Update parquet files (step 15 of 476)

spaces/123Kumar/vits-uma-genshin-honkai123/Docker/vits.sh

@@ -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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spaces/1gistliPinn/ChatGPT4/Examples/Descargar Gateway B2 Teacher Book Pdf LINK.md

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spaces/1gistliPinn/ChatGPT4/Examples/Download Film Si Doel Anak Sekolahan Full 14 BEST.md

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Become the Drift King with Drift Clash Online Racing Mod APK Android 1.md

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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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spaces/1phancelerku/anime-remove-background/Download Traffic Racer MOD APK for Android The Ultimate Racing Experience.md

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- <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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spaces/1phancelerku/anime-remove-background/FIFA Mobile v18.1.01 MOD APK Play in World Cup Stadiums with Official Licenses.md

@@ -1,132 +0,0 @@
-
-<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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-<p>So what are you waiting for? Read on and discover how FIFA Mobile v18.1.01 mod apk can take your soccer game to the next level.</p>
- <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>
-<ul>
-<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>
-<ul>
-<li><strong>Unlimited money</strong>: You can get unlimited coins and points to buy players, upgrade your team, and unlock features without spending real money.</li>
-<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>
-</ol>
-<p>Congratulations, you have successfully downloaded and installed FIFA Mobile v18.1.01 mod apk on your device. Now you can enjoy all the benefits of the modified version of the game and have more fun with FIFA Mobile.</p>
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-fifa mobile v18 2023 season 23 update download free unlimited money coins points tokens players teams kits stadiums icons heroes manager mode world cup mode offline online hack cheat menu mega obb data revdl rexdl 5play android latest version full premium pro cracked patched vip.</p>
- <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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spaces/7hao/bingo/Dockerfile

@@ -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

spaces/801artistry/RVC801/diffq/__init__.py

@@ -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

spaces/A00001/bingothoo/src/components/header.tsx

@@ -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>
-  )
-}

spaces/AIConsultant/MusicGen/audiocraft/__init__.py

@@ -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'

spaces/AIFILMS/generate_human_motion/pyrender/setup.py

@@ -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='[email protected]',
-    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
-)

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/predict.py

@@ -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)

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-coslr-preciseBN_in1k.py

@@ -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')
-]

spaces/Ababababababbababa/Ashaar/poetry_diacritizer/create_configs.py

@@ -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))

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

@@ -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;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/YAMLMake.js

@@ -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;

spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.py

@@ -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)
-
-# ----------------------------------------------------------------------------

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/transformer_temporal.md

@@ -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

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/zh/quicktour.md

@@ -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[���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。

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/transformer_temporal.py

@@ -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)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py

@@ -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

spaces/Andy1621/uniformer_image_detection/configs/vfnet/vfnet_r50_fpn_1x_coco.py

@@ -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)

spaces/Andy1621/uniformer_image_detection/mmdet/datasets/voc.py

@@ -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

spaces/Andy1621/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x512_160k_ade20k.py

@@ -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))

spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './encnet_r50-d8_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformerv2_demo/transforms.py

@@ -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))

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

@@ -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

spaces/Arnx/MusicGenXvAKN/tests/common_utils/wav_utils.py

@@ -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)

spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/tuneavideo_text2video.py

@@ -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,
-            )

spaces/AsakuraMizu/moe-tts/text/mandarin.py

@@ -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

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

@@ -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)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/Misc/mmdet_mask_rcnn_R_50_FPN_1x.py

@@ -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

spaces/Axolotlily/Interpolate/README.md

@@ -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

spaces/BasToTheMax/22h-vintedois-diffusion-v0-1/README.md

@@ -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

spaces/Benebene/Chat-question-answering/app.py

@@ -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)

spaces/Benson/text-generation/Examples/Buscar En La Lista De Miembros.md

@@ -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 />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Choque Mini Descarga Pc.md

@@ -1,63 +0,0 @@
-<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 />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Fonte Clash Royale.md

@@ -1,61 +0,0 @@
-<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 />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/__init__.py

@@ -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())

spaces/CVPR/LIVE/pybind11/tools/check-style.sh

@@ -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

spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/transform_reduce.h

@@ -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 
-

spaces/CVPR/LIVE/thrust/thrust/unique.h

@@ -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>
-

spaces/Cat125/text-generator-v2/utils.py

@@ -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()

spaces/Celestinian/Topic-Detection/app.py

@@ -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()

spaces/ChandraMohanNayal/AutoGPT/autogpt/speech/base.py

@@ -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

spaces/ChongCJ/fish/README.md

@@ -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

spaces/Clebersla/RVC_V2_Huggingface_Version/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py

@@ -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

spaces/CuriousDolphin/MobileSAM/app.py

@@ -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()

spaces/Cybsechuman/Consistency_analysis/README.md

@@ -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