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spaces/1acneusushi/gradio-2dmoleculeeditor/Wake Up Sid 720p Dvdrip Torrent.md

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-</ol>
- <p>Kurikulum az also specifies the content standards and learning outcomes for each subject and course. Content standards describe the essential knowledge and skills that students should acquire in each subject area. Learning outcomes describe the expected achievements of students at the end of each sub-level of general education. For example:</p>
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-kurikulum azərbaycan ədəbiyyatı<br />
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- <ul>
-<li>The content standard for Azerbaijani language and literature in primary education is: "Students will develop their linguistic competence in Azerbaijani language by listening, speaking, reading, and writing in various situations and contexts. They will also develop their literary competence by exploring and appreciating different genres and forms of Azerbaijani literature."</li>
- <li>The learning outcome for Azerbaijani language and literature in primary education is: "By the end of primary education, students will be able to communicate effectively in oral and written forms in Azerbaijani language using appropriate vocabulary, grammar, and style. They will also be able to analyze and interpret different texts and works of Azerbaijani literature using basic literary concepts and techniques."</li> <h2>The benefits and challenges of kurikulum az</h2>
-<p>Kurikulum az has many benefits for the improvement of the quality and relevance of education in Azerbaijan. Some of these benefits are:</p>
- <ul>
-<li>Kurikulum az helps students to develop the competencies and skills that are in high demand in the modern world, such as critical thinking, creativity, communication, collaboration, digital literacy, civic literacy, etc.</li>
-<li>Kurikulum az enables students to learn in a more meaningful and engaging way, by connecting their learning to real-life situations, problems, and contexts.</li>
-<li>Kurikulum az empowers students to take more responsibility and ownership of their learning, by giving them more choice, voice, and agency in their learning process.</li>
-<li>Kurikulum az supports teachers to adopt more effective and innovative teaching methods, such as inquiry-based learning, project-based learning, cooperative learning, etc.</li>
-<li>Kurikulum az involves parents and other stakeholders in the education system, by encouraging their participation and feedback in the curriculum development, implementation, and evaluation.</li>
-<li>Kurikulum az reflects and promotes the national identity, culture, values, and aspirations of Azerbaijan, by integrating them into the curriculum content and outcomes.</li>
-</ul>
- <p>However, kurikulum az also faces some challenges and difficulties in its implementation and evaluation. Some of these challenges are:</p>
- <ul>
-<li>Kurikulum az requires a lot of resources and support for its successful implementation, such as adequate funding, infrastructure, equipment, materials, training, etc.</li>
-<li>Kurikulum az demands a lot of changes and adjustments from the teachers, students, parents, and other actors in the education system, such as new roles, responsibilities, expectations, attitudes, behaviors, etc.</li>
-<li>Kurikulum az poses a lot of questions and uncertainties about its effectiveness and impact on the students' learning outcomes and achievements, such as how to measure, monitor, assess, and evaluate them.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, kurikulum az is a modern curriculum model that aims to provide students with the knowledge, skills, values, and attitudes that they need to succeed in the 21st century. It is based on the principles of student-centeredness, competency-basedness, and outcome-orientedness. It covers all levels of general education from preschool to higher education. It defines eight key competencies that students should develop throughout their education. It also specifies the content standards and learning outcomes for each subject and course. Kurikulum az has many benefits for the improvement of the quality and relevance of education in Azerbaijan. However, it also faces some challenges and difficulties in its implementation and evaluation. Therefore, it is important to provide continuous support and feedback to all the stakeholders involved in kurikulum az and to monitor and improve its effectiveness and impact on the students' learning outcomes and achievements.</p>
- <p>Do you have any questions or comments about kurikulum az? If so, please share them with us in the comment section below. We would love to hear from you!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers about kurikulum az:</p>
- <ol>
-<li><b>What is the difference between kurikulum az and derslik?</b></li>
-<p>Derslik is a term that refers to the textbooks that are used in schools. Kurikulum az is a term that refers to the curriculum model that guides the teaching, learning, and assessment processes in schools. Derslik is one of the tools that supports kurikulum az, but it is not the only one. Kurikulum az also uses other tools such as teacher guides, student workbooks, digital resources, etc.</p>
- <li><b>How can I access kurikulum az online?</b></li>
-<p>You can access kurikulum az online through the official website of the Ministry of Education of Azerbaijan: <a href="">www.edu.gov.az</a>. There you can find all the information, documents, and resources related to kurikulum az.</p>
- <li><b>How can I give feedback or suggestions about kurikulum az?</b></li>
-<p>You can give feedback or suggestions about kurikulum az through various channels such as email, phone, social media, or online surveys. You can also contact your local education authorities or school administration for any issues or concerns related to kurikulum az.</p>
- <li><b>How can I get involved or participate in kurikulum az?</b></li>
-<p>You can get involved or participate in kurikulum az by taking an active role in your own or your child's education. For example, you can:</p>
-<ul>
-<li>Read and understand the goals, principles, and - content standards and learning outcomes of kurikulum az - Support and encourage your child's learning at home and at school - Communicate and cooperate with your child's teachers and school administration - Participate in school events, activities, and decision-making processes - Join or form parent-teacher associations or other community groups that support education - Volunteer or donate to educational initiatives or projects</ul>
- <li><b>What are some examples of good practices or success stories of kurikulum az?</b></li>
-<p>There are many examples of good practices or success stories of kurikulum az that showcase the positive impact of kurikulum az on students, teachers, schools, and society. For example:</p>
-<ul>
-<li>Some schools have implemented innovative projects that integrate kurikulum az with local needs and resources, such as environmental education, cultural heritage, social entrepreneurship, etc.</li>
-<li>Some teachers have adopted new pedagogical methods that enhance student engagement, motivation, and achievement, such as gamification, flipped classroom, blended learning, etc.</li>
-<li>Some students have demonstrated outstanding performance and achievements in national and international competitions, assessments, and exhibitions, such as Olympiads, PISA, STEM Expo, etc.</li>
-<li>Some parents and communities have expressed their satisfaction and appreciation for the quality and relevance of education provided by kurikulum az.</li>
-</ul></p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Castle Clash Mod Apk 2022 Enjoy the Best Features of the Game with No Ads.md

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-<h1>Castle Clash Mod Apk 2022: A Guide for Beginners</h1>
-<p>Are you looking for a fun and exciting strategy game that will keep you hooked for hours? Do you want to experience the thrill of building your own castle, commanding your own army, and conquering your enemies? If yes, then you should try Castle Clash Mod Apk 2022, the latest version of the popular mobile game that has millions of fans around the world.</p>
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-<p>In this article, we will tell you everything you need to know about Castle Clash Mod Apk 2022, including what it is, how to download and install it, how to play it, and how to get unlimited money and gems in the game. By the end of this article, you will be ready to join the epic adventure of Castle Clash Mod Apk 2022 and become a world ruler.</p>
- <h2>What is Castle Clash?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Castle Clash is a free-to-play mobile strategy game from Playrix that was released in 2013. It is one of the most popular games in the genre, with over 100 million downloads on Google Play Store alone. The game is available for both Android and iOS devices.</p>
-<p>Castle Clash is a game where you can create your own kingdom, recruit and train your own troops, build and upgrade your own buildings, and fight against other players or computer-controlled enemies. You can choose from ten different medieval lords, each with their own unique troops and buildings. You can also join or create guilds, participate in events, complete quests, and collect rewards.</p>
-<h3>How to download and install Castle Clash Mod Apk 2022</h3>
-<p>If you want to enjoy the game with more features and benefits, you can download and install Castle Clash Mod Apk 2022, which is a modified version of the original game that gives you access to unlimited money and gems, as well as other perks. Here are the steps to download and install Castle Clash Mod Apk 2022:</p>
-<ol>
-<li>Go to  or any other trusted website that offers Castle Clash Mod Apk 2022.</li>
-<li>Click on the download button and wait for the file to be downloaded on your device.</li>
-<li>Go to your device's settings and enable the installation of apps from unknown sources.</li>
-<li>Locate the downloaded file on your device and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to be completed.</li>
-<li>Launch the game and enjoy Castle Clash Mod Apk 2022.</li>
-</ol>
-<h3>What are the benefits of using Castle Clash Mod Apk 2022</h3>
-<p>There are many benefits of using Castle Clash Mod Apk 2022, such as:</p>
-<ul>
-<li>You can get unlimited money and gems in the game, which you can use to buy anything you want, such as troops, buildings, upgrades, items, etc.</li>
-<li>You can unlock all the lords, troops, buildings, and modes in the game without having to spend real money or wait for long hours.</li>
-<li>You can enjoy faster loading times, smoother gameplay, better graphics, and more stability in the game.</li>
-<li>You can have more fun and excitement in the game without any limitations or restrictions <h2>How to play Castle Clash Mod Apk 2022</h2>
-<h3>The basics of building your castle and army</h3>
-<p>Once you have installed Castle Clash Mod Apk 2022, you can start playing the game by creating your own castle and army. Here are some of the basic steps to follow:</p>
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-<ul>
-<li>Choose a lord that suits your playstyle and strategy. Each lord has different strengths and weaknesses, as well as different troops and buildings.</li>
-<li>Build your castle by placing various buildings, such as barracks, towers, walls, mines, vaults, etc. You can upgrade your buildings to make them stronger and more efficient.</li>
-<li>Recruit and train your troops by using the barracks. You can choose from different types of troops, such as infantry, archers, cavalry, mages, etc. You can also upgrade your troops to improve their skills and abilities.</li>
-<li>Defend your castle from enemy attacks by using your towers, walls, traps, heroes, etc. You can also use spells and items to boost your defense.</li>
-<li>Attack other players' castles or computer-controlled enemies by using your troops, heroes, spells, items, etc. You can also use strategies and tactics to overcome your opponents.</li>
-</ul>
-<h3>The different game modes and challenges</h3>
-<p>Castle Clash Mod Apk 2022 offers a variety of game modes and challenges that will test your skills and keep you entertained. Some of the game modes and challenges are:</p>
-<ul>
-<li>Arena: A mode where you can compete with other players in real-time battles and rank up in the leaderboard.</li>
-<li>Guild Wars: A mode where you can join or create a guild and fight with other guilds for glory and rewards.</li>
-<li>Dungeon: A mode where you can explore different dungeons and face various enemies and bosses.</li>
-<li>Raid: A mode where you can raid other players' castles and loot their resources.</li>
-<li>HBM: A mode where you can defend your castle from waves of enemies and earn rewards.</li>
-<li>Trial: A mode where you can challenge yourself with different scenarios and difficulties.</li>
-</ul>
-<h3>The best tips and tricks for winning battles and raids</h3>
-<p>If you want to win more battles and raids in Castle Clash Mod Apk 2022, you should follow these tips and tricks:</p>
-<ul>
-<li>Know your enemy: Before you attack or defend, you should scout your enemy's castle and troops and plan your strategy accordingly.</li>
-<li>Use the right troops: Depending on the situation, you should use the right troops for the job. For example, infantry are good for breaking walls, archers are good for sniping towers, cavalry are good for flanking enemies, etc.</li>
-<li>Use the right heroes: Heroes are powerful units that can turn the tide of battle. You should use the right heroes for the right roles. For example, some heroes are good for offense, some are good for defense, some are good for support, etc.</li>
-<li>Use the right spells and items: Spells and items are useful tools that can enhance your performance in battle. You should use the right spells and items for the right situations. For example, some spells and items can heal your units, some can damage your enemies, some can buff your allies, etc.</li>
-<li>Use the right strategies and tactics: Strategies and tactics are important factors that can determine the outcome of battle. You should use the right strategies and tactics for the right scenarios. For example, some strategies and tactics are good for attacking, some are good for defending, some are good for ambushes, etc.</li>
-</ul> <h2>How to get unlimited money and gems in Castle Clash Mod Apk 2022</h2>
-<h3>The advantages of having unlimited resources in the game</h3>
-<p>One of the main reasons why many players use Castle Clash Mod Apk 2022 is because it gives them unlimited money and gems in the game. Money and gems are the two main currencies in Castle Clash, and they are used for various purposes, such as:</p>
-<ul>
-<li>Buying and upgrading troops, buildings, heroes, spells, items, etc.</li>
-<li>Speeding up the construction and training time of your units and structures.</li>
-<li>Unlocking new lords, troops, buildings, and modes in the game.</li>
-<li>Participating in special events, quests, and rewards.</li>
-<li>Enhancing your gameplay experience and enjoyment.</li>
-</ul>
-<p>Having unlimited money and gems in the game can give you a huge advantage over other players who have to spend real money or wait for long hours to get them. You can have more fun and freedom in the game without any limitations or restrictions.</p>
-<h3>The methods of getting free money and gems in Castle Clash Mod Apk 2022</h3>
-<p>There are two main methods of getting free money and gems in Castle Clash Mod Apk 2022. They are:</p>
-<ul>
-<li>Using the modded version of the game: This is the easiest and most convenient method of getting unlimited money and gems in the game. All you have to do is download and install Castle Clash Mod Apk 2022 from a trusted website, such as , and launch the game. You will automatically get unlimited money and gems in your account, which you can use as you wish.</li>
-<li>Using online generators or hacks: This is another method of getting free money and gems in the game, but it is more risky and complicated. You have to use online tools or websites that claim to generate or hack money and gems for you, such as  or . You have to enter your username or email, select the amount of money and gems you want, and complete some verification steps. Then, you will supposedly get the money and gems in your account.</li>
-</ul>
-<h3>The precautions and risks of using Castle Clash Mod Apk 2022</h3>
-<p>While using Castle Clash Mod Apk 2022 can be tempting and beneficial, it also comes with some precautions and risks that you should be aware of. Some of them are:</p>
-<ul>
-<li>You may get banned from the game: The developers of Castle Clash do not approve of using modded versions or hacks of the game, as they consider it cheating and unfair. They may detect your activity and ban your account from the game permanently.</li>
-<li>You may get viruses or malware on your device: Some websites or tools that offer Castle Clash Mod Apk 2022 or hacks may be malicious or fraudulent. They may contain viruses or malware that can harm your device or steal your personal information.</li>
-<li>You may lose your progress or data: Some modded versions or hacks of the game may not be compatible with the original version or updates of the game. They may cause errors or glitches that can corrupt your progress or data in the game.</li>
-<li>You may lose your interest or challenge in the game: Having unlimited money and gems in the game may make it too easy or boring for you. You may lose your interest or challenge in the game, as you will not have any goals or obstacles to overcome.</li>
-</ul>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action</h3>
-<p>In conclusion, Castle Clash Mod Apk 2022 is a modified version of the original Castle Clash game that gives you unlimited money and gems in the game, as well as other features and benefits. It is a fun and exciting strategy game where you can build your own castle, recruit your own army, and fight against other players or enemies. You can download and install Castle Clash Mod Apk 2022 from a trusted website, such as , or use online generators or hacks to get free money and gems in the game. However, you should also be careful of the precautions and risks of using Castle Clash Mod Apk 2022, such as getting banned from the game, getting viruses or malware on your device, losing your progress or data, or losing your interest or challenge in the game.</p>
-<p>If you are interested in trying out Castle Clash Mod Apk 2022, you can follow the steps we have provided in this article. We hope you have enjoyed this article and learned something new about Castle Clash Mod Apk 2022. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h <h2>FAQs</h2>
-<h3>What is the difference between Castle Clash and Castle Clash Mod Apk 2022?</h3>
-<p>Castle Clash is the original version of the game, while Castle Clash Mod Apk 2022 is a modified version of the game that gives you unlimited money and gems, as well as other features and benefits.</p>
-<h3>Is Castle Clash Mod Apk 2022 safe to use?</h3>
-<p>Castle Clash Mod Apk 2022 is safe to use if you download and install it from a trusted website, such as . However, you should also be aware of the precautions and risks of using it, such as getting banned from the game, getting viruses or malware on your device, losing your progress or data, or losing your interest or challenge in the game.</p>
-<h3>How can I update Castle Clash Mod Apk 2022?</h3>
-<p>You can update Castle Clash Mod Apk 2022 by visiting the same website where you downloaded and installed it, and downloading and installing the latest version of the mod. You should also backup your progress and data before updating, in case something goes wrong.</p>
-<h3>Can I play Castle Clash Mod Apk 2022 with my friends?</h3>
-<p>Yes, you can play Castle Clash Mod Apk 2022 with your friends, as long as they also have the same modded version of the game. You can join or create guilds, chat with other players, and cooperate or compete with them in various game modes and challenges.</p>
-<h3>Can I play Castle Clash Mod Apk 2022 offline?</h3>
-<p>No, you cannot play Castle Clash Mod Apk 2022 offline, as it requires an internet connection to run. You need to be online to access the game's servers, features, and content.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download Ludo for PC and Challenge Your Friends Online.md

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-<h1>How to Download Ludo for PC and Enjoy Its Benefits</h1>
-<p>Ludo is one of the most popular board games in the world, especially in India, where it originated. It is a game that can be played by anyone, regardless of age or skill level. It is also a game that can offer many benefits, such as improving your cognitive abilities, social skills, and confidence. But did you know that you can also play Ludo on your PC? In this article, we will show you how to download Ludo for PC using an Android emulator, and what are the advantages of playing Ludo on PC with BlueStacks.</p>
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-<h3>Ludo is a classic board game that originated in India</h3>
-<p>Ludo is a board game that is played by two to four players. Each player has four tokens of the same color, which they have to move around the board according to the roll of a dice. The objective of the game is to be the first player to move all four tokens into their home triangle in the center of the board. Along the way, players can capture their opponents' tokens by landing on the same square as them, or block their path by forming a chain with their own tokens. The game is based on an ancient Indian game called Pachisi, which was played by kings and queens in medieval times.</p>
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-<p>Ludo is not just a simple game that you play for entertainment. It is also a game that can help you develop various skills and qualities that are useful in life. For example, playing Ludo can help you:</p>
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- <h2>How to Download Ludo for PC Using an Android Emulator</h2>
-<h3>An Android emulator is a software that allows you to run Android apps on your PC</h3>
-<p>If you want to play Ludo on your PC, you will need an Android emulator. An Android emulator is a software that mimics the Android operating system on your PC, allowing you to run Android apps and games on your computer. There are many Android emulators available online, but one of the best and most popular ones is BlueStacks.</p>
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-<p>BlueStacks is a free and easy-to-use Android emulator that has millions of users worldwide. It is compatible with Windows and Mac computers, and it supports a wide range of Android apps and games, including Ludo. With BlueStacks, you can download and play Ludo on your PC in just a few steps. Here's how:</p>
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- <h4>How to install BlueStacks on your PC</h4>
-<ol>
-<li>Go to the official website of BlueStacks at [bluestacks.com] and click on the "Download BlueStacks" button.</li>
-<li>Wait for the download to finish and then run the installer file.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Launch BlueStacks on your PC and sign in with your Google account or create a new one.</li>
-</ol>
- <h4>How to access the Google Play Store and search for Ludo, Ludo King, or Ludo Club on BlueStacks</h4>
-<ol>
-<li>On the home screen of BlueStacks, click on the "Google Play" icon to open the Google Play Store.</li>
-<li>In the search bar, type "Ludo" and hit enter. You will see a list of Ludo games available for download.</li>
-<li>You can choose any Ludo game that you like, such as Ludo King or Ludo Club, which are some of the most popular and highly rated ones.</li>
-<li>Click on the game that you want to download and then click on the "Install" button.</li>
-<li>Wait for the installation to finish and then click on the "Open" button.</li>
-</ol>
- <h4>How to install and launch the Ludo game of your choice on BlueStacks</h4>
-<ol>
-<li>Once you have installed the Ludo game that you want to play, you will see its icon on the home screen of BlueStacks.</li>
-<li>Click on the icon to launch the game and start playing.</li>
-<li>You can adjust the settings of the game according to your preferences, such as the sound, language, graphics, etc.</li>
-<li>You can also customize your profile by choosing your name, avatar, color, etc.</li>
-<li>You can play Ludo in different modes, such as online multiplayer, local multiplayer, or against the computer.</li>
-</ol>
- <h2>Benefits of Playing Ludo on PC with BlueStacks</h2>
-<h3>You can enjoy a larger and better display of the game on your PC screen</h3>
-<p>One of the main benefits of playing Ludo on PC with BlueStacks is that you can enjoy a larger and better display of the game on your PC screen. You can see the board more clearly and appreciate the details more. You can also zoom in or out as you wish. Playing Ludo on a bigger screen can enhance your visual experience and make you feel more immersed in the game.</p>
- <h3>You can play with your friends and family online or offline, or against the computer</h3>
-<p>Another benefit of playing Ludo on PC with BlueStacks is that you can play with your friends and family online or offline, or against the computer. You can invite your friends or family members to join you in an online multiplayer mode, where you can chat with them and have fun together. You can also play with them offline by connecting your devices through Bluetooth or Wi-Fi. Alternatively, you can play against the computer in a single-player mode, where you can choose the difficulty level and practice your skills.</p>
- <h3>You can use various features and enhancements of BlueStacks to improve your gaming experience</h3>
-<p>A third benefit of playing Ludo on PC with BlueStacks is that you can use various features and enhancements of BlueStacks to improve your gaming experience. For example, you can use the following features of BlueStacks:</p>
-<ul>
-<li>Multi-instance: You can play multiple Ludo games at the same time on different windows, or play other games or apps while playing Ludo.</li>
-<li>Macro recorder: You can record and replay your actions in the game, such as rolling the dice, moving the tokens, etc.</li>
-<li>Keymapping: You can customize the keyboard and mouse controls for the game, such as assigning keys for different actions, changing the sensitivity, etc.</li>
-<li>Eco mode: You can lower the CPU and RAM usage of BlueStacks, which can improve the performance and speed of the game.</li>
-<li>Real-time translation: You can translate the text and voice chat in the game to any language that you want, which can help you communicate with other players from different countries.</li>
-</ul>
-<p>These are just some of the features that BlueStacks offers to enhance your gaming experience. You can explore more features and settings of BlueStacks by clicking on the menu icon on the top right corner of the emulator.</p>
- <h2>Conclusion</h2>
-<p>Ludo is a great game that can provide you with many benefits, such as improving your brain function, social skills, and happiness. But playing Ludo on PC with BlueStacks can make your gaming experience even better, as you can enjoy a larger and better display, play with your friends and family online or offline, or against the computer, and use various features and enhancements of BlueStacks to improve your performance and fun. So what are you waiting for? Download BlueStacks today and start playing Ludo on your PC!</p>
- <h2>FAQs</h2>
-<h3>What are some of the social benefits of playing Ludo?</h3>
-<p>Some of the social benefits of playing Ludo are:</p>
-<ul>
-<li>You can make new friends and connect with old ones by playing online with other players around the world.</li>
-<li>You can strengthen your bond with your family members by playing offline with them through Bluetooth or Wi-Fi.</li>
-<li>You can improve your communication and cooperation skills by chatting and working with your teammates in the game.</li>
-<li>You can learn about different cultures and languages by playing with people from different countries and using the real-time translation feature of BlueStacks.</li>
-</ul>
- <h3>What are some of the skills that you can develop by playing Ludo?</h3>
-<p>Some of the skills that you can develop by playing Ludo are:</p>
-<ul>
-<li>You can enhance your logical thinking, problem-solving, analysis, and decision-making abilities by planning your moves ahead and anticipating your opponents' actions.</li>
-<li>You can boost your memory, concentration, and attention span by keeping track of your tokens and dice rolls.</li>
-<li>You can increase your creativity and imagination by choosing different themes and avatars for the board and your profile.</li>
-<li>You can develop your strategy and tactics by using different methods and tricks to win the game.</li>
-</ul>
- <h3>How can you play Ludo online with other players around the world?</h3>
-<p>You can play Ludo online with other players around the world by following these steps:</p>
-<ol>
-<li>Launch the Ludo game that you have downloaded on BlueStacks.</li>
-<li>Select the online multiplayer mode from the main menu.</li>
-<li>Choose whether you want to play with two, three, or four players.</li>
-<li>Select whether you want to play with random players or invite your friends by sharing a code.</li>
-<li>Wait for the game to start and enjoy playing with other players around the world.</li>
-</ol>
- <h3>How can you change the theme of the board in Ludo?</h3>
-<p>You can change the theme of the board in Ludo by following these steps:</p>
-<ol>
-<li>Launch the Ludo game that you have downloaded on BlueStacks.</li>
-<li>Select the settings icon from the main menu.</li>
-<li>Select the theme option from the settings menu.</li>
-<li>Choose from various themes available for the board, such as nature, Egypt, disco, etc.</li>
-<li>Apply the theme that you like and enjoy playing on a different board.</li>
-</ol>
- <h3>How can you win the game of Ludo?</h3>
-<p>You can win the game of Ludo by following these tips:</p>
-<ul>
-<li>Roll the dice carefully and try to get a six as often as possible. A six will allow you to move a token out of your base or move an existing token six squares ahead. It will also give you another chance to roll again.</li>
-<li>Move your tokens strategically and try to capture your opponents' tokens by landing on the same square as them. This will send their tokens back to their base and delay their progress.</li>
-<li>Protect your tokens from being captured by forming a chain with two or more of your tokens on the same square. This will make them immune to capture by your opponents.</li>
-<li>Avoid landing on the star squares, as they are the most vulnerable to capture by your opponents. Instead, try to land on the safe squares, which are marked with a shield icon. These will protect your tokens from being captured.</li>
-<li>Move your tokens as fast as possible to reach your home triangle in the center of the board. Once you have moved all four of your tokens into your home triangle, you will win the game.</li>
-</ul>
- <p>I hope you enjoyed reading this article and learned how to download Ludo for PC and enjoy its benefits. Now, go ahead and try playing Ludo on your PC with BlueStacks and have fun!</p> 401be4b1e0<br />
-<br />
-<br />

spaces/2023Liu2023/bingo/tailwind.config.js

@@ -1,48 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/components/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/app/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/ui/**/*.{js,ts,jsx,tsx,mdx}',
-  ],
-  "darkMode": "class",
-  theme: {
-    extend: {
-      colors: {
-        'primary-blue': 'rgb(var(--color-primary-blue) / <alpha-value>)',
-        secondary: 'rgb(var(--color-secondary) / <alpha-value>)',
-        'primary-background': 'rgb(var(--primary-background) / <alpha-value>)',
-        'primary-text': 'rgb(var(--primary-text) / <alpha-value>)',
-        'secondary-text': 'rgb(var(--secondary-text) / <alpha-value>)',
-        'light-text': 'rgb(var(--light-text) / <alpha-value>)',
-        'primary-border': 'rgb(var(--primary-border) / <alpha-value>)',
-      },
-      keyframes: {
-        slideDownAndFade: {
-          from: { opacity: 0, transform: 'translateY(-2px)' },
-          to: { opacity: 1, transform: 'translateY(0)' },
-        },
-        slideLeftAndFade: {
-          from: { opacity: 0, transform: 'translateX(2px)' },
-          to: { opacity: 1, transform: 'translateX(0)' },
-        },
-        slideUpAndFade: {
-          from: { opacity: 0, transform: 'translateY(2px)' },
-          to: { opacity: 1, transform: 'translateY(0)' },
-        },
-        slideRightAndFade: {
-          from: { opacity: 0, transform: 'translateX(2px)' },
-          to: { opacity: 1, transform: 'translateX(0)' },
-        },
-      },
-      animation: {
-        slideDownAndFade: 'slideDownAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideLeftAndFade: 'slideLeftAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideUpAndFade: 'slideUpAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideRightAndFade: 'slideRightAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-      },
-    },
-  },
-  plugins: [require('@headlessui/tailwindcss'), require('tailwind-scrollbar')],
-}

spaces/232labs/VToonify/vtoonify/model/raft/train_standard.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-mkdir -p checkpoints
-python -u train.py --name raft-chairs --stage chairs --validation chairs --gpus 0 1 --num_steps 100000 --batch_size 10 --lr 0.0004 --image_size 368 496 --wdecay 0.0001
-python -u train.py --name raft-things --stage things --validation sintel --restore_ckpt checkpoints/raft-chairs.pth --gpus 0 1 --num_steps 100000 --batch_size 6 --lr 0.000125 --image_size 400 720 --wdecay 0.0001
-python -u train.py --name raft-sintel --stage sintel --validation sintel --restore_ckpt checkpoints/raft-things.pth --gpus 0 1 --num_steps 100000 --batch_size 6 --lr 0.000125 --image_size 368 768 --wdecay 0.00001 --gamma=0.85
-python -u train.py --name raft-kitti  --stage kitti --validation kitti --restore_ckpt checkpoints/raft-sintel.pth --gpus 0 1 --num_steps 50000 --batch_size 6 --lr 0.0001 --image_size 288 960 --wdecay 0.00001 --gamma=0.85

spaces/AHzizi/WaifuVoiceGen/modules.py

@@ -1,388 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x

spaces/AIFILMS/generate_human_motion/pyrender/pyrender/offscreen.py

@@ -1,160 +0,0 @@
-"""Wrapper for offscreen rendering.
-
-Author: Matthew Matl
-"""
-import os
-
-from .renderer import Renderer
-from .constants import RenderFlags
-
-
-class OffscreenRenderer(object):
-    """A wrapper for offscreen rendering.
-
-    Parameters
-    ----------
-    viewport_width : int
-        The width of the main viewport, in pixels.
-    viewport_height : int
-        The height of the main viewport, in pixels.
-    point_size : float
-        The size of screen-space points in pixels.
-    """
-
-    def __init__(self, viewport_width, viewport_height, point_size=1.0):
-        self.viewport_width = viewport_width
-        self.viewport_height = viewport_height
-        self.point_size = point_size
-
-        self._platform = None
-        self._renderer = None
-        self._create()
-
-    @property
-    def viewport_width(self):
-        """int : The width of the main viewport, in pixels.
-        """
-        return self._viewport_width
-
-    @viewport_width.setter
-    def viewport_width(self, value):
-        self._viewport_width = int(value)
-
-    @property
-    def viewport_height(self):
-        """int : The height of the main viewport, in pixels.
-        """
-        return self._viewport_height
-
-    @viewport_height.setter
-    def viewport_height(self, value):
-        self._viewport_height = int(value)
-
-    @property
-    def point_size(self):
-        """float : The pixel size of points in point clouds.
-        """
-        return self._point_size
-
-    @point_size.setter
-    def point_size(self, value):
-        self._point_size = float(value)
-
-    def render(self, scene, flags=RenderFlags.NONE, seg_node_map=None):
-        """Render a scene with the given set of flags.
-
-        Parameters
-        ----------
-        scene : :class:`Scene`
-            A scene to render.
-        flags : int
-            A bitwise or of one or more flags from :class:`.RenderFlags`.
-        seg_node_map : dict
-            A map from :class:`.Node` objects to (3,) colors for each.
-            If specified along with flags set to :attr:`.RenderFlags.SEG`,
-            the color image will be a segmentation image.
-
-        Returns
-        -------
-        color_im : (h, w, 3) uint8 or (h, w, 4) uint8
-            The color buffer in RGB format, or in RGBA format if
-            :attr:`.RenderFlags.RGBA` is set.
-            Not returned if flags includes :attr:`.RenderFlags.DEPTH_ONLY`.
-        depth_im : (h, w) float32
-            The depth buffer in linear units.
-        """
-        self._platform.make_current()
-        # If platform does not support dynamically-resizing framebuffers,
-        # destroy it and restart it
-        if (self._platform.viewport_height != self.viewport_height or
-                self._platform.viewport_width != self.viewport_width):
-            if not self._platform.supports_framebuffers():
-                self.delete()
-                self._create()
-
-        self._platform.make_current()
-        self._renderer.viewport_width = self.viewport_width
-        self._renderer.viewport_height = self.viewport_height
-        self._renderer.point_size = self.point_size
-
-        if self._platform.supports_framebuffers():
-            flags |= RenderFlags.OFFSCREEN
-            retval = self._renderer.render(scene, flags, seg_node_map)
-        else:
-            self._renderer.render(scene, flags, seg_node_map)
-            depth = self._renderer.read_depth_buf()
-            if flags & RenderFlags.DEPTH_ONLY:
-                retval = depth
-            else:
-                color = self._renderer.read_color_buf()
-                retval = color, depth
-
-        # Make the platform not current
-        self._platform.make_uncurrent()
-        return retval
-
-    def delete(self):
-        """Free all OpenGL resources.
-        """
-        self._platform.make_current()
-        self._renderer.delete()
-        self._platform.delete_context()
-        del self._renderer
-        del self._platform
-        self._renderer = None
-        self._platform = None
-        import gc
-        gc.collect()
-
-    def _create(self):
-        if 'PYOPENGL_PLATFORM' not in os.environ:
-            from pyrender.platforms.pyglet_platform import PygletPlatform
-            self._platform = PygletPlatform(self.viewport_width,
-                                            self.viewport_height)
-        elif os.environ['PYOPENGL_PLATFORM'] == 'egl':
-            from pyrender.platforms import egl
-            device_id = int(os.environ.get('EGL_DEVICE_ID', '0'))
-            egl_device = egl.get_device_by_index(device_id)
-            self._platform = egl.EGLPlatform(self.viewport_width,
-                                             self.viewport_height,
-                                             device=egl_device)
-        elif os.environ['PYOPENGL_PLATFORM'] == 'osmesa':
-            from pyrender.platforms.osmesa import OSMesaPlatform
-            self._platform = OSMesaPlatform(self.viewport_width,
-                                            self.viewport_height)
-        else:
-            raise ValueError('Unsupported PyOpenGL platform: {}'.format(
-                os.environ['PYOPENGL_PLATFORM']
-            ))
-        self._platform.init_context()
-        self._platform.make_current()
-        self._renderer = Renderer(self.viewport_width, self.viewport_height)
-
-    def __del__(self):
-        try:
-            self.delete()
-        except Exception:
-            pass
-
-
-__all__ = ['OffscreenRenderer']

spaces/AIFILMS/generate_human_motion/pyrender/pyrender/texture.py

@@ -1,259 +0,0 @@
-"""Textures, conforming to the glTF 2.0 standards as specified in
-https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-texture
-
-Author: Matthew Matl
-"""
-import numpy as np
-
-from OpenGL.GL import *
-
-from .utils import format_texture_source
-from .sampler import Sampler
-
-
-class Texture(object):
-    """A texture and its sampler.
-
-    Parameters
-    ----------
-    name : str, optional
-        The user-defined name of this object.
-    sampler : :class:`Sampler`
-        The sampler used by this texture.
-    source : (h,w,c) uint8 or (h,w,c) float or :class:`PIL.Image.Image`
-        The image used by this texture. If None, the texture is created
-        empty and width and height must be specified.
-    source_channels : str
-        Either `D`, `R`, `RG`, `GB`, `RGB`, or `RGBA`. Indicates the
-        channels to extract from `source`. Any missing channels will be filled
-        with `1.0`.
-    width : int, optional
-        For empty textures, the width of the texture buffer.
-    height : int, optional
-        For empty textures, the height of the texture buffer.
-    tex_type : int
-        Either GL_TEXTURE_2D or GL_TEXTURE_CUBE.
-    data_format : int
-        For now, just GL_FLOAT.
-    """
-
-    def __init__(self,
-                 name=None,
-                 sampler=None,
-                 source=None,
-                 source_channels=None,
-                 width=None,
-                 height=None,
-                 tex_type=GL_TEXTURE_2D,
-                 data_format=GL_UNSIGNED_BYTE):
-        self.source_channels = source_channels
-        self.name = name
-        self.sampler = sampler
-        self.source = source
-        self.width = width
-        self.height = height
-        self.tex_type = tex_type
-        self.data_format = data_format
-
-        self._texid = None
-        self._is_transparent = False
-
-    @property
-    def name(self):
-        """str : The user-defined name of this object.
-        """
-        return self._name
-
-    @name.setter
-    def name(self, value):
-        if value is not None:
-            value = str(value)
-        self._name = value
-
-    @property
-    def sampler(self):
-        """:class:`Sampler` : The sampler used by this texture.
-        """
-        return self._sampler
-
-    @sampler.setter
-    def sampler(self, value):
-        if value is None:
-            value = Sampler()
-        self._sampler = value
-
-    @property
-    def source(self):
-        """(h,w,c) uint8 or float or :class:`PIL.Image.Image` : The image
-        used in this texture.
-        """
-        return self._source
-
-    @source.setter
-    def source(self, value):
-        if value is None:
-            self._source = None
-        else:
-            self._source = format_texture_source(value, self.source_channels)
-        self._is_transparent = False
-
-    @property
-    def source_channels(self):
-        """str : The channels that were extracted from the original source.
-        """
-        return self._source_channels
-
-    @source_channels.setter
-    def source_channels(self, value):
-        self._source_channels = value
-
-    @property
-    def width(self):
-        """int : The width of the texture buffer.
-        """
-        return self._width
-
-    @width.setter
-    def width(self, value):
-        self._width = value
-
-    @property
-    def height(self):
-        """int : The height of the texture buffer.
-        """
-        return self._height
-
-    @height.setter
-    def height(self, value):
-        self._height = value
-
-    @property
-    def tex_type(self):
-        """int : The type of the texture.
-        """
-        return self._tex_type
-
-    @tex_type.setter
-    def tex_type(self, value):
-        self._tex_type = value
-
-    @property
-    def data_format(self):
-        """int : The format of the texture data.
-        """
-        return self._data_format
-
-    @data_format.setter
-    def data_format(self, value):
-        self._data_format = value
-
-    def is_transparent(self, cutoff=1.0):
-        """bool : If True, the texture is partially transparent.
-        """
-        if self._is_transparent is None:
-            self._is_transparent = False
-            if self.source_channels == 'RGBA' and self.source is not None:
-                if np.any(self.source[:,:,3] < cutoff):
-                    self._is_transparent = True
-        return self._is_transparent
-
-    def delete(self):
-        """Remove this texture from the OpenGL context.
-        """
-        self._unbind()
-        self._remove_from_context()
-
-    ##################
-    # OpenGL code
-    ##################
-    def _add_to_context(self):
-        if self._texid is not None:
-            raise ValueError('Texture already loaded into OpenGL context')
-
-        fmt = GL_DEPTH_COMPONENT
-        if self.source_channels == 'R':
-            fmt = GL_RED
-        elif self.source_channels == 'RG' or self.source_channels == 'GB':
-            fmt = GL_RG
-        elif self.source_channels == 'RGB':
-            fmt = GL_RGB
-        elif self.source_channels == 'RGBA':
-            fmt = GL_RGBA
-
-        # Generate the OpenGL texture
-        self._texid = glGenTextures(1)
-        glBindTexture(self.tex_type, self._texid)
-
-        # Flip data for OpenGL buffer
-        data = None
-        width = self.width
-        height = self.height
-        if self.source is not None:
-            data = np.ascontiguousarray(np.flip(self.source, axis=0).flatten())
-            width = self.source.shape[1]
-            height = self.source.shape[0]
-
-        # Bind texture and generate mipmaps
-        glTexImage2D(
-            self.tex_type, 0, fmt, width, height, 0, fmt,
-            self.data_format, data
-        )
-        if self.source is not None:
-            glGenerateMipmap(self.tex_type)
-
-        if self.sampler.magFilter is not None:
-            glTexParameteri(
-                self.tex_type, GL_TEXTURE_MAG_FILTER, self.sampler.magFilter
-            )
-        else:
-            if self.source is not None:
-                glTexParameteri(self.tex_type, GL_TEXTURE_MAG_FILTER, GL_LINEAR)
-            else:
-                glTexParameteri(self.tex_type, GL_TEXTURE_MAG_FILTER, GL_NEAREST)
-        if self.sampler.minFilter is not None:
-            glTexParameteri(
-                self.tex_type, GL_TEXTURE_MIN_FILTER, self.sampler.minFilter
-            )
-        else:
-            if self.source is not None:
-                glTexParameteri(self.tex_type, GL_TEXTURE_MIN_FILTER, GL_LINEAR_MIPMAP_LINEAR)
-            else:
-                glTexParameteri(self.tex_type, GL_TEXTURE_MIN_FILTER, GL_NEAREST)
-
-        glTexParameteri(self.tex_type, GL_TEXTURE_WRAP_S, self.sampler.wrapS)
-        glTexParameteri(self.tex_type, GL_TEXTURE_WRAP_T, self.sampler.wrapT)
-        border_color = 255 * np.ones(4).astype(np.uint8)
-        if self.data_format == GL_FLOAT:
-            border_color = np.ones(4).astype(np.float32)
-        glTexParameterfv(
-            self.tex_type, GL_TEXTURE_BORDER_COLOR,
-            border_color
-        )
-
-        # Unbind texture
-        glBindTexture(self.tex_type, 0)
-
-    def _remove_from_context(self):
-        if self._texid is not None:
-            # TODO OPENGL BUG?
-            # glDeleteTextures(1, [self._texid])
-            glDeleteTextures([self._texid])
-            self._texid = None
-
-    def _in_context(self):
-        return self._texid is not None
-
-    def _bind(self):
-        # TODO HANDLE INDEXING INTO OTHER UV's
-        glBindTexture(self.tex_type, self._texid)
-
-    def _unbind(self):
-        glBindTexture(self.tex_type, 0)
-
-    def _bind_as_depth_attachment(self):
-        glFramebufferTexture2D(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
-                               self.tex_type, self._texid, 0)
-
-    def _bind_as_color_attachment(self):
-        glFramebufferTexture2D(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0,
-                               self.tex_type, self._texid, 0)

spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/commons/rel_transformer_history.py

@@ -1,628 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-from text_to_speech.utils.commons.hparams import hparams
-from text_to_speech.modules.commons.layers import Embedding
-
-import transformers
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-class Encoder(nn.Module):
-    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.,
-                 window_size=None, block_length=None, pre_ln=False, **kwargs):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.block_length = block_length
-        self.pre_ln = pre_ln
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size,
-                                   p_dropout=p_dropout, block_length=block_length))
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-        if pre_ln:
-            self.last_ln = LayerNorm(hidden_channels)
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        for i in range(self.n_layers):
-            x = x * x_mask
-            x_ = x
-            if self.pre_ln:
-                x = self.norm_layers_1[i](x)
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = x_ + y
-            if not self.pre_ln:
-                x = self.norm_layers_1[i](x)
-
-            x_ = x
-            if self.pre_ln:
-                x = self.norm_layers_2[i](x)
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = x_ + y
-            if not self.pre_ln:
-                x = self.norm_layers_2[i](x)
-        if self.pre_ln:
-            x = self.last_ln(x)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0.,
-                 block_length=None, proximal_bias=False, proximal_init=False):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.p_dropout = p_dropout
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels ** -0.5
-            self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-            self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        if proximal_init:
-            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
-            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
-        if self.window_size is not None:
-            assert t_s == t_t, "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings)
-            rel_logits = self._relative_position_to_absolute_position(rel_logits)
-            scores_local = rel_logits / math.sqrt(self.k_channels)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-                scores = scores * block_mask + -1e4 * (1 - block_mask)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-            output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-        output = output.transpose(2, 3).contiguous().view(b, d, t_t)  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
-        x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(x * x_mask)
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        return x * x_mask
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-4):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        n_dims = len(x.shape)
-        mean = torch.mean(x, 1, keepdim=True)
-        variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
-
-        x = (x - mean) * torch.rsqrt(variance + self.eps)
-
-        shape = [1, -1] + [1] * (n_dims - 2)
-        x = x * self.gamma.view(*shape) + self.beta.view(*shape)
-        return x
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(
-            nn.ReLU(),
-            nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class RelTransformerEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout=0.0,
-                 window_size=4,
-                 block_length=None,
-                 prenet=True,
-                 pre_ln=True,
-                 ):
-
-        super().__init__()
-
-        self.n_vocab = n_vocab
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.block_length = block_length
-        self.prenet = prenet
-        if n_vocab > 0:
-            self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0)
-
-        if prenet:
-            self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels,
-                                    kernel_size=5, n_layers=3, p_dropout=0)
-        self.encoder = Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            window_size=window_size,
-            block_length=block_length,
-            pre_ln=pre_ln,
-        )
-
-    def forward(self, x, x_mask=None):
-        if self.n_vocab > 0:
-            x_lengths = (x > 0).long().sum(-1)
-            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-        else:
-            x_lengths = (x.abs().sum(-1) > 0).long().sum(-1)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-        if self.prenet:
-            x = self.pre(x, x_mask)
-        x = self.encoder(x, x_mask)
-        return x.transpose(1, 2)
-
-
-def group_hidden_by_segs(h, seg_ids, max_len):
-    """
-    :param h: [B, T, H]
-    :param seg_ids: [B, T]
-    :return: h_ph: [B, T_ph, H]
-    """
-    B, T, H = h.shape
-    h_gby_segs = h.new_zeros([B, max_len + 1, H]).scatter_add_(1, seg_ids[:, :, None].repeat([1, 1, H]), h)
-    all_ones = h.new_ones(h.shape[:2])
-    cnt_gby_segs = h.new_zeros([B, max_len + 1]).scatter_add_(1, seg_ids, all_ones).contiguous()
-    h_gby_segs = h_gby_segs[:, 1:]
-    cnt_gby_segs = cnt_gby_segs[:, 1:]
-    h_gby_segs = h_gby_segs / torch.clamp(cnt_gby_segs[:, :, None], min=1)
-    # assert h_gby_segs.shape[-1] == 192
-    return h_gby_segs
-
-def postprocess_word2ph(word_encoding, ph2word):
-    word_encoding = F.pad(word_encoding,[0,0,1,0])
-    ph2word_ = ph2word[:, :, None].repeat([1, 1, word_encoding.shape[-1]])
-    out = torch.gather(word_encoding, 1, ph2word_)  # [B, T, H]
-    return out
-
-
-class Pooler(nn.Module):
-    """
-    Parameter-free poolers to get the sentence embedding
-    'cls': [CLS] representation with BERT/RoBERTa's MLP pooler.
-    'cls_before_pooler': [CLS] representation without the original MLP pooler.
-    'avg': average of the last layers' hidden states at each token.
-    'avg_top2': average of the last two layers.
-    'avg_first_last': average of the first and the last layers.
-    """
-    def __init__(self, pooler_type):
-        super().__init__()
-        self.pooler_type = pooler_type
-        assert self.pooler_type in ["cls", "cls_before_pooler", "avg", "avg_top2", "avg_first_last"], "unrecognized pooling type %s" % self.pooler_type
-
-    def forward(self, attention_mask, outputs):
-        last_hidden = outputs.last_hidden_state
-        pooler_output = outputs.pooler_output
-        hidden_states = outputs.hidden_states
-
-        if self.pooler_type in ['cls_before_pooler', 'cls']:
-            return last_hidden[:, 0]
-        elif self.pooler_type == "avg":
-            return ((last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1))
-        elif self.pooler_type == "avg_first_last":
-            first_hidden = hidden_states[0]
-            last_hidden = hidden_states[-1]
-            pooled_result = ((first_hidden + last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
-            return pooled_result
-        elif self.pooler_type == "avg_top2":
-            second_last_hidden = hidden_states[-2]
-            last_hidden = hidden_states[-1]
-            pooled_result = ((last_hidden + second_last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
-            return pooled_result
-        else:
-            raise NotImplementedError
-
-
-class Similarity(nn.Module):
-    """
-    Dot product or cosine similarity
-    """
-
-    def __init__(self, temp):
-        super().__init__()
-        self.temp = temp
-        self.cos = nn.CosineSimilarity(dim=-1)
-        self.record = None
-        self.pos_avg = 0.0
-        self.neg_avg = 0.0
-
-    def forward(self, x, y):
-        sim = self.cos(x, y)
-        self.record = sim.detach() # [64,64]
-        min_size = min(self.record.shape[0], self.record.shape[1]) # 64
-        num_item = self.record.shape[0] * self.record.shape[1] # 4096
-        self.pos_avg = self.record.diag().sum() / min_size
-        if num_item - min_size == 0:
-            self.neg_avg = (self.record.sum() - self.record.diag().sum()) / 1
-            return sim / self.temp
-        if torch.any(torch.isnan(self.record)).item() is True:
-            print("we got self.record has nan when compute neg_avg")
-        if torch.any(torch.isnan(self.record.diag())).item() is True:
-            print("we got self.record.diag() has nan when compute neg_avg")
-        self.neg_avg = (self.record.sum() - self.record.diag().sum()) / (num_item - min_size)
-            
-        return sim / self.temp
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, hidden_size):
-        super().__init__()
-        self.dense = nn.Linear(hidden_size, hidden_size)
-        self.transform_act_fn = F.gelu
-        self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-12)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, hid_dim, out_dim):
-        super().__init__()
-        self.transform = BertPredictionHeadTransform(hid_dim)
-        self.decoder = nn.Linear(hid_dim, out_dim, bias=False)
-        self.bias = nn.Parameter(torch.zeros(out_dim))
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-# V2_2
-# change add to concat.
-# now support finetune BERT
-# grad_bert=0.1 & trainable_block_idx=0
-class BERTRelTransformerEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout=0.0,
-                 window_size=4,
-                 block_length=None,
-                 prenet=True,
-                 pre_ln=True,
-                 ):
-
-        super().__init__()
-
-        self.n_vocab = n_vocab
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.block_length = block_length
-        self.prenet = prenet
-        if n_vocab > 0:
-            self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0)
-
-        if prenet:
-            self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels,
-                                    kernel_size=5, n_layers=3, p_dropout=0)
-        self.encoder1 = Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers//2,
-            kernel_size,
-            p_dropout,
-            window_size=window_size,
-            block_length=block_length,
-            pre_ln=pre_ln,
-        )
-
-        self.encoder2 = Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers - n_layers//2,
-            kernel_size,
-            p_dropout,
-            window_size=window_size,
-            block_length=block_length,
-            pre_ln=pre_ln,
-        )
-
-        if hparams['ds_name'] in ['ljspeech', 'libritts']:
-            model_name = 'bert-base-uncased'
-        elif hparams['ds_name'] in ['biaobei']:
-            model_name = 'bert-base-chinese'
-        else:
-            raise NotImplementedError()
-            
-        config_kwargs = {'cache_dir': None, 'revision': 'main', 'use_auth_token': None}
-        self.tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
-        config = transformers.AutoConfig.from_pretrained(model_name, **config_kwargs)
-        self.bert =  transformers.AutoModelForMaskedLM.from_pretrained(
-                        model_name,
-                        config=config,
-            )
-        self.cl_head = BertLMPredictionHead(768, 768)
-        trainable_start_block = hparams.get("trainable_start_block", 10)
-        for k, v in self.bert.named_parameters():
-            if 'embeddings' in k:
-                v.requires_grad = False
-            elif 'encoder.layer' in k:
-                block_idx =  int(k.split(".")[3])
-                if block_idx < trainable_start_block:
-                    v.requires_grad = False
-                else:
-                    v.requires_grad = True
-            elif 'cls' in k:
-                v.requires_grad = True
-            else:
-                print("Unhandled key: {}, set to requires_grad...".format(k))
-                v.requires_grad = True
-
-        self.bert_combine = nn.Sequential(*[
-            nn.Conv1d(768 + hidden_channels, hidden_channels, 3, 1, 1),
-            nn.ReLU(),
-            ])
-        self.pooler = Pooler("avg")
-        self.sim = Similarity(temp=0.05)
-        
-    def forward(self, x, x_mask=None, bert_feats=None, ph2word=None, **kwargs):
-        if self.n_vocab > 0:
-            x_lengths = (x > 0).long().sum(-1)
-            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-        else:
-            x_lengths = (x.abs().sum(-1) > 0).long().sum(-1)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-        if self.prenet:
-            x = self.pre(x, x_mask)
-        x = self.encoder1(x, x_mask)
-        bert_outputs = self.bert.bert(bert_feats['bert_input_ids'],
-                                    attention_mask=bert_feats['bert_attention_mask'],
-                                    token_type_ids=bert_feats['bert_token_type_ids'],)
-        bert_embedding = bert_outputs['last_hidden_state']
-        grad_bert = hparams.get("grad_bert", 0.1)
-        bert_embedding = bert_embedding.detach() * (1-grad_bert) + bert_embedding * grad_bert
-        bert_word_embedding = group_hidden_by_segs(bert_embedding, bert_feats['bert_token2word'], bert_feats['bert_token2word'].max().item())
-        bert_ph_embedding = postprocess_word2ph(bert_word_embedding, ph2word)
-        bert_ph_embedding = bert_ph_embedding.transpose(1,2)
-        x = torch.cat([x, bert_ph_embedding], dim=1)
-        x = self.bert_combine(x)
-        x = self.encoder2(x, x_mask)
-        return x.transpose(1, 2)
-
-

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb8-150e_deepfashion2_short_sleeved_outwear_256x192/td_hm_res50_4xb8-150e_deepfashion2_short_sleeved_outwear_256x192.py

@@ -1,2861 +0,0 @@
-default_scope = 'mmpose'
-default_hooks = dict(
-    timer=dict(type='IterTimerHook'),
-    logger=dict(type='LoggerHook', interval=50),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    checkpoint=dict(
-        type='CheckpointHook', interval=10, save_best='PCK', rule='greater'),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-    visualization=dict(type='PoseVisualizationHook', enable=False))
-custom_hooks = [dict(type='SyncBuffersHook')]
-env_cfg = dict(
-    cudnn_benchmark=False,
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
-    dist_cfg=dict(backend='nccl'))
-vis_backends = [dict(type='LocalVisBackend')]
-visualizer = dict(
-    type='PoseLocalVisualizer',
-    vis_backends=[dict(type='LocalVisBackend'),
-                  dict(type='WandbVisBackend')],
-    name='visualizer')
-log_processor = dict(
-    type='LogProcessor', window_size=50, by_epoch=True, num_digits=6)
-log_level = 'INFO'
-load_from = None
-resume = False
-backend_args = dict(backend='local')
-train_cfg = dict(by_epoch=True, max_epochs=150, val_interval=10)
-val_cfg = dict()
-test_cfg = dict()
-colors = dict(
-    sss=[255, 128, 0],
-    lss=[255, 0, 128],
-    sso=[128, 0, 255],
-    lso=[0, 128, 255],
-    vest=[0, 128, 128],
-    sling=[0, 0, 128],
-    shorts=[128, 128, 128],
-    trousers=[128, 0, 128],
-    skirt=[64, 128, 128],
-    ssd=[64, 64, 128],
-    lsd=[128, 64, 0],
-    vd=[128, 64, 255],
-    sd=[128, 64, 0])
-dataset_info = dict(
-    dataset_name='deepfashion2',
-    paper_info=dict(
-        author=
-        'Yuying Ge and Ruimao Zhang and Lingyun Wu and Xiaogang Wang and Xiaoou Tang and Ping Luo',
-        title=
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-        container=
-        'Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)',
-        year='2019',
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-            swap='lso_kpt35'),
-        127:
-        dict(
-            name='lso_kpt39',
-            id=127,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt36'),
-        128:
-        dict(name='vest_kpt1', id=128, color=[0, 128, 128], type='', swap=''),
-        129:
-        dict(
-            name='vest_kpt2',
-            id=129,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt6'),
-        130:
-        dict(
-            name='vest_kpt3',
-            id=130,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt5'),
-        131:
-        dict(name='vest_kpt4', id=131, color=[0, 128, 128], type='', swap=''),
-        132:
-        dict(
-            name='vest_kpt5',
-            id=132,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt3'),
-        133:
-        dict(
-            name='vest_kpt6',
-            id=133,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt2'),
-        134:
-        dict(
-            name='vest_kpt7',
-            id=134,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt15'),
-        135:
-        dict(
-            name='vest_kpt8',
-            id=135,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt14'),
-        136:
-        dict(
-            name='vest_kpt9',
-            id=136,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt13'),
-        137:
-        dict(
-            name='vest_kpt10',
-            id=137,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt12'),
-        138:
-        dict(name='vest_kpt11', id=138, color=[0, 128, 128], type='', swap=''),
-        139:
-        dict(
-            name='vest_kpt12',
-            id=139,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt10'),
-        140:
-        dict(name='vest_kpt13', id=140, color=[0, 128, 128], type='', swap=''),
-        141:
-        dict(
-            name='vest_kpt14',
-            id=141,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt8'),
-        142:
-        dict(
-            name='vest_kpt15',
-            id=142,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt7'),
-        143:
-        dict(name='sling_kpt1', id=143, color=[0, 0, 128], type='', swap=''),
-        144:
-        dict(
-            name='sling_kpt2',
-            id=144,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt6'),
-        145:
-        dict(
-            name='sling_kpt3',
-            id=145,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt5'),
-        146:
-        dict(name='sling_kpt4', id=146, color=[0, 0, 128], type='', swap=''),
-        147:
-        dict(
-            name='sling_kpt5',
-            id=147,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt3'),
-        148:
-        dict(
-            name='sling_kpt6',
-            id=148,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt2'),
-        149:
-        dict(
-            name='sling_kpt7',
-            id=149,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt15'),
-        150:
-        dict(
-            name='sling_kpt8',
-            id=150,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt14'),
-        151:
-        dict(
-            name='sling_kpt9',
-            id=151,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt13'),
-        152:
-        dict(
-            name='sling_kpt10',
-            id=152,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt12'),
-        153:
-        dict(name='sling_kpt11', id=153, color=[0, 0, 128], type='', swap=''),
-        154:
-        dict(
-            name='sling_kpt12',
-            id=154,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt10'),
-        155:
-        dict(
-            name='sling_kpt13',
-            id=155,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt9'),
-        156:
-        dict(
-            name='sling_kpt14',
-            id=156,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt8'),
-        157:
-        dict(
-            name='sling_kpt15',
-            id=157,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt7'),
-        158:
-        dict(
-            name='shorts_kpt1',
-            id=158,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt3'),
-        159:
-        dict(
-            name='shorts_kpt2',
-            id=159,
-            color=[128, 128, 128],
-            type='',
-            swap=''),
-        160:
-        dict(
-            name='shorts_kpt3',
-            id=160,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt1'),
-        161:
-        dict(
-            name='shorts_kpt4',
-            id=161,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt10'),
-        162:
-        dict(
-            name='shorts_kpt5',
-            id=162,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt9'),
-        163:
-        dict(
-            name='shorts_kpt6',
-            id=163,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt8'),
-        164:
-        dict(
-            name='shorts_kpt7',
-            id=164,
-            color=[128, 128, 128],
-            type='',
-            swap=''),
-        165:
-        dict(
-            name='shorts_kpt8',
-            id=165,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt6'),
-        166:
-        dict(
-            name='shorts_kpt9',
-            id=166,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt5'),
-        167:
-        dict(
-            name='shorts_kpt10',
-            id=167,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt4'),
-        168:
-        dict(
-            name='trousers_kpt1',
-            id=168,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt3'),
-        169:
-        dict(
-            name='trousers_kpt2',
-            id=169,
-            color=[128, 0, 128],
-            type='',
-            swap=''),
-        170:
-        dict(
-            name='trousers_kpt3',
-            id=170,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt1'),
-        171:
-        dict(
-            name='trousers_kpt4',
-            id=171,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt14'),
-        172:
-        dict(
-            name='trousers_kpt5',
-            id=172,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt13'),
-        173:
-        dict(
-            name='trousers_kpt6',
-            id=173,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt12'),
-        174:
-        dict(
-            name='trousers_kpt7',
-            id=174,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt11'),
-        175:
-        dict(
-            name='trousers_kpt8',
-            id=175,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt10'),
-        176:
-        dict(
-            name='trousers_kpt9',
-            id=176,
-            color=[128, 0, 128],
-            type='',
-            swap=''),
-        177:
-        dict(
-            name='trousers_kpt10',
-            id=177,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt8'),
-        178:
-        dict(
-            name='trousers_kpt11',
-            id=178,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt7'),
-        179:
-        dict(
-            name='trousers_kpt12',
-            id=179,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt6'),
-        180:
-        dict(
-            name='trousers_kpt13',
-            id=180,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt5'),
-        181:
-        dict(
-            name='trousers_kpt14',
-            id=181,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt4'),
-        182:
-        dict(
-            name='skirt_kpt1',
-            id=182,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt3'),
-        183:
-        dict(
-            name='skirt_kpt2', id=183, color=[64, 128, 128], type='', swap=''),
-        184:
-        dict(
-            name='skirt_kpt3',
-            id=184,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt1'),
-        185:
-        dict(
-            name='skirt_kpt4',
-            id=185,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt8'),
-        186:
-        dict(
-            name='skirt_kpt5',
-            id=186,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt7'),
-        187:
-        dict(
-            name='skirt_kpt6', id=187, color=[64, 128, 128], type='', swap=''),
-        188:
-        dict(
-            name='skirt_kpt7',
-            id=188,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt5'),
-        189:
-        dict(
-            name='skirt_kpt8',
-            id=189,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt4'),
-        190:
-        dict(name='ssd_kpt1', id=190, color=[64, 64, 128], type='', swap=''),
-        191:
-        dict(
-            name='ssd_kpt2',
-            id=191,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt6'),
-        192:
-        dict(
-            name='ssd_kpt3',
-            id=192,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt5'),
-        193:
-        dict(name='ssd_kpt4', id=193, color=[64, 64, 128], type='', swap=''),
-        194:
-        dict(
-            name='ssd_kpt5',
-            id=194,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt3'),
-        195:
-        dict(
-            name='ssd_kpt6',
-            id=195,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt2'),
-        196:
-        dict(
-            name='ssd_kpt7',
-            id=196,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt29'),
-        197:
-        dict(
-            name='ssd_kpt8',
-            id=197,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt28'),
-        198:
-        dict(
-            name='ssd_kpt9',
-            id=198,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt27'),
-        199:
-        dict(
-            name='ssd_kpt10',
-            id=199,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt26'),
-        200:
-        dict(
-            name='ssd_kpt11',
-            id=200,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt25'),
-        201:
-        dict(
-            name='ssd_kpt12',
-            id=201,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt24'),
-        202:
-        dict(
-            name='ssd_kpt13',
-            id=202,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt23'),
-        203:
-        dict(
-            name='ssd_kpt14',
-            id=203,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt22'),
-        204:
-        dict(
-            name='ssd_kpt15',
-            id=204,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt21'),
-        205:
-        dict(
-            name='ssd_kpt16',
-            id=205,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt20'),
-        206:
-        dict(
-            name='ssd_kpt17',
-            id=206,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt19'),
-        207:
-        dict(name='ssd_kpt18', id=207, color=[64, 64, 128], type='', swap=''),
-        208:
-        dict(
-            name='ssd_kpt19',
-            id=208,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt17'),
-        209:
-        dict(
-            name='ssd_kpt20',
-            id=209,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt16'),
-        210:
-        dict(
-            name='ssd_kpt21',
-            id=210,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt15'),
-        211:
-        dict(
-            name='ssd_kpt22',
-            id=211,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt14'),
-        212:
-        dict(
-            name='ssd_kpt23',
-            id=212,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt13'),
-        213:
-        dict(
-            name='ssd_kpt24',
-            id=213,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt12'),
-        214:
-        dict(
-            name='ssd_kpt25',
-            id=214,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt11'),
-        215:
-        dict(
-            name='ssd_kpt26',
-            id=215,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt10'),
-        216:
-        dict(
-            name='ssd_kpt27',
-            id=216,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt9'),
-        217:
-        dict(
-            name='ssd_kpt28',
-            id=217,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt8'),
-        218:
-        dict(
-            name='ssd_kpt29',
-            id=218,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt7'),
-        219:
-        dict(name='lsd_kpt1', id=219, color=[128, 64, 0], type='', swap=''),
-        220:
-        dict(
-            name='lsd_kpt2',
-            id=220,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt6'),
-        221:
-        dict(
-            name='lsd_kpt3',
-            id=221,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt5'),
-        222:
-        dict(name='lsd_kpt4', id=222, color=[128, 64, 0], type='', swap=''),
-        223:
-        dict(
-            name='lsd_kpt5',
-            id=223,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt3'),
-        224:
-        dict(
-            name='lsd_kpt6',
-            id=224,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt2'),
-        225:
-        dict(
-            name='lsd_kpt7',
-            id=225,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt37'),
-        226:
-        dict(
-            name='lsd_kpt8',
-            id=226,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt36'),
-        227:
-        dict(
-            name='lsd_kpt9',
-            id=227,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt35'),
-        228:
-        dict(
-            name='lsd_kpt10',
-            id=228,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt34'),
-        229:
-        dict(
-            name='lsd_kpt11',
-            id=229,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt33'),
-        230:
-        dict(
-            name='lsd_kpt12',
-            id=230,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt32'),
-        231:
-        dict(
-            name='lsd_kpt13',
-            id=231,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt31'),
-        232:
-        dict(
-            name='lsd_kpt14',
-            id=232,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt30'),
-        233:
-        dict(
-            name='lsd_kpt15',
-            id=233,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt29'),
-        234:
-        dict(
-            name='lsd_kpt16',
-            id=234,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt28'),
-        235:
-        dict(
-            name='lsd_kpt17',
-            id=235,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt27'),
-        236:
-        dict(
-            name='lsd_kpt18',
-            id=236,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt26'),
-        237:
-        dict(
-            name='lsd_kpt19',
-            id=237,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt25'),
-        238:
-        dict(
-            name='lsd_kpt20',
-            id=238,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt24'),
-        239:
-        dict(
-            name='lsd_kpt21',
-            id=239,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt23'),
-        240:
-        dict(name='lsd_kpt22', id=240, color=[128, 64, 0], type='', swap=''),
-        241:
-        dict(
-            name='lsd_kpt23',
-            id=241,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt21'),
-        242:
-        dict(
-            name='lsd_kpt24',
-            id=242,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt20'),
-        243:
-        dict(
-            name='lsd_kpt25',
-            id=243,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt19'),
-        244:
-        dict(
-            name='lsd_kpt26',
-            id=244,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt18'),
-        245:
-        dict(
-            name='lsd_kpt27',
-            id=245,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt17'),
-        246:
-        dict(
-            name='lsd_kpt28',
-            id=246,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt16'),
-        247:
-        dict(
-            name='lsd_kpt29',
-            id=247,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt15'),
-        248:
-        dict(
-            name='lsd_kpt30',
-            id=248,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt14'),
-        249:
-        dict(
-            name='lsd_kpt31',
-            id=249,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt13'),
-        250:
-        dict(
-            name='lsd_kpt32',
-            id=250,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt12'),
-        251:
-        dict(
-            name='lsd_kpt33',
-            id=251,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt11'),
-        252:
-        dict(
-            name='lsd_kpt34',
-            id=252,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt10'),
-        253:
-        dict(
-            name='lsd_kpt35',
-            id=253,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt9'),
-        254:
-        dict(
-            name='lsd_kpt36',
-            id=254,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt8'),
-        255:
-        dict(
-            name='lsd_kpt37',
-            id=255,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt7'),
-        256:
-        dict(name='vd_kpt1', id=256, color=[128, 64, 255], type='', swap=''),
-        257:
-        dict(
-            name='vd_kpt2',
-            id=257,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt6'),
-        258:
-        dict(
-            name='vd_kpt3',
-            id=258,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt5'),
-        259:
-        dict(name='vd_kpt4', id=259, color=[128, 64, 255], type='', swap=''),
-        260:
-        dict(
-            name='vd_kpt5',
-            id=260,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt3'),
-        261:
-        dict(
-            name='vd_kpt6',
-            id=261,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt2'),
-        262:
-        dict(
-            name='vd_kpt7',
-            id=262,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt19'),
-        263:
-        dict(
-            name='vd_kpt8',
-            id=263,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt18'),
-        264:
-        dict(
-            name='vd_kpt9',
-            id=264,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt17'),
-        265:
-        dict(
-            name='vd_kpt10',
-            id=265,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt16'),
-        266:
-        dict(
-            name='vd_kpt11',
-            id=266,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt15'),
-        267:
-        dict(
-            name='vd_kpt12',
-            id=267,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt14'),
-        268:
-        dict(name='vd_kpt13', id=268, color=[128, 64, 255], type='', swap=''),
-        269:
-        dict(
-            name='vd_kpt14',
-            id=269,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt12'),
-        270:
-        dict(
-            name='vd_kpt15',
-            id=270,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt11'),
-        271:
-        dict(
-            name='vd_kpt16',
-            id=271,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt10'),
-        272:
-        dict(
-            name='vd_kpt17',
-            id=272,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt9'),
-        273:
-        dict(
-            name='vd_kpt18',
-            id=273,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt8'),
-        274:
-        dict(
-            name='vd_kpt19',
-            id=274,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt7'),
-        275:
-        dict(name='sd_kpt1', id=275, color=[128, 64, 0], type='', swap=''),
-        276:
-        dict(
-            name='sd_kpt2',
-            id=276,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt6'),
-        277:
-        dict(
-            name='sd_kpt3',
-            id=277,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt5'),
-        278:
-        dict(name='sd_kpt4', id=278, color=[128, 64, 0], type='', swap=''),
-        279:
-        dict(
-            name='sd_kpt5',
-            id=279,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt3'),
-        280:
-        dict(
-            name='sd_kpt6',
-            id=280,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt2'),
-        281:
-        dict(
-            name='sd_kpt7',
-            id=281,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt19'),
-        282:
-        dict(
-            name='sd_kpt8',
-            id=282,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt18'),
-        283:
-        dict(
-            name='sd_kpt9',
-            id=283,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt17'),
-        284:
-        dict(
-            name='sd_kpt10',
-            id=284,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt16'),
-        285:
-        dict(
-            name='sd_kpt11',
-            id=285,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt15'),
-        286:
-        dict(
-            name='sd_kpt12',
-            id=286,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt14'),
-        287:
-        dict(name='sd_kpt13', id=287, color=[128, 64, 0], type='', swap=''),
-        288:
-        dict(
-            name='sd_kpt14',
-            id=288,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt12'),
-        289:
-        dict(
-            name='sd_kpt15',
-            id=289,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt11'),
-        290:
-        dict(
-            name='sd_kpt16',
-            id=290,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt10'),
-        291:
-        dict(
-            name='sd_kpt17',
-            id=291,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt9'),
-        292:
-        dict(
-            name='sd_kpt18',
-            id=292,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt8'),
-        293:
-        dict(
-            name='sd_kpt19',
-            id=293,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt7')
-    }),
-    skeleton_info=dict({
-        0:
-        dict(link=('sss_kpt1', 'sss_kpt2'), id=0, color=[255, 128, 0]),
-        1:
-        dict(link=('sss_kpt2', 'sss_kpt7'), id=1, color=[255, 128, 0]),
-        2:
-        dict(link=('sss_kpt7', 'sss_kpt8'), id=2, color=[255, 128, 0]),
-        3:
-        dict(link=('sss_kpt8', 'sss_kpt9'), id=3, color=[255, 128, 0]),
-        4:
-        dict(link=('sss_kpt9', 'sss_kpt10'), id=4, color=[255, 128, 0]),
-        5:
-        dict(link=('sss_kpt10', 'sss_kpt11'), id=5, color=[255, 128, 0]),
-        6:
-        dict(link=('sss_kpt11', 'sss_kpt12'), id=6, color=[255, 128, 0]),
-        7:
-        dict(link=('sss_kpt12', 'sss_kpt13'), id=7, color=[255, 128, 0]),
-        8:
-        dict(link=('sss_kpt13', 'sss_kpt14'), id=8, color=[255, 128, 0]),
-        9:
-        dict(link=('sss_kpt14', 'sss_kpt15'), id=9, color=[255, 128, 0]),
-        10:
-        dict(link=('sss_kpt15', 'sss_kpt16'), id=10, color=[255, 128, 0]),
-        11:
-        dict(link=('sss_kpt16', 'sss_kpt17'), id=11, color=[255, 128, 0]),
-        12:
-        dict(link=('sss_kpt17', 'sss_kpt18'), id=12, color=[255, 128, 0]),
-        13:
-        dict(link=('sss_kpt18', 'sss_kpt19'), id=13, color=[255, 128, 0]),
-        14:
-        dict(link=('sss_kpt19', 'sss_kpt20'), id=14, color=[255, 128, 0]),
-        15:
-        dict(link=('sss_kpt20', 'sss_kpt21'), id=15, color=[255, 128, 0]),
-        16:
-        dict(link=('sss_kpt21', 'sss_kpt22'), id=16, color=[255, 128, 0]),
-        17:
-        dict(link=('sss_kpt22', 'sss_kpt23'), id=17, color=[255, 128, 0]),
-        18:
-        dict(link=('sss_kpt23', 'sss_kpt24'), id=18, color=[255, 128, 0]),
-        19:
-        dict(link=('sss_kpt24', 'sss_kpt25'), id=19, color=[255, 128, 0]),
-        20:
-        dict(link=('sss_kpt25', 'sss_kpt6'), id=20, color=[255, 128, 0]),
-        21:
-        dict(link=('sss_kpt6', 'sss_kpt1'), id=21, color=[255, 128, 0]),
-        22:
-        dict(link=('sss_kpt2', 'sss_kpt3'), id=22, color=[255, 128, 0]),
-        23:
-        dict(link=('sss_kpt3', 'sss_kpt4'), id=23, color=[255, 128, 0]),
-        24:
-        dict(link=('sss_kpt4', 'sss_kpt5'), id=24, color=[255, 128, 0]),
-        25:
-        dict(link=('sss_kpt5', 'sss_kpt6'), id=25, color=[255, 128, 0]),
-        26:
-        dict(link=('lss_kpt1', 'lss_kpt2'), id=26, color=[255, 0, 128]),
-        27:
-        dict(link=('lss_kpt2', 'lss_kpt7'), id=27, color=[255, 0, 128]),
-        28:
-        dict(link=('lss_kpt7', 'lss_kpt8'), id=28, color=[255, 0, 128]),
-        29:
-        dict(link=('lss_kpt8', 'lss_kpt9'), id=29, color=[255, 0, 128]),
-        30:
-        dict(link=('lss_kpt9', 'lss_kpt10'), id=30, color=[255, 0, 128]),
-        31:
-        dict(link=('lss_kpt10', 'lss_kpt11'), id=31, color=[255, 0, 128]),
-        32:
-        dict(link=('lss_kpt11', 'lss_kpt12'), id=32, color=[255, 0, 128]),
-        33:
-        dict(link=('lss_kpt12', 'lss_kpt13'), id=33, color=[255, 0, 128]),
-        34:
-        dict(link=('lss_kpt13', 'lss_kpt14'), id=34, color=[255, 0, 128]),
-        35:
-        dict(link=('lss_kpt14', 'lss_kpt15'), id=35, color=[255, 0, 128]),
-        36:
-        dict(link=('lss_kpt15', 'lss_kpt16'), id=36, color=[255, 0, 128]),
-        37:
-        dict(link=('lss_kpt16', 'lss_kpt17'), id=37, color=[255, 0, 128]),
-        38:
-        dict(link=('lss_kpt17', 'lss_kpt18'), id=38, color=[255, 0, 128]),
-        39:
-        dict(link=('lss_kpt18', 'lss_kpt19'), id=39, color=[255, 0, 128]),
-        40:
-        dict(link=('lss_kpt19', 'lss_kpt20'), id=40, color=[255, 0, 128]),
-        41:
-        dict(link=('lss_kpt20', 'lss_kpt21'), id=41, color=[255, 0, 128]),
-        42:
-        dict(link=('lss_kpt21', 'lss_kpt22'), id=42, color=[255, 0, 128]),
-        43:
-        dict(link=('lss_kpt22', 'lss_kpt23'), id=43, color=[255, 0, 128]),
-        44:
-        dict(link=('lss_kpt23', 'lss_kpt24'), id=44, color=[255, 0, 128]),
-        45:
-        dict(link=('lss_kpt24', 'lss_kpt25'), id=45, color=[255, 0, 128]),
-        46:
-        dict(link=('lss_kpt25', 'lss_kpt26'), id=46, color=[255, 0, 128]),
-        47:
-        dict(link=('lss_kpt26', 'lss_kpt27'), id=47, color=[255, 0, 128]),
-        48:
-        dict(link=('lss_kpt27', 'lss_kpt28'), id=48, color=[255, 0, 128]),
-        49:
-        dict(link=('lss_kpt28', 'lss_kpt29'), id=49, color=[255, 0, 128]),
-        50:
-        dict(link=('lss_kpt29', 'lss_kpt30'), id=50, color=[255, 0, 128]),
-        51:
-        dict(link=('lss_kpt30', 'lss_kpt31'), id=51, color=[255, 0, 128]),
-        52:
-        dict(link=('lss_kpt31', 'lss_kpt32'), id=52, color=[255, 0, 128]),
-        53:
-        dict(link=('lss_kpt32', 'lss_kpt33'), id=53, color=[255, 0, 128]),
-        54:
-        dict(link=('lss_kpt33', 'lss_kpt6'), id=54, color=[255, 0, 128]),
-        55:
-        dict(link=('lss_kpt6', 'lss_kpt5'), id=55, color=[255, 0, 128]),
-        56:
-        dict(link=('lss_kpt5', 'lss_kpt4'), id=56, color=[255, 0, 128]),
-        57:
-        dict(link=('lss_kpt4', 'lss_kpt3'), id=57, color=[255, 0, 128]),
-        58:
-        dict(link=('lss_kpt3', 'lss_kpt2'), id=58, color=[255, 0, 128]),
-        59:
-        dict(link=('lss_kpt6', 'lss_kpt1'), id=59, color=[255, 0, 128]),
-        60:
-        dict(link=('sso_kpt1', 'sso_kpt4'), id=60, color=[128, 0, 255]),
-        61:
-        dict(link=('sso_kpt4', 'sso_kpt7'), id=61, color=[128, 0, 255]),
-        62:
-        dict(link=('sso_kpt7', 'sso_kpt8'), id=62, color=[128, 0, 255]),
-        63:
-        dict(link=('sso_kpt8', 'sso_kpt9'), id=63, color=[128, 0, 255]),
-        64:
-        dict(link=('sso_kpt9', 'sso_kpt10'), id=64, color=[128, 0, 255]),
-        65:
-        dict(link=('sso_kpt10', 'sso_kpt11'), id=65, color=[128, 0, 255]),
-        66:
-        dict(link=('sso_kpt11', 'sso_kpt12'), id=66, color=[128, 0, 255]),
-        67:
-        dict(link=('sso_kpt12', 'sso_kpt13'), id=67, color=[128, 0, 255]),
-        68:
-        dict(link=('sso_kpt13', 'sso_kpt14'), id=68, color=[128, 0, 255]),
-        69:
-        dict(link=('sso_kpt14', 'sso_kpt15'), id=69, color=[128, 0, 255]),
-        70:
-        dict(link=('sso_kpt15', 'sso_kpt16'), id=70, color=[128, 0, 255]),
-        71:
-        dict(link=('sso_kpt16', 'sso_kpt31'), id=71, color=[128, 0, 255]),
-        72:
-        dict(link=('sso_kpt31', 'sso_kpt30'), id=72, color=[128, 0, 255]),
-        73:
-        dict(link=('sso_kpt30', 'sso_kpt2'), id=73, color=[128, 0, 255]),
-        74:
-        dict(link=('sso_kpt2', 'sso_kpt3'), id=74, color=[128, 0, 255]),
-        75:
-        dict(link=('sso_kpt3', 'sso_kpt4'), id=75, color=[128, 0, 255]),
-        76:
-        dict(link=('sso_kpt1', 'sso_kpt6'), id=76, color=[128, 0, 255]),
-        77:
-        dict(link=('sso_kpt6', 'sso_kpt25'), id=77, color=[128, 0, 255]),
-        78:
-        dict(link=('sso_kpt25', 'sso_kpt24'), id=78, color=[128, 0, 255]),
-        79:
-        dict(link=('sso_kpt24', 'sso_kpt23'), id=79, color=[128, 0, 255]),
-        80:
-        dict(link=('sso_kpt23', 'sso_kpt22'), id=80, color=[128, 0, 255]),
-        81:
-        dict(link=('sso_kpt22', 'sso_kpt21'), id=81, color=[128, 0, 255]),
-        82:
-        dict(link=('sso_kpt21', 'sso_kpt20'), id=82, color=[128, 0, 255]),
-        83:
-        dict(link=('sso_kpt20', 'sso_kpt19'), id=83, color=[128, 0, 255]),
-        84:
-        dict(link=('sso_kpt19', 'sso_kpt18'), id=84, color=[128, 0, 255]),
-        85:
-        dict(link=('sso_kpt18', 'sso_kpt17'), id=85, color=[128, 0, 255]),
-        86:
-        dict(link=('sso_kpt17', 'sso_kpt29'), id=86, color=[128, 0, 255]),
-        87:
-        dict(link=('sso_kpt29', 'sso_kpt28'), id=87, color=[128, 0, 255]),
-        88:
-        dict(link=('sso_kpt28', 'sso_kpt27'), id=88, color=[128, 0, 255]),
-        89:
-        dict(link=('sso_kpt27', 'sso_kpt26'), id=89, color=[128, 0, 255]),
-        90:
-        dict(link=('sso_kpt26', 'sso_kpt5'), id=90, color=[128, 0, 255]),
-        91:
-        dict(link=('sso_kpt5', 'sso_kpt6'), id=91, color=[128, 0, 255]),
-        92:
-        dict(link=('lso_kpt1', 'lso_kpt2'), id=92, color=[0, 128, 255]),
-        93:
-        dict(link=('lso_kpt2', 'lso_kpt7'), id=93, color=[0, 128, 255]),
-        94:
-        dict(link=('lso_kpt7', 'lso_kpt8'), id=94, color=[0, 128, 255]),
-        95:
-        dict(link=('lso_kpt8', 'lso_kpt9'), id=95, color=[0, 128, 255]),
-        96:
-        dict(link=('lso_kpt9', 'lso_kpt10'), id=96, color=[0, 128, 255]),
-        97:
-        dict(link=('lso_kpt10', 'lso_kpt11'), id=97, color=[0, 128, 255]),
-        98:
-        dict(link=('lso_kpt11', 'lso_kpt12'), id=98, color=[0, 128, 255]),
-        99:
-        dict(link=('lso_kpt12', 'lso_kpt13'), id=99, color=[0, 128, 255]),
-        100:
-        dict(link=('lso_kpt13', 'lso_kpt14'), id=100, color=[0, 128, 255]),
-        101:
-        dict(link=('lso_kpt14', 'lso_kpt15'), id=101, color=[0, 128, 255]),
-        102:
-        dict(link=('lso_kpt15', 'lso_kpt16'), id=102, color=[0, 128, 255]),
-        103:
-        dict(link=('lso_kpt16', 'lso_kpt17'), id=103, color=[0, 128, 255]),
-        104:
-        dict(link=('lso_kpt17', 'lso_kpt18'), id=104, color=[0, 128, 255]),
-        105:
-        dict(link=('lso_kpt18', 'lso_kpt19'), id=105, color=[0, 128, 255]),
-        106:
-        dict(link=('lso_kpt19', 'lso_kpt20'), id=106, color=[0, 128, 255]),
-        107:
-        dict(link=('lso_kpt20', 'lso_kpt39'), id=107, color=[0, 128, 255]),
-        108:
-        dict(link=('lso_kpt39', 'lso_kpt38'), id=108, color=[0, 128, 255]),
-        109:
-        dict(link=('lso_kpt38', 'lso_kpt4'), id=109, color=[0, 128, 255]),
-        110:
-        dict(link=('lso_kpt4', 'lso_kpt3'), id=110, color=[0, 128, 255]),
-        111:
-        dict(link=('lso_kpt3', 'lso_kpt2'), id=111, color=[0, 128, 255]),
-        112:
-        dict(link=('lso_kpt1', 'lso_kpt6'), id=112, color=[0, 128, 255]),
-        113:
-        dict(link=('lso_kpt6', 'lso_kpt33'), id=113, color=[0, 128, 255]),
-        114:
-        dict(link=('lso_kpt33', 'lso_kpt32'), id=114, color=[0, 128, 255]),
-        115:
-        dict(link=('lso_kpt32', 'lso_kpt31'), id=115, color=[0, 128, 255]),
-        116:
-        dict(link=('lso_kpt31', 'lso_kpt30'), id=116, color=[0, 128, 255]),
-        117:
-        dict(link=('lso_kpt30', 'lso_kpt29'), id=117, color=[0, 128, 255]),
-        118:
-        dict(link=('lso_kpt29', 'lso_kpt28'), id=118, color=[0, 128, 255]),
-        119:
-        dict(link=('lso_kpt28', 'lso_kpt27'), id=119, color=[0, 128, 255]),
-        120:
-        dict(link=('lso_kpt27', 'lso_kpt26'), id=120, color=[0, 128, 255]),
-        121:
-        dict(link=('lso_kpt26', 'lso_kpt25'), id=121, color=[0, 128, 255]),
-        122:
-        dict(link=('lso_kpt25', 'lso_kpt24'), id=122, color=[0, 128, 255]),
-        123:
-        dict(link=('lso_kpt24', 'lso_kpt23'), id=123, color=[0, 128, 255]),
-        124:
-        dict(link=('lso_kpt23', 'lso_kpt22'), id=124, color=[0, 128, 255]),
-        125:
-        dict(link=('lso_kpt22', 'lso_kpt21'), id=125, color=[0, 128, 255]),
-        126:
-        dict(link=('lso_kpt21', 'lso_kpt37'), id=126, color=[0, 128, 255]),
-        127:
-        dict(link=('lso_kpt37', 'lso_kpt36'), id=127, color=[0, 128, 255]),
-        128:
-        dict(link=('lso_kpt36', 'lso_kpt35'), id=128, color=[0, 128, 255]),
-        129:
-        dict(link=('lso_kpt35', 'lso_kpt34'), id=129, color=[0, 128, 255]),
-        130:
-        dict(link=('lso_kpt34', 'lso_kpt5'), id=130, color=[0, 128, 255]),
-        131:
-        dict(link=('lso_kpt5', 'lso_kpt6'), id=131, color=[0, 128, 255]),
-        132:
-        dict(link=('vest_kpt1', 'vest_kpt2'), id=132, color=[0, 128, 128]),
-        133:
-        dict(link=('vest_kpt2', 'vest_kpt7'), id=133, color=[0, 128, 128]),
-        134:
-        dict(link=('vest_kpt7', 'vest_kpt8'), id=134, color=[0, 128, 128]),
-        135:
-        dict(link=('vest_kpt8', 'vest_kpt9'), id=135, color=[0, 128, 128]),
-        136:
-        dict(link=('vest_kpt9', 'vest_kpt10'), id=136, color=[0, 128, 128]),
-        137:
-        dict(link=('vest_kpt10', 'vest_kpt11'), id=137, color=[0, 128, 128]),
-        138:
-        dict(link=('vest_kpt11', 'vest_kpt12'), id=138, color=[0, 128, 128]),
-        139:
-        dict(link=('vest_kpt12', 'vest_kpt13'), id=139, color=[0, 128, 128]),
-        140:
-        dict(link=('vest_kpt13', 'vest_kpt14'), id=140, color=[0, 128, 128]),
-        141:
-        dict(link=('vest_kpt14', 'vest_kpt15'), id=141, color=[0, 128, 128]),
-        142:
-        dict(link=('vest_kpt15', 'vest_kpt6'), id=142, color=[0, 128, 128]),
-        143:
-        dict(link=('vest_kpt6', 'vest_kpt1'), id=143, color=[0, 128, 128]),
-        144:
-        dict(link=('vest_kpt2', 'vest_kpt3'), id=144, color=[0, 128, 128]),
-        145:
-        dict(link=('vest_kpt3', 'vest_kpt4'), id=145, color=[0, 128, 128]),
-        146:
-        dict(link=('vest_kpt4', 'vest_kpt5'), id=146, color=[0, 128, 128]),
-        147:
-        dict(link=('vest_kpt5', 'vest_kpt6'), id=147, color=[0, 128, 128]),
-        148:
-        dict(link=('sling_kpt1', 'sling_kpt2'), id=148, color=[0, 0, 128]),
-        149:
-        dict(link=('sling_kpt2', 'sling_kpt8'), id=149, color=[0, 0, 128]),
-        150:
-        dict(link=('sling_kpt8', 'sling_kpt9'), id=150, color=[0, 0, 128]),
-        151:
-        dict(link=('sling_kpt9', 'sling_kpt10'), id=151, color=[0, 0, 128]),
-        152:
-        dict(link=('sling_kpt10', 'sling_kpt11'), id=152, color=[0, 0, 128]),
-        153:
-        dict(link=('sling_kpt11', 'sling_kpt12'), id=153, color=[0, 0, 128]),
-        154:
-        dict(link=('sling_kpt12', 'sling_kpt13'), id=154, color=[0, 0, 128]),
-        155:
-        dict(link=('sling_kpt13', 'sling_kpt14'), id=155, color=[0, 0, 128]),
-        156:
-        dict(link=('sling_kpt14', 'sling_kpt6'), id=156, color=[0, 0, 128]),
-        157:
-        dict(link=('sling_kpt2', 'sling_kpt7'), id=157, color=[0, 0, 128]),
-        158:
-        dict(link=('sling_kpt6', 'sling_kpt15'), id=158, color=[0, 0, 128]),
-        159:
-        dict(link=('sling_kpt2', 'sling_kpt3'), id=159, color=[0, 0, 128]),
-        160:
-        dict(link=('sling_kpt3', 'sling_kpt4'), id=160, color=[0, 0, 128]),
-        161:
-        dict(link=('sling_kpt4', 'sling_kpt5'), id=161, color=[0, 0, 128]),
-        162:
-        dict(link=('sling_kpt5', 'sling_kpt6'), id=162, color=[0, 0, 128]),
-        163:
-        dict(link=('sling_kpt1', 'sling_kpt6'), id=163, color=[0, 0, 128]),
-        164:
-        dict(
-            link=('shorts_kpt1', 'shorts_kpt4'), id=164, color=[128, 128,
-                                                                128]),
-        165:
-        dict(
-            link=('shorts_kpt4', 'shorts_kpt5'), id=165, color=[128, 128,
-                                                                128]),
-        166:
-        dict(
-            link=('shorts_kpt5', 'shorts_kpt6'), id=166, color=[128, 128,
-                                                                128]),
-        167:
-        dict(
-            link=('shorts_kpt6', 'shorts_kpt7'), id=167, color=[128, 128,
-                                                                128]),
-        168:
-        dict(
-            link=('shorts_kpt7', 'shorts_kpt8'), id=168, color=[128, 128,
-                                                                128]),
-        169:
-        dict(
-            link=('shorts_kpt8', 'shorts_kpt9'), id=169, color=[128, 128,
-                                                                128]),
-        170:
-        dict(
-            link=('shorts_kpt9', 'shorts_kpt10'),
-            id=170,
-            color=[128, 128, 128]),
-        171:
-        dict(
-            link=('shorts_kpt10', 'shorts_kpt3'),
-            id=171,
-            color=[128, 128, 128]),
-        172:
-        dict(
-            link=('shorts_kpt3', 'shorts_kpt2'), id=172, color=[128, 128,
-                                                                128]),
-        173:
-        dict(
-            link=('shorts_kpt2', 'shorts_kpt1'), id=173, color=[128, 128,
-                                                                128]),
-        174:
-        dict(
-            link=('trousers_kpt1', 'trousers_kpt4'),
-            id=174,
-            color=[128, 0, 128]),
-        175:
-        dict(
-            link=('trousers_kpt4', 'trousers_kpt5'),
-            id=175,
-            color=[128, 0, 128]),
-        176:
-        dict(
-            link=('trousers_kpt5', 'trousers_kpt6'),
-            id=176,
-            color=[128, 0, 128]),
-        177:
-        dict(
-            link=('trousers_kpt6', 'trousers_kpt7'),
-            id=177,
-            color=[128, 0, 128]),
-        178:
-        dict(
-            link=('trousers_kpt7', 'trousers_kpt8'),
-            id=178,
-            color=[128, 0, 128]),
-        179:
-        dict(
-            link=('trousers_kpt8', 'trousers_kpt9'),
-            id=179,
-            color=[128, 0, 128]),
-        180:
-        dict(
-            link=('trousers_kpt9', 'trousers_kpt10'),
-            id=180,
-            color=[128, 0, 128]),
-        181:
-        dict(
-            link=('trousers_kpt10', 'trousers_kpt11'),
-            id=181,
-            color=[128, 0, 128]),
-        182:
-        dict(
-            link=('trousers_kpt11', 'trousers_kpt12'),
-            id=182,
-            color=[128, 0, 128]),
-        183:
-        dict(
-            link=('trousers_kpt12', 'trousers_kpt13'),
-            id=183,
-            color=[128, 0, 128]),
-        184:
-        dict(
-            link=('trousers_kpt13', 'trousers_kpt14'),
-            id=184,
-            color=[128, 0, 128]),
-        185:
-        dict(
-            link=('trousers_kpt14', 'trousers_kpt3'),
-            id=185,
-            color=[128, 0, 128]),
-        186:
-        dict(
-            link=('trousers_kpt3', 'trousers_kpt2'),
-            id=186,
-            color=[128, 0, 128]),
-        187:
-        dict(
-            link=('trousers_kpt2', 'trousers_kpt1'),
-            id=187,
-            color=[128, 0, 128]),
-        188:
-        dict(link=('skirt_kpt1', 'skirt_kpt4'), id=188, color=[64, 128, 128]),
-        189:
-        dict(link=('skirt_kpt4', 'skirt_kpt5'), id=189, color=[64, 128, 128]),
-        190:
-        dict(link=('skirt_kpt5', 'skirt_kpt6'), id=190, color=[64, 128, 128]),
-        191:
-        dict(link=('skirt_kpt6', 'skirt_kpt7'), id=191, color=[64, 128, 128]),
-        192:
-        dict(link=('skirt_kpt7', 'skirt_kpt8'), id=192, color=[64, 128, 128]),
-        193:
-        dict(link=('skirt_kpt8', 'skirt_kpt3'), id=193, color=[64, 128, 128]),
-        194:
-        dict(link=('skirt_kpt3', 'skirt_kpt2'), id=194, color=[64, 128, 128]),
-        195:
-        dict(link=('skirt_kpt2', 'skirt_kpt1'), id=195, color=[64, 128, 128]),
-        196:
-        dict(link=('ssd_kpt1', 'ssd_kpt2'), id=196, color=[64, 64, 128]),
-        197:
-        dict(link=('ssd_kpt2', 'ssd_kpt7'), id=197, color=[64, 64, 128]),
-        198:
-        dict(link=('ssd_kpt7', 'ssd_kpt8'), id=198, color=[64, 64, 128]),
-        199:
-        dict(link=('ssd_kpt8', 'ssd_kpt9'), id=199, color=[64, 64, 128]),
-        200:
-        dict(link=('ssd_kpt9', 'ssd_kpt10'), id=200, color=[64, 64, 128]),
-        201:
-        dict(link=('ssd_kpt10', 'ssd_kpt11'), id=201, color=[64, 64, 128]),
-        202:
-        dict(link=('ssd_kpt11', 'ssd_kpt12'), id=202, color=[64, 64, 128]),
-        203:
-        dict(link=('ssd_kpt12', 'ssd_kpt13'), id=203, color=[64, 64, 128]),
-        204:
-        dict(link=('ssd_kpt13', 'ssd_kpt14'), id=204, color=[64, 64, 128]),
-        205:
-        dict(link=('ssd_kpt14', 'ssd_kpt15'), id=205, color=[64, 64, 128]),
-        206:
-        dict(link=('ssd_kpt15', 'ssd_kpt16'), id=206, color=[64, 64, 128]),
-        207:
-        dict(link=('ssd_kpt16', 'ssd_kpt17'), id=207, color=[64, 64, 128]),
-        208:
-        dict(link=('ssd_kpt17', 'ssd_kpt18'), id=208, color=[64, 64, 128]),
-        209:
-        dict(link=('ssd_kpt18', 'ssd_kpt19'), id=209, color=[64, 64, 128]),
-        210:
-        dict(link=('ssd_kpt19', 'ssd_kpt20'), id=210, color=[64, 64, 128]),
-        211:
-        dict(link=('ssd_kpt20', 'ssd_kpt21'), id=211, color=[64, 64, 128]),
-        212:
-        dict(link=('ssd_kpt21', 'ssd_kpt22'), id=212, color=[64, 64, 128]),
-        213:
-        dict(link=('ssd_kpt22', 'ssd_kpt23'), id=213, color=[64, 64, 128]),
-        214:
-        dict(link=('ssd_kpt23', 'ssd_kpt24'), id=214, color=[64, 64, 128]),
-        215:
-        dict(link=('ssd_kpt24', 'ssd_kpt25'), id=215, color=[64, 64, 128]),
-        216:
-        dict(link=('ssd_kpt25', 'ssd_kpt26'), id=216, color=[64, 64, 128]),
-        217:
-        dict(link=('ssd_kpt26', 'ssd_kpt27'), id=217, color=[64, 64, 128]),
-        218:
-        dict(link=('ssd_kpt27', 'ssd_kpt28'), id=218, color=[64, 64, 128]),
-        219:
-        dict(link=('ssd_kpt28', 'ssd_kpt29'), id=219, color=[64, 64, 128]),
-        220:
-        dict(link=('ssd_kpt29', 'ssd_kpt6'), id=220, color=[64, 64, 128]),
-        221:
-        dict(link=('ssd_kpt6', 'ssd_kpt5'), id=221, color=[64, 64, 128]),
-        222:
-        dict(link=('ssd_kpt5', 'ssd_kpt4'), id=222, color=[64, 64, 128]),
-        223:
-        dict(link=('ssd_kpt4', 'ssd_kpt3'), id=223, color=[64, 64, 128]),
-        224:
-        dict(link=('ssd_kpt3', 'ssd_kpt2'), id=224, color=[64, 64, 128]),
-        225:
-        dict(link=('ssd_kpt6', 'ssd_kpt1'), id=225, color=[64, 64, 128]),
-        226:
-        dict(link=('lsd_kpt1', 'lsd_kpt2'), id=226, color=[128, 64, 0]),
-        227:
-        dict(link=('lsd_kpt2', 'lsd_kpt7'), id=228, color=[128, 64, 0]),
-        228:
-        dict(link=('lsd_kpt7', 'lsd_kpt8'), id=228, color=[128, 64, 0]),
-        229:
-        dict(link=('lsd_kpt8', 'lsd_kpt9'), id=229, color=[128, 64, 0]),
-        230:
-        dict(link=('lsd_kpt9', 'lsd_kpt10'), id=230, color=[128, 64, 0]),
-        231:
-        dict(link=('lsd_kpt10', 'lsd_kpt11'), id=231, color=[128, 64, 0]),
-        232:
-        dict(link=('lsd_kpt11', 'lsd_kpt12'), id=232, color=[128, 64, 0]),
-        233:
-        dict(link=('lsd_kpt12', 'lsd_kpt13'), id=233, color=[128, 64, 0]),
-        234:
-        dict(link=('lsd_kpt13', 'lsd_kpt14'), id=234, color=[128, 64, 0]),
-        235:
-        dict(link=('lsd_kpt14', 'lsd_kpt15'), id=235, color=[128, 64, 0]),
-        236:
-        dict(link=('lsd_kpt15', 'lsd_kpt16'), id=236, color=[128, 64, 0]),
-        237:
-        dict(link=('lsd_kpt16', 'lsd_kpt17'), id=237, color=[128, 64, 0]),
-        238:
-        dict(link=('lsd_kpt17', 'lsd_kpt18'), id=238, color=[128, 64, 0]),
-        239:
-        dict(link=('lsd_kpt18', 'lsd_kpt19'), id=239, color=[128, 64, 0]),
-        240:
-        dict(link=('lsd_kpt19', 'lsd_kpt20'), id=240, color=[128, 64, 0]),
-        241:
-        dict(link=('lsd_kpt20', 'lsd_kpt21'), id=241, color=[128, 64, 0]),
-        242:
-        dict(link=('lsd_kpt21', 'lsd_kpt22'), id=242, color=[128, 64, 0]),
-        243:
-        dict(link=('lsd_kpt22', 'lsd_kpt23'), id=243, color=[128, 64, 0]),
-        244:
-        dict(link=('lsd_kpt23', 'lsd_kpt24'), id=244, color=[128, 64, 0]),
-        245:
-        dict(link=('lsd_kpt24', 'lsd_kpt25'), id=245, color=[128, 64, 0]),
-        246:
-        dict(link=('lsd_kpt25', 'lsd_kpt26'), id=246, color=[128, 64, 0]),
-        247:
-        dict(link=('lsd_kpt26', 'lsd_kpt27'), id=247, color=[128, 64, 0]),
-        248:
-        dict(link=('lsd_kpt27', 'lsd_kpt28'), id=248, color=[128, 64, 0]),
-        249:
-        dict(link=('lsd_kpt28', 'lsd_kpt29'), id=249, color=[128, 64, 0]),
-        250:
-        dict(link=('lsd_kpt29', 'lsd_kpt30'), id=250, color=[128, 64, 0]),
-        251:
-        dict(link=('lsd_kpt30', 'lsd_kpt31'), id=251, color=[128, 64, 0]),
-        252:
-        dict(link=('lsd_kpt31', 'lsd_kpt32'), id=252, color=[128, 64, 0]),
-        253:
-        dict(link=('lsd_kpt32', 'lsd_kpt33'), id=253, color=[128, 64, 0]),
-        254:
-        dict(link=('lsd_kpt33', 'lsd_kpt34'), id=254, color=[128, 64, 0]),
-        255:
-        dict(link=('lsd_kpt34', 'lsd_kpt35'), id=255, color=[128, 64, 0]),
-        256:
-        dict(link=('lsd_kpt35', 'lsd_kpt36'), id=256, color=[128, 64, 0]),
-        257:
-        dict(link=('lsd_kpt36', 'lsd_kpt37'), id=257, color=[128, 64, 0]),
-        258:
-        dict(link=('lsd_kpt37', 'lsd_kpt6'), id=258, color=[128, 64, 0]),
-        259:
-        dict(link=('lsd_kpt6', 'lsd_kpt5'), id=259, color=[128, 64, 0]),
-        260:
-        dict(link=('lsd_kpt5', 'lsd_kpt4'), id=260, color=[128, 64, 0]),
-        261:
-        dict(link=('lsd_kpt4', 'lsd_kpt3'), id=261, color=[128, 64, 0]),
-        262:
-        dict(link=('lsd_kpt3', 'lsd_kpt2'), id=262, color=[128, 64, 0]),
-        263:
-        dict(link=('lsd_kpt6', 'lsd_kpt1'), id=263, color=[128, 64, 0]),
-        264:
-        dict(link=('vd_kpt1', 'vd_kpt2'), id=264, color=[128, 64, 255]),
-        265:
-        dict(link=('vd_kpt2', 'vd_kpt7'), id=265, color=[128, 64, 255]),
-        266:
-        dict(link=('vd_kpt7', 'vd_kpt8'), id=266, color=[128, 64, 255]),
-        267:
-        dict(link=('vd_kpt8', 'vd_kpt9'), id=267, color=[128, 64, 255]),
-        268:
-        dict(link=('vd_kpt9', 'vd_kpt10'), id=268, color=[128, 64, 255]),
-        269:
-        dict(link=('vd_kpt10', 'vd_kpt11'), id=269, color=[128, 64, 255]),
-        270:
-        dict(link=('vd_kpt11', 'vd_kpt12'), id=270, color=[128, 64, 255]),
-        271:
-        dict(link=('vd_kpt12', 'vd_kpt13'), id=271, color=[128, 64, 255]),
-        272:
-        dict(link=('vd_kpt13', 'vd_kpt14'), id=272, color=[128, 64, 255]),
-        273:
-        dict(link=('vd_kpt14', 'vd_kpt15'), id=273, color=[128, 64, 255]),
-        274:
-        dict(link=('vd_kpt15', 'vd_kpt16'), id=274, color=[128, 64, 255]),
-        275:
-        dict(link=('vd_kpt16', 'vd_kpt17'), id=275, color=[128, 64, 255]),
-        276:
-        dict(link=('vd_kpt17', 'vd_kpt18'), id=276, color=[128, 64, 255]),
-        277:
-        dict(link=('vd_kpt18', 'vd_kpt19'), id=277, color=[128, 64, 255]),
-        278:
-        dict(link=('vd_kpt19', 'vd_kpt6'), id=278, color=[128, 64, 255]),
-        279:
-        dict(link=('vd_kpt6', 'vd_kpt5'), id=279, color=[128, 64, 255]),
-        280:
-        dict(link=('vd_kpt5', 'vd_kpt4'), id=280, color=[128, 64, 255]),
-        281:
-        dict(link=('vd_kpt4', 'vd_kpt3'), id=281, color=[128, 64, 255]),
-        282:
-        dict(link=('vd_kpt3', 'vd_kpt2'), id=282, color=[128, 64, 255]),
-        283:
-        dict(link=('vd_kpt6', 'vd_kpt1'), id=283, color=[128, 64, 255]),
-        284:
-        dict(link=('sd_kpt1', 'sd_kpt2'), id=284, color=[128, 64, 0]),
-        285:
-        dict(link=('sd_kpt2', 'sd_kpt8'), id=285, color=[128, 64, 0]),
-        286:
-        dict(link=('sd_kpt8', 'sd_kpt9'), id=286, color=[128, 64, 0]),
-        287:
-        dict(link=('sd_kpt9', 'sd_kpt10'), id=287, color=[128, 64, 0]),
-        288:
-        dict(link=('sd_kpt10', 'sd_kpt11'), id=288, color=[128, 64, 0]),
-        289:
-        dict(link=('sd_kpt11', 'sd_kpt12'), id=289, color=[128, 64, 0]),
-        290:
-        dict(link=('sd_kpt12', 'sd_kpt13'), id=290, color=[128, 64, 0]),
-        291:
-        dict(link=('sd_kpt13', 'sd_kpt14'), id=291, color=[128, 64, 0]),
-        292:
-        dict(link=('sd_kpt14', 'sd_kpt15'), id=292, color=[128, 64, 0]),
-        293:
-        dict(link=('sd_kpt15', 'sd_kpt16'), id=293, color=[128, 64, 0]),
-        294:
-        dict(link=('sd_kpt16', 'sd_kpt17'), id=294, color=[128, 64, 0]),
-        295:
-        dict(link=('sd_kpt17', 'sd_kpt18'), id=295, color=[128, 64, 0]),
-        296:
-        dict(link=('sd_kpt18', 'sd_kpt6'), id=296, color=[128, 64, 0]),
-        297:
-        dict(link=('sd_kpt6', 'sd_kpt5'), id=297, color=[128, 64, 0]),
-        298:
-        dict(link=('sd_kpt5', 'sd_kpt4'), id=298, color=[128, 64, 0]),
-        299:
-        dict(link=('sd_kpt4', 'sd_kpt3'), id=299, color=[128, 64, 0]),
-        300:
-        dict(link=('sd_kpt3', 'sd_kpt2'), id=300, color=[128, 64, 0]),
-        301:
-        dict(link=('sd_kpt2', 'sd_kpt7'), id=301, color=[128, 64, 0]),
-        302:
-        dict(link=('sd_kpt6', 'sd_kpt19'), id=302, color=[128, 64, 0]),
-        303:
-        dict(link=('sd_kpt6', 'sd_kpt1'), id=303, color=[128, 64, 0])
-    }),
-    joint_weights=[
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0
-    ],
-    sigmas=[])
-param_scheduler = [
-    dict(
-        type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False),
-    dict(
-        type='MultiStepLR',
-        begin=0,
-        end=150,
-        milestones=[100, 130],
-        gamma=0.1,
-        by_epoch=True)
-]
-optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005))
-auto_scale_lr = dict(base_batch_size=512)
-dataset_type = 'DeepFashion2Dataset'
-data_mode = 'topdown'
-data_root = 'data/deepfashion2/'
-codec = dict(
-    type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)
-train_pipeline = [
-    dict(type='LoadImage'),
-    dict(type='GetBBoxCenterScale'),
-    dict(type='RandomFlip', direction='horizontal'),
-    dict(
-        type='RandomBBoxTransform',
-        shift_prob=0,
-        rotate_factor=60,
-        scale_factor=(0.75, 1.25)),
-    dict(type='TopdownAffine', input_size=(192, 256)),
-    dict(
-        type='GenerateTarget',
-        encoder=dict(
-            type='MSRAHeatmap',
-            input_size=(192, 256),
-            heatmap_size=(48, 64),
-            sigma=2)),
-    dict(type='PackPoseInputs')
-]
-val_pipeline = [
-    dict(type='LoadImage', backend_args=dict(backend='local')),
-    dict(type='GetBBoxCenterScale'),
-    dict(type='TopdownAffine', input_size=(192, 256)),
-    dict(type='PackPoseInputs')
-]
-train_dataloader = dict(
-    batch_size=8,
-    num_workers=6,
-    persistent_workers=True,
-    sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=dict(
-        type='DeepFashion2Dataset',
-        data_root='data/deepfashion2/',
-        data_mode='topdown',
-        ann_file='train/deepfashion2_short_sleeved_outwear.json',
-        data_prefix=dict(img='train/image/'),
-        pipeline=[
-            dict(type='LoadImage'),
-            dict(type='GetBBoxCenterScale'),
-            dict(type='RandomFlip', direction='horizontal'),
-            dict(
-                type='RandomBBoxTransform',
-                shift_prob=0,
-                rotate_factor=60,
-                scale_factor=(0.75, 1.25)),
-            dict(type='TopdownAffine', input_size=(192, 256)),
-            dict(
-                type='GenerateTarget',
-                encoder=dict(
-                    type='MSRAHeatmap',
-                    input_size=(192, 256),
-                    heatmap_size=(48, 64),
-                    sigma=2)),
-            dict(type='PackPoseInputs')
-        ]))
-val_dataloader = dict(
-    batch_size=8,
-    num_workers=6,
-    persistent_workers=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=dict(
-        type='DeepFashion2Dataset',
-        data_root='data/deepfashion2/',
-        data_mode='topdown',
-        ann_file='validation/deepfashion2_short_sleeved_outwear.json',
-        data_prefix=dict(img='validation/image/'),
-        test_mode=True,
-        pipeline=[
-            dict(type='LoadImage', backend_args=dict(backend='local')),
-            dict(type='GetBBoxCenterScale'),
-            dict(type='TopdownAffine', input_size=(192, 256)),
-            dict(type='PackPoseInputs')
-        ]))
-test_dataloader = dict(
-    batch_size=8,
-    num_workers=6,
-    persistent_workers=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=dict(
-        type='DeepFashion2Dataset',
-        data_root='data/deepfashion2/',
-        data_mode='topdown',
-        ann_file='validation/deepfashion2_short_sleeved_outwear.json',
-        data_prefix=dict(img='validation/image/'),
-        test_mode=True,
-        pipeline=[
-            dict(type='LoadImage', backend_args=dict(backend='local')),
-            dict(type='GetBBoxCenterScale'),
-            dict(type='TopdownAffine', input_size=(192, 256)),
-            dict(type='PackPoseInputs')
-        ]))
-channel_cfg = dict(
-    num_output_channels=294,
-    dataset_joints=294,
-    dataset_channel=[[
-        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-        20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
-        38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-        56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
-        74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
-        92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
-        108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
-        122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
-        136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
-        150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
-        178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
-        192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
-        206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
-        220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
-        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
-        248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,
-        262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,
-        276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
-        290, 291, 292, 293
-    ]],
-    inference_channel=[
-        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-        20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
-        38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-        56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
-        74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
-        92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
-        108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
-        122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
-        136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
-        150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
-        178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
-        192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
-        206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
-        220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
-        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
-        248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,
-        262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,
-        276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
-        290, 291, 292, 293
-    ])
-model = dict(
-    type='TopdownPoseEstimator',
-    data_preprocessor=dict(
-        type='PoseDataPreprocessor',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        bgr_to_rgb=True),
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
-    head=dict(
-        type='HeatmapHead',
-        in_channels=2048,
-        out_channels=294,
-        loss=dict(type='KeypointMSELoss', use_target_weight=True),
-        decoder=dict(
-            type='MSRAHeatmap',
-            input_size=(192, 256),
-            heatmap_size=(48, 64),
-            sigma=2)),
-    test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True))
-val_evaluator = [
-    dict(type='PCKAccuracy', thr=0.2),
-    dict(type='AUC'),
-    dict(type='EPE')
-]
-test_evaluator = [
-    dict(type='PCKAccuracy', thr=0.2),
-    dict(type='AUC'),
-    dict(type='EPE')
-]
-launcher = 'pytorch'
-work_dir = './work_dirs/td_hm_res50_4xb8-150e_deepfashion2_short_sleeved_outwear_256x192'

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/server/websearch/generateQuery.ts

@@ -1,13 +0,0 @@
-import type { Message } from "$lib/types/Message";
-import { generateFromDefaultEndpoint } from "../generateFromDefaultEndpoint";
-import { defaultModel } from "../models";
-
-export async function generateQuery(messages: Message[]) {
-	const promptSearchQuery = defaultModel.webSearchQueryPromptRender({ messages });
-	const searchQuery = await generateFromDefaultEndpoint(promptSearchQuery).then((query) => {
-		const arr = query.split(/\r?\n/);
-		return arr[0].length > 0 ? arr[0] : arr[1];
-	});
-
-	return searchQuery;
-}

spaces/Adapter/T2I-Adapter/ldm/inference_base.py

@@ -1,282 +0,0 @@
-import argparse
-import torch
-from omegaconf import OmegaConf
-
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.plms import PLMSSampler
-from ldm.modules.encoders.adapter import Adapter, StyleAdapter, Adapter_light
-from ldm.modules.extra_condition.api import ExtraCondition
-from ldm.util import fix_cond_shapes, load_model_from_config, read_state_dict
-
-DEFAULT_NEGATIVE_PROMPT = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
-                          'fewer digits, cropped, worst quality, low quality'
-
-
-def get_base_argument_parser() -> argparse.ArgumentParser:
-    """get the base argument parser for inference scripts"""
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--outdir',
-        type=str,
-        help='dir to write results to',
-        default=None,
-    )
-
-    parser.add_argument(
-        '--prompt',
-        type=str,
-        nargs='?',
-        default=None,
-        help='positive prompt',
-    )
-
-    parser.add_argument(
-        '--neg_prompt',
-        type=str,
-        default=DEFAULT_NEGATIVE_PROMPT,
-        help='negative prompt',
-    )
-
-    parser.add_argument(
-        '--cond_path',
-        type=str,
-        default=None,
-        help='condition image path',
-    )
-
-    parser.add_argument(
-        '--cond_inp_type',
-        type=str,
-        default='image',
-        help='the type of the input condition image, take depth T2I as example, the input can be raw image, '
-        'which depth will be calculated, or the input can be a directly a depth map image',
-    )
-
-    parser.add_argument(
-        '--sampler',
-        type=str,
-        default='ddim',
-        choices=['ddim', 'plms'],
-        help='sampling algorithm, currently, only ddim and plms are supported, more are on the way',
-    )
-
-    parser.add_argument(
-        '--steps',
-        type=int,
-        default=50,
-        help='number of sampling steps',
-    )
-
-    parser.add_argument(
-        '--sd_ckpt',
-        type=str,
-        default='models/sd-v1-4.ckpt',
-        help='path to checkpoint of stable diffusion model, both .ckpt and .safetensor are supported',
-    )
-
-    parser.add_argument(
-        '--vae_ckpt',
-        type=str,
-        default=None,
-        help='vae checkpoint, anime SD models usually have seperate vae ckpt that need to be loaded',
-    )
-
-    parser.add_argument(
-        '--adapter_ckpt',
-        type=str,
-        default=None,
-        help='path to checkpoint of adapter',
-    )
-
-    parser.add_argument(
-        '--config',
-        type=str,
-        default='configs/stable-diffusion/sd-v1-inference.yaml',
-        help='path to config which constructs SD model',
-    )
-
-    parser.add_argument(
-        '--max_resolution',
-        type=float,
-        default=512 * 512,
-        help='max image height * width, only for computer with limited vram',
-    )
-
-    parser.add_argument(
-        '--resize_short_edge',
-        type=int,
-        default=None,
-        help='resize short edge of the input image, if this arg is set, max_resolution will not be used',
-    )
-
-    parser.add_argument(
-        '--C',
-        type=int,
-        default=4,
-        help='latent channels',
-    )
-
-    parser.add_argument(
-        '--f',
-        type=int,
-        default=8,
-        help='downsampling factor',
-    )
-
-    parser.add_argument(
-        '--scale',
-        type=float,
-        default=7.5,
-        help='unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))',
-    )
-
-    parser.add_argument(
-        '--cond_tau',
-        type=float,
-        default=1.0,
-        help='timestamp parameter that determines until which step the adapter is applied, '
-        'similar as Prompt-to-Prompt tau')
-
-    parser.add_argument(
-        '--cond_weight',
-        type=float,
-        default=1.0,
-        help='the adapter features are multiplied by the cond_weight. The larger the cond_weight, the more aligned '
-        'the generated image and condition will be, but the generated quality may be reduced',
-    )
-
-    parser.add_argument(
-        '--seed',
-        type=int,
-        default=42,
-    )
-
-    parser.add_argument(
-        '--n_samples',
-        type=int,
-        default=4,
-        help='# of samples to generate',
-    )
-
-    return parser
-
-
-def get_sd_models(opt):
-    """
-    build stable diffusion model, sampler
-    """
-    # SD
-    config = OmegaConf.load(f"{opt.config}")
-    model = load_model_from_config(config, opt.sd_ckpt, opt.vae_ckpt)
-    sd_model = model.to(opt.device)
-
-    # sampler
-    if opt.sampler == 'plms':
-        sampler = PLMSSampler(model)
-    elif opt.sampler == 'ddim':
-        sampler = DDIMSampler(model)
-    else:
-        raise NotImplementedError
-
-    return sd_model, sampler
-
-
-def get_t2i_adapter_models(opt):
-    config = OmegaConf.load(f"{opt.config}")
-    model = load_model_from_config(config, opt.sd_ckpt, opt.vae_ckpt)
-    adapter_ckpt_path = getattr(opt, f'{opt.which_cond}_adapter_ckpt', None)
-    if adapter_ckpt_path is None:
-        adapter_ckpt_path = getattr(opt, 'adapter_ckpt')
-    adapter_ckpt = read_state_dict(adapter_ckpt_path)
-    new_state_dict = {}
-    for k, v in adapter_ckpt.items():
-        if not k.startswith('adapter.'):
-            new_state_dict[f'adapter.{k}'] = v
-        else:
-            new_state_dict[k] = v
-    m, u = model.load_state_dict(new_state_dict, strict=False)
-    if len(u) > 0:
-        print(f"unexpected keys in loading adapter ckpt {adapter_ckpt_path}:")
-        print(u)
-
-    model = model.to(opt.device)
-
-    # sampler
-    if opt.sampler == 'plms':
-        sampler = PLMSSampler(model)
-    elif opt.sampler == 'ddim':
-        sampler = DDIMSampler(model)
-    else:
-        raise NotImplementedError
-
-    return model, sampler
-
-
-def get_cond_ch(cond_type: ExtraCondition):
-    if cond_type == ExtraCondition.sketch or cond_type == ExtraCondition.canny:
-        return 1
-    return 3
-
-
-def get_adapters(opt, cond_type: ExtraCondition):
-    adapter = {}
-    cond_weight = getattr(opt, f'{cond_type.name}_weight', None)
-    if cond_weight is None:
-        cond_weight = getattr(opt, 'cond_weight')
-    adapter['cond_weight'] = cond_weight
-
-    if cond_type == ExtraCondition.style:
-        adapter['model'] = StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8).to(opt.device)
-    elif cond_type == ExtraCondition.color:
-        adapter['model'] = Adapter_light(
-            cin=64 * get_cond_ch(cond_type),
-            channels=[320, 640, 1280, 1280],
-            nums_rb=4).to(opt.device)
-    else:
-        adapter['model'] = Adapter(
-            cin=64 * get_cond_ch(cond_type),
-            channels=[320, 640, 1280, 1280][:4],
-            nums_rb=2,
-            ksize=1,
-            sk=True,
-            use_conv=False).to(opt.device)
-    ckpt_path = getattr(opt, f'{cond_type.name}_adapter_ckpt', None)
-    if ckpt_path is None:
-        ckpt_path = getattr(opt, 'adapter_ckpt')
-    adapter['model'].load_state_dict(torch.load(ckpt_path))
-
-    return adapter
-
-
-def diffusion_inference(opt, model, sampler, adapter_features, append_to_context=None):
-    # get text embedding
-    c = model.get_learned_conditioning([opt.prompt])
-    if opt.scale != 1.0:
-        uc = model.get_learned_conditioning([opt.neg_prompt])
-    else:
-        uc = None
-    c, uc = fix_cond_shapes(model, c, uc)
-
-    if not hasattr(opt, 'H'):
-        opt.H = 512
-        opt.W = 512
-    shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
-
-    samples_latents, _ = sampler.sample(
-        S=opt.steps,
-        conditioning=c,
-        batch_size=1,
-        shape=shape,
-        verbose=False,
-        unconditional_guidance_scale=opt.scale,
-        unconditional_conditioning=uc,
-        x_T=None,
-        features_adapter=adapter_features,
-        append_to_context=append_to_context,
-        cond_tau=opt.cond_tau,
-    )
-
-    x_samples = model.decode_first_stage(samples_latents)
-    x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
-    return x_samples

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

@@ -1,10 +0,0 @@
-import Space from '../../space/Space.js';
-
-var CreateSpace = function (scene, data, view, styles, customBuilders) {
-    var gameObject = new Space(scene);
-    // Don't add Zone into scene
-    // this.scene.add.existing(gameObject);
-    return gameObject;
-}
-
-export default CreateSpace;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/PercentToPosition.js

@@ -1,13 +0,0 @@
-const Linear = Phaser.Math.Linear;
-
-var PercentToPosition = function (t, startPoint, endPoint, out) {
-    if (out === undefined) {
-        out = tmpOut;
-    }
-    out.x = Linear(startPoint.x, endPoint.x, t);
-    out.y = Linear(startPoint.y, endPoint.y, t);
-    return out;
-}
-var tmpOut = {};
-
-export default PercentToPosition;

spaces/Akmyradov/TurkmenTTSweSTT/asr.py

@@ -1,41 +0,0 @@
-import librosa
-from transformers import Wav2Vec2ForCTC, AutoProcessor
-import torch
-
-ASR_SAMPLING_RATE = 16_000
-
-
-MODEL_ID = "facebook/mms-1b-all"
-
-processor = AutoProcessor.from_pretrained(MODEL_ID)
-model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
-
-
-def transcribe(microphone, file_upload, lang):
-
-    warn_output = ""
-    if (microphone is not None) and (file_upload is not None):
-        warn_output = (
-            "WARNING: You've uploaded an audio file and used the microphone. "
-            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
-        )
-    elif (microphone is None) and (file_upload is None):
-        return "ERROR: You have to either use the microphone or upload an audio file"
-
-    audio_fp = microphone if microphone is not None else file_upload
-    audio_samples = librosa.load(audio_fp, sr=ASR_SAMPLING_RATE, mono=True)[0]
-
-    lang_code = lang.split(":")[0]
-    processor.tokenizer.set_target_lang(lang_code)
-    model.load_adapter(lang_code)
-
-    inputs = processor(
-        audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt"
-    )
-
-    with torch.no_grad():
-        outputs = model(**inputs).logits
-
-    ids = torch.argmax(outputs, dim=-1)[0]
-    transcription = processor.decode(ids)
-    return warn_output + transcription

spaces/Alichuan/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py

@@ -1,30 +0,0 @@
-import re
-import opencc
-
-
-dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
-            'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
-            'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
-            'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
-            'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen',
-            'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'}
-
-converters = {}
-
-for dialect in dialects.values():
-    try:
-        converters[dialect] = opencc.OpenCC(dialect)
-    except:
-        pass
-
-
-def ngu_dialect_to_ipa(text, dialect):
-    dialect = dialects[dialect]
-    text = converters[dialect].convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text

spaces/Amrrs/DragGan-Inversion/PTI/torch_utils/ops/upfirdn2d.h

@@ -1,59 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <cuda_runtime.h>
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct upfirdn2d_kernel_params
-{
-    const void*     x;
-    const float*    f;
-    void*           y;
-
-    int2            up;
-    int2            down;
-    int2            pad0;
-    int             flip;
-    float           gain;
-
-    int4            inSize;         // [width, height, channel, batch]
-    int4            inStride;
-    int2            filterSize;     // [width, height]
-    int2            filterStride;
-    int4            outSize;        // [width, height, channel, batch]
-    int4            outStride;
-    int             sizeMinor;
-    int             sizeMajor;
-
-    int             loopMinor;
-    int             loopMajor;
-    int             loopX;
-    int             launchMinor;
-    int             launchMajor;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct upfirdn2d_kernel_spec
-{
-    void*   kernel;
-    int     tileOutW;
-    int     tileOutH;
-    int     loopMinor;
-    int     loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/controlnet/test_flax_controlnet.py

@@ -1,127 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import unittest
-
-from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
-from diffusers.utils import is_flax_available, load_image, slow
-from diffusers.utils.testing_utils import require_flax
-
-
-if is_flax_available():
-    import jax
-    import jax.numpy as jnp
-    from flax.jax_utils import replicate
-    from flax.training.common_utils import shard
-
-
-@slow
-@require_flax
-class FlaxControlNetPipelineIntegrationTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-
-    def test_canny(self):
-        controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
-            "lllyasviel/sd-controlnet-canny", from_pt=True, dtype=jnp.bfloat16
-        )
-        pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16
-        )
-        params["controlnet"] = controlnet_params
-
-        prompts = "bird"
-        num_samples = jax.device_count()
-        prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
-
-        canny_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        )
-        processed_image = pipe.prepare_image_inputs([canny_image] * num_samples)
-
-        rng = jax.random.PRNGKey(0)
-        rng = jax.random.split(rng, jax.device_count())
-
-        p_params = replicate(params)
-        prompt_ids = shard(prompt_ids)
-        processed_image = shard(processed_image)
-
-        images = pipe(
-            prompt_ids=prompt_ids,
-            image=processed_image,
-            params=p_params,
-            prng_seed=rng,
-            num_inference_steps=50,
-            jit=True,
-        ).images
-        assert images.shape == (jax.device_count(), 1, 768, 512, 3)
-
-        images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
-        image_slice = images[0, 253:256, 253:256, -1]
-
-        output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
-        expected_slice = jnp.array(
-            [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]
-        )
-        print(f"output_slice: {output_slice}")
-        assert jnp.abs(output_slice - expected_slice).max() < 1e-2
-
-    def test_pose(self):
-        controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
-            "lllyasviel/sd-controlnet-openpose", from_pt=True, dtype=jnp.bfloat16
-        )
-        pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16
-        )
-        params["controlnet"] = controlnet_params
-
-        prompts = "Chef in the kitchen"
-        num_samples = jax.device_count()
-        prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
-
-        pose_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"
-        )
-        processed_image = pipe.prepare_image_inputs([pose_image] * num_samples)
-
-        rng = jax.random.PRNGKey(0)
-        rng = jax.random.split(rng, jax.device_count())
-
-        p_params = replicate(params)
-        prompt_ids = shard(prompt_ids)
-        processed_image = shard(processed_image)
-
-        images = pipe(
-            prompt_ids=prompt_ids,
-            image=processed_image,
-            params=p_params,
-            prng_seed=rng,
-            num_inference_steps=50,
-            jit=True,
-        ).images
-        assert images.shape == (jax.device_count(), 1, 768, 512, 3)
-
-        images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
-        image_slice = images[0, 253:256, 253:256, -1]
-
-        output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
-        expected_slice = jnp.array(
-            [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]
-        )
-        print(f"output_slice: {output_slice}")
-        assert jnp.abs(output_slice - expected_slice).max() < 1e-2

spaces/Andy1621/uniformer_image_detection/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py

@@ -1,8 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-
-model = dict(
-    neck=dict(
-        type='PAFPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        num_outs=5))

spaces/Andy1621/uniformer_image_detection/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py

@@ -1,16 +0,0 @@
-_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        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',
-        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))

spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py

@@ -1,2 +0,0 @@
-_base_ = './pspnet_r50-d8_480x480_40k_pascal_context_59.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/Training_PRO/train_utils.py

@@ -1,279 +0,0 @@
-import os
-from modules import shared, utils
-from pathlib import Path
-import json
-
-def list_subfoldersByTime(directory):
-
-    if not directory.endswith('/'):
-        directory += '/'
-    subfolders = []
-    path = directory
-    name_list = os.listdir(path)
-    full_list = [os.path.join(path,i) for i in name_list]
-    time_sorted_list = sorted(full_list, key=os.path.getmtime,reverse=True)
-
-    for entry in time_sorted_list:
-        if os.path.isdir(entry):
-            entry_str = f"{entry}"  # Convert entry to a string
-            full_path = entry_str
-            entry_str = entry_str.replace('\\','/')
-            entry_str = entry_str.replace(f"{directory}", "")  # Remove directory part
-            subfolders.append(entry_str)
-
-    return subfolders
-
-def get_available_loras_local(_sortedByTime):
-    
-    model_dir = shared.args.lora_dir  # Update with the appropriate directory path
-    subfolders = []
-    if _sortedByTime:
-        subfolders = list_subfoldersByTime(model_dir)
-    else:
-        subfolders = utils.get_available_loras()        
-
-    return subfolders
-
-
-# FPHAM SPLIT BY SENTENCE BLOCK ===============
-     
-def split_sentences(text: str, cutoff_len: int):
-    sentences = []
-    sentence = ''
-    delimiters = ['. ', '? ', '! ', '... ', '.\n', '?\n', '!\n','...\n','</s>','<//>']
-    abbreviations = ['Mr. ', 'Mrs. ', 'Dr. ', 'Ms. ', 'St. ', 'Prof. ', 'Jr. ', 'Ltd. ', 'Capt. ', 'Col. ', 'Gen. ', 'Ave. ', 'Blvd. ', 'Co. ', 'Corp. ', 'Dept. ', 'Est. ', 'Gov. ', 'Inc. ', 'Ph.D. ', 'Univ. ']
-    errors = 0
-    max_cut = cutoff_len-1
-    prev_char = ''  
-
-    for char in text:
-        sentence += char
-
-    
-        if (any(sentence.endswith(delimiter) for delimiter in delimiters) and
-            not (prev_char.isupper() and len(sentence) >= 3 and sentence[-3] != ' ') and 
-            not any(sentence.endswith(abbreviation) for abbreviation in abbreviations)):
-            tokens = shared.tokenizer.encode(sentence)
-            
-            if len(tokens) > max_cut:
-                tokens = tokens[:max_cut]
-                sentence = shared.tokenizer.decode(tokens, skip_special_tokens=True)
-                errors = errors + 1
-
-            sentences.append({'text': sentence, 'size': len(tokens)})
-            
-            sentence = ''
-
-        prev_char = char
-
-    if sentence:
-        tokens = shared.tokenizer.encode(sentence)
-        if len(tokens) > max_cut:
-            tokens = tokens[:max_cut]
-            sentence = shared.tokenizer.decode(tokens, skip_special_tokens=True)  
-            errors = errors + 1
-
-        sentences.append({'text': sentence, 'size': len(tokens)})
-
-    if errors > 0:
-        print(f"Trimmed sentences beyond Cutoff Length: {errors}")
-
-    return sentences
-
-# The goal of following code is to create blocks of text + overlapping blocks while:
-# respects sentence boundaries
-# always uses all the text 
-# hard cut defined by hard_cut_string or </s> will always end at the end of data block
-# no overlapping blocks will be created across hard cut or across </s> token
-
-def precise_cut(text: str, overlap: bool, min_chars_cut: int, eos_to_hc: bool, cutoff_len: int, hard_cut_string: str, debug_slicer:bool):
-
-    EOSX_str = '<//>' #hardcut placeholder
-    EOS_str = '</s>' 
-    print("Precise raw text slicer: ON")
-    
-    cut_string = hard_cut_string.replace('\\n', '\n')
-    text = text.replace(cut_string, EOSX_str)
-    sentences = split_sentences(text, cutoff_len)
-
-    print(f"Sentences: {len(sentences)}")
-    sentencelist = []
-    currentSentence = ''
-    totalLength = 0
-    max_cut = cutoff_len-1
-    half_cut = cutoff_len//2
-    halfcut_length = 0
-
-    edgeindex = []
-    half_index = 0
-
-    for index, item in enumerate(sentences):
-        
-        if halfcut_length+ item['size'] < half_cut:
-            halfcut_length += item['size']
-            half_index = index
-        else:
-            edgeindex.append(half_index)
-            halfcut_length = -2 * max_cut
-
-
-        if totalLength + item['size'] < max_cut and not currentSentence.endswith(EOSX_str): 
-            currentSentence += item['text']
-            totalLength += item['size']
-        else:
-
-            if len(currentSentence.strip()) > min_chars_cut:
-                sentencelist.append(currentSentence.strip())
-
-            currentSentence = item['text']
-            totalLength = item['size']
-            halfcut_length = item['size']
-            
-    if len(currentSentence.strip()) > min_chars_cut:    
-        sentencelist.append(currentSentence.strip())
-
-    unique_blocks = len(sentencelist)
-    print(f"Text Blocks: {unique_blocks}")
-
-    #overlap strategies: 
-    # don't overlap across HARD CUT (EOSX)
-    if overlap:
-        for edge_idx in edgeindex:
-            currentSentence = ''
-            totalLength = 0
-
-            for item in sentences[edge_idx:]:
-                if totalLength + item['size'] < max_cut:
-                    currentSentence += item['text']
-                    totalLength += item['size']
-                else:
-                    #if by chance EOSX is at the end then it's acceptable
-                    if currentSentence.endswith(EOSX_str) and len(currentSentence.strip()) > min_chars_cut:
-                            sentencelist.append(currentSentence.strip())    
-                    # otherwise don't cross hard cut    
-                    elif EOSX_str not in currentSentence and len(currentSentence.strip()) > min_chars_cut:
-                        sentencelist.append(currentSentence.strip())
-                    
-                    currentSentence = ''
-                    totalLength = 0
-                    break
-        
-        print(f"+ Overlapping blocks: {len(sentencelist)-unique_blocks}")
-
-    num_EOS = 0
-    for i in range(len(sentencelist)):
-        if eos_to_hc:
-            sentencelist[i] = sentencelist[i].replace(EOSX_str, EOS_str)
-        else:
-            sentencelist[i] = sentencelist[i].replace(EOSX_str, '')
-        
-        #someone may have had stop strings in the raw text...
-        sentencelist[i] = sentencelist[i].replace("</s></s>", EOS_str)
-        num_EOS += sentencelist[i].count(EOS_str)
-
-    if num_EOS > 0:
-        print(f"+ EOS count: {num_EOS}")
-
-    #final check for useless lines
-    sentencelist = [item for item in sentencelist if item.strip() != "</s>"]
-    sentencelist = [item for item in sentencelist if item.strip() != ""]
-
-
-    if debug_slicer:
-                    # Write the log file
-        Path('logs').mkdir(exist_ok=True)
-        sentencelist_dict = {index: sentence for index, sentence in enumerate(sentencelist)}
-        output_file = "logs/sentencelist.json"
-        with open(output_file, 'w') as f:
-            json.dump(sentencelist_dict, f,indent=2)
-        
-        print("Saved sentencelist.json in logs folder")
-    
-    return sentencelist   
-
-
-def sliding_block_cut(text: str, min_chars_cut: int, eos_to_hc: bool, cutoff_len: int, hard_cut_string: str, debug_slicer:bool):
-
-    EOSX_str = '<//>' #hardcut placeholder
-    EOS_str = '</s>' 
-    print("Mega Block Overlap: ON")
-    
-    cut_string = hard_cut_string.replace('\\n', '\n')
-    text = text.replace(cut_string, EOSX_str)
-    sentences = split_sentences(text, cutoff_len)
-
-    print(f"Sentences: {len(sentences)}")
-    sentencelist = []
-    
-    max_cut = cutoff_len-1
-
-    #print(f"max_cut: {max_cut}")
-    advancing_to = 0
-
-    prev_block_lastsentence = ""
-    
-
-    for i in range(len(sentences)):
-        totalLength = 0
-        currentSentence = ''
-        lastsentence = ""
-        
-        if i >= advancing_to:
-            for k in range(i, len(sentences)):
-                
-                current_length = sentences[k]['size']
-
-                if totalLength + current_length <= max_cut and not currentSentence.endswith(EOSX_str):
-                    currentSentence += sentences[k]['text']
-                    totalLength += current_length
-                    lastsentence = sentences[k]['text']
-                else:
-                    if len(currentSentence.strip()) > min_chars_cut:
-                        if prev_block_lastsentence!=lastsentence:
-                            sentencelist.append(currentSentence.strip())
-                            prev_block_lastsentence = lastsentence
-                        
-                    advancing_to = 0
-                    if currentSentence.endswith(EOSX_str):
-                        advancing_to = k
-
-                    currentSentence = ""
-                    totalLength = 0
-                    break
-            
-            if currentSentence != "":
-                if len(currentSentence.strip()) > min_chars_cut:
-                    sentencelist.append(currentSentence.strip())
-
-    unique_blocks = len(sentencelist)
-    print(f"Text Blocks: {unique_blocks}")
-    num_EOS = 0
-    for i in range(len(sentencelist)):
-        if eos_to_hc:
-            sentencelist[i] = sentencelist[i].replace(EOSX_str, EOS_str)
-        else:
-            sentencelist[i] = sentencelist[i].replace(EOSX_str, '')
-        
-        #someone may have had stop strings in the raw text...
-        sentencelist[i] = sentencelist[i].replace("</s></s>", EOS_str)
-        num_EOS += sentencelist[i].count(EOS_str)
-
-    if num_EOS > 0:
-        print(f"+ EOS count: {num_EOS}")
-
-    #final check for useless lines
-    sentencelist = [item for item in sentencelist if item.strip() != "</s>"]
-    sentencelist = [item for item in sentencelist if item.strip() != ""]
-
-
-    if debug_slicer:
-                    # Write the log file
-        Path('logs').mkdir(exist_ok=True)
-        sentencelist_dict = {index: sentence for index, sentence in enumerate(sentencelist)}
-        output_file = "logs/sentencelist.json"
-        with open(output_file, 'w') as f:
-            json.dump(sentencelist_dict, f,indent=2)
-        
-        print("Saved sentencelist.json in logs folder")
-    
-    return sentencelist   

spaces/ArtGAN/Segment-Anything-Video/app.py

@@ -1,319 +0,0 @@
-import gradio as gr
-from demo import automask_image_app, automask_video_app, sahi_autoseg_app
-
-
-
-def image_app():
-    with gr.Blocks():
-        with gr.Row():
-            with gr.Column():
-                seg_automask_image_file = gr.Image(type="filepath").style(height=260)
-                with gr.Row():
-                    with gr.Column():
-                        seg_automask_image_model_type = gr.Dropdown(
-                            choices=[
-                                "vit_h",
-                                "vit_l",
-                                "vit_b",
-                            ],
-                            value="vit_l",
-                            label="Model Type",
-                        )
-
-                        seg_automask_image_min_area = gr.Number(
-                            value=0,
-                            label="Min Area",
-                        )
-                    with gr.Row():
-                        with gr.Column():
-                            seg_automask_image_points_per_side = gr.Slider(
-                                minimum=0,
-                                maximum=32,
-                                step=2,
-                                value=16,
-                                label="Points per Side",
-                            )
-
-                            seg_automask_image_points_per_batch = gr.Slider(
-                                minimum=0,
-                                maximum=64,
-                                step=2,
-                                value=32,
-                                label="Points per Batch",
-                            )
-
-                seg_automask_image_predict = gr.Button(value="Generator")
-
-            with gr.Column():
-                output_image = gr.Image()
-
-        seg_automask_image_predict.click(
-            fn=automask_image_app,
-            inputs=[
-                seg_automask_image_file,
-                seg_automask_image_model_type,
-                seg_automask_image_points_per_side,
-                seg_automask_image_points_per_batch,
-                seg_automask_image_min_area,
-            ],
-            outputs=[output_image],
-        )
-        
-        gr.Examples(
-            examples=[
-                [
-                    "testv3.jpeg",
-                    "vit_l",
-                    16,
-                    32,
-                    0,
-                ],
-            
-            ],
-            fn=automask_image_app,
-            inputs=[
-                seg_automask_image_file,
-                seg_automask_image_model_type,
-                seg_automask_image_points_per_side,
-                seg_automask_image_points_per_batch,
-                seg_automask_image_min_area,
-            ],  
-            outputs=[output_image],
-            cache_examples=True,
-        )
-
-
-def video_app():
-    with gr.Blocks():
-        with gr.Row():
-            with gr.Column():
-                seg_automask_video_file = gr.Video().style(height=260)
-                with gr.Row():
-                    with gr.Column():
-                        seg_automask_video_model_type = gr.Dropdown(
-                            choices=[
-                                "vit_h",
-                                "vit_l",
-                                "vit_b",
-                            ],
-                            value="vit_l",
-                            label="Model Type",
-                        )
-                        seg_automask_video_min_area = gr.Number(
-                            value=1000,
-                            label="Min Area",
-                        )
-
-                    with gr.Row():
-                        with gr.Column():
-                            seg_automask_video_points_per_side = gr.Slider(
-                                minimum=0,
-                                maximum=32,
-                                step=2,
-                                value=16,
-                                label="Points per Side",
-                            )
-
-                            seg_automask_video_points_per_batch = gr.Slider(
-                                minimum=0,
-                                maximum=64,
-                                step=2,
-                                value=32,
-                                label="Points per Batch",
-                            )
-
-                seg_automask_video_predict = gr.Button(value="Generator")
-            with gr.Column():
-                output_video = gr.Video()
-
-        seg_automask_video_predict.click(
-            fn=automask_video_app,
-            inputs=[
-                seg_automask_video_file,
-                seg_automask_video_model_type,
-                seg_automask_video_points_per_side,
-                seg_automask_video_points_per_batch,
-                seg_automask_video_min_area,
-            ],
-            outputs=[output_video],
-        )
-
-        gr.Examples(
-            examples=[
-                [
-                    "testv2.mp4",
-                    "vit_l",
-                    16,
-                    32,
-                    0,
-                ],  
-            ],
-            fn=automask_video_app,
-            inputs=[
-                seg_automask_video_file,
-                seg_automask_video_model_type,
-                seg_automask_video_points_per_side,
-                seg_automask_video_points_per_batch,
-                seg_automask_video_min_area,
-            ],
-            outputs=[output_video],
-            cache_examples=True,
-        )
-
-
-def sahi_app():
-    with gr.Blocks():
-        with gr.Row():
-            with gr.Column():
-                sahi_image_file = gr.Image(type="filepath").style(height=260)
-                sahi_autoseg_model_type = gr.Dropdown(
-                    choices=[
-                        "vit_h",
-                        "vit_l",
-                        "vit_b",
-                    ],
-                    value="vit_l",
-                    label="Sam Model Type",
-                )
-
-                with gr.Row():
-                    with gr.Column():
-                        sahi_model_type = gr.Dropdown(
-                            choices=[
-                                "yolov5",
-                                "yolov8",
-                            ],
-                            value="yolov5",
-                            label="Detector Model Type",
-                        )
-                        sahi_image_size = gr.Slider(
-                            minimum=0,
-                            maximum=1280,
-                            step=32,
-                            value=640,
-                            label="Image Size",
-                        )
-
-                        sahi_overlap_width = gr.Slider(
-                            minimum=0,
-                            maximum=1,
-                            step=0.1,
-                            value=0.2,
-                            label="Overlap Width",
-                        )
-
-                        sahi_slice_width = gr.Slider(
-                            minimum=0,
-                            maximum=640,
-                            step=32,
-                            value=256,
-                            label="Slice Width",
-                        )
-
-                    with gr.Row():
-                        with gr.Column():
-                            sahi_model_path = gr.Dropdown(
-                                choices=[
-                                    "yolov5l.pt",
-                                    "yolov5l6.pt",
-                                    "yolov8l.pt",
-                                    "yolov8x.pt",
-                                ],
-                                value="yolov5l6.pt",
-                                label="Detector Model Path",
-                            )
-
-                            sahi_conf_th = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                step=0.1,
-                                value=0.2,
-                                label="Confidence Threshold",
-                            )
-                            sahi_overlap_height = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                step=0.1,
-                                value=0.2,
-                                label="Overlap Height",
-                            )
-                            sahi_slice_height = gr.Slider(
-                                minimum=0,
-                                maximum=640,
-                                step=32,
-                                value=256,
-                                label="Slice Height",
-                            )
-                sahi_image_predict = gr.Button(value="Generator")
-
-            with gr.Column():
-                output_image = gr.Image()
-
-        sahi_image_predict.click(
-            fn=sahi_autoseg_app,
-            inputs=[
-                sahi_image_file,
-                sahi_autoseg_model_type,
-                sahi_model_type,
-                sahi_model_path,
-                sahi_conf_th,
-                sahi_image_size,
-                sahi_slice_height,
-                sahi_slice_width,
-                sahi_overlap_height,
-                sahi_overlap_width,
-            ],
-            outputs=[output_image],
-        )
-        
-        gr.Examples(
-            examples=[
-                [
-                    "testv1.jpg",
-                    "vit_l",
-                    "yolov5",
-                    "yolov5l6.pt",
-                    0.2,
-                    1280,
-                    256,
-                    256,
-                    0.2,
-                    0.2,
-                ],
-            ],
-            fn=sahi_autoseg_app,
-            inputs=[
-                sahi_image_file,
-                sahi_autoseg_model_type,
-                sahi_model_type,
-                sahi_model_path,
-                sahi_conf_th,
-                sahi_image_size,
-                sahi_slice_height,
-                sahi_slice_width,
-                sahi_overlap_height,
-                sahi_overlap_width,
-            ],
-            outputs=[output_image],
-            cache_examples=True,
-        )
-
-
-def metaseg_app():
-    app = gr.Blocks()
-    with app:
-        with gr.Row():
-            with gr.Column():
-                with gr.Tab("Image"):
-                    image_app()
-                with gr.Tab("Video"):
-                    video_app()
-                with gr.Tab("SAHI"):
-                    sahi_app()
-
-    app.queue(concurrency_count=1)
-    app.launch(debug=True, enable_queue=True)
-
-
-if __name__ == "__main__":
-    metaseg_app()

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/config/GroundingDINO_SwinT_OGC.py

@@ -1,43 +0,0 @@
-batch_size = 1
-modelname = "groundingdino"
-backbone = "swin_T_224_1k"
-position_embedding = "sine"
-pe_temperatureH = 20
-pe_temperatureW = 20
-return_interm_indices = [1, 2, 3]
-backbone_freeze_keywords = None
-enc_layers = 6
-dec_layers = 6
-pre_norm = False
-dim_feedforward = 2048
-hidden_dim = 256
-dropout = 0.0
-nheads = 8
-num_queries = 900
-query_dim = 4
-num_patterns = 0
-num_feature_levels = 4
-enc_n_points = 4
-dec_n_points = 4
-two_stage_type = "standard"
-two_stage_bbox_embed_share = False
-two_stage_class_embed_share = False
-transformer_activation = "relu"
-dec_pred_bbox_embed_share = True
-dn_box_noise_scale = 1.0
-dn_label_noise_ratio = 0.5
-dn_label_coef = 1.0
-dn_bbox_coef = 1.0
-embed_init_tgt = True
-dn_labelbook_size = 2000
-max_text_len = 256
-text_encoder_type = "bert-base-uncased"
-use_text_enhancer = True
-use_fusion_layer = True
-use_checkpoint = True
-use_transformer_ckpt = True
-use_text_cross_attention = True
-text_dropout = 0.0
-fusion_dropout = 0.0
-fusion_droppath = 0.1
-sub_sentence_present = True

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

@@ -1,237 +0,0 @@
-""" PEP 610 """
-import json
-import re
-import urllib.parse
-from typing import Any, Dict, Iterable, Optional, Type, TypeVar, Union
-
-__all__ = [
-    "DirectUrl",
-    "DirectUrlValidationError",
-    "DirInfo",
-    "ArchiveInfo",
-    "VcsInfo",
-]
-
-T = TypeVar("T")
-
-DIRECT_URL_METADATA_NAME = "direct_url.json"
-ENV_VAR_RE = re.compile(r"^\$\{[A-Za-z0-9-_]+\}(:\$\{[A-Za-z0-9-_]+\})?$")
-
-
-class DirectUrlValidationError(Exception):
-    pass
-
-
-def _get(
-    d: Dict[str, Any], expected_type: Type[T], key: str, default: Optional[T] = None
-) -> Optional[T]:
-    """Get value from dictionary and verify expected type."""
-    if key not in d:
-        return default
-    value = d[key]
-    if not isinstance(value, expected_type):
-        raise DirectUrlValidationError(
-            "{!r} has unexpected type for {} (expected {})".format(
-                value, key, expected_type
-            )
-        )
-    return value
-
-
-def _get_required(
-    d: Dict[str, Any], expected_type: Type[T], key: str, default: Optional[T] = None
-) -> T:
-    value = _get(d, expected_type, key, default)
-    if value is None:
-        raise DirectUrlValidationError(f"{key} must have a value")
-    return value
-
-
-def _exactly_one_of(infos: Iterable[Optional["InfoType"]]) -> "InfoType":
-    infos = [info for info in infos if info is not None]
-    if not infos:
-        raise DirectUrlValidationError(
-            "missing one of archive_info, dir_info, vcs_info"
-        )
-    if len(infos) > 1:
-        raise DirectUrlValidationError(
-            "more than one of archive_info, dir_info, vcs_info"
-        )
-    assert infos[0] is not None
-    return infos[0]
-
-
-def _filter_none(**kwargs: Any) -> Dict[str, Any]:
-    """Make dict excluding None values."""
-    return {k: v for k, v in kwargs.items() if v is not None}
-
-
-class VcsInfo:
-    name = "vcs_info"
-
-    def __init__(
-        self,
-        vcs: str,
-        commit_id: str,
-        requested_revision: Optional[str] = None,
-    ) -> None:
-        self.vcs = vcs
-        self.requested_revision = requested_revision
-        self.commit_id = commit_id
-
-    @classmethod
-    def _from_dict(cls, d: Optional[Dict[str, Any]]) -> Optional["VcsInfo"]:
-        if d is None:
-            return None
-        return cls(
-            vcs=_get_required(d, str, "vcs"),
-            commit_id=_get_required(d, str, "commit_id"),
-            requested_revision=_get(d, str, "requested_revision"),
-        )
-
-    def _to_dict(self) -> Dict[str, Any]:
-        return _filter_none(
-            vcs=self.vcs,
-            requested_revision=self.requested_revision,
-            commit_id=self.commit_id,
-        )
-
-
-class ArchiveInfo:
-    name = "archive_info"
-
-    def __init__(
-        self,
-        hash: Optional[str] = None,
-        hashes: Optional[Dict[str, str]] = None,
-    ) -> None:
-        # set hashes before hash, since the hash setter will further populate hashes
-        self.hashes = hashes
-        self.hash = hash
-
-    @property
-    def hash(self) -> Optional[str]:
-        return self._hash
-
-    @hash.setter
-    def hash(self, value: Optional[str]) -> None:
-        if value is not None:
-            # Auto-populate the hashes key to upgrade to the new format automatically.
-            # We don't back-populate the legacy hash key from hashes.
-            try:
-                hash_name, hash_value = value.split("=", 1)
-            except ValueError:
-                raise DirectUrlValidationError(
-                    f"invalid archive_info.hash format: {value!r}"
-                )
-            if self.hashes is None:
-                self.hashes = {hash_name: hash_value}
-            elif hash_name not in self.hashes:
-                self.hashes = self.hashes.copy()
-                self.hashes[hash_name] = hash_value
-        self._hash = value
-
-    @classmethod
-    def _from_dict(cls, d: Optional[Dict[str, Any]]) -> Optional["ArchiveInfo"]:
-        if d is None:
-            return None
-        return cls(hash=_get(d, str, "hash"), hashes=_get(d, dict, "hashes"))
-
-    def _to_dict(self) -> Dict[str, Any]:
-        return _filter_none(hash=self.hash, hashes=self.hashes)
-
-
-class DirInfo:
-    name = "dir_info"
-
-    def __init__(
-        self,
-        editable: bool = False,
-    ) -> None:
-        self.editable = editable
-
-    @classmethod
-    def _from_dict(cls, d: Optional[Dict[str, Any]]) -> Optional["DirInfo"]:
-        if d is None:
-            return None
-        return cls(editable=_get_required(d, bool, "editable", default=False))
-
-    def _to_dict(self) -> Dict[str, Any]:
-        return _filter_none(editable=self.editable or None)
-
-
-InfoType = Union[ArchiveInfo, DirInfo, VcsInfo]
-
-
-class DirectUrl:
-    def __init__(
-        self,
-        url: str,
-        info: InfoType,
-        subdirectory: Optional[str] = None,
-    ) -> None:
-        self.url = url
-        self.info = info
-        self.subdirectory = subdirectory
-
-    def _remove_auth_from_netloc(self, netloc: str) -> str:
-        if "@" not in netloc:
-            return netloc
-        user_pass, netloc_no_user_pass = netloc.split("@", 1)
-        if (
-            isinstance(self.info, VcsInfo)
-            and self.info.vcs == "git"
-            and user_pass == "git"
-        ):
-            return netloc
-        if ENV_VAR_RE.match(user_pass):
-            return netloc
-        return netloc_no_user_pass
-
-    @property
-    def redacted_url(self) -> str:
-        """url with user:password part removed unless it is formed with
-        environment variables as specified in PEP 610, or it is ``git``
-        in the case of a git URL.
-        """
-        purl = urllib.parse.urlsplit(self.url)
-        netloc = self._remove_auth_from_netloc(purl.netloc)
-        surl = urllib.parse.urlunsplit(
-            (purl.scheme, netloc, purl.path, purl.query, purl.fragment)
-        )
-        return surl
-
-    def validate(self) -> None:
-        self.from_dict(self.to_dict())
-
-    @classmethod
-    def from_dict(cls, d: Dict[str, Any]) -> "DirectUrl":
-        return DirectUrl(
-            url=_get_required(d, str, "url"),
-            subdirectory=_get(d, str, "subdirectory"),
-            info=_exactly_one_of(
-                [
-                    ArchiveInfo._from_dict(_get(d, dict, "archive_info")),
-                    DirInfo._from_dict(_get(d, dict, "dir_info")),
-                    VcsInfo._from_dict(_get(d, dict, "vcs_info")),
-                ]
-            ),
-        )
-
-    def to_dict(self) -> Dict[str, Any]:
-        res = _filter_none(
-            url=self.redacted_url,
-            subdirectory=self.subdirectory,
-        )
-        res[self.info.name] = self.info._to_dict()
-        return res
-
-    @classmethod
-    def from_json(cls, s: str) -> "DirectUrl":
-        return cls.from_dict(json.loads(s))
-
-    def to_json(self) -> str:
-        return json.dumps(self.to_dict(), sort_keys=True)
-
-    def is_local_editable(self) -> bool:
-        return isinstance(self.info, DirInfo) and self.info.editable

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/resolvelib/structs.py

@@ -1,170 +0,0 @@
-import itertools
-
-from .compat import collections_abc
-
-
-class DirectedGraph(object):
-    """A graph structure with directed edges."""
-
-    def __init__(self):
-        self._vertices = set()
-        self._forwards = {}  # <key> -> Set[<key>]
-        self._backwards = {}  # <key> -> Set[<key>]
-
-    def __iter__(self):
-        return iter(self._vertices)
-
-    def __len__(self):
-        return len(self._vertices)
-
-    def __contains__(self, key):
-        return key in self._vertices
-
-    def copy(self):
-        """Return a shallow copy of this graph."""
-        other = DirectedGraph()
-        other._vertices = set(self._vertices)
-        other._forwards = {k: set(v) for k, v in self._forwards.items()}
-        other._backwards = {k: set(v) for k, v in self._backwards.items()}
-        return other
-
-    def add(self, key):
-        """Add a new vertex to the graph."""
-        if key in self._vertices:
-            raise ValueError("vertex exists")
-        self._vertices.add(key)
-        self._forwards[key] = set()
-        self._backwards[key] = set()
-
-    def remove(self, key):
-        """Remove a vertex from the graph, disconnecting all edges from/to it."""
-        self._vertices.remove(key)
-        for f in self._forwards.pop(key):
-            self._backwards[f].remove(key)
-        for t in self._backwards.pop(key):
-            self._forwards[t].remove(key)
-
-    def connected(self, f, t):
-        return f in self._backwards[t] and t in self._forwards[f]
-
-    def connect(self, f, t):
-        """Connect two existing vertices.
-
-        Nothing happens if the vertices are already connected.
-        """
-        if t not in self._vertices:
-            raise KeyError(t)
-        self._forwards[f].add(t)
-        self._backwards[t].add(f)
-
-    def iter_edges(self):
-        for f, children in self._forwards.items():
-            for t in children:
-                yield f, t
-
-    def iter_children(self, key):
-        return iter(self._forwards[key])
-
-    def iter_parents(self, key):
-        return iter(self._backwards[key])
-
-
-class IteratorMapping(collections_abc.Mapping):
-    def __init__(self, mapping, accessor, appends=None):
-        self._mapping = mapping
-        self._accessor = accessor
-        self._appends = appends or {}
-
-    def __repr__(self):
-        return "IteratorMapping({!r}, {!r}, {!r})".format(
-            self._mapping,
-            self._accessor,
-            self._appends,
-        )
-
-    def __bool__(self):
-        return bool(self._mapping or self._appends)
-
-    __nonzero__ = __bool__  # XXX: Python 2.
-
-    def __contains__(self, key):
-        return key in self._mapping or key in self._appends
-
-    def __getitem__(self, k):
-        try:
-            v = self._mapping[k]
-        except KeyError:
-            return iter(self._appends[k])
-        return itertools.chain(self._accessor(v), self._appends.get(k, ()))
-
-    def __iter__(self):
-        more = (k for k in self._appends if k not in self._mapping)
-        return itertools.chain(self._mapping, more)
-
-    def __len__(self):
-        more = sum(1 for k in self._appends if k not in self._mapping)
-        return len(self._mapping) + more
-
-
-class _FactoryIterableView(object):
-    """Wrap an iterator factory returned by `find_matches()`.
-
-    Calling `iter()` on this class would invoke the underlying iterator
-    factory, making it a "collection with ordering" that can be iterated
-    through multiple times, but lacks random access methods presented in
-    built-in Python sequence types.
-    """
-
-    def __init__(self, factory):
-        self._factory = factory
-        self._iterable = None
-
-    def __repr__(self):
-        return "{}({})".format(type(self).__name__, list(self))
-
-    def __bool__(self):
-        try:
-            next(iter(self))
-        except StopIteration:
-            return False
-        return True
-
-    __nonzero__ = __bool__  # XXX: Python 2.
-
-    def __iter__(self):
-        iterable = (
-            self._factory() if self._iterable is None else self._iterable
-        )
-        self._iterable, current = itertools.tee(iterable)
-        return current
-
-
-class _SequenceIterableView(object):
-    """Wrap an iterable returned by find_matches().
-
-    This is essentially just a proxy to the underlying sequence that provides
-    the same interface as `_FactoryIterableView`.
-    """
-
-    def __init__(self, sequence):
-        self._sequence = sequence
-
-    def __repr__(self):
-        return "{}({})".format(type(self).__name__, self._sequence)
-
-    def __bool__(self):
-        return bool(self._sequence)
-
-    __nonzero__ = __bool__  # XXX: Python 2.
-
-    def __iter__(self):
-        return iter(self._sequence)
-
-
-def build_iter_view(matches):
-    """Build an iterable view from the value returned by `find_matches()`."""
-    if callable(matches):
-        return _FactoryIterableView(matches)
-    if not isinstance(matches, collections_abc.Sequence):
-        matches = list(matches)
-    return _SequenceIterableView(matches)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/webencodings/mklabels.py

@@ -1,59 +0,0 @@
-"""
-
-    webencodings.mklabels
-    ~~~~~~~~~~~~~~~~~~~~~
-
-    Regenarate the webencodings.labels module.
-
-    :copyright: Copyright 2012 by Simon Sapin
-    :license: BSD, see LICENSE for details.
-
-"""
-
-import json
-try:
-    from urllib import urlopen
-except ImportError:
-    from urllib.request import urlopen
-
-
-def assert_lower(string):
-    assert string == string.lower()
-    return string
-
-
-def generate(url):
-    parts = ['''\
-"""
-
-    webencodings.labels
-    ~~~~~~~~~~~~~~~~~~~
-
-    Map encoding labels to their name.
-
-    :copyright: Copyright 2012 by Simon Sapin
-    :license: BSD, see LICENSE for details.
-
-"""
-
-# XXX Do not edit!
-# This file is automatically generated by mklabels.py
-
-LABELS = {
-''']
-    labels = [
-        (repr(assert_lower(label)).lstrip('u'),
-         repr(encoding['name']).lstrip('u'))
-        for category in json.loads(urlopen(url).read().decode('ascii'))
-        for encoding in category['encodings']
-        for label in encoding['labels']]
-    max_len = max(len(label) for label, name in labels)
-    parts.extend(
-        '    %s:%s %s,\n' % (label, ' ' * (max_len - len(label)), name)
-        for label, name in labels)
-    parts.append('}')
-    return ''.join(parts)
-
-
-if __name__ == '__main__':
-    print(generate('http://encoding.spec.whatwg.org/encodings.json'))

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

@@ -1,35 +0,0 @@
-"""For backward compatibility, expose main functions from
-``setuptools.config.setupcfg``
-"""
-import warnings
-from functools import wraps
-from textwrap import dedent
-from typing import Callable, TypeVar, cast
-
-from .._deprecation_warning import SetuptoolsDeprecationWarning
-from . import setupcfg
-
-Fn = TypeVar("Fn", bound=Callable)
-
-__all__ = ('parse_configuration', 'read_configuration')
-
-
-def _deprecation_notice(fn: Fn) -> Fn:
-    @wraps(fn)
-    def _wrapper(*args, **kwargs):
-        msg = f"""\
-        As setuptools moves its configuration towards `pyproject.toml`,
-        `{__name__}.{fn.__name__}` became deprecated.
-
-        For the time being, you can use the `{setupcfg.__name__}` module
-        to access a backward compatible API, but this module is provisional
-        and might be removed in the future.
-        """
-        warnings.warn(dedent(msg), SetuptoolsDeprecationWarning, stacklevel=2)
-        return fn(*args, **kwargs)
-
-    return cast(Fn, _wrapper)
-
-
-read_configuration = _deprecation_notice(setupcfg.read_configuration)
-parse_configuration = _deprecation_notice(setupcfg.parse_configuration)

spaces/Avinash-12035/MyGenAIChatBot/app.py

@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """hi Avinash, your youthful and witty personal assistant! At 18 years old, he's full of energy and always eager to help. Avinash's goal is to assist you with any questions or problems you might have. His enthusiasm shines through in every response, making interactions with his enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.

spaces/Benson/text-generation/Examples/Anime Avatar.md

@@ -1,101 +0,0 @@
-<br />
-<h1>Qué es un avatar de anime y por qué deberías tener uno</h1>
- <p>Anime avatar es un término que se refiere a una imagen digital o personaje que te representa en línea, utilizando el estilo de anime, que es una forma de animación de Japón. Los avatares de anime se están volviendo más populares y populares, ya que ofrecen muchos beneficios para los usuarios en línea que quieren expresarse, conectarse con otros y divertirse. En este artículo, explicaremos qué son los avatares de anime, cómo pueden beneficiarte, cuáles son algunos ejemplos y tendencias de los avatares de anime, y cómo puedes crear tu propio avatar de anime en cuatro sencillos pasos. </p>
-<h2>anime avatar</h2><br /><p><b><b>Download Zip</b> &#10022;&#10022;&#10022; <a href="https://bltlly.com/2v6J87">https://bltlly.com/2v6J87</a></b></p><br /><br />
- <h2>La definición y el origen del anime avatar</h2>
- <p>Para entender qué son los avatares de anime, primero tenemos que entender qué son anime y avatares por separado. </p>
- <h3>Anime como un estilo de animación de Japón</h3>
- <p>Anime es una palabra derivada de la animación, y se refiere a un estilo de animación que se originó en Japón. El anime se caracteriza por su estilo artístico distintivo, que a menudo presenta ojos grandes y expresivos, cabello colorido, expresiones exageradas y movimientos dinámicos. Anime también cubre una amplia gama de géneros, temas e historias, apelando a diversos públicos y gustos. Anime tiene una larga historia y una gran base de fans, tanto en Japón y en todo el mundo. Algunos ejemplos de series de anime famosas son Naruto, One Piece, Dragon Ball, Sailor Moon, Pokemon, Attack on Titan, My Hero Academia, Demon Slayer, etc.</p>
- <h3>Avatar como representación digital de uno mismo</h3>
-
- <h3>Avatar de anime como una combinación de anime y avatar</h3>
- <p>Anime avatar es un término que combina anime y avatar, lo que significa una imagen digital o personaje que te representa en línea usando el estilo de anime. Un avatar de anime puede ser una imagen o modelo 2D o 3D que imita la apariencia y los movimientos de un personaje de anime. Un avatar de anime también puede tener varias características y opciones que le permiten personalizar su apariencia, expresiones, voz, ropa, accesorios, antecedentes, etc. Un avatar de anime se puede usar para varios propósitos y ocasiones en línea, como chatear, jugar, transmitir, socializar, etc.</p>
- <h2>Los beneficios de tener un avatar de anime</h2>
- <p>Tener un avatar de anime puede ofrecerle muchos beneficios en línea. Aquí están algunos de ellos:</p>
-<p></p>
- <h3>Expresar tu personalidad y creatividad</h3>
- <p>Uno de los principales beneficios de tener un avatar de anime es que te permite expresar tu personalidad y creatividad en línea. Puedes elegir un avatar de anime que refleje tus rasgos, preferencias, intereses, aficiones, estados de ánimo, etc., o crear uno que sea completamente original y único. También puedes cambiar tu avatar de anime de acuerdo a diferentes situaciones y contextos en línea. Por ejemplo, puedes tener diferentes avatares de anime para diferentes plataformas, juegos, géneros, estados de ánimo, etc. También puedes dar rienda suelta a tu creatividad e imaginación diseñando tu avatar de anime con varias opciones y características. Puedes hacer tu avatar de anime tan realista o fantástico como quieras, y experimentar con diferentes estilos y combinaciones. </p>
- <h3>Unirse a la creciente comunidad de fans del anime</h3>
-
- <h3>Mejorar su presencia en línea y el compromiso</h3>
- <p>Un tercer beneficio de tener un avatar de anime es que puede mejorar su presencia en línea y el compromiso. Un avatar de anime puede ayudarte a destacar entre la multitud y atraer más atención y seguidores en línea. Un avatar de anime también puede hacerte más memorable y reconocible en línea, ya que puede crear una fuerte impresión visual e identidad para ti. Un avatar de anime también puede hacerte más atractivo e interactivo en línea, ya que puede transmitir tus emociones y expresiones de manera más vívida y dinámica. Un avatar de anime también puede hacerte más entretenido y divertido en línea, ya que puede agregar humor, encanto y personalidad a tu contenido en línea. </p>
- <h2>Los ejemplos y tendencias de anime avatar</h2>
- <p>Los avatares de anime no son un fenómeno nuevo, pero se han vuelto más populares y populares en los últimos años. Aquí hay algunos ejemplos y tendencias de avatares de anime:</p>
- <h3>Los populares generadores de avatar de anime y plataformas</h3>
- <p>Hay muchos generadores de avatar de anime y plataformas disponibles en línea que le permiten crear su propio avatar de anime fácil y rápidamente. Algunos de los más populares son:</p>
- <tabla>
-<tr>
-<th>Nombre</th>
-<th>Descripción</th>
-<th>URL</th>
-</tr>
-<tr>
-<td>Picrew</td>
-<td>Un sitio web japonés que alberga miles de creadores de avatar de anime generados por el usuario con varios estilos y opciones. </td>
-<td>(https://picrew.me/)</td>
-</tr>
-<tr>
-<td>Vroid Studio</td>
-<td>Un software 3D gratuito que te permite crear tus propios modelos de avatar de anime 3D con alta calidad y detalle. </td>
-<td>(https://vroid.com/en/studio)</td>
-</tr>
-<tr>
-<td>VRChat</td>
-<td>Una plataforma de realidad virtual social que te permite crear, subir y usar tus propios avatares de anime en 3D en varios mundos virtuales y escenarios. </td>
-<td>(https://www.vrchat.com/)</td>
-</tr>
-<tr>
-<td>Zepeto</td>
-<td>Una aplicación móvil que te permite crear tus propios avatares de anime en 3D con expresiones y movimientos faciales realistas. </td>
-<td>(https://zepeto.me/)</td>
-</tr>
-
-<td>FaceRig</td>
-<td>Un software que te permite usar tu webcam para animar tus propios avatares de anime 2D o 3D en tiempo real. </td>
-<td>(https://facerig.com/)</td>
-</tr>
-</table> <h3>La adopción de avatar de anime por personalidades en línea</h3>
- <p>Otra tendencia de los avatares de anime es que han sido adoptados por muchas personalidades en línea, tales como serpentinas, influencers, celebridades, etc. Algunos de ellos utilizan avatares de anime como su persona en línea principal o alternativa, mientras que otros los usan como una forma de experimentar con diferentes estilos y géneros. Algunos ejemplos de personalidades en línea que usan avatares de anime son:</p>
- <ul>
-<li>Kizuna AI: Un YouTuber virtual que es considerado el primer y más popular anime avatar streamer. Ella tiene más de 4 millones de suscriptores en YouTube y es conocida por su personalidad linda y energética. </li>
-<li>CodeMiko: Un streamer virtual que utiliza un avatar de anime en 3D que es controlado por un traje de captura de movimiento. Tiene más de 1 millón de seguidores en Twitch y es conocida por sus transmisiones interactivas e inmersivas. </li>
-<li>Lil Nas X: Un rapero y cantante que utiliza un anime en 3D avatar para realizar su hit canción "Montero (Call Me By Your Name)" en un concierto virtual en Roblox. Atrajo a más de 30 millones de espectadores y recibió comentarios positivos de los fans. </li>
-<li>Belle Delphine: Una modelo e influencer que usó un avatar de anime en 2D para bromear con sus fans en el Día de los Inocentes. Ella fingió ser una serpentina virtual y subió un video de su avatar de anime bailando y cantando. </li>
-<li>Pokimane: Un streamer y jugador que utiliza un avatar de anime en 2D para transmitir en Twitch como una broma. Ella sorprendió a sus fans con su avatar de anime, que se basó en su verdadera apariencia y voz. </li>
-</ul>
- <h3>Las posibilidades futuras de avatar de anime con AI y VR</h3>
- <p>Una tercera tendencia de los avatares de anime es que tienen el potencial de evolucionar y mejorar con el avance de las tecnologías de IA y RV. Algunos de los posibles escenarios futuros de avatares de anime son:</p>
- <ul>
-
-<li>Avatares de anime que pueden aprender de su comportamiento, preferencias y comentarios, y adaptarse a sus necesidades y expectativas utilizando modelos y sistemas de IA. </li>
-<li>Avatares de anime que pueden interactuar contigo y con otros usuarios en tiempo real usando chatbots y agentes de IA. </li>
-<li>Avatares de anime que se pueden experimentar en plena inmersión y presencia usando auriculares y dispositivos de VR. </li>
-<li>Avatares de anime que se pueden personalizar y personalizar usando herramientas e interfaces de realidad virtual. </li>
-</ul>
- <h2>Cómo crear tu propio avatar de anime en cuatro sencillos pasos</h2>
- <p>Si estás interesado en crear tu propio avatar de anime, aquí hay cuatro sencillos pasos que puedes seguir:</p>
- <h3>Elige un generador de avatar de anime que se adapte a tus necesidades</h3>
- <p>El primer paso es elegir un generador de avatar de anime que se adapte a sus necesidades. Hay muchos generadores de avatar de anime disponibles en línea, cada uno con sus propias ventajas y desventajas. Usted debe considerar factores tales como el estilo, calidad, características, opciones, facilidad de uso, costo, etc., del generador de avatar de anime. También puedes comparar diferentes generadores de avatar de anime leyendo reseñas, viendo tutoriales o probando demos. También puede consultar la tabla de arriba para algunos generadores de avatar de anime populares. </p>
- <h3>Personaliza tu avatar de anime con varias opciones y características</h3>
- <p>El segundo paso es personalizar tu avatar de anime con varias opciones y características. Dependiendo del generador de avatar de anime que elijas, puedes personalizar aspectos como la cara, cabello, ojos, nariz, boca, piel, cuerpo, ropa, accesorios, etc., de tu avatar de anime. También puede ajustar los colores, tamaños, formas, posiciones, ángulos, etc., de estos aspectos. También puedes añadir efectos como sombras, iluminación, filtros, etc., para mejorar tu avatar de anime. También puedes previsualizar tu avatar de anime en diferentes poses y expresiones. </p> <h3>Guarda y descarga tu avatar de anime en alta calidad</h3>
-
- <h3>Comparte y usa tu avatar de anime en diferentes plataformas y ocasiones</h3>
- <p>El cuarto y último paso es compartir y usar tu avatar de anime en diferentes plataformas y ocasiones. Puedes usar tu avatar de anime para varios propósitos y ocasiones en línea, como chatear, jugar, transmitir, socializar, etc. También puedes compartir tu avatar de anime con tus amigos, familiares, fans, seguidores, etc., en línea. También puedes subir tu avatar de anime a diferentes plataformas y sitios web que admiten avatares de anime, como VRChat, FaceRig, Zepeto, etc. También puedes imprimir tu avatar de anime en diferentes productos y materiales, como pegatinas, carteles, camisas, tazas, etc.</p>
- <h2>Conclusión y preguntas frecuentes</h2>
- <p>Los avatares de anime son imágenes digitales o personajes que te representan en línea usando el estilo de anime. Los avatares de anime se están volviendo más populares y populares, ya que ofrecen muchos beneficios para los usuarios en línea que quieren expresarse, conectarse con otros y divertirse. Los avatares de anime también están evolucionando y mejorando con el avance de las tecnologías de IA y VR. Puede crear su propio avatar de anime en cuatro sencillos pasos: elegir un generador de avatar de anime que se adapte a sus necesidades, personalizar su avatar de anime con varias opciones y características, guardar y descargar su avatar de anime en alta calidad, y compartir y utilizar su avatar de anime en diferentes plataformas y ocasiones. </p>
- <p>Aquí hay algunas preguntas frecuentes sobre avatares de anime:</p>
- <ul>
-<li>Q: ¿Cuánto cuesta crear un avatar de anime? </li>
-<li>A: Depende del generador de avatar de anime que elijas. Algunos generadores de avatar de anime son de uso gratuito, mientras que otros pueden cobrar una tarifa o requerir una suscripción. Usted debe comprobar el precio y los términos de servicio del generador de avatar anime antes de usarlo. </li>
-<li>Q: ¿Cuánto tiempo se tarda en crear un avatar de anime? </li>
-
-<li>Q: ¿Cómo puedo hacer que mi avatar de anime se parezca más a mí? </li>
-<li>A: Hay algunos consejos y trucos que pueden ayudarte a hacer que tu avatar de anime se parezca más a ti. Por ejemplo, puedes usar una foto tuya como referencia o una plantilla para tu avatar de anime. También puedes ajustar las proporciones, colores, formas, etc., de tu avatar de anime para que coincida con tu apariencia real. También puedes añadir detalles como gafas, piercings, tatuajes, etc. </li>
-<li>Q: ¿Cómo puedo hacer mi avatar de anime más único y original? </li>
-<li>A: Hay algunos consejos y trucos que pueden ayudarte a hacer tu avatar de anime más único y original. Por ejemplo, puedes mezclar y combinar diferentes estilos y géneros de anime para tu avatar de anime. También puedes añadir elementos como fantasía, ciencia ficción, terror, etc., a tu avatar de anime. También puedes experimentar con diferentes efectos como filtros, sombras, iluminación, etc., a tu avatar de anime. También puedes usar tu creatividad e imaginación para crear tu avatar de anime. </li>
-<li>Q: ¿Cómo puedo proteger mi avatar de anime de ser robado o copiado? </li>
-<li>A: Hay algunos consejos y trucos que pueden ayudarte a proteger tu avatar de anime de ser robado o copiado. Por ejemplo, puedes agregar una marca de agua o una firma a tu avatar de anime. También puedes usar una herramienta de búsqueda de imagen inversa para comprobar si tu avatar de anime ha sido utilizado por alguien más sin tu permiso. También puedes reportar o tomar acciones legales contra cualquiera que robe o copie tu avatar de anime. </li>
-</ul>
- <p>Espero que este artículo te haya ayudado a aprender más sobre los avatares de anime y cómo crear los tuyos. Si usted tiene alguna pregunta o retroalimentación, por favor no dude en dejar un comentario a continuación. Gracias por leer y divertirse con su avatar de anime! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/service.py

@@ -1,199 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import os
-
-from botocore.docs.bcdoc.restdoc import DocumentStructure
-from botocore.docs.service import ServiceDocumenter as BaseServiceDocumenter
-from botocore.exceptions import DataNotFoundError
-
-import boto3
-from boto3.docs.client import Boto3ClientDocumenter
-from boto3.docs.resource import ResourceDocumenter, ServiceResourceDocumenter
-from boto3.utils import ServiceContext
-
-
-class ServiceDocumenter(BaseServiceDocumenter):
-    # The path used to find examples
-    EXAMPLE_PATH = os.path.join(os.path.dirname(boto3.__file__), 'examples')
-
-    def __init__(self, service_name, session, root_docs_path):
-        super().__init__(
-            service_name=service_name,
-            # I know that this is an internal attribute, but the botocore session
-            # is needed to load the paginator and waiter models.
-            session=session._session,
-            root_docs_path=root_docs_path,
-        )
-        self._boto3_session = session
-        self._client = self._boto3_session.client(service_name)
-        self._service_resource = None
-        if self._service_name in self._boto3_session.get_available_resources():
-            self._service_resource = self._boto3_session.resource(service_name)
-        self.sections = [
-            'title',
-            'client',
-            'paginators',
-            'waiters',
-            'resources',
-            'examples',
-        ]
-        self._root_docs_path = root_docs_path
-        self._USER_GUIDE_LINK = (
-            'https://boto3.amazonaws.com/'
-            'v1/documentation/api/latest/guide/resources.html'
-        )
-
-    def document_service(self):
-        """Documents an entire service.
-
-        :returns: The reStructured text of the documented service.
-        """
-        doc_structure = DocumentStructure(
-            self._service_name, section_names=self.sections, target='html'
-        )
-        self.title(doc_structure.get_section('title'))
-
-        self.client_api(doc_structure.get_section('client'))
-        self.paginator_api(doc_structure.get_section('paginators'))
-        self.waiter_api(doc_structure.get_section('waiters'))
-        if self._service_resource:
-            self.resource_section(doc_structure.get_section('resources'))
-        self._document_examples(doc_structure.get_section('examples'))
-        return doc_structure.flush_structure()
-
-    def client_api(self, section):
-        examples = None
-        try:
-            examples = self.get_examples(self._service_name)
-        except DataNotFoundError:
-            pass
-
-        Boto3ClientDocumenter(
-            self._client, self._root_docs_path, examples
-        ).document_client(section)
-
-    def resource_section(self, section):
-        section.style.h2('Resources')
-        section.style.new_line()
-        section.write(
-            'Resources are available in boto3 via the '
-            '``resource`` method. For more detailed instructions '
-            'and examples on the usage of resources, see the '
-            'resources '
-        )
-        section.style.external_link(
-            title='user guide',
-            link=self._USER_GUIDE_LINK,
-        )
-        section.write('.')
-        section.style.new_line()
-        section.style.new_line()
-        section.write('The available resources are:')
-        section.style.new_line()
-        section.style.toctree()
-        self._document_service_resource(section)
-        self._document_resources(section)
-
-    def _document_service_resource(self, section):
-        # Create a new DocumentStructure for each Service Resource and add contents.
-        service_resource_doc = DocumentStructure(
-            'service-resource', target='html'
-        )
-        breadcrumb_section = service_resource_doc.add_new_section('breadcrumb')
-        breadcrumb_section.style.ref(
-            self._client.__class__.__name__, f'../../{self._service_name}'
-        )
-        breadcrumb_section.write(' / Resource / ServiceResource')
-        ServiceResourceDocumenter(
-            self._service_resource, self._session, self._root_docs_path
-        ).document_resource(service_resource_doc)
-        # Write collections in individual/nested files.
-        # Path: <root>/reference/services/<service>/<resource_name>/<collection_name>.rst
-        resource_name = self._service_resource.meta.resource_model.name
-        if resource_name == self._service_name:
-            resource_name = 'service-resource'
-        service_resource_dir_path = os.path.join(
-            self._root_docs_path,
-            f'{self._service_name}',
-            f'{resource_name.lower()}',
-        )
-        service_resource_doc.write_to_file(service_resource_dir_path, 'index')
-        section.style.tocitem(f'{self._service_name}/{resource_name}/index')
-
-    def _document_resources(self, section):
-        temp_identifier_value = 'foo'
-        loader = self._session.get_component('data_loader')
-        json_resource_model = loader.load_service_model(
-            self._service_name, 'resources-1'
-        )
-        service_model = self._service_resource.meta.client.meta.service_model
-        for resource_name in json_resource_model['resources']:
-            resource_model = json_resource_model['resources'][resource_name]
-            resource_cls = (
-                self._boto3_session.resource_factory.load_from_definition(
-                    resource_name=resource_name,
-                    single_resource_json_definition=resource_model,
-                    service_context=ServiceContext(
-                        service_name=self._service_name,
-                        resource_json_definitions=json_resource_model[
-                            'resources'
-                        ],
-                        service_model=service_model,
-                        service_waiter_model=None,
-                    ),
-                )
-            )
-            identifiers = resource_cls.meta.resource_model.identifiers
-            args = []
-            for _ in identifiers:
-                args.append(temp_identifier_value)
-            resource = resource_cls(*args, client=self._client)
-            # Create a new DocumentStructure for each Resource and add contents.
-            resource_name = resource.meta.resource_model.name.lower()
-            resource_doc = DocumentStructure(resource_name, target='html')
-            breadcrumb_section = resource_doc.add_new_section('breadcrumb')
-            breadcrumb_section.style.ref(
-                self._client.__class__.__name__, f'../../{self._service_name}'
-            )
-            breadcrumb_section.write(
-                f' / Resource / {resource.meta.resource_model.name}'
-            )
-            ResourceDocumenter(
-                resource, self._session, self._root_docs_path
-            ).document_resource(
-                resource_doc.add_new_section(resource.meta.resource_model.name)
-            )
-            # Write collections in individual/nested files.
-            # Path: <root>/reference/services/<service>/<resource_name>/<index>.rst
-            service_resource_dir_path = os.path.join(
-                self._root_docs_path,
-                f'{self._service_name}',
-                f'{resource_name}',
-            )
-            resource_doc.write_to_file(service_resource_dir_path, 'index')
-            section.style.tocitem(
-                f'{self._service_name}/{resource_name}/index'
-            )
-
-    def _get_example_file(self):
-        return os.path.realpath(
-            os.path.join(self.EXAMPLE_PATH, self._service_name + '.rst')
-        )
-
-    def _document_examples(self, section):
-        examples_file = self._get_example_file()
-        if os.path.isfile(examples_file):
-            section.style.h2('Examples')
-            section.style.new_line()
-            with open(examples_file) as f:
-                section.write(f.read())

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/exceptions.py

@@ -1,323 +0,0 @@
-from __future__ import absolute_import
-
-from .packages.six.moves.http_client import IncompleteRead as httplib_IncompleteRead
-
-# Base Exceptions
-
-
-class HTTPError(Exception):
-    """Base exception used by this module."""
-
-    pass
-
-
-class HTTPWarning(Warning):
-    """Base warning used by this module."""
-
-    pass
-
-
-class PoolError(HTTPError):
-    """Base exception for errors caused within a pool."""
-
-    def __init__(self, pool, message):
-        self.pool = pool
-        HTTPError.__init__(self, "%s: %s" % (pool, message))
-
-    def __reduce__(self):
-        # For pickling purposes.
-        return self.__class__, (None, None)
-
-
-class RequestError(PoolError):
-    """Base exception for PoolErrors that have associated URLs."""
-
-    def __init__(self, pool, url, message):
-        self.url = url
-        PoolError.__init__(self, pool, message)
-
-    def __reduce__(self):
-        # For pickling purposes.
-        return self.__class__, (None, self.url, None)
-
-
-class SSLError(HTTPError):
-    """Raised when SSL certificate fails in an HTTPS connection."""
-
-    pass
-
-
-class ProxyError(HTTPError):
-    """Raised when the connection to a proxy fails."""
-
-    def __init__(self, message, error, *args):
-        super(ProxyError, self).__init__(message, error, *args)
-        self.original_error = error
-
-
-class DecodeError(HTTPError):
-    """Raised when automatic decoding based on Content-Type fails."""
-
-    pass
-
-
-class ProtocolError(HTTPError):
-    """Raised when something unexpected happens mid-request/response."""
-
-    pass
-
-
-#: Renamed to ProtocolError but aliased for backwards compatibility.
-ConnectionError = ProtocolError
-
-
-# Leaf Exceptions
-
-
-class MaxRetryError(RequestError):
-    """Raised when the maximum number of retries is exceeded.
-
-    :param pool: The connection pool
-    :type pool: :class:`~urllib3.connectionpool.HTTPConnectionPool`
-    :param string url: The requested Url
-    :param exceptions.Exception reason: The underlying error
-
-    """
-
-    def __init__(self, pool, url, reason=None):
-        self.reason = reason
-
-        message = "Max retries exceeded with url: %s (Caused by %r)" % (url, reason)
-
-        RequestError.__init__(self, pool, url, message)
-
-
-class HostChangedError(RequestError):
-    """Raised when an existing pool gets a request for a foreign host."""
-
-    def __init__(self, pool, url, retries=3):
-        message = "Tried to open a foreign host with url: %s" % url
-        RequestError.__init__(self, pool, url, message)
-        self.retries = retries
-
-
-class TimeoutStateError(HTTPError):
-    """Raised when passing an invalid state to a timeout"""
-
-    pass
-
-
-class TimeoutError(HTTPError):
-    """Raised when a socket timeout error occurs.
-
-    Catching this error will catch both :exc:`ReadTimeoutErrors
-    <ReadTimeoutError>` and :exc:`ConnectTimeoutErrors <ConnectTimeoutError>`.
-    """
-
-    pass
-
-
-class ReadTimeoutError(TimeoutError, RequestError):
-    """Raised when a socket timeout occurs while receiving data from a server"""
-
-    pass
-
-
-# This timeout error does not have a URL attached and needs to inherit from the
-# base HTTPError
-class ConnectTimeoutError(TimeoutError):
-    """Raised when a socket timeout occurs while connecting to a server"""
-
-    pass
-
-
-class NewConnectionError(ConnectTimeoutError, PoolError):
-    """Raised when we fail to establish a new connection. Usually ECONNREFUSED."""
-
-    pass
-
-
-class EmptyPoolError(PoolError):
-    """Raised when a pool runs out of connections and no more are allowed."""
-
-    pass
-
-
-class ClosedPoolError(PoolError):
-    """Raised when a request enters a pool after the pool has been closed."""
-
-    pass
-
-
-class LocationValueError(ValueError, HTTPError):
-    """Raised when there is something wrong with a given URL input."""
-
-    pass
-
-
-class LocationParseError(LocationValueError):
-    """Raised when get_host or similar fails to parse the URL input."""
-
-    def __init__(self, location):
-        message = "Failed to parse: %s" % location
-        HTTPError.__init__(self, message)
-
-        self.location = location
-
-
-class URLSchemeUnknown(LocationValueError):
-    """Raised when a URL input has an unsupported scheme."""
-
-    def __init__(self, scheme):
-        message = "Not supported URL scheme %s" % scheme
-        super(URLSchemeUnknown, self).__init__(message)
-
-        self.scheme = scheme
-
-
-class ResponseError(HTTPError):
-    """Used as a container for an error reason supplied in a MaxRetryError."""
-
-    GENERIC_ERROR = "too many error responses"
-    SPECIFIC_ERROR = "too many {status_code} error responses"
-
-
-class SecurityWarning(HTTPWarning):
-    """Warned when performing security reducing actions"""
-
-    pass
-
-
-class SubjectAltNameWarning(SecurityWarning):
-    """Warned when connecting to a host with a certificate missing a SAN."""
-
-    pass
-
-
-class InsecureRequestWarning(SecurityWarning):
-    """Warned when making an unverified HTTPS request."""
-
-    pass
-
-
-class SystemTimeWarning(SecurityWarning):
-    """Warned when system time is suspected to be wrong"""
-
-    pass
-
-
-class InsecurePlatformWarning(SecurityWarning):
-    """Warned when certain TLS/SSL configuration is not available on a platform."""
-
-    pass
-
-
-class SNIMissingWarning(HTTPWarning):
-    """Warned when making a HTTPS request without SNI available."""
-
-    pass
-
-
-class DependencyWarning(HTTPWarning):
-    """
-    Warned when an attempt is made to import a module with missing optional
-    dependencies.
-    """
-
-    pass
-
-
-class ResponseNotChunked(ProtocolError, ValueError):
-    """Response needs to be chunked in order to read it as chunks."""
-
-    pass
-
-
-class BodyNotHttplibCompatible(HTTPError):
-    """
-    Body should be :class:`http.client.HTTPResponse` like
-    (have an fp attribute which returns raw chunks) for read_chunked().
-    """
-
-    pass
-
-
-class IncompleteRead(HTTPError, httplib_IncompleteRead):
-    """
-    Response length doesn't match expected Content-Length
-
-    Subclass of :class:`http.client.IncompleteRead` to allow int value
-    for ``partial`` to avoid creating large objects on streamed reads.
-    """
-
-    def __init__(self, partial, expected):
-        super(IncompleteRead, self).__init__(partial, expected)
-
-    def __repr__(self):
-        return "IncompleteRead(%i bytes read, %i more expected)" % (
-            self.partial,
-            self.expected,
-        )
-
-
-class InvalidChunkLength(HTTPError, httplib_IncompleteRead):
-    """Invalid chunk length in a chunked response."""
-
-    def __init__(self, response, length):
-        super(InvalidChunkLength, self).__init__(
-            response.tell(), response.length_remaining
-        )
-        self.response = response
-        self.length = length
-
-    def __repr__(self):
-        return "InvalidChunkLength(got length %r, %i bytes read)" % (
-            self.length,
-            self.partial,
-        )
-
-
-class InvalidHeader(HTTPError):
-    """The header provided was somehow invalid."""
-
-    pass
-
-
-class ProxySchemeUnknown(AssertionError, URLSchemeUnknown):
-    """ProxyManager does not support the supplied scheme"""
-
-    # TODO(t-8ch): Stop inheriting from AssertionError in v2.0.
-
-    def __init__(self, scheme):
-        # 'localhost' is here because our URL parser parses
-        # localhost:8080 -> scheme=localhost, remove if we fix this.
-        if scheme == "localhost":
-            scheme = None
-        if scheme is None:
-            message = "Proxy URL had no scheme, should start with http:// or https://"
-        else:
-            message = (
-                "Proxy URL had unsupported scheme %s, should use http:// or https://"
-                % scheme
-            )
-        super(ProxySchemeUnknown, self).__init__(message)
-
-
-class ProxySchemeUnsupported(ValueError):
-    """Fetching HTTPS resources through HTTPS proxies is unsupported"""
-
-    pass
-
-
-class HeaderParsingError(HTTPError):
-    """Raised by assert_header_parsing, but we convert it to a log.warning statement."""
-
-    def __init__(self, defects, unparsed_data):
-        message = "%s, unparsed data: %r" % (defects or "Unknown", unparsed_data)
-        super(HeaderParsingError, self).__init__(message)
-
-
-class UnrewindableBodyError(HTTPError):
-    """urllib3 encountered an error when trying to rewind a body"""
-
-    pass

spaces/BramVanroy/mateo-demo/README.md

@@ -1,12 +0,0 @@
----
-title: MATEO
-emoji: 🎈
-colorFrom: green
-colorTo: green
-sdk: docker
-app_port: 7860
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Burcin/ExtractiveSummarizer/app.py

@@ -1,118 +0,0 @@
-import gradio as gr
-from gradio.mix import Parallel, Series
-import wikipedia
-import spacy
-from spacy.lang.en.stop_words import STOP_WORDS
-from string import punctuation
-import nltk
-nltk.download('wordnet', quiet=True)
-nltk.download('punkt', quiet=True)
-from nltk.stem import WordNetLemmatizer
-from heapq import nlargest
-import warnings
-from sklearn.feature_extraction.text import TfidfVectorizer
-import numpy as np
-
-warnings.filterwarnings("ignore")
-
-def get_wiki_original_text(inp):
-    text = wikipedia.summary(inp)
-    return text
-   
-
-
-def get_wiki_summary_by_lem(inp):
-    text = wikipedia.summary(inp)
-
-    print(text)
-
-    stopwords = list(STOP_WORDS)
-
-    lemmatizer = WordNetLemmatizer()
-    tokens = [lemmatizer.lemmatize(str(token).lower()) for token in nltk.word_tokenize(text) if str(token) not in punctuation and str(token).lower() not in stopwords and len(token) >1]
-    word_counts = {}
-
-    for token in tokens:
-        if token in word_counts.keys():
-            word_counts[token] += 1
-        else:
-            word_counts[token] = 1
-
-        
-
-    sentence_scores = {}
-
-    for sentence in nltk.sent_tokenize(text):
-        sentence_scores[sentence] = 0
-        for wrd in nltk.word_tokenize(sentence):
-            if lemmatizer.lemmatize(str(wrd).lower()) in word_counts.keys():
-                sentence_scores[sentence] += word_counts[lemmatizer.lemmatize(str(wrd).lower())]
-
-    summary_length = 0
-
-    if len(sentence_scores) > 5 :
-        summary_length = int(len(sentence_scores)*0.20)
-    else:
-        summary_length = int(len(sentence_scores)*0.50)
-        
-    summary = str()
-
-    for sentence in nltk.sent_tokenize(text):
-        for i in range(0,summary_length):
-            if str(sentence).find(str(nlargest(summary_length, sentence_scores, key = sentence_scores.get)[i])) == 0:
-                summary += str(sentence).replace('\n','')
-                summary += ' '
-                
-                
-    print('\033[1m' + "Summarized Text" + '\033[0m')
-
-    return summary
-
-
-def get_wiki_summary_by_tfidf(inp):
-    text = wikipedia.summary(inp)
-
-    tfidf_vectorizer = TfidfVectorizer(ngram_range=(1,3))
-    
-    all_sentences = [str(sent) for sent in nltk.sent_tokenize(text)]
-    sentence_vectors = tfidf_vectorizer.fit_transform(all_sentences)
-    
-    sentence_scores_vector = np.hstack(np.array(sentence_vectors.sum(axis=1)))
-
-    sentence_scores = dict(zip(all_sentences, sentence_scores_vector))
-
-    summary_length = 0
-
-    if len(sentence_scores) > 5 :
-        summary_length = int(len(sentence_scores)*0.20)
-    else:
-        summary_length = int(len(sentence_scores)*0.50)
-        
-    summary = str()
-
-    for sentence in nltk.sent_tokenize(text):
-        for i in range(0,summary_length):
-            if str(sentence).find(str(nlargest(summary_length, sentence_scores, key = sentence_scores.get)[i])) == 0:
-                summary += str(sentence).replace('\n','')
-                summary += ' '
-                
-                
-    return summary
-
-
-
-desc =  """This interface allows you to summarize Wikipedia contents. Only requirement is to write the topic and it collects content by fetching from Wikipedia. For summarization this model uses 2 different extractive summarization methods and the number of sentences in the output depends on the length of the original text."""
-
-
-sample = [['Europe'],['Great Depression'],['Crocodile Dundee']]
-
-
-iface = Parallel(gr.Interface(fn=get_wiki_original_text, inputs=gr.inputs.Textbox(label="Text"), outputs="text", description='Original Text'),
-                 gr.Interface(fn=get_wiki_summary_by_lem, inputs=gr.inputs.Textbox(label="Text"), outputs="text", description='Summary 1'),
-                 gr.Interface(fn=get_wiki_summary_by_tfidf, inputs=gr.inputs.Textbox(label="Text"), outputs="text", description='Summary 2'),
-                 title= 'Text Summarizer', 
-                 description = desc,
-                 examples=sample, 
-                 inputs = gr.inputs.Textbox(label="Text"))
-
-iface.launch(inline = False)

spaces/CVPR/LIVE/edge_query.h

@@ -1,7 +0,0 @@
-#pragma once
-
-struct EdgeQuery {
-	int shape_group_id;
-    int shape_id;
-    bool hit; // Do we hit the specified shape_group_id & shape_id?
-};

spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/general_copy.h

@@ -1,147 +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 general_copy.h
- *  \brief Sequential copy algorithms for general iterators.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/raw_reference_cast.h>
-#include <thrust/detail/type_traits.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace sequential
-{
-namespace general_copy_detail
-{
-
-
-template<typename T1, typename T2>
-struct lazy_is_assignable
-  : thrust::detail::is_assignable<
-      typename T1::type,
-      typename T2::type
-    >
-{};
-
-
-// sometimes OutputIterator's reference type is reported as void
-// in that case, just assume that we're able to assign to it OK
-template<typename InputIterator, typename OutputIterator>
-struct reference_is_assignable
-  : thrust::detail::eval_if<
-      thrust::detail::is_same<
-        typename thrust::iterator_reference<OutputIterator>::type, void
-      >::value,
-      thrust::detail::true_type,
-      lazy_is_assignable<
-        thrust::iterator_reference<OutputIterator>,
-        thrust::iterator_reference<InputIterator>
-      >
-    >::type
-{};
-
-
-// introduce an iterator assign helper to deal with assignments from
-// a wrapped reference
-
-__thrust_exec_check_disable__
-template<typename OutputIterator, typename InputIterator>
-inline __host__ __device__
-typename thrust::detail::enable_if<
-  reference_is_assignable<InputIterator,OutputIterator>::value
->::type
-iter_assign(OutputIterator dst, InputIterator src)
-{
-  *dst = *src;
-}
-
-
-__thrust_exec_check_disable__
-template<typename OutputIterator, typename InputIterator>
-inline __host__ __device__
-typename thrust::detail::disable_if<
-  reference_is_assignable<InputIterator,OutputIterator>::value
->::type
-iter_assign(OutputIterator dst, InputIterator src)
-{
-  typedef typename thrust::iterator_value<InputIterator>::type value_type;
-
-  // insert a temporary and hope for the best
-  *dst = static_cast<value_type>(*src);
-}
-
-
-} // end general_copy_detail
-
-
-__thrust_exec_check_disable__
-template<typename InputIterator,
-         typename OutputIterator>
-__host__ __device__
-  OutputIterator general_copy(InputIterator first,
-                              InputIterator last,
-                              OutputIterator result)
-{
-  for(; first != last; ++first, ++result)
-  {
-    // gcc 4.2 crashes while instantiating iter_assign
-#if (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC) && (THRUST_GCC_VERSION < 40300)
-    *result = *first;
-#else
-    general_copy_detail::iter_assign(result, first);
-#endif
-  }
-
-  return result;
-} // end general_copy()
-
-
-__thrust_exec_check_disable__
-template<typename InputIterator,
-         typename Size,
-         typename OutputIterator>
-__host__ __device__
-  OutputIterator general_copy_n(InputIterator first,
-                                Size n,
-                                OutputIterator result)
-{
-  for(; n > Size(0); ++first, ++result, --n)
-  {
-    // gcc 4.2 crashes while instantiating iter_assign
-#if (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC) && (THRUST_GCC_VERSION < 40300)
-    *result = *first;
-#else
-    general_copy_detail::iter_assign(result, first);
-#endif
-  }
-
-  return result;
-} // end general_copy_n()
-
-
-} // end namespace sequential
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-

spaces/CVPR/regionclip-demo/setup.py

@@ -1,247 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import glob
-import os
-import shutil
-from os import path
-from setuptools import find_packages, setup
-from typing import List
-import torch
-from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
-from torch.utils.hipify import hipify_python
-
-torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
-assert torch_ver >= [1, 6], "Requires PyTorch >= 1.6"
-
-
-def get_version():
-    init_py_path = path.join(path.abspath(path.dirname(__file__)), "detectron2", "__init__.py")
-    init_py = open(init_py_path, "r").readlines()
-    version_line = [l.strip() for l in init_py if l.startswith("__version__")][0]
-    version = version_line.split("=")[-1].strip().strip("'\"")
-
-    # The following is used to build release packages.
-    # Users should never use it.
-    suffix = os.getenv("D2_VERSION_SUFFIX", "")
-    version = version + suffix
-    if os.getenv("BUILD_NIGHTLY", "0") == "1":
-        from datetime import datetime
-
-        date_str = datetime.today().strftime("%y%m%d")
-        version = version + ".dev" + date_str
-
-        new_init_py = [l for l in init_py if not l.startswith("__version__")]
-        new_init_py.append('__version__ = "{}"\n'.format(version))
-        with open(init_py_path, "w") as f:
-            f.write("".join(new_init_py))
-    return version
-
-
-def get_extensions():
-    this_dir = path.dirname(path.abspath(__file__))
-    extensions_dir = path.join(this_dir, "detectron2", "layers", "csrc")
-
-    main_source = path.join(extensions_dir, "vision.cpp")
-    sources = glob.glob(path.join(extensions_dir, "**", "*.cpp"))
-
-    from torch.utils.cpp_extension import ROCM_HOME
-
-    is_rocm_pytorch = (
-        True if ((torch.version.hip is not None) and (ROCM_HOME is not None)) else False
-    )
-
-    hipify_ver = (
-        [int(x) for x in torch.utils.hipify.__version__.split(".")]
-        if hasattr(torch.utils.hipify, "__version__")
-        else [0, 0, 0]
-    )
-
-    if is_rocm_pytorch and hipify_ver < [1, 0, 0]:  # TODO not needed since pt1.8
-
-        # Earlier versions of hipification and extension modules were not
-        # transparent, i.e. would require an explicit call to hipify, and the
-        # hipification would introduce "hip" subdirectories, possibly changing
-        # the relationship between source and header files.
-        # This path is maintained for backwards compatibility.
-
-        hipify_python.hipify(
-            project_directory=this_dir,
-            output_directory=this_dir,
-            includes="/detectron2/layers/csrc/*",
-            show_detailed=True,
-            is_pytorch_extension=True,
-        )
-
-        source_cuda = glob.glob(path.join(extensions_dir, "**", "hip", "*.hip")) + glob.glob(
-            path.join(extensions_dir, "hip", "*.hip")
-        )
-
-        shutil.copy(
-            "detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h",
-            "detectron2/layers/csrc/box_iou_rotated/hip/box_iou_rotated_utils.h",
-        )
-        shutil.copy(
-            "detectron2/layers/csrc/deformable/deform_conv.h",
-            "detectron2/layers/csrc/deformable/hip/deform_conv.h",
-        )
-
-        sources = [main_source] + sources
-        sources = [
-            s
-            for s in sources
-            if not is_rocm_pytorch or torch_ver < [1, 7] or not s.endswith("hip/vision.cpp")
-        ]
-
-    else:
-
-        # common code between cuda and rocm platforms,
-        # for hipify version [1,0,0] and later.
-
-        source_cuda = glob.glob(path.join(extensions_dir, "**", "*.cu")) + glob.glob(
-            path.join(extensions_dir, "*.cu")
-        )
-
-        sources = [main_source] + sources
-
-    extension = CppExtension
-
-    extra_compile_args = {"cxx": []}
-    define_macros = []
-
-    if (torch.cuda.is_available() and ((CUDA_HOME is not None) or is_rocm_pytorch)) or os.getenv(
-        "FORCE_CUDA", "0"
-    ) == "1":
-        extension = CUDAExtension
-        sources += source_cuda
-
-        if not is_rocm_pytorch:
-            define_macros += [("WITH_CUDA", None)]
-            extra_compile_args["nvcc"] = [
-                "-O3",
-                "-DCUDA_HAS_FP16=1",
-                "-D__CUDA_NO_HALF_OPERATORS__",
-                "-D__CUDA_NO_HALF_CONVERSIONS__",
-                "-D__CUDA_NO_HALF2_OPERATORS__",
-            ]
-        else:
-            define_macros += [("WITH_HIP", None)]
-            extra_compile_args["nvcc"] = []
-
-        if torch_ver < [1, 7]:
-            # supported by https://github.com/pytorch/pytorch/pull/43931
-            CC = os.environ.get("CC", None)
-            if CC is not None:
-                extra_compile_args["nvcc"].append("-ccbin={}".format(CC))
-
-    include_dirs = [extensions_dir]
-
-    ext_modules = [
-        extension(
-            "detectron2._C",
-            sources,
-            include_dirs=include_dirs,
-            define_macros=define_macros,
-            extra_compile_args=extra_compile_args,
-        )
-    ]
-
-    return ext_modules
-
-
-def get_model_zoo_configs() -> List[str]:
-    """
-    Return a list of configs to include in package for model zoo. Copy over these configs inside
-    detectron2/model_zoo.
-    """
-
-    # Use absolute paths while symlinking.
-    source_configs_dir = path.join(path.dirname(path.realpath(__file__)), "configs")
-    destination = path.join(
-        path.dirname(path.realpath(__file__)), "detectron2", "model_zoo", "configs"
-    )
-    # Symlink the config directory inside package to have a cleaner pip install.
-
-    # Remove stale symlink/directory from a previous build.
-    if path.exists(source_configs_dir):
-        if path.islink(destination):
-            os.unlink(destination)
-        elif path.isdir(destination):
-            shutil.rmtree(destination)
-
-    if not path.exists(destination):
-        try:
-            os.symlink(source_configs_dir, destination)
-        except OSError:
-            # Fall back to copying if symlink fails: ex. on Windows.
-            shutil.copytree(source_configs_dir, destination)
-
-    config_paths = glob.glob("configs/**/*.yaml", recursive=True) + glob.glob(
-        "configs/**/*.py", recursive=True
-    )
-    return config_paths
-
-
-# For projects that are relative small and provide features that are very close
-# to detectron2's core functionalities, we install them under detectron2.projects
-PROJECTS = {
-    # "detectron2.projects.point_rend": "projects/PointRend/point_rend",
-    # "detectron2.projects.deeplab": "projects/DeepLab/deeplab",
-    # "detectron2.projects.panoptic_deeplab": "projects/Panoptic-DeepLab/panoptic_deeplab",
-}
-
-setup(
-    name="detectron2",
-    version=get_version(),
-    author="FAIR",
-    url="https://github.com/facebookresearch/detectron2",
-    description="Detectron2 is FAIR's next-generation research "
-    "platform for object detection and segmentation.",
-    packages=find_packages(exclude=("configs", "tests*")) + list(PROJECTS.keys()),
-    package_dir=PROJECTS,
-    package_data={"detectron2.model_zoo": get_model_zoo_configs()},
-    python_requires=">=3.6",
-    install_requires=[
-        # Do not add opencv here. Just like pytorch, user should install
-        # opencv themselves, preferrably by OS's package manager, or by
-        # choosing the proper pypi package name at https://github.com/skvark/opencv-python
-        "termcolor>=1.1",
-        "Pillow>=7.1",  # or use pillow-simd for better performance
-        "yacs>=0.1.6",
-        "tabulate",
-        "cloudpickle",
-        "matplotlib",
-        "tqdm>4.29.0",
-        "tensorboard",
-        # Lock version of fvcore/iopath because they may have breaking changes
-        # NOTE: when updating fvcore/iopath version, make sure fvcore depends
-        # on compatible version of iopath.
-        "fvcore>=0.1.5,<0.1.6",  # required like this to make it pip installable
-        "iopath>=0.1.7,<0.1.9",
-        "pycocotools>=2.0.2",  # corresponds to https://github.com/ppwwyyxx/cocoapi
-        "future",  # used by caffe2
-        "pydot",  # used to save caffe2 SVGs
-        "dataclasses; python_version<'3.7'",
-        "omegaconf>=2.1.0rc1",
-        "hydra-core>=1.1.0rc1",
-        "black==21.4b2",
-        # When adding to the list, may need to update docs/requirements.txt
-        # or add mock in docs/conf.py
-    ],
-    extras_require={
-        "all": [
-            "shapely",
-            "pygments>=2.2",
-            "psutil",
-            "panopticapi @ https://github.com/cocodataset/panopticapi/archive/master.zip",
-        ],
-        "dev": [
-            "flake8==3.8.1",
-            "isort==4.3.21",
-            "flake8-bugbear",
-            "flake8-comprehensions",
-        ],
-    },
-    ext_modules=get_extensions(),
-    cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
-)

spaces/Chintan-Donda/KKMS-KSSW-HF/src/kkms_kssw.py

@@ -1,77 +0,0 @@
-import os
-
-import src.constants as constants_utils
-import src.langchain_utils as langchain_utils
-import src.weather as weather_utils
-import src.mandi_price as mandi_utils
-import src.translator as translator_utils
-import src.web_crawler as web_crawler_utils
-
-import logging
-logger = logging.getLogger(__name__)
-logging.basicConfig(
-    format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
-)
-
-import warnings
-warnings.filterwarnings('ignore')
-
-
-
-class KKMS_KSSW:
-    def __init__(self):
-        self.index_type = constants_utils.INDEX_TYPE
-        self.load_from_existing_index_store = constants_utils.LOAD_FROM_EXISTING_INDEX_STORE
-
-        # Instantiate langchain_utils class object
-        self.langchain_utils_obj = langchain_utils.LANGCHAIN_UTILS(
-            index_type=self.index_type,
-            load_from_existing_index_store=self.load_from_existing_index_store
-        )
-        # Instantiate Mandi Price utils class object
-        self.mandi_utils_obj = mandi_utils.MANDI_PRICE()
-        # Instantiate Weather class object
-        self.weather_utils_obj = weather_utils.WEATHER()
-        # Instantiate translator_utils class object
-        self.translator_utils_obj = translator_utils.TRANSLATOR()
-
-
-
-    # Initialize index (vector store)
-    def load_create_index(self):
-        logger.info(f"Load/Create index")
-        self.langchain_utils_obj.load_create_index()
-
-
-    # Upload data and update the index
-    def upload_data(
-        self,
-        doc_type,
-        files_or_urls,
-        index_category
-    ):
-        logger.info(f"Uploading data")
-        self.langchain_utils_obj.upload_data(
-            doc_type=doc_type,
-            files_or_urls=files_or_urls,
-            index_category=index_category
-        )
-
-
-    # Define query on index to retrieve the most relevant top K documents from the vector store
-    def query(
-        self,
-        question,
-        question_category
-    ):
-        '''
-            Args:
-                mode: can be any of [default, embedding]
-                response_mode: can be any of [default, compact, tree_summarize]
-        '''
-        logger.info(f"Querying from index/vector store")
-        
-        return self.langchain_utils_obj.query(
-            question=question,
-            question_category=question_category
-        )

spaces/ChrisPreston/diff-svc_minato_aqua/modules/diff/diffusion.py

@@ -1,312 +0,0 @@
-from collections import deque
-from functools import partial
-from inspect import isfunction
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-from tqdm import tqdm
-
-from modules.encoder import SvcEncoder
-from training.train_pipeline import Batch2Loss
-from utils.hparams import hparams
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-# gaussian diffusion trainer class
-
-def extract(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
-
-
-def linear_beta_schedule(timesteps, max_beta=hparams.get('max_beta', 0.01)):
-    """
-    linear schedule
-    """
-    betas = np.linspace(1e-4, max_beta, timesteps)
-    return betas
-
-
-def cosine_beta_schedule(timesteps, s=0.008):
-    """
-    cosine schedule
-    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
-    """
-    steps = timesteps + 1
-    x = np.linspace(0, steps, steps)
-    alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
-    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
-    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
-    return np.clip(betas, a_min=0, a_max=0.999)
-
-
-beta_schedule = {
-    "cosine": cosine_beta_schedule,
-    "linear": linear_beta_schedule,
-}
-
-
-class GaussianDiffusion(nn.Module):
-    def __init__(self, phone_encoder, out_dims, denoise_fn,
-                 timesteps=1000, K_step=1000, loss_type=hparams.get('diff_loss_type', 'l1'), betas=None, spec_min=None,
-                 spec_max=None):
-        super().__init__()
-        self.denoise_fn = denoise_fn
-        self.fs2 = SvcEncoder(phone_encoder, out_dims)
-        self.mel_bins = out_dims
-
-        if exists(betas):
-            betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas
-        else:
-            if 'schedule_type' in hparams.keys():
-                betas = beta_schedule[hparams['schedule_type']](timesteps)
-            else:
-                betas = cosine_beta_schedule(timesteps)
-
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.K_step = K_step
-        self.loss_type = loss_type
-
-        self.noise_list = deque(maxlen=4)
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        self.register_buffer('spec_min', torch.FloatTensor(spec_min)[None, None, :hparams['keep_bins']])
-        self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']])
-
-    def q_mean_variance(self, x_start, t):
-        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, cond, clip_denoised: bool):
-        noise_pred = self.denoise_fn(x, t, cond=cond)
-        x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred)
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond, clip_denoised=clip_denoised)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False):
-        """
-        Use the PLMS method from [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778).
-        """
-
-        def get_x_pred(x, noise_t, t):
-            a_t = extract(self.alphas_cumprod, t, x.shape)
-            a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t)), x.shape)
-            a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt()
-
-            x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / (
-                    a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
-            x_pred = x + x_delta
-
-            return x_pred
-
-        noise_list = self.noise_list
-        noise_pred = self.denoise_fn(x, t, cond=cond)
-
-        if len(noise_list) == 0:
-            x_pred = get_x_pred(x, noise_pred, t)
-            noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond)
-            noise_pred_prime = (noise_pred + noise_pred_prev) / 2
-        elif len(noise_list) == 1:
-            noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2
-        elif len(noise_list) == 2:
-            noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12
-        elif len(noise_list) >= 3:
-            noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24
-
-        x_prev = get_x_pred(x, noise_pred_prime, t)
-        noise_list.append(noise_pred)
-
-        return x_prev
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (
-                extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
-        )
-
-    def p_losses(self, x_start, t, cond, noise=None, nonpadding=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        x_recon = self.denoise_fn(x_noisy, t, cond)
-
-        if self.loss_type == 'l1':
-            if nonpadding is not None:
-                loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean()
-            else:
-                # print('are you sure w/o nonpadding?')
-                loss = (noise - x_recon).abs().mean()
-
-        elif self.loss_type == 'l2':
-            loss = F.mse_loss(noise, x_recon)
-        else:
-            raise NotImplementedError()
-
-        return loss
-
-    def forward(self, hubert, mel2ph=None, spk_embed=None,
-                ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs):
-        '''
-            conditioning diffusion, use fastspeech2 encoder output as the condition
-        '''
-        ret = self.fs2(hubert, mel2ph, spk_embed, None, f0, uv, energy,
-                       skip_decoder=True, infer=infer, **kwargs)
-        cond = ret['decoder_inp'].transpose(1, 2)
-        b, *_, device = *hubert.shape, hubert.device
-
-        if not infer:
-            Batch2Loss.module4(
-                self.p_losses,
-                self.norm_spec(ref_mels), cond, ret, self.K_step, b, device
-            )
-        else:
-            if 'use_gt_mel' in kwargs.keys() and kwargs['use_gt_mel']:
-                t = kwargs['add_noise_step']
-                print('===>using ground truth mel as start, please make sure parameter "key==0" !')
-                fs2_mels = ref_mels
-                fs2_mels = self.norm_spec(fs2_mels)
-                fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :]
-                x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long())
-            else:
-                t = self.K_step
-                shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2])
-                x = torch.randn(shape, device=device)
-            if hparams.get('pndm_speedup') and hparams['pndm_speedup'] > 1:
-                self.noise_list = deque(maxlen=4)
-                iteration_interval = hparams['pndm_speedup']
-                for i in tqdm(reversed(range(0, t, iteration_interval)), desc='sample time step',
-                              total=t // iteration_interval):
-                    x = self.p_sample_plms(x, torch.full((b,), i, device=device, dtype=torch.long), iteration_interval,
-                                           cond)
-            else:
-                for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
-                    x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
-            x = x[:, 0].transpose(1, 2)
-            if mel2ph is not None:  # for singing
-                ret['mel_out'] = self.denorm_spec(x) * ((mel2ph > 0).float()[:, :, None])
-            else:
-                ret['mel_out'] = self.denorm_spec(x)
-        return ret
-
-    def norm_spec(self, x):
-        return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
-
-    def denorm_spec(self, x):
-        return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min
-
-    def out2mel(self, x):
-        return x
-
-
-class OfflineGaussianDiffusion(GaussianDiffusion):
-    def forward(self, txt_tokens, mel2ph=None, spk_embed=None,
-                ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs):
-        b, *_, device = *txt_tokens.shape, txt_tokens.device
-
-        ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy,
-                       skip_decoder=True, infer=True, **kwargs)
-        cond = ret['decoder_inp'].transpose(1, 2)
-        fs2_mels = ref_mels[1]
-        ref_mels = ref_mels[0]
-
-        if not infer:
-            t = torch.randint(0, self.K_step, (b,), device=device).long()
-            x = ref_mels
-            x = self.norm_spec(x)
-            x = x.transpose(1, 2)[:, None, :, :]  # [B, 1, M, T]
-            ret['diff_loss'] = self.p_losses(x, t, cond)
-        else:
-            t = self.K_step
-            fs2_mels = self.norm_spec(fs2_mels)
-            fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :]
-
-            x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long())
-
-            if hparams.get('gaussian_start') is not None and hparams['gaussian_start']:
-                print('===> gaussion start.')
-                shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2])
-                x = torch.randn(shape, device=device)
-            for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
-                x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
-            x = x[:, 0].transpose(1, 2)
-            ret['mel_out'] = self.denorm_spec(x)
-
-        return ret