Update parquet files (step 85 of 397)

spaces/0xSpleef/openchat-openchat_8192/README.md

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----
-title: Openchat-openchat 8192
-emoji: 🌍
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----
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spaces/0xtanmoysamanta/espnet-kan-bayashi_ljspeech_vits/README.md

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-title: Espnet-kan-bayashi Ljspeech Vits
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----
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download and Install Microsoft Office 32-bit Version Online or Offline.md

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spaces/1phancelerku/anime-remove-background/AP KGBV Teaching Jobs 2023 How to Apply for Principal PGT CRT PET Posts in KGBV Schools.md

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-<h1>APKGVB Notification 2023: Everything You Need to Know</h1>
-<p>If you are interested in working as a teacher or studying in a residential school for girls in Andhra Pradesh, then you should not miss the APKGVB notification 2023. This notification is released by the Samagra Shiksha, Government of Andhra Pradesh, to invite online applications for filling up of vacant posts of teaching staff and students in all the Kasturba Gandhi Balika Vidyalayas (KGBVs) located across the state. In this article, we will tell you everything you need to know about the APKGVB notification 2023, including what is APKGVB, what is the recruitment and admission process, how to apply online, and more.</p>
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-<p>APKGVB stands for Andhra Pradesh Kasturba Gandhi Balika Vidyalaya. It is a scheme launched by the Government of India in August 2004, under the Sarva Shiksha Abhiyan (SSA), to provide quality education to girls from disadvantaged sections of society. The scheme aims to set up residential schools at upper primary level for girls belonging to SC, ST, OBC, minority communities and families below the poverty line (BPL) in educationally backward blocks. The scheme was later extended to cover girls in secondary level as well.</p>
- <h3>The objectives and features of APKGVB</h3>
-<p>The main objectives of APKGVB are:</p>
-<ul>
-<li>To ensure access and quality education to girls from marginalized groups by providing them with a safe and supportive environment.</li>
-<li>To reduce gender disparities in education and enhance the self-esteem and confidence of girls.</li>
-<li>To promote girls' participation in co-curricular activities, life skills education, vocational training and career guidance.</li>
-<li>To facilitate the transition of girls from school to higher education or work.</li>
-</ul>
-<p>Some of the key features of APKGVB are:</p>
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-<li>The schools are run by local bodies or NGOs with financial assistance from the central and state governments.</li>
-<li>The schools provide free boarding, lodging, uniforms, textbooks, stationery and health care to the students.</li>
-<li>The schools follow the state curriculum and use local language as the medium of instruction.</li>
-<li>The schools have qualified and trained female teachers and staff.</li>
-<li>The schools have well-equipped classrooms, libraries, laboratories, computer rooms, playgrounds and other facilities.</li>
-<li>The schools have a child-friendly and gender-sensitive environment that respects the diversity and dignity of girls.</li>
-</ul>
- <h3>The benefits and achievements of APKGVB</h3>
-<p>APKGVB has been successful in achieving its goals and bringing positive changes in the lives of girls. Some of the benefits and achievements of APKGVB are:</p>
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-<li>APKGVB has increased the enrollment, retention and completion rates of girls in upper primary and secondary education.</li>
-<li>APKGVB has improved the learning outcomes and academic performance of girls.</li>
-<li>APKGVB has reduced the dropout rate, absenteeism rate and child marriage rate among girls.</li>
-<li>APKGVB has empowered girls with knowledge, skills, values and attitudes that enable them to face challenges and opportunities in life.</li>
-<li>APKGVB has created awareness among parents, community members and stakeholders about the importance of girls' education.</li>
-</ul>
- <h2>What is the APKGVB notification 2023?</h2> <p>The APKGVB notification 2023 is a document that contains all the information regarding the recruitment of teaching staff and the admission of students in the KGBVs for the academic year 2023-24. The notification is released by the Samagra Shiksha, Government of Andhra Pradesh, on its official website apkgbv.apcfss.in. The notification covers two aspects:</p>
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- <h3>The recruitment process for teaching staff in KGBVs</h3>
-<p>The Samagra Shiksha invites online applications from eligible women candidates for filling up of 1358 vacant posts of teaching staff in all the KGBVs across the state. The posts include Principal, Post Graduate Teachers (PGT), Contract Residential Teachers (CRT) and Physical Education Teachers (PET). The recruitment is done on a contractual basis for a period of one year or till regular recruitment is made, whichever is earlier.</p>
- <h4>Eligibility criteria and application fee</h4>
-<p>The candidates who wish to apply for the teaching staff recruitment must fulfill the following eligibility criteria:</p>
-<table>
-<tr><th>Post</th><th>Qualification</th><th>Age Limit</th><th>Application Fee</th></tr>
-<tr><td>Principal</td><td>Post Graduation Degree with B.Ed. from a recognized university with at least 50% marks in aggregate for OCs, 45% for BCs and 40% for SC/ST/Differently abled persons.</td><td>Not more than 45 years as on 01.07.2023</td><td>Rs. 500/-</td></tr>
-<tr><td>PGT</td><td>Post Graduation Degree in the relevant subject with B.Ed. from a recognized university with at least 50% marks in aggregate for OCs, 45% for BCs and 40% for SC/ST/Differently abled persons.</td><td>Not more than 44 years as on 01.07.2023</td><td>Rs. 500/-</td></tr>
-<tr><td>CRT</td><td>Graduation Degree in the relevant subject with B.Ed. from a recognized university with at least 50% marks in aggregate for OCs, 45% for BCs and 40% for SC/ST/Differently abled persons.</td><td>Not more than 39 years as on 01.07.2023</td><td>Rs. 500/-</td></tr>
-<tr><td>PET</td><td>Intermediate with D.P.Ed./B.P.Ed./M.P.Ed. from a recognized board or university.</td><td>Not more than 39 years as on 01.07.2023</td><td>Rs. 250/-</td></tr>
-</table>
- <h4>Timeline and selection procedure</h4>
-<p>The candidates who are interested and eligible can apply online through the official website apkgbv.apcfss.in from May 30, 2023 to June 05, 2023. The candidates have to pay the application fee through online mode only using debit card/credit card/net banking etc. The candidates have to upload their scanned copies of photograph, signature and relevant documents while applying online.</p>
-<p>The selection of the candidates will be done on the basis of merit list prepared by the State Office at the ratio of 1:3 for each post. The merit list will be based on the academic qualifications, professional qualifications and experience of the candidates as per the weightage given below:</p>
-<table>
-<tr><th>Post</th><th>Acedemic Qualifications (Max Marks)</th><th>Professional Qualifications (Max Marks)</th><th>Experience (Max Marks)</th></tr>
-<tr><td>Principal</td><td>30 (10 marks each for SSC, Intermediate and Graduation)</td><td>20 (10 marks each for Post Graduation and B.Ed.)</td><td>50 (10 marks each for one year of experience as Principal/PGT/CRT/PET in any residential school)</td></tr>
-<tr><td>PGT</td><td>30 (10 marks each for SSC, Intermediate and Graduation)</td><td>20 (10 marks each for Post Graduation and B.Ed.)</td><td>50 (10 marks each for one year of experience as PGT/CRT/PET in any residential school)</td></tr>
-<tr><td>CRT</td><td>30 (10 marks each for SSC, Intermediate and Graduation)</td><td>20 (10 marks each for B.Ed.)</td><td>50 (10 marks each for one year of experience as CRT/PET in any residential school)</td></tr>
-<tr><td>PET</td><td>30 (10 marks each for SSC, Intermediate and D.P.Ed./B.P.Ed./M.P.Ed.)</td><td>20 (10 marks each for Graduation)</td><td>50 (10 marks each for one year of experience as PET in any residential school)</td></tr>
-</table>
-<p>The candidates who are shortlisted in the merit list will be called for certificate verification and demo/interview at the district level. The final selection will be based on the performance of the candidates in the demo/interview and the availability of vacancies.</p>
- <h4>Vacancy details and salary structure</h4>
-<p>The vacancy details for the teaching staff recruitment are as follows:</p>
-<table>
-<tr><th>Post</th><th>No. of Vacancies</th><th>Salary per month</th></tr>
-<tr><td>Principal</td><td>44</td><td>Rs. 40,000/-</td></tr>
-<tr><td>PGT</td><td>313</td><td>Rs. 31,000/-</td></tr>
-<tr><td>CRT</td><td>897</td><td>Rs. 21,000/-</td></tr>
-<tr><td>PET</td><td>104</td><td>Rs. 12,000/-</td></tr>
-</table>
-<p>The salary structure for the teaching staff is subject to revision as per the norms of the Samagra Shiksha.</p>
- <h3>The admission process for students in KGBVs</h3>
-<p>The Samagra Shiksha also invites online applications from eligible girl students for admission into Class VI to X in all the KGBVs across the state. The admission is done on a merit-cum-reservation basis for a total of 36,720 seats available in 918 KGBVs.</p>
- <h4>Eligibility criteria and application fee</h4>
-<p>The girl students who wish to apply for the admission in KGBVs must fulfill the following eligibility criteria:</p>
-<ul>
-<li>The girl student must belong to SC, ST, OBC, minority community or BPL family.</li>
-<li>The girl student must have passed Class V to IX from any recognized school in Andhra Pradesh.</li>
-<li>The girl student must not be enrolled in any other residential school or hostel.</li>
-<li>The girl student must not be suffering from any contagious disease or disability.</li>
-<li>The girl student must be willing to stay in the KGBV hostel and follow its rules and regulations.</li>
-</ul>
-<p>The girl students who are eligible can apply online through the official website apkgbv.apcfss.in without paying any application fee.</p>
- <h4>Timeline and selection procedure</h4>
-<p>The girl students who are interested and eligible can apply online through the official website apkgbv.apcfss.in from June 10, 2023 to June 20, 2023. The girl students have to upload their scanned copies of photograph, signature and relevant documents while applying online.</p>
-<p>The selection of the girl students will be done on the basis of merit list prepared by the District Project Office at the ratio of 1:2 for each seat. The merit list will be based on the marks obtained by the girl students in their previous class. The merit list will be displayed on the notice board of the concerned KGBV and on the official website apkgbv.apcfss.in by June 25, 2023.</p>
-<p>The girl students who are shortlisted in the merit list will be called for certificate verification and counseling at the district level. The final selection will be based on the verification of documents and the availability of seats.</p>
- <h4>Reservation policy and seat allotment</h4>
-<p>The reservation policy for the admission of girl students in KGBVs is as follows:</p>
-<ul>
-<li>15% of seats are reserved for SCs, 6% for STs, 29% for BCs, 15% for minorities and 3% for differently abled persons.</li>
-<li>33% of seats are reserved for girls from BPL families irrespective of their caste or community.</li>
-<li>In case of non-availability of eligible candidates in any category, the seats will be filled up by eligible candidates from other categories as per merit.</li>
-<li>In case of non-availability of eligible candidates from any district, the seats will be filled up by eligible candidates from other districts as per merit.</li>
-<li>In case of non-availability of eligible candidates from any state, the seats will be filled up by eligible candidates from other states as per merit.</li>
-<li>The seat allotment will be done by the District Project Office based on the preferences given by the girl students during counseling.</li>
-<li>The girl students who are allotted seats in KGBVs have to report to their respective schools by June 30, 2023 with their original certificates and other documents.</li>
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- <h2>How to apply for APKGVB notification 2023?</h2>
-<p>If you are interested in applying for the APKGVB notification 2023, either as a teaching staff or as a student, you have to follow the steps given below:</p>
- <h3>The steps to apply online for teaching staff recruitment</h3>
-<ol>
-<li>Visit the official website apkgbv.apcfss.in and click on the link "Online Application for Teaching Staff Recruitment 2023".</li>
-<li>Read the instructions carefully and click on the "Proceed" button.</li>
-<li>Fill in the basic details such as name, date of birth, gender, mobile number, email id, etc. and click on the "Submit" button.</li>
-<li>You will receive an OTP on your registered mobile number and email id. Enter the OTP and click on the "Verify" button.</li>
-<li>You will get a registration number and password. Note them down for future reference.</li>
-<li>Login with your registration number and password and fill in the personal details, educational details, experience details, etc. and click on the "Save" button.</li>
-<li>Upload your scanned photograph, signature and relevant documents in the prescribed format and size and click on the "Upload" button.</li>
-<li>Pay the application fee through online mode using debit card/credit card/net banking etc. and click on the "Pay" button.</li>
-<li>Take a printout of the application form and fee receipt for future reference.</li>
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-<ol>
-<li>Visit the official website apkgbv.apcfss.in and click on the link "Online Application for Student Admission 2023".</li>
-<li>Read the instructions carefully and click on the "Proceed" button.</li>
-<li>Fill in the basic details such as name, date of birth, gender, caste, community, BPL status, etc. and click on the "Submit" button.</li>
-<li>You will receive an OTP on your registered mobile number. Enter the OTP and click on the "Verify" button.</li>
-<li>You will get a registration number and password. Note them down for future reference.</li>
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-<li>Upload your scanned photograph, signature and relevant documents in the prescribed format and size and click on the "Upload" button.</li>
-<li>Take a printout of the application form for future reference.</li>
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- <h2>Conclusion</h2>
-<p>The APKGVB notification 2023 is a great opportunity for women candidates who want to pursue a career as a teacher and for girl students who want to get quality education in a residential school. The notification provides all the details about the eligibility criteria, application process, selection process, vacancy details, reservation policy, etc. for both teaching staff recruitment and student admission. The candidates who are interested and eligible can apply online through the official website apkgbv.apcfss.in before the last date. The candidates who are selected will be able to work or study in one of the best KGBVs in Andhra Pradesh.</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the frequently asked questions about the APKGVB notification 2023:</p>
- <h4>Q: When will the APKGVB notification 2023 be released?</h4>
-<p>A: The APKGVB notification 2023 is expected to be released by May 2023 on the official website apkgbv.apcfss.in.</p>
- <h4>Q: How many vacancies are there for teaching staff recruitment in KGBVs?</h4>
-<p>A: There are 1358 vacancies for teaching staff recruitment in KGBVs, including 44 for Principal, 313 for PGT, 897 for CRT and 104 for PET.</p>
- <h4>Q: How many seats are there for student admission in KGBVs?</h4>
-<p>A: There are 36,720 seats for student admission in KGBVs, including 9180 seats for Class VI, 9180 seats for Class VII, 9180 seats for Class VIII, 9180 seats for Class IX and 9180 seats for Class X.</p>
- <h4>Q: What is the application fee for teaching staff recruitment in KGBVs?</h4>
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- <h4>Q: What is the application fee for student admission in KGBVs?</h4>
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- <h4>Q: How can I contact the Samagra Shiksha for any queries or grievances regarding the APKGVB notification 2023?</h4>
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-<li>Email: [email protected]</li>
-<li>Phone: 040-23317140, 040-23317141</li>
-<li>Address: Samagra Shiksha, Government of Andhra Pradesh, 5th Floor, Anjaneya Towers, Ibrahimpatnam, Vijayawada - 521456</li>
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-<p>The gameplay of Candy Crush Soda Saga is similar to that of Candy Crush Saga. You have to match three or more candies of the same color to clear them from the board. You can also create special candies by matching four or more candies in different shapes, such as striped, wrapped, or fish candies. These special candies can have various effects, such as clearing a whole row, column, or area of candies.</p>
-<p>The game has different objectives depending on the level type. For example, in soda levels, you have to switch the bottles and match candies to release purple soda and save the candy bears. In frosting levels, you have to match candies to smash the ice and set the candy bears free. In honey levels, you have to match candies next to honeycomb to release the trapped candy bears. In jam levels, you have to spread the jam across the board.</p>
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-<p>The game has over 10,000 levels to play, each with different layouts, obstacles, and challenges. You have a limited number of moves or time to complete each level. If you run out of moves or time before reaching the goal, you will lose a life. You can earn up to five lives at a time, which regenerate over time or can be purchased with real money. You can also use boosters, such as lollipop hammers or color bombs, to help <p>you clear the board or make special moves. You can also earn stars and gold bars by completing levels, which can be used to unlock new episodes, buy boosters, or access special features.</p>
- <h3>A sequel to the popular Candy Crush Saga</h3>
-<p>Candy Crush Soda Saga is a sequel to Candy Crush Saga, which was launched in 2012 and became a global phenomenon. Candy Crush Saga is based on the classic game Candy Crush, which was created by King in 2011. The game has been downloaded over 2.7 billion times and has more than 270 million monthly active users. The game has also inspired several spin-offs, such as Candy Crush Jelly Saga, Candy Crush Friends Saga, and Candy Crush All Stars.</p>
-<p>Candy Crush Soda Saga follows the adventures of Kimmy, the sister of Tiffi, the main character of Candy Crush Saga. Kimmy is looking for her lost sister and travels through the Candy Kingdom, meeting new friends and foes along the way. The game introduces new characters, such as Mr. Toffee, Yeti, Bubblegum Troll, and Percy the Penguin. The game also features new graphics, animations, sound effects, and music that enhance the candy-themed experience.</p>
- <h3>A game with different modes, levels, and challenges</h3>
-<p>Candy Crush Soda Saga is a game that offers a lot of variety and fun for players of all ages and skill levels. The game has different modes that test your abilities and creativity. For example, in Live Events mode, you can compete with other players in real-time for prizes and glory. In Quests mode, you can complete daily tasks and earn rewards. In Team mode, you can join or create a team with other players and chat, share lives, and play together.</p>
-<p>The game also has different levels that challenge your strategy and logic. For example, in Boss levels, you have to face off against powerful enemies that have special abilities and tricks. In Super Hard levels, you have to overcome extra difficult obstacles and puzzles. In Treasure Hunt levels, you have to find hidden treasures and collect them.</p>
-<p>The game also has different challenges that add more excitement and fun to the gameplay. For example, in Bubblegum Hill challenge, you have to climb a mountain of bubblegum and collect as many gold crowns as possible. In Soda Squad challenge, you have to work with your team to fill a soda meter and win rewards. In Rainbow Rapids challenge, you have to match candies on rainbow-colored tiles and create rainbow streaks.</p>
- <h2>How to download and install Candy Crush Soda Saga on PC Windows 7?</h2>
-<p>If you want to enjoy Candy Crush Soda Saga on a bigger screen and with better performance, you can download and install it on your PC Windows 7 for free. There are two main options for doing this: downloading from the Microsoft Store or downloading from a third-party platform.</p>
- <h3>Option 1: Download from the Microsoft Store</h3>
-<p>The Microsoft Store is the official app store for Windows devices. It offers a wide range of apps and games that are compatible with Windows 7 or later versions. You can download Candy Crush Soda Saga from the Microsoft Store by following these steps:</p>
- <h4>Step 1: Open the Microsoft Store app</h4>
-<p>To open the Microsoft Store app, you can click on the Start button on the bottom left corner of your screen and type "Microsoft Store" in the search box. Alternatively, you can press the Windows key + S on your keyboard and type "Microsoft Store" in the search box.</p>
- <h4>Step 2: Search for Candy Crush Soda Saga</h4>
-<p>To search for Candy Crush Soda Saga in the Microsoft Store app, you can click on the magnifying glass icon on the top right corner of the app window and type "Candy Crush Soda Saga" in the search box. Alternatively, you can press Ctrl + F on your keyboard and type "Candy Crush Soda Saga" in the search box.</p>
- <h4>Step 3: Click on Get or Install to download the game</h4>
-<p>To download Candy Crush Soda Saga from the Microsoft Store app, you can click on the Get or Install button next to the game's name and icon. This will start downloading the game to your PC Windows 7. You may need to sign in with your Microsoft account or create one if you don't have one already.</p>
- <h4>Step 4: Launch the game from the Start menu or the desktop shortcut</h4>
-<p>To launch Candy Crush Soda Saga from your PC Windows 7, you can click on the Start button on the bottom left corner of your screen and scroll down to find the game's name and icon under "C". Alternatively, you can press the Windows key + Q on your keyboard and type "Candy Crush Soda Saga" in the search box. You can also find a desktop shortcut for the game on your desktop and double-click on it to launch the game.</p>
- <h3>Option 2: Download from a third-party platform</h3>
-<p>If you prefer to download Candy Crush Soda Saga from a different source than the Microsoft Store, you can use a third-party platform that offers PC games. Some of the most popular platforms are Steam, Epic Games, GOG, and itch.io. You can download Candy Crush Soda Saga from any of these platforms by following these steps:</p>
- <h4>Step 1: Choose a platform such as Steam, Epic Games, GOG, or itch.io</h4>
-<p>To choose a platform to download Candy Crush Soda Saga from, you can visit their official websites and compare their features, prices, and reviews. You can also check if they have any discounts, deals, or free games available. Some of the factors to consider when choosing a platform are:</p>
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-<li>The compatibility of the platform with your PC Windows 7</li>
-<li>The security and reliability of the platform and its payment methods</li>
-<li>The availability and quality of customer support and community forums</li>
-<li>The variety and exclusivity of games and genres offered by the platform</li>
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- <h4>Step 2: Create an account and log in to the platform</h4>
-<p>To create an account and log in to the platform of your choice, you can follow the instructions on their website or app. You may need to provide some personal information, such as your name, email address, password, and payment details. You may also need to verify your account through email or phone. Once you have created an account and logged in to the platform, you can access its features and browse its games.</p>
- <h4>Step 3: Search for Candy Crush Soda Saga and purchase or download the game</h4>
-<p>To search for Candy Crush Soda Saga on the platform of your choice, you can use the search bar or filter options to find the game's name and icon. You can also check the game's description, screenshots, videos, ratings, reviews, and system requirements. Depending on the platform, you may need to purchase or download the game before playing it. Some platforms may offer free trials or demos of the game. You can also check if there are any updates or patches available for the game.</p>
- <h4>Step 4: Launch the game from the platform's library or launcher</h4>
-<p>To launch Candy Crush Soda Saga from the platform of your choice, you can go to your library or launcher and find the game's name and icon. You can also create a desktop shortcut for the game if you want. You can then click on Play or Launch to start playing the game. You may need to log in to your account or connect to the internet to play the game.</p>
- <h2>What are the benefits of playing Candy Crush Soda Saga?</h2>
-<p>Candy Crush Soda Saga is not only a fun and addictive game, but also a beneficial one. Playing this game can have positive effects on your mental and emotional well-being. Here are some of the benefits of playing Candy Crush Soda Saga:</p>
- <h3>It can improve your cognitive skills and memory</h3>
-<p>Playing Candy Crush Soda Saga can stimulate your brain and enhance your cognitive skills, such as attention, concentration, problem-solving, logic, spatial awareness, pattern recognition, and memory. These skills are essential for learning, working, and everyday life. By matching candies and creating special combinations, you can train your brain to process information faster and more efficiently. By completing levels and advancing through episodes, you can challenge your brain to remember details and strategies.</p>
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-<p>Playing Candy Crush Soda Saga can also help you relax and unwind from stress and boredom. The game has colorful graphics, cheerful music, cute characters, and satisfying sound effects that can create a positive mood and atmosphere. The game also has simple rules and easy controls that can make you feel comfortable and confident. The game also has different modes and levels that can keep you entertained and engaged for hours. The game also has a rewarding system that can make you feel accomplished and motivated. By playing Candy Crush Soda Saga, you can escape from the worries and pressures of reality and enjoy a sweet and refreshing adventure.</p>
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-<p>Playing Candy Crush Soda Saga can also help you connect and interact with other people who share your passion for the game. The game has a social feature that allows you to link your Facebook account and see your friends' progress and scores. You can also send and receive lives, boosters, and messages from your friends. You can also join or create a team with other players and chat, share lives, and play together. You can also compete with other players in live events or leaderboards and show off your skills and achievements. By playing Candy Crush Soda Saga, you can have fun and make new friends at the same time.</p>
- <h2>What are some tips and tricks for playing Candy Crush Soda Saga?</h2>
-<p>Candy Crush Soda Saga is a game that requires strategy and skill to master. If you want to improve your performance and progress faster in the game, you might want to follow some tips and tricks that can help you beat the levels and challenges. Here are some of them:</p>
- <h3>Focus on clearing soda bottles and raising the soda level</h3>
-<p>In soda levels, the main objective is to switch the bottles and match candies to release purple soda and save the candy bears. The more soda you release, the higher the soda level will rise. The higher the soda level, the easier it will be to match candies and clear the board. Therefore, you should focus on clearing soda bottles as soon as possible and raising the soda level as high as possible. You should also try to match candies near the bottom of the board, as this will create more cascades and opportunities to clear more bottles.</p>
- <h3>Use special candies and combos to clear obstacles and ice</h3>
-<p>In frosting levels, the main objective is to match candies to smash the ice and set the candy bears free. The ice can be thick or thin, depending on the level. The thicker the ice, the more times you have to match candies next to it to break it. Therefore, you should use special candies and combos to clear obstacles and ice faster and more efficiently. Special candies are created by matching four or more candies in different shapes, such as striped, wrapped, or fish candies. Combos are created by matching two or more special candies together, such as striped + striped, striped + wrapped, or wrapped + wrapped. These special candies and combos can have various effects, such as clearing a whole row, column, or area of candies.</p>
- <h3>Keep an eye on the bubble bears and don't let them float away</h3>
-<p>In honey levels, the main objective is to match candies next to honeycomb to release the trapped candy bears. The honeycomb can be thick or thin, depending on the level. The thicker the honeycomb, the more times you have to match candies next to it to break it. However, there is another challenge in these levels: the bubble bears. These are candy bears that are surrounded by bubbles and float up when you match candies below them. If they reach the top of the board, they will disappear and you will lose them. Therefore, you should keep an eye on the bubble bears and don't let them float away. You should try to match candies next to them or use special candies or combos to pop their bubbles.</p>
- <h3>Plan your moves ahead and save your boosters for hard levels</h3>
-<p>In jam levels, the main objective is to spread the jam across the board. The jam is a sticky substance that covers some of the candies or tiles on the board. To spread the jam, you have to match candies on top of it or use special candies or combos to splash it. However, you have a limited number of moves or time to spread the jam to all the tiles on the board. Therefore, you should plan your moves ahead and save your boosters for hard levels. Boosters are items that can help you clear the board or make special moves. You can earn boosters by completing levels, quests, events, or challenges. You can also buy boosters with real money. Some of the boosters are lollipop hammers, color bombs, striped brushes, free switches, and extra moves.</p>
- <h2>Conclusion</h2>
-<p>Candy Crush Soda Saga is a game that can offer you hours of fun and enjoyment. It is a game that can improve your cognitive skills and memory, reduce your stress and boredom, and provide you with social interaction and entertainment. It is also a game that can challenge your strategy and logic with different modes, levels, and obstacles. If you want to play this game on your PC Windows 7 for free, you can download it from the Microsoft Store or from a third-party platform. You can also follow some tips and tricks to help you master the game and beat the levels. So what are you waiting for? Download Candy Crush Soda Saga today and join Kimmy on her sweet and fizzy adventure!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Candy Crush Soda Saga:</p>
- <h3>Q: How do I sync my progress across different devices?</h3>
-<p>A: To sync your progress across different devices, you need to link your game to your Facebook account or your King account. You can do this by tapping on the settings icon on the main screen and choosing "Connect" or "Log in". Once you have linked your game to your account, you can access your progress on any device that has the game installed.</p>
- <h3>Q: How do I get more lives?</h3>
-<p>A: To get more lives, you have several options. You can wait for your lives to regenerate over time, which takes about 30 minutes per life. You can ask your friends for lives, which they can send you through Facebook or the game's app. You can join or create a team and share lives with your teammates. You can also buy lives with real money or gold bars.</p>
- <h3>Q: How do I get more gold bars?</h3>
-<p>A: To get more gold bars, you have several options. You can earn gold bars by completing levels, quests, events, or challenges. You can also buy gold bars with real money or redeem them with gift cards or coupons. You can also get gold bars from your friends or teammates as gifts.</p>
- <h3>Q: How do I unlock new episodes?</h3>
-<p>A: To unlock new episodes, you need to complete all the levels in the previous episode. You may also need to pay a certain amount of gold bars or ask your friends for tickets to unlock the next episode. Some episodes may also have special requirements or conditions to unlock them.</p>
- <h3>Q: How do I contact customer support?</h3>
-<p>A: To contact customer support, you can visit the official website of King and go to the "Help Center" section. There you can find answers to common questions, report a problem, give feedback, or chat with an agent.</p> 401be4b1e0<br />
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- <p>Guardian Tales is a pixel RPG game that combines gacha elements, puzzle-solving, and action combat. You can collect over 50 heroes and 100 weapons, each with their own unique abilities and skills. You can also explore various worlds, dungeons, and bosses, as well as challenge other players in real-time PvP battles.</p>
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- <p>In this article, we will show you how to download and play Guardian Tales JP on your Android devices or PC using an emulator. We will also share some tips and tricks for beginners who want to start their adventure in Kanterbury, the world of Guardian Tales.</p>
- <h2>How to Download Guardian Tales JP on Android Devices</h2>
- <p>If you have an Android device, you can download Guardian Tales JP from the Google Play Store. However, you will need to change your region settings to Japan first. Here are the steps to do so:</p>
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-<li>Select Japan as your country. You might need to add a payment method from Japan to do this.</li>
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-<li>Search for "Guardian Tales" in the Play Store. You should see the Japanese version of the game with the title "ガーディアンテイルズ".</li>
-<li>Tap on Install and wait for the game to download.</li>
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- <p>If you want to play Guardian Tales JP on your PC, you will need to use an Android emulator. An emulator is a software that allows you to run Android apps on your computer. There are many emulators available online, but we recommend using BlueStacks or MuMu Player as they are popular and reliable options.</p>
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- <ol>
-<li>Download and install an Android emulator on your PC. You can choose from BlueStacks or MuMu Player.</li>
-<li>Open the emulator and complete Google sign-in to access the Play Store.</li>
-<li>Change your region settings to Japan following the same steps as above.</li>
-<li>Search for "Guardian Tales" in the Play Store. You should see the Japanese version of the game with the title "ガーディアンテイルズ".</li>
-<li>Click to install Guardian Tales JP from the search results.</li>
-<li>Once installation completes, click the game icon to start the game.</li>
-<li>Enjoy playing Guardian Tales JP on your PC with the emulator.</li>
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- <p>You can also customize your keyboard and mouse controls using the Advanced Keymapping feature in BlueStacks or MuMu Player. This will allow you to play Guardian Tales JP more comfortably and efficiently on your PC.</p>
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- <p>Now that you have downloaded Guardian Tales JP, you might be wondering how to play it well. Don't worry, we have some tips and tricks for beginners who want to have a smooth start in the game. Here are some of them:</p>
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- <p>When you start the game, you will be able to choose one of four starter heroes: Knight, Warrior, Mage, or Archer. Each hero has their own strengths and weaknesses, as well as different roles and playstyles. You can check their stats and skills before making your choice.</p>
- <p>However, if you are not satisfied with your starter hero, you can reroll for a better one. Rerolling means resetting your game data and starting over until you get the hero you want. To reroll in Guardian Tales JP, you need to do the following:</p>
- <ol>
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-<li>Collect the free gems and tickets from the mailbox and events.</li>
-<li>Go to the summon menu and use your gems and tickets to pull for heroes and weapons.</li>
-<li>If you get a good hero or weapon, keep playing. If not, go to the settings menu and tap on "Delete Account".</li>
-<li>Confirm your decision and restart the game.</li>
-<li>Repeat the process until you get your desired hero or weapon.</li>
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- <p>The best heroes to aim for are those with a rarity of 3 stars, as they have higher stats and skills than lower rarity heroes. Some of the most popular 3-star heroes are Marina, Bari, Nari, Oghma, Bianca, and Eugene. You can also check the tier list for more information on the best heroes and weapons in the game.</p>
- <h3>Complete the Story Mode and Side Quests for Rewards</h3>
- <p>One of the main features of Guardian Tales is its story mode, which consists of 10 chapters with different themes and settings. The story mode is not only fun and engaging, but also rewarding. You can earn gems, gold, experience, items, and even new heroes by completing the story mode.</p>
- <p>However, don't just rush through the main quests. You should also pay attention to the side quests, which are marked with a yellow exclamation point on the map. Side quests are optional missions that give you more insight into the characters and the world of Guardian Tales. They also reward you with more gems, gold, experience, items, and sometimes even costumes for your heroes.</p>
- <p>Therefore, try to complete as many side quests as possible while progressing through the story mode. You can also replay the story mode stages on higher difficulties for more rewards and challenges.</p>
- <h3>Join a Guild and Participate in Raids and Events</h3>
- <p>Another way to enjoy Guardian Tales is to join a guild and participate in raids and events. A guild is a group of players who can chat, cooperate, and compete with each other. You can join an existing guild or create your own guild with your friends.</p>
- <p>By joining a guild, you can access various benefits such as guild buffs, guild shop, guild attendance rewards, and guild missions. You can also participate in guild raids, which are special battles that require teamwork and strategy. Guild raids reward you with raid coins, which you can use to buy exclusive items from the raid shop.</p>
- <p>Besides guild raids, you can also participate in various events that are held regularly in Guardian Tales. Events are limited-time missions that offer unique rewards such as gems, gold, items, costumes, heroes, and weapons. Some events are also collab events that feature characters from other popular games or anime series. For example, there was a collab event with Re:Zero in 2021 that allowed players to obtain Rem, Ram, Emilia, Subaru, Beatrice, and Roswaal as playable heroes.</p>
- <p>Therefore, try to join a guild and participate in raids and events as much as possible. They will not only make your game more fun and social but also help you progress faster and easier.</p>
- <h3>Upgrade Your Heroes, Weapons, and Accessories</h3>
- <p>As you play Guardian Tales JP , you will need to upgrade your heroes, weapons, and accessories to make them stronger and more effective. There are several ways to do this, such as leveling up, awakening, evolution, limit breaking, and enhancement.</p>
- <p>Leveling up is the simplest way to increase your heroes' and weapons' stats. You can level up your heroes by using experience points (XP) that you earn from battles or items. You can level up your weapons by using weapon XP that you earn from dismantling other weapons or items.</p>
- <p>Awakening is a process that unlocks new skills and abilities for your heroes and weapons. You can awaken your heroes by using awakening stones that you obtain from the awakening dungeon or events. You can awaken your weapons by using magic metal that you obtain from dismantling other weapons or events.</p>
- <p>Evolution is a process that increases the rarity and potential of your heroes and weapons. You can evolve your heroes by using hero crystals that you obtain from summoning or events. You can evolve your weapons by using weapon hammers that you obtain from the evolution dungeon or events.</p>
- <p>Limit breaking is a process that increases the maximum level and stats of your heroes and weapons. You can limit break your heroes by using hero shards that you obtain from summoning or events. You can limit break your weapons by using weapon shards that you obtain from summoning or events.</p>
- <p>Enhancement is a process that adds extra effects and bonuses to your accessories. You can enhance your accessories by using enhancement stones that you obtain from the enhancement dungeon or events.</p>
- <p>Therefore, try to upgrade your heroes, weapons, and accessories as much as possible. They will make a huge difference in your performance and results in the game.</p>
- <h3>Explore the Floating Island and Customize Your Base</h3>
- <p>The last tip we have for beginners is to explore the floating island and customize your base. The floating island is a feature that allows you to create and decorate your own base with various buildings, facilities, and items. You can also invite your heroes and friends to visit your base and interact with them.</p>
- <p>The floating island is not only a place to relax and have fun but also a source of income and resources. You can collect gold, gems, items, and energy from the buildings and facilities in your base. You can also complete quests and missions related to the floating island for more rewards.</p>
- <p>To access the floating island, you need to tap on the island icon on the top right corner of the screen. You can then use the edit mode to place and move buildings, facilities, and items on your base. You can also use the visit mode to see how your base looks like and interact with your heroes and friends.</p>
- <p>Some of the buildings and facilities you can build on your base are:</p>
- <table>
-<tr>
-<th>Name</th>
-<th>Description</th>
-<th>Benefits</th>
-</tr>
-<tr>
-<td>Inn</td>
-<td>A place where your heroes can rest and recover.</td>
-<td>Increases hero XP over time.</td>
-</tr>
-<tr>
-<td>Tower</td>
-<td>A place where you can store and display your weapons.</td>
-<td>Increases weapon XP over time.</td>
-</tr>
-<tr>
-<td>Shop</td>
-<td>A place where you can buy and sell items.</td>
-<td>Generates gold over time.</td>
-</tr>
-<tr>
-<td>Cafe</td>
-<td>A place where you can serve drinks and snacks to your heroes.</td>
-<td>Increases hero affection over time.</td>
-</tr>
-<tr>
-<td>Mine</td>
-<td>A place where you can dig for minerals and gems.</td>
-<td>Generates gems over time.</td>
-</tr>
-<tr>
-<td>Factory</td>
-<td>A place where you can produce items and materials.</td>
-<td>Generates items over time.</td>
-</tr>
-<tr>
-<td>Battery</td ><td>A place where you can store and recharge energy.</td ><td>Generates energy over time.</td ></tr ></table ><p >Therefore, try to explore the floating island and customize your base as much as possible. They will not only make your game more enjoyable but also help you progress faster and easier.</p ><h2 >Conclusion: Summary of the Main Points and a Call to Action</h2 ><p >In conclusion, Guardian Tales JP is a pixel RPG game that has some exclusive features and content that are different from the global version. If you want to try it out, you can download it on your Android devices or PC using an emulator. You can also follow our tips and tricks for beginners who want to have a smooth start in the game. We hope this article has been helpful and informative for you.</p ><p >If you liked this article, please share it with your friends who are also interested in Guardian Tales JP. You can also leave a comment below and let us know what you think about the game. And if you want to learn more about Guardian Tales JP, you can visit the official website or follow the social media accounts of the game. Thank you for reading and have a great day!</p>
- <h2>FAQs: Five Common Questions and Answers About Guardian Tales JP</h2>
- <p>Here are some of the most frequently asked questions and answers about Guardian Tales JP. If you have any other questions, feel free to ask them in the comments section.</p>
- <h3>Q: Is Guardian Tales JP free to play?</h3>
- <p>A: Yes, Guardian Tales JP is free to play. You can download and play the game without spending any money. However, there are some optional in-game purchases that can enhance your gaming experience, such as gems, costumes, and packages. You can buy these with real money or earn them through various methods in the game.</p>
- <h3>Q: Can I play Guardian Tales JP with my friends?</h3>
- <p>A: Yes, you can play Guardian Tales JP with your friends. You can add them as friends in the game and chat with them, visit their bases, and send them gifts. You can also invite them to join your guild or team up with them in co-op mode, arena mode, or colosseum mode. Playing with your friends can make the game more fun and rewarding.</p>
- <h3>Q: How can I change the language of Guardian Tales JP?</h3>
- <p>A: Unfortunately, you cannot change the language of Guardian Tales JP. The game is only available in Japanese, and there is no option to switch to other languages. If you want to play the game in English or other languages, you will have to download the global version of Guardian Tales instead.</p>
- <h3>Q: How can I transfer my data from the global version to the Japanese version of Guardian Tales?</h3>
- <p>A: Unfortunately, you cannot transfer your data from the global version to the Japanese version of Guardian Tales. The two versions are separate and have different servers, accounts, and data. If you want to play the Japanese version of Guardian Tales, you will have to start from scratch.</p>
- <h3>Q: How can I contact the customer service of Guardian Tales JP?</h3>
- <p>A: If you have any problems or issues with Guardian Tales JP, you can contact the customer service of the game by following these steps:</p>
- <ol>
-<li>Go to the settings menu and tap on "Customer Service".</li>
-<li>Tap on "Contact Us" and fill out the form with your details and inquiry.</li>
-<li>Tap on "Send" and wait for a reply from the customer service team.</li>
-</ol>
- <p>You can also check the FAQ section for more information and solutions to common problems.</p> 197e85843d<br />
-<br />
-<br />

spaces/AIConsultant/MusicGen/audiocraft/metrics/__init__.py

@@ -1,14 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""Metrics like CLAP score, FAD, KLD, Visqol, Chroma similarity, etc.
-"""
-# flake8: noqa
-from .clap_consistency import CLAPTextConsistencyMetric, TextConsistencyMetric
-from .chroma_cosinesim import ChromaCosineSimilarityMetric
-from .fad import FrechetAudioDistanceMetric
-from .kld import KLDivergenceMetric, PasstKLDivergenceMetric
-from .rvm import RelativeVolumeMel
-from .visqol import ViSQOL

spaces/AIWaves/Debate/src/agents/Action/base_action.py

@@ -1,48 +0,0 @@
-from Memory import Memory
-class Action:
-    """
-    The basic action unit of agent
-    """
-    def __init__(self,**kwargs):
-        self.response = None
-        self.is_user = False
-        self.res_dict = {}
-        self.name = ""
-        self.role = ""
-        for key,value in kwargs.items():
-            setattr(self,key,value)
-    
-    
-    def process(self):
-        """
-        processing action
-        Rerutn : memory(Memory)
-        """
-        response = self.response
-        send_name = self.name
-        send_role = self.role
-        all = ""
-        for res in response:
-            all += res
-        parse = f"{send_name}:"
-        
-        # 将里面对话的第三人称删了
-        # The third person in the dialogue was deleted.
-        while parse in all:
-            index = all.index(parse) + len(parse)
-            all = all[index:]
-        
-        if not self.is_user:
-            print(f"{send_name}({send_role}):{all}")
-                # for software
-        if "<title>" in all:
-            title = extract(all,"title")
-            python = extract(all,"python")
-            os.makedirs("output_code", exist_ok=True)
-            file_name = "output_code/" + title
-            with open(file_name, "w", encoding="utf-8") as f:
-                f.write(python)
-        memory = Memory(send_role, send_name, all)
-        return memory
-    
-    

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td_hm_res50_4xb16-120e_deepfashion2_long_sleeved_outwear_256x192.py

@@ -1,172 +0,0 @@
-_base_ = [
-    '../../../_base_/default_runtime.py',
-    '../../../_base_/datasets/deepfashion2.py'
-]
-
-default_hooks = dict(checkpoint=dict(save_best='PCK', rule='greater'))
-
-resume = False  # 断点恢复
-load_from = None  # 模型权重加载
-train_cfg = dict(by_epoch=True, max_epochs=120, val_interval=10)  # 训练轮数，测试间隔
-param_scheduler = [
-    dict(  # warmup策略
-        type='LinearLR',
-        begin=0,
-        end=500,
-        start_factor=0.001,
-        by_epoch=False),
-    dict(  # scheduler
-        type='MultiStepLR',
-        begin=0,
-        end=60,
-        milestones=[20, 40],
-        gamma=0.1,
-        by_epoch=True)
-]
-optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005))  # 优化器和学习率
-auto_scale_lr = dict(base_batch_size=512)  # 根据batch_size自动缩放学习率
-
-backend_args = dict(backend='local')  # 数据加载后端设置，默认从本地硬盘加载
-dataset_type = 'DeepFashion2Dataset'  # 数据集类名  DeepFashionDataset
-data_mode = 'topdown'  # 算法结构类型，用于指定标注信息加载策略
-data_root = 'data/deepfashion2/'  # 数据存放路径
-# 定义数据编解码器，用于生成target和对pred进行解码，同时包含了输入图片和输出heatmap尺寸等信息
-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=codec['input_size']),
-    dict(type='GenerateTarget', encoder=codec),
-    dict(type='PackPoseInputs')
-]
-val_pipeline = [  # 测试时数据增强
-    dict(type='LoadImage', backend_args=backend_args),  # 加载图片
-    dict(type='GetBBoxCenterScale'),  # 根据bbox获取center和scale
-    dict(type='TopdownAffine', input_size=codec['input_size']),  # 根据变换矩阵更新目标数据
-    dict(type='PackPoseInputs')  # 对target进行打包用于训练
-]
-train_dataloader = dict(  # 训练数据加载
-    batch_size=16,  # 批次大小
-    num_workers=6,  # 数据加载进程数
-    persistent_workers=True,  # 在不活跃时维持进程不终止，避免反复启动进程的开销
-    sampler=dict(type='DefaultSampler', shuffle=True),  # 采样策略，打乱数据
-    dataset=dict(
-        type=dataset_type,  # 数据集类名
-        data_root=data_root,  # 数据集路径
-        data_mode=data_mode,  # 算法类型
-        ann_file='train/deepfashion2_long_sleeved_outwear.json',  # 标注文件路径
-        data_prefix=dict(img='train/image/'),  # 图像路径
-        pipeline=train_pipeline  # 数据流水线
-    ))
-val_dataloader = dict(
-    batch_size=16,
-    num_workers=6,
-    persistent_workers=True,  # 在不活跃时维持进程不终止，避免反复启动进程的开销
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),  # 采样策略，不进行打乱
-    dataset=dict(
-        type=dataset_type,  # 数据集类名
-        data_root=data_root,  # 数据集路径
-        data_mode=data_mode,  # 算法类型
-        ann_file='validation/deepfashion2_long_sleeved_outwear.json',  # 标注文件路径
-        data_prefix=dict(img='validation/image/'),  # 图像路径
-        test_mode=True,  # 测试模式开关
-        pipeline=val_pipeline  # 数据流水线
-    ))
-test_dataloader = val_dataloader  # 默认情况下不区分验证集和测试集，用户根据需要来自行定义
-
-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',  # 模型结构决定了算法流程
-    data_preprocessor=dict(  # 数据归一化和通道顺序调整，作为模型的一部分
-        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',  # 预训练参数，只加载backbone权重用于迁移学习
-            checkpoint='torchvision://resnet50')),
-    head=dict(  # 模型头部
-        type='HeatmapHead',
-        in_channels=2048,
-        out_channels=channel_cfg['num_output_channels'],
-        # deconv_out_channels=None,
-        loss=dict(type='KeypointMSELoss', use_target_weight=True),  # 损失函数
-        decoder=codec),  # 解码器，将heatmap解码成坐标值
-    test_cfg=dict(
-        flip_test=True,  # 开启测试时水平翻转集成
-        flip_mode='heatmap',  # 对heatmap进行翻转
-        shift_heatmap=True,  # 对翻转后的结果进行平移提高精度
-    ))
-
-val_evaluator = [
-    dict(type='PCKAccuracy', thr=0.2),
-    dict(type='AUC'),
-    dict(type='EPE'),
-]
-test_evaluator = val_evaluator  # 默认情况下不区分验证集和测试集，用户根据需要来自行定义
-
-visualizer = dict(
-    vis_backends=[dict(type='LocalVisBackend'),
-                  dict(type='WandbVisBackend')])

spaces/Abhilashvj/planogram-compliance/data/scripts/get_imagenet.sh

@@ -1,51 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Download ILSVRC2012 ImageNet dataset https://image-net.org
-# Example usage: bash data/scripts/get_imagenet.sh
-# parent
-# ├── yolov5
-# └── datasets
-#     └── imagenet  ← downloads here
-
-# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
-if [ "$#" -gt 0 ]; then
-  for opt in "$@"; do
-    case "${opt}" in
-    --train) train=true ;;
-    --val) val=true ;;
-    esac
-  done
-else
-  train=true
-  val=true
-fi
-
-# Make dir
-d='../datasets/imagenet' # unzip directory
-mkdir -p $d && cd $d
-
-# Download/unzip train
-if [ "$train" == "true" ]; then
-  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
-  mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
-  tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
-  find . -name "*.tar" | while read NAME; do
-    mkdir -p "${NAME%.tar}"
-    tar -xf "${NAME}" -C "${NAME%.tar}"
-    rm -f "${NAME}"
-  done
-  cd ..
-fi
-
-# Download/unzip val
-if [ "$val" == "true" ]; then
-  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
-  mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
-  wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
-fi
-
-# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
-# rm train/n04266014/n04266014_10835.JPEG
-
-# TFRecords (optional)
-# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/GetAllChildrenSizers.js

@@ -1,14 +0,0 @@
-var GetAllChildrenSizers = function (out) {
-    if (out === undefined) {
-        out = [];
-    }
-    var startIdx = out.length;
-    var children = this.getChildrenSizers(out);
-    var endIdx = out.length;
-    for (var i = startIdx; i < endIdx; i++) {
-        children[i].getAllChildrenSizers(out);
-    }
-
-    return out;
-}
-export default GetAllChildrenSizers;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/RemoveChildMethods.js

@@ -1,39 +0,0 @@
-import RemoveChild from './utils/RemoveChild.js';
-import GetParentSizerMethods from './GetParentSizerMethods.js';
-
-const RemoveItem = Phaser.Utils.Array.Remove;
-
-export default {
-    removeFromParentSizer() {
-        var parent = GetParentSizerMethods.getParentSizer(gameObject);
-        if (parent) {
-            parent.remove(this);
-        }
-        return this;
-    },
-
-    removeBackground(gameObject, destroyChild) {
-        if (this.backgroundChildren === undefined) {
-            return this;
-        }
-
-        if (this.getParentSizer(gameObject) !== this) {
-            return this;
-        }
-
-        RemoveItem(this.backgroundChildren, gameObject);
-        RemoveChild.call(this, gameObject, destroyChild);
-        return this;
-    },
-
-    removeAllBackgrounds(destroyChild) {
-        if (this.backgroundChildren === undefined) {
-            return this;
-        }
-
-        for (var i = this.backgroundChildren.length - 1; i >= 0; i--) {
-            this.remove(this.backgroundChildren[i], destroyChild);
-        }
-        return this;
-    },
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/intouching/InTouching.js

@@ -1,2 +0,0 @@
-import InTouching from '../../../plugins/intouching.js'
-export default InTouching;

spaces/AlekseyKorshuk/rugpt3/README.md

@@ -1,37 +0,0 @@
----
-title: Rugpt3
-emoji: 📚
-colorFrom: green
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/facial_recognition/model_irse.py

@@ -1,84 +0,0 @@
-from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
-from models.facial_recognition.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
-
-"""
-Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Backbone(Module):
-	def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
-		super(Backbone, self).__init__()
-		assert input_size in [112, 224], "input_size should be 112 or 224"
-		assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
-		assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
-		blocks = get_blocks(num_layers)
-		if mode == 'ir':
-			unit_module = bottleneck_IR
-		elif mode == 'ir_se':
-			unit_module = bottleneck_IR_SE
-		self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
-									  BatchNorm2d(64),
-									  PReLU(64))
-		if input_size == 112:
-			self.output_layer = Sequential(BatchNorm2d(512),
-			                               Dropout(drop_ratio),
-			                               Flatten(),
-			                               Linear(512 * 7 * 7, 512),
-			                               BatchNorm1d(512, affine=affine))
-		else:
-			self.output_layer = Sequential(BatchNorm2d(512),
-			                               Dropout(drop_ratio),
-			                               Flatten(),
-			                               Linear(512 * 14 * 14, 512),
-			                               BatchNorm1d(512, affine=affine))
-
-		modules = []
-		for block in blocks:
-			for bottleneck in block:
-				modules.append(unit_module(bottleneck.in_channel,
-										   bottleneck.depth,
-										   bottleneck.stride))
-		self.body = Sequential(*modules)
-
-	def forward(self, x):
-		x = self.input_layer(x)
-		x = self.body(x)
-		x = self.output_layer(x)
-		return l2_norm(x)
-
-
-def IR_50(input_size):
-	"""Constructs a ir-50 model."""
-	model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
-	return model
-
-
-def IR_101(input_size):
-	"""Constructs a ir-101 model."""
-	model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
-	return model
-
-
-def IR_152(input_size):
-	"""Constructs a ir-152 model."""
-	model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
-	return model
-
-
-def IR_SE_50(input_size):
-	"""Constructs a ir_se-50 model."""
-	model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
-	return model
-
-
-def IR_SE_101(input_size):
-	"""Constructs a ir_se-101 model."""
-	model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
-	return model
-
-
-def IR_SE_152(input_size):
-	"""Constructs a ir_se-152 model."""
-	model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
-	return model

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

@@ -1,103 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "upfirdn2d.h"
-
-//------------------------------------------------------------------------
-
-static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
-    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
-    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
-    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
-    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
-    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
-    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
-    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
-    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
-
-    // Initialize CUDA kernel parameters.
-    upfirdn2d_kernel_params p;
-    p.x             = x.data_ptr();
-    p.f             = f.data_ptr<float>();
-    p.y             = y.data_ptr();
-    p.up            = make_int2(upx, upy);
-    p.down          = make_int2(downx, downy);
-    p.pad0          = make_int2(padx0, pady0);
-    p.flip          = (flip) ? 1 : 0;
-    p.gain          = gain;
-    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
-    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
-    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
-    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
-    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
-    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
-    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
-
-    // Choose CUDA kernel.
-    upfirdn2d_kernel_spec spec;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        spec = choose_upfirdn2d_kernel<scalar_t>(p);
-    });
-
-    // Set looping options.
-    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
-    p.loopMinor     = spec.loopMinor;
-    p.loopX         = spec.loopX;
-    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
-    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
-
-    // Compute grid size.
-    dim3 blockSize, gridSize;
-    if (spec.tileOutW < 0) // large
-    {
-        blockSize = dim3(4, 32, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
-            p.launchMajor);
-    }
-    else // small
-    {
-        blockSize = dim3(256, 1, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
-            p.launchMajor);
-    }
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("upfirdn2d", &upfirdn2d);
-}
-
-//------------------------------------------------------------------------

spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_configs/global_config.py

@@ -1,12 +0,0 @@
-# Device
-cuda_visible_devices = '0'
-device = 'cuda:0'
-
-# Logs
-training_step = 1
-image_rec_result_log_snapshot = 100
-pivotal_training_steps = 0
-model_snapshot_interval = 400
-
-# Run name to be updated during PTI
-run_name = 'exp'

spaces/Anar0140/6.AI.Dashboard.Wiki.Chat.Cognitive.HTML5/style.css

@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/training/text2image.md

@@ -1,277 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-
-# Text-to-image
-
-<Tip warning={true}>
-
-The text-to-image fine-tuning script is experimental. It's easy to overfit and run into issues like catastrophic forgetting. We recommend you explore different hyperparameters to get the best results on your dataset.
-
-</Tip>
-
-Text-to-image models like Stable Diffusion generate an image from a text prompt. This guide will show you how to finetune the [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) model on your own dataset with PyTorch and Flax. All the training scripts for text-to-image finetuning used in this guide can be found in this [repository](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) if you're interested in taking a closer look.
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-```bash
-pip install git+https://github.com/huggingface/diffusers.git
-pip install -U -r requirements.txt
-```
-
-And initialize an [🤗 Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-If you have already cloned the repo, then you won't need to go through these steps. Instead, you can pass the path to your local checkout to the training script and it will be loaded from there.
-
-## Hardware requirements
-
-Using `gradient_checkpointing` and `mixed_precision`, it should be possible to finetune the model on a single 24GB GPU. For higher `batch_size`'s and faster training, it's better to use GPUs with more than 30GB of GPU memory. You can also use JAX/Flax for fine-tuning on TPUs or GPUs, which will be covered [below](#flax-jax-finetuning).
-
-You can reduce your memory footprint even more by enabling memory efficient attention with xFormers. Make sure you have [xFormers installed](./optimization/xformers) and pass the `--enable_xformers_memory_efficient_attention` flag to the training script.
-
-xFormers is not available for Flax.
-
-## Upload model to Hub
-
-Store your model on the Hub by adding the following argument to the training script:
-
-```bash
-  --push_to_hub
-```
-
-## Save and load checkpoints
-
-It is a good idea to regularly save checkpoints in case anything happens during training. To save a checkpoint, pass the following argument to the training script:
-
-```bash
-  --checkpointing_steps=500
-```
-
-Every 500 steps, the full training state is saved in a subfolder in the `output_dir`. The checkpoint has the format `checkpoint-` followed by the number of steps trained so far. For example, `checkpoint-1500` is a checkpoint saved after 1500 training steps.
-
-To load a checkpoint to resume training, pass the argument `--resume_from_checkpoint` to the training script and specify the checkpoint you want to resume from. For example, the following argument resumes training from the checkpoint saved after 1500 training steps:
-
-```bash
-  --resume_from_checkpoint="checkpoint-1500"
-```
-
-## Fine-tuning
-
-<frameworkcontent>
-<pt>
-Launch the [PyTorch training script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) for a fine-tuning run on the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset like this.
-
-Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument.
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export dataset_name="lambdalabs/pokemon-blip-captions"
-
-accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --dataset_name=$dataset_name \
-  --use_ema \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --gradient_checkpointing \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --output_dir="sd-pokemon-model" \
-  --push_to_hub
-```
-
-To finetune on your own dataset, prepare the dataset according to the format required by 🤗 [Datasets](https://huggingface.co/docs/datasets/index). You can [upload your dataset to the Hub](https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub), or you can [prepare a local folder with your files](https://huggingface.co/docs/datasets/image_dataset#imagefolder).
-
-Modify the script if you want to use custom loading logic. We left pointers in the code in the appropriate places to help you. 🤗 The example script below shows how to finetune on a local dataset in `TRAIN_DIR` and where to save the model to in `OUTPUT_DIR`:
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export TRAIN_DIR="path_to_your_dataset"
-export OUTPUT_DIR="path_to_save_model"
-
-accelerate launch train_text_to_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --train_data_dir=$TRAIN_DIR \
-  --use_ema \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --gradient_checkpointing \
-  --mixed_precision="fp16" \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --lr_scheduler="constant" 
-  --lr_warmup_steps=0 \
-  --output_dir=${OUTPUT_DIR} \
-  --push_to_hub
-```
-
-#### Training with multiple GPUs
-
-`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)
-for running distributed training with `accelerate`. Here is an example command:
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export dataset_name="lambdalabs/pokemon-blip-captions"
-
-accelerate launch --mixed_precision="fp16" --multi_gpu  train_text_to_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --dataset_name=$dataset_name \
-  --use_ema \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --gradient_checkpointing \
-  --max_train_steps=15000 \ 
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --output_dir="sd-pokemon-model" \
-  --push_to_hub
-```
-
-</pt>
-<jax>
-With Flax, it's possible to train a Stable Diffusion model faster on TPUs and GPUs thanks to [@duongna211](https://github.com/duongna21). This is very efficient on TPU hardware but works great on GPUs too. The Flax training script doesn't support features like gradient checkpointing or gradient accumulation yet, so you'll need a GPU with at least 30GB of memory or a TPU v3.
-
-Before running the script, make sure you have the requirements installed:
-
-```bash
-pip install -U -r requirements_flax.txt
-```
-
-Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument.
-
-Now you can launch the [Flax training script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_flax.py) like this:
-
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-export dataset_name="lambdalabs/pokemon-blip-captions"
-
-python train_text_to_image_flax.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --dataset_name=$dataset_name \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --output_dir="sd-pokemon-model" \
-  --push_to_hub
-```
-
-To finetune on your own dataset, prepare the dataset according to the format required by 🤗 [Datasets](https://huggingface.co/docs/datasets/index). You can [upload your dataset to the Hub](https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub), or you can [prepare a local folder with your files](https://huggingface.co/docs/datasets/image_dataset#imagefolder).
-
-Modify the script if you want to use custom loading logic. We left pointers in the code in the appropriate places to help you. 🤗 The example script below shows how to finetune on a local dataset in `TRAIN_DIR`:
-
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export TRAIN_DIR="path_to_your_dataset"
-
-python train_text_to_image_flax.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --train_data_dir=$TRAIN_DIR \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --mixed_precision="fp16" \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --output_dir="sd-pokemon-model" \
-  --push_to_hub
-```
-</jax>
-</frameworkcontent>
-
-## Training with Min-SNR weighting
-
-We support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps to achieve faster convergence
-by rebalancing the loss. In order to use it, one needs to set the `--snr_gamma` argument. The recommended
-value when using it is 5.0. 
-
-You can find [this project on Weights and Biases](https://wandb.ai/sayakpaul/text2image-finetune-minsnr) that compares the loss surfaces of the following setups:
-
-* Training without the Min-SNR weighting strategy
-* Training with the Min-SNR weighting strategy (`snr_gamma` set to 5.0)
-* Training with the Min-SNR weighting strategy (`snr_gamma` set to 1.0)
-
-For our small Pokemons dataset, the effects of Min-SNR weighting strategy might not appear to be pronounced, but for larger datasets, we believe the effects will be more pronounced.
-
-Also, note that in this example, we either predict `epsilon` (i.e., the noise) or the `v_prediction`. For both of these cases, the formulation of the Min-SNR weighting strategy that we have used holds. 
-
-<Tip warning={true}>
-
-Training with Min-SNR weighting strategy is only supported in PyTorch.
-
-</Tip>
-
-## LoRA
-
-You can also use Low-Rank Adaptation of Large Language Models (LoRA), a fine-tuning technique for accelerating training large models, for fine-tuning text-to-image models. For more details, take a look at the [LoRA training](lora#text-to-image) guide.
-
-## Inference
-
-Now you can load the fine-tuned model for inference by passing the model path or model name on the Hub to the [`StableDiffusionPipeline`]:
-
-<frameworkcontent>
-<pt>
-```python
-from diffusers import StableDiffusionPipeline
-
-model_path = "path_to_saved_model"
-pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
-pipe.to("cuda")
-
-image = pipe(prompt="yoda").images[0]
-image.save("yoda-pokemon.png")
-```
-</pt>
-<jax>
-```python
-import jax
-import numpy as np
-from flax.jax_utils import replicate
-from flax.training.common_utils import shard
-from diffusers import FlaxStableDiffusionPipeline
-
-model_path = "path_to_saved_model"
-pipe, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)
-
-prompt = "yoda pokemon"
-prng_seed = jax.random.PRNGKey(0)
-num_inference_steps = 50
-
-num_samples = jax.device_count()
-prompt = num_samples * [prompt]
-prompt_ids = pipeline.prepare_inputs(prompt)
-
-# shard inputs and rng
-params = replicate(params)
-prng_seed = jax.random.split(prng_seed, jax.device_count())
-prompt_ids = shard(prompt_ids)
-
-images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
-images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
-image.save("yoda-pokemon.png")
-```
-</jax>
-</frameworkcontent>

spaces/Andy1621/uniformer_image_detection/mmdet/models/necks/hrfpn.py

@@ -1,102 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, caffe2_xavier_init
-from torch.utils.checkpoint import checkpoint
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class HRFPN(nn.Module):
-    """HRFPN (High Resolution Feature Pyramids)
-
-    paper: `High-Resolution Representations for Labeling Pixels and Regions
-    <https://arxiv.org/abs/1904.04514>`_.
-
-    Args:
-        in_channels (list): number of channels for each branch.
-        out_channels (int): output channels of feature pyramids.
-        num_outs (int): number of output stages.
-        pooling_type (str): pooling for generating feature pyramids
-            from {MAX, AVG}.
-        conv_cfg (dict): dictionary to construct and config conv layer.
-        norm_cfg (dict): dictionary to construct and config norm layer.
-        with_cp  (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed.
-        stride (int): stride of 3x3 convolutional layers
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_outs=5,
-                 pooling_type='AVG',
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 with_cp=False,
-                 stride=1):
-        super(HRFPN, self).__init__()
-        assert isinstance(in_channels, list)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_ins = len(in_channels)
-        self.num_outs = num_outs
-        self.with_cp = with_cp
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-
-        self.reduction_conv = ConvModule(
-            sum(in_channels),
-            out_channels,
-            kernel_size=1,
-            conv_cfg=self.conv_cfg,
-            act_cfg=None)
-
-        self.fpn_convs = nn.ModuleList()
-        for i in range(self.num_outs):
-            self.fpn_convs.append(
-                ConvModule(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    padding=1,
-                    stride=stride,
-                    conv_cfg=self.conv_cfg,
-                    act_cfg=None))
-
-        if pooling_type == 'MAX':
-            self.pooling = F.max_pool2d
-        else:
-            self.pooling = F.avg_pool2d
-
-    def init_weights(self):
-        """Initialize the weights of module."""
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                caffe2_xavier_init(m)
-
-    def forward(self, inputs):
-        """Forward function."""
-        assert len(inputs) == self.num_ins
-        outs = [inputs[0]]
-        for i in range(1, self.num_ins):
-            outs.append(
-                F.interpolate(inputs[i], scale_factor=2**i, mode='bilinear'))
-        out = torch.cat(outs, dim=1)
-        if out.requires_grad and self.with_cp:
-            out = checkpoint(self.reduction_conv, out)
-        else:
-            out = self.reduction_conv(out)
-        outs = [out]
-        for i in range(1, self.num_outs):
-            outs.append(self.pooling(out, kernel_size=2**i, stride=2**i))
-        outputs = []
-
-        for i in range(self.num_outs):
-            if outs[i].requires_grad and self.with_cp:
-                tmp_out = checkpoint(self.fpn_convs[i], outs[i])
-            else:
-                tmp_out = self.fpn_convs[i](outs[i])
-            outputs.append(tmp_out)
-        return tuple(outputs)

spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py

@@ -1,9 +0,0 @@
-_base_ = './ocrnet_hr18_512x512_80k_ade20k.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w18_small',
-    backbone=dict(
-        extra=dict(
-            stage1=dict(num_blocks=(2, )),
-            stage2=dict(num_blocks=(2, 2)),
-            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
-            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

spaces/AnishKumbhar/ChatBot/README.md

@@ -1,13 +0,0 @@
----
-title: ChatBot
-emoji: 📚
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: llama2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ankush05/Newcode/README.md

@@ -1,12 +0,0 @@
----
-title: Newcode
-emoji: 📚
-colorFrom: gray
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.27.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/diffusionmodules/util.py

@@ -1,270 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
-
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-        ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
-                                   "dtype": torch.get_autocast_gpu_dtype(),
-                                   "cache_enabled": torch.is_autocast_cache_enabled()}
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad(), \
-                torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=timesteps.device)
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-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()

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

@@ -1,18 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""CacheControl import Interface.
-
-Make it easy to import from cachecontrol without long namespaces.
-"""
-__author__ = "Eric Larson"
-__email__ = "[email protected]"
-__version__ = "0.12.11"
-
-from .wrapper import CacheControl
-from .adapter import CacheControlAdapter
-from .controller import CacheController
-
-import logging
-logging.getLogger(__name__).addHandler(logging.NullHandler())

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/util/url.py

@@ -1,435 +0,0 @@
-from __future__ import absolute_import
-
-import re
-from collections import namedtuple
-
-from ..exceptions import LocationParseError
-from ..packages import six
-
-url_attrs = ["scheme", "auth", "host", "port", "path", "query", "fragment"]
-
-# We only want to normalize urls with an HTTP(S) scheme.
-# urllib3 infers URLs without a scheme (None) to be http.
-NORMALIZABLE_SCHEMES = ("http", "https", None)
-
-# Almost all of these patterns were derived from the
-# 'rfc3986' module: https://github.com/python-hyper/rfc3986
-PERCENT_RE = re.compile(r"%[a-fA-F0-9]{2}")
-SCHEME_RE = re.compile(r"^(?:[a-zA-Z][a-zA-Z0-9+-]*:|/)")
-URI_RE = re.compile(
-    r"^(?:([a-zA-Z][a-zA-Z0-9+.-]*):)?"
-    r"(?://([^\\/?#]*))?"
-    r"([^?#]*)"
-    r"(?:\?([^#]*))?"
-    r"(?:#(.*))?$",
-    re.UNICODE | re.DOTALL,
-)
-
-IPV4_PAT = r"(?:[0-9]{1,3}\.){3}[0-9]{1,3}"
-HEX_PAT = "[0-9A-Fa-f]{1,4}"
-LS32_PAT = "(?:{hex}:{hex}|{ipv4})".format(hex=HEX_PAT, ipv4=IPV4_PAT)
-_subs = {"hex": HEX_PAT, "ls32": LS32_PAT}
-_variations = [
-    #                            6( h16 ":" ) ls32
-    "(?:%(hex)s:){6}%(ls32)s",
-    #                       "::" 5( h16 ":" ) ls32
-    "::(?:%(hex)s:){5}%(ls32)s",
-    # [               h16 ] "::" 4( h16 ":" ) ls32
-    "(?:%(hex)s)?::(?:%(hex)s:){4}%(ls32)s",
-    # [ *1( h16 ":" ) h16 ] "::" 3( h16 ":" ) ls32
-    "(?:(?:%(hex)s:)?%(hex)s)?::(?:%(hex)s:){3}%(ls32)s",
-    # [ *2( h16 ":" ) h16 ] "::" 2( h16 ":" ) ls32
-    "(?:(?:%(hex)s:){0,2}%(hex)s)?::(?:%(hex)s:){2}%(ls32)s",
-    # [ *3( h16 ":" ) h16 ] "::"    h16 ":"   ls32
-    "(?:(?:%(hex)s:){0,3}%(hex)s)?::%(hex)s:%(ls32)s",
-    # [ *4( h16 ":" ) h16 ] "::"              ls32
-    "(?:(?:%(hex)s:){0,4}%(hex)s)?::%(ls32)s",
-    # [ *5( h16 ":" ) h16 ] "::"              h16
-    "(?:(?:%(hex)s:){0,5}%(hex)s)?::%(hex)s",
-    # [ *6( h16 ":" ) h16 ] "::"
-    "(?:(?:%(hex)s:){0,6}%(hex)s)?::",
-]
-
-UNRESERVED_PAT = r"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._\-~"
-IPV6_PAT = "(?:" + "|".join([x % _subs for x in _variations]) + ")"
-ZONE_ID_PAT = "(?:%25|%)(?:[" + UNRESERVED_PAT + "]|%[a-fA-F0-9]{2})+"
-IPV6_ADDRZ_PAT = r"\[" + IPV6_PAT + r"(?:" + ZONE_ID_PAT + r")?\]"
-REG_NAME_PAT = r"(?:[^\[\]%:/?#]|%[a-fA-F0-9]{2})*"
-TARGET_RE = re.compile(r"^(/[^?#]*)(?:\?([^#]*))?(?:#.*)?$")
-
-IPV4_RE = re.compile("^" + IPV4_PAT + "$")
-IPV6_RE = re.compile("^" + IPV6_PAT + "$")
-IPV6_ADDRZ_RE = re.compile("^" + IPV6_ADDRZ_PAT + "$")
-BRACELESS_IPV6_ADDRZ_RE = re.compile("^" + IPV6_ADDRZ_PAT[2:-2] + "$")
-ZONE_ID_RE = re.compile("(" + ZONE_ID_PAT + r")\]$")
-
-_HOST_PORT_PAT = ("^(%s|%s|%s)(?::0*?(|0|[1-9][0-9]{0,4}))?$") % (
-    REG_NAME_PAT,
-    IPV4_PAT,
-    IPV6_ADDRZ_PAT,
-)
-_HOST_PORT_RE = re.compile(_HOST_PORT_PAT, re.UNICODE | re.DOTALL)
-
-UNRESERVED_CHARS = set(
-    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._-~"
-)
-SUB_DELIM_CHARS = set("!$&'()*+,;=")
-USERINFO_CHARS = UNRESERVED_CHARS | SUB_DELIM_CHARS | {":"}
-PATH_CHARS = USERINFO_CHARS | {"@", "/"}
-QUERY_CHARS = FRAGMENT_CHARS = PATH_CHARS | {"?"}
-
-
-class Url(namedtuple("Url", url_attrs)):
-    """
-    Data structure for representing an HTTP URL. Used as a return value for
-    :func:`parse_url`. Both the scheme and host are normalized as they are
-    both case-insensitive according to RFC 3986.
-    """
-
-    __slots__ = ()
-
-    def __new__(
-        cls,
-        scheme=None,
-        auth=None,
-        host=None,
-        port=None,
-        path=None,
-        query=None,
-        fragment=None,
-    ):
-        if path and not path.startswith("/"):
-            path = "/" + path
-        if scheme is not None:
-            scheme = scheme.lower()
-        return super(Url, cls).__new__(
-            cls, scheme, auth, host, port, path, query, fragment
-        )
-
-    @property
-    def hostname(self):
-        """For backwards-compatibility with urlparse. We're nice like that."""
-        return self.host
-
-    @property
-    def request_uri(self):
-        """Absolute path including the query string."""
-        uri = self.path or "/"
-
-        if self.query is not None:
-            uri += "?" + self.query
-
-        return uri
-
-    @property
-    def netloc(self):
-        """Network location including host and port"""
-        if self.port:
-            return "%s:%d" % (self.host, self.port)
-        return self.host
-
-    @property
-    def url(self):
-        """
-        Convert self into a url
-
-        This function should more or less round-trip with :func:`.parse_url`. The
-        returned url may not be exactly the same as the url inputted to
-        :func:`.parse_url`, but it should be equivalent by the RFC (e.g., urls
-        with a blank port will have : removed).
-
-        Example: ::
-
-            >>> U = parse_url('http://google.com/mail/')
-            >>> U.url
-            'http://google.com/mail/'
-            >>> Url('http', 'username:password', 'host.com', 80,
-            ... '/path', 'query', 'fragment').url
-            'http://username:[email protected]:80/path?query#fragment'
-        """
-        scheme, auth, host, port, path, query, fragment = self
-        url = u""
-
-        # We use "is not None" we want things to happen with empty strings (or 0 port)
-        if scheme is not None:
-            url += scheme + u"://"
-        if auth is not None:
-            url += auth + u"@"
-        if host is not None:
-            url += host
-        if port is not None:
-            url += u":" + str(port)
-        if path is not None:
-            url += path
-        if query is not None:
-            url += u"?" + query
-        if fragment is not None:
-            url += u"#" + fragment
-
-        return url
-
-    def __str__(self):
-        return self.url
-
-
-def split_first(s, delims):
-    """
-    .. deprecated:: 1.25
-
-    Given a string and an iterable of delimiters, split on the first found
-    delimiter. Return two split parts and the matched delimiter.
-
-    If not found, then the first part is the full input string.
-
-    Example::
-
-        >>> split_first('foo/bar?baz', '?/=')
-        ('foo', 'bar?baz', '/')
-        >>> split_first('foo/bar?baz', '123')
-        ('foo/bar?baz', '', None)
-
-    Scales linearly with number of delims. Not ideal for large number of delims.
-    """
-    min_idx = None
-    min_delim = None
-    for d in delims:
-        idx = s.find(d)
-        if idx < 0:
-            continue
-
-        if min_idx is None or idx < min_idx:
-            min_idx = idx
-            min_delim = d
-
-    if min_idx is None or min_idx < 0:
-        return s, "", None
-
-    return s[:min_idx], s[min_idx + 1 :], min_delim
-
-
-def _encode_invalid_chars(component, allowed_chars, encoding="utf-8"):
-    """Percent-encodes a URI component without reapplying
-    onto an already percent-encoded component.
-    """
-    if component is None:
-        return component
-
-    component = six.ensure_text(component)
-
-    # Normalize existing percent-encoded bytes.
-    # Try to see if the component we're encoding is already percent-encoded
-    # so we can skip all '%' characters but still encode all others.
-    component, percent_encodings = PERCENT_RE.subn(
-        lambda match: match.group(0).upper(), component
-    )
-
-    uri_bytes = component.encode("utf-8", "surrogatepass")
-    is_percent_encoded = percent_encodings == uri_bytes.count(b"%")
-    encoded_component = bytearray()
-
-    for i in range(0, len(uri_bytes)):
-        # Will return a single character bytestring on both Python 2 & 3
-        byte = uri_bytes[i : i + 1]
-        byte_ord = ord(byte)
-        if (is_percent_encoded and byte == b"%") or (
-            byte_ord < 128 and byte.decode() in allowed_chars
-        ):
-            encoded_component += byte
-            continue
-        encoded_component.extend(b"%" + (hex(byte_ord)[2:].encode().zfill(2).upper()))
-
-    return encoded_component.decode(encoding)
-
-
-def _remove_path_dot_segments(path):
-    # See http://tools.ietf.org/html/rfc3986#section-5.2.4 for pseudo-code
-    segments = path.split("/")  # Turn the path into a list of segments
-    output = []  # Initialize the variable to use to store output
-
-    for segment in segments:
-        # '.' is the current directory, so ignore it, it is superfluous
-        if segment == ".":
-            continue
-        # Anything other than '..', should be appended to the output
-        elif segment != "..":
-            output.append(segment)
-        # In this case segment == '..', if we can, we should pop the last
-        # element
-        elif output:
-            output.pop()
-
-    # If the path starts with '/' and the output is empty or the first string
-    # is non-empty
-    if path.startswith("/") and (not output or output[0]):
-        output.insert(0, "")
-
-    # If the path starts with '/.' or '/..' ensure we add one more empty
-    # string to add a trailing '/'
-    if path.endswith(("/.", "/..")):
-        output.append("")
-
-    return "/".join(output)
-
-
-def _normalize_host(host, scheme):
-    if host:
-        if isinstance(host, six.binary_type):
-            host = six.ensure_str(host)
-
-        if scheme in NORMALIZABLE_SCHEMES:
-            is_ipv6 = IPV6_ADDRZ_RE.match(host)
-            if is_ipv6:
-                # IPv6 hosts of the form 'a::b%zone' are encoded in a URL as
-                # such per RFC 6874: 'a::b%25zone'. Unquote the ZoneID
-                # separator as necessary to return a valid RFC 4007 scoped IP.
-                match = ZONE_ID_RE.search(host)
-                if match:
-                    start, end = match.span(1)
-                    zone_id = host[start:end]
-
-                    if zone_id.startswith("%25") and zone_id != "%25":
-                        zone_id = zone_id[3:]
-                    else:
-                        zone_id = zone_id[1:]
-                    zone_id = "%" + _encode_invalid_chars(zone_id, UNRESERVED_CHARS)
-                    return host[:start].lower() + zone_id + host[end:]
-                else:
-                    return host.lower()
-            elif not IPV4_RE.match(host):
-                return six.ensure_str(
-                    b".".join([_idna_encode(label) for label in host.split(".")])
-                )
-    return host
-
-
-def _idna_encode(name):
-    if name and any(ord(x) >= 128 for x in name):
-        try:
-            from pip._vendor import idna
-        except ImportError:
-            six.raise_from(
-                LocationParseError("Unable to parse URL without the 'idna' module"),
-                None,
-            )
-        try:
-            return idna.encode(name.lower(), strict=True, std3_rules=True)
-        except idna.IDNAError:
-            six.raise_from(
-                LocationParseError(u"Name '%s' is not a valid IDNA label" % name), None
-            )
-    return name.lower().encode("ascii")
-
-
-def _encode_target(target):
-    """Percent-encodes a request target so that there are no invalid characters"""
-    path, query = TARGET_RE.match(target).groups()
-    target = _encode_invalid_chars(path, PATH_CHARS)
-    query = _encode_invalid_chars(query, QUERY_CHARS)
-    if query is not None:
-        target += "?" + query
-    return target
-
-
-def parse_url(url):
-    """
-    Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
-    performed to parse incomplete urls. Fields not provided will be None.
-    This parser is RFC 3986 and RFC 6874 compliant.
-
-    The parser logic and helper functions are based heavily on
-    work done in the ``rfc3986`` module.
-
-    :param str url: URL to parse into a :class:`.Url` namedtuple.
-
-    Partly backwards-compatible with :mod:`urlparse`.
-
-    Example::
-
-        >>> parse_url('http://google.com/mail/')
-        Url(scheme='http', host='google.com', port=None, path='/mail/', ...)
-        >>> parse_url('google.com:80')
-        Url(scheme=None, host='google.com', port=80, path=None, ...)
-        >>> parse_url('/foo?bar')
-        Url(scheme=None, host=None, port=None, path='/foo', query='bar', ...)
-    """
-    if not url:
-        # Empty
-        return Url()
-
-    source_url = url
-    if not SCHEME_RE.search(url):
-        url = "//" + url
-
-    try:
-        scheme, authority, path, query, fragment = URI_RE.match(url).groups()
-        normalize_uri = scheme is None or scheme.lower() in NORMALIZABLE_SCHEMES
-
-        if scheme:
-            scheme = scheme.lower()
-
-        if authority:
-            auth, _, host_port = authority.rpartition("@")
-            auth = auth or None
-            host, port = _HOST_PORT_RE.match(host_port).groups()
-            if auth and normalize_uri:
-                auth = _encode_invalid_chars(auth, USERINFO_CHARS)
-            if port == "":
-                port = None
-        else:
-            auth, host, port = None, None, None
-
-        if port is not None:
-            port = int(port)
-            if not (0 <= port <= 65535):
-                raise LocationParseError(url)
-
-        host = _normalize_host(host, scheme)
-
-        if normalize_uri and path:
-            path = _remove_path_dot_segments(path)
-            path = _encode_invalid_chars(path, PATH_CHARS)
-        if normalize_uri and query:
-            query = _encode_invalid_chars(query, QUERY_CHARS)
-        if normalize_uri and fragment:
-            fragment = _encode_invalid_chars(fragment, FRAGMENT_CHARS)
-
-    except (ValueError, AttributeError):
-        return six.raise_from(LocationParseError(source_url), None)
-
-    # For the sake of backwards compatibility we put empty
-    # string values for path if there are any defined values
-    # beyond the path in the URL.
-    # TODO: Remove this when we break backwards compatibility.
-    if not path:
-        if query is not None or fragment is not None:
-            path = ""
-        else:
-            path = None
-
-    # Ensure that each part of the URL is a `str` for
-    # backwards compatibility.
-    if isinstance(url, six.text_type):
-        ensure_func = six.ensure_text
-    else:
-        ensure_func = six.ensure_str
-
-    def ensure_type(x):
-        return x if x is None else ensure_func(x)
-
-    return Url(
-        scheme=ensure_type(scheme),
-        auth=ensure_type(auth),
-        host=ensure_type(host),
-        port=port,
-        path=ensure_type(path),
-        query=ensure_type(query),
-        fragment=ensure_type(fragment),
-    )
-
-
-def get_host(url):
-    """
-    Deprecated. Use :func:`parse_url` instead.
-    """
-    p = parse_url(url)
-    return p.scheme or "http", p.hostname, p.port

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/cmd.py

@@ -1,436 +0,0 @@
-"""distutils.cmd
-
-Provides the Command class, the base class for the command classes
-in the distutils.command package.
-"""
-
-import sys
-import os
-import re
-from distutils.errors import DistutilsOptionError
-from distutils import util, dir_util, file_util, archive_util, dep_util
-from distutils import log
-
-
-class Command:
-    """Abstract base class for defining command classes, the "worker bees"
-    of the Distutils.  A useful analogy for command classes is to think of
-    them as subroutines with local variables called "options".  The options
-    are "declared" in 'initialize_options()' and "defined" (given their
-    final values, aka "finalized") in 'finalize_options()', both of which
-    must be defined by every command class.  The distinction between the
-    two is necessary because option values might come from the outside
-    world (command line, config file, ...), and any options dependent on
-    other options must be computed *after* these outside influences have
-    been processed -- hence 'finalize_options()'.  The "body" of the
-    subroutine, where it does all its work based on the values of its
-    options, is the 'run()' method, which must also be implemented by every
-    command class.
-    """
-
-    # 'sub_commands' formalizes the notion of a "family" of commands,
-    # eg. "install" as the parent with sub-commands "install_lib",
-    # "install_headers", etc.  The parent of a family of commands
-    # defines 'sub_commands' as a class attribute; it's a list of
-    #    (command_name : string, predicate : unbound_method | string | None)
-    # tuples, where 'predicate' is a method of the parent command that
-    # determines whether the corresponding command is applicable in the
-    # current situation.  (Eg. we "install_headers" is only applicable if
-    # we have any C header files to install.)  If 'predicate' is None,
-    # that command is always applicable.
-    #
-    # 'sub_commands' is usually defined at the *end* of a class, because
-    # predicates can be unbound methods, so they must already have been
-    # defined.  The canonical example is the "install" command.
-    sub_commands = []
-
-    # -- Creation/initialization methods -------------------------------
-
-    def __init__(self, dist):
-        """Create and initialize a new Command object.  Most importantly,
-        invokes the 'initialize_options()' method, which is the real
-        initializer and depends on the actual command being
-        instantiated.
-        """
-        # late import because of mutual dependence between these classes
-        from distutils.dist import Distribution
-
-        if not isinstance(dist, Distribution):
-            raise TypeError("dist must be a Distribution instance")
-        if self.__class__ is Command:
-            raise RuntimeError("Command is an abstract class")
-
-        self.distribution = dist
-        self.initialize_options()
-
-        # Per-command versions of the global flags, so that the user can
-        # customize Distutils' behaviour command-by-command and let some
-        # commands fall back on the Distribution's behaviour.  None means
-        # "not defined, check self.distribution's copy", while 0 or 1 mean
-        # false and true (duh).  Note that this means figuring out the real
-        # value of each flag is a touch complicated -- hence "self._dry_run"
-        # will be handled by __getattr__, below.
-        # XXX This needs to be fixed.
-        self._dry_run = None
-
-        # verbose is largely ignored, but needs to be set for
-        # backwards compatibility (I think)?
-        self.verbose = dist.verbose
-
-        # Some commands define a 'self.force' option to ignore file
-        # timestamps, but methods defined *here* assume that
-        # 'self.force' exists for all commands.  So define it here
-        # just to be safe.
-        self.force = None
-
-        # The 'help' flag is just used for command-line parsing, so
-        # none of that complicated bureaucracy is needed.
-        self.help = 0
-
-        # 'finalized' records whether or not 'finalize_options()' has been
-        # called.  'finalize_options()' itself should not pay attention to
-        # this flag: it is the business of 'ensure_finalized()', which
-        # always calls 'finalize_options()', to respect/update it.
-        self.finalized = 0
-
-    # XXX A more explicit way to customize dry_run would be better.
-    def __getattr__(self, attr):
-        if attr == 'dry_run':
-            myval = getattr(self, "_" + attr)
-            if myval is None:
-                return getattr(self.distribution, attr)
-            else:
-                return myval
-        else:
-            raise AttributeError(attr)
-
-    def ensure_finalized(self):
-        if not self.finalized:
-            self.finalize_options()
-        self.finalized = 1
-
-    # Subclasses must define:
-    #   initialize_options()
-    #     provide default values for all options; may be customized by
-    #     setup script, by options from config file(s), or by command-line
-    #     options
-    #   finalize_options()
-    #     decide on the final values for all options; this is called
-    #     after all possible intervention from the outside world
-    #     (command-line, option file, etc.) has been processed
-    #   run()
-    #     run the command: do whatever it is we're here to do,
-    #     controlled by the command's various option values
-
-    def initialize_options(self):
-        """Set default values for all the options that this command
-        supports.  Note that these defaults may be overridden by other
-        commands, by the setup script, by config files, or by the
-        command-line.  Thus, this is not the place to code dependencies
-        between options; generally, 'initialize_options()' implementations
-        are just a bunch of "self.foo = None" assignments.
-
-        This method must be implemented by all command classes.
-        """
-        raise RuntimeError(
-            "abstract method -- subclass %s must override" % self.__class__
-        )
-
-    def finalize_options(self):
-        """Set final values for all the options that this command supports.
-        This is always called as late as possible, ie.  after any option
-        assignments from the command-line or from other commands have been
-        done.  Thus, this is the place to code option dependencies: if
-        'foo' depends on 'bar', then it is safe to set 'foo' from 'bar' as
-        long as 'foo' still has the same value it was assigned in
-        'initialize_options()'.
-
-        This method must be implemented by all command classes.
-        """
-        raise RuntimeError(
-            "abstract method -- subclass %s must override" % self.__class__
-        )
-
-    def dump_options(self, header=None, indent=""):
-        from distutils.fancy_getopt import longopt_xlate
-
-        if header is None:
-            header = "command options for '%s':" % self.get_command_name()
-        self.announce(indent + header, level=log.INFO)
-        indent = indent + "  "
-        for (option, _, _) in self.user_options:
-            option = option.translate(longopt_xlate)
-            if option[-1] == "=":
-                option = option[:-1]
-            value = getattr(self, option)
-            self.announce(indent + "{} = {}".format(option, value), level=log.INFO)
-
-    def run(self):
-        """A command's raison d'etre: carry out the action it exists to
-        perform, controlled by the options initialized in
-        'initialize_options()', customized by other commands, the setup
-        script, the command-line, and config files, and finalized in
-        'finalize_options()'.  All terminal output and filesystem
-        interaction should be done by 'run()'.
-
-        This method must be implemented by all command classes.
-        """
-        raise RuntimeError(
-            "abstract method -- subclass %s must override" % self.__class__
-        )
-
-    def announce(self, msg, level=1):
-        """If the current verbosity level is of greater than or equal to
-        'level' print 'msg' to stdout.
-        """
-        log.log(level, msg)
-
-    def debug_print(self, msg):
-        """Print 'msg' to stdout if the global DEBUG (taken from the
-        DISTUTILS_DEBUG environment variable) flag is true.
-        """
-        from distutils.debug import DEBUG
-
-        if DEBUG:
-            print(msg)
-            sys.stdout.flush()
-
-    # -- Option validation methods -------------------------------------
-    # (these are very handy in writing the 'finalize_options()' method)
-    #
-    # NB. the general philosophy here is to ensure that a particular option
-    # value meets certain type and value constraints.  If not, we try to
-    # force it into conformance (eg. if we expect a list but have a string,
-    # split the string on comma and/or whitespace).  If we can't force the
-    # option into conformance, raise DistutilsOptionError.  Thus, command
-    # classes need do nothing more than (eg.)
-    #   self.ensure_string_list('foo')
-    # and they can be guaranteed that thereafter, self.foo will be
-    # a list of strings.
-
-    def _ensure_stringlike(self, option, what, default=None):
-        val = getattr(self, option)
-        if val is None:
-            setattr(self, option, default)
-            return default
-        elif not isinstance(val, str):
-            raise DistutilsOptionError(
-                "'{}' must be a {} (got `{}`)".format(option, what, val)
-            )
-        return val
-
-    def ensure_string(self, option, default=None):
-        """Ensure that 'option' is a string; if not defined, set it to
-        'default'.
-        """
-        self._ensure_stringlike(option, "string", default)
-
-    def ensure_string_list(self, option):
-        r"""Ensure that 'option' is a list of strings.  If 'option' is
-        currently a string, we split it either on /,\s*/ or /\s+/, so
-        "foo bar baz", "foo,bar,baz", and "foo,   bar baz" all become
-        ["foo", "bar", "baz"].
-        """
-        val = getattr(self, option)
-        if val is None:
-            return
-        elif isinstance(val, str):
-            setattr(self, option, re.split(r',\s*|\s+', val))
-        else:
-            if isinstance(val, list):
-                ok = all(isinstance(v, str) for v in val)
-            else:
-                ok = False
-            if not ok:
-                raise DistutilsOptionError(
-                    "'{}' must be a list of strings (got {!r})".format(option, val)
-                )
-
-    def _ensure_tested_string(self, option, tester, what, error_fmt, default=None):
-        val = self._ensure_stringlike(option, what, default)
-        if val is not None and not tester(val):
-            raise DistutilsOptionError(
-                ("error in '%s' option: " + error_fmt) % (option, val)
-            )
-
-    def ensure_filename(self, option):
-        """Ensure that 'option' is the name of an existing file."""
-        self._ensure_tested_string(
-            option, os.path.isfile, "filename", "'%s' does not exist or is not a file"
-        )
-
-    def ensure_dirname(self, option):
-        self._ensure_tested_string(
-            option,
-            os.path.isdir,
-            "directory name",
-            "'%s' does not exist or is not a directory",
-        )
-
-    # -- Convenience methods for commands ------------------------------
-
-    def get_command_name(self):
-        if hasattr(self, 'command_name'):
-            return self.command_name
-        else:
-            return self.__class__.__name__
-
-    def set_undefined_options(self, src_cmd, *option_pairs):
-        """Set the values of any "undefined" options from corresponding
-        option values in some other command object.  "Undefined" here means
-        "is None", which is the convention used to indicate that an option
-        has not been changed between 'initialize_options()' and
-        'finalize_options()'.  Usually called from 'finalize_options()' for
-        options that depend on some other command rather than another
-        option of the same command.  'src_cmd' is the other command from
-        which option values will be taken (a command object will be created
-        for it if necessary); the remaining arguments are
-        '(src_option,dst_option)' tuples which mean "take the value of
-        'src_option' in the 'src_cmd' command object, and copy it to
-        'dst_option' in the current command object".
-        """
-        # Option_pairs: list of (src_option, dst_option) tuples
-        src_cmd_obj = self.distribution.get_command_obj(src_cmd)
-        src_cmd_obj.ensure_finalized()
-        for (src_option, dst_option) in option_pairs:
-            if getattr(self, dst_option) is None:
-                setattr(self, dst_option, getattr(src_cmd_obj, src_option))
-
-    def get_finalized_command(self, command, create=1):
-        """Wrapper around Distribution's 'get_command_obj()' method: find
-        (create if necessary and 'create' is true) the command object for
-        'command', call its 'ensure_finalized()' method, and return the
-        finalized command object.
-        """
-        cmd_obj = self.distribution.get_command_obj(command, create)
-        cmd_obj.ensure_finalized()
-        return cmd_obj
-
-    # XXX rename to 'get_reinitialized_command()'? (should do the
-    # same in dist.py, if so)
-    def reinitialize_command(self, command, reinit_subcommands=0):
-        return self.distribution.reinitialize_command(command, reinit_subcommands)
-
-    def run_command(self, command):
-        """Run some other command: uses the 'run_command()' method of
-        Distribution, which creates and finalizes the command object if
-        necessary and then invokes its 'run()' method.
-        """
-        self.distribution.run_command(command)
-
-    def get_sub_commands(self):
-        """Determine the sub-commands that are relevant in the current
-        distribution (ie., that need to be run).  This is based on the
-        'sub_commands' class attribute: each tuple in that list may include
-        a method that we call to determine if the subcommand needs to be
-        run for the current distribution.  Return a list of command names.
-        """
-        commands = []
-        for (cmd_name, method) in self.sub_commands:
-            if method is None or method(self):
-                commands.append(cmd_name)
-        return commands
-
-    # -- External world manipulation -----------------------------------
-
-    def warn(self, msg):
-        log.warn("warning: %s: %s\n", self.get_command_name(), msg)
-
-    def execute(self, func, args, msg=None, level=1):
-        util.execute(func, args, msg, dry_run=self.dry_run)
-
-    def mkpath(self, name, mode=0o777):
-        dir_util.mkpath(name, mode, dry_run=self.dry_run)
-
-    def copy_file(
-        self, infile, outfile, preserve_mode=1, preserve_times=1, link=None, level=1
-    ):
-        """Copy a file respecting verbose, dry-run and force flags.  (The
-        former two default to whatever is in the Distribution object, and
-        the latter defaults to false for commands that don't define it.)"""
-        return file_util.copy_file(
-            infile,
-            outfile,
-            preserve_mode,
-            preserve_times,
-            not self.force,
-            link,
-            dry_run=self.dry_run,
-        )
-
-    def copy_tree(
-        self,
-        infile,
-        outfile,
-        preserve_mode=1,
-        preserve_times=1,
-        preserve_symlinks=0,
-        level=1,
-    ):
-        """Copy an entire directory tree respecting verbose, dry-run,
-        and force flags.
-        """
-        return dir_util.copy_tree(
-            infile,
-            outfile,
-            preserve_mode,
-            preserve_times,
-            preserve_symlinks,
-            not self.force,
-            dry_run=self.dry_run,
-        )
-
-    def move_file(self, src, dst, level=1):
-        """Move a file respecting dry-run flag."""
-        return file_util.move_file(src, dst, dry_run=self.dry_run)
-
-    def spawn(self, cmd, search_path=1, level=1):
-        """Spawn an external command respecting dry-run flag."""
-        from distutils.spawn import spawn
-
-        spawn(cmd, search_path, dry_run=self.dry_run)
-
-    def make_archive(
-        self, base_name, format, root_dir=None, base_dir=None, owner=None, group=None
-    ):
-        return archive_util.make_archive(
-            base_name,
-            format,
-            root_dir,
-            base_dir,
-            dry_run=self.dry_run,
-            owner=owner,
-            group=group,
-        )
-
-    def make_file(
-        self, infiles, outfile, func, args, exec_msg=None, skip_msg=None, level=1
-    ):
-        """Special case of 'execute()' for operations that process one or
-        more input files and generate one output file.  Works just like
-        'execute()', except the operation is skipped and a different
-        message printed if 'outfile' already exists and is newer than all
-        files listed in 'infiles'.  If the command defined 'self.force',
-        and it is true, then the command is unconditionally run -- does no
-        timestamp checks.
-        """
-        if skip_msg is None:
-            skip_msg = "skipping %s (inputs unchanged)" % outfile
-
-        # Allow 'infiles' to be a single string
-        if isinstance(infiles, str):
-            infiles = (infiles,)
-        elif not isinstance(infiles, (list, tuple)):
-            raise TypeError("'infiles' must be a string, or a list or tuple of strings")
-
-        if exec_msg is None:
-            exec_msg = "generating {} from {}".format(outfile, ', '.join(infiles))
-
-        # If 'outfile' must be regenerated (either because it doesn't
-        # exist, is out-of-date, or the 'force' flag is true) then
-        # perform the action that presumably regenerates it
-        if self.force or dep_util.newer_group(infiles, outfile):
-            self.execute(func, args, exec_msg, level)
-        # Otherwise, print the "skip" message
-        else:
-            log.debug(skip_msg)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/dataset_mapper.py

@@ -1,191 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import logging
-import numpy as np
-from typing import List, Optional, Union
-import torch
-
-from detectron2.config import configurable
-
-from . import detection_utils as utils
-from . import transforms as T
-
-"""
-This file contains the default mapping that's applied to "dataset dicts".
-"""
-
-__all__ = ["DatasetMapper"]
-
-
-class DatasetMapper:
-    """
-    A callable which takes a dataset dict in Detectron2 Dataset format,
-    and map it into a format used by the model.
-
-    This is the default callable to be used to map your dataset dict into training data.
-    You may need to follow it to implement your own one for customized logic,
-    such as a different way to read or transform images.
-    See :doc:`/tutorials/data_loading` for details.
-
-    The callable currently does the following:
-
-    1. Read the image from "file_name"
-    2. Applies cropping/geometric transforms to the image and annotations
-    3. Prepare data and annotations to Tensor and :class:`Instances`
-    """
-
-    @configurable
-    def __init__(
-        self,
-        is_train: bool,
-        *,
-        augmentations: List[Union[T.Augmentation, T.Transform]],
-        image_format: str,
-        use_instance_mask: bool = False,
-        use_keypoint: bool = False,
-        instance_mask_format: str = "polygon",
-        keypoint_hflip_indices: Optional[np.ndarray] = None,
-        precomputed_proposal_topk: Optional[int] = None,
-        recompute_boxes: bool = False,
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            is_train: whether it's used in training or inference
-            augmentations: a list of augmentations or deterministic transforms to apply
-            image_format: an image format supported by :func:`detection_utils.read_image`.
-            use_instance_mask: whether to process instance segmentation annotations, if available
-            use_keypoint: whether to process keypoint annotations if available
-            instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation
-                masks into this format.
-            keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices`
-            precomputed_proposal_topk: if given, will load pre-computed
-                proposals from dataset_dict and keep the top k proposals for each image.
-            recompute_boxes: whether to overwrite bounding box annotations
-                by computing tight bounding boxes from instance mask annotations.
-        """
-        if recompute_boxes:
-            assert use_instance_mask, "recompute_boxes requires instance masks"
-        # fmt: off
-        self.is_train               = is_train
-        self.augmentations          = T.AugmentationList(augmentations)
-        self.image_format           = image_format
-        self.use_instance_mask      = use_instance_mask
-        self.instance_mask_format   = instance_mask_format
-        self.use_keypoint           = use_keypoint
-        self.keypoint_hflip_indices = keypoint_hflip_indices
-        self.proposal_topk          = precomputed_proposal_topk
-        self.recompute_boxes        = recompute_boxes
-        # fmt: on
-        logger = logging.getLogger(__name__)
-        mode = "training" if is_train else "inference"
-        logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}")
-
-    @classmethod
-    def from_config(cls, cfg, is_train: bool = True):
-        augs = utils.build_augmentation(cfg, is_train)
-        if cfg.INPUT.CROP.ENABLED and is_train:
-            augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))
-            recompute_boxes = cfg.MODEL.MASK_ON
-        else:
-            recompute_boxes = False
-
-        ret = {
-            "is_train": is_train,
-            "augmentations": augs,
-            "image_format": cfg.INPUT.FORMAT,
-            "use_instance_mask": cfg.MODEL.MASK_ON,
-            "instance_mask_format": cfg.INPUT.MASK_FORMAT,
-            "use_keypoint": cfg.MODEL.KEYPOINT_ON,
-            "recompute_boxes": recompute_boxes,
-        }
-
-        if cfg.MODEL.KEYPOINT_ON:
-            ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)
-
-        if cfg.MODEL.LOAD_PROPOSALS:
-            ret["precomputed_proposal_topk"] = (
-                cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN
-                if is_train
-                else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST
-            )
-        return ret
-
-    def _transform_annotations(self, dataset_dict, transforms, image_shape):
-        # USER: Modify this if you want to keep them for some reason.
-        for anno in dataset_dict["annotations"]:
-            if not self.use_instance_mask:
-                anno.pop("segmentation", None)
-            if not self.use_keypoint:
-                anno.pop("keypoints", None)
-
-        # USER: Implement additional transformations if you have other types of data
-        annos = [
-            utils.transform_instance_annotations(
-                obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices
-            )
-            for obj in dataset_dict.pop("annotations")
-            if obj.get("iscrowd", 0) == 0
-        ]
-        instances = utils.annotations_to_instances(
-            annos, image_shape, mask_format=self.instance_mask_format
-        )
-
-        # After transforms such as cropping are applied, the bounding box may no longer
-        # tightly bound the object. As an example, imagine a triangle object
-        # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight
-        # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to
-        # the intersection of original bounding box and the cropping box.
-        if self.recompute_boxes:
-            instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
-        dataset_dict["instances"] = utils.filter_empty_instances(instances)
-
-    def __call__(self, dataset_dict):
-        """
-        Args:
-            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
-
-        Returns:
-            dict: a format that builtin models in detectron2 accept
-        """
-        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
-        # USER: Write your own image loading if it's not from a file
-        image = utils.read_image(dataset_dict["file_name"], format=self.image_format)
-        utils.check_image_size(dataset_dict, image)
-
-        # USER: Remove if you don't do semantic/panoptic segmentation.
-        if "sem_seg_file_name" in dataset_dict:
-            sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2)
-        else:
-            sem_seg_gt = None
-
-        aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
-        transforms = self.augmentations(aug_input)
-        image, sem_seg_gt = aug_input.image, aug_input.sem_seg
-
-        image_shape = image.shape[:2]  # h, w
-        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
-        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
-        # Therefore it's important to use torch.Tensor.
-        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
-        if sem_seg_gt is not None:
-            dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
-
-        # USER: Remove if you don't use pre-computed proposals.
-        # Most users would not need this feature.
-        if self.proposal_topk is not None:
-            utils.transform_proposals(
-                dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk
-            )
-
-        if not self.is_train:
-            # USER: Modify this if you want to keep them for some reason.
-            dataset_dict.pop("annotations", None)
-            dataset_dict.pop("sem_seg_file_name", None)
-            return dataset_dict
-
-        if "annotations" in dataset_dict:
-            self._transform_annotations(dataset_dict, transforms, image_shape)
-
-        return dataset_dict

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/build.py

@@ -1,24 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from detectron2.utils.registry import Registry
-
-PROPOSAL_GENERATOR_REGISTRY = Registry("PROPOSAL_GENERATOR")
-PROPOSAL_GENERATOR_REGISTRY.__doc__ = """
-Registry for proposal generator, which produces object proposals from feature maps.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-The call should return a `nn.Module` object.
-"""
-
-from . import rpn, rrpn  # noqa F401 isort:skip
-
-
-def build_proposal_generator(cfg, input_shape):
-    """
-    Build a proposal generator from `cfg.MODEL.PROPOSAL_GENERATOR.NAME`.
-    The name can be "PrecomputedProposals" to use no proposal generator.
-    """
-    name = cfg.MODEL.PROPOSAL_GENERATOR.NAME
-    if name == "PrecomputedProposals":
-        return None
-
-    return PROPOSAL_GENERATOR_REGISTRY.get(name)(cfg, input_shape)

spaces/BetterAPI/BetterChat/src/lib/utils/sum.ts

@@ -1,3 +0,0 @@
-export function sum(nums: number[]): number {
-	return nums.reduce((a, b) => a + b, 0);
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/compat.py

@@ -1,350 +0,0 @@
-# Copyright 2012-2014 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
-#
-# http://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 copy
-import datetime
-import sys
-import inspect
-import warnings
-import hashlib
-from http.client import HTTPMessage
-import logging
-import shlex
-import re
-import os
-from collections import OrderedDict
-from collections.abc import MutableMapping
-from math import floor
-
-from botocore.vendored import six
-from botocore.exceptions import MD5UnavailableError
-from dateutil.tz import tzlocal
-from urllib3 import exceptions
-
-logger = logging.getLogger(__name__)
-
-
-class HTTPHeaders(HTTPMessage):
-    pass
-
-from urllib.parse import (
-    quote,
-    urlencode,
-    unquote,
-    unquote_plus,
-    urlparse,
-    urlsplit,
-    urlunsplit,
-    urljoin,
-    parse_qsl,
-    parse_qs,
-)
-from http.client import HTTPResponse
-from io import IOBase as _IOBase
-from base64 import encodebytes
-from email.utils import formatdate
-from itertools import zip_longest
-file_type = _IOBase
-zip = zip
-
-# In python3, unquote takes a str() object, url decodes it,
-# then takes the bytestring and decodes it to utf-8.
-unquote_str = unquote_plus
-
-def set_socket_timeout(http_response, timeout):
-    """Set the timeout of the socket from an HTTPResponse.
-
-    :param http_response: An instance of ``httplib.HTTPResponse``
-
-    """
-    http_response._fp.fp.raw._sock.settimeout(timeout)
-
-def accepts_kwargs(func):
-    # In python3.4.1, there's backwards incompatible
-    # changes when using getargspec with functools.partials.
-    return inspect.getfullargspec(func)[2]
-
-def ensure_unicode(s, encoding=None, errors=None):
-    # NOOP in Python 3, because every string is already unicode
-    return s
-
-def ensure_bytes(s, encoding='utf-8', errors='strict'):
-    if isinstance(s, str):
-        return s.encode(encoding, errors)
-    if isinstance(s, bytes):
-        return s
-    raise ValueError(f"Expected str or bytes, received {type(s)}.")
-
-
-try:
-    import xml.etree.cElementTree as ETree
-except ImportError:
-    # cElementTree does not exist from Python3.9+
-    import xml.etree.ElementTree as ETree
-XMLParseError = ETree.ParseError
-import json
-
-
-def filter_ssl_warnings():
-    # Ignore warnings related to SNI as it is not being used in validations.
-    warnings.filterwarnings(
-        'ignore',
-        message="A true SSLContext object is not available.*",
-        category=exceptions.InsecurePlatformWarning,
-        module=r".*urllib3\.util\.ssl_",
-    )
-
-
-@classmethod
-def from_dict(cls, d):
-    new_instance = cls()
-    for key, value in d.items():
-        new_instance[key] = value
-    return new_instance
-
-
-@classmethod
-def from_pairs(cls, pairs):
-    new_instance = cls()
-    for key, value in pairs:
-        new_instance[key] = value
-    return new_instance
-
-
-HTTPHeaders.from_dict = from_dict
-HTTPHeaders.from_pairs = from_pairs
-
-
-def copy_kwargs(kwargs):
-    """
-    This used to be a compat shim for 2.6 but is now just an alias.
-    """
-    copy_kwargs = copy.copy(kwargs)
-    return copy_kwargs
-
-
-def total_seconds(delta):
-    """
-    Returns the total seconds in a ``datetime.timedelta``.
-
-    This used to be a compat shim for 2.6 but is now just an alias.
-
-    :param delta: The timedelta object
-    :type delta: ``datetime.timedelta``
-    """
-    return delta.total_seconds()
-
-
-# Checks to see if md5 is available on this system. A given system might not
-# have access to it for various reasons, such as FIPS mode being enabled.
-try:
-    hashlib.md5()
-    MD5_AVAILABLE = True
-except ValueError:
-    MD5_AVAILABLE = False
-
-
-def get_md5(*args, **kwargs):
-    """
-    Attempts to get an md5 hashing object.
-
-    :param raise_error_if_unavailable: raise an error if md5 is unavailable on
-        this system. If False, None will be returned if it is unavailable.
-    :type raise_error_if_unavailable: bool
-    :param args: Args to pass to the MD5 constructor
-    :param kwargs: Key word arguments to pass to the MD5 constructor
-    :return: An MD5 hashing object if available. If it is unavailable, None
-        is returned if raise_error_if_unavailable is set to False.
-    """
-    if MD5_AVAILABLE:
-        return hashlib.md5(*args, **kwargs)
-    else:
-        raise MD5UnavailableError()
-
-
-def compat_shell_split(s, platform=None):
-    if platform is None:
-        platform = sys.platform
-
-    if platform == "win32":
-        return _windows_shell_split(s)
-    else:
-        return shlex.split(s)
-
-
-def _windows_shell_split(s):
-    """Splits up a windows command as the built-in command parser would.
-
-    Windows has potentially bizarre rules depending on where you look. When
-    spawning a process via the Windows C runtime (which is what python does
-    when you call popen) the rules are as follows:
-
-    https://docs.microsoft.com/en-us/cpp/cpp/parsing-cpp-command-line-arguments
-
-    To summarize:
-
-    * Only space and tab are valid delimiters
-    * Double quotes are the only valid quotes
-    * Backslash is interpreted literally unless it is part of a chain that
-      leads up to a double quote. Then the backslashes escape the backslashes,
-      and if there is an odd number the final backslash escapes the quote.
-
-    :param s: The command string to split up into parts.
-    :return: A list of command components.
-    """
-    if not s:
-        return []
-
-    components = []
-    buff = []
-    is_quoted = False
-    num_backslashes = 0
-    for character in s:
-        if character == '\\':
-            # We can't simply append backslashes because we don't know if
-            # they are being used as escape characters or not. Instead we
-            # keep track of how many we've encountered and handle them when
-            # we encounter a different character.
-            num_backslashes += 1
-        elif character == '"':
-            if num_backslashes > 0:
-                # The backslashes are in a chain leading up to a double
-                # quote, so they are escaping each other.
-                buff.append('\\' * int(floor(num_backslashes / 2)))
-                remainder = num_backslashes % 2
-                num_backslashes = 0
-                if remainder == 1:
-                    # The number of backslashes is uneven, so they are also
-                    # escaping the double quote, so it needs to be added to
-                    # the current component buffer.
-                    buff.append('"')
-                    continue
-
-            # We've encountered a double quote that is not escaped,
-            # so we toggle is_quoted.
-            is_quoted = not is_quoted
-
-            # If there are quotes, then we may want an empty string. To be
-            # safe, we add an empty string to the buffer so that we make
-            # sure it sticks around if there's nothing else between quotes.
-            # If there is other stuff between quotes, the empty string will
-            # disappear during the joining process.
-            buff.append('')
-        elif character in [' ', '\t'] and not is_quoted:
-            # Since the backslashes aren't leading up to a quote, we put in
-            # the exact number of backslashes.
-            if num_backslashes > 0:
-                buff.append('\\' * num_backslashes)
-                num_backslashes = 0
-
-            # Excess whitespace is ignored, so only add the components list
-            # if there is anything in the buffer.
-            if buff:
-                components.append(''.join(buff))
-                buff = []
-        else:
-            # Since the backslashes aren't leading up to a quote, we put in
-            # the exact number of backslashes.
-            if num_backslashes > 0:
-                buff.append('\\' * num_backslashes)
-                num_backslashes = 0
-            buff.append(character)
-
-    # Quotes must be terminated.
-    if is_quoted:
-        raise ValueError(f"No closing quotation in string: {s}")
-
-    # There may be some leftover backslashes, so we need to add them in.
-    # There's no quote so we add the exact number.
-    if num_backslashes > 0:
-        buff.append('\\' * num_backslashes)
-
-    # Add the final component in if there is anything in the buffer.
-    if buff:
-        components.append(''.join(buff))
-
-    return components
-
-
-def get_tzinfo_options():
-    # Due to dateutil/dateutil#197, Windows may fail to parse times in the past
-    # with the system clock. We can alternatively fallback to tzwininfo when
-    # this happens, which will get time info from the Windows registry.
-    if sys.platform == 'win32':
-        from dateutil.tz import tzwinlocal
-
-        return (tzlocal, tzwinlocal)
-    else:
-        return (tzlocal,)
-
-
-# Detect if CRT is available for use
-try:
-    import awscrt.auth
-
-    # Allow user opt-out if needed
-    disabled = os.environ.get('BOTO_DISABLE_CRT', "false")
-    HAS_CRT = not disabled.lower() == 'true'
-except ImportError:
-    HAS_CRT = False
-
-
-########################################################
-#              urllib3 compat backports                #
-########################################################
-
-# Vendoring IPv6 validation regex patterns from urllib3
-# https://github.com/urllib3/urllib3/blob/7e856c0/src/urllib3/util/url.py
-IPV4_PAT = r"(?:[0-9]{1,3}\.){3}[0-9]{1,3}"
-IPV4_RE = re.compile("^" + IPV4_PAT + "$")
-HEX_PAT = "[0-9A-Fa-f]{1,4}"
-LS32_PAT = "(?:{hex}:{hex}|{ipv4})".format(hex=HEX_PAT, ipv4=IPV4_PAT)
-_subs = {"hex": HEX_PAT, "ls32": LS32_PAT}
-_variations = [
-    #                            6( h16 ":" ) ls32
-    "(?:%(hex)s:){6}%(ls32)s",
-    #                       "::" 5( h16 ":" ) ls32
-    "::(?:%(hex)s:){5}%(ls32)s",
-    # [               h16 ] "::" 4( h16 ":" ) ls32
-    "(?:%(hex)s)?::(?:%(hex)s:){4}%(ls32)s",
-    # [ *1( h16 ":" ) h16 ] "::" 3( h16 ":" ) ls32
-    "(?:(?:%(hex)s:)?%(hex)s)?::(?:%(hex)s:){3}%(ls32)s",
-    # [ *2( h16 ":" ) h16 ] "::" 2( h16 ":" ) ls32
-    "(?:(?:%(hex)s:){0,2}%(hex)s)?::(?:%(hex)s:){2}%(ls32)s",
-    # [ *3( h16 ":" ) h16 ] "::"    h16 ":"   ls32
-    "(?:(?:%(hex)s:){0,3}%(hex)s)?::%(hex)s:%(ls32)s",
-    # [ *4( h16 ":" ) h16 ] "::"              ls32
-    "(?:(?:%(hex)s:){0,4}%(hex)s)?::%(ls32)s",
-    # [ *5( h16 ":" ) h16 ] "::"              h16
-    "(?:(?:%(hex)s:){0,5}%(hex)s)?::%(hex)s",
-    # [ *6( h16 ":" ) h16 ] "::"
-    "(?:(?:%(hex)s:){0,6}%(hex)s)?::",
-]
-
-UNRESERVED_PAT = (
-    r"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._!\-~"
-)
-IPV6_PAT = "(?:" + "|".join([x % _subs for x in _variations]) + ")"
-ZONE_ID_PAT = "(?:%25|%)(?:[" + UNRESERVED_PAT + "]|%[a-fA-F0-9]{2})+"
-IPV6_ADDRZ_PAT = r"\[" + IPV6_PAT + r"(?:" + ZONE_ID_PAT + r")?\]"
-IPV6_ADDRZ_RE = re.compile("^" + IPV6_ADDRZ_PAT + "$")
-
-# These are the characters that are stripped by post-bpo-43882 urlparse().
-UNSAFE_URL_CHARS = frozenset('\t\r\n')
-
-# Detect if gzip is available for use
-try:
-    import gzip
-    HAS_GZIP = True
-except ImportError:
-    HAS_GZIP = False

spaces/BigSalmon/BackTranslation/app.py

@@ -1,117 +0,0 @@
-from deep_translator import GoogleTranslator
-import streamlit as st
-
-st.set_page_config(page_title='Language Translator (Adaptation of https://github.com/Ompramod9921/Language_translator)')
-
-hide_streamlit_style = """
-            <style>
-            #MainMenu {visibility: hidden;}
-            footer {visibility: hidden;}
-            footer:after {
-            content: 'Adaptation of https://github.com/Ompramod9921/Language_translator (om pram)' 
-            visibility: visible;
-            }
-            </style>
-            """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-
-st.markdown("<h1 style='text-align: center; font-size: 24px; color: voilet;font-family: Droid Sans'>Language Translator (Adaptation of https://github.com/Ompramod9921/Language_translator)</h1>", unsafe_allow_html=True)
-st.write("****")
-
-text = st.text_area("Enter text:",height=None,max_chars=None,key=None,help="Enter your text here -")
-st.write("****")
-
-option1 = st.selectbox('Input language',('english','hindi','afrikaans', 'albanian', 'amharic', 'arabic', 'armenian', 'azerbaijani', 'basque', 'belarusian', 'bengali', 'bosnian', 'bulgarian', 'catalan', 'cebuano', 'chichewa', 'chinese', 'chinese (simplified)', 'chinese (traditional)', 'corsican', 'croatian', 'czech', 'danish', 'dutch', 'esperanto', 'estonian', 'filipino', 'finnish', 'french', 'frisian', 'galician', 'georgian', 'german', 'greek', 'gujarati', 'haitian creole', 'hausa', 'hawaiian', 'hebrew', 'hmong', 'hungarian', 'icelandic', 'igbo', 'indonesian', 'irish', 'italian', 'japanese', 'javanese', 'kannada', 'kazakh', 'khmer', 'korean', 'kurdish (kurmanji)', 'kyrgyz', 'lao', 'latin', 'latvian', 'lithuanian', 'luxembourgish', 'macedonian', 'malagasy', 'malay', 'malayalam', 'maltese', 'maori', 'marathi', 'mongolian', 'myanmar (burmese)', 'nepali', 'norwegian', 'pashto', 'persian', 'polish', 'portuguese', 'punjabi', 'romanian', 'russian', 'samoan', 'scots gaelic', 'serbian', 'sesotho', 'shona', 'sindhi', 'sinhala', 'slovak', 'slovenian', 'somali', 'spanish', 'sundanese', 'swahili', 'swedish', 'tajik', 'tamil', 'telugu', 'thai', 'turkish', 'ukrainian', 'urdu', 'uzbek', 'vietnamese', 'welsh', 'xhosa', 'yiddish', 'yoruba', 'zulu', 'Filipino'))
-option2 = st.selectbox('Output language',('english','hindi','afrikaans', 'albanian', 'amharic', 'arabic', 'armenian', 'azerbaijani', 'basque', 'belarusian', 'bengali', 'bosnian', 'bulgarian', 'catalan', 'cebuano', 'chichewa', 'chinese', 'chinese (simplified)', 'chinese (traditional)', 'corsican', 'croatian', 'czech', 'danish', 'dutch', 'esperanto', 'estonian', 'filipino', 'finnish', 'french', 'frisian', 'galician', 'georgian', 'german', 'greek', 'gujarati', 'haitian creole', 'hausa', 'hawaiian', 'hebrew', 'hmong', 'hungarian', 'icelandic', 'igbo', 'indonesian', 'irish', 'italian', 'japanese', 'javanese', 'kannada', 'kazakh', 'khmer', 'korean', 'kurdish (kurmanji)', 'kyrgyz', 'lao', 'latin', 'latvian', 'lithuanian', 'luxembourgish', 'macedonian', 'malagasy', 'malay', 'malayalam', 'maltese', 'maori', 'marathi', 'mongolian', 'myanmar (burmese)', 'nepali', 'norwegian', 'pashto', 'persian', 'polish', 'portuguese', 'punjabi', 'romanian', 'russian', 'samoan', 'scots gaelic', 'serbian', 'sesotho', 'shona', 'sindhi', 'sinhala', 'slovak', 'slovenian', 'somali', 'spanish', 'sundanese', 'swahili', 'swedish', 'tajik', 'tamil', 'telugu', 'thai', 'turkish', 'ukrainian', 'urdu', 'uzbek', 'vietnamese', 'welsh', 'xhosa', 'yiddish', 'yoruba', 'zulu', 'Filipino'))
-st.write("****")
-
-if st.button('Translate Sentence'):
-    st.write(" ")
-    st.write(" ")
-    if text == "":
-        st.warning('Please **enter text** for translation')
-
-    else:
-        if option1 == option2 :
-            st.error("source and target language can't be the same")
-        else :
-            translated = GoogleTranslator(source=option1,target=option2).translate(text=text)
-            st.write("Translated text -")
-            st.info(str(translated))
-            translated_text = str(translated)
-            back_translated = GoogleTranslator(source=option2,target=option1).translate(text=translated_text)
-            st.write("Back Translated text -")
-            st.info(str(back_translated))
-
-if st.button('Back Translate: Multiple Languages'):
-    st.write(" ")
-    st.write(" ")
-    if text == "":
-        st.warning('Please **enter text** for translation')
-    else:
-        if option1 == option2 :
-            st.error("source and target language can't be the same")
-        else:
-            translated = GoogleTranslator(source=option1,target=option2).translate(text=text)
-            st.write("Translated text -")
-            st.info(str(translated))
-            translated_text = str(translated)
-            back_translated = GoogleTranslator(source=option2,target=option1).translate(text=translated_text)
-            st.write("Back Translated text -")
-            st.info(str(back_translated))
-        
-            translated = GoogleTranslator(source=option1,target="albanian").translate(text=text)
-            st.write("Translated text -")
-            st.info(str(translated))
-            translated_text = str(translated)
-            back_translated = GoogleTranslator(source="albanian",target=option1).translate(text=translated_text)
-            st.write("Back Translated text -")
-            st.info(str(back_translated))
-            
-            translated = GoogleTranslator(source=option1,target="greek").translate(text=text)
-            st.write("Translated text -")
-            st.info(str(translated))
-            translated_text = str(translated)
-            back_translated = GoogleTranslator(source="greek",target=option1).translate(text=translated_text)
-            st.write("Back Translated text -")
-            st.info(str(back_translated))
-            
-            translated = GoogleTranslator(source=option1,target="italian").translate(text=text)
-            st.write("Translated text -")
-            st.info(str(translated))
-            translated_text = str(translated)
-            back_translated = GoogleTranslator(source="italian",target=option1).translate(text=translated_text)
-            st.write("Back Translated text -")
-            st.info(str(back_translated))
-            
-            translated = GoogleTranslator(source=option1,target="polish").translate(text=text)
-            st.write("Translated text -")
-            st.info(str(translated))
-            translated_text = str(translated)
-            back_translated = GoogleTranslator(source="polish",target=option1).translate(text=translated_text)
-            st.write("Back Translated text -")
-            st.info(str(back_translated))
-            
-            translated = GoogleTranslator(source=option1,target="spanish").translate(text=text)
-            st.write("Translated text -")
-            st.info(str(translated))
-            translated_text = str(translated)
-            back_translated = GoogleTranslator(source="spanish",target=option1).translate(text=translated_text)
-            st.write("Back Translated text -")
-            st.info(str(back_translated))
-            
-            translated = GoogleTranslator(source=option1,target="galician").translate(text=text)
-            st.write("Translated text -")
-            st.info(str(translated))
-            translated_text = str(translated)
-            back_translated = GoogleTranslator(source="galician",target=option1).translate(text=translated_text)
-            st.write("Back Translated text -")
-            st.info(str(back_translated))
-            
-            translated = GoogleTranslator(source=option1,target="dutch").translate(text=text)
-            st.write("Translated text -")
-            st.info(str(translated))
-            translated_text = str(translated)
-            back_translated = GoogleTranslator(source="dutch",target=option1).translate(text=translated_text)
-            st.write("Back Translated text -")
-            st.info(str(back_translated))

spaces/CVPR/LIVE/thrust/thrust/detail/static_assert.h

@@ -1,92 +0,0 @@
-/*
- *  Copyright 2008-2018 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.
- */
-
-/*
- * (C) Copyright John Maddock 2000.
- * 
- * Distributed under the Boost Software License, Version 1.0.
- * (See accompanying NOTICE file for the complete license)
- *
- * For more information, see http://www.boost.org
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/detail/preprocessor.h>
-
-namespace thrust
-{
-
-namespace detail
-{
-
-template <typename, bool x>
-struct depend_on_instantiation
-{
-  THRUST_INLINE_INTEGRAL_MEMBER_CONSTANT bool value = x;
-};
-
-#if THRUST_CPP_DIALECT >= 2011
-
-#  if THRUST_CPP_DIALECT >= 2017
-#    define THRUST_STATIC_ASSERT(B)        static_assert(B)
-#  else
-#    define THRUST_STATIC_ASSERT(B)        static_assert(B, "static assertion failed")
-#  endif
-#  define THRUST_STATIC_ASSERT_MSG(B, msg) static_assert(B, msg)
-
-#else // Older than C++11.
-
-// HP aCC cannot deal with missing names for template value parameters.
-template <bool x> struct STATIC_ASSERTION_FAILURE;
-
-template <> struct STATIC_ASSERTION_FAILURE<true> {};
-
-// HP aCC cannot deal with missing names for template value parameters.
-template <int x> struct static_assert_test {};
-
-#if    (  (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC)                  \
-       && (THRUST_GCC_VERSION >= 40800))                                      \
-    || (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_CLANG)
-  // Clang and GCC 4.8+ will complain about this typedef being unused unless we
-  // annotate it as such.
-#  define THRUST_STATIC_ASSERT(B)                                             \
-    typedef ::thrust::detail::static_assert_test<                             \
-      sizeof(::thrust::detail::STATIC_ASSERTION_FAILURE<(bool)(B)>)           \
-    >                                                                         \
-      THRUST_PP_CAT2(thrust_static_assert_typedef_, __LINE__)                 \
-      __attribute__((unused))                                                 \
-    /**/      
-#else
-#  define THRUST_STATIC_ASSERT(B)                                             \
-    typedef ::thrust::detail::static_assert_test<                             \
-      sizeof(::thrust::detail::STATIC_ASSERTION_FAILURE<(bool)(B)>)           \
-    >                                                                         \
-      THRUST_PP_CAT2(thrust_static_assert_typedef_, __LINE__)                 \
-    /**/      
-#endif
-
-#define THRUST_STATIC_ASSERT_MSG(B, msg) THRUST_STATIC_ASSERT(B)
-
-#endif // THRUST_CPP_DIALECT >= 2011
-
-} // namespace detail
-
-} // end namespace thrust
-
-

spaces/CVPR/LIVE/thrust/thrust/detail/type_traits/iterator/is_discard_iterator.h

@@ -1,40 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/iterator/discard_iterator.h>
-
-namespace thrust
-{
-namespace detail
-{
-
-template <typename Iterator>
-struct is_discard_iterator
-  : public thrust::detail::false_type
-{};
-
-template <typename System>
-struct is_discard_iterator< thrust::discard_iterator<System> >
- : public thrust::detail::true_type
-{};
-
-} // end namespace detail
-} // end namespace thrust
-

spaces/CVPR/Text2Human/app.py

@@ -1,158 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import argparse
-import os
-import pathlib
-import subprocess
-
-import gradio as gr
-
-if os.getenv('SYSTEM') == 'spaces':
-    import mim
-
-    mim.uninstall('mmcv-full', confirm_yes=True)
-    mim.install('mmcv-full==1.5.2', is_yes=True)
-
-    with open('patch') as f:
-        subprocess.run('patch -p1'.split(), cwd='Text2Human', stdin=f)
-
-from model import Model
-
-DESCRIPTION = '''# Text2Human
-
-This is an unofficial demo for <a href="https://github.com/yumingj/Text2Human">https://github.com/yumingj/Text2Human</a> made by <a href="https://huggingface.co/spaces/hysts/Text2Human">@hysts</a>.
-You can modify sample steps and seeds. By varying seeds, you can sample different human images under the same pose, shape description, and texture description. The larger the sample steps, the better quality of the generated images. (The default value of sample steps is 256 in the original repo.)
-
-Label image generation step can be skipped. However, in that case, the input label image must be 512x256 in size and must contain only the specified colors.
-'''
-FOOTER = '<img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=hysts.text2human" />'
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    return parser.parse_args()
-
-
-def set_example_image(example: list) -> dict:
-    return gr.Image.update(value=example[0])
-
-
-def set_example_text(example: list) -> dict:
-    return gr.Textbox.update(value=example[0])
-
-
-def main():
-    args = parse_args()
-    model = Model(args.device)
-
-    with gr.Blocks(theme=args.theme, css='style.css') as demo:
-        gr.Markdown(DESCRIPTION)
-
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    input_image = gr.Image(label='Input Pose Image',
-                                           type='pil',
-                                           elem_id='input-image')
-                    pose_data = gr.Variable()
-                with gr.Row():
-                    paths = sorted(pathlib.Path('pose_images').glob('*.png'))
-                    example_images = gr.Dataset(components=[input_image],
-                                                samples=[[path.as_posix()]
-                                                         for path in paths])
-
-                with gr.Row():
-                    shape_text = gr.Textbox(
-                        label='Shape Description',
-                        placeholder=
-                        '''<gender>, <sleeve length>, <length of lower clothing>, <outer clothing type>, <other accessories1>, ...
-Note: The outer clothing type and accessories can be omitted.''')
-                with gr.Row():
-                    shape_example_texts = gr.Dataset(
-                        components=[shape_text],
-                        samples=[['man, sleeveless T-shirt, long pants'],
-                                 ['woman, short-sleeve T-shirt, short jeans']])
-                with gr.Row():
-                    generate_label_button = gr.Button('Generate Label Image')
-
-            with gr.Column():
-                with gr.Row():
-                    label_image = gr.Image(label='Label Image',
-                                           type='numpy',
-                                           elem_id='label-image')
-
-                with gr.Row():
-                    texture_text = gr.Textbox(
-                        label='Texture Description',
-                        placeholder=
-                        '''<upper clothing texture>, <lower clothing texture>, <outer clothing texture>
-Note: Currently, only 5 types of textures are supported, i.e., pure color, stripe/spline, plaid/lattice, floral, denim.'''
-                    )
-                with gr.Row():
-                    texture_example_texts = gr.Dataset(
-                        components=[texture_text],
-                        samples=[['pure color, denim'], ['floral, stripe']])
-                with gr.Row():
-                    sample_steps = gr.Slider(10,
-                                             300,
-                                             value=10,
-                                             step=10,
-                                             label='Sample Steps')
-                with gr.Row():
-                    seed = gr.Slider(0, 1000000, value=0, step=1, label='Seed')
-                with gr.Row():
-                    generate_human_button = gr.Button('Generate Human')
-
-            with gr.Column():
-                with gr.Row():
-                    result = gr.Image(label='Result',
-                                      type='numpy',
-                                      elem_id='result-image')
-
-        gr.Markdown(FOOTER)
-
-        input_image.change(fn=model.process_pose_image,
-                           inputs=input_image,
-                           outputs=pose_data)
-        generate_label_button.click(fn=model.generate_label_image,
-                                    inputs=[
-                                        pose_data,
-                                        shape_text,
-                                    ],
-                                    outputs=label_image)
-        generate_human_button.click(fn=model.generate_human,
-                                    inputs=[
-                                        label_image,
-                                        texture_text,
-                                        sample_steps,
-                                        seed,
-                                    ],
-                                    outputs=result)
-        example_images.click(fn=set_example_image,
-                             inputs=example_images,
-                             outputs=example_images.components)
-        shape_example_texts.click(fn=set_example_text,
-                                  inputs=shape_example_texts,
-                                  outputs=shape_example_texts.components)
-        texture_example_texts.click(fn=set_example_text,
-                                    inputs=texture_example_texts,
-                                    outputs=texture_example_texts.components)
-
-    demo.launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()

spaces/CVPR/WALT/mmdet/core/bbox/match_costs/builder.py

@@ -1,8 +0,0 @@
-from mmcv.utils import Registry, build_from_cfg
-
-MATCH_COST = Registry('Match Cost')
-
-
-def build_match_cost(cfg, default_args=None):
-    """Builder of IoU calculator."""
-    return build_from_cfg(cfg, MATCH_COST, default_args)

spaces/CVPR/WALT/mmdet/models/dense_heads/dense_test_mixins.py

@@ -1,100 +0,0 @@
-from inspect import signature
-
-import torch
-
-from mmdet.core import bbox2result, bbox_mapping_back, multiclass_nms
-
-
-class BBoxTestMixin(object):
-    """Mixin class for test time augmentation of bboxes."""
-
-    def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas):
-        """Merge augmented detection bboxes and scores.
-
-        Args:
-            aug_bboxes (list[Tensor]): shape (n, 4*#class)
-            aug_scores (list[Tensor] or None): shape (n, #class)
-            img_shapes (list[Tensor]): shape (3, ).
-
-        Returns:
-            tuple: (bboxes, scores)
-        """
-        recovered_bboxes = []
-        for bboxes, img_info in zip(aug_bboxes, img_metas):
-            img_shape = img_info[0]['img_shape']
-            scale_factor = img_info[0]['scale_factor']
-            flip = img_info[0]['flip']
-            flip_direction = img_info[0]['flip_direction']
-            bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip,
-                                       flip_direction)
-            recovered_bboxes.append(bboxes)
-        bboxes = torch.cat(recovered_bboxes, dim=0)
-        if aug_scores is None:
-            return bboxes
-        else:
-            scores = torch.cat(aug_scores, dim=0)
-            return bboxes, scores
-
-    def aug_test_bboxes(self, feats, img_metas, rescale=False):
-        """Test det bboxes with test time augmentation.
-
-        Args:
-            feats (list[Tensor]): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxCxHxW,
-                which contains features for all images in the batch.
-            img_metas (list[list[dict]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch. each dict has image information.
-            rescale (bool, optional): Whether to rescale the results.
-                Defaults to False.
-
-        Returns:
-            list[ndarray]: bbox results of each class
-        """
-        # check with_nms argument
-        gb_sig = signature(self.get_bboxes)
-        gb_args = [p.name for p in gb_sig.parameters.values()]
-        if hasattr(self, '_get_bboxes'):
-            gbs_sig = signature(self._get_bboxes)
-        else:
-            gbs_sig = signature(self._get_bboxes_single)
-        gbs_args = [p.name for p in gbs_sig.parameters.values()]
-        assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \
-            f'{self.__class__.__name__}' \
-            ' does not support test-time augmentation'
-
-        aug_bboxes = []
-        aug_scores = []
-        aug_factors = []  # score_factors for NMS
-        for x, img_meta in zip(feats, img_metas):
-            # only one image in the batch
-            outs = self.forward(x)
-            bbox_inputs = outs + (img_meta, self.test_cfg, False, False)
-            bbox_outputs = self.get_bboxes(*bbox_inputs)[0]
-            aug_bboxes.append(bbox_outputs[0])
-            aug_scores.append(bbox_outputs[1])
-            # bbox_outputs of some detectors (e.g., ATSS, FCOS, YOLOv3)
-            # contains additional element to adjust scores before NMS
-            if len(bbox_outputs) >= 3:
-                aug_factors.append(bbox_outputs[2])
-
-        # after merging, bboxes will be rescaled to the original image size
-        merged_bboxes, merged_scores = self.merge_aug_bboxes(
-            aug_bboxes, aug_scores, img_metas)
-        merged_factors = torch.cat(aug_factors, dim=0) if aug_factors else None
-        det_bboxes, det_labels = multiclass_nms(
-            merged_bboxes,
-            merged_scores,
-            self.test_cfg.score_thr,
-            self.test_cfg.nms,
-            self.test_cfg.max_per_img,
-            score_factors=merged_factors)
-
-        if rescale:
-            _det_bboxes = det_bboxes
-        else:
-            _det_bboxes = det_bboxes.clone()
-            _det_bboxes[:, :4] *= det_bboxes.new_tensor(
-                img_metas[0][0]['scale_factor'])
-        bbox_results = bbox2result(_det_bboxes, det_labels, self.num_classes)
-        return bbox_results

spaces/CVPR/regionclip-demo/detectron2/structures/rotated_boxes.py

@@ -1,505 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import math
-from typing import List, Tuple
-import torch
-
-from detectron2.layers.rotated_boxes import pairwise_iou_rotated
-
-from .boxes import Boxes, _maybe_jit_unused
-
-
-class RotatedBoxes(Boxes):
-    """
-    This structure stores a list of rotated boxes as a Nx5 torch.Tensor.
-    It supports some common methods about boxes
-    (`area`, `clip`, `nonempty`, etc),
-    and also behaves like a Tensor
-    (support indexing, `to(device)`, `.device`, and iteration over all boxes)
-    """
-
-    def __init__(self, tensor: torch.Tensor):
-        """
-        Args:
-            tensor (Tensor[float]): a Nx5 matrix.  Each row is
-                (x_center, y_center, width, height, angle),
-                in which angle is represented in degrees.
-                While there's no strict range restriction for it,
-                the recommended principal range is between [-180, 180) degrees.
-
-        Assume we have a horizontal box B = (x_center, y_center, width, height),
-        where width is along the x-axis and height is along the y-axis.
-        The rotated box B_rot (x_center, y_center, width, height, angle)
-        can be seen as:
-
-        1. When angle == 0:
-           B_rot == B
-        2. When angle > 0:
-           B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CCW;
-        3. When angle < 0:
-           B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CW.
-
-        Mathematically, since the right-handed coordinate system for image space
-        is (y, x), where y is top->down and x is left->right, the 4 vertices of the
-        rotated rectangle :math:`(yr_i, xr_i)` (i = 1, 2, 3, 4) can be obtained from
-        the vertices of the horizontal rectangle :math:`(y_i, x_i)` (i = 1, 2, 3, 4)
-        in the following way (:math:`\\theta = angle*\\pi/180` is the angle in radians,
-        :math:`(y_c, x_c)` is the center of the rectangle):
-
-        .. math::
-
-            yr_i = \\cos(\\theta) (y_i - y_c) - \\sin(\\theta) (x_i - x_c) + y_c,
-
-            xr_i = \\sin(\\theta) (y_i - y_c) + \\cos(\\theta) (x_i - x_c) + x_c,
-
-        which is the standard rigid-body rotation transformation.
-
-        Intuitively, the angle is
-        (1) the rotation angle from y-axis in image space
-        to the height vector (top->down in the box's local coordinate system)
-        of the box in CCW, and
-        (2) the rotation angle from x-axis in image space
-        to the width vector (left->right in the box's local coordinate system)
-        of the box in CCW.
-
-        More intuitively, consider the following horizontal box ABCD represented
-        in (x1, y1, x2, y2): (3, 2, 7, 4),
-        covering the [3, 7] x [2, 4] region of the continuous coordinate system
-        which looks like this:
-
-        .. code:: none
-
-            O--------> x
-            |
-            |  A---B
-            |  |   |
-            |  D---C
-            |
-            v y
-
-        Note that each capital letter represents one 0-dimensional geometric point
-        instead of a 'square pixel' here.
-
-        In the example above, using (x, y) to represent a point we have:
-
-        .. math::
-
-            O = (0, 0), A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4)
-
-        We name vector AB = vector DC as the width vector in box's local coordinate system, and
-        vector AD = vector BC as the height vector in box's local coordinate system. Initially,
-        when angle = 0 degree, they're aligned with the positive directions of x-axis and y-axis
-        in the image space, respectively.
-
-        For better illustration, we denote the center of the box as E,
-
-        .. code:: none
-
-            O--------> x
-            |
-            |  A---B
-            |  | E |
-            |  D---C
-            |
-            v y
-
-        where the center E = ((3+7)/2, (2+4)/2) = (5, 3).
-
-        Also,
-
-        .. math::
-
-            width = |AB| = |CD| = 7 - 3 = 4,
-            height = |AD| = |BC| = 4 - 2 = 2.
-
-        Therefore, the corresponding representation for the same shape in rotated box in
-        (x_center, y_center, width, height, angle) format is:
-
-        (5, 3, 4, 2, 0),
-
-        Now, let's consider (5, 3, 4, 2, 90), which is rotated by 90 degrees
-        CCW (counter-clockwise) by definition. It looks like this:
-
-        .. code:: none
-
-            O--------> x
-            |   B-C
-            |   | |
-            |   |E|
-            |   | |
-            |   A-D
-            v y
-
-        The center E is still located at the same point (5, 3), while the vertices
-        ABCD are rotated by 90 degrees CCW with regard to E:
-        A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5)
-
-        Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to
-        vector AD or vector BC (the top->down height vector in box's local coordinate system),
-        or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right
-        width vector in box's local coordinate system).
-
-        .. math::
-
-            width = |AB| = |CD| = 5 - 1 = 4,
-            height = |AD| = |BC| = 6 - 4 = 2.
-
-        Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise)
-        by definition? It looks like this:
-
-        .. code:: none
-
-            O--------> x
-            |   D-A
-            |   | |
-            |   |E|
-            |   | |
-            |   C-B
-            v y
-
-        The center E is still located at the same point (5, 3), while the vertices
-        ABCD are rotated by 90 degrees CW with regard to E:
-        A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1)
-
-        .. math::
-
-            width = |AB| = |CD| = 5 - 1 = 4,
-            height = |AD| = |BC| = 6 - 4 = 2.
-
-        This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU
-        will be 1. However, these two will generate different RoI Pooling results and
-        should not be treated as an identical box.
-
-        On the other hand, it's easy to see that (X, Y, W, H, A) is identical to
-        (X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be
-        identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is
-        equivalent to rotating the same shape 90 degrees CW.
-
-        We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180):
-
-        .. code:: none
-
-            O--------> x
-            |
-            |  C---D
-            |  | E |
-            |  B---A
-            |
-            v y
-
-        .. math::
-
-            A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2),
-
-            width = |AB| = |CD| = 7 - 3 = 4,
-            height = |AD| = |BC| = 4 - 2 = 2.
-
-        Finally, this is a very inaccurate (heavily quantized) illustration of
-        how (5, 3, 4, 2, 60) looks like in case anyone wonders:
-
-        .. code:: none
-
-            O--------> x
-            |     B\
-            |    /  C
-            |   /E /
-            |  A  /
-            |   `D
-            v y
-
-        It's still a rectangle with center of (5, 3), width of 4 and height of 2,
-        but its angle (and thus orientation) is somewhere between
-        (5, 3, 4, 2, 0) and (5, 3, 4, 2, 90).
-        """
-        device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu")
-        tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
-        if tensor.numel() == 0:
-            # Use reshape, so we don't end up creating a new tensor that does not depend on
-            # the inputs (and consequently confuses jit)
-            tensor = tensor.reshape((0, 5)).to(dtype=torch.float32, device=device)
-        assert tensor.dim() == 2 and tensor.size(-1) == 5, tensor.size()
-
-        self.tensor = tensor
-
-    def clone(self) -> "RotatedBoxes":
-        """
-        Clone the RotatedBoxes.
-
-        Returns:
-            RotatedBoxes
-        """
-        return RotatedBoxes(self.tensor.clone())
-
-    @_maybe_jit_unused
-    def to(self, device: torch.device):
-        # Boxes are assumed float32 and does not support to(dtype)
-        return RotatedBoxes(self.tensor.to(device=device))
-
-    def area(self) -> torch.Tensor:
-        """
-        Computes the area of all the boxes.
-
-        Returns:
-            torch.Tensor: a vector with areas of each box.
-        """
-        box = self.tensor
-        area = box[:, 2] * box[:, 3]
-        return area
-
-    def normalize_angles(self) -> None:
-        """
-        Restrict angles to the range of [-180, 180) degrees
-        """
-        self.tensor[:, 4] = (self.tensor[:, 4] + 180.0) % 360.0 - 180.0
-
-    def clip(self, box_size: Tuple[int, int], clip_angle_threshold: float = 1.0) -> None:
-        """
-        Clip (in place) the boxes by limiting x coordinates to the range [0, width]
-        and y coordinates to the range [0, height].
-
-        For RRPN:
-        Only clip boxes that are almost horizontal with a tolerance of
-        clip_angle_threshold to maintain backward compatibility.
-
-        Rotated boxes beyond this threshold are not clipped for two reasons:
-
-        1. There are potentially multiple ways to clip a rotated box to make it
-           fit within the image.
-        2. It's tricky to make the entire rectangular box fit within the image
-           and still be able to not leave out pixels of interest.
-
-        Therefore we rely on ops like RoIAlignRotated to safely handle this.
-
-        Args:
-            box_size (height, width): The clipping box's size.
-            clip_angle_threshold:
-                Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees),
-                we do the clipping as horizontal boxes.
-        """
-        h, w = box_size
-
-        # normalize angles to be within (-180, 180] degrees
-        self.normalize_angles()
-
-        idx = torch.where(torch.abs(self.tensor[:, 4]) <= clip_angle_threshold)[0]
-
-        # convert to (x1, y1, x2, y2)
-        x1 = self.tensor[idx, 0] - self.tensor[idx, 2] / 2.0
-        y1 = self.tensor[idx, 1] - self.tensor[idx, 3] / 2.0
-        x2 = self.tensor[idx, 0] + self.tensor[idx, 2] / 2.0
-        y2 = self.tensor[idx, 1] + self.tensor[idx, 3] / 2.0
-
-        # clip
-        x1.clamp_(min=0, max=w)
-        y1.clamp_(min=0, max=h)
-        x2.clamp_(min=0, max=w)
-        y2.clamp_(min=0, max=h)
-
-        # convert back to (xc, yc, w, h)
-        self.tensor[idx, 0] = (x1 + x2) / 2.0
-        self.tensor[idx, 1] = (y1 + y2) / 2.0
-        # make sure widths and heights do not increase due to numerical errors
-        self.tensor[idx, 2] = torch.min(self.tensor[idx, 2], x2 - x1)
-        self.tensor[idx, 3] = torch.min(self.tensor[idx, 3], y2 - y1)
-
-    def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
-        """
-        Find boxes that are non-empty.
-        A box is considered empty, if either of its side is no larger than threshold.
-
-        Returns:
-            Tensor: a binary vector which represents
-            whether each box is empty (False) or non-empty (True).
-        """
-        box = self.tensor
-        widths = box[:, 2]
-        heights = box[:, 3]
-        keep = (widths > threshold) & (heights > threshold)
-        return keep
-
-    def __getitem__(self, item) -> "RotatedBoxes":
-        """
-        Returns:
-            RotatedBoxes: Create a new :class:`RotatedBoxes` by indexing.
-
-        The following usage are allowed:
-
-        1. `new_boxes = boxes[3]`: return a `RotatedBoxes` which contains only one box.
-        2. `new_boxes = boxes[2:10]`: return a slice of boxes.
-        3. `new_boxes = boxes[vector]`, where vector is a torch.ByteTensor
-           with `length = len(boxes)`. Nonzero elements in the vector will be selected.
-
-        Note that the returned RotatedBoxes might share storage with this RotatedBoxes,
-        subject to Pytorch's indexing semantics.
-        """
-        if isinstance(item, int):
-            return RotatedBoxes(self.tensor[item].view(1, -1))
-        b = self.tensor[item]
-        assert b.dim() == 2, "Indexing on RotatedBoxes with {} failed to return a matrix!".format(
-            item
-        )
-        return RotatedBoxes(b)
-
-    def __len__(self) -> int:
-        return self.tensor.shape[0]
-
-    def __repr__(self) -> str:
-        return "RotatedBoxes(" + str(self.tensor) + ")"
-
-    def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:
-        """
-        Args:
-            box_size (height, width): Size of the reference box covering
-                [0, width] x [0, height]
-            boundary_threshold (int): Boxes that extend beyond the reference box
-                boundary by more than boundary_threshold are considered "outside".
-
-        For RRPN, it might not be necessary to call this function since it's common
-        for rotated box to extend to outside of the image boundaries
-        (the clip function only clips the near-horizontal boxes)
-
-        Returns:
-            a binary vector, indicating whether each box is inside the reference box.
-        """
-        height, width = box_size
-
-        cnt_x = self.tensor[..., 0]
-        cnt_y = self.tensor[..., 1]
-        half_w = self.tensor[..., 2] / 2.0
-        half_h = self.tensor[..., 3] / 2.0
-        a = self.tensor[..., 4]
-        c = torch.abs(torch.cos(a * math.pi / 180.0))
-        s = torch.abs(torch.sin(a * math.pi / 180.0))
-        # This basically computes the horizontal bounding rectangle of the rotated box
-        max_rect_dx = c * half_w + s * half_h
-        max_rect_dy = c * half_h + s * half_w
-
-        inds_inside = (
-            (cnt_x - max_rect_dx >= -boundary_threshold)
-            & (cnt_y - max_rect_dy >= -boundary_threshold)
-            & (cnt_x + max_rect_dx < width + boundary_threshold)
-            & (cnt_y + max_rect_dy < height + boundary_threshold)
-        )
-
-        return inds_inside
-
-    def get_centers(self) -> torch.Tensor:
-        """
-        Returns:
-            The box centers in a Nx2 array of (x, y).
-        """
-        return self.tensor[:, :2]
-
-    def scale(self, scale_x: float, scale_y: float) -> None:
-        """
-        Scale the rotated box with horizontal and vertical scaling factors
-        Note: when scale_factor_x != scale_factor_y,
-        the rotated box does not preserve the rectangular shape when the angle
-        is not a multiple of 90 degrees under resize transformation.
-        Instead, the shape is a parallelogram (that has skew)
-        Here we make an approximation by fitting a rotated rectangle to the parallelogram.
-        """
-        self.tensor[:, 0] *= scale_x
-        self.tensor[:, 1] *= scale_y
-        theta = self.tensor[:, 4] * math.pi / 180.0
-        c = torch.cos(theta)
-        s = torch.sin(theta)
-
-        # In image space, y is top->down and x is left->right
-        # Consider the local coordintate system for the rotated box,
-        # where the box center is located at (0, 0), and the four vertices ABCD are
-        # A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2)
-        # the midpoint of the left edge AD of the rotated box E is:
-        # E = (A+D)/2 = (-w / 2, 0)
-        # the midpoint of the top edge AB of the rotated box F is:
-        # F(0, -h / 2)
-        # To get the old coordinates in the global system, apply the rotation transformation
-        # (Note: the right-handed coordinate system for image space is yOx):
-        # (old_x, old_y) = (s * y + c * x, c * y - s * x)
-        # E(old) = (s * 0 + c * (-w/2), c * 0 - s * (-w/2)) = (-c * w / 2, s * w / 2)
-        # F(old) = (s * (-h / 2) + c * 0, c * (-h / 2) - s * 0) = (-s * h / 2, -c * h / 2)
-        # After applying the scaling factor (sfx, sfy):
-        # E(new) = (-sfx * c * w / 2, sfy * s * w / 2)
-        # F(new) = (-sfx * s * h / 2, -sfy * c * h / 2)
-        # The new width after scaling tranformation becomes:
-
-        # w(new) = |E(new) - O| * 2
-        #        = sqrt[(sfx * c * w / 2)^2 + (sfy * s * w / 2)^2] * 2
-        #        = sqrt[(sfx * c)^2 + (sfy * s)^2] * w
-        # i.e., scale_factor_w = sqrt[(sfx * c)^2 + (sfy * s)^2]
-        #
-        # For example,
-        # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_w == scale_factor_x;
-        # when |angle| = 90, c = 0, |s| = 1, scale_factor_w == scale_factor_y
-        self.tensor[:, 2] *= torch.sqrt((scale_x * c) ** 2 + (scale_y * s) ** 2)
-
-        # h(new) = |F(new) - O| * 2
-        #        = sqrt[(sfx * s * h / 2)^2 + (sfy * c * h / 2)^2] * 2
-        #        = sqrt[(sfx * s)^2 + (sfy * c)^2] * h
-        # i.e., scale_factor_h = sqrt[(sfx * s)^2 + (sfy * c)^2]
-        #
-        # For example,
-        # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_h == scale_factor_y;
-        # when |angle| = 90, c = 0, |s| = 1, scale_factor_h == scale_factor_x
-        self.tensor[:, 3] *= torch.sqrt((scale_x * s) ** 2 + (scale_y * c) ** 2)
-
-        # The angle is the rotation angle from y-axis in image space to the height
-        # vector (top->down in the box's local coordinate system) of the box in CCW.
-        #
-        # angle(new) = angle_yOx(O - F(new))
-        #            = angle_yOx( (sfx * s * h / 2, sfy * c * h / 2) )
-        #            = atan2(sfx * s * h / 2, sfy * c * h / 2)
-        #            = atan2(sfx * s, sfy * c)
-        #
-        # For example,
-        # when sfx == sfy, angle(new) == atan2(s, c) == angle(old)
-        self.tensor[:, 4] = torch.atan2(scale_x * s, scale_y * c) * 180 / math.pi
-
-    @classmethod
-    @_maybe_jit_unused
-    def cat(cls, boxes_list: List["RotatedBoxes"]) -> "RotatedBoxes":
-        """
-        Concatenates a list of RotatedBoxes into a single RotatedBoxes
-
-        Arguments:
-            boxes_list (list[RotatedBoxes])
-
-        Returns:
-            RotatedBoxes: the concatenated RotatedBoxes
-        """
-        assert isinstance(boxes_list, (list, tuple))
-        if len(boxes_list) == 0:
-            return cls(torch.empty(0))
-        assert all([isinstance(box, RotatedBoxes) for box in boxes_list])
-
-        # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input
-        cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))
-        return cat_boxes
-
-    @property
-    def device(self) -> torch.device:
-        return self.tensor.device
-
-    @torch.jit.unused
-    def __iter__(self):
-        """
-        Yield a box as a Tensor of shape (5,) at a time.
-        """
-        yield from self.tensor
-
-
-def pairwise_iou(boxes1: RotatedBoxes, boxes2: RotatedBoxes) -> None:
-    """
-    Given two lists of rotated boxes of size N and M,
-    compute the IoU (intersection over union)
-    between **all** N x M pairs of boxes.
-    The box order must be (x_center, y_center, width, height, angle).
-
-    Args:
-        boxes1, boxes2 (RotatedBoxes):
-            two `RotatedBoxes`. Contains N & M rotated boxes, respectively.
-
-    Returns:
-        Tensor: IoU, sized [N,M].
-    """
-
-    return pairwise_iou_rotated(boxes1.tensor, boxes2.tensor)

spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/GroundingDINO/backbone/__init__.py

@@ -1 +0,0 @@
-from .backbone import build_backbone

spaces/CjangCjengh/Shanghainese-TTS/monotonic_align/core.py

@@ -1,35 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-  b = paths.shape[0]
-  max_neg_val=-1e9
-  for i in range(int(b)):
-    path = paths[i]
-    value = values[i]
-    t_y = t_ys[i]
-    t_x = t_xs[i]
-
-    v_prev = v_cur = 0.0
-    index = t_x - 1
-
-    for y in range(t_y):
-      for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-        if x == y:
-          v_cur = max_neg_val
-        else:
-          v_cur = value[y-1, x]
-        if x == 0:
-          if y == 0:
-            v_prev = 0.
-          else:
-            v_prev = max_neg_val
-        else:
-          v_prev = value[y-1, x-1]
-        value[y, x] += max(v_prev, v_cur)
-
-    for y in range(t_y - 1, -1, -1):
-      path[y, index] = 1
-      if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
-        index = index - 1

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/midas/blocks.py

@@ -1,342 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .vit import (
-    _make_pretrained_vitb_rn50_384,
-    _make_pretrained_vitl16_384,
-    _make_pretrained_vitb16_384,
-    forward_vit,
-)
-
-def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
-    if backbone == "vitl16_384":
-        pretrained = _make_pretrained_vitl16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [256, 512, 1024, 1024], features, groups=groups, expand=expand
-        )  # ViT-L/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb_rn50_384":
-        pretrained = _make_pretrained_vitb_rn50_384(
-            use_pretrained,
-            hooks=hooks,
-            use_vit_only=use_vit_only,
-            use_readout=use_readout,
-        )
-        scratch = _make_scratch(
-            [256, 512, 768, 768], features, groups=groups, expand=expand
-        )  # ViT-H/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb16_384":
-        pretrained = _make_pretrained_vitb16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [96, 192, 384, 768], features, groups=groups, expand=expand
-        )  # ViT-B/16 - 84.6% Top1 (backbone)
-    elif backbone == "resnext101_wsl":
-        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
-        scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand)     # efficientnet_lite3  
-    elif backbone == "efficientnet_lite3":
-        pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
-        scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand)  # efficientnet_lite3     
-    else:
-        print(f"Backbone '{backbone}' not implemented")
-        assert False
-        
-    return pretrained, scratch
-
-
-def _make_scratch(in_shape, out_shape, groups=1, expand=False):
-    scratch = nn.Module()
-
-    out_shape1 = out_shape
-    out_shape2 = out_shape
-    out_shape3 = out_shape
-    out_shape4 = out_shape
-    if expand==True:
-        out_shape1 = out_shape
-        out_shape2 = out_shape*2
-        out_shape3 = out_shape*4
-        out_shape4 = out_shape*8
-
-    scratch.layer1_rn = nn.Conv2d(
-        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer2_rn = nn.Conv2d(
-        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer3_rn = nn.Conv2d(
-        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer4_rn = nn.Conv2d(
-        in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-
-    return scratch
-
-
-def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
-    efficientnet = torch.hub.load(
-        "rwightman/gen-efficientnet-pytorch",
-        "tf_efficientnet_lite3",
-        pretrained=use_pretrained,
-        exportable=exportable
-    )
-    return _make_efficientnet_backbone(efficientnet)
-
-
-def _make_efficientnet_backbone(effnet):
-    pretrained = nn.Module()
-
-    pretrained.layer1 = nn.Sequential(
-        effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
-    )
-    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
-    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
-    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
-
-    return pretrained
-    
-
-def _make_resnet_backbone(resnet):
-    pretrained = nn.Module()
-    pretrained.layer1 = nn.Sequential(
-        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
-    )
-
-    pretrained.layer2 = resnet.layer2
-    pretrained.layer3 = resnet.layer3
-    pretrained.layer4 = resnet.layer4
-
-    return pretrained
-
-
-def _make_pretrained_resnext101_wsl(use_pretrained):
-    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
-    return _make_resnet_backbone(resnet)
-
-
-
-class Interpolate(nn.Module):
-    """Interpolation module.
-    """
-
-    def __init__(self, scale_factor, mode, align_corners=False):
-        """Init.
-
-        Args:
-            scale_factor (float): scaling
-            mode (str): interpolation mode
-        """
-        super(Interpolate, self).__init__()
-
-        self.interp = nn.functional.interpolate
-        self.scale_factor = scale_factor
-        self.mode = mode
-        self.align_corners = align_corners
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: interpolated data
-        """
-
-        x = self.interp(
-            x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
-        )
-
-        return x
-
-
-class ResidualConvUnit(nn.Module):
-    """Residual convolution module.
-    """
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        out = self.relu(x)
-        out = self.conv1(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-
-        return out + x
-
-
-class FeatureFusionBlock(nn.Module):
-    """Feature fusion block.
-    """
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock, self).__init__()
-
-        self.resConfUnit1 = ResidualConvUnit(features)
-        self.resConfUnit2 = ResidualConvUnit(features)
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            output += self.resConfUnit1(xs[1])
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=True
-        )
-
-        return output
-
-
-
-
-class ResidualConvUnit_custom(nn.Module):
-    """Residual convolution module.
-    """
-
-    def __init__(self, features, activation, bn):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.bn = bn
-
-        self.groups=1
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
-        )
-        
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
-        )
-
-        if self.bn==True:
-            self.bn1 = nn.BatchNorm2d(features)
-            self.bn2 = nn.BatchNorm2d(features)
-
-        self.activation = activation
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        
-        out = self.activation(x)
-        out = self.conv1(out)
-        if self.bn==True:
-            out = self.bn1(out)
-       
-        out = self.activation(out)
-        out = self.conv2(out)
-        if self.bn==True:
-            out = self.bn2(out)
-
-        if self.groups > 1:
-            out = self.conv_merge(out)
-
-        return self.skip_add.add(out, x)
-
-        # return out + x
-
-
-class FeatureFusionBlock_custom(nn.Module):
-    """Feature fusion block.
-    """
-
-    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock_custom, self).__init__()
-
-        self.deconv = deconv
-        self.align_corners = align_corners
-
-        self.groups=1
-
-        self.expand = expand
-        out_features = features
-        if self.expand==True:
-            out_features = features//2
-        
-        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
-
-        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
-        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
-        
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            res = self.resConfUnit1(xs[1])
-            output = self.skip_add.add(output, res)
-            # output += res
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
-        )
-
-        output = self.out_conv(output)
-
-        return output
-

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/io_.py

@@ -1,216 +0,0 @@
-#coding=utf-8
-'''
-Created on 2016年9月27日
-
-@author: dengdan
-
-Tool  functions for file system operation and I/O. 
-In the style of linux shell commands
-'''
-import os
-import pickle as pkl
-# import commands
-import logging
-
-# import util
-
-def mkdir(path):
-    """
-    If the target directory does not exists, it and its parent directories will created. 
-    """
-    path = get_absolute_path(path)
-    if not exists(path):
-        os.makedirs(path)
-    return path
-
-def make_parent_dir(path):
-    """make the parent directories for a file."""
-    parent_dir = get_dir(path)
-    mkdir(parent_dir)
-    
-    
-def pwd():
-    return os.getcwd()
-
-def dump(path, obj):
-    path = get_absolute_path(path)
-    parent_path = get_dir(path)
-    mkdir(parent_path)
-    with open(path, 'w') as f:
-        logging.info('dumping file:' + path);
-        pkl.dump(obj, f)
-
-def load(path):
-    path = get_absolute_path(path)
-    with open(path, 'r') as f:
-        data = pkl.load(f)
-    return data
-
-def join_path(a, *p):
-    return os.path.join(a, *p)
-
-def is_dir(path):
-    path = get_absolute_path(path)
-    return os.path.isdir(path)
-
-
-def is_path(path):
-    path = get_absolute_path(path)
-    return os.path.ispath(path)
-    
-def get_dir(path):
-    '''
-    return the directory it belongs to.
-    if path is a directory itself, itself will be return 
-    '''
-    path = get_absolute_path(path)
-    if is_dir(path):
-        return path;
-    return os.path.split(path)[0]
-
-def get_filename(path):
-    return os.path.split(path)[1]
-
-def get_absolute_path(p):
-    if p.startswith('~'):
-        p = os.path.expanduser(p)
-    return os.path.abspath(p)
-
-def cd(p):
-    p = get_absolute_path(p)
-    os.chdir(p)
-    
-# def ls(path = '.', suffix = None):
-#     """
-#     list files in a directory.
-#     return file names in a list
-#     """
-#     path = get_absolute_path(path)
-#     files = os.listdir(path)
-#
-#     if suffix is None:
-#         return files
-#
-#     filtered = []
-#     for f in files:
-#         if util.str.ends_with(f, suffix, ignore_case = True):
-#             filtered.append(f)
-#
-#     return filtered
-
-def find_files(pattern):
-    import glob
-    return glob.glob(pattern)
-
-def read_lines(p):
-    """return the text in a file in lines as a list """
-    p = get_absolute_path(p)
-    f = open(p,'r')
-    return f.readlines()
-    
-def write_lines(p, lines):
-    p = get_absolute_path(p)
-    make_parent_dir(p)
-    with open(p, 'w') as f:
-        for line in lines:
-            f.write(line)
-            
-
-# def cat(p):
-#     """return the text in a file as a whole"""
-#     cmd = 'cat ' + p
-#     return commands.getoutput(cmd)
-
-def exists(path):
-    path = get_absolute_path(path)
-    return os.path.exists(path)
-
-def load_mat(path):
-    import scipy.io as sio
-    path = get_absolute_path(path)
-    return sio.loadmat(path)
-
-def dump_mat(path, dict_obj, append = True):
-    import scipy.io as sio
-    path = get_absolute_path(path)
-    make_parent_dir(path)
-    sio.savemat(file_name = path, mdict =  dict_obj, appendmat = append)
-    
-def dir_mat(path):
-    '''
-    list the variables in mat file.
-    return a list: [(name, shape, dtype), ...]
-    '''
-    import scipy.io as sio
-    path = get_absolute_path(path)
-    return sio.whosmat(path)
-    
-SIZE_UNIT_K = 1024
-SIZE_UNIT_M = SIZE_UNIT_K ** 2
-SIZE_UNIT_G = SIZE_UNIT_K ** 3
-def get_file_size(path, unit = SIZE_UNIT_K):
-    size = os.path.getsize(get_absolute_path(path))
-    return size * 1.0 / unit
-    
-    
-def create_h5(path):
-    import h5py
-    path = get_absolute_path(path)
-    make_parent_dir(path)
-    return h5py.File(path, 'w');
-
-def open_h5(path, mode = 'r'):
-    import h5py
-    path = get_absolute_path(path)
-    return h5py.File(path, mode);
-    
-def read_h5(h5, key):
-    return h5[key][:]
-def read_h5_attrs(h5, key, attrs):
-    return h5[key].attrs[attrs]
-    
-def copy(src, dest):
-    import shutil
-    shutil.copy(get_absolute_path(src), get_absolute_path(dest))
-    
-cp = copy
-
-def remove(p):
-    import os
-    os.remove(get_absolute_path(p))
-rm = remove
-
-# def search(pattern, path, file_only = True):
-#     """
-#     Search files whose name matches the give pattern. The search scope
-#     is the directory and sub-directories of 'path'.
-#     """
-#     path = get_absolute_path(path)
-#     pattern_here = util.io.join_path(path, pattern)
-#     targets = []
-#
-#     # find matchings in current directory
-#     candidates = find_files(pattern_here)
-#     for can in candidates:
-#         if util.io.is_dir(can) and file_only:
-#             continue
-#         else:
-#             targets.append(can)
-#
-#     # find matching in sub-dirs
-#     files = ls(path)
-#     for f in files:
-#         fpath = util.io.join_path(path, f)
-#         if is_dir(fpath):
-#             targets_in_sub_dir = search(pattern, fpath, file_only)
-#             targets.extend(targets_in_sub_dir)
-#     return targets
-
-def dump_json(path, data):
-    import json
-    path = get_absolute_path(path)
-    make_parent_dir(path)
-
-    with open(path, 'w') as f:
-        json.dump(data, f)
-    return path

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-4ccfb72c.css

@@ -1 +0,0 @@
-.wrap.svelte-1sc8eck{display:flex;flex-direction:column;flex-flow:column;margin:0;padding:0;height:100%}.codemirror-wrapper.svelte-1sc8eck{height:100%;overflow:auto}.cm-editor{height:100%}.cm-selectionBackground{background-color:#b9d2ff30!important}.cm-focused{outline:none!important}button.svelte-qi7jcw{position:relative;cursor:pointer;padding:5px;width:22px;height:22px}.check.svelte-qi7jcw{position:absolute;top:0;right:0;z-index:var(--layer-top);background:var(--background-fill-primary);padding:var(--size-1);width:100%;height:100%;color:var(--body-text-color)}a.svelte-14d303a{position:relative;cursor:pointer;padding:5px;width:22px;height:22px}.copied.svelte-14d303a{color:var(--color-green-500)}.check.svelte-14d303a{position:absolute;top:0;right:0;z-index:var(--layer-top);background:var(--background-fill-primary);padding:var(--size-1);width:100%;height:100%;color:var(--body-text-color)}div.svelte-1yin446{display:flex;position:absolute;top:var(--block-label-margin);right:var(--block-label-margin);align-items:center;z-index:var(--layer-2);transition:.15s;box-shadow:var(--shadow-drop);border:1px solid var(--border-color-primary);border-top:none;border-right:none;border-radius:var(--block-label-right-radius);background:var(--block-label-background-fill);overflow:hidden;color:var(--block-label-text-color);font:var(--font);font-size:var(--button-small-text-size)}

spaces/Dagfinn1962/stablediffusion-articlera/theme.css

@@ -1 +0,0 @@
-{"theme": {"background_accent": "*primary_500", "background_accent_soft": "#919cbf", "background_accent_soft_dark": "*neutral_700", "background_primary": "#586794", "background_primary_dark": "*neutral_950", "background_secondary": "#586794", "background_secondary_dark": "*neutral_900", "block_background": "#7280ad", "block_background_dark": "#31395294", "block_border_color": "*border_color_primary", "block_border_color_dark": "*border_color_primary", "block_border_width": "1px", "block_info_color": "#f8f8f2", "block_info_color_dark": "#f8f8f2", "block_info_text_size": "*text_sm", "block_info_text_weight": "400", "block_label_background": "*background_primary", "block_label_background_dark": "*background_secondary", "block_label_border_color": "*border_color_primary", "block_label_border_color_dark": "*border_color_primary", "block_label_border_width": "1px", "block_label_icon_color": "*block_label_text_color", "block_label_margin": "0", "block_label_padding": "*spacing_sm *spacing_lg", "block_label_radius": "calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px) 0", "block_label_right_radius": "0 calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px)", "block_label_text_color": "#f8f8f2", "block_label_text_color_dark": "#f8f8f2", "block_label_text_size": "*text_sm", "block_label_text_weight": "400", "block_padding": "*spacing_xl calc(*spacing_xl + 2px)", "block_radius": "*radius_lg", "block_shadow": "none", "block_title_background": "none", "block_title_border_color": "none", "block_title_border_width": "0px", "block_title_padding": "0", "block_title_radius": "none", "block_title_text_color": "#f8f8f2", "block_title_text_color_dark": "#f8f8f2", "block_title_text_size": "*text_md", "block_title_text_weight": "400", "body_background": "#586794", "body_background_dark": "*background_primary", "body_text_color": "#f8f8f2", "body_text_color_dark": "#f8f8f2", "body_text_color_subdued": "#f8f8f2", "body_text_color_subdued_dark": "*neutral_400", "body_text_size": "*text_md", "body_text_weight": "400", "border_color_accent": "#818eb6", "border_color_accent_dark": "*neutral_600", "border_color_primary": "*neutral_200", "border_color_primary_dark": "*neutral_700", "button_border_width": "*input_border_width", "button_cancel_background": "*button_secondary_background", "button_cancel_background_dark": "*button_secondary_background", "button_cancel_background_hover": "*button_cancel_background", "button_cancel_background_hover_dark": "*button_cancel_background", "button_cancel_border_color": "*button_secondary_border_color", "button_cancel_border_color_dark": "*button_secondary_border_color", "button_cancel_border_color_hover": "*button_cancel_border_color", "button_cancel_border_color_hover_dark": "*button_cancel_border_color", "button_cancel_text_color": "*button_secondary_text_color", "button_cancel_text_color_dark": "*button_secondary_text_color", "button_cancel_text_color_hover": "*button_cancel_text_color", "button_cancel_text_color_hover_dark": "*button_cancel_text_color", "button_large_padding": "*spacing_lg calc(2 * *spacing_lg)", "button_large_radius": "*radius_lg", "button_large_text_size": "*text_lg", "button_large_text_weight": "600", "button_primary_background": "#ffa1d7", "button_primary_background_dark": "#ff79c6", "button_primary_background_hover": "*button_primary_background", "button_primary_background_hover_dark": "*button_primary_background", "button_primary_border_color": "*primary_200", "button_primary_border_color_dark": "*primary_600", "button_primary_border_color_hover": "*button_primary_border_color", "button_primary_border_color_hover_dark": "*button_primary_border_color", "button_primary_text_color": "*primary_600", "button_primary_text_color_dark": "white", "button_primary_text_color_hover": "*button_primary_text_color", "button_primary_text_color_hover_dark": "*button_primary_text_color", "button_secondary_background": "*neutral_200", "button_secondary_background_dark": "*neutral_600", "button_secondary_background_hover": "*button_secondary_background", "button_secondary_background_hover_dark": "*button_secondary_background", "button_secondary_border_color": "*neutral_200", "button_secondary_border_color_dark": "*neutral_600", "button_secondary_border_color_hover": "*button_secondary_border_color", "button_secondary_border_color_hover_dark": "*button_secondary_border_color", "button_secondary_text_color": "#f8f8f2", "button_secondary_text_color_dark": "white", "button_secondary_text_color_hover": "*button_secondary_text_color", "button_secondary_text_color_hover_dark": "*button_secondary_text_color", "button_shadow": "none", "button_shadow_active": "none", "button_shadow_hover": "none", "button_small_padding": "*spacing_sm calc(2 * *spacing_sm)", "button_small_radius": "*radius_lg", "button_small_text_size": "*text_md", "button_small_text_weight": "400", "button_transition": "background-color 0.2s ease", "checkbox_background": "*background_primary", "checkbox_background_dark": "*neutral_800", "checkbox_background_focus": "*checkbox_background", "checkbox_background_focus_dark": "*checkbox_background", "checkbox_background_hover": "*checkbox_background", "checkbox_background_hover_dark": "*checkbox_background", "checkbox_background_selected": "#ff79c6", "checkbox_background_selected_dark": "#ff79c6", "checkbox_border_color": "*neutral_300", "checkbox_border_color_dark": "*neutral_700", "checkbox_border_color_focus": "*secondary_500", "checkbox_border_color_focus_dark": "*secondary_500", "checkbox_border_color_hover": "*neutral_300", "checkbox_border_color_hover_dark": "*neutral_600", "checkbox_border_color_selected": "*secondary_600", "checkbox_border_color_selected_dark": "*secondary_600", "checkbox_border_radius": "*radius_sm", "checkbox_border_width": "*input_border_width", "checkbox_label_background": "*button_secondary_background", "checkbox_label_background_dark": "*button_secondary_background", "checkbox_label_background_hover": "*button_secondary_background_hover", "checkbox_label_background_hover_dark": "*button_secondary_background_hover", "checkbox_label_background_selected": "*checkbox_label_background", "checkbox_label_background_selected_dark": "*checkbox_label_background", "checkbox_label_border_color": "*border_color_primary", "checkbox_label_border_color_dark": "*border_color_primary", "checkbox_label_border_color_hover": "*checkbox_label_border_color", "checkbox_label_border_color_hover_dark": "*checkbox_label_border_color", "checkbox_label_border_width": "*input_border_width", "checkbox_label_gap": "*spacing_lg", "checkbox_label_padding": "*spacing_md calc(2 * *spacing_md)", "checkbox_label_shadow": "none", "checkbox_label_text_size": "*text_md", "checkbox_label_text_weight": "400", "checkbox_shadow": "*input_shadow", "checkbox_text_color": "*body_text_color", "checkbox_text_color_dark": "*body_text_color", "checkbox_text_color_selected": "*checkbox_text_color", "checkbox_text_color_selected_dark": "*checkbox_text_color", "container_radius": "*radius_lg", "embed_radius": "*radius_lg", "error_background": "#fee2e2", "error_background_dark": "*background_primary", "error_border_color": "#fecaca", "error_border_color_dark": "*border_color_primary", "error_border_width": "1px", "error_color": "#ef4444", "error_color_dark": "#ef4444", "font": "'Poppins'", "font_mono": "'IBM Plex Mono', 'ui-monospace', 'Consolas', monospace", "form_gap_width": "0px", "header_text_weight": "600", "input_background": "*neutral_100", "input_background_dark": "*neutral_700", "input_background_focus": "*secondary_500", "input_background_focus_dark": "*secondary_600", "input_background_hover": "*input_background", "input_background_hover_dark": "*input_background", "input_border_color": "*border_color_primary", "input_border_color_dark": "*border_color_primary", "input_border_color_focus": "*secondary_300", "input_border_color_focus_dark": "*neutral_700", "input_border_color_hover": "*input_border_color", "input_border_color_hover_dark": "*input_border_color", "input_border_width": "0px", "input_padding": "*spacing_xl", "input_placeholder_color": "*neutral_400", "input_placeholder_color_dark": "*neutral_500", "input_radius": "*radius_lg", "input_shadow": "none", "input_shadow_focus": "*input_shadow", "input_text_size": "*text_md", "input_text_weight": "400", "layout_gap": "*spacing_xxl", "link_text_color": "*secondary_600", "link_text_color_active": "*secondary_600", "link_text_color_active_dark": "*secondary_500", "link_text_color_dark": "*secondary_500", "link_text_color_hover": "*secondary_700", "link_text_color_hover_dark": "*secondary_400", "link_text_color_visited": "*secondary_500", "link_text_color_visited_dark": "*secondary_600", "loader_color": "*background_accent", "neutral_100": "#919cbf", "neutral_200": "#818eb6", "neutral_300": "#7280ad", "neutral_400": "#6272a4", "neutral_50": "#a1aac8", "neutral_500": "#586794", "neutral_600": "#4e5b83", "neutral_700": "#455073", "neutral_800": "#3b4462", "neutral_900": "#313952", "neutral_950": "#272e42", "panel_background": "*background_secondary", "panel_background_dark": "#31395294", "panel_border_color": "*border_color_primary", "panel_border_color_dark": "*border_color_primary", "panel_border_width": "0", "primary_100": "#fce7f3", "primary_200": "#fbcfe8", "primary_300": "#f9a8d4", "primary_400": "#f472b6", "primary_50": "#fdf2f8", "primary_500": "#ec4899", "primary_600": "#db2777", "primary_700": "#be185d", "primary_800": "#9d174d", "primary_900": "#831843", "primary_950": "#6e1a3d", "prose_text_size": "*text_md", "prose_text_weight": "400", "radius_lg": "8px", "radius_md": "6px", "radius_sm": "4px", "radius_xl": "12px", "radius_xs": "2px", "radius_xxl": "22px", "radius_xxs": "1px", "secondary_100": "#dbeafe", "secondary_200": "#bfdbfe", "secondary_300": "#93c5fd", "secondary_400": "#60a5fa", "secondary_50": "#eff6ff", "secondary_500": "#3b82f6", "secondary_600": "#2563eb", "secondary_700": "#1d4ed8", "secondary_800": "#1e40af", "secondary_900": "#1e3a8a", "secondary_950": "#1d3660", "section_header_text_size": "*text_md", "section_header_text_weight": "400", "shadow_drop": "rgba(0,0,0,0.05) 0px 1px 2px 0px", "shadow_drop_lg": "0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1)", "shadow_inset": "rgba(0,0,0,0.05) 0px 2px 4px 0px inset", "shadow_spread": "3px", "shadow_spread_dark": "1px", "slider_color": "#ffa1d7", "slider_color_dark": "#ff79c6", "spacing_lg": "8px", "spacing_md": "6px", "spacing_sm": "4px", "spacing_xl": "10px", "spacing_xs": "2px", "spacing_xxl": "16px", "spacing_xxs": "1px", "stat_color_background": "*primary_300", "stat_color_background_dark": "*primary_500", "table_border_color": "*neutral_300", "table_border_color_dark": "*neutral_700", "table_even_background": "#7280ad", "table_even_background_dark": "*neutral_950", "table_odd_background": "*neutral_50", "table_odd_background_dark": "*neutral_900", "table_radius": "*radius_lg", "table_row_focus": "*background_accent_soft", "table_row_focus_dark": "*background_accent_soft", "text_lg": "16px", "text_md": "14px", "text_sm": "12px", "text_xl": "22px", "text_xs": "10px", "text_xxl": "26px", "text_xxs": "9px"}, "version": "0.3.2"}

spaces/Djacon/emotion_detection/files/js/summarizer.js

@@ -1,213 +0,0 @@
-// Form Divs
-const sumText  = document.getElementById('sum-text-div');
-const sumFile  = document.getElementById('sum-file-div')
-const sumVideo = document.getElementById('sum-video-div');
-
-// Form Data
-const selectOption  = document.getElementById('sum-type');
-const sumTextInput  = document.getElementById('sum-text-input');
-const sumFileInput  = document.getElementById('sum-file-input');
-const sumVideoInput = document.getElementById('sum-video-input');
-
-// Error Output Section
-const sumError = document.getElementById('sum-err');
-
-// Result Section
-const extractText = document.getElementById('extracted-text');
-const summaryText = document.getElementById('summarized-text');
-
-// Word Counter
-const wordsCount = document.getElementById('word-counter');
-
-// Tabs
-const original = document.getElementById('sum-original');
-const summary  = document.getElementById('sum-summary');
-const showOriginal = document.getElementById('show-original');
-const showSummary  = document.getElementById('show-summary');
-
-const MAX_SIZE = 20000;
-
-
-function _summarize() {
-    var xhr = new XMLHttpRequest();
-    xhr.open('POST', '/predict_summarization', true);
-    xhr.setRequestHeader('Content-Type', 'application/json');
- 
-    var data = JSON.stringify({ 'sum_type': selectOption.value, 'text': extractText.value });
-
-    xhr.onreadystatechange = function () {
-        if (xhr.readyState === 4 && xhr.status === 200) {
-            result = xhr.responseText.split('\\n').join('\n');
-            summaryText.value = result.slice(1, -1);
-            _show_summary();
-        }
-    };
-
-    xhr.send(data);
-    return;
-}
-
-function _extractFile() {
-    const file = sumFileInput.files[0];
-    if (file.type === 'text/plain') {
-        const reader = new FileReader();
-        reader.onload = function() {
-            sumTextInput.value = reader.result.slice(0, MAX_SIZE);
-        };
-        reader.readAsText(file, 'CP1251');
-        return;
-    } else if (file.type === 'application/pdf') {
-        sumTextInput.value = '';
-        const reader = new FileReader();
-        reader.onload = function (e) {
-            const pdfData = e.target.result;
-            pdfjsLib.getDocument(pdfData).promise.then(function (pdfDocument) {
-                for (let pageNum = 1; pageNum <= pdfDocument.numPages; pageNum++) {
-                    pdfDocument.getPage(pageNum).then(function (pdfPage) {
-                      pdfPage.getTextContent().then(function (textContent) {
-                        let size = sumTextInput.value.length;
-                        let pageText = [];
-                        for (const textItem of textContent.items) {
-                            pageText.push(textItem.str);
-                            size += textItem.str.length;
-                            if (size > MAX_SIZE) break;
-                        }
-                        sumTextInput.value += pageText.join(' ');
-                      });
-                    });
-                  }
-            });
-        };
-        reader.readAsDataURL(file);
-    }
-    return;
-}
-
-
-async function summarize(event) {
-    event.preventDefault();
-
-    switch (selectOption.value) {
-        case 'sum-text':
-            len = sumTextInput.value.trim().length
-            if (len < 250) {
-                sumError.innerText = `The text size should be at least 250 characters (${len} < 250)`;
-                sumError.classList.remove('hidden');
-                return;
-            }
-            break;
-        case 'sum-video':
-            regex = /^((((http)s?:\/\/)?((www\.)|(m\.))?youtube.com\/watch\?([^\?]*&)?v=.+)|(((http)s?:\/\/)?youtu.be\/([^\?=]+)(\?[^?]+)?))$/
-            if (!sumVideoInput.value.match(regex)) {
-                sumError.innerText = 'Invalid youtube link';
-                sumError.classList.remove('hidden');
-                return;
-            }
-            break;
-    }
-
-    sumError.classList.add('hidden');
-
-    _show_summary();
-
-    // Here we can finally summarize data
-    summaryText.value = 'Please wait...';
-    switch (selectOption.value) {
-        case 'sum-text':
-            extractText.value = sumTextInput.value.trim().slice(0, MAX_SIZE);
-            break;
-        case 'sum-video':
-            extractText.value = sumVideoInput.value.slice(0, MAX_SIZE);
-            break;
-    }
-    _summarize();
-}
-
-
-function _update_option() {
-    switch (selectOption.value) {
-        case 'sum-text':
-            sumText.classList.remove('hidden');
-            sumVideo.classList.add('hidden');
-
-            sumTextInput.setAttribute('required', '');
-            sumVideoInput.removeAttribute('required');
-            break;
-        case 'sum-video':
-            sumText.classList.add('hidden');
-            sumVideo.classList.remove('hidden');
-
-            sumTextInput.removeAttribute('required');
-            sumVideoInput.setAttribute('required', '');
-            break;
-    }
-    sumError.classList.add('hidden');
-}
-
-function _update_counter() {
-    let text = sumTextInput.value.trim()
-    if (text === '') {
-        sumFile.classList.remove('hidden');
-        wordsCount.classList.add('hidden');
-        return;
-    }
-
-    sumFile.classList.add('hidden');
-    wordsCount.classList.remove('hidden');
-    wordsCount.innerHTML = `Words: ${text.split(/\s+/).length} | Chars: ${text.length}`
-}
-
-
-function _show_summary() {
-    showOriginal.classList.remove('bg-gray-100');
-    showSummary.classList.add('bg-gray-100');
-
-    summary.classList.remove('hidden');
-    original.classList.add('hidden');
-}
-
-function _show_original() {
-    showOriginal.classList.add('bg-gray-100');
-    showSummary.classList.remove('bg-gray-100');
-
-    original.classList.remove('hidden');
-    summary.classList.add('hidden');
-}
-
-
-document.addEventListener('DOMContentLoaded', function () {
-    selectOption.addEventListener('change', _update_option);
-
-    var submitButton = document.getElementById('submit');
-    submitButton.addEventListener('click', summarize);
-
-    sumFileInput.addEventListener('change', async function() {
-        const allowedTypes = ['application/pdf', 'text/plain'];
-        const file = sumFileInput.files[0];
-
-        if (!file) {
-            sumError.classList.remove('hidden');
-            return;
-        }
-
-        if (!allowedTypes.includes(file.type)) {
-            sumError.innerText = 'Not supported type (Only `.pdf` or `.txt`)';
-            sumError.classList.remove('hidden');
-            return;
-        }
-
-        // Back to main option
-        selectOption.options[0].selected = true;
-        _update_option();
-        _extractFile();
-
-        await (new Promise(resolve => setTimeout(resolve, 1000)));
-        _update_counter();
-        sumError.classList.add('hidden');
-    });
-
-    sumTextInput.addEventListener('input', _update_counter);
-
-    showSummary.addEventListener('click', _show_summary);
-    showOriginal.addEventListener('click', _show_original);
-});

spaces/DrSong/ChatGLM-6B-ChatBot/README.md

@@ -1,13 +0,0 @@
----
-title: ChatGLM 6B ChatBot
-emoji: 🐨
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference