
-        return [gr.update() for k in elements]  # Dummy, do nothing
-    else:
-        return [state[k] if k in state else gr.update() for k in elements]
-
-
-class ToolButton(gr.Button, gr.components.IOComponent):
-    """Small button with single emoji as text, fits inside gradio forms"""
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def get_block_name(self):
-        return "button"
-
-
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_class):
-    def refresh():
-        refresh_method()
-        args = refreshed_args() if callable(refreshed_args) else refreshed_args
-
-        for k, v in args.items():
-            setattr(refresh_component, k, v)
-
-        return gr.update(**(args or {}))
-
-    refresh_button = ToolButton(value=refresh_symbol, elem_classes=elem_class)
-    refresh_button.click(
-        fn=refresh,
-        inputs=[],
-        outputs=[refresh_component]
-    )
-    return refresh_button
-
-
-def create_delete_button(**kwargs):
-    return ToolButton(value=delete_symbol, **kwargs)
-
-
-def create_save_button(**kwargs):
-    return ToolButton(value=save_symbol, **kwargs)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/decision_transformer/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/decision_transformer/__init__.py
deleted file mode 100644
index 44070229aaa8591cb967a4ca7ff4867873072f8a..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/decision_transformer/__init__.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright 2020 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.
-from typing import TYPE_CHECKING
-
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
-
-
-_import_structure = {
-    "configuration_decision_transformer": [
-        "DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
-        "DecisionTransformerConfig",
-    ],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_decision_transformer"] = [
-        "DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "DecisionTransformerGPT2Model",
-        "DecisionTransformerGPT2PreTrainedModel",
-        "DecisionTransformerModel",
-        "DecisionTransformerPreTrainedModel",
-    ]
-
-
-if TYPE_CHECKING:
-    from .configuration_decision_transformer import (
-        DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
-        DecisionTransformerConfig,
-    )
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_decision_transformer import (
-            DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
-            DecisionTransformerGPT2Model,
-            DecisionTransformerGPT2PreTrainedModel,
-            DecisionTransformerModel,
-            DecisionTransformerPreTrainedModel,
-        )
-
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/donut/configuration_donut_swin.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/donut/configuration_donut_swin.py
deleted file mode 100644
index c7d6792467fe3fba2514a5fb88e7f92353408365..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/donut/configuration_donut_swin.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# coding=utf-8
-# Copyright 2022 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.
-""" Donut Swin Transformer model configuration"""
-
-from ...configuration_utils import PretrainedConfig
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
-    # See all Donut models at https://huggingface.co/models?filter=donut-swin
-}
-
-
-class DonutSwinConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`DonutSwinModel`]. It is used to instantiate a
-    Donut model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the Donut
-    [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        image_size (`int`, *optional*, defaults to 224):
-            The size (resolution) of each image.
-        patch_size (`int`, *optional*, defaults to 4):
-            The size (resolution) of each patch.
-        num_channels (`int`, *optional*, defaults to 3):
-            The number of input channels.
-        embed_dim (`int`, *optional*, defaults to 96):
-            Dimensionality of patch embedding.
-        depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`):
-            Depth of each layer in the Transformer encoder.
-        num_heads (`list(int)`, *optional*, defaults to `[3, 6, 12, 24]`):
-            Number of attention heads in each layer of the Transformer encoder.
-        window_size (`int`, *optional*, defaults to 7):
-            Size of windows.
-        mlp_ratio (`float`, *optional*, defaults to 4.0):
-            Ratio of MLP hidden dimensionality to embedding dimensionality.
-        qkv_bias (`bool`, *optional*, defaults to `True`):
-            Whether or not a learnable bias should be added to the queries, keys and values.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
-            The dropout probability for all fully connected layers in the embeddings and encoder.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        drop_path_rate (`float`, *optional*, defaults to 0.1):
-            Stochastic depth rate.
-        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
-            `"selu"` and `"gelu_new"` are supported.
-        use_absolute_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether or not to add absolute position embeddings to the patch embeddings.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
-            The epsilon used by the layer normalization layers.
-
-    Example:
-
-    ```python
-    >>> from transformers import DonutSwinConfig, DonutSwinModel
-
-    >>> # Initializing a Donut naver-clova-ix/donut-base style configuration
-    >>> configuration = DonutSwinConfig()
-
-    >>> # Randomly initializing a model from the naver-clova-ix/donut-base style configuration
-    >>> model = DonutSwinModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "donut-swin"
-
-    attribute_map = {
-        "num_attention_heads": "num_heads",
-        "num_hidden_layers": "num_layers",
-    }
-
-    def __init__(
-        self,
-        image_size=224,
-        patch_size=4,
-        num_channels=3,
-        embed_dim=96,
-        depths=[2, 2, 6, 2],
-        num_heads=[3, 6, 12, 24],
-        window_size=7,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        hidden_dropout_prob=0.0,
-        attention_probs_dropout_prob=0.0,
-        drop_path_rate=0.1,
-        hidden_act="gelu",
-        use_absolute_embeddings=False,
-        initializer_range=0.02,
-        layer_norm_eps=1e-5,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        self.image_size = image_size
-        self.patch_size = patch_size
-        self.num_channels = num_channels
-        self.embed_dim = embed_dim
-        self.depths = depths
-        self.num_layers = len(depths)
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.mlp_ratio = mlp_ratio
-        self.qkv_bias = qkv_bias
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.drop_path_rate = drop_path_rate
-        self.hidden_act = hidden_act
-        self.use_absolute_embeddings = use_absolute_embeddings
-        self.layer_norm_eps = layer_norm_eps
-        self.initializer_range = initializer_range
-        # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
-        # this indicates the channel dimension after the last stage of the model
-        self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv3/tokenization_layoutlmv3_fast.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv3/tokenization_layoutlmv3_fast.py
deleted file mode 100644
index 3d7445e4493117d8abed741de7b7a84c515dad8f..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv3/tokenization_layoutlmv3_fast.py
+++ /dev/null
@@ -1,855 +0,0 @@
-# coding=utf-8
-# Copyright 2022 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.
-"""
-Fast tokenization class for LayoutLMv3. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus
-and _encode_plus, in which the Rust tokenizer is used.
-"""
-
-import json
-from typing import Dict, List, Optional, Tuple, Union
-
-from tokenizers import pre_tokenizers, processors
-
-from ...tokenization_utils_base import (
-    BatchEncoding,
-    EncodedInput,
-    PaddingStrategy,
-    PreTokenizedInput,
-    TensorType,
-    TextInput,
-    TextInputPair,
-    TruncationStrategy,
-)
-from ...tokenization_utils_fast import PreTrainedTokenizerFast
-from ...utils import add_end_docstrings, logging
-from .tokenization_layoutlmv3 import (
-    LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING,
-    LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING,
-    LayoutLMv3Tokenizer,
-)
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/raw/main/vocab.json",
-        "microsoft/layoutlmv3-large": "https://huggingface.co/microsoft/layoutlmv3-large/raw/main/vocab.json",
-    },
-    "merges_file": {
-        "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/raw/main/merges.txt",
-        "microsoft/layoutlmv3-large": "https://huggingface.co/microsoft/layoutlmv3-large/raw/main/merges.txt",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "microsoft/layoutlmv3-base": 512,
-    "microsoft/layoutlmv3-large": 512,
-}
-
-
-class LayoutLMv3TokenizerFast(PreTrainedTokenizerFast):
-    r"""
-    Construct a "fast" LayoutLMv3 tokenizer (backed by HuggingFace's *tokenizers* library). Based on BPE.
-
-    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
-    refer to this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        merges_file (`str`):
-            Path to the merges file.
-        errors (`str`, *optional*, defaults to `"replace"`):
-            Paradigm to follow when decoding bytes to UTF-8. See
-            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
-        bos_token (`str`, *optional*, defaults to `"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-
-            <Tip>
-
-            When building a sequence using special tokens, this is not the token that is used for the beginning of
-            sequence. The token used is the `cls_token`.
-
-            </Tip>
-
-        eos_token (`str`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-
-            <Tip>
-
-            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
-            The token used is the `sep_token`.
-
-            </Tip>
-
-        sep_token (`str`, *optional*, defaults to `"</s>"`):
-            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
-            sequence classification or for a text and a question for question answering. It is also used as the last
-            token of a sequence built with special tokens.
-        cls_token (`str`, *optional*, defaults to `"<s>"`):
-            The classifier token which is used when doing sequence classification (classification of the whole sequence
-            instead of per-token classification). It is the first token of the sequence when built with special tokens.
-        unk_token (`str`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        pad_token (`str`, *optional*, defaults to `"<pad>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        mask_token (`str`, *optional*, defaults to `"<mask>"`):
-            The token used for masking values. This is the token used when training this model with masked language
-            modeling. This is the token which the model will try to predict.
-        add_prefix_space (`bool`, *optional*, defaults to `False`):
-            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
-            other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
-        trim_offsets (`bool`, *optional*, defaults to `True`):
-            Whether the post processing step should trim offsets to avoid including whitespaces.
-        cls_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
-            The bounding box to use for the special [CLS] token.
-        sep_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
-            The bounding box to use for the special [SEP] token.
-        pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
-            The bounding box to use for the special [PAD] token.
-        pad_token_label (`int`, *optional*, defaults to -100):
-            The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
-            CrossEntropyLoss.
-        only_label_first_subword (`bool`, *optional*, defaults to `True`):
-            Whether or not to only label the first subword, in case word labels are provided.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "attention_mask"]
-    slow_tokenizer_class = LayoutLMv3Tokenizer
-
-    def __init__(
-        self,
-        vocab_file=None,
-        merges_file=None,
-        tokenizer_file=None,
-        errors="replace",
-        bos_token="<s>",
-        eos_token="</s>",
-        sep_token="</s>",
-        cls_token="<s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        mask_token="<mask>",
-        add_prefix_space=True,
-        trim_offsets=True,
-        cls_token_box=[0, 0, 0, 0],
-        sep_token_box=[0, 0, 0, 0],
-        pad_token_box=[0, 0, 0, 0],
-        pad_token_label=-100,
-        only_label_first_subword=True,
-        **kwargs,
-    ):
-        super().__init__(
-            vocab_file,
-            merges_file,
-            tokenizer_file=tokenizer_file,
-            errors=errors,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            sep_token=sep_token,
-            cls_token=cls_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            mask_token=mask_token,
-            add_prefix_space=add_prefix_space,
-            trim_offsets=trim_offsets,
-            cls_token_box=cls_token_box,
-            sep_token_box=sep_token_box,
-            pad_token_box=pad_token_box,
-            pad_token_label=pad_token_label,
-            only_label_first_subword=only_label_first_subword,
-            **kwargs,
-        )
-
-        pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
-        if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
-            pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
-            pre_tok_state["add_prefix_space"] = add_prefix_space
-            self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
-
-        self.add_prefix_space = add_prefix_space
-
-        tokenizer_component = "post_processor"
-        tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
-        if tokenizer_component_instance:
-            state = json.loads(tokenizer_component_instance.__getstate__())
-
-            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
-            if "sep" in state:
-                state["sep"] = tuple(state["sep"])
-            if "cls" in state:
-                state["cls"] = tuple(state["cls"])
-
-            changes_to_apply = False
-
-            if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
-                state["add_prefix_space"] = add_prefix_space
-                changes_to_apply = True
-
-            if state.get("trim_offsets", trim_offsets) != trim_offsets:
-                state["trim_offsets"] = trim_offsets
-                changes_to_apply = True
-
-            if changes_to_apply:
-                component_class = getattr(processors, state.pop("type"))
-                new_value = component_class(**state)
-                setattr(self.backend_tokenizer, tokenizer_component, new_value)
-
-        # additional properties
-        self.cls_token_box = cls_token_box
-        self.sep_token_box = sep_token_box
-        self.pad_token_box = pad_token_box
-        self.pad_token_label = pad_token_label
-        self.only_label_first_subword = only_label_first_subword
-
-    @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.__call__
-    def __call__(
-        self,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
-        text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
-        boxes: Union[List[List[int]], List[List[List[int]]]] = None,
-        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
-        sequences with word-level normalized bounding boxes and optional labels.
-
-        Args:
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
-                (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
-                words).
-            text_pair (`List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
-                (pretokenized string).
-            boxes (`List[List[int]]`, `List[List[List[int]]]`):
-                Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
-            word_labels (`List[int]`, `List[List[int]]`, *optional*):
-                Word-level integer labels (for token classification tasks such as FUNSD, CORD).
-        """
-
-        # Input type checking for clearer error
-        def _is_valid_text_input(t):
-            if isinstance(t, str):
-                # Strings are fine
-                return True
-            elif isinstance(t, (list, tuple)):
-                # List are fine as long as they are...
-                if len(t) == 0:
-                    # ... empty
-                    return True
-                elif isinstance(t[0], str):
-                    # ... list of strings
-                    return True
-                elif isinstance(t[0], (list, tuple)):
-                    # ... list with an empty list or with a list of strings
-                    return len(t[0]) == 0 or isinstance(t[0][0], str)
-                else:
-                    return False
-            else:
-                return False
-
-        if text_pair is not None:
-            # in case text + text_pair are provided, text = questions, text_pair = words
-            if not _is_valid_text_input(text):
-                raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
-            if not isinstance(text_pair, (list, tuple)):
-                raise ValueError(
-                    "Words must be of type `List[str]` (single pretokenized example), "
-                    "or `List[List[str]]` (batch of pretokenized examples)."
-                )
-        else:
-            # in case only text is provided => must be words
-            if not isinstance(text, (list, tuple)):
-                raise ValueError(
-                    "Words must be of type `List[str]` (single pretokenized example), "
-                    "or `List[List[str]]` (batch of pretokenized examples)."
-                )
-
-        if text_pair is not None:
-            is_batched = isinstance(text, (list, tuple))
-        else:
-            is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
-
-        words = text if text_pair is None else text_pair
-        if boxes is None:
-            raise ValueError("You must provide corresponding bounding boxes")
-        if is_batched:
-            if len(words) != len(boxes):
-                raise ValueError("You must provide words and boxes for an equal amount of examples")
-            for words_example, boxes_example in zip(words, boxes):
-                if len(words_example) != len(boxes_example):
-                    raise ValueError("You must provide as many words as there are bounding boxes")
-        else:
-            if len(words) != len(boxes):
-                raise ValueError("You must provide as many words as there are bounding boxes")
-
-        if is_batched:
-            if text_pair is not None and len(text) != len(text_pair):
-                raise ValueError(
-                    f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
-                    f" {len(text_pair)}."
-                )
-            batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
-            is_pair = bool(text_pair is not None)
-            return self.batch_encode_plus(
-                batch_text_or_text_pairs=batch_text_or_text_pairs,
-                is_pair=is_pair,
-                boxes=boxes,
-                word_labels=word_labels,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-        else:
-            return self.encode_plus(
-                text=text,
-                text_pair=text_pair,
-                boxes=boxes,
-                word_labels=word_labels,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-
-    @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.batch_encode_plus
-    def batch_encode_plus(
-        self,
-        batch_text_or_text_pairs: Union[
-            List[TextInput],
-            List[TextInputPair],
-            List[PreTokenizedInput],
-        ],
-        is_pair: bool = None,
-        boxes: Optional[List[List[List[int]]]] = None,
-        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return self._batch_encode_plus(
-            batch_text_or_text_pairs=batch_text_or_text_pairs,
-            is_pair=is_pair,
-            boxes=boxes,
-            word_labels=word_labels,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.tokenize
-    def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
-        batched_input = [(text, pair)] if pair else [text]
-        encodings = self._tokenizer.encode_batch(
-            batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
-        )
-
-        return encodings[0].tokens
-
-    @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.encode_plus
-    def encode_plus(
-        self,
-        text: Union[TextInput, PreTokenizedInput],
-        text_pair: Optional[PreTokenizedInput] = None,
-        boxes: Optional[List[List[int]]] = None,
-        word_labels: Optional[List[int]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
-        `__call__` should be used instead.
-
-        Args:
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
-            text_pair (`List[str]` or `List[int]`, *optional*):
-                Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
-                list of list of strings (words of a batch of examples).
-        """
-
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return self._encode_plus(
-            text=text,
-            boxes=boxes,
-            text_pair=text_pair,
-            word_labels=word_labels,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-    def _batch_encode_plus(
-        self,
-        batch_text_or_text_pairs: Union[
-            List[TextInput],
-            List[TextInputPair],
-            List[PreTokenizedInput],
-        ],
-        is_pair: bool = None,
-        boxes: Optional[List[List[List[int]]]] = None,
-        word_labels: Optional[List[List[int]]] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[str] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-    ) -> BatchEncoding:
-        if not isinstance(batch_text_or_text_pairs, list):
-            raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")
-
-        # Set the truncation and padding strategy and restore the initial configuration
-        self.set_truncation_and_padding(
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-        )
-
-        if is_pair:
-            batch_text_or_text_pairs = [(text.split(), text_pair) for text, text_pair in batch_text_or_text_pairs]
-
-        encodings = self._tokenizer.encode_batch(
-            batch_text_or_text_pairs,
-            add_special_tokens=add_special_tokens,
-            is_pretokenized=True,  # we set this to True as LayoutLMv3 always expects pretokenized inputs
-        )
-
-        # Convert encoding to dict
-        # `Tokens` has type: Tuple[
-        #                       List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
-        #                       List[EncodingFast]
-        #                    ]
-        # with nested dimensions corresponding to batch, overflows, sequence length
-        tokens_and_encodings = [
-            self._convert_encoding(
-                encoding=encoding,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=True
-                if word_labels is not None
-                else return_offsets_mapping,  # we use offsets to create the labels
-                return_length=return_length,
-                verbose=verbose,
-            )
-            for encoding in encodings
-        ]
-
-        # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
-        # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
-        # (we say ~ because the number of overflow varies with the example in the batch)
-        #
-        # To match each overflowing sample with the original sample in the batch
-        # we add an overflow_to_sample_mapping array (see below)
-        sanitized_tokens = {}
-        for key in tokens_and_encodings[0][0].keys():
-            stack = [e for item, _ in tokens_and_encodings for e in item[key]]
-            sanitized_tokens[key] = stack
-        sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
-
-        # If returning overflowing tokens, we need to return a mapping
-        # from the batch idx to the original sample
-        if return_overflowing_tokens:
-            overflow_to_sample_mapping = []
-            for i, (toks, _) in enumerate(tokens_and_encodings):
-                overflow_to_sample_mapping += [i] * len(toks["input_ids"])
-            sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
-
-        for input_ids in sanitized_tokens["input_ids"]:
-            self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
-
-        # create the token boxes
-        token_boxes = []
-        for batch_index in range(len(sanitized_tokens["input_ids"])):
-            if return_overflowing_tokens:
-                original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
-            else:
-                original_index = batch_index
-            token_boxes_example = []
-            for id, sequence_id, word_id in zip(
-                sanitized_tokens["input_ids"][batch_index],
-                sanitized_encodings[batch_index].sequence_ids,
-                sanitized_encodings[batch_index].word_ids,
-            ):
-                if word_id is not None:
-                    if is_pair and sequence_id == 0:
-                        token_boxes_example.append(self.pad_token_box)
-                    else:
-                        token_boxes_example.append(boxes[original_index][word_id])
-                else:
-                    if id == self.cls_token_id:
-                        token_boxes_example.append(self.cls_token_box)
-                    elif id == self.sep_token_id:
-                        token_boxes_example.append(self.sep_token_box)
-                    elif id == self.pad_token_id:
-                        token_boxes_example.append(self.pad_token_box)
-                    else:
-                        raise ValueError("Id not recognized")
-            token_boxes.append(token_boxes_example)
-
-        sanitized_tokens["bbox"] = token_boxes
-
-        # optionally, create the labels
-        if word_labels is not None:
-            labels = []
-            for batch_index in range(len(sanitized_tokens["input_ids"])):
-                if return_overflowing_tokens:
-                    original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
-                else:
-                    original_index = batch_index
-                labels_example = []
-                previous_token_empty = False
-                for id, offset, word_id in zip(
-                    sanitized_tokens["input_ids"][batch_index],
-                    sanitized_tokens["offset_mapping"][batch_index],
-                    sanitized_encodings[batch_index].word_ids,
-                ):
-                    if word_id is not None:
-                        if self.only_label_first_subword:
-                            if offset[0] == 0 and not previous_token_empty:
-                                # Use the real label id for the first token of the word, and padding ids for the remaining tokens
-                                labels_example.append(word_labels[original_index][word_id])
-                            else:
-                                labels_example.append(self.pad_token_label)
-                            if offset == (0, 0):
-                                previous_token_empty = True
-                            else:
-                                previous_token_empty = False
-                        else:
-                            labels_example.append(word_labels[original_index][word_id])
-                    else:
-                        labels_example.append(self.pad_token_label)
-                labels.append(labels_example)
-
-            sanitized_tokens["labels"] = labels
-            # finally, remove offsets if the user didn't want them
-            if not return_offsets_mapping:
-                del sanitized_tokens["offset_mapping"]
-
-        return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
-
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast._encode_plus
-    def _encode_plus(
-        self,
-        text: Union[TextInput, PreTokenizedInput],
-        text_pair: Optional[PreTokenizedInput] = None,
-        boxes: Optional[List[List[int]]] = None,
-        word_labels: Optional[List[int]] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[bool] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        # make it a batched input
-        # 2 options:
-        # 1) only text, in case text must be a list of str
-        # 2) text + text_pair, in which case text = str and text_pair a list of str
-        batched_input = [(text, text_pair)] if text_pair else [text]
-        batched_boxes = [boxes]
-        batched_word_labels = [word_labels] if word_labels is not None else None
-        batched_output = self._batch_encode_plus(
-            batched_input,
-            is_pair=bool(text_pair is not None),
-            boxes=batched_boxes,
-            word_labels=batched_word_labels,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        # Return tensor is None, then we can remove the leading batch axis
-        # Overflowing tokens are returned as a batch of output so we keep them in this case
-        if return_tensors is None and not return_overflowing_tokens:
-            batched_output = BatchEncoding(
-                {
-                    key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
-                    for key, value in batched_output.items()
-                },
-                batched_output.encodings,
-            )
-
-        self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
-
-        return batched_output
-
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast._pad
-    def _pad(
-        self,
-        encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
-        max_length: Optional[int] = None,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        pad_to_multiple_of: Optional[int] = None,
-        return_attention_mask: Optional[bool] = None,
-    ) -> dict:
-        """
-        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
-
-        Args:
-            encoded_inputs:
-                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
-            max_length: maximum length of the returned list and optionally padding length (see below).
-                Will truncate by taking into account the special tokens.
-            padding_strategy: PaddingStrategy to use for padding.
-
-                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
-                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
-                - PaddingStrategy.DO_NOT_PAD: Do not pad
-                The tokenizer padding sides are defined in self.padding_side:
-
-                    - 'left': pads on the left of the sequences
-                    - 'right': pads on the right of the sequences
-            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
-                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
-                `>= 7.5` (Volta).
-            return_attention_mask:
-                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
-        """
-        # Load from model defaults
-        if return_attention_mask is None:
-            return_attention_mask = "attention_mask" in self.model_input_names
-
-        required_input = encoded_inputs[self.model_input_names[0]]
-
-        if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = len(required_input)
-
-        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
-            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
-
-        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
-
-        # Initialize attention mask if not present.
-        if return_attention_mask and "attention_mask" not in encoded_inputs:
-            encoded_inputs["attention_mask"] = [1] * len(required_input)
-
-        if needs_to_be_padded:
-            difference = max_length - len(required_input)
-            if self.padding_side == "right":
-                if return_attention_mask:
-                    encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
-                if "token_type_ids" in encoded_inputs:
-                    encoded_inputs["token_type_ids"] = (
-                        encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
-                    )
-                if "bbox" in encoded_inputs:
-                    encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
-                if "labels" in encoded_inputs:
-                    encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
-                if "special_tokens_mask" in encoded_inputs:
-                    encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
-                encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
-            elif self.padding_side == "left":
-                if return_attention_mask:
-                    encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
-                if "token_type_ids" in encoded_inputs:
-                    encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
-                        "token_type_ids"
-                    ]
-                if "bbox" in encoded_inputs:
-                    encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
-                if "labels" in encoded_inputs:
-                    encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
-                if "special_tokens_mask" in encoded_inputs:
-                    encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
-                encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
-            else:
-                raise ValueError("Invalid padding strategy:" + str(self.padding_side))
-
-        return encoded_inputs
-
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.save_vocabulary
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
-        return tuple(files)
-
-    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
-        if token_ids_1 is None:
-            return output
-
-        return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Args:
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not:
-        make use of token type ids, therefore a list of zeros is returned.
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-        Returns:
-            `List[int]`: List of zeros.
-        """
-        sep = [self.sep_token_id]
-        cls = [self.cls_token_id]
-
-        if token_ids_1 is None:
-            return len(cls + token_ids_0 + sep) * [0]
-        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/diffusion/diffusion.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/diffusion/diffusion.py
deleted file mode 100644
index decc1d31503e93e6611b02ced7b9c6f00b95db58..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/diffusion/diffusion.py
+++ /dev/null
@@ -1,317 +0,0 @@
-from collections import deque
-from functools import partial
-from inspect import isfunction
-import torch.nn.functional as F
-import librosa.sequence
-import numpy as np
-import torch
-from torch import nn
-from tqdm import tqdm
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def extract(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
-
-
-def linear_beta_schedule(timesteps, max_beta=0.02):
-    """
-    linear schedule
-    """
-    betas = np.linspace(1e-4, max_beta, timesteps)
-    return betas
-
-
-def cosine_beta_schedule(timesteps, s=0.008):
-    """
-    cosine schedule
-    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
-    """
-    steps = timesteps + 1
-    x = np.linspace(0, steps, steps)
-    alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
-    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
-    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
-    return np.clip(betas, a_min=0, a_max=0.999)
-
-
-beta_schedule = {
-    "cosine": cosine_beta_schedule,
-    "linear": linear_beta_schedule,
-}
-
-
-class GaussianDiffusion(nn.Module):
-    def __init__(self, 
-                denoise_fn, 
-                out_dims=128,
-                timesteps=1000, 
-                k_step=1000,
-                max_beta=0.02,
-                spec_min=-12, 
-                spec_max=2):
-        super().__init__()
-        self.denoise_fn = denoise_fn
-        self.out_dims = out_dims
-        betas = beta_schedule['linear'](timesteps, max_beta=max_beta)
-
-        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.k_step = k_step
-
-        self.noise_list = deque(maxlen=4)
-
-        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)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        self.register_buffer('spec_min', torch.FloatTensor([spec_min])[None, None, :out_dims])
-        self.register_buffer('spec_max', torch.FloatTensor([spec_max])[None, None, :out_dims])
-
-    def q_mean_variance(self, x_start, t):
-        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, cond):
-        noise_pred = self.denoise_fn(x, t, cond=cond)
-        x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred)
-
-        x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False):
-        """
-        Use the PLMS method from
-        [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778).
-        """
-
-        def get_x_pred(x, noise_t, t):
-            a_t = extract(self.alphas_cumprod, t, x.shape)
-            a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t)), x.shape)
-            a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt()
-
-            x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / (
-                    a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
-            x_pred = x + x_delta
-
-            return x_pred
-
-        noise_list = self.noise_list
-        noise_pred = self.denoise_fn(x, t, cond=cond)
-
-        if len(noise_list) == 0:
-            x_pred = get_x_pred(x, noise_pred, t)
-            noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond)
-            noise_pred_prime = (noise_pred + noise_pred_prev) / 2
-        elif len(noise_list) == 1:
-            noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2
-        elif len(noise_list) == 2:
-            noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12
-        else:
-            noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24
-
-        x_prev = get_x_pred(x, noise_pred_prime, t)
-        noise_list.append(noise_pred)
-
-        return x_prev
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (
-                extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
-        )
-
-    def p_losses(self, x_start, t, cond, noise=None, loss_type='l2'):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        x_recon = self.denoise_fn(x_noisy, t, cond)
-
-        if loss_type == 'l1':
-            loss = (noise - x_recon).abs().mean()
-        elif loss_type == 'l2':
-            loss = F.mse_loss(noise, x_recon)
-        else:
-            raise NotImplementedError()
-
-        return loss
-
-    def forward(self, 
-                condition, 
-                gt_spec=None, 
-                infer=True, 
-                infer_speedup=10, 
-                method='dpm-solver',
-                k_step=300,
-                use_tqdm=True):
-        """
-            conditioning diffusion, use fastspeech2 encoder output as the condition
-        """
-        cond = condition.transpose(1, 2)
-        b, device = condition.shape[0], condition.device
-
-        if not infer:
-            spec = self.norm_spec(gt_spec)
-            t = torch.randint(0, self.k_step, (b,), device=device).long()
-            norm_spec = spec.transpose(1, 2)[:, None, :, :]  # [B, 1, M, T]
-            return self.p_losses(norm_spec, t, cond=cond)
-        else:
-            shape = (cond.shape[0], 1, self.out_dims, cond.shape[2])
-            
-            if gt_spec is None:
-                t = self.k_step
-                x = torch.randn(shape, device=device)
-            else:
-                t = k_step
-                norm_spec = self.norm_spec(gt_spec)
-                norm_spec = norm_spec.transpose(1, 2)[:, None, :, :]
-                x = self.q_sample(x_start=norm_spec, t=torch.tensor([t - 1], device=device).long())
-                        
-            if method is not None and infer_speedup > 1:
-                if method == 'dpm-solver':
-                    from .dpm_solver_pytorch import NoiseScheduleVP, model_wrapper, DPM_Solver
-                    # 1. Define the noise schedule.
-                    noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t])
-
-                    # 2. Convert your discrete-time `model` to the continuous-time
-                    # noise prediction model. Here is an example for a diffusion model
-                    # `model` with the noise prediction type ("noise") .
-                    def my_wrapper(fn):
-                        def wrapped(x, t, **kwargs):
-                            ret = fn(x, t, **kwargs)
-                            if use_tqdm:
-                                self.bar.update(1)
-                            return ret
-
-                        return wrapped
-
-                    model_fn = model_wrapper(
-                        my_wrapper(self.denoise_fn),
-                        noise_schedule,
-                        model_type="noise",  # or "x_start" or "v" or "score"
-                        model_kwargs={"cond": cond}
-                    )
-
-                    # 3. Define dpm-solver and sample by singlestep DPM-Solver.
-                    # (We recommend singlestep DPM-Solver for unconditional sampling)
-                    # You can adjust the `steps` to balance the computation
-                    # costs and the sample quality.
-                    dpm_solver = DPM_Solver(model_fn, noise_schedule)
-
-                    steps = t // infer_speedup
-                    if use_tqdm:
-                        self.bar = tqdm(desc="sample time step", total=steps)
-                    x = dpm_solver.sample(
-                        x,
-                        steps=steps,
-                        order=3,
-                        skip_type="time_uniform",
-                        method="singlestep",
-                    )
-                    if use_tqdm:
-                        self.bar.close()
-                elif method == 'pndm':
-                    self.noise_list = deque(maxlen=4)
-                    if use_tqdm:
-                        for i in tqdm(
-                                reversed(range(0, t, infer_speedup)), desc='sample time step',
-                                total=t // infer_speedup,
-                        ):
-                            x = self.p_sample_plms(
-                                x, torch.full((b,), i, device=device, dtype=torch.long),
-                                infer_speedup, cond=cond
-                            )
-                    else:
-                        for i in reversed(range(0, t, infer_speedup)):
-                            x = self.p_sample_plms(
-                                x, torch.full((b,), i, device=device, dtype=torch.long),
-                                infer_speedup, cond=cond
-                            )
-                else:
-                    raise NotImplementedError(method)
-            else:
-                if use_tqdm:
-                    for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
-                        x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
-                else:
-                    for i in reversed(range(0, t)):
-                        x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
-            x = x.squeeze(1).transpose(1, 2)  # [B, T, M]
-            return self.denorm_spec(x)
-
-    def norm_spec(self, x):
-        return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
-
-    def denorm_spec(self, x):
-        return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min
diff --git a/spaces/zhang-wei-jian/docker/node_modules/escape-html/Readme.md b/spaces/zhang-wei-jian/docker/node_modules/escape-html/Readme.md
deleted file mode 100644
index 653d9eaa793317827ce724c4a0756110e9356fc8..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/escape-html/Readme.md
+++ /dev/null
@@ -1,43 +0,0 @@
-
-# escape-html
-
-  Escape string for use in HTML
-
-## Example
-
-```js
-var escape = require('escape-html');
-var html = escape('foo & bar');
-// -> foo &amp; bar
-```
-
-## Benchmark
-
-```
-$ npm run-script bench
-
-> escape-html@1.0.3 bench nodejs-escape-html
-> node benchmark/index.js
-
-
-  http_parser@1.0
-  node@0.10.33
-  v8@3.14.5.9
-  ares@1.9.0-DEV
-  uv@0.10.29
-  zlib@1.2.3
-  modules@11
-  openssl@1.0.1j
-
-  1 test completed.
-  2 tests completed.
-  3 tests completed.
-
-  no special characters    x 19,435,271 ops/sec ±0.85% (187 runs sampled)
-  single special character x  6,132,421 ops/sec ±0.67% (194 runs sampled)
-  many special characters  x  3,175,826 ops/sec ±0.65% (193 runs sampled)
-```
-
-## License
-
-  MIT
\ No newline at end of file
diff --git a/spaces/zhanghaohui/szu-gpt-academic/check_proxy.py b/spaces/zhanghaohui/szu-gpt-academic/check_proxy.py
deleted file mode 100644
index 977802db49babe079a191dbda6815c216e156548..0000000000000000000000000000000000000000
--- a/spaces/zhanghaohui/szu-gpt-academic/check_proxy.py
+++ /dev/null
@@ -1,159 +0,0 @@
-
-def check_proxy(proxies):
-    import requests
-    proxies_https = proxies['https'] if proxies is not None else '无'
-    try:
-        response = requests.get("https://ipapi.co/json/",
-                                proxies=proxies, timeout=4)
-        data = response.json()
-        print(f'查询代理的地理位置，返回的结果是{data}')
-        if 'country_name' in data:
-            country = data['country_name']
-            result = f"代理配置 {proxies_https}, 代理所在地：{country}"
-        elif 'error' in data:
-            result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
-        print(result)
-        return result
-    except:
-        result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
-        print(result)
-        return result
-
-
-def backup_and_download(current_version, remote_version):
-    """
-    一键更新协议：备份和下载
-    """
-    from toolbox import get_conf
-    import shutil
-    import os
-    import requests
-    import zipfile
-    os.makedirs(f'./history', exist_ok=True)
-    backup_dir = f'./history/backup-{current_version}/'
-    new_version_dir = f'./history/new-version-{remote_version}/'
-    if os.path.exists(new_version_dir):
-        return new_version_dir
-    os.makedirs(new_version_dir)
-    shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
-    proxies, = get_conf('proxies')
-    r = requests.get(
-        'https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
-    zip_file_path = backup_dir+'/master.zip'
-    with open(zip_file_path, 'wb+') as f:
-        f.write(r.content)
-    dst_path = new_version_dir
-    with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
-        for zip_info in zip_ref.infolist():
-            dst_file_path = os.path.join(dst_path, zip_info.filename)
-            if os.path.exists(dst_file_path):
-                os.remove(dst_file_path)
-            zip_ref.extract(zip_info, dst_path)
-    return new_version_dir
-
-
-def patch_and_restart(path):
-    """
-    一键更新协议：覆盖和重启
-    """
-    from distutils import dir_util
-    import shutil
-    import os
-    import sys
-    import time
-    import glob
-    from colorful import print亮黄, print亮绿, print亮红
-    # if not using config_private, move origin config.py as config_private.py
-    if not os.path.exists('config_private.py'):
-        print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
-              '另外您可以随时在history子文件夹下找回旧版的程序。')
-        shutil.copyfile('config.py', 'config_private.py')
-    path_new_version = glob.glob(path + '/*-master')[0]
-    dir_util.copy_tree(path_new_version, './')
-    print亮绿('代码已经更新，即将更新pip包依赖……')
-    for i in reversed(range(5)): time.sleep(1); print(i)
-    try: 
-        import subprocess
-        subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
-    except:
-        print亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
-    print亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
-    print亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
-    print(' ------------------------------ -----------------------------------')
-    for i in reversed(range(8)): time.sleep(1); print(i)
-    os.execl(sys.executable, sys.executable, *sys.argv)
-
-
-def get_current_version():
-    import json
-    try:
-        with open('./version', 'r', encoding='utf8') as f:
-            current_version = json.loads(f.read())['version']
-    except:
-        current_version = ""
-    return current_version
-
-
-def auto_update(raise_error=False):
-    """
-    一键更新协议：查询版本和用户意见
-    """
-    try:
-        from toolbox import get_conf
-        import requests
-        import time
-        import json
-        proxies, = get_conf('proxies')
-        response = requests.get(
-            "https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
-        remote_json_data = json.loads(response.text)
-        remote_version = remote_json_data['version']
-        if remote_json_data["show_feature"]:
-            new_feature = "新功能：" + remote_json_data["new_feature"]
-        else:
-            new_feature = ""
-        with open('./version', 'r', encoding='utf8') as f:
-            current_version = f.read()
-            current_version = json.loads(current_version)['version']
-        if (remote_version - current_version) >= 0.01:
-            from colorful import print亮黄
-            print亮黄(
-                f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
-            print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
-            user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')
-            if user_instruction in ['Y', 'y']:
-                path = backup_and_download(current_version, remote_version)
-                try:
-                    patch_and_restart(path)
-                except:
-                    msg = '更新失败。'
-                    if raise_error:
-                        from toolbox import trimmed_format_exc
-                        msg += trimmed_format_exc()
-                    print(msg)
-            else:
-                print('自动更新程序：已禁用')
-                return
-        else:
-            return
-    except:
-        msg = '自动更新程序：已禁用'
-        if raise_error:
-            from toolbox import trimmed_format_exc
-            msg += trimmed_format_exc()
-        print(msg)
-
-def warm_up_modules():
-    print('正在执行一些模块的预热...')
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    enc.encode("模块预热", disallowed_special=())
-    enc = model_info["gpt-4"]['tokenizer']
-    enc.encode("模块预热", disallowed_special=())
-
-if __name__ == '__main__':
-    import os
-    os.environ['no_proxy'] = '*'  # 避免代理网络产生意外污染
-    from toolbox import get_conf
-    proxies, = get_conf('proxies')
-    check_proxy(proxies)
diff --git a/spaces/zhangjf/chatbot/README.md b/spaces/zhangjf/chatbot/README.md
deleted file mode 100644
index dd44cd8c4779a168fed6b459127a673a280cd22c..0000000000000000000000000000000000000000
--- a/spaces/zhangjf/chatbot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Chatbot
-emoji: 👀
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.19.1
-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/zhoupin30/zhoupin30/src/components/header.tsx b/spaces/zhoupin30/zhoupin30/src/components/header.tsx
deleted file mode 100644
index dc298b722154d1ac6d7a7e148204605562d6cc58..0000000000000000000000000000000000000000
--- a/spaces/zhoupin30/zhoupin30/src/components/header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import * as React from 'react'
-import { UserMenu } from './user-menu'
-
-export async function Header() {
-  return (
-    <header className="sticky top-0 z-50 flex items-center justify-between w-full h-16 px-4 border-b shrink-0 bg-gradient-to-b from-background/10 via-background/50 to-background/80 backdrop-blur-xl">
-      <div className="flex items-center justify-end space-x-2 w-full">
-        <UserMenu />
-      </div>
-    </header>
-  )
-}
diff --git a/spaces/zideliu/styledrop/timm/loss/asymmetric_loss.py b/spaces/zideliu/styledrop/timm/loss/asymmetric_loss.py
deleted file mode 100644
index a8b10f9c797c2cb3b2652302717b592dada216f3..0000000000000000000000000000000000000000
--- a/spaces/zideliu/styledrop/timm/loss/asymmetric_loss.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class AsymmetricLossMultiLabel(nn.Module):
-    def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False):
-        super(AsymmetricLossMultiLabel, self).__init__()
-
-        self.gamma_neg = gamma_neg
-        self.gamma_pos = gamma_pos
-        self.clip = clip
-        self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss
-        self.eps = eps
-
-    def forward(self, x, y):
-        """"
-        Parameters
-        ----------
-        x: input logits
-        y: targets (multi-label binarized vector)
-        """
-
-        # Calculating Probabilities
-        x_sigmoid = torch.sigmoid(x)
-        xs_pos = x_sigmoid
-        xs_neg = 1 - x_sigmoid
-
-        # Asymmetric Clipping
-        if self.clip is not None and self.clip > 0:
-            xs_neg = (xs_neg + self.clip).clamp(max=1)
-
-        # Basic CE calculation
-        los_pos = y * torch.log(xs_pos.clamp(min=self.eps))
-        los_neg = (1 - y) * torch.log(xs_neg.clamp(min=self.eps))
-        loss = los_pos + los_neg
-
-        # Asymmetric Focusing
-        if self.gamma_neg > 0 or self.gamma_pos > 0:
-            if self.disable_torch_grad_focal_loss:
-                torch._C.set_grad_enabled(False)
-            pt0 = xs_pos * y
-            pt1 = xs_neg * (1 - y)  # pt = p if t > 0 else 1-p
-            pt = pt0 + pt1
-            one_sided_gamma = self.gamma_pos * y + self.gamma_neg * (1 - y)
-            one_sided_w = torch.pow(1 - pt, one_sided_gamma)
-            if self.disable_torch_grad_focal_loss:
-                torch._C.set_grad_enabled(True)
-            loss *= one_sided_w
-
-        return -loss.sum()
-
-
-class AsymmetricLossSingleLabel(nn.Module):
-    def __init__(self, gamma_pos=1, gamma_neg=4, eps: float = 0.1, reduction='mean'):
-        super(AsymmetricLossSingleLabel, self).__init__()
-
-        self.eps = eps
-        self.logsoftmax = nn.LogSoftmax(dim=-1)
-        self.targets_classes = []  # prevent gpu repeated memory allocation
-        self.gamma_pos = gamma_pos
-        self.gamma_neg = gamma_neg
-        self.reduction = reduction
-
-    def forward(self, inputs, target, reduction=None):
-        """"
-        Parameters
-        ----------
-        x: input logits
-        y: targets (1-hot vector)
-        """
-
-        num_classes = inputs.size()[-1]
-        log_preds = self.logsoftmax(inputs)
-        self.targets_classes = torch.zeros_like(inputs).scatter_(1, target.long().unsqueeze(1), 1)
-
-        # ASL weights
-        targets = self.targets_classes
-        anti_targets = 1 - targets
-        xs_pos = torch.exp(log_preds)
-        xs_neg = 1 - xs_pos
-        xs_pos = xs_pos * targets
-        xs_neg = xs_neg * anti_targets
-        asymmetric_w = torch.pow(1 - xs_pos - xs_neg,
-                                 self.gamma_pos * targets + self.gamma_neg * anti_targets)
-        log_preds = log_preds * asymmetric_w
-
-        if self.eps > 0:  # label smoothing
-            self.targets_classes.mul_(1 - self.eps).add_(self.eps / num_classes)
-
-        # loss calculation
-        loss = - self.targets_classes.mul(log_preds)
-
-        loss = loss.sum(dim=-1)
-        if self.reduction == 'mean':
-            loss = loss.mean()
-
-        return loss
diff --git a/spaces/zuhuri/OpenAI_chatgpt-turbo-UI/app.py b/spaces/zuhuri/OpenAI_chatgpt-turbo-UI/app.py
deleted file mode 100644
index 05ff0da5cf0178c3edc62efb255265c0151dc945..0000000000000000000000000000000000000000
--- a/spaces/zuhuri/OpenAI_chatgpt-turbo-UI/app.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import gradio as gr
-import requests
-import csv
-import openai
-
-prompt_templates = {"Default ChatGPT": ""}
-
-def get_empty_state():
-    return {"total_tokens": 0, "messages": []}
-
-def download_prompt_templates():
-    url = "https://raw.githubusercontent.com/f/awesome-chatgpt-prompts/main/prompts.csv"
-    try:
-        response = requests.get(url)
-        reader = csv.reader(response.text.splitlines())
-        next(reader)  # skip the header row
-        for row in reader:
-            if len(row) >= 2:
-                act = row[0].strip('"')
-                prompt = row[1].strip('"')
-                prompt_templates[act] = prompt
-
-    except requests.exceptions.RequestException as e:
-        print(f"An error occurred while downloading prompt templates: {e}")
-        return
-
-    choices = list(prompt_templates.keys())
-    choices = choices[:1] + sorted(choices[1:])
-    return gr.update(value=choices[0], choices=choices)
-
-def on_token_change(user_token):
-    openai.api_key = user_token
-
-def on_prompt_template_change(prompt_template):
-    if not isinstance(prompt_template, str): return
-    return prompt_templates[prompt_template]
-
-def submit_message(user_token, prompt, prompt_template, temperature, max_tokens, context_length, state):
-
-    history = state['messages']
-
-    if not prompt:
-        return gr.update(value=''), [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: {state['total_tokens']}", state
-    
-    prompt_template = prompt_templates[prompt_template]
-
-    system_prompt = []
-    if prompt_template:
-        system_prompt = [{ "role": "system", "content": prompt_template }]
-
-    prompt_msg = { "role": "user", "content": prompt }
-
-    if not user_token:
-        history.append(prompt_msg)
-        history.append({
-            "role": "system",
-            "content": "Error: OpenAI API Key is not set."
-        })
-        return '', [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: 0", state
-    
-    try:
-        completion = openai.ChatCompletion.create(model="gpt-4", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
-
-        history.append(prompt_msg)
-        history.append(completion.choices[0].message.to_dict())
-
-        state['total_tokens'] += completion['usage']['total_tokens']
-    
-    except Exception as e:
-        history.append(prompt_msg)
-        history.append({
-            "role": "system",
-            "content": f"Error: {e}"
-        })
-
-    total_tokens_used_msg = f"Total tokens used: {state['total_tokens']}"
-    chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)]
-
-    return '', chat_messages, total_tokens_used_msg, state
-
-def clear_conversation():
-    return gr.update(value=None, visible=True), None, "", get_empty_state()
-
-
-css = """
-      #col-container {max-width: 80%; margin-left: auto; margin-right: auto;}
-      #chatbox {min-height: 400px;}
-      #header {text-align: center;}
-      #prompt_template_preview {padding: 1em; border-width: 1px; border-style: solid; border-color: #e0e0e0; border-radius: 4px;}
-      #total_tokens_str {text-align: right; font-size: 0.8em; color: #666;}
-      #label {font-size: 0.8em; padding: 0.5em; margin: 0;}
-      .message { font-size: 1.2em; }
-      """
-
-with gr.Blocks(css=css) as demo:
-    
-    state = gr.State(get_empty_state())
-
-
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("""## OpenAI ChatGPT UI
-                    Using the ofiicial API (gpt-4 model)
-                    Prompt templates from [awesome-chatgpt-prompts](https://github.com/f/awesome-chatgpt-prompts).""",
-                    elem_id="header")
-
-        with gr.Row():
-            with gr.Column():
-                chatbot = gr.Chatbot(elem_id="chatbox")
-                input_message = gr.Textbox(show_label=False, placeholder="Enter text and press enter", visible=True).style(container=False)
-                btn_submit = gr.Button("Submit")
-                total_tokens_str = gr.Markdown(elem_id="total_tokens_str")
-                btn_clear_conversation = gr.Button("🔃 Start New Conversation")
-            with gr.Column():
-                gr.Markdown("Enter your OpenAI API Key. You can get one [here](https://platform.openai.com/account/api-keys).", elem_id="label")
-                user_token = gr.Textbox(value='', placeholder="OpenAI API Key", type="password", show_label=False)
-                prompt_template = gr.Dropdown(label="Set a custom insruction for the chatbot:", choices=list(prompt_templates.keys()))
-                prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview")
-                with gr.Accordion("Advanced parameters", open=False):
-                    temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, label="Temperature", info="Higher = more creative/chaotic")
-                    max_tokens = gr.Slider(minimum=100, maximum=4096, value=1000, step=1, label="Max tokens per response")
-                    context_length = gr.Slider(minimum=1, maximum=10, value=2, step=1, label="Context length", info="Number of previous messages to send to the chatbot. Be careful with high values, it can blow up the token budget quickly.")
-
-    btn_submit.click(submit_message, [user_token, input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state])
-    input_message.submit(submit_message, [user_token, input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state])
-    btn_clear_conversation.click(clear_conversation, [], [input_message, chatbot, total_tokens_str, state])
-    prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview])
-    user_token.change(on_token_change, inputs=[user_token], outputs=[])
-
-    
-    demo.load(download_prompt_templates, inputs=None, outputs=[prompt_template], queur=False)
-
-
-demo.queue(concurrency_count=10)
-demo.launch(height='800px')
\ No newline at end of file