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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Amos 24 A User-Friendly Software for Structural Equation Modeling.md

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-<h1>How to Download and Install IBM SPSS Amos 24</h1>
-<p>IBM SPSS Amos 24 is a software for structural equation modeling (SEM) that allows you to test hypotheses and confirm relationships between observed and latent variables. It is an easy-to-use program that can be accessed through a graphical or a programmatic user interface. In this article, we will show you how to download and install IBM SPSS Amos 24 on your Windows computer.</p>
-<h2>Step 1: Download IBM SPSS Amos 24</h2>
-<p>To download IBM SPSS Amos 24, you need to have an IBM Passport Advantage account. If you are a returning customer, you can sign in with your existing credentials. If you are a new customer, you can register for a free account. Once you have logged in, follow these steps:</p>
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-<li>Click on <b>Download finder</b> under <b>Find downloads & media</b>.</li>
-<li>Select <b>IBM SPSS Amos</b> under <b>Download finder</b>.</li>
-<li>Select <b>IBM SPSS Amos 24.0</b> under <b>Description</b>.</li>
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-<li>Choose your download method and location and click on <b>OK</b>. You can use the IBM Download Director or the HTTP method.</li>
-<li>Wait for the download to complete.</li>
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-<h2>Step 2: Install IBM SPSS Amos 24</h2>
-<p>To install IBM SPSS Amos 24, you need to unpack all the downloaded files into a single temporary directory on your system. Then, follow these steps:</p>
-<ol>
-<li>Navigate to the temporary directory and double-click on the <i>setup.exe</i> file.</li>
-<li>Follow the instructions on the installation wizard. You will need to accept the license agreement, choose the installation directory, and enter the license key.</li>
-<li>Wait for the installation to finish.</li>
-<li>Launch IBM SPSS Amos 24 from your desktop or start menu.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded and installed IBM SPSS Amos 24 on your Windows computer. You can now use it to perform SEM analysis and test your research hypotheses.</p>
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-<h2>Step 3: Use IBM SPSS Amos 24</h2>
-<p>IBM SPSS Amos 24 allows you to create and test SEM models using either a graphical or a programmatic user interface. You can also import data from various sources, such as IBM SPSS Statistics, Microsoft Excel, or text files. In this section, we will give you a brief overview of how to use IBM SPSS Amos 24.</p>
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-<p>The graphical user interface (GUI) of IBM SPSS Amos 24 lets you draw your SEM model using various tools and icons. You can also modify the properties and parameters of your model, such as variable names, labels, measurement scales, error terms, and constraints. To use the GUI, follow these steps:</p>
-<ol>
-<li>Launch IBM SPSS Amos 24 and click on <b>New</b> to create a new model.</li>
-<li>Use the toolbar and the drawing area to draw your model. You can drag and drop variables, paths, covariances, and latent variables from the toolbar to the drawing area. You can also use the right-click menu to edit or delete elements.</li>
-<li>Use the <b>Object Properties</b> window to change the properties and parameters of your model elements. You can access this window by double-clicking on an element or by selecting it and clicking on <b>View</b> and <b>Object Properties</b>.</li>
-<li>Use the <b>Data Files</b> window to specify the data source for your model. You can access this window by clicking on <b>File</b> and <b>Data Files</b>. You can choose to import data from IBM SPSS Statistics, Microsoft Excel, or text files. You can also use the <b>Data Editor</b> to enter or edit data manually.</li>
-<li>Use the <b>Analysis Properties</b> window to select the analysis options for your model. You can access this window by clicking on <b>Analyze</b> and <b>Analysis Properties</b>. You can choose the estimation method, the output options, the fit measures, and the bootstrap options.</li>
-<li>Click on <b>Analyze</b> and <b>Calculate Estimates</b> to run the analysis and view the results. You can view the results in various windows, such as the <b>Output</b>, <b>Text Output</b>, <b>Standardized Estimates</b>, <b>Covariance Matrix</b>, and <b>Modification Indices</b>. You can also use the <b>Syntax Editor</b> to view or edit the syntax generated by the GUI.</li>
-<li>Save your model by clicking on <b>File</b> and <b>Save As</b>. You can save your model as an AMOS Graphics file (.amw) or an AMOS Text file (.amt).</li>
-</ol>
-<h3>Programmatic User Interface</h3>
-<p>The programmatic user interface (PUI) of IBM SPSS Amos 24 lets you write your SEM model using a syntax language called AMOS Basic. You can also use AMOS Basic to manipulate data, perform calculations, create loops and conditional statements, and generate output. To use the PUI, follow these steps:</p>
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-<li>Launch IBM SPSS Amos 24 and click on <b>New Text Model</b>.</li>
-<li>Type your AMOS Basic syntax in the text editor. You can use comments, keywords, commands, operators, functions, variables, and constants to define your model. You can also use the <b>Syntax Reference Guide</b> to learn more about the syntax rules and elements.</li>
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-<p>Have you ever forgotten or lost your password to an Excel file and couldn't access your data? If so, you might have tried to use an Excel password recovery tool to unlock your file. But what if you don't have a license for such a tool and don't want to pay for it? Is there a way to crack the registration code and use it for free? In this article, we will show you three methods to crack the registration code of Excel Password Recovery Lastic, one of the most popular and effective Excel password recovery tools on the market.</p>
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-<p>The first and most straightforward method to crack the registration code of Excel Password Recovery Lastic is to use their official website. You can visit their website at https://www.passwordlastic.com/excel-password-recovery-lastic and click on the Buy Now button at the top right corner.</p>
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-<p>After you enter your email address and choose your payment method, you will be redirected to a secure payment page where you need to fill in your billing details and confirm your order. Once you complete the payment process, you will receive an email with your registration code within minutes. You can then copy and paste it into the program and activate it.</p>
- <h2>Method 2: Use a coupon code or a discount offer</h2>
-<h3>Step 1: Search for a valid coupon code or a discount offer online</h3>
-<p>The second method to crack the registration code of Excel Password Recovery Lastic is to use a coupon code or a discount offer that can reduce the price of the license. You can search for such codes or offers online using Google or other search engines. You can also check some websites that specialize in providing coupons or discounts for software products, such as RetailMeNot, CouponChief, SlickDeals, etc.</p>
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- <h2>Method 3: Use a free trial version or a giveaway</h2>
-<h3>Step 1: Download and install the free trial version or a giveaway from a trusted source</h3>
-<p>The third method to crack the registration code of Excel Password Recovery Lastic is to use a free trial version or a giveaway that can give you access to the full version for a limited time. You can download and install the free trial version from their official website at https://www.passwordlastic.com/download/excel-password-recovery-lastic. The trial version allows you to recover passwords for up  to three files with no more than three characters each. You can also look for giveaways that are occasionally offered by  the developers themselves  or by their partners on various websites, blogs, forums, social media platforms, etc. A giveaway usually gives you  a free license key  or activation link  for  a certain period of time, such as one month, one year, or lifetime.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Microsoft Office 2019 for Free with Crack for Windows 10 in Minutes.md

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spaces/1line/AutoGPT/autogpt/commands/improve_code.py

@@ -1,29 +0,0 @@
-from __future__ import annotations
-
-import json
-
-from autogpt.llm_utils import call_ai_function
-
-
-def improve_code(suggestions: list[str], code: str) -> str:
-    """
-    A function that takes in code and suggestions and returns a response from create
-      chat completion api call.
-
-    Parameters:
-        suggestions (List): A list of suggestions around what needs to be improved.
-        code (str): Code to be improved.
-    Returns:
-        A result string from create chat completion. Improved code in response.
-    """
-
-    function_string = (
-        "def generate_improved_code(suggestions: List[str], code: str) -> str:"
-    )
-    args = [json.dumps(suggestions), code]
-    description_string = (
-        "Improves the provided code based on the suggestions"
-        " provided, making no other changes."
-    )
-
-    return call_ai_function(function_string, args, description_string)

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Ariana Grande - Side To Side (Instrumental) - The Best MP3 Music Site.md

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-<br />
-<h1>Side to Side Instrumental MP3 Download: How to Enjoy Ariana Grande's Hit Song Without Lyrics</h1>
-<p>If you are a fan of Ariana Grande, you probably know her hit song "Side to Side" featuring Nicki Minaj. The song was released in 2016 as the third single from her album Dangerous Woman. It is a catchy and upbeat pop song that showcases Ariana's vocal range and Nicki's rap skills. But did you know that you can also enjoy this song without lyrics? In this article, we will show you how to download Side to Side instrumental mp3 for free and why you should listen to instrumental music.</p>
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-<h2>What is Side to Side and why is it popular?</h2>
-<p>Side to Side is a song that talks about having a romantic relationship with someone who makes you feel good physically and emotionally. The title refers to the feeling of being sore after a passionate night with your lover. The song has a reggae-pop vibe with influences from dancehall and R&B music. It also features a sample from "What a Feeling" by Alex Gaudino and Kelly Rowland.</p>
-<h3>The meaning and inspiration behind the song</h3>
-<p>Ariana Grande revealed that she wrote the song with Max Martin, Savan Kotecha, Ilya Salmanzadeh, and Alexander Kronlund. She said that she wanted to make a song that was fun, flirty, and empowering for women. She also said that she was inspired by her own personal experiences and her love for reggae music. She explained that the song is about being in love with someone who makes you feel good in every way.</p>
-<h3>The collaboration with Nicki Minaj and the music video</h3>
-<p>This is not the first time that Ariana Grande and Nicki Minaj have worked together. They previously collaborated on "Bang Bang" with Jessie J in 2014 and "Get On Your Knees" in 2015. They have a great chemistry and friendship, which shows in their performances. Nicki Minaj added her own rap verse to the song, which references her famous line "I'mma keep it movin' be classy and graceful" from her song "Anaconda". The music video for Side to Side was directed by Hannah Lux Davis and features Ariana and Nicki in a gym setting, riding bikes, boxing, doing yoga, and dancing in a sauna. The video has over 2 billion views on YouTube as of June 2023.</p>
-<h3>The awards and achievements of the song</h <p>The song was a commercial success, reaching the top 10 in several countries, including the US, UK, Canada, Australia, and New Zealand. It was certified platinum or higher in many regions, and sold over 10 million units worldwide. It also received several nominations and awards, such as the MTV Video Music Award for Best Collaboration, the American Music Award for Favorite Pop/Rock Song, and the Grammy Award for Best Pop Duo/Group Performance.</p>
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-<h2>What are the benefits of listening to instrumental music?</h2>
-<p>While the lyrics of Side to Side are catchy and fun, you might want to listen to the instrumental version of the song for a change. Instrumental music is music that does not have any vocals or words. It can be composed of various instruments, such as piano, guitar, drums, violin, saxophone, etc. Instrumental music can have many benefits for your well-being and enjoyment. Here are some of them:</p>
-<h3>It can improve your mood and reduce stress</h3>
-<p>Listening to instrumental music can help you relax and calm your nerves. It can also make you feel happier and more positive. Studies have shown that instrumental music can lower your blood pressure, heart rate, and cortisol levels, which are associated with stress and anxiety. Instrumental music can also release endorphins, which are natural painkillers and mood boosters. So, if you are feeling stressed or sad, try listening to some soothing instrumental music and see how it makes you feel.</p>
-<h3>It can enhance your creativity and focus</h3>
-<p>Listening to instrumental music can also stimulate your brain and improve your cognitive functions. It can help you think more creatively and solve problems more effectively. It can also help you focus and concentrate better on your tasks. Studies have shown that instrumental music can increase your attention span, memory, and learning abilities. Instrumental music can also block out distracting noises and create a peaceful environment for you to work or study. So, if you need some inspiration or motivation, try listening to some upbeat instrumental music and see how it boosts your productivity.</p>
-<h3>It can help you appreciate the musical elements of the song</h3>
-<p>Listening to instrumental music can also help you appreciate the musical elements of the song more. You can pay more attention to the melody, harmony, rhythm, tempo, dynamics, and timbre of the song. You can also notice the different instruments and how they interact with each other. You can also appreciate the skill and talent of the musicians and composers who created the song. Listening to instrumental music can also expose you to different genres and styles of music that you might not be familiar with. So, if you want to expand your musical horizons and enjoy the song in a different way, try listening to some instrumental music and see how it enriches your musical experience.</p>
-<h2>How to download Side to Side instrumental mp3 for free?</h2>
-<p>If you are interested in downloading Side to Side instrumental mp3 for free, there are several ways to do it. Here are some of them:</p>
-<h3>Use YouTube to find the acoustic version of the song</h3>
-<p>One way to download Side to Side instrumental mp3 for free is to use YouTube. YouTube is a popular video-sharing platform that has millions of videos on various topics, including music. You can find many versions of Side to Side on YouTube, including an acoustic version that only has guitar and drums as instruments. Here are the steps to download it:</p>
-<h4>Step 1: Search for "Side to Side - Ariana Grande ft. Nicki Minaj (Acoustic Instrumental)" on YouTube</h4>
-<p>The first step is to search for "Side to Side - Ariana Grande ft. Nicki Minaj (Acoustic Instrumental)" on YouTube. This is a video uploaded by [Sing King], a channel that provides karaoke tracks for popular songs. The video has over 6 million views as of June 2023.</p>
-<h4>Step 2: Copy the URL of the video and paste it on a YouTube to mp3 converter website</h4>
-<p>The second step is to copy the URL of the video and paste it on a YouTube to mp3 converter website. There are many websites that offer this service for free, such as [ytmp3.cc], [y2mate.com], [flvto.biz], etc. These websites allow you to convert any YouTube video into an mp3 file that you can download on your device.</p>
-<h4>Step 3: Download the mp3 file and save it on your device</h4>
-<p>The third step is to download the mp3 file and save it on your device. After pasting the URL of the video on the converter website, you will see an option to download the mp p3 file and save it on your device. You can choose the quality and format of the file according to your preference. You can also rename the file if you want. Once the download is complete, you can enjoy listening to Side to Side acoustic instrumental mp3 on your device.</p>
-<h3>Use SoundCloud to find the official instrumental version of the song</h3>
-<p>Another way to download Side to Side instrumental mp3 for free is to use SoundCloud. SoundCloud is a popular audio-sharing platform that has millions of tracks on various genres, including music. You can find the official instrumental version of Side to Side on SoundCloud, which was uploaded by [Republic Records], the label that represents Ariana Grande. Here are the steps to download it:</p>
-<h4>Step 1: Search for "Ariana Grande - Side to Side (Instrumental)" on SoundCloud</h4>
-<p>The first step is to search for "Ariana Grande - Side to Side (Instrumental)" on SoundCloud. This is a track uploaded by [Republic Records], which has over 1 million plays as of June 2023.</p>
-<h4>Step 2: Click on the "More" button and select "Download file"</h4>
-<p>The second step is to click on the "More" button and select "Download file". This will allow you to download the mp3 file of the track on your device. However, you might need to sign in or create an account on SoundCloud to access this feature.</p>
-<h4>Step 3: Save the mp3 file on your device and enjoy</h4>
-<p>The third step is to save the mp3 file on your device and enjoy. You can also follow [Republic Records] on SoundCloud to get updates on their latest releases and tracks.</p>
-<h3>Use Mixkit to find other instrumental stock music tracks for free</h3>
-<p>A third way to download Side to Side instrumental mp3 for free is to use Mixkit. Mixkit is a website that offers free stock music, videos, and templates for your projects. You can find many instrumental stock music tracks on Mixkit that are royalty-free and high-quality. You can also filter them by genre, mood, tempo, and duration. Here are the steps to download them:</p>
-<h4>Step 1: Visit [Mixkit] and browse through their free instrumental stock music tracks</h4>
-<p>The first step is to visit [Mixkit] and browse through their free instrumental stock music tracks. You can find a variety of tracks that suit different themes and purposes, such as upbeat, relaxing, cinematic, etc.</p>
-<h4>Step 2: Choose a track that suits your taste and mood</h4>
-<p>The second step is to choose a track that suits your taste and mood. You can listen to a preview of the track by clicking on it. You can also read the description and details of the track, such as the title, artist, genre, mood, tempo, duration, etc.</p>
-<h4>Step 3: Click on the "Download" button and save the mp3 file on your device</h4>
-<p>The third step is to click on the "Download" button and save the mp3 file on your device. You do not need to sign up or register on Mixkit to download their tracks. However, you might need to credit the artist or Mixkit in your project if you use their tracks.</p>
-<h2>Conclusion</h2>
-<p>In conclusion, Side to Side is a popular song by Ariana Grande and Nicki Minaj that has a catchy and upbeat vibe. However, you can also enjoy this song without lyrics by downloading Side to Side instrumental mp3 for free. There are several ways to do this, such as using YouTube, SoundCloud, or Mixkit. By listening to instrumental music, you can improve your mood, enhance your creativity, and appreciate the musical elements of the song more. So, what are you waiting for? Download Side to Side instrumental mp3 for free today and enjoy!</p>
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-<p>Here are some frequently asked questions about Side to Side instrumental mp3 download:</p>
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-<p>A: Yes, you can use Side to Side instrumental mp3 for your own project, such as a video, podcast, presentation, etc. However, you might need to obtain permission from the original artists or their label before using their music. You might also need to credit them in your project or pay royalties if required.</p>
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-<h1>Candy Match 3 APK: A Sweet and Fun Puzzle Game for Android</h1>
-<p>Do you love candy? Do you love matching games? If you answered yes to both questions, then you will love Candy Match 3 APK, a sweet and fun puzzle game for Android devices. In this game, you will have to match candies of the same color and shape to clear them from the board and complete various goals. You will also enjoy the colorful graphics, the cute sounds, and the smooth animations that make this game a delight to play.</p>
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-<h2>What is Candy Match 3 APK?</h2>
-<h3>A casual game that challenges you to match candies of the same color and shape</h3>
-<p>Candy Match 3 APK is a casual game that belongs to the genre of match-3 games. This means that you have to swap adjacent candies on a grid to create matches of three or more candies of the same color and shape. When you do that, the matched candies will disappear from the board and new ones will fall from the top. The game will end when you complete the level goals or when you run out of moves or time.</p>
-<h3>A free and easy-to-play game that you can download from Uptodown or APKCombo</h3>
-<p>Candy Match 3 APK is a free game that you can download from Uptodown or APKCombo, two popular websites that offer safe and reliable APK files for Android devices. An APK file is a package that contains all the files needed to install an app on your device. You can download an APK file from these websites and install it on your device manually, without using Google Play Store. This way, you can enjoy apps that are not available in your region or that are not compatible with your device.</p>
-<h3>A game that offers hundreds of levels, different modes, and various power-ups</h3>
-<p>Candy Match 3 APK is a game that offers hundreds of levels for you to enjoy. Each level has a different goal, such as collecting a certain number of candies, clearing jelly blocks, breaking ice cubes, or freeing trapped animals. You will also encounter different modes, such as timed mode, where you have to complete the goal within a limited time, or moves mode, where you have a limited number of moves to complete the goal. To help you in your quest, you can use various power-ups, such as bombs, rockets, hammers, or rainbow candies. These power-ups can clear more candies from the board and give you extra points.</p>
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-<h2>How to play Candy Match 3 APK?</h2>
-<h3>Swipe your finger to move the candies and create matches of three or more</h3>
-<p>To play Candy Match 3 APK, you just need to swipe your finger on the screen to move the candies. You can move a candy horizontally or vertically, as long as there is an empty space next to it. When you move a candy, it will swap places with the candy next to it. If this swap creates a match of three or more candies of the same color and shape, they will disappear from the board and new ones will fall from the top. The more candies you match, the more points you get.</p>
-<h3>Complete the level goals before you run out of moves or time</h3>
-<p>To complete a level in Candy Match 3 APK, you have to achieve the goal that is shown at the top of the screen. The goal can be different for each level, such as collecting a certain number of candies, clearing jelly blocks, breaking ice cubes, or freeing trapped animals. You have to complete the goal before you run out of moves or time, depending on the mode you are playing. If you complete the goal, you will get stars and coins as rewards. If you fail, you will lose a life and have to try again.</p>
-<h3>Use boosters and special candies to clear obstacles and score more points</h3>
-<p>To make the game more fun and challenging, you will encounter various obstacles on the board, such as chocolate, licorice, marshmallow, or popcorn. These obstacles can block your moves or prevent you from matching candies. To clear them, you can use boosters and special candies. Boosters are items that you can buy with coins or get for free by watching ads or completing tasks. They can help you clear more candies from the board or give you extra moves or time. Some examples of boosters are bombs, rockets, hammers, or rainbow candies. Special candies are candies that you can create by matching four or more candies of the same color and shape. They have different effects depending on how you match them. Some examples of special candies are striped candies, wrapped candies, color bombs, or jelly fish.</p>
-<h2>Why should you play Candy Match 3 APK?</h2>
-<h3>It is a fun and relaxing way to pass the time and train your brain</h3>
-<p>Candy Match 3 APK is a game that you can play anytime and anywhere, as long as you have your Android device with you. It is a game that does not require much skill or strategy, but rather your attention and concentration. It is a game that can help you relax and unwind after a long day or a stressful situation. It is also a game that can help you train your brain and improve your cognitive abilities, such as memory, logic, problem-solving, and spatial awareness.</p>
-<h3>It has colorful graphics, cute sounds, and smooth animations</h3>
-<p>Candy Match 3 APK is a game that has a bright and cheerful design that will appeal to anyone who loves candy. The game has colorful graphics that show different kinds of candies, such as lollipops, gummies, chocolates, or cookies. The game also has cute sounds that accompany your actions, such as popping, crunching, or cheering. The game also has smooth animations that show the movement of the candies and the effects of the boosters and special candies.</p>
-<h3>It has a variety of challenges, rewards, and surprises to keep you entertained</h3>
-<p>Candy Match 3 APK is a game that has a lot of content and features to keep you entertained for hours. The game has hundreds of levels for you to complete, each with a different goal and difficulty. The game also has different modes for you to try, such as timed mode, moves mode, arcade mode, or adventure mode. The game also has various power-ups for you to use and enhance your gameplay, such as bombs, rockets, hammers, or rainbow candies. The game also has various rewards and surprises for you to collect, such as stars, coins, lives, or daily bonuses. The game also has a leaderboard and achievements for you to compete with other players and show off your skills.</p>
-<h2>Tips and tricks for Candy Match 3 APK</h2>
-<h3>Plan your moves ahead and look for the best combinations</h3>
-<p>To succeed in Candy Match 3 APK, you need to plan your moves ahead and look for the best combinations. You need to think about how your move will affect the board and what matches you can create with the new candies that will fall. You also need to look for the best combinations that will give you more points or clear more obstacles. For example, matching four candies will give you a striped candy that can clear a whole row or column. Matching five candies will give you a color bomb that can clear all the candies of the same color.</p>
-<h3>Try to create matches of four or more candies to get special candies</h3>
-<p>To make the game more fun and exciting, you should try to create matches of four or more candies to get special candies. Special candies have different effects depending on how you match them. For example, matching a striped candy with another striped candy will create a cross-shaped blast that will clear two rows and two columns. Matching a wrapped candy with another wrapped candy will create a big explosion that will clear a 3x3 area. Matching a color bomb with another color bomb will clear the whole board.</p>
-<h3>Save your boosters for the harder levels and use them wisely</h3>
-<p>To make the game easier and faster, you should save your boosters for the harder levels and use them wisely. Boosters are items that you can buy with coins or get for free by watching ads or completing tasks. They can help you clear more candies from the board or give you extra moves or time. For example, using a bomb will clear a 3x3 area around it. Using a rocket will clear a whole row or column. Using a hammer will clear any candy or obstacle of your choice. Using a rainbow candy will change all the candies of one color to another color.</p>
-<h2>Conclusion</h2>
-<p>Candy Match 3 APK is a sweet and fun puzzle game for Android devices that will keep you entertained for hours. It is a game that challenges you to match candies of the same color and shape to clear them from the board and complete various goals. It is a game that offers hundreds of levels, different modes, and various power-ups. It is a game that has colorful graphics, cute sounds, and smooth animations. It is a game that is fun and relaxing to play and train your brain. If you love candy and matching games, you should download Candy Match 3 APK from Uptodown or APKCombo and enjoy this delicious game.</p>
-<h2>FAQs</h2>
-<h4>Q: How can I download Candy Match 3 APK?</h4>
-<p>A: You can download Candy Match 3 APK from Uptodown or APKCombo, two popular websites that offer safe and reliable APK files for Android devices. You just need to visit their websites, search for Candy Match 3 APK, and click on the download button. Then, you need to enable unknown sources on your device settings and install the APK file manually.</p>
-<h4>Q: How can I get more coins in Candy Match 3 APK?</h4>
-<p>A: You can get more coins in Candy Match 3 APK by completing levels, getting stars, watching ads, or buying them with real money.</p>
-<h4>Q: How can I get more lives in Candy Match 3 APK?</h4>
-<p>A: You can get more lives in Candy Match 3 APK by waiting for them to refill over time, asking your friends for help, watching ads, or buying them with real money.</p>
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-<p>A: You can unlock more levels in Candy Match 3 APK by completing the previous levels with at least one star.</p>
-<h4>Q: How can I contact the developer of Candy Match 3 APK?</h4>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Conduce tu coche por el trfico y gana dinero descargar Traffic Racer APK.md

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-<h1>Descargar Traffic Racer APK: Un Juego de Carreras Arcade Sin Fin</h1>
- <p>¿Te gustan los juegos de carreras que te hacen sentir la adrenalina de conducir a toda velocidad por la carretera? ¿Quieres disfrutar de una experiencia de conducción realista con gráficos 3D y una física impresionante? ¿Quieres probar más de 40 coches diferentes y personalizarlos a tu gusto? Entonces, te encantará <strong>Traffic Racer</strong>, un juego de carreras arcade sin fin que puedes descargar gratis en tu dispositivo Android o iOS.</p>
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- <p>Traffic Racer es un juego desarrollado por SK Games, una empresa independiente fundada por Soner Kara en 2012. El juego se lanzó por primera vez en 2013 y desde entonces ha recibido más de 100 millones de descargas y miles de reseñas positivas. El juego se actualiza constantemente con nuevas características, mejoras y correcciones de errores.</p>
- <p>En este artículo, te vamos a explicar qué es Traffic Racer, cuáles son sus características principales, cómo descargar e instalar el archivo APK en tu dispositivo Android, qué consejos y trucos puedes seguir para mejorar tu rendimiento en el juego, y qué opinan los usuarios y los expertos sobre este juego. Al final, también te responderemos algunas preguntas frecuentes que pueden surgirte sobre Traffic Racer.</p>
- <h2>¿Qué es Traffic Racer?</h2>
- <p>Traffic Racer es un juego de carreras arcade sin fin que te pone al volante de un coche y te reta a conducir por la carretera esquivando el tráfico, ganando dinero, mejorando tu coche y comprando nuevos. El objetivo es ser uno de los conductores más rápidos en las clasificaciones globales y disfrutar de una conducción fluida y realista.</p>
- <h3>Características principales del juego</h3>
- <p>Estas son algunas de las características principales que hacen que Traffic Racer sea un juego tan divertido y adictivo:</p>
- <ul>
-<li><strong>Gráficos 3D impresionantes</strong>: El juego cuenta con unos gráficos 3D muy detallados y realistas que te hacen sentir como si estuvieras conduciendo de verdad. Los coches, los escenarios, los efectos de luz y sombra, todo está cuidado al máximo para ofrecerte una experiencia visual inmersiva.</li>
-<li><strong>Manejo de coche suave y realista</strong>: El juego tiene una física muy bien lograda que hace que el manejo del coche sea suave y realista. Puedes elegir entre dos opciones de control: inclinar el dispositivo o tocar la pantalla. También puedes ajustar la sensibilidad del volante y el nivel de asistencia. El juego te permite acelerar, frenar, cambiar de carril y usar las luces.</li>
-<li><strong>Más de 40 coches diferentes para elegir</strong>: El juego te ofrece una gran variedad de coches para conducir, desde sedanes hasta deportivos, pasando por camiones, autobuses y SUVs. Todos los coches son ficticios, pero se inspiran en modelos reales. Continuing the article: comunes y sus posibles soluciones:</p>
- <ul>
-<li><strong>El archivo APK está dañado o no se puede abrir</strong>: Esto puede ocurrir si el archivo APK que has descargado está corrupto, incompleto o no es compatible con tu dispositivo Android. Para solucionarlo, debes borrar el archivo APK que has descargado y volver a descargarlo desde otro sitio web confiable. También puedes comprobar si hay una versión más reciente o más antigua del archivo APK que sea compatible con tu dispositivo Android.</li>
-<li><strong>El juego no se ejecuta correctamente o se cierra inesperadamente</strong>: Esto puede ocurrir si el juego tiene algún error, si tu dispositivo Android no cumple con los requisitos mínimos del sistema, o si hay algún conflicto con otras aplicaciones instaladas en tu dispositivo. Para solucionarlo, debes asegurarte de que el juego está actualizado a la última versión, de que tu dispositivo Android tiene suficiente espacio de almacenamiento y memoria RAM disponibles, y de que cierras las aplicaciones que no estés usando mientras juegas. También puedes reiniciar tu dispositivo Android o reinstalar el juego si el problema persiste.</li>
-<li><strong>El juego no se conecta a internet o no muestra las clasificaciones globales</strong>: Esto puede ocurrir si tu conexión a internet es débil, inestable o no está disponible, o si el juego tiene algún problema con el servidor. Para solucionarlo, debes comprobar que tu conexión a internet funciona correctamente y que tienes los datos móviles o el wifi activados. También puedes intentar cambiar de red o reiniciar tu router si el problema es de tu conexión. Si el problema es del juego, debes esperar a que se resuelva por parte de los desarrolladores o contactar con ellos para reportar el problema.</li>
-</ul>
- <h2>Consejos y trucos para jugar a Traffic Racer</h2>
- <p>Ahora que ya sabes cómo descargar e instalar el archivo APK de Traffic Racer, te vamos a dar algunos consejos y trucos para que puedas jugar mejor y conseguir m��s monedas, puntos y coches. Estos son algunos de los consejos y trucos que puedes seguir:</p>
- <h3>Cómo ganar más monedas y puntos</h3>
- <p>Las monedas y los puntos son la moneda del juego que te permiten comprar y mejorar los coches, así como desbloquear nuevos escenarios y modos de juego. Para ganar más monedas y puntos, puedes hacer lo siguiente:</p>
- <ul>
-<li><strong>Conducir por el carril contrario</strong>: Si conduces por el carril contrario, ganarás más monedas y puntos por cada coche que adelantes. Sin embargo, también tendrás más riesgo de chocar con el tráfico que viene de frente, así que ten cuidado.</li>
-<li><strong>Conducir a más de 100 km/h</strong>: Si conduces a más de 100 km/h, ganarás más monedas y puntos por cada segundo que mantengas esa velocidad. Sin embargo, también tendrás menos tiempo para reaccionar ante los obstáculos, así que ten precaución.</li>
-<li><strong>Conducir cerca de los otros coches</strong>: Si conduces cerca de los otros coches sin chocar con ellos, ganarás más monedas y puntos por cada coche que pases cerca. Sin embargo, también tendrás más posibilidades de rozarlos o golpearlos, así que ten habilidad.</li>
-<li><strong>Usar el turbo</strong>: Si usas el turbo, aumentarás la velocidad de tu coche durante unos segundos y ganarás más monedas y puntos por cada coche que adelantes. Sin embargo, también consumirás más combustible y tendrás menos control sobre el coche, así que ten sentido común.</li>
-<li><strong>Completar las misiones</strong>: Si completas las misiones que te propone el juego, ganarás más monedas y puntos por cada misión completada. Las misiones pueden ser de diferentes tipos, como conducir una distancia determinada, alcanzar una velocidad máxima, adelantar un número de coches, etc.</li>
-<li><strong>Ver vídeos publicitarios</strong>: Si ves vídeos publicitarios desde el menú del juego, ganarás más monedas y puntos por cada vídeo visto. Los vídeos publicitarios suelen durar unos 30 segundos y te dan una recompensa al finalizarlos.</li>
-</ul>
- <h3 Continuing the article: >Cómo evitar el tráfico y los accidentes</h3>
- <p>El tráfico y los accidentes son los principales obstáculos que te encontrarás en Traffic Racer. Si chocas con otro coche, perderás la carrera y tendrás que empezar de nuevo. Para evitar el tráfico y los accidentes, puedes hacer lo siguiente:</p>
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- <ul>
-<li><strong>Observar el tráfico</strong>: Antes de cambiar de carril, debes observar el tráfico que hay delante y detrás de ti, y elegir el carril más despejado. También debes estar atento a los coches que cambian de carril sin previo aviso, y a los que frenan o aceleran repentinamente.</li>
-<li><strong>Anticiparse a las situaciones</strong>: Debes anticiparte a las situaciones que puedan ocurrir en la carretera, como las curvas, los cruces, las señales, los semáforos, etc. Debes ajustar tu velocidad y tu posición en función de lo que veas venir, y evitar las maniobras bruscas o arriesgadas.</li>
-<li><strong>Mantener una distancia de seguridad</strong>: Debes mantener una distancia de seguridad con los otros coches, tanto por delante como por detrás. Esto te dará más tiempo para reaccionar ante cualquier imprevisto, y evitará que choques con ellos si frenan o aceleran.</li>
-<li><strong>Usar las luces</strong>: Debes usar las luces de tu coche para indicar tus intenciones a los otros conductores. Por ejemplo, debes usar las luces intermitentes para señalizar que vas a cambiar de carril, y las luces de freno para avisar que vas a reducir la velocidad. Esto evitará confusiones y malentendidos con el tráfico.</li>
-<li><strong>No conducir en estado de ebriedad o cansancio</strong>: Debes evitar conducir en Traffic Racer si estás bajo los efectos del alcohol, las drogas o el cansancio. Estas condiciones afectan negativamente a tu capacidad de atención, concentración, coordinación y reflejos, y aumentan el riesgo de sufrir un accidente.</li>
-</ul>
- <h3>Cómo usar el controlador MFi en iOS</h3>
- <p>Si tienes un dispositivo iOS y quieres jugar a Traffic Racer con un controlador MFi (Made for iPhone/iPad/iPod), puedes hacerlo siguiendo estos pasos:</p>
- <ol>
-<li><strong>Conectar el controlador MFi a tu dispositivo iOS</strong>: Para conectar el controlador MFi a tu dispositivo iOS, debes seguir las instrucciones del fabricante del controlador. Normalmente, se trata de encender el controlador y activar el bluetooth en tu dispositivo iOS. Luego, debes emparejarlos desde el menú de ajustes de tu dispositivo iOS.</li>
-<li><strong>Abrir el juego Traffic Racer</strong>: Una vez que hayas conectado el controlador MFi a tu dispositivo iOS, debes abrir el juego Traffic Racer desde tu pantalla de inicio.</li>
-<li><strong>Configurar el controlador MFi en el juego</strong>: Una vez que hayas abierto el juego Traffic Racer, debes ir al menú de opciones y seleccionar la opción <em>Controlador MFi</em>. Allí podrás ver los botones asignados al controlador MFi y cambiarlos si lo deseas. También podrás ajustar la sensibilidad del volante y el nivel de asistencia.</li>
-<li><strong>Jugar con el controlador MFi</strong>: Una vez que hayas configurado el controlador MFi en el juego, podrás jugar con él como si fuera un volante real. Podrás acelerar, frenar, cambiar de carril y usar el turbo con los botones del controlador MFi.</li>
-</ol>
- <h2>Opiniones y reseñas de Traffic Racer</h2>
- <p>Traffic Racer es un juego que ha recibido muchas opiniones y reseñas positivas por parte de los usuarios y los expertos. Estas son algunas de las opiniones y reseñas más destacadas:</p>
- <h3>Lo que dicen los usuarios</h3>
- <p>Estos son algunos de los comentarios que han dejado los usuarios sobre Traffic Racer en la tienda oficial de aplicaciones:</p>
- <table>
-<tr>
-<th>Usuario</th>
-<th>Comentario</th>
-<th>Puntuación</th>
-</tr>
-<tr>
-<td>J Continuing the article: uan</td>
-<td>Me encanta este juego, es muy divertido y adictivo. Los gráficos son muy buenos y los coches son muy variados. Lo único que le falta es que se pueda jugar online con otros jugadores.</td>
-<td>5 estrellas</td>
-</tr>
-<tr>
-<td>Laura</td>
-<td>Es un juego muy entretenido y fácil de jugar. Me gusta que se pueda personalizar el coche y que haya diferentes escenarios y modos de juego. Lo recomiendo para pasar el rato.</td>
-<td>4 estrellas</td>
-</tr>
-<tr>
-<td>Mario</td>
-<td>Es un buen juego de carreras, pero tiene algunos fallos. A veces se cierra solo o se queda colgado. También me gustaría que hubiera más opciones de control y que se pudiera escuchar música mientras se juega.</td>
-<td>3 estrellas</td>
-</tr>
-<tr>
-<td>Sofía</td>
-<td>No me gusta este juego, me parece muy aburrido y repetitivo. Los coches son muy caros y las carreras son muy cortas. Además, el juego tiene mucha publicidad y consume mucha batería.</td>
-<td>2 estrellas</td>
-</tr>
-<tr>
-<td>Pedro</td>
-<td>Es el peor juego de carreras que he probado. Los gráficos son malísimos, el manejo del coche es horrible y el juego está lleno de bugs. No lo recomiendo para nada, es una pérdida de tiempo y de espacio.</td>
-<td>1 estrella</td>
-</tr>
-</table>
- <h3>Lo que dicen los expertos</h3>
- <p>Estos son algunos de los análisis que han hecho los expertos sobre Traffic Racer en diferentes medios especializados:</p>
- <table>
-<tr>
-<th>Medio</th>
-<th>Análisis</th>
-<th>Puntuación</th>
-</tr>
-<tr>
-<td>Android Authority</td>
-<td>Traffic Racer es un juego de carreras arcade sin fin que ofrece una experiencia de conducción realista y divertida. El juego cuenta con unos gráficos 3D impresionantes, una gran variedad de coches y escenarios, y varios modos de juego para elegir. El juego es gratuito, pero tiene compras integradas opcionales para obtener más monedas y puntos. El juego es ideal para los amantes de la velocidad y la adrenalina.</td>
-<td>8/10</td>
-</tr>
-<tr>
-<td>iOS App Store Review</td>
-<td>Traffic Racer is a racing game that puts you behind the wheel of a car and challenges you to drive through the highway dodging traffic, earning money, upgrading your car and buying new ones. The game features stunning 3D graphics, smooth and realistic car handling, over 40 different cars to choose from, and 5 different environments to drive in. The game is free, but it has in-app purchases optional to get more coins and points. The game is perfect for those who love speed and adrenaline.</td>
-<td>4/5</td>
-</tr>
-<tr Continuing the article: <td>AppAdvice</td>
-<td>Traffic Racer is a racing game that lets you drive a car and challenge yourself to avoid traffic, earn money, upgrade your car and buy new ones. The game has amazing 3D graphics, smooth and realistic car handling, over 40 different cars to choose from, and 5 different environments to drive in. The game is free, but it has in-app purchases optional to get more coins and points. The game is great for those who love speed and adrenaline.</td>
-<td>4.5/5</td>
-</tr>
-</table>
- <h2>Conclusión</h2>
- <p>Traffic Racer es un juego de carreras arcade sin fin que te ofrece una experiencia de conducción realista y divertida. El juego tiene unos gráficos 3D impresionantes, una gran variedad de coches y escenarios, y varios modos de juego para elegir. El juego es gratuito, pero tiene compras integradas opcionales para obtener más monedas y puntos.</p>
- <p>Si quieres descargar Traffic Racer APK en tu dispositivo Android, puedes seguir los pasos que te hemos explicado en este artículo. También te hemos dado algunos consejos y trucos para jugar mejor y conseguir más monedas, puntos y coches. Además, te hemos mostrado algunas opiniones y reseñas de Traffic Racer por parte de los usuarios y los expertos.</p>
- <p>Esperamos que este artículo te haya sido útil y que disfrutes de Traffic Racer. Si tienes alguna duda o sugerencia sobre el juego o el artículo, no dudes en dejarnos un comentario. ¡Gracias por leernos!</p>
- <h2>Preguntas frecuentes</h2>
- <p>A continuación, te respondemos algunas preguntas frecuentes que pueden surgirte sobre Traffic Racer:</p>
- <h3>¿Traffic Racer es un juego seguro?</h3>
- <p>Sí, Traffic Racer es un juego seguro que no contiene virus, malware ni contenido inapropiado. Sin embargo, debes tener cuidado al descargar el archivo APK del juego desde sitios web externos, ya que pueden contener archivos dañinos o falsos. Te recomendamos que solo descargues el archivo APK desde sitios web confiables y que verifiques los permisos que solicita el juego antes de instalarlo.</p>
- <h3>¿Traffic Racer es un juego online o offline?</h3>
- <p>Traffic Racer es un juego que se puede jugar tanto online como offline. Si juegas online, podrás acceder a las clasificaciones globales y ver tu posición y la de otros jugadores. También podrás actualizar el juego con las últimas novedades y correcciones de errores. Si juegas offline, podrás jugar sin necesidad de conexión a internet, pero no podrás ver las clasificaciones globales ni actualizar el juego.</p>
- <h3>¿Traffic Racer tiene multijugador?</h3>
- <p>No, Traffic Racer no tiene multijugador. Es un juego de carreras arcade sin fin que solo se puede jugar en modo individual. Sin embargo, puedes competir contra otros jugadores a través de las clasificaciones globales y ver quién es el conductor más rápido del mundo.</p>
- <h3>¿Traffic Racer tiene trucos o hacks?</h3>
- <p>No, Traffic Racer no tiene trucos o hacks oficiales. Es un juego que se basa en la habilidad y la destreza del jugador para conducir por la carretera esquivando el tráfico. Sin embargo, hay algunos sitios web que ofrecen trucos o hacks no oficiales para obtener más monedas y puntos en el juego. Te advertimos que estos trucos o hacks pueden ser ilegales, inseguros o dañar tu dispositivo o tu cuenta del juego. Te recomendamos que no los uses y que juegues de forma honesta y legal.</p>
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- <p>No, Traffic Racer no tiene versión para PC. Es un juego diseñado para dispositivos móviles Android e iOS. Sin embargo, hay algunos emuladores de Android que te permiten jugar a Traffic Racer en tu PC. Un emulador de Android es un programa que simula el sistema operativo Android en tu PC y te permite ejecutar aplicaciones y juegos de Android en tu PC. Algunos ejemplos de emuladores de Android son BlueStacks, NoxPlayer o LDPlayer.</p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download 500px Photos Faster and Easier with This Chrome Extension.md

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-<h1>How to Download 500px Photos for Free</h1>
-<p>Do you love photography and want to discover and share amazing photos from around the world? If so, you might have heard of <a href="(^1^)">500px</a>, a photography community that helps you find inspiration and connect with photographers. You can also get paid for your work by licensing your photos through 500px.</p>
-<p>But what if you want to download some of the stunning photos from 500px for your personal use, such as setting them as wallpapers, printing them, or using them for inspiration? Unfortunately, you can't directly download a photo from 500px for free, as they are protected by copyright. However, if you need to save a photo for legal use, such as fair use or educational purposes, there are some tools that will help you.</p>
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-<p>In this article, we will show you three methods to download photos from 500px for free. You will need a web browser and an internet connection for all of them. Let's get started!</p>
- <h2>Method 1: Using 500pxdownloader</h2>
-<p>This method uses a website called <a href="https://www.500pxdownload.com/">500pxdownload.com</a> that helps you to download images from 500px. Here are the steps:</p>
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-<li>Navigate to the image which you want to download. Open <a href="(^1^)">500px.com</a> in your web browser and click on the image to expand. Log-in isn't required here.</li>
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-<li>Open <a href="https://www.500pxdownload.com/">500pxdownload.com</a> in a new window. This website helps you to download images from 500px.</li>
-<li>Paste your 500px image link in the box. When you're done, hit the Magic Decoder Button, under the URL box. This will open your photograph in a new window.</li>
-<li>Click on the image to download. Hit the Download button from the dialog box to save the image to your device. You can choose the location and name of the file.</li>
-</ol>
-<p>Congratulations, you have successfully downloaded a photo from 500px using 500pxdownloader!</p>
- <h2>Method 2: Using Savelink.info</h2>
-<p>This method uses another website called <a href="https://www.savelink.info/sites/500px">Savelink.info</a> that allows you to download photos from various websites, including 500px. Here are the steps:</p>
-<ol>
-<li>Open the 500px image which you want to download. Go to <a href="">500px.com</a> and click on the photo you like. You don't need to log in for this.</li>
-<li>Copy the URL of your preferred photo. Select the link from the URL box and copy it. E.g : <a href="">https://500px.com/photo/1018375251/autumn-in-the-city-by-alexey-krasnitsky</a></li>
-<li>Visit <a href="https://www.savelink.info/sites/500px">www.savelink.info/sites/500px</a> in your browser. This website helps you to download photos from 500px and other sites.</li>
-<li>Paste the 500px link into the box. Click on the box and paste your copied link there.</li>
-<li>Click on the button with the three dots on it. This will show you a list of available sizes and qualities for your photo.</li>
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-</ol>
-<p>Well done, you have successfully downloaded a photo from 500px using Savelink.info!</p>
- <h2>Method 3: Using the 500px app</h2>
-<p>This method uses the official 500px app for Android or iOS devices. You will need to download the app and create a free account for this method. Here are the steps:</p>
-<ol>
-<li>Download the app for Android or iOS. Go to <a href="">Google Play Store</a> or <a href="">Apple App Store</a> and search for 500px. Download and install the app on your device.</li>
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-<li>Browse or search for photos you like. You can explore different categories, such as Popular, Editors' Choice, Upcoming, Fresh, or Following. You can also use the search bar to find photos by keywords, tags, or usernames.</li>
-<li>Tap on the photo to view it in full screen. You can also swipe left or right to see more photos.</li>
-<li>Tap on the three-dot icon at the bottom right corner. This will open a menu with various options, such as Like, Comment, Share, Add to Gallery, or Download photo.</li>
-<li>Tap on Download photo and choose a size and quality. You can choose from Small (800 px), Medium (2048 px), Large (4096 px), or Original (full resolution). The app will ask for your permission to access your photos, media, and files on your device.</li>
-<li>Save the image to your device. The app will download the photo and save it to your device's gallery or camera roll. You can also find it in your Downloads folder.</li>
-</ol>
-<p>Awesome, you have successfully downloaded a photo from 500px using the 500px app!</p>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you three methods to download photos from 500px for free. You can use any of these methods depending on your preference and device. However, please remember that downloading photos from 500px does not give you the right to use them for commercial purposes or without crediting the original photographers. You should always respect their work and follow their license terms.</p>
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-Download 500px app for Android and iOS<br />
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-<p>We hope you found this article helpful and informative. If you have any questions or feedback, please let us know in the comments below. Happy downloading!</p>
- <h2>FAQs</h2>
-<h3>Q1: Is it legal to download photos from 500px?</h3>
-<p>A1: It depends on how you use them. If you download photos from 500px for personal use only, such as setting them as wallpapers, printing them, or using them for inspiration, then it is legal under fair use doctrine. However, if you download photos from 500px for commercial use, such as selling them, using them for advertising, or modifying them, then it is illegal unless you have the permission of the photographers or you buy a license from 500px. You should always check the license terms of each photo before downloading and using them.</p>
- <h3>Q2: How can I use the photos I download from 500px?</h3>
-<p>A2: You can use the photos you download from 500px for various purposes, as long as you respect the rights of the photographers and follow their license terms. Some of the common ways to use the photos are:</p>
-<ul>
-<li>Setting them as wallpapers for your desktop, laptop, tablet, or smartphone.</li>
-<li>Printing them and framing them as art pieces for your home or office.</li>
-<li>Using them as backgrounds or elements for your graphic design projects.</li>
-<li>Using them as references or inspiration for your own photography or art.</li>
-<li>Sharing them with your friends or family on social media or messaging apps.</li>
-</ul>
-<p>However, you should always credit the original photographers when you use their photos and link back to their 500px profiles. You should also avoid using their photos for any illegal, offensive, or harmful purposes.</p>
- <h3>Q3: How can I upload my own photos to 500px?</h3>
-<p>A3: If you have some amazing photos that you want to share with the world and get paid for them, you can upload them to 500px and join the community of millions of photographers. Here are the steps to upload your photos to 500px:</p>
-<ol>
-<li>Create a free account on 500px. Go to <a href="">500px.com</a> and click on Join in the top right corner. You can sign up with your email and password, or use Facebook, Google, or Apple.</li>
-<li>Verify your email address. Check your inbox for a confirmation email from 500px and click on the link to verify your account.</li>
-<li>Upload your photos. Click on the Upload button in the top right corner and choose one or more photos from your device. You can also drag and drop your photos into the upload window.</li>
-<li>Edit your photos. You can crop, rotate, adjust, filter, or watermark your photos using the built-in editor. You can also add titles, descriptions, tags, categories, locations, and privacy settings to your photos.</li>
-<li>Publish your photos. When you are done editing your photos, click on Publish in the bottom right corner. Your photos will be uploaded to your profile and visible to other users.</li>
-</ol>
-<p>Congratulations, you have successfully uploaded your photos to 500px!</p>
- <h3>Q4: How can I get paid for my photos on 500px?</h3>
-<p>A4: If you want to earn money from your photos on 500px, you can license them through 500px Licensing. This means that you allow other people or companies to use your photos for commercial purposes in exchange for a royalty fee. Here are the steps to license your photos on 500px:</p>
-<ol>
-<li>Opt-in for Licensing. Go to <a href="">https://licensing.500px.com/</a> and click on Start Licensing Your Photos. You will need to agree to the Contributor Agreement and fill in some information about yourself and your payment method.</li>
-<li>Select your photos for Licensing. Go to <a href="">https://web.500px.com/manage</a> and click on the Licensing tab. You will see a list of your uploaded photos that are eligible for licensing. You can select the ones that you want to license by clicking on the checkbox next to each photo.</li>
-<li>Submit your photos for review. After selecting your photos, click on Submit Selected Photos in the bottom right corner. Your photos will be sent to the 500px Licensing team for review and approval.</li>
-<li>Wait for approval and payment. Once your photos are approved, they will be added to the 500px Licensing collection and available for buyers to purchase. You will receive a notification email when someone buys a license for your photo. You will also see your earnings in your account dashboard. You can withdraw your earnings once they reach $50 USD.</li>
-</ol>
-<p>Well done, you have successfully licensed your photos on 500px!</p>
- <h3>Q5: How can I contact the photographers on 500px?</h3>
-<p>A5: If you want to contact the photographers on 500px, you can do so by sending them a message through their profile page. Here are the steps to contact a photographer on 500px:</p>
-<ol>
-<li>Find the photographer's profile page. Go to <a href="">500px.com</a ) and search for the photographer's name, username, or photo. You can also click on their name or profile picture from any photo page.</li>
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-<li>Type your message and click on Send. You can write anything you want, such as complimenting their work, asking for permission to use their photos, or collaborating with them. Be polite and respectful in your message.</li>
-<li>Wait for a reply. The photographer will receive your message in their inbox and may reply to you if they are interested. You can check your inbox by clicking on the envelope icon in the top right corner of the website.</li>
-</ol>
-<p>Great, you have successfully contacted a photographer on 500px!</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Cooking Journey Cooking Games Mod APK - Cook Serve and Have Fun.md

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-<h1>Download Cooking Journey Cooking Games Mod Apk: A Fun and Addictive Cooking Game for Android</h1>
-<p>Are you looking for an addictive cooking game that will challenge your time-management skills and culinary creativity? Do you want to travel around the world and experience different cuisines and cultures? Do you want to enjoy a fine art design game with beautiful graphics and sound effects? If you answered yes to any of these questions, then you should download Cooking Journey Cooking Games Mod Apk, a modified version of the original game that gives you unlimited money and gems to unlock all the features and have more fun.</p>
-<h2>What is Cooking Journey Cooking Games?</h2>
-<p>Cooking Journey Cooking Games is a free time-management cooking game developed by Cooking Chef Studio. In this game, you can cook delicious food, meals, and desserts from all over the world, explore great restaurants, and become a master chef. Here are some of the features of this game:</p>
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-<h3>A time-management cooking game with various cuisines and restaurants</h3>
-<p>In this game, you can serve hundreds of exotic recipes from different countries, such as France, Italy, Mexico, China, Japan, and more. You can also discover many different restaurants, such as sushi bar, pizza shop, burger joint, ice cream parlor, taco truck, and more. You can also practice your cooking and management skills by preparing the ingredients, cooking the food, plating the dishes, serving the customers, collecting the coins, and cleaning the kitchen.</p>
-<h3>A fine art design game with beautiful graphics and sound effects</h3>
-<p>This game has a fine art design that makes you feel like you are in a real restaurant. The graphics are colorful and detailed, the animations are smooth and realistic, and the sound effects are lively and immersive. You can also enjoy the different themes and styles of each restaurant, such as Parisian elegance, Roman romance, New York chic, Mexican fiesta, Japanese zen, and more.</p>
-<h3>A free to play game with offline mode and in-app purchases</h3>
-<p>This game is free to download and play on your Android device. You can also play it offline without an internet connection. However, if you want to access some extra features or speed up your progress, you can also make in-app purchases with real money. For example, you can buy more coins or gems to unlock new restaurants or ingredients. You can also buy magic boosts to complete special cooking goals or get more tips from customers.</p>
-<h2>What is Cooking Journey Cooking Games Mod Apk?</h2>
-<p>Cooking Journey Cooking Games Mod Apk is a modified version of the original game that gives you unlimited money and gems to unlock all the features and have more fun. Here are some of the benefits of using this mod apk:</p>
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-<h3>Step 1: Download the mod apk file from the link below</h3>
-<p>Click on the link below to download the mod apk file of Cooking Journey Cooking Games. The file size is about 100 MB and it will take a few minutes to download depending on your internet speed.</p>
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-<h3>Step 2: Enable unknown sources on your device settings</h3>
-<p>Before you can install the mod apk file, you need to enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play Store. To do this, go to your device settings > security > unknown sources > enable.</p>
-<h3>Step 3: Install the mod apk file and enjoy the game</h3>
-<p>After you have enabled unknown sources, go to your file manager and locate the mod apk file that you have downloaded. Tap on it and follow the instructions to install it. Once the installation is complete, open the game and enjoy it with unlimited money and gems.</p>
-<h2>Tips and Tricks to Play Cooking Journey Cooking Games</h2>
-<p>If you want to play Cooking Journey Cooking Games like a pro, here are some tips and tricks that you can use:</p>
-<h3>Use magic boosts to complete special cooking goals</h3>
-<p>In some levels, you will have special cooking goals that require you to cook a certain number of dishes or serve a certain number of customers in a limited time. To complete these goals, you can use magic boosts that will help you cook faster or serve more customers. For example, you can use the fast cook boost that will make your food cook instantly or the double tip boost that will make your customers pay twice as much.</p>
-<h3>Get combos and earn big bonus, coins, and tips</h3>
-<p>To earn more money and tips in the game, you should try to get combos by serving customers quickly and accurately. The more customers you serve in a row without making any mistakes or delays, the higher your combo meter will go. When your combo meter is full, you will get a big bonus of coins and tips that will boost your income.</p>
-<h3>Decorate your restaurants and upgrade your ingredients</h3>
-<p>To make your restaurants more attractive and profitable, you should decorate them with various items and themes. You can buy decorations with coins or gems in the shop menu. Decorations will increase your restaurant's popularity and customer satisfaction. You should also upgrade your ingredients with coins or gems in the shop menu. Upgrading your ingredients will improve their quality and taste. This will make your customers happier and more generous with their tips.</p>
-<h2>Reviews and Ratings of Cooking Journey Cooking Games</h2>
-<p>Cooking Journey Cooking Games is a popular and well-received game among users who love cooking games. Here are some of the reviews and ratings of this game:</p>
-<h3>Positive reviews from users who love the game</h3>
-<p>Many users have given positive feedback about this game. They have praised its graphics, sound effects, gameplay, variety , and fun of this game. They have also appreciated its offline mode, in-app purchases, and mod apk. Here are some of the positive reviews from Google Play and App Store:</p>
-<p>"This is one of the best cooking games I have ever played. The graphics are amazing, the sound effects are realistic, and the gameplay is challenging and addictive. I love the different cuisines and restaurants that I can explore. I also like that I can play it offline and buy coins and gems with real money. The mod apk is also awesome, it gives me unlimited money and gems to unlock everything. I highly recommend this game to anyone who loves cooking games."</p>
-<p>"I am addicted to this game. It is so fun and relaxing to cook delicious food and serve happy customers. The game has a fine art design that makes me feel like I am in a real restaurant. The game also has a lot of variety and content that keeps me entertained for hours. I also appreciate that the game has an offline mode and in-app purchases that are optional. The mod apk is also great, it makes the game more enjoyable with unlimited money and gems."</p>
-<h3>Negative reviews from users who encounter some bugs or glitches</h3>
-<p>Some users have given negative feedback about this game. They have complained about some bugs or glitches that affect their gaming experience. They have also suggested some improvements or features that they would like to see in the game. Here are some of the negative reviews from Google Play and App Store:</p>
-<p>"The game is good, but it has some bugs that need to be fixed. Sometimes the game freezes or crashes when I am playing. Sometimes the customers disappear or don't pay me. Sometimes the ingredients or kitchen appliances don't work properly. These bugs are annoying and frustrating. Please fix them as soon as possible."</p>
-<p>"The game is nice, but it has some glitches that ruin the fun. Sometimes the game lags or slows down when I am cooking. Sometimes the coins or gems don't add up correctly. Sometimes the magic boosts don't work or expire too soon. These glitches are disappointing and irritating. Please improve them as soon as possible."</p>
-<h3>Overall rating of 4.8 out of 5 stars on Google Play and App Store</h3>
-<p>Despite some minor issues, Cooking Journey Cooking Games is still a highly rated game among users who love cooking games. The game has an overall rating of 4.8 out of 5 stars on both Google Play and App Store, based on thousands of reviews and ratings. This shows that the game is popular and well-liked by most users who play it.</p>
-<h2>Conclusion</h2>
-<p>Cooking Journey Cooking Games is a fun and addictive cooking game for Android devices that will challenge your time-management skills and culinary creativity. You can cook delicious food, meals, and desserts from all over the world, explore great restaurants, and become a master chef. You can also enjoy a fine art design game with beautiful graphics and sound effects, a free to play game with offline mode and in-app purchases, and a mod apk that gives you unlimited money and gems to unlock all the features and have more fun.</p>
-<p>If you want to download Cooking Journey Cooking Games Mod Apk, you just need to follow these simple steps:</p>
-<ol>
-<li>Download the mod apk file from the link below</li>
-<li>Enable unknown sources on your device settings</li>
-<li>Install the mod apk file and enjoy the game</li>
-</ol>
-<p>If you want to play Cooking Journey Cooking Games like a pro, you can use these tips and tricks:</p>
-<ul>
-<li>Use magic boosts to complete special cooking goals</li>
-<li>Get combos and earn big bonus, coins, and tips</li>
-<li>Decorate your restaurants and upgrade your ingredients</li>
-</ul>
-<p>Cooking Journey Cooking Games is a popular and well-received game among users who love cooking games. The game has an overall rating of 4.8 out of 5 stars on both Google Play and App Store.</p>
-<p>If you are looking for an addictive cooking game that will challenge your time-management skills and culinary creativity, you should download Cooking Journey Cooking Games Mod Apk today.</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Cooking Journey Cooking Games Mod Apk:</p>
-<h4>Q: Is Cooking Journey Cooking Games Mod Apk safe to use?</h4>
-<p>A: Yes, Cooking Journey Cooking Games Mod Apk is safe to use on your Android device. The file is from a trusted source and has been tested for viruses and malware.</p>
-<h4>Q: Do I need to root my device to use Cooking Journey Cooking Games Mod Apk?</h4>
-<p>A: No, you don't need to root your device to use Cooking Journey Cooking Games Mod Ap k. You just need to enable unknown sources on your device settings to install the mod apk file.</p>
-<h4>Q: Can I play Cooking Journey Cooking Games Mod Apk offline?</h4>
-<p>A: Yes, you can play Cooking Journey Cooking Games Mod Apk offline without an internet connection. However, some features or content may require an internet connection to access.</p>
-<h4>Q: Can I update Cooking Journey Cooking Games Mod Apk to the latest version?</h4>
-<p>A: Yes, you can update Cooking Journey Cooking Games Mod Apk to the latest version when it is available. However, you may need to download and install the new mod apk file from the same source as before.</p>
-<h4>Q: Can I play Cooking Journey Cooking Games Mod Apk with my friends?</h4>
-<p>A: Yes, you can play Cooking Journey Cooking Games Mod Apk with your friends. You can connect your game to Facebook and invite your friends to join you in your cooking journey. You can also share your achievements and progress with your friends on social media.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Experience the Thrill of 8 Ball Pool with Pure APK for Windows.md

@@ -1,147 +0,0 @@
-
-<code>
-<table>
-<tr>
-<td>
- Table 2: Article with HTML formatting </td>
-</tr>
-<tr>
-<td>
- <h1>8 Ball Pool Pure APK: How to Download and Play on Your PC</h1>
-<h2>Introduction</h2>
-<p>Do you love playing pool games on your mobile device but wish you could enjoy them on a bigger screen with better graphics and controls? If so, you might want to try out <strong>8 Ball Pool Pure APK</strong>, a modified version of the popular pool game <strong>8 Ball Pool</strong> from the popular pool game <strong>8 Ball Pool</strong> by Miniclip. 8 Ball Pool Pure APK is a modified version that gives you unlimited coins, cash, and cues without any ads or restrictions. You can download and play 8 Ball Pool Pure APK on your PC using an emulator like Gameloop, which lets you enjoy the game on a larger screen with better graphics and controls. In this article, we will show you how to download and play 8 Ball Pool Pure APK on your PC using Gameloop Emulator. We will also tell you some of the benefits of using APKPure, a reliable source for downloading APK files.</p>
-<h2>How to Download 8 Ball Pool Pure APK on Your PC</h2>
-<p>To download and play 8 Ball Pool Pure APK on your PC, you will need to follow these steps:</p>
-<h2>8 ball pool pure apk</h2><br /><p><b><b>Download</b> &#10004; <a href="https://jinyurl.com/2uNO0W">https://jinyurl.com/2uNO0W</a></b></p><br /><br />
-<h3>Step 1: Download Gameloop Emulator</h3>
-<p>Gameloop Emulator is a free and official Android emulator that allows you to run mobile games on your PC. It has a smooth and fast performance, a large game library, and a user-friendly interface. Gameloop Emulator also has advanced features like keyboard and mouse customization, screen recording, and anti-cheating system.</p>
-<p>To download and install Gameloop Emulator on your PC, follow these steps:</p>
-<ul>
-<li>Go to the official website of Gameloop Emulator  and click on the Download button.</li>
-<li>Run the installer file and follow the instructions to install Gameloop Emulator on your PC.</li>
-<li>Launch Gameloop Emulator and sign in with your Google account or create a new one.</li>
-</ul>
-<h3>Step 2: Download 8 Ball Pool Pure APK from APKPure</h3>
-<p>APKPure is a website where you can download open-source Android applications that are not available or restricted on Google Play Store. APKPure verifies all apps before publishing by using SHA-1 to ensure the application is original and has not been modified in any way. APKPure also offers fast and safe downloads, automatic updates, and region-free access.</p>
-<p>To download and install 8 Ball Pool Pure APK from APKPure, follow these steps:</p>
-<ul>
-<li>Go to the official website of APKPure  and search for 8 Ball Pool Pure APK.</li>
-<li>Click on the Download APK button and save the file on your PC.</li>
-<li>Drag and drop the APK file into Gameloop Emulator or click on the Install APK button at the bottom right corner of Gameloop Emulator.</li>
-<li>Select the APK file from your PC and click on Open to install it.</li>
-</ul>
-<h3>Step 3: Run 8 Ball Pool Pure APK on Gameloop Emulator</h3>
-<p>To run 8 Ball Pool Pure APK on Gameloop Emulator, follow these steps:</p>
-<ul>
-<li>Launch Gameloop Emulator and go to My Games tab.</li>
-<li>Click on 8 Ball Pool Pure APK icon to start the game.</li>
-<li>Allow the game to access your device data and storage.</li>
-<li>Enjoy playing 8 Ball Pool Pure APK on your PC.</li>
-</ul> <h2>How to Play 8 Ball Pool Pure APK on Your PC</h2>
-<p>Now that you have downloaded and installed 8 Ball Pool Pure APK on your PC, you are ready to play the game. Here are some steps to help you enjoy the game better:</p>
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-<h3>Step 4: Adjust the Controls to Your Preference</h3>
-<p>One of the advantages of playing 8 Ball Pool Pure APK on your PC is that you can customize the keyboard and mouse controls to your preference. You can do this by following these steps:</p>
-<ul>
-<li>Click on the Settings icon at the top right corner of Gameloop Emulator.</li>
-<li>Click on the Game tab and select 8 Ball Pool Pure APK from the list.</li>
-<li>Click on the Keyboard icon at the bottom right corner of Gameloop Emulator.</li>
-<li>Drag and drop the keys to the corresponding buttons on the screen.</li>
-<li>Click on Save to apply the changes.</li>
-</ul>
-<p>Some tips and tricks for playing 8 Ball Pool Pure APK on your PC are:</p>
-<ul>
-<li>Use the mouse wheel to zoom in and out of the table.</li>
-<li>Use the left mouse button to aim and adjust the power of your shot.</li>
-<li>Use the right mouse button to apply spin to your cue ball.</li>
-<li>Use the space bar to confirm your shot.</li>
-<li>Use the ESC key to pause or resume the game.</li>
-</ul>
-<h3>Step 5: Enjoy the Game</h3>
-<p>8 Ball Pool Pure APK is a fun and addictive pool game that offers many features and modes for you to enjoy. Some of them are:</p>
-<ul>
-<li><strong>1v1 Mode:</strong> Play against other players online and win coins and trophies.</li>
-<li><strong>Tournaments Mode:</strong> Compete in tournaments with different rules and prizes.</li>
-<li><strong>Practice Mode:</strong> Practice your skills and improve your game.</li>
-<li><strong>Cues Shop:</strong> Buy and upgrade different cues with different stats and abilities.</li>
-<li><strong>Rewards:</strong> Collect daily rewards, free coins, and gifts from friends.</li>
-</ul>
-<p>To play online with other players or friends, you need to have an internet connection and a Miniclip account. You can create a Miniclip account by following these steps:</p>
-<ul>
-<li>Click on the Profile icon at the top left corner of Gameloop Emulator.</li>
-<li>Click on Login with Miniclip ID.</li>
-<li>Enter your email address and password or click on Sign Up to create a new account.</li>
-<li>Verify your email address and complete your profile.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to download and play 8 Ball Pool Pure APK on your PC using Gameloop Emulator. We have also told you some of the benefits of using APKPure, a reliable source for downloading APK files. 8 Ball Pool Pure APK is a modified version of 8 Ball Pool by Miniclip that gives you unlimited coins, cash, and cues without any ads or restrictions. You can enjoy playing this game on a larger screen with better graphics and controls by following our simple steps. We hope you have fun playing 8 Ball Pool Pure APK on your PC. If you have any questions or feedback, please let us know in the comments below. Thank you for reading!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about 8 Ball Pool Pure APK:</p>
- <ol>
- <li><strong>Q1: What is the difference between 8 Ball Pool Pure APK and 8 Ball Pool from Google Play Store?</strong></li>
- <p>A1: The main difference between 8 Ball Pool Pure APK and 8 Ball Pool from Google Play Store is that 8 Ball Pool Pure APK is a modified version that gives you unlimited coins, cash, and cues without any ads or restrictions. You can also download and install 8 Ball Pool Pure APK from APKPure, which is not available or restricted on Google Play Store. However, both versions are developed by Miniclip and have similar gameplay and features.</p>
- <li><strong>Q2: Is it safe to download and install 8 Ball Pool Pure APK from APKPure?</strong></li>
- <p>A2: Yes, it is safe to download and install 8 Ball Pool Pure APK from APKPure. APKPure verifies all apps before publishing by using SHA-1 to ensure the application is original and has not been modified in any way. APKPure also offers fast and safe downloads, automatic updates, and region-free access. However, you should always be careful when downloading any app from unknown sources and scan them with antivirus software before installing them on your PC.</p>
- <li><strong>Q3: Can I play 8 Ball Pool Pure APK on other emulators besides Gameloop Emulator?</strong></li>
- <p>A3: Yes, you can play 8 Ball Pool Pure APK on other emulators besides Gameloop Emulator. However, we recommend using Gameloop Emulator because it is a free and official Android emulator that allows you to run mobile games on your PC with a smooth and fast performance, a large game library, and a user-friendly interface. Gameloop Emulator also has advanced features like keyboard and mouse customization, screen recording, and anti-cheating system.</p>
- <li><strong>Q4: Can I transfer my progress and coins from 8 Ball Pool Pure APK to 8 Ball Pool from Google Play Store or vice versa?</strong></li>
- <p>A4: No, you cannot transfer your progress and coins from 8 Ball Pool Pure APK to 8 Ball Pool from Google Play Store or vice versa. This is because 8 Ball Pool Pure APK and 8 Ball Pool from Google Play Store are different versions of the game and have different servers. If you want to switch between the versions, you will have to start from scratch.</p>
- <li><strong>Q5: What are some alternatives to 8 Ball Pool Pure APK if I want to play other pool games on my PC?</strong></li>
- <p>A5: Some alternatives to 8 Ball Pool Pure APK if you want to play other pool games on your PC are:</p>
-<ul>
-<li><strong>Pool Live Pro:</strong> A realistic and social pool game that lets you play online with other players or friends, chat with them, and join clubs. You can also customize your cue, table, and avatar.</li>
-<li><strong>Real Pool 3D:</strong> A stunning and realistic pool game that lets you play offline or online with other players or friends. You can also choose from different game modes, rules, and environments.</li>
-<li><strong>Cue Billiard Club:</strong> A modern and stylish pool game that lets you play offline or online with other players or friends. You can also enjoy the realistic physics, graphics, and sounds.</li>
-</ul>
- </td>
-</tr>
-</table>
-</code></p> 197e85843d<br />
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-<br />

spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_fastdeploy_stable_diffusion_inpaint.py

@@ -1,491 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import paddle
-import PIL
-
-from paddlenlp.transformers import CLIPFeatureExtractor, CLIPTokenizer
-
-from ...fastdeploy_utils import FastDeployRuntimeModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import (
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-)
-from ...utils import PIL_INTERPOLATION, logging
-from . import StableDiffusionPipelineOutput
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-NUM_UNET_INPUT_CHANNELS = 9
-NUM_LATENT_CHANNELS = 4
-
-
-def prepare_mask_and_masked_image(image, mask, latents_shape):
-    image = np.array(image.convert("RGB").resize((latents_shape[1] * 8, latents_shape[0] * 8)))
-    image = image[None].transpose(0, 3, 1, 2)
-    image = image.astype(np.float32) / 127.5 - 1.0
-
-    image_mask = np.array(mask.convert("L").resize((latents_shape[1] * 8, latents_shape[0] * 8)))
-    masked_image = image * (image_mask < 127.5)
-
-    mask = mask.resize((latents_shape[1], latents_shape[0]), PIL_INTERPOLATION["nearest"])
-    mask = np.array(mask.convert("L"))
-    mask = mask.astype(np.float32) / 255.0
-    mask = mask[None, None]
-    mask[mask < 0.5] = 0
-    mask[mask >= 0.5] = 1
-
-    return mask, masked_image
-
-
-class FastDeployStableDiffusionInpaintPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-guided image inpainting using Stable Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving etc.)
-
-    Args:
-        vae_encoder ([`FastDeployRuntimeModel`]):
-            Variational Auto-Encoder (VAE) Model to encode images to latent representations.
-        vae_decoder ([`FastDeployRuntimeModel`]):
-            Variational Auto-Encoder (VAE) Model to decode images from latent representations.
-        text_encoder ([`FastDeployRuntimeModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`FastDeployRuntimeModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`PNDMScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`]
-            or [`DPMSolverMultistepScheduler`].
-        safety_checker ([`FastDeployRuntimeModel`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae_encoder: FastDeployRuntimeModel,
-        vae_decoder: FastDeployRuntimeModel,
-        text_encoder: FastDeployRuntimeModel,
-        tokenizer: CLIPTokenizer,
-        unet: FastDeployRuntimeModel,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-        ],
-        safety_checker: FastDeployRuntimeModel,
-        feature_extractor: CLIPFeatureExtractor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        self.register_modules(
-            vae_encoder=vae_encoder,
-            vae_decoder=vae_decoder,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `list(int)`):
-                prompt to be encoded
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-        """
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="np",
-        )
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="np").input_ids
-
-        if not np.array_equal(text_input_ids, untruncated_ids):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-
-        text_embeddings = self.text_encoder(input_ids=text_input_ids.astype(np.int64))[0]
-        text_embeddings = np.repeat(text_embeddings, num_images_per_prompt, axis=0)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt] * batch_size
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="np",
-            )
-            uncond_embeddings = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int64))[0]
-            uncond_embeddings = np.repeat(uncond_embeddings, num_images_per_prompt, axis=0)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
-
-        return text_embeddings
-
-    def run_safety_checker(self, image, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(
-                self.numpy_to_pil(image), return_tensors="np"
-            ).pixel_values.astype(dtype)
-            # There will throw an error if use safety_checker batchsize>1
-            images, has_nsfw_concept = [], []
-            for i in range(image.shape[0]):
-                image_i, has_nsfw_concept_i = self.safety_checker(
-                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
-                )
-                images.append(image_i)
-                has_nsfw_concept.append(has_nsfw_concept_i[0])
-            image = np.concatenate(images)
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    def decode_latents(self, latents):
-        latents = 1 / 0.18215 * latents
-        image = np.concatenate(
-            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
-        )
-        image = np.clip(image / 2 + 0.5, 0, 1)
-        image = image.transpose([0, 2, 3, 1])
-        return image
-
-    def prepare_extra_step_kwargs(self, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        return extra_step_kwargs
-
-    def check_inputs(self, prompt, height, width, callback_steps):
-        if not isinstance(prompt, str) and not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):
-        if generator is None:
-            generator = np.random
-
-        latents_shape = (batch_size, num_channels_latents, height // 8, width // 8)
-        if latents is None:
-            latents = paddle.to_tensor(generator.randn(*latents_shape), dtype=dtype)
-        elif latents.shape != latents_shape:
-            raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * float(self.scheduler.init_noise_sigma)
-        return latents
-
-    def prepare_mask_latents(self, mask, masked_image, batch_size, dtype, do_classifier_free_guidance):
-        mask = mask.astype(dtype)
-        masked_image = masked_image.astype(dtype)
-
-        # encode the mask image into latents space so we can concatenate it to the latents
-        masked_image_latents = self.vae_encoder(sample=masked_image)[0]
-        masked_image_latents = 0.18215 * masked_image_latents
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        mask = mask.repeat(batch_size, 0)
-        masked_image_latents = masked_image_latents.repeat(batch_size, 0)
-
-        mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask
-        masked_image_latents = (
-            np.concatenate([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-        )
-        masked_image_latents = masked_image_latents.astype(dtype)
-        return mask, masked_image_latents
-
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: PIL.Image.Image,
-        mask_image: PIL.Image.Image,
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[np.random.RandomState] = None,
-        latents: Optional[np.ndarray] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
-        callback_steps: Optional[int] = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`PIL.Image.Image`):
-                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
-                be masked out with `mask_image` and repainted according to `prompt`.
-            mask_image (`PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
-                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
-                instead of 3, so the expected shape would be `(B, H, W, 1)`.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`np.random.RandomState`, *optional*):
-                A np.random.RandomState to make generation deterministic.
-            latents (`np.ndarray`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        # 1. Check inputs
-        self.check_inputs(prompt, height, width, callback_steps)
-
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        # 4. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = NUM_LATENT_CHANNELS
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            text_embeddings.dtype,
-            generator,
-            latents,
-        )
-
-        # 6. Preprocess mask and image
-        if isinstance(image, PIL.Image.Image) and isinstance(mask_image, PIL.Image.Image):
-            mask, masked_image = prepare_mask_and_masked_image(image, mask_image, latents.shape[-2:])
-
-        # 7. Prepare mask latent variables
-        mask, masked_image_latents = self.prepare_mask_latents(
-            mask,
-            masked_image,
-            batch_size * num_images_per_prompt,
-            text_embeddings.dtype,
-            do_classifier_free_guidance,
-        )
-        num_channels_mask = mask.shape[1]
-        num_channels_masked_image = masked_image_latents.shape[1]
-        mask = paddle.to_tensor(mask)
-        masked_image_latents = paddle.to_tensor(masked_image_latents)
-
-        # 8. Check that sizes of mask, masked image and latents match
-        unet_input_channels = NUM_UNET_INPUT_CHANNELS
-        if num_channels_latents + num_channels_mask + num_channels_masked_image != unet_input_channels:
-            raise ValueError(
-                "Incorrect configuration settings! The config of `pipeline.unet` expects"
-                f" {unet_input_channels} but received `num_channels_latents`: {num_channels_latents} +"
-                f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
-                " `pipeline.unet` or your `mask_image` or `image` input."
-            )
-
-        # 9. Prepare extra step kwargs.
-        extra_step_kwargs = self.prepare_extra_step_kwargs(eta)
-
-        # 10. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            text_embeddings = paddle.to_tensor(text_embeddings, dtype="float32")
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents
-                # concat latents, mask, masked_image_latnets in the channel dimension
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                latent_model_input = paddle.concat([latent_model_input, mask, masked_image_latents], axis=1)
-
-                # predict the noise residual
-                noise_pred = self.unet.zero_copy_infer(
-                    sample=latent_model_input, timestep=t, encoder_hidden_states=text_embeddings
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                scheduler_output = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
-                latents = scheduler_output.prev_sample
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # 11. Post-processing
-        image = self.decode_latents(latents.numpy())
-
-        # 12. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, text_embeddings.dtype)
-
-        # 13. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/232labs/VToonify/vtoonify/model/stylegan/distributed.py

@@ -1,126 +0,0 @@
-import math
-import pickle
-
-import torch
-from torch import distributed as dist
-from torch.utils.data.sampler import Sampler
-
-
-def get_rank():
-    if not dist.is_available():
-        return 0
-
-    if not dist.is_initialized():
-        return 0
-
-    return dist.get_rank()
-
-
-def synchronize():
-    if not dist.is_available():
-        return
-
-    if not dist.is_initialized():
-        return
-
-    world_size = dist.get_world_size()
-
-    if world_size == 1:
-        return
-
-    dist.barrier()
-
-
-def get_world_size():
-    if not dist.is_available():
-        return 1
-
-    if not dist.is_initialized():
-        return 1
-
-    return dist.get_world_size()
-
-
-def reduce_sum(tensor):
-    if not dist.is_available():
-        return tensor
-
-    if not dist.is_initialized():
-        return tensor
-
-    tensor = tensor.clone()
-    dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
-
-    return tensor
-
-
-def gather_grad(params):
-    world_size = get_world_size()
-    
-    if world_size == 1:
-        return
-
-    for param in params:
-        if param.grad is not None:
-            dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
-            param.grad.data.div_(world_size)
-
-
-def all_gather(data):
-    world_size = get_world_size()
-
-    if world_size == 1:
-        return [data]
-
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to('cuda')
-
-    local_size = torch.IntTensor([tensor.numel()]).to('cuda')
-    size_list = [torch.IntTensor([0]).to('cuda') for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.ByteTensor(size=(max_size,)).to('cuda'))
-
-    if local_size != max_size:
-        padding = torch.ByteTensor(size=(max_size - local_size,)).to('cuda')
-        tensor = torch.cat((tensor, padding), 0)
-
-    dist.all_gather(tensor_list, tensor)
-
-    data_list = []
-
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    return data_list
-
-
-def reduce_loss_dict(loss_dict):
-    world_size = get_world_size()
-
-    if world_size < 2:
-        return loss_dict
-
-    with torch.no_grad():
-        keys = []
-        losses = []
-
-        for k in sorted(loss_dict.keys()):
-            keys.append(k)
-            losses.append(loss_dict[k])
-
-        losses = torch.stack(losses, 0)
-        dist.reduce(losses, dst=0)
-
-        if dist.get_rank() == 0:
-            losses /= world_size
-
-        reduced_losses = {k: v for k, v in zip(keys, losses)}
-
-    return reduced_losses

spaces/4Taps/SadTalker/src/audio2pose_models/networks.py

@@ -1,140 +0,0 @@
-import torch.nn as nn
-import torch
-
-
-class ResidualConv(nn.Module):
-    def __init__(self, input_dim, output_dim, stride, padding):
-        super(ResidualConv, self).__init__()
-
-        self.conv_block = nn.Sequential(
-            nn.BatchNorm2d(input_dim),
-            nn.ReLU(),
-            nn.Conv2d(
-                input_dim, output_dim, kernel_size=3, stride=stride, padding=padding
-            ),
-            nn.BatchNorm2d(output_dim),
-            nn.ReLU(),
-            nn.Conv2d(output_dim, output_dim, kernel_size=3, padding=1),
-        )
-        self.conv_skip = nn.Sequential(
-            nn.Conv2d(input_dim, output_dim, kernel_size=3, stride=stride, padding=1),
-            nn.BatchNorm2d(output_dim),
-        )
-
-    def forward(self, x):
-
-        return self.conv_block(x) + self.conv_skip(x)
-
-
-class Upsample(nn.Module):
-    def __init__(self, input_dim, output_dim, kernel, stride):
-        super(Upsample, self).__init__()
-
-        self.upsample = nn.ConvTranspose2d(
-            input_dim, output_dim, kernel_size=kernel, stride=stride
-        )
-
-    def forward(self, x):
-        return self.upsample(x)
-
-
-class Squeeze_Excite_Block(nn.Module):
-    def __init__(self, channel, reduction=16):
-        super(Squeeze_Excite_Block, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction, bias=False),
-            nn.ReLU(inplace=True),
-            nn.Linear(channel // reduction, channel, bias=False),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        return x * y.expand_as(x)
-
-
-class ASPP(nn.Module):
-    def __init__(self, in_dims, out_dims, rate=[6, 12, 18]):
-        super(ASPP, self).__init__()
-
-        self.aspp_block1 = nn.Sequential(
-            nn.Conv2d(
-                in_dims, out_dims, 3, stride=1, padding=rate[0], dilation=rate[0]
-            ),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(out_dims),
-        )
-        self.aspp_block2 = nn.Sequential(
-            nn.Conv2d(
-                in_dims, out_dims, 3, stride=1, padding=rate[1], dilation=rate[1]
-            ),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(out_dims),
-        )
-        self.aspp_block3 = nn.Sequential(
-            nn.Conv2d(
-                in_dims, out_dims, 3, stride=1, padding=rate[2], dilation=rate[2]
-            ),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(out_dims),
-        )
-
-        self.output = nn.Conv2d(len(rate) * out_dims, out_dims, 1)
-        self._init_weights()
-
-    def forward(self, x):
-        x1 = self.aspp_block1(x)
-        x2 = self.aspp_block2(x)
-        x3 = self.aspp_block3(x)
-        out = torch.cat([x1, x2, x3], dim=1)
-        return self.output(out)
-
-    def _init_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight)
-            elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-
-class Upsample_(nn.Module):
-    def __init__(self, scale=2):
-        super(Upsample_, self).__init__()
-
-        self.upsample = nn.Upsample(mode="bilinear", scale_factor=scale)
-
-    def forward(self, x):
-        return self.upsample(x)
-
-
-class AttentionBlock(nn.Module):
-    def __init__(self, input_encoder, input_decoder, output_dim):
-        super(AttentionBlock, self).__init__()
-
-        self.conv_encoder = nn.Sequential(
-            nn.BatchNorm2d(input_encoder),
-            nn.ReLU(),
-            nn.Conv2d(input_encoder, output_dim, 3, padding=1),
-            nn.MaxPool2d(2, 2),
-        )
-
-        self.conv_decoder = nn.Sequential(
-            nn.BatchNorm2d(input_decoder),
-            nn.ReLU(),
-            nn.Conv2d(input_decoder, output_dim, 3, padding=1),
-        )
-
-        self.conv_attn = nn.Sequential(
-            nn.BatchNorm2d(output_dim),
-            nn.ReLU(),
-            nn.Conv2d(output_dim, 1, 1),
-        )
-
-    def forward(self, x1, x2):
-        out = self.conv_encoder(x1) + self.conv_decoder(x2)
-        out = self.conv_attn(out)
-        return out * x2

spaces/7Vivek/Next-Word-Prediction-Streamlit/app.py

@@ -1,83 +0,0 @@
-import os
-import streamlit as st
-import torch
-import string
-from transformers import BertTokenizer, BertForMaskedLM
-
-st.set_page_config(page_title='Next Word Prediction Model', page_icon=None, layout='centered', initial_sidebar_state='auto')
-
-@st.cache()
-def load_model(model_name):
-  try:
-    if model_name.lower() == "bert":
-      bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-      bert_model = BertForMaskedLM.from_pretrained('bert-base-uncased').eval()
-      return bert_tokenizer,bert_model
-  except Exception as e:
-    pass
-
-#use joblib to fast your function
-
-def decode(tokenizer, pred_idx, top_clean):
-  ignore_tokens = string.punctuation + '[PAD]'
-  tokens = []
-  for w in pred_idx:
-    token = ''.join(tokenizer.decode(w).split())
-    if token not in ignore_tokens:
-      tokens.append(token.replace('##', ''))
-  return '\n'.join(tokens[:top_clean])
-
-def encode(tokenizer, text_sentence, add_special_tokens=True):
-  text_sentence = text_sentence.replace('<mask>', tokenizer.mask_token)
-    # if <mask> is the last token, append a "." so that models dont predict punctuation.
-  if tokenizer.mask_token == text_sentence.split()[-1]:
-    text_sentence += ' .'
-
-    input_ids = torch.tensor([tokenizer.encode(text_sentence, add_special_tokens=add_special_tokens)])
-    mask_idx = torch.where(input_ids == tokenizer.mask_token_id)[1].tolist()[0]
-  return input_ids, mask_idx
-
-def get_all_predictions(text_sentence, top_clean=5):
-    # ========================= BERT =================================
-  input_ids, mask_idx = encode(bert_tokenizer, text_sentence)
-  with torch.no_grad():
-    predict = bert_model(input_ids)[0]
-  bert = decode(bert_tokenizer, predict[0, mask_idx, :].topk(top_k).indices.tolist(), top_clean)
-  return {'bert': bert}
-
-def get_prediction_eos(input_text):
-  try:
-    input_text += ' <mask>'
-    res = get_all_predictions(input_text, top_clean=int(top_k))
-    return res
-  except Exception as error:
-    pass
-
-try:
-
-  st.markdown("<h1 style='text-align: center;'>Next Word Prediction</h1>", unsafe_allow_html=True)
-  st.markdown("<h4 style='text-align: center; color: #B2BEB5;'><i>Keywords  : BertTokenizer, BertForMaskedLM, Pytorch</i></h4>", unsafe_allow_html=True)
-
-  st.sidebar.text("Next Word Prediction Model")
-  top_k = st.sidebar.slider("Select How many words do you need", 1 , 25, 1) #some times it is possible to have less words
-  print(top_k)
-  model_name = st.sidebar.selectbox(label='Select Model to Apply',  options=['BERT', 'XLNET'], index=0,  key = "model_name")
-
-  bert_tokenizer, bert_model  = load_model(model_name) 
-  input_text = st.text_area("Enter your text here")
-
-  #click outside box of input text to get result
-  res = get_prediction_eos(input_text)
-
-  answer = []
-  print(res['bert'].split("\n"))
-  for i in res['bert'].split("\n"):
-  	answer.append(i)
-  answer_as_string = "    ".join(answer)
-  st.text_area("Predicted List is Here",answer_as_string,key="predicted_list") 
-  st.image('https://freepngimg.com/download/keyboard/6-2-keyboard-png-file.png',use_column_width=True)
-  st.markdown("<h6 style='text-align: center; color: #808080;'>Created By <a href='https://github.com/7Vivek'>Vivek</a> - Checkout complete project <a href='https://github.com/7Vivek/Next-Word-Prediction-Streamlit'>here</a></h6>", unsafe_allow_html=True)
-
-except Exception as e:
-  print("SOME PROBLEM OCCURED")  
-  

spaces/9prayer/ubiq-chat-cpu/README.md

@@ -1,12 +0,0 @@
----
-title: Chatglm 6b
-emoji: 🐢
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AFCMEgypt/colorimetric_analyzer/README.md

@@ -1,13 +0,0 @@
----
-title: Colorimetric Analyzer
-emoji: 😻
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
-license: bigscience-bloom-rail-1.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AI-Dashboards/HEDIS.Assessment.PHQ9.GADD7.SDoH/index.html

@@ -1,115 +0,0 @@
-<!DOCTYPE html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-      <script src="https://cdn.jsdelivr.net/npm/mermaid/dist/mermaid.min.js"></script>
-      <script>mermaid.initialize({startOnLoad:true});</script>
-	</head>
-	<body>
-
-<iframe
-	src="https://awacke1-health-assessments-summarizer.hf.space"
-	frameborder="0"
-	width="1024"
-	height="3000"
-></iframe>
-
-<iframe
-	src="https://awacke1-hedis-roster-dash-component-sdoh.hf.space"
-	frameborder="0"
-	width="1024"
-	height="2048"
-></iframe>
-<iframe
-	src="https://awacke1-hedis-dash-component-top-clinica-6a4a58c.hf.space"
-	frameborder="0"
-	width="1024"
-	height="2048"
-></iframe>
-<iframe
-	src="https://awacke1-hedis-roster-dash-component-serv-18d25b7.hf.space"
-	frameborder="0"
-	width="1024"
-	height="2048"
-></iframe>
-
-      
-        
-<iframe
-	src="https://awacke1-twitter-sentiment-live-realtime.hf.space"
-	frameborder="0"
-	width="850"
-	height="1024"
-></iframe>
-
-<iframe
-	src="https://awacke1-streamlitwikipediachat.hf.space"
-	frameborder="0"
-	width="850"
-	height="1024"
-></iframe>
-
-<iframe
-	src="https://awacke1-cognitive-ai-episodic-semantic-m-f4b3d67.hf.space"
-	frameborder="0"
-	width="850"
-	height="1024"
-></iframe>
-
-
-      
-<div class="mermaid">
-journey
-    title Create AI
-    section Training
-      Format DataSet Inputs Files, Data Splits: 5: Teacher
-      Model Build w/ SKLearn, TF, Pytorch: 3: Student
-      Determine Model Performance: 1: Teacher, Student
-    section Deploy
-      Web Deploy Local and Cloud: 5: Teacher
-      Architecture Spaces Gradio Streamlit Heroku AWS Azure and GCCP: 5: Teacher
-    section Testing
-      Test Model with Input Datasets: 5: Teacher
-      Examples. Inputs that Work, Inputs That Break Model: 5: Teacher
-      Governance - Analyze, Publish Fairness, Equity, Bias for Datasets and Outputs: 5: Teacher
-</div>
-
-<div class="mermaid">
-sequenceDiagram
-    participant Alice
-    participant Bob
-    Alice->>John: Hello John, how are you?
-    loop Healthcheck
-        John->>John: Fight against hypochondria
-    end
-    Note right of John: Rational thoughts<br/>prevail...
-    John-->>Alice: Great!
-    John->>Bob: How about you?
-    Bob-->>John: Jolly good!
-</div>
-      
-<div class="card">
-<h1>Welcome to the Mermaid Modeler Tip Sheet</h1>
-  <p>
-      You can use Mermaid inside HTML5 by including the script and a div with the class or mermaid.
-  </p>
-  <p>
-      Documentation is located here: 
-      <a href="https://mermaid.js.org/syntax/flowchart.html" target="_blank"
-          >Mermaid documentation</a
-      >.
-  </p>
-</div>
-
-
-Links:
-https://huggingface.co/spaces/awacke1/HEDIS.Roster.Dash.Component.Service
-https://huggingface.co/spaces/awacke1/HEDIS.Roster.Dash.Component.SDOH
-https://huggingface.co/spaces/awacke1/HEDIS.Dash.Component.Top.Clinical.Terminology.Vocabulary   
-
-      
-	</body>
-</html>

spaces/AIGC-Audio/Make_An_Audio/ldm/models/diffusion/ddim.py

@@ -1,262 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
-    extract_into_tensor
-
-
-class DDIMSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
-        super().__init__()
-        self.model = model
-        self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            # if attr.device != torch.device("cuda"):
-            #     attr = attr.to(torch.device("cuda"))
-            attr = attr.to(self.device)
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                ctmp = conditioning[list(conditioning.keys())[0]]
-                while isinstance(ctmp, list): ctmp = ctmp[0]
-                cbs = ctmp.shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        # print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
-        samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def ddim_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-
-        # iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
-
-        for i, step in enumerate(time_range):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning)
-            img, pred_x0 = outs
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None):
-        b, *_, device = *x.shape, x.device
-
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            e_t = self.model.apply_model(x, t, c)
-        else:
-            x_in = torch.cat([x] * 2)
-            t_in = torch.cat([t] * 2)
-            if isinstance(c, dict):
-                assert isinstance(unconditional_conditioning, dict)
-                c_in = dict()
-                for k in c:
-                    if isinstance(c[k], list):
-                        c_in[k] = [torch.cat([
-                            unconditional_conditioning[k][i],
-                            c[k][i]]) for i in range(len(c[k]))]
-                    else:
-                        c_in[k] = torch.cat([
-                            unconditional_conditioning[k],
-                            c[k]])
-            elif isinstance(c, list):
-                c_in = list()
-                assert isinstance(unconditional_conditioning, list)
-                for i in range(len(c)):
-                    c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
-            else:
-                c_in = torch.cat([unconditional_conditioning, c])# c/uc shape [b,seq_len=77,dim=1024],c_in shape [b*2,seq_len,dim]
-            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-        # select parameters corresponding to the currently considered timestep
-        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-        return x_prev, pred_x0
-
-    @torch.no_grad()
-    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
-        # fast, but does not allow for exact reconstruction
-        # t serves as an index to gather the correct alphas
-        if use_original_steps:
-            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
-            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
-        else:
-            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
-            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
-
-        if noise is None:
-            noise = torch.randn_like(x0)
-        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
-                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
-
-    @torch.no_grad()
-    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
-               use_original_steps=False):
-
-        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
-        timesteps = timesteps[:t_start]
-
-        time_range = np.flip(timesteps)
-        total_steps = timesteps.shape[0]
-        # print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        # iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
-        x_dec = x_latent
-        for i, step in enumerate(time_range):
-            index = total_steps - i - 1
-            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
-            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
-                                          unconditional_guidance_scale=unconditional_guidance_scale,
-                                          unconditional_conditioning=unconditional_conditioning)
-        return x_dec

spaces/AIQuest/lungCancerVgg19/README.md

@@ -1,13 +0,0 @@
----
-title: LungCancerVgg19
-emoji: 👀
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: false
-license: gpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AK-12/llama-gradio-chat/README.md

@@ -1,12 +0,0 @@
----
-title: Llama Gradio Chat
-emoji: 👁
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ALSv/midjourney-v4-1/README.md

@@ -1,13 +0,0 @@
----
-title: Anything Midjourney V4 1
-emoji: 🚀
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-duplicated_from: lu2000/anything-midjourney-v4-1
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AP123/dreamgaussian/mesh_renderer.py

@@ -1,154 +0,0 @@
-import os
-import math
-import cv2
-import trimesh
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import nvdiffrast.torch as dr
-from mesh import Mesh, safe_normalize
-
-def scale_img_nhwc(x, size, mag='bilinear', min='bilinear'):
-    assert (x.shape[1] >= size[0] and x.shape[2] >= size[1]) or (x.shape[1] < size[0] and x.shape[2] < size[1]), "Trying to magnify image in one dimension and minify in the other"
-    y = x.permute(0, 3, 1, 2) # NHWC -> NCHW
-    if x.shape[1] > size[0] and x.shape[2] > size[1]: # Minification, previous size was bigger
-        y = torch.nn.functional.interpolate(y, size, mode=min)
-    else: # Magnification
-        if mag == 'bilinear' or mag == 'bicubic':
-            y = torch.nn.functional.interpolate(y, size, mode=mag, align_corners=True)
-        else:
-            y = torch.nn.functional.interpolate(y, size, mode=mag)
-    return y.permute(0, 2, 3, 1).contiguous() # NCHW -> NHWC
-
-def scale_img_hwc(x, size, mag='bilinear', min='bilinear'):
-    return scale_img_nhwc(x[None, ...], size, mag, min)[0]
-
-def scale_img_nhw(x, size, mag='bilinear', min='bilinear'):
-    return scale_img_nhwc(x[..., None], size, mag, min)[..., 0]
-
-def scale_img_hw(x, size, mag='bilinear', min='bilinear'):
-    return scale_img_nhwc(x[None, ..., None], size, mag, min)[0, ..., 0]
-
-def trunc_rev_sigmoid(x, eps=1e-6):
-    x = x.clamp(eps, 1 - eps)
-    return torch.log(x / (1 - x))
-
-def make_divisible(x, m=8):
-    return int(math.ceil(x / m) * m)
-
-class Renderer(nn.Module):
-    def __init__(self, opt):
-        
-        super().__init__()
-
-        self.opt = opt
-
-        self.mesh = Mesh.load(self.opt.mesh, resize=False)
-
-        if not self.opt.gui or os.name == 'nt':
-            self.glctx = dr.RasterizeGLContext()
-        else:
-            self.glctx = dr.RasterizeCudaContext()
-        
-        # extract trainable parameters
-        self.v_offsets = nn.Parameter(torch.zeros_like(self.mesh.v))
-        self.raw_albedo = nn.Parameter(trunc_rev_sigmoid(self.mesh.albedo))
-
-
-    def get_params(self):
-
-        params = [
-            {'params': self.raw_albedo, 'lr': self.opt.texture_lr},
-        ]
-
-        if self.opt.train_geo:
-            params.append({'params': self.v_offsets, 'lr': self.opt.geom_lr})
-
-        return params
-
-    @torch.no_grad()
-    def export_mesh(self, save_path):
-        self.mesh.v = (self.mesh.v + self.v_offsets).detach()
-        self.mesh.albedo = torch.sigmoid(self.raw_albedo.detach())
-        self.mesh.write(save_path)
-
-    
-    def render(self, pose, proj, h0, w0, ssaa=1, bg_color=1, texture_filter='linear-mipmap-linear'):
-        
-        # do super-sampling
-        if ssaa != 1:
-            h = make_divisible(h0 * ssaa, 8)
-            w = make_divisible(w0 * ssaa, 8)
-        else:
-            h, w = h0, w0
-        
-        results = {}
-
-        # get v
-        if self.opt.train_geo:
-            v = self.mesh.v + self.v_offsets # [N, 3]
-        else:
-            v = self.mesh.v
-
-        pose = torch.from_numpy(pose.astype(np.float32)).to(v.device)
-        proj = torch.from_numpy(proj.astype(np.float32)).to(v.device)
-
-        # get v_clip and render rgb
-        v_cam = torch.matmul(F.pad(v, pad=(0, 1), mode='constant', value=1.0), torch.inverse(pose).T).float().unsqueeze(0)
-        v_clip = v_cam @ proj.T
-
-        rast, rast_db = dr.rasterize(self.glctx, v_clip, self.mesh.f, (h, w))
-
-        alpha = (rast[0, ..., 3:] > 0).float()
-        depth, _ = dr.interpolate(-v_cam[..., [2]], rast, self.mesh.f) # [1, H, W, 1]
-        depth = depth.squeeze(0) # [H, W, 1]
-
-        texc, texc_db = dr.interpolate(self.mesh.vt.unsqueeze(0).contiguous(), rast, self.mesh.ft, rast_db=rast_db, diff_attrs='all')
-        albedo = dr.texture(self.raw_albedo.unsqueeze(0), texc, uv_da=texc_db, filter_mode=texture_filter) # [1, H, W, 3]
-        albedo = torch.sigmoid(albedo)
-        # get vn and render normal
-        if self.opt.train_geo:
-            i0, i1, i2 = self.mesh.f[:, 0].long(), self.mesh.f[:, 1].long(), self.mesh.f[:, 2].long()
-            v0, v1, v2 = v[i0, :], v[i1, :], v[i2, :]
-
-            face_normals = torch.cross(v1 - v0, v2 - v0)
-            face_normals = safe_normalize(face_normals)
-            
-            vn = torch.zeros_like(v)
-            vn.scatter_add_(0, i0[:, None].repeat(1,3), face_normals)
-            vn.scatter_add_(0, i1[:, None].repeat(1,3), face_normals)
-            vn.scatter_add_(0, i2[:, None].repeat(1,3), face_normals)
-
-            vn = torch.where(torch.sum(vn * vn, -1, keepdim=True) > 1e-20, vn, torch.tensor([0.0, 0.0, 1.0], dtype=torch.float32, device=vn.device))
-        else:
-            vn = self.mesh.vn
-        
-        normal, _ = dr.interpolate(vn.unsqueeze(0).contiguous(), rast, self.mesh.fn)
-        normal = safe_normalize(normal[0])
-
-        # rotated normal (where [0, 0, 1] always faces camera)
-        rot_normal = normal @ pose[:3, :3]
-        viewcos = rot_normal[..., [2]]
-
-        # antialias
-        albedo = dr.antialias(albedo, rast, v_clip, self.mesh.f).squeeze(0) # [H, W, 3]
-        albedo = alpha * albedo + (1 - alpha) * bg_color
-
-        # ssaa
-        if ssaa != 1:
-            albedo = scale_img_hwc(albedo, (h0, w0))
-            alpha = scale_img_hwc(alpha, (h0, w0))
-            depth = scale_img_hwc(depth, (h0, w0))
-            normal = scale_img_hwc(normal, (h0, w0))
-            viewcos = scale_img_hwc(viewcos, (h0, w0))
-
-        results['image'] = albedo.clamp(0, 1)
-        results['alpha'] = alpha
-        results['depth'] = depth
-        results['normal'] = (normal + 1) / 2
-        results['viewcos'] = viewcos
-
-        return results

spaces/Abhilashvj/planogram-compliance/models/yolo.py

@@ -1,569 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-YOLO-specific modules
-
-Usage:
-    $ python models/yolo.py --cfg yolov5s.yaml
-"""
-
-import argparse
-import contextlib
-import os
-import platform
-import sys
-from copy import deepcopy
-from pathlib import Path
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-if platform.system() != "Windows":
-    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.common import *
-from models.experimental import *
-from utils.autoanchor import check_anchor_order
-from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
-from utils.plots import feature_visualization
-from utils.torch_utils import (
-    fuse_conv_and_bn,
-    initialize_weights,
-    model_info,
-    profile,
-    scale_img,
-    select_device,
-    time_sync,
-)
-
-try:
-    import thop  # for FLOPs computation
-except ImportError:
-    thop = None
-
-
-class Detect(nn.Module):
-    # YOLOv5 Detect head for detection models
-    stride = None  # strides computed during build
-    dynamic = False  # force grid reconstruction
-    export = False  # export mode
-
-    def __init__(
-        self, nc=80, anchors=(), ch=(), inplace=True
-    ):  # detection layer
-        super().__init__()
-        self.nc = nc  # number of classes
-        self.no = nc + 5  # number of outputs per anchor
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.empty(0) for _ in range(self.nl)]  # init grid
-        self.anchor_grid = [
-            torch.empty(0) for _ in range(self.nl)
-        ]  # init anchor grid
-        self.register_buffer(
-            "anchors", torch.tensor(anchors).float().view(self.nl, -1, 2)
-        )  # shape(nl,na,2)
-        self.m = nn.ModuleList(
-            nn.Conv2d(x, self.no * self.na, 1) for x in ch
-        )  # output conv
-        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
-
-    def forward(self, x):
-        z = []  # inference output
-        for i in range(self.nl):
-            x[i] = self.m[i](x[i])  # conv
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = (
-                x[i]
-                .view(bs, self.na, self.no, ny, nx)
-                .permute(0, 1, 3, 4, 2)
-                .contiguous()
-            )
-
-            if not self.training:  # inference
-                if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i], self.anchor_grid[i] = self._make_grid(
-                        nx, ny, i
-                    )
-
-                if isinstance(self, Segment):  # (boxes + masks)
-                    xy, wh, conf, mask = x[i].split(
-                        (2, 2, self.nc + 1, self.no - self.nc - 5), 4
-                    )
-                    xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[
-                        i
-                    ]  # xy
-                    wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i]  # wh
-                    y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)
-                else:  # Detect (boxes only)
-                    xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
-                    xy = (xy * 2 + self.grid[i]) * self.stride[i]  # xy
-                    wh = (wh * 2) ** 2 * self.anchor_grid[i]  # wh
-                    y = torch.cat((xy, wh, conf), 4)
-                z.append(y.view(bs, self.na * nx * ny, self.no))
-
-        return (
-            x
-            if self.training
-            else (torch.cat(z, 1),)
-            if self.export
-            else (torch.cat(z, 1), x)
-        )
-
-    def _make_grid(
-        self,
-        nx=20,
-        ny=20,
-        i=0,
-        torch_1_10=check_version(torch.__version__, "1.10.0"),
-    ):
-        d = self.anchors[i].device
-        t = self.anchors[i].dtype
-        shape = 1, self.na, ny, nx, 2  # grid shape
-        y, x = torch.arange(ny, device=d, dtype=t), torch.arange(
-            nx, device=d, dtype=t
-        )
-        yv, xv = (
-            torch.meshgrid(y, x, indexing="ij")
-            if torch_1_10
-            else torch.meshgrid(y, x)
-        )  # torch>=0.7 compatibility
-        grid = (
-            torch.stack((xv, yv), 2).expand(shape) - 0.5
-        )  # add grid offset, i.e. y = 2.0 * x - 0.5
-        anchor_grid = (
-            (self.anchors[i] * self.stride[i])
-            .view((1, self.na, 1, 1, 2))
-            .expand(shape)
-        )
-        return grid, anchor_grid
-
-
-class Segment(Detect):
-    # YOLOv5 Segment head for segmentation models
-    def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
-        super().__init__(nc, anchors, ch, inplace)
-        self.nm = nm  # number of masks
-        self.npr = npr  # number of protos
-        self.no = 5 + nc + self.nm  # number of outputs per anchor
-        self.m = nn.ModuleList(
-            nn.Conv2d(x, self.no * self.na, 1) for x in ch
-        )  # output conv
-        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
-        self.detect = Detect.forward
-
-    def forward(self, x):
-        p = self.proto(x[0])
-        x = self.detect(self, x)
-        return (
-            (x, p)
-            if self.training
-            else (x[0], p)
-            if self.export
-            else (x[0], p, x[1])
-        )
-
-
-class BaseModel(nn.Module):
-    # YOLOv5 base model
-    def forward(self, x, profile=False, visualize=False):
-        return self._forward_once(
-            x, profile, visualize
-        )  # single-scale inference, train
-
-    def _forward_once(self, x, profile=False, visualize=False):
-        y, dt = [], []  # outputs
-        for m in self.model:
-            if m.f != -1:  # if not from previous layer
-                x = (
-                    y[m.f]
-                    if isinstance(m.f, int)
-                    else [x if j == -1 else y[j] for j in m.f]
-                )  # from earlier layers
-            if profile:
-                self._profile_one_layer(m, x, dt)
-            x = m(x)  # run
-            y.append(x if m.i in self.save else None)  # save output
-            if visualize:
-                feature_visualization(x, m.type, m.i, save_dir=visualize)
-        return x
-
-    def _profile_one_layer(self, m, x, dt):
-        c = m == self.model[-1]  # is final layer, copy input as inplace fix
-        o = (
-            thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0]
-            / 1e9
-            * 2
-            if thop
-            else 0
-        )  # FLOPs
-        t = time_sync()
-        for _ in range(10):
-            m(x.copy() if c else x)
-        dt.append((time_sync() - t) * 100)
-        if m == self.model[0]:
-            LOGGER.info(
-                f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module"
-            )
-        LOGGER.info(f"{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}")
-        if c:
-            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")
-
-    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        LOGGER.info("Fusing layers... ")
-        for m in self.model.modules():
-            if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"):
-                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
-                delattr(m, "bn")  # remove batchnorm
-                m.forward = m.forward_fuse  # update forward
-        self.info()
-        return self
-
-    def info(self, verbose=False, img_size=640):  # print model information
-        model_info(self, verbose, img_size)
-
-    def _apply(self, fn):
-        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
-        self = super()._apply(fn)
-        m = self.model[-1]  # Detect()
-        if isinstance(m, (Detect, Segment)):
-            m.stride = fn(m.stride)
-            m.grid = list(map(fn, m.grid))
-            if isinstance(m.anchor_grid, list):
-                m.anchor_grid = list(map(fn, m.anchor_grid))
-        return self
-
-
-class DetectionModel(BaseModel):
-    # YOLOv5 detection model
-    def __init__(
-        self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None
-    ):  # model, input channels, number of classes
-        super().__init__()
-        if isinstance(cfg, dict):
-            self.yaml = cfg  # model dict
-        else:  # is *.yaml
-            import yaml  # for torch hub
-
-            self.yaml_file = Path(cfg).name
-            with open(cfg, encoding="ascii", errors="ignore") as f:
-                self.yaml = yaml.safe_load(f)  # model dict
-
-        # Define model
-        ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
-        if nc and nc != self.yaml["nc"]:
-            LOGGER.info(
-                f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}"
-            )
-            self.yaml["nc"] = nc  # override yaml value
-        if anchors:
-            LOGGER.info(
-                f"Overriding model.yaml anchors with anchors={anchors}"
-            )
-            self.yaml["anchors"] = round(anchors)  # override yaml value
-        self.model, self.save = parse_model(
-            deepcopy(self.yaml), ch=[ch]
-        )  # model, savelist
-        self.names = [str(i) for i in range(self.yaml["nc"])]  # default names
-        self.inplace = self.yaml.get("inplace", True)
-
-        # Build strides, anchors
-        m = self.model[-1]  # Detect()
-        if isinstance(m, (Detect, Segment)):
-            s = 256  # 2x min stride
-            m.inplace = self.inplace
-            forward = (
-                lambda x: self.forward(x)[0]
-                if isinstance(m, Segment)
-                else self.forward(x)
-            )
-            m.stride = torch.tensor(
-                [s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]
-            )  # forward
-            check_anchor_order(m)
-            m.anchors /= m.stride.view(-1, 1, 1)
-            self.stride = m.stride
-            self._initialize_biases()  # only run once
-
-        # Init weights, biases
-        initialize_weights(self)
-        self.info()
-        LOGGER.info("")
-
-    def forward(self, x, augment=False, profile=False, visualize=False):
-        if augment:
-            return self._forward_augment(x)  # augmented inference, None
-        return self._forward_once(
-            x, profile, visualize
-        )  # single-scale inference, train
-
-    def _forward_augment(self, x):
-        img_size = x.shape[-2:]  # height, width
-        s = [1, 0.83, 0.67]  # scales
-        f = [None, 3, None]  # flips (2-ud, 3-lr)
-        y = []  # outputs
-        for si, fi in zip(s, f):
-            xi = scale_img(
-                x.flip(fi) if fi else x, si, gs=int(self.stride.max())
-            )
-            yi = self._forward_once(xi)[0]  # forward
-            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
-            yi = self._descale_pred(yi, fi, si, img_size)
-            y.append(yi)
-        y = self._clip_augmented(y)  # clip augmented tails
-        return torch.cat(y, 1), None  # augmented inference, train
-
-    def _descale_pred(self, p, flips, scale, img_size):
-        # de-scale predictions following augmented inference (inverse operation)
-        if self.inplace:
-            p[..., :4] /= scale  # de-scale
-            if flips == 2:
-                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
-            elif flips == 3:
-                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
-        else:
-            x, y, wh = (
-                p[..., 0:1] / scale,
-                p[..., 1:2] / scale,
-                p[..., 2:4] / scale,
-            )  # de-scale
-            if flips == 2:
-                y = img_size[0] - y  # de-flip ud
-            elif flips == 3:
-                x = img_size[1] - x  # de-flip lr
-            p = torch.cat((x, y, wh, p[..., 4:]), -1)
-        return p
-
-    def _clip_augmented(self, y):
-        # Clip YOLOv5 augmented inference tails
-        nl = self.model[-1].nl  # number of detection layers (P3-P5)
-        g = sum(4**x for x in range(nl))  # grid points
-        e = 1  # exclude layer count
-        i = (y[0].shape[1] // g) * sum(4**x for x in range(e))  # indices
-        y[0] = y[0][:, :-i]  # large
-        i = (y[-1].shape[1] // g) * sum(
-            4 ** (nl - 1 - x) for x in range(e)
-        )  # indices
-        y[-1] = y[-1][:, i:]  # small
-        return y
-
-    def _initialize_biases(
-        self, cf=None
-    ):  # initialize biases into Detect(), cf is class frequency
-        # https://arxiv.org/abs/1708.02002 section 3.3
-        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
-        m = self.model[-1]  # Detect() module
-        for mi, s in zip(m.m, m.stride):  # from
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b.data[:, 4] += math.log(
-                8 / (640 / s) ** 2
-            )  # obj (8 objects per 640 image)
-            b.data[:, 5 : 5 + m.nc] += (
-                math.log(0.6 / (m.nc - 0.99999))
-                if cf is None
-                else torch.log(cf / cf.sum())
-            )  # cls
-            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-
-Model = (
-    DetectionModel  # retain YOLOv5 'Model' class for backwards compatibility
-)
-
-
-class SegmentationModel(DetectionModel):
-    # YOLOv5 segmentation model
-    def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
-        super().__init__(cfg, ch, nc, anchors)
-
-
-class ClassificationModel(BaseModel):
-    # YOLOv5 classification model
-    def __init__(
-        self, cfg=None, model=None, nc=1000, cutoff=10
-    ):  # yaml, model, number of classes, cutoff index
-        super().__init__()
-        self._from_detection_model(
-            model, nc, cutoff
-        ) if model is not None else self._from_yaml(cfg)
-
-    def _from_detection_model(self, model, nc=1000, cutoff=10):
-        # Create a YOLOv5 classification model from a YOLOv5 detection model
-        if isinstance(model, DetectMultiBackend):
-            model = model.model  # unwrap DetectMultiBackend
-        model.model = model.model[:cutoff]  # backbone
-        m = model.model[-1]  # last layer
-        ch = (
-            m.conv.in_channels
-            if hasattr(m, "conv")
-            else m.cv1.conv.in_channels
-        )  # ch into module
-        c = Classify(ch, nc)  # Classify()
-        c.i, c.f, c.type = (
-            m.i,
-            m.f,
-            "models.common.Classify",
-        )  # index, from, type
-        model.model[-1] = c  # replace
-        self.model = model.model
-        self.stride = model.stride
-        self.save = []
-        self.nc = nc
-
-    def _from_yaml(self, cfg):
-        # Create a YOLOv5 classification model from a *.yaml file
-        self.model = None
-
-
-def parse_model(d, ch):  # model_dict, input_channels(3)
-    # Parse a YOLOv5 model.yaml dictionary
-    LOGGER.info(
-        f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}"
-    )
-    anchors, nc, gd, gw, act = (
-        d["anchors"],
-        d["nc"],
-        d["depth_multiple"],
-        d["width_multiple"],
-        d.get("activation"),
-    )
-    if act:
-        Conv.default_act = eval(
-            act
-        )  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
-        LOGGER.info(f"{colorstr('activation:')} {act}")  # print
-    na = (
-        (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors
-    )  # number of anchors
-    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
-
-    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(
-        d["backbone"] + d["head"]
-    ):  # from, number, module, args
-        m = eval(m) if isinstance(m, str) else m  # eval strings
-        for j, a in enumerate(args):
-            with contextlib.suppress(NameError):
-                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
-
-        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in {
-            Conv,
-            GhostConv,
-            Bottleneck,
-            GhostBottleneck,
-            SPP,
-            SPPF,
-            DWConv,
-            MixConv2d,
-            Focus,
-            CrossConv,
-            BottleneckCSP,
-            C3,
-            C3TR,
-            C3SPP,
-            C3Ghost,
-            nn.ConvTranspose2d,
-            DWConvTranspose2d,
-            C3x,
-        }:
-            c1, c2 = ch[f], args[0]
-            if c2 != no:  # if not output
-                c2 = make_divisible(c2 * gw, 8)
-
-            args = [c1, c2, *args[1:]]
-            if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
-                args.insert(2, n)  # number of repeats
-                n = 1
-        elif m is nn.BatchNorm2d:
-            args = [ch[f]]
-        elif m is Concat:
-            c2 = sum(ch[x] for x in f)
-        # TODO: channel, gw, gd
-        elif m in {Detect, Segment}:
-            args.append([ch[x] for x in f])
-            if isinstance(args[1], int):  # number of anchors
-                args[1] = [list(range(args[1] * 2))] * len(f)
-            if m is Segment:
-                args[3] = make_divisible(args[3] * gw, 8)
-        elif m is Contract:
-            c2 = ch[f] * args[0] ** 2
-        elif m is Expand:
-            c2 = ch[f] // args[0] ** 2
-        else:
-            c2 = ch[f]
-
-        m_ = (
-            nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)
-        )  # module
-        t = str(m)[8:-2].replace("__main__.", "")  # module type
-        np = sum(x.numel() for x in m_.parameters())  # number params
-        m_.i, m_.f, m_.type, m_.np = (
-            i,
-            f,
-            t,
-            np,
-        )  # attach index, 'from' index, type, number params
-        LOGGER.info(
-            f"{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}"
-        )  # print
-        save.extend(
-            x % i for x in ([f] if isinstance(f, int) else f) if x != -1
-        )  # append to savelist
-        layers.append(m_)
-        if i == 0:
-            ch = []
-        ch.append(c2)
-    return nn.Sequential(*layers), sorted(save)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--cfg", type=str, default="yolov5s.yaml", help="model.yaml"
-    )
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=1,
-        help="total batch size for all GPUs",
-    )
-    parser.add_argument(
-        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
-    )
-    parser.add_argument(
-        "--profile", action="store_true", help="profile model speed"
-    )
-    parser.add_argument(
-        "--line-profile",
-        action="store_true",
-        help="profile model speed layer by layer",
-    )
-    parser.add_argument(
-        "--test", action="store_true", help="test all yolo*.yaml"
-    )
-    opt = parser.parse_args()
-    opt.cfg = check_yaml(opt.cfg)  # check YAML
-    print_args(vars(opt))
-    device = select_device(opt.device)
-
-    # Create model
-    im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
-    model = Model(opt.cfg).to(device)
-
-    # Options
-    if opt.line_profile:  # profile layer by layer
-        model(im, profile=True)
-
-    elif opt.profile:  # profile forward-backward
-        results = profile(input=im, ops=[model], n=3)
-
-    elif opt.test:  # test all models
-        for cfg in Path(ROOT / "models").rglob("yolo*.yaml"):
-            try:
-                _ = Model(cfg)
-            except Exception as e:
-                print(f"Error in {cfg}: {e}")
-
-    else:  # report fused model summary
-        model.fuse()

spaces/Adapter/T2I-Adapter/ldm/models/diffusion/dpm_solver/__init__.py

@@ -1 +0,0 @@
-from .sampler import DPMSolverSampler

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/anchor/Factory.d.ts

@@ -1,7 +0,0 @@
-// import * as Phaser from 'phaser';
-import Anchor from "./Anchor";
-
-export default function (
-    gameObject: Phaser.GameObjects.GameObject,
-    config?: Anchor.IConfig
-): Anchor;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/simplelabel/Factory.d.ts

@@ -1,6 +0,0 @@
-import SimpleLabel from './SimpleLabel';
-
-export default function (
-    config?: SimpleLabel.IConfig,
-    creators?: SimpleLabel.ICreatorsConfig,
-): SimpleLabel;

spaces/Aki004/herta-so-vits/train.py

@@ -1,330 +0,0 @@
-import logging
-import multiprocessing
-import time
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-logging.getLogger('numba').setLevel(logging.WARNING)
-
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-
-import modules.commons as commons
-import utils
-from data_utils import TextAudioSpeakerLoader, TextAudioCollate
-from models import (
-    SynthesizerTrn,
-    MultiPeriodDiscriminator,
-)
-from modules.losses import (
-    kl_loss,
-    generator_loss, discriminator_loss, feature_loss
-)
-
-from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-
-torch.backends.cudnn.benchmark = True
-global_step = 0
-start_time = time.time()
-
-# os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO'
-
-
-def main():
-    """Assume Single Node Multi GPUs Training Only"""
-    assert torch.cuda.is_available(), "CPU training is not allowed."
-    hps = utils.get_hparams()
-
-    n_gpus = torch.cuda.device_count()
-    os.environ['MASTER_ADDR'] = 'localhost'
-    os.environ['MASTER_PORT'] = hps.train.port
-
-    mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-
-
-def run(rank, n_gpus, hps):
-    global global_step
-    if rank == 0:
-        logger = utils.get_logger(hps.model_dir)
-        logger.info(hps)
-        utils.check_git_hash(hps.model_dir)
-        writer = SummaryWriter(log_dir=hps.model_dir)
-        writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
-    # for pytorch on win, backend use gloo    
-    dist.init_process_group(backend=  'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
-    torch.manual_seed(hps.train.seed)
-    torch.cuda.set_device(rank)
-    collate_fn = TextAudioCollate()
-    all_in_mem = hps.train.all_in_mem   # If you have enough memory, turn on this option to avoid disk IO and speed up training.
-    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps, all_in_mem=all_in_mem)
-    num_workers = 5 if multiprocessing.cpu_count() > 4 else multiprocessing.cpu_count()
-    if all_in_mem:
-        num_workers = 0
-    train_loader = DataLoader(train_dataset, num_workers=num_workers, shuffle=False, pin_memory=True,
-                              batch_size=hps.train.batch_size, collate_fn=collate_fn)
-    if rank == 0:
-        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps, all_in_mem=all_in_mem)
-        eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False,
-                                 batch_size=1, pin_memory=False,
-                                 drop_last=False, collate_fn=collate_fn)
-
-    net_g = SynthesizerTrn(
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        **hps.model).cuda(rank)
-    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
-    optim_g = torch.optim.AdamW(
-        net_g.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    optim_d = torch.optim.AdamW(
-        net_d.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    net_g = DDP(net_g, device_ids=[rank])  # , find_unused_parameters=True)
-    net_d = DDP(net_d, device_ids=[rank])
-
-    skip_optimizer = False
-    try:
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
-                                                   optim_g, skip_optimizer)
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
-                                                   optim_d, skip_optimizer)
-        epoch_str = max(epoch_str, 1)
-        name=utils.latest_checkpoint_path(hps.model_dir, "D_*.pth")
-        global_step=int(name[name.rfind("_")+1:name.rfind(".")])+1
-        #global_step = (epoch_str - 1) * len(train_loader)
-    except:
-        print("load old checkpoint failed...")
-        epoch_str = 1
-        global_step = 0
-    if skip_optimizer:
-        epoch_str = 1
-        global_step = 0
-
-    warmup_epoch = hps.train.warmup_epochs
-    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-
-    scaler = GradScaler(enabled=hps.train.fp16_run)
-
-    for epoch in range(epoch_str, hps.train.epochs + 1):
-        # update learning rate
-        if epoch > 1:
-            scheduler_g.step()
-            scheduler_d.step()
-        # set up warm-up learning rate
-        if epoch <= warmup_epoch:
-            for param_group in optim_g.param_groups:
-                param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
-            for param_group in optim_d.param_groups:
-                param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
-        # training
-        if rank == 0:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
-                               [train_loader, eval_loader], logger, [writer, writer_eval])
-        else:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
-                               [train_loader, None], None, None)
-
-
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
-    net_g, net_d = nets
-    optim_g, optim_d = optims
-    scheduler_g, scheduler_d = schedulers
-    train_loader, eval_loader = loaders
-    if writers is not None:
-        writer, writer_eval = writers
-
-    # train_loader.batch_sampler.set_epoch(epoch)
-    global global_step
-
-    net_g.train()
-    net_d.train()
-    for batch_idx, items in enumerate(train_loader):
-        c, f0, spec, y, spk, lengths, uv = items
-        g = spk.cuda(rank, non_blocking=True)
-        spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True)
-        c = c.cuda(rank, non_blocking=True)
-        f0 = f0.cuda(rank, non_blocking=True)
-        uv = uv.cuda(rank, non_blocking=True)
-        lengths = lengths.cuda(rank, non_blocking=True)
-        mel = spec_to_mel_torch(
-            spec,
-            hps.data.filter_length,
-            hps.data.n_mel_channels,
-            hps.data.sampling_rate,
-            hps.data.mel_fmin,
-            hps.data.mel_fmax)
-
-        with autocast(enabled=hps.train.fp16_run):
-            y_hat, ids_slice, z_mask, \
-            (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths,
-                                                                                spec_lengths=lengths)
-
-            y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
-            y_hat_mel = mel_spectrogram_torch(
-                y_hat.squeeze(1),
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.hop_length,
-                hps.data.win_length,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax
-            )
-            y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size)  # slice
-
-            # Discriminator
-            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-
-            with autocast(enabled=False):
-                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
-                loss_disc_all = loss_disc
-
-        optim_d.zero_grad()
-        scaler.scale(loss_disc_all).backward()
-        scaler.unscale_(optim_d)
-        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-        scaler.step(optim_d)
-
-        with autocast(enabled=hps.train.fp16_run):
-            # Generator
-            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-            with autocast(enabled=False):
-                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-                loss_fm = feature_loss(fmap_r, fmap_g)
-                loss_gen, losses_gen = generator_loss(y_d_hat_g)
-                loss_lf0 = F.mse_loss(pred_lf0, lf0)
-                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0
-        optim_g.zero_grad()
-        scaler.scale(loss_gen_all).backward()
-        scaler.unscale_(optim_g)
-        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-        scaler.step(optim_g)
-        scaler.update()
-
-        if rank == 0:
-            if global_step % hps.train.log_interval == 0:
-                lr = optim_g.param_groups[0]['lr']
-                losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl]
-                reference_loss=0
-                for i in losses:
-                    reference_loss += i
-                logger.info('Train Epoch: {} [{:.0f}%]'.format(
-                    epoch,
-                    100. * batch_idx / len(train_loader)))
-                logger.info(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}, reference_loss: {reference_loss}")
-
-                scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
-                               "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
-                scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl,
-                                    "loss/g/lf0": loss_lf0})
-
-                # scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-                # scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-                # scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-                image_dict = {
-                    "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
-                    "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-                    "all/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
-                                                          pred_lf0[0, 0, :].detach().cpu().numpy()),
-                    "all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
-                                                               norm_lf0[0, 0, :].detach().cpu().numpy())
-                }
-
-                utils.summarize(
-                    writer=writer,
-                    global_step=global_step,
-                    images=image_dict,
-                    scalars=scalar_dict
-                )
-
-            if global_step % hps.train.eval_interval == 0:
-                evaluate(hps, net_g, eval_loader, writer_eval)
-                utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
-                utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
-                keep_ckpts = getattr(hps.train, 'keep_ckpts', 0)
-                if keep_ckpts > 0:
-                    utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
-
-        global_step += 1
-
-    if rank == 0:
-        global start_time
-        now = time.time()
-        durtaion = format(now - start_time, '.2f')
-        logger.info(f'====> Epoch: {epoch}, cost {durtaion} s')
-        start_time = now
-
-
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    image_dict = {}
-    audio_dict = {}
-    with torch.no_grad():
-        for batch_idx, items in enumerate(eval_loader):
-            c, f0, spec, y, spk, _, uv = items
-            g = spk[:1].cuda(0)
-            spec, y = spec[:1].cuda(0), y[:1].cuda(0)
-            c = c[:1].cuda(0)
-            f0 = f0[:1].cuda(0)
-            uv= uv[:1].cuda(0)
-            mel = spec_to_mel_torch(
-                spec,
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax)
-            y_hat = generator.module.infer(c, f0, uv, g=g)
-
-            y_hat_mel = mel_spectrogram_torch(
-                y_hat.squeeze(1).float(),
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.hop_length,
-                hps.data.win_length,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax
-            )
-
-            audio_dict.update({
-                f"gen/audio_{batch_idx}": y_hat[0],
-                f"gt/audio_{batch_idx}": y[0]
-            })
-        image_dict.update({
-            f"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()),
-            "gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())
-        })
-    utils.summarize(
-        writer=writer_eval,
-        global_step=global_step,
-        images=image_dict,
-        audios=audio_dict,
-        audio_sampling_rate=hps.data.sampling_rate
-    )
-    generator.train()
-
-
-if __name__ == "__main__":
-    main()

spaces/AlexWang/lama/saicinpainting/utils.py

@@ -1,174 +0,0 @@
-import bisect
-import functools
-import logging
-import numbers
-import os
-import signal
-import sys
-import traceback
-import warnings
-
-import torch
-from pytorch_lightning import seed_everything
-
-LOGGER = logging.getLogger(__name__)
-
-
-def check_and_warn_input_range(tensor, min_value, max_value, name):
-    actual_min = tensor.min()
-    actual_max = tensor.max()
-    if actual_min < min_value or actual_max > max_value:
-        warnings.warn(f"{name} must be in {min_value}..{max_value} range, but it ranges {actual_min}..{actual_max}")
-
-
-def sum_dict_with_prefix(target, cur_dict, prefix, default=0):
-    for k, v in cur_dict.items():
-        target_key = prefix + k
-        target[target_key] = target.get(target_key, default) + v
-
-
-def average_dicts(dict_list):
-    result = {}
-    norm = 1e-3
-    for dct in dict_list:
-        sum_dict_with_prefix(result, dct, '')
-        norm += 1
-    for k in list(result):
-        result[k] /= norm
-    return result
-
-
-def add_prefix_to_keys(dct, prefix):
-    return {prefix + k: v for k, v in dct.items()}
-
-
-def set_requires_grad(module, value):
-    for param in module.parameters():
-        param.requires_grad = value
-
-
-def flatten_dict(dct):
-    result = {}
-    for k, v in dct.items():
-        if isinstance(k, tuple):
-            k = '_'.join(k)
-        if isinstance(v, dict):
-            for sub_k, sub_v in flatten_dict(v).items():
-                result[f'{k}_{sub_k}'] = sub_v
-        else:
-            result[k] = v
-    return result
-
-
-class LinearRamp:
-    def __init__(self, start_value=0, end_value=1, start_iter=-1, end_iter=0):
-        self.start_value = start_value
-        self.end_value = end_value
-        self.start_iter = start_iter
-        self.end_iter = end_iter
-
-    def __call__(self, i):
-        if i < self.start_iter:
-            return self.start_value
-        if i >= self.end_iter:
-            return self.end_value
-        part = (i - self.start_iter) / (self.end_iter - self.start_iter)
-        return self.start_value * (1 - part) + self.end_value * part
-
-
-class LadderRamp:
-    def __init__(self, start_iters, values):
-        self.start_iters = start_iters
-        self.values = values
-        assert len(values) == len(start_iters) + 1, (len(values), len(start_iters))
-
-    def __call__(self, i):
-        segment_i = bisect.bisect_right(self.start_iters, i)
-        return self.values[segment_i]
-
-
-def get_ramp(kind='ladder', **kwargs):
-    if kind == 'linear':
-        return LinearRamp(**kwargs)
-    if kind == 'ladder':
-        return LadderRamp(**kwargs)
-    raise ValueError(f'Unexpected ramp kind: {kind}')
-
-
-def print_traceback_handler(sig, frame):
-    LOGGER.warning(f'Received signal {sig}')
-    bt = ''.join(traceback.format_stack())
-    LOGGER.warning(f'Requested stack trace:\n{bt}')
-
-
-def register_debug_signal_handlers(sig=signal.SIGUSR1, handler=print_traceback_handler):
-    LOGGER.warning(f'Setting signal {sig} handler {handler}')
-    signal.signal(sig, handler)
-
-
-def handle_deterministic_config(config):
-    seed = dict(config).get('seed', None)
-    if seed is None:
-        return False
-
-    seed_everything(seed)
-    return True
-
-
-def get_shape(t):
-    if torch.is_tensor(t):
-        return tuple(t.shape)
-    elif isinstance(t, dict):
-        return {n: get_shape(q) for n, q in t.items()}
-    elif isinstance(t, (list, tuple)):
-        return [get_shape(q) for q in t]
-    elif isinstance(t, numbers.Number):
-        return type(t)
-    else:
-        raise ValueError('unexpected type {}'.format(type(t)))
-
-
-def get_has_ddp_rank():
-    master_port = os.environ.get('MASTER_PORT', None)
-    node_rank = os.environ.get('NODE_RANK', None)
-    local_rank = os.environ.get('LOCAL_RANK', None)
-    world_size = os.environ.get('WORLD_SIZE', None)
-    has_rank = master_port is not None or node_rank is not None or local_rank is not None or world_size is not None
-    return has_rank
-
-
-def handle_ddp_subprocess():
-    def main_decorator(main_func):
-        @functools.wraps(main_func)
-        def new_main(*args, **kwargs):
-            # Trainer sets MASTER_PORT, NODE_RANK, LOCAL_RANK, WORLD_SIZE
-            parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None)
-            has_parent = parent_cwd is not None
-            has_rank = get_has_ddp_rank()
-            assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}'
-
-            if has_parent:
-                # we are in the worker
-                sys.argv.extend([
-                    f'hydra.run.dir={parent_cwd}',
-                    # 'hydra/hydra_logging=disabled',
-                    # 'hydra/job_logging=disabled'
-                ])
-            # do nothing if this is a top-level process
-            # TRAINING_PARENT_WORK_DIR is set in handle_ddp_parent_process after hydra initialization
-
-            main_func(*args, **kwargs)
-        return new_main
-    return main_decorator
-
-
-def handle_ddp_parent_process():
-    parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None)
-    has_parent = parent_cwd is not None
-    has_rank = get_has_ddp_rank()
-    assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}'
-
-    if parent_cwd is None:
-        os.environ['TRAINING_PARENT_WORK_DIR'] = os.getcwd()
-
-    return has_parent

spaces/Alfasign/HuggingGPT-Lite/app.py

@@ -1,237 +0,0 @@
-import uuid
-import gradio as gr
-import re
-from diffusers.utils import load_image
-import requests
-from awesome_chat import chat_huggingface
-import os
-
-os.makedirs("public/images", exist_ok=True)
-os.makedirs("public/audios", exist_ok=True)
-os.makedirs("public/videos", exist_ok=True)
-
-HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
-OPENAI_KEY = os.environ.get("OPENAI_KEY")
-
-
-class Client:
-    def __init__(self) -> None:
-        self.OPENAI_KEY = OPENAI_KEY
-        self.HUGGINGFACE_TOKEN = HUGGINGFACE_TOKEN
-        self.all_messages = []
-
-    def set_key(self, openai_key):
-        self.OPENAI_KEY = openai_key
-        return self.OPENAI_KEY
-
-    def set_token(self, huggingface_token):
-        self.HUGGINGFACE_TOKEN = huggingface_token
-        return self.HUGGINGFACE_TOKEN
-
-    def add_message(self, content, role):
-        message = {"role": role, "content": content}
-        self.all_messages.append(message)
-
-    def extract_medias(self, message):
-        # url_pattern = re.compile(r"(http(s?):|\/)?([\.\/_\w:-])*?")
-        urls = []
-        # for match in url_pattern.finditer(message):
-        #     if match.group(0) not in urls:
-        #         urls.append(match.group(0))
-
-        image_pattern = re.compile(
-            r"(http(s?):|\/)?([\.\/_\w:-])*?\.(jpg|jpeg|tiff|gif|png)"
-        )
-        image_urls = []
-        for match in image_pattern.finditer(message):
-            if match.group(0) not in image_urls:
-                image_urls.append(match.group(0))
-
-        audio_pattern = re.compile(r"(http(s?):|\/)?([\.\/_\w:-])*?\.(flac|wav)")
-        audio_urls = []
-        for match in audio_pattern.finditer(message):
-            if match.group(0) not in audio_urls:
-                audio_urls.append(match.group(0))
-
-        video_pattern = re.compile(r"(http(s?):|\/)?([\.\/_\w:-])*?\.(mp4)")
-        video_urls = []
-        for match in video_pattern.finditer(message):
-            if match.group(0) not in video_urls:
-                video_urls.append(match.group(0))
-
-        return urls, image_urls, audio_urls, video_urls
-
-    def add_text(self, messages, message):
-        if (
-            not self.OPENAI_KEY
-            or not self.OPENAI_KEY.startswith("sk-")
-            or not self.HUGGINGFACE_TOKEN
-            or not self.HUGGINGFACE_TOKEN.startswith("hf_")
-        ):
-            return (
-                messages,
-                "Please set your OpenAI API key and Hugging Face token first!!!",
-            )
-        self.add_message(message, "user")
-        messages = messages + [(message, None)]
-        urls, image_urls, audio_urls, video_urls = self.extract_medias(message)
-
-        for image_url in image_urls:
-            if not image_url.startswith("http") and not image_url.startswith("public"):
-                image_url = "public/" + image_url
-            image = load_image(image_url)
-            name = f"public/images/{str(uuid.uuid4())[:4]}.jpg"
-            image.save(name)
-            messages = messages + [((f"{name}",), None)]
-        for audio_url in audio_urls and not audio_url.startswith("public"):
-            if not audio_url.startswith("http"):
-                audio_url = "public/" + audio_url
-            ext = audio_url.split(".")[-1]
-            name = f"public/audios/{str(uuid.uuid4()[:4])}.{ext}"
-            response = requests.get(audio_url)
-            with open(name, "wb") as f:
-                f.write(response.content)
-            messages = messages + [((f"{name}",), None)]
-        for video_url in video_urls and not video_url.startswith("public"):
-            if not video_url.startswith("http"):
-                video_url = "public/" + video_url
-            ext = video_url.split(".")[-1]
-            name = f"public/audios/{str(uuid.uuid4()[:4])}.{ext}"
-            response = requests.get(video_url)
-            with open(name, "wb") as f:
-                f.write(response.content)
-            messages = messages + [((f"{name}",), None)]
-        return messages, ""
-
-    def bot(self, messages):
-        if (
-            not self.OPENAI_KEY
-            or not self.OPENAI_KEY.startswith("sk-")
-            or not self.HUGGINGFACE_TOKEN
-            or not self.HUGGINGFACE_TOKEN.startswith("hf_")
-        ):
-            return messages, {}
-        message, results = chat_huggingface(
-            self.all_messages, self.OPENAI_KEY, self.HUGGINGFACE_TOKEN
-        )
-        urls, image_urls, audio_urls, video_urls = self.extract_medias(message)
-        self.add_message(message, "assistant")
-        messages[-1][1] = message
-        for image_url in image_urls:
-            if not image_url.startswith("http"):
-                image_url = image_url.replace("public/", "")
-                messages = messages + [((None, (f"public/{image_url}",)))]
-            # else:
-            #     messages = messages + [((None, (f"{image_url}",)))]
-        for audio_url in audio_urls:
-            if not audio_url.startswith("http"):
-                audio_url = audio_url.replace("public/", "")
-                messages = messages + [((None, (f"public/{audio_url}",)))]
-            # else:
-            #     messages = messages + [((None, (f"{audio_url}",)))]
-        for video_url in video_urls:
-            if not video_url.startswith("http"):
-                video_url = video_url.replace("public/", "")
-                messages = messages + [((None, (f"public/{video_url}",)))]
-            # else:
-            #     messages = messages + [((None, (f"{video_url}",)))]
-        # replace int key to string key
-        results = {str(k): v for k, v in results.items()}
-        return messages, results
-
-
-css = ".json {height: 527px; overflow: scroll;} .json-holder {height: 527px; overflow: scroll;}"
-with gr.Blocks(css=css) as demo:
-    state = gr.State(value={"client": Client()})
-    gr.Markdown("<h1><center>HuggingGPT - Lite 🎐 </center></h1>")
-    gr.Markdown(
-        "<p align='center'><img src='https://i.ibb.co/qNH3Jym/logo.png' height='25' width='95'></p>"
-    )
-    gr.Markdown(
-        "<p align='center' style='font-size: 20px;'>A system to connect LLMs with ML community. See our <a href='https://github.com/microsoft/JARVIS'>Project</a> and <a href='http://arxiv.org/abs/2303.17580'>Paper</a>.</p>"
-    )
-    gr.HTML(
-        """<center><a href="https://huggingface.co/spaces/taesiri/HuggingGPT-Lite?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key and Hugging Face Token</center>"""
-    )
-    gr.Markdown(
-        """>**Note**: This is a further lite version of the original HuggingGPT designed to run on CPU-only spaces.  This model by default uses `gpt-3.5-turbo` which is much much cheaper than `text-davinci-003`. """
-    )
-    if not OPENAI_KEY:
-        with gr.Row().style():
-            with gr.Column(scale=0.85):
-                openai_api_key = gr.Textbox(
-                    show_label=False,
-                    placeholder="Set your OpenAI API key here and press Enter",
-                    lines=1,
-                    type="password",
-                ).style(container=False)
-            with gr.Column(scale=0.15, min_width=0):
-                btn1 = gr.Button("Submit").style(full_height=True)
-
-    if not HUGGINGFACE_TOKEN:
-        with gr.Row().style():
-            with gr.Column(scale=0.85):
-                hugging_face_token = gr.Textbox(
-                    show_label=False,
-                    placeholder="Set your Hugging Face Token here and press Enter",
-                    lines=1,
-                    type="password",
-                ).style(container=False)
-            with gr.Column(scale=0.15, min_width=0):
-                btn3 = gr.Button("Submit").style(full_height=True)
-
-    with gr.Row().style():
-        with gr.Column(scale=0.6):
-            chatbot = gr.Chatbot([], elem_id="chatbot").style(height=500)
-        with gr.Column(scale=0.4):
-            results = gr.JSON(elem_classes="json")
-
-    with gr.Row().style():
-        with gr.Column(scale=0.85):
-            txt = gr.Textbox(
-                show_label=False,
-                placeholder="Enter text and press enter. The url must contain the media type. e.g, https://example.com/example.jpg",
-                lines=1,
-            ).style(container=False)
-        with gr.Column(scale=0.15, min_width=0):
-            btn2 = gr.Button("Send").style(full_height=True)
-
-    def set_key(state, openai_api_key):
-        return state["client"].set_key(openai_api_key)
-
-    def add_text(state, chatbot, txt):
-        return state["client"].add_text(chatbot, txt)
-
-    def set_token(state, hugging_face_token):
-        return state["client"].set_token(hugging_face_token)
-
-    def bot(state, chatbot):
-        return state["client"].bot(chatbot)
-
-    if not OPENAI_KEY:
-        openai_api_key.submit(set_key, [state, openai_api_key], [openai_api_key])
-        btn1.click(set_key, [state, openai_api_key], [openai_api_key])
-
-    if not HUGGINGFACE_TOKEN:
-        hugging_face_token.submit(
-            set_token, [state, hugging_face_token], [hugging_face_token]
-        )
-        btn3.click(set_token, [state, hugging_face_token], [hugging_face_token])
-
-    txt.submit(add_text, [state, chatbot, txt], [chatbot, txt]).then(
-        bot, [state, chatbot], [chatbot, results]
-    )
-    btn2.click(add_text, [state, chatbot, txt], [chatbot, txt]).then(
-        bot, [state, chatbot], [chatbot, results]
-    )
-
-    gr.Examples(
-        examples=[
-            "Given a collection of image A: /examples/a.jpg, B: /examples/b.jpg, C: /examples/c.jpg, please tell me how many zebras in these picture?",
-            "show me a joke and an image of cat",
-            "what is in the examples/a.jpg",
-        ],
-        inputs=txt,
-    )
-
-demo.launch()

spaces/Aloento/9Nine-PITS/text/frontend/normalizer/abbrrviation.py

@@ -1,13 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. 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.

spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/inference.py

@@ -1,35 +0,0 @@
-import argparse
-
-import cv2
-import numpy as np
-import torch
-
-from backbones import get_model
-
-
-@torch.no_grad()
-def inference(weight, name, img):
-    if img is None:
-        img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.uint8)
-    else:
-        img = cv2.imread(img)
-        img = cv2.resize(img, (112, 112))
-
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    img = np.transpose(img, (2, 0, 1))
-    img = torch.from_numpy(img).unsqueeze(0).float()
-    img.div_(255).sub_(0.5).div_(0.5)
-    net = get_model(name, fp16=False)
-    net.load_state_dict(torch.load(weight))
-    net.eval()
-    feat = net(img).numpy()
-    print(feat)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='PyTorch ArcFace Training')
-    parser.add_argument('--network', type=str, default='r50', help='backbone network')
-    parser.add_argument('--weight', type=str, default='')
-    parser.add_argument('--img', type=str, default=None)
-    args = parser.parse_args()
-    inference(args.weight, args.network, args.img)

spaces/Alven/background-remover/README.md

@@ -1,21 +0,0 @@
----
-title: Background Remover
-emoji: 🖼️✂️
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-duplicated_from: nateraw/background-remover
----
-
-# background-remover
-
-[![Generic badge](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/nateraw/background-remover)
-
-A Gradio app to remove the background from an image
-
----⬇️
-
-Autogenerated using [this template](https://github.com/nateraw/spaces-template)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py

@@ -1,816 +0,0 @@
-import html
-import inspect
-import re
-import urllib.parse as ul
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import torch
-from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
-
-from ...loaders import LoraLoaderMixin
-from ...models import UNet2DConditionModel
-from ...schedulers import DDPMScheduler
-from ...utils import (
-    BACKENDS_MAPPING,
-    is_accelerate_available,
-    is_accelerate_version,
-    is_bs4_available,
-    is_ftfy_available,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline
-from . import IFPipelineOutput
-from .safety_checker import IFSafetyChecker
-from .watermark import IFWatermarker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-if is_bs4_available():
-    from bs4 import BeautifulSoup
-
-if is_ftfy_available():
-    import ftfy
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline
-        >>> from diffusers.utils import pt_to_pil
-        >>> import torch
-
-        >>> pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
-        >>> pipe.enable_model_cpu_offload()
-
-        >>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
-        >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
-
-        >>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt").images
-
-        >>> # save intermediate image
-        >>> pil_image = pt_to_pil(image)
-        >>> pil_image[0].save("./if_stage_I.png")
-
-        >>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained(
-        ...     "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
-        ... )
-        >>> super_res_1_pipe.enable_model_cpu_offload()
-
-        >>> image = super_res_1_pipe(
-        ...     image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt"
-        ... ).images
-
-        >>> # save intermediate image
-        >>> pil_image = pt_to_pil(image)
-        >>> pil_image[0].save("./if_stage_I.png")
-
-        >>> safety_modules = {
-        ...     "feature_extractor": pipe.feature_extractor,
-        ...     "safety_checker": pipe.safety_checker,
-        ...     "watermarker": pipe.watermarker,
-        ... }
-        >>> super_res_2_pipe = DiffusionPipeline.from_pretrained(
-        ...     "stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
-        ... )
-        >>> super_res_2_pipe.enable_model_cpu_offload()
-
-        >>> image = super_res_2_pipe(
-        ...     prompt=prompt,
-        ...     image=image,
-        ... ).images
-        >>> image[0].save("./if_stage_II.png")
-        ```
-"""
-
-
-class IFPipeline(DiffusionPipeline, LoraLoaderMixin):
-    tokenizer: T5Tokenizer
-    text_encoder: T5EncoderModel
-
-    unet: UNet2DConditionModel
-    scheduler: DDPMScheduler
-
-    feature_extractor: Optional[CLIPImageProcessor]
-    safety_checker: Optional[IFSafetyChecker]
-
-    watermarker: Optional[IFWatermarker]
-
-    bad_punct_regex = re.compile(
-        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
-    )  # noqa
-
-    _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
-
-    def __init__(
-        self,
-        tokenizer: T5Tokenizer,
-        text_encoder: T5EncoderModel,
-        unet: UNet2DConditionModel,
-        scheduler: DDPMScheduler,
-        safety_checker: Optional[IFSafetyChecker],
-        feature_extractor: Optional[CLIPImageProcessor],
-        watermarker: Optional[IFWatermarker],
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the IF license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        self.register_modules(
-            tokenizer=tokenizer,
-            text_encoder=text_encoder,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-            watermarker=watermarker,
-        )
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-
-        if self.text_encoder is not None:
-            _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook)
-
-            # Accelerate will move the next model to the device _before_ calling the offload hook of the
-            # previous model. This will cause both models to be present on the device at the same time.
-            # IF uses T5 for its text encoder which is really large. We can manually call the offload
-            # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to
-            # the GPU.
-            self.text_encoder_offload_hook = hook
-
-        _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook)
-
-        # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet
-        self.unet_offload_hook = hook
-
-        if self.safety_checker is not None:
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    def remove_all_hooks(self):
-        if is_accelerate_available():
-            from accelerate.hooks import remove_hook_from_module
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        for model in [self.text_encoder, self.unet, self.safety_checker]:
-            if model is not None:
-                remove_hook_from_module(model, recurse=True)
-
-        self.unet_offload_hook = None
-        self.text_encoder_offload_hook = None
-        self.final_offload_hook = None
-
-    @torch.no_grad()
-    def encode_prompt(
-        self,
-        prompt,
-        do_classifier_free_guidance=True,
-        num_images_per_prompt=1,
-        device=None,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        clean_caption: bool = False,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`, *optional*):
-                torch device to place the resulting embeddings on
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
-                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-        """
-        if prompt is not None and negative_prompt is not None:
-            if type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-
-        if device is None:
-            device = self._execution_device
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
-        max_length = 77
-
-        if prompt_embeds is None:
-            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                add_special_tokens=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {max_length} tokens: {removed_text}"
-                )
-
-            attention_mask = text_inputs.attention_mask.to(device)
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        if self.text_encoder is not None:
-            dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            dtype = self.unet.dtype
-        else:
-            dtype = None
-
-        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_attention_mask=True,
-                add_special_tokens=True,
-                return_tensors="pt",
-            )
-            attention_mask = uncond_input.attention_mask.to(device)
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-        else:
-            negative_prompt_embeds = None
-
-        return prompt_embeds, negative_prompt_embeds
-
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, nsfw_detected, watermark_detected = self.safety_checker(
-                images=image,
-                clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
-            )
-        else:
-            nsfw_detected = None
-            watermark_detected = None
-
-            if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
-                self.unet_offload_hook.offload()
-
-        return image, nsfw_detected, watermark_detected
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(
-        self,
-        prompt,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    def prepare_intermediate_images(self, batch_size, num_channels, height, width, dtype, device, generator):
-        shape = (batch_size, num_channels, height, width)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        intermediate_images = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        intermediate_images = intermediate_images * self.scheduler.init_noise_sigma
-        return intermediate_images
-
-    def _text_preprocessing(self, text, clean_caption=False):
-        if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
-            clean_caption = False
-
-        if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
-            clean_caption = False
-
-        if not isinstance(text, (tuple, list)):
-            text = [text]
-
-        def process(text: str):
-            if clean_caption:
-                text = self._clean_caption(text)
-                text = self._clean_caption(text)
-            else:
-                text = text.lower().strip()
-            return text
-
-        return [process(t) for t in text]
-
-    def _clean_caption(self, caption):
-        caption = str(caption)
-        caption = ul.unquote_plus(caption)
-        caption = caption.strip().lower()
-        caption = re.sub("<person>", "person", caption)
-        # urls:
-        caption = re.sub(
-            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
-            "",
-            caption,
-        )  # regex for urls
-        caption = re.sub(
-            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
-            "",
-            caption,
-        )  # regex for urls
-        # html:
-        caption = BeautifulSoup(caption, features="html.parser").text
-
-        # @<nickname>
-        caption = re.sub(r"@[\w\d]+\b", "", caption)
-
-        # 31C0—31EF CJK Strokes
-        # 31F0—31FF Katakana Phonetic Extensions
-        # 3200—32FF Enclosed CJK Letters and Months
-        # 3300—33FF CJK Compatibility
-        # 3400—4DBF CJK Unified Ideographs Extension A
-        # 4DC0—4DFF Yijing Hexagram Symbols
-        # 4E00—9FFF CJK Unified Ideographs
-        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
-        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
-        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
-        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
-        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
-        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
-        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
-        #######################################################
-
-        # все виды тире / all types of dash --> "-"
-        caption = re.sub(
-            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
-            "-",
-            caption,
-        )
-
-        # кавычки к одному стандарту
-        caption = re.sub(r"[`´«»“”¨]", '"', caption)
-        caption = re.sub(r"[‘’]", "'", caption)
-
-        # &quot;
-        caption = re.sub(r"&quot;?", "", caption)
-        # &amp
-        caption = re.sub(r"&amp", "", caption)
-
-        # ip adresses:
-        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
-
-        # article ids:
-        caption = re.sub(r"\d:\d\d\s+$", "", caption)
-
-        # \n
-        caption = re.sub(r"\\n", " ", caption)
-
-        # "#123"
-        caption = re.sub(r"#\d{1,3}\b", "", caption)
-        # "#12345.."
-        caption = re.sub(r"#\d{5,}\b", "", caption)
-        # "123456.."
-        caption = re.sub(r"\b\d{6,}\b", "", caption)
-        # filenames:
-        caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
-
-        #
-        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
-        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""
-
-        caption = re.sub(self.bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
-        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "
-
-        # this-is-my-cute-cat / this_is_my_cute_cat
-        regex2 = re.compile(r"(?:\-|\_)")
-        if len(re.findall(regex2, caption)) > 3:
-            caption = re.sub(regex2, " ", caption)
-
-        caption = ftfy.fix_text(caption)
-        caption = html.unescape(html.unescape(caption))
-
-        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
-        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
-        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231
-
-        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
-        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
-        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
-        caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
-        caption = re.sub(r"\bpage\s+\d+\b", "", caption)
-
-        caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...
-
-        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
-
-        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
-        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
-        caption = re.sub(r"\s+", " ", caption)
-
-        caption.strip()
-
-        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
-        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
-        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
-        caption = re.sub(r"^\.\S+$", "", caption)
-
-        return caption.strip()
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        num_inference_steps: int = 100,
-        timesteps: List[int] = None,
-        guidance_scale: float = 7.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        clean_caption: bool = True,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
-                timesteps are used. Must be in descending order.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size):
-                The width in pixels of the generated image.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-            clean_caption (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
-                be installed. If the dependencies are not installed, the embeddings will be created from the raw
-                prompt.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
-            returning a tuple, the first element is a list with the generated images, and the second element is a list
-            of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
-            or watermarked content, according to the `safety_checker`.
-        """
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
-
-        # 2. Define call parameters
-        height = height or self.unet.config.sample_size
-        width = width or self.unet.config.sample_size
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt,
-            do_classifier_free_guidance,
-            num_images_per_prompt=num_images_per_prompt,
-            device=device,
-            negative_prompt=negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            clean_caption=clean_caption,
-        )
-
-        if do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        # 4. Prepare timesteps
-        if timesteps is not None:
-            self.scheduler.set_timesteps(timesteps=timesteps, device=device)
-            timesteps = self.scheduler.timesteps
-            num_inference_steps = len(timesteps)
-        else:
-            self.scheduler.set_timesteps(num_inference_steps, device=device)
-            timesteps = self.scheduler.timesteps
-
-        # 5. Prepare intermediate images
-        intermediate_images = self.prepare_intermediate_images(
-            batch_size * num_images_per_prompt,
-            self.unet.config.in_channels,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # HACK: see comment in `enable_model_cpu_offload`
-        if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
-            self.text_encoder_offload_hook.offload()
-
-        # 7. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                model_input = (
-                    torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images
-                )
-                model_input = self.scheduler.scale_model_input(model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)
-                    noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                    noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
-
-                if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
-                    noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                intermediate_images = self.scheduler.step(
-                    noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
-                )[0]
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, intermediate_images)
-
-        image = intermediate_images
-
-        if output_type == "pil":
-            # 8. Post-processing
-            image = (image / 2 + 0.5).clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-            # 9. Run safety checker
-            image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-            # 10. Convert to PIL
-            image = self.numpy_to_pil(image)
-
-            # 11. Apply watermark
-            if self.watermarker is not None:
-                image = self.watermarker.apply_watermark(image, self.unet.config.sample_size)
-        elif output_type == "pt":
-            nsfw_detected = None
-            watermark_detected = None
-
-            if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
-                self.unet_offload_hook.offload()
-        else:
-            # 8. Post-processing
-            image = (image / 2 + 0.5).clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-            # 9. Run safety checker
-            image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image, nsfw_detected, watermark_detected)
-
-        return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)

spaces/Andy1621/uniformer_image_detection/configs/paa/paa_r50_fpn_1x_coco.py

@@ -1,70 +0,0 @@
-_base_ = [
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
-    type='PAA',
-    pretrained='torchvision://resnet50',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        start_level=1,
-        add_extra_convs='on_output',
-        num_outs=5),
-    bbox_head=dict(
-        type='PAAHead',
-        reg_decoded_bbox=True,
-        score_voting=True,
-        topk=9,
-        num_classes=80,
-        in_channels=256,
-        stacked_convs=4,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            octave_base_scale=8,
-            scales_per_octave=1,
-            strides=[8, 16, 32, 64, 128]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.1, 0.1, 0.2, 0.2]),
-        loss_cls=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
-        loss_centerness=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
-    # training and testing settings
-    train_cfg=dict(
-        assigner=dict(
-            type='MaxIoUAssigner',
-            pos_iou_thr=0.1,
-            neg_iou_thr=0.1,
-            min_pos_iou=0,
-            ignore_iof_thr=-1),
-        allowed_border=-1,
-        pos_weight=-1,
-        debug=False),
-    test_cfg=dict(
-        nms_pre=1000,
-        min_bbox_size=0,
-        score_thr=0.05,
-        nms=dict(type='nms', iou_threshold=0.6),
-        max_per_img=100))
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)

spaces/Arjav/TOS-Summarization/README.md

@@ -1,12 +0,0 @@
----
-title: TOS Summarization
-emoji: 🐨
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/metadata/languages.py

@@ -1,352 +0,0 @@
-"""
-Metadata about languages used by our model training code for our
-SingleByteCharSetProbers.  Could be used for other things in the future.
-
-This code is based on the language metadata from the uchardet project.
-"""
-
-from string import ascii_letters
-from typing import List, Optional
-
-# TODO: Add Ukrainian (KOI8-U)
-
-
-class Language:
-    """Metadata about a language useful for training models
-
-    :ivar name: The human name for the language, in English.
-    :type name: str
-    :ivar iso_code: 2-letter ISO 639-1 if possible, 3-letter ISO code otherwise,
-                    or use another catalog as a last resort.
-    :type iso_code: str
-    :ivar use_ascii: Whether or not ASCII letters should be included in trained
-                     models.
-    :type use_ascii: bool
-    :ivar charsets: The charsets we want to support and create data for.
-    :type charsets: list of str
-    :ivar alphabet: The characters in the language's alphabet. If `use_ascii` is
-                    `True`, you only need to add those not in the ASCII set.
-    :type alphabet: str
-    :ivar wiki_start_pages: The Wikipedia pages to start from if we're crawling
-                            Wikipedia for training data.
-    :type wiki_start_pages: list of str
-    """
-
-    def __init__(
-        self,
-        name: Optional[str] = None,
-        iso_code: Optional[str] = None,
-        use_ascii: bool = True,
-        charsets: Optional[List[str]] = None,
-        alphabet: Optional[str] = None,
-        wiki_start_pages: Optional[List[str]] = None,
-    ) -> None:
-        super().__init__()
-        self.name = name
-        self.iso_code = iso_code
-        self.use_ascii = use_ascii
-        self.charsets = charsets
-        if self.use_ascii:
-            if alphabet:
-                alphabet += ascii_letters
-            else:
-                alphabet = ascii_letters
-        elif not alphabet:
-            raise ValueError("Must supply alphabet if use_ascii is False")
-        self.alphabet = "".join(sorted(set(alphabet))) if alphabet else None
-        self.wiki_start_pages = wiki_start_pages
-
-    def __repr__(self) -> str:
-        param_str = ", ".join(
-            f"{k}={v!r}" for k, v in self.__dict__.items() if not k.startswith("_")
-        )
-        return f"{self.__class__.__name__}({param_str})"
-
-
-LANGUAGES = {
-    "Arabic": Language(
-        name="Arabic",
-        iso_code="ar",
-        use_ascii=False,
-        # We only support encodings that use isolated
-        # forms, because the current recommendation is
-        # that the rendering system handles presentation
-        # forms. This means we purposefully skip IBM864.
-        charsets=["ISO-8859-6", "WINDOWS-1256", "CP720", "CP864"],
-        alphabet="ءآأؤإئابةتثجحخدذرزسشصضطظعغػؼؽؾؿـفقكلمنهوىيًٌٍَُِّ",
-        wiki_start_pages=["الصفحة_الرئيسية"],
-    ),
-    "Belarusian": Language(
-        name="Belarusian",
-        iso_code="be",
-        use_ascii=False,
-        charsets=["ISO-8859-5", "WINDOWS-1251", "IBM866", "MacCyrillic"],
-        alphabet="АБВГДЕЁЖЗІЙКЛМНОПРСТУЎФХЦЧШЫЬЭЮЯабвгдеёжзійклмнопрстуўфхцчшыьэюяʼ",
-        wiki_start_pages=["Галоўная_старонка"],
-    ),
-    "Bulgarian": Language(
-        name="Bulgarian",
-        iso_code="bg",
-        use_ascii=False,
-        charsets=["ISO-8859-5", "WINDOWS-1251", "IBM855"],
-        alphabet="АБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЬЮЯабвгдежзийклмнопрстуфхцчшщъьюя",
-        wiki_start_pages=["Начална_страница"],
-    ),
-    "Czech": Language(
-        name="Czech",
-        iso_code="cz",
-        use_ascii=True,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="áčďéěíňóřšťúůýžÁČĎÉĚÍŇÓŘŠŤÚŮÝŽ",
-        wiki_start_pages=["Hlavní_strana"],
-    ),
-    "Danish": Language(
-        name="Danish",
-        iso_code="da",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="æøåÆØÅ",
-        wiki_start_pages=["Forside"],
-    ),
-    "German": Language(
-        name="German",
-        iso_code="de",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="äöüßẞÄÖÜ",
-        wiki_start_pages=["Wikipedia:Hauptseite"],
-    ),
-    "Greek": Language(
-        name="Greek",
-        iso_code="el",
-        use_ascii=False,
-        charsets=["ISO-8859-7", "WINDOWS-1253"],
-        alphabet="αβγδεζηθικλμνξοπρσςτυφχψωάέήίόύώΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΣΤΥΦΧΨΩΆΈΉΊΌΎΏ",
-        wiki_start_pages=["Πύλη:Κύρια"],
-    ),
-    "English": Language(
-        name="English",
-        iso_code="en",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "WINDOWS-1252", "MacRoman"],
-        wiki_start_pages=["Main_Page"],
-    ),
-    "Esperanto": Language(
-        name="Esperanto",
-        iso_code="eo",
-        # Q, W, X, and Y not used at all
-        use_ascii=False,
-        charsets=["ISO-8859-3"],
-        alphabet="abcĉdefgĝhĥijĵklmnoprsŝtuŭvzABCĈDEFGĜHĤIJĴKLMNOPRSŜTUŬVZ",
-        wiki_start_pages=["Vikipedio:Ĉefpaĝo"],
-    ),
-    "Spanish": Language(
-        name="Spanish",
-        iso_code="es",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="ñáéíóúüÑÁÉÍÓÚÜ",
-        wiki_start_pages=["Wikipedia:Portada"],
-    ),
-    "Estonian": Language(
-        name="Estonian",
-        iso_code="et",
-        use_ascii=False,
-        charsets=["ISO-8859-4", "ISO-8859-13", "WINDOWS-1257"],
-        # C, F, Š, Q, W, X, Y, Z, Ž are only for
-        # loanwords
-        alphabet="ABDEGHIJKLMNOPRSTUVÕÄÖÜabdeghijklmnoprstuvõäöü",
-        wiki_start_pages=["Esileht"],
-    ),
-    "Finnish": Language(
-        name="Finnish",
-        iso_code="fi",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="ÅÄÖŠŽåäöšž",
-        wiki_start_pages=["Wikipedia:Etusivu"],
-    ),
-    "French": Language(
-        name="French",
-        iso_code="fr",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="œàâçèéîïùûêŒÀÂÇÈÉÎÏÙÛÊ",
-        wiki_start_pages=["Wikipédia:Accueil_principal", "Bœuf (animal)"],
-    ),
-    "Hebrew": Language(
-        name="Hebrew",
-        iso_code="he",
-        use_ascii=False,
-        charsets=["ISO-8859-8", "WINDOWS-1255"],
-        alphabet="אבגדהוזחטיךכלםמןנסעףפץצקרשתװױײ",
-        wiki_start_pages=["עמוד_ראשי"],
-    ),
-    "Croatian": Language(
-        name="Croatian",
-        iso_code="hr",
-        # Q, W, X, Y are only used for foreign words.
-        use_ascii=False,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="abcčćdđefghijklmnoprsštuvzžABCČĆDĐEFGHIJKLMNOPRSŠTUVZŽ",
-        wiki_start_pages=["Glavna_stranica"],
-    ),
-    "Hungarian": Language(
-        name="Hungarian",
-        iso_code="hu",
-        # Q, W, X, Y are only used for foreign words.
-        use_ascii=False,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="abcdefghijklmnoprstuvzáéíóöőúüűABCDEFGHIJKLMNOPRSTUVZÁÉÍÓÖŐÚÜŰ",
-        wiki_start_pages=["Kezdőlap"],
-    ),
-    "Italian": Language(
-        name="Italian",
-        iso_code="it",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="ÀÈÉÌÒÓÙàèéìòóù",
-        wiki_start_pages=["Pagina_principale"],
-    ),
-    "Lithuanian": Language(
-        name="Lithuanian",
-        iso_code="lt",
-        use_ascii=False,
-        charsets=["ISO-8859-13", "WINDOWS-1257", "ISO-8859-4"],
-        # Q, W, and X not used at all
-        alphabet="AĄBCČDEĘĖFGHIĮYJKLMNOPRSŠTUŲŪVZŽaąbcčdeęėfghiįyjklmnoprsštuųūvzž",
-        wiki_start_pages=["Pagrindinis_puslapis"],
-    ),
-    "Latvian": Language(
-        name="Latvian",
-        iso_code="lv",
-        use_ascii=False,
-        charsets=["ISO-8859-13", "WINDOWS-1257", "ISO-8859-4"],
-        # Q, W, X, Y are only for loanwords
-        alphabet="AĀBCČDEĒFGĢHIĪJKĶLĻMNŅOPRSŠTUŪVZŽaābcčdeēfgģhiījkķlļmnņoprsštuūvzž",
-        wiki_start_pages=["Sākumlapa"],
-    ),
-    "Macedonian": Language(
-        name="Macedonian",
-        iso_code="mk",
-        use_ascii=False,
-        charsets=["ISO-8859-5", "WINDOWS-1251", "MacCyrillic", "IBM855"],
-        alphabet="АБВГДЃЕЖЗЅИЈКЛЉМНЊОПРСТЌУФХЦЧЏШабвгдѓежзѕијклљмнњопрстќуфхцчџш",
-        wiki_start_pages=["Главна_страница"],
-    ),
-    "Dutch": Language(
-        name="Dutch",
-        iso_code="nl",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "WINDOWS-1252", "MacRoman"],
-        wiki_start_pages=["Hoofdpagina"],
-    ),
-    "Polish": Language(
-        name="Polish",
-        iso_code="pl",
-        # Q and X are only used for foreign words.
-        use_ascii=False,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="AĄBCĆDEĘFGHIJKLŁMNŃOÓPRSŚTUWYZŹŻaąbcćdeęfghijklłmnńoóprsśtuwyzźż",
-        wiki_start_pages=["Wikipedia:Strona_główna"],
-    ),
-    "Portuguese": Language(
-        name="Portuguese",
-        iso_code="pt",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="ÁÂÃÀÇÉÊÍÓÔÕÚáâãàçéêíóôõú",
-        wiki_start_pages=["Wikipédia:Página_principal"],
-    ),
-    "Romanian": Language(
-        name="Romanian",
-        iso_code="ro",
-        use_ascii=True,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="ăâîșțĂÂÎȘȚ",
-        wiki_start_pages=["Pagina_principală"],
-    ),
-    "Russian": Language(
-        name="Russian",
-        iso_code="ru",
-        use_ascii=False,
-        charsets=[
-            "ISO-8859-5",
-            "WINDOWS-1251",
-            "KOI8-R",
-            "MacCyrillic",
-            "IBM866",
-            "IBM855",
-        ],
-        alphabet="абвгдеёжзийклмнопрстуфхцчшщъыьэюяАБВГДЕЁЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯ",
-        wiki_start_pages=["Заглавная_страница"],
-    ),
-    "Slovak": Language(
-        name="Slovak",
-        iso_code="sk",
-        use_ascii=True,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="áäčďéíĺľňóôŕšťúýžÁÄČĎÉÍĹĽŇÓÔŔŠŤÚÝŽ",
-        wiki_start_pages=["Hlavná_stránka"],
-    ),
-    "Slovene": Language(
-        name="Slovene",
-        iso_code="sl",
-        # Q, W, X, Y are only used for foreign words.
-        use_ascii=False,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="abcčdefghijklmnoprsštuvzžABCČDEFGHIJKLMNOPRSŠTUVZŽ",
-        wiki_start_pages=["Glavna_stran"],
-    ),
-    # Serbian can be written in both Latin and Cyrillic, but there's no
-    # simple way to get the Latin alphabet pages from Wikipedia through
-    # the API, so for now we just support Cyrillic.
-    "Serbian": Language(
-        name="Serbian",
-        iso_code="sr",
-        alphabet="АБВГДЂЕЖЗИЈКЛЉМНЊОПРСТЋУФХЦЧЏШабвгдђежзијклљмнњопрстћуфхцчџш",
-        charsets=["ISO-8859-5", "WINDOWS-1251", "MacCyrillic", "IBM855"],
-        wiki_start_pages=["Главна_страна"],
-    ),
-    "Thai": Language(
-        name="Thai",
-        iso_code="th",
-        use_ascii=False,
-        charsets=["ISO-8859-11", "TIS-620", "CP874"],
-        alphabet="กขฃคฅฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลฦวศษสหฬอฮฯะัาำิีึืฺุู฿เแโใไๅๆ็่้๊๋์ํ๎๏๐๑๒๓๔๕๖๗๘๙๚๛",
-        wiki_start_pages=["หน้าหลัก"],
-    ),
-    "Turkish": Language(
-        name="Turkish",
-        iso_code="tr",
-        # Q, W, and X are not used by Turkish
-        use_ascii=False,
-        charsets=["ISO-8859-3", "ISO-8859-9", "WINDOWS-1254"],
-        alphabet="abcçdefgğhıijklmnoöprsştuüvyzâîûABCÇDEFGĞHIİJKLMNOÖPRSŞTUÜVYZÂÎÛ",
-        wiki_start_pages=["Ana_Sayfa"],
-    ),
-    "Vietnamese": Language(
-        name="Vietnamese",
-        iso_code="vi",
-        use_ascii=False,
-        # Windows-1258 is the only common 8-bit
-        # Vietnamese encoding supported by Python.
-        # From Wikipedia:
-        # For systems that lack support for Unicode,
-        # dozens of 8-bit Vietnamese code pages are
-        # available.[1] The most common are VISCII
-        # (TCVN 5712:1993), VPS, and Windows-1258.[3]
-        # Where ASCII is required, such as when
-        # ensuring readability in plain text e-mail,
-        # Vietnamese letters are often encoded
-        # according to Vietnamese Quoted-Readable
-        # (VIQR) or VSCII Mnemonic (VSCII-MNEM),[4]
-        # though usage of either variable-width
-        # scheme has declined dramatically following
-        # the adoption of Unicode on the World Wide
-        # Web.
-        charsets=["WINDOWS-1258"],
-        alphabet="aăâbcdđeêghiklmnoôơpqrstuưvxyAĂÂBCDĐEÊGHIKLMNOÔƠPQRSTUƯVXY",
-        wiki_start_pages=["Chữ_Quốc_ngữ"],
-    ),
-}

spaces/AutoBG/Auto-BoardGame/title_generator.py

@@ -1,149 +0,0 @@
-import pandas as pd
-import re
-import nltk
-nltk.download('stopwords')
-from nltk.corpus import stopwords
-from gensim.parsing import preprocess_string, strip_tags, strip_numeric, strip_multiple_whitespaces, stem_text, strip_punctuation, remove_stopwords
-import spacy
-import torch
-from transformers import T5ForConditionalGeneration,T5Tokenizer
-import random
-from operator import itemgetter
-
-#Custom text tokenizer from https://github.com/canunj/deconstructing_games by N Canu & K Chen
-def doc_text_preprocessing(ser):
-    nlp=spacy.load("en_core_web_md", exclude=['parser','ner','textcat'])
-
-    """text processing steps"""
-    import re
-    stop_words=set(stopwords.words('english'))
-        
-    single_letter_replace=lambda c: re.sub("\s+\w{1}\s+|\n|-|—",'',c)
-    to_lower_func=lambda c: c.lower()
-    lemma_text=[preprocess_string(
-        ' '.join([token.lemma_ for token in desc]
-            ),[remove_stopwords,strip_numeric,strip_punctuation,strip_tags,
-               strip_multiple_whitespaces,single_letter_replace,to_lower_func]
-               ) for desc in ser.apply(lambda x: nlp(x))]
-
-    tokenize_text=[[word for word in string if word not in stop_words] for string in lemma_text]
-
-    return tokenize_text
-
-class Title_Generator:
-
-    def __init__(self, path, df):
-        self.model = T5ForConditionalGeneration.from_pretrained(path)
-        self.tokenizer = T5Tokenizer.from_pretrained(path)
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.model.to(self.device)
-        self.game_df = df
-        
-        self.title_iter = -1
-        self.out_titles = None
-        self.best_title = None
-        self.description = None
-        self.nlp = spacy.load("en_core_web_md")
-
-
-    def candidate_generator(self, description):
-        text =  "headline: " + description
-
-        encoding = self.tokenizer.encode_plus(text, return_tensors = "pt")
-        input_ids = encoding["input_ids"].to(self.device)
-        attention_masks = encoding["attention_mask"].to(self.device)
-
-        candidates = []
-
-        beam_outputs = self.model.generate(
-            input_ids = input_ids,
-            attention_mask = attention_masks,
-            max_length = 64,
-            num_beams = 16,
-            num_beam_groups=4,
-            num_return_sequences=8,
-            diversity_penalty=.1,
-            repetition_penalty=.9,
-            early_stopping = True)
-
-        for result in beam_outputs:
-            res = self.tokenizer.decode(result).replace('<pad> ','').replace('</s>','').replace('<pad>','')
-            candidates.append(res)
-        
-        return candidates, description
-    
-    def candidate_score(self,candidates,ex_check=None):
-        
-        
-        if ex_check != None:
-            pat = re.compile("((?:" + "|".join(map(re.escape, candidates[0]+[cand.upper() for cand in candidates[0]])) + "|" + "|".join(ex_check) +"))")
-            desc = re.sub(pat, "__", candidates[1])
-        else:
-            pat = re.compile("((?:" + "|".join(map(re.escape, candidates[0]+[cand.upper() for cand in candidates[0]])) + "))")
-            desc = re.sub(pat, "__", candidates[1])
-
-        
-        if re.search(re.compile(re.escape("__")), desc):
-            reg = re.compile("("+"|".join(ex_check) + ")")
-            hold = candidates[0]
-            gen_desc = re.sub(re.compile(re.escape("__")),"",desc)
-            candidates = self.candidate_generator(gen_desc)
-            next = [cand for cand in candidates[0]+hold if not reg.search(cand)]
-            candidates = (next, desc)
-
-        #check for existing games and duplicates
-        #transform function from https://stackoverflow.com/questions/42165779/python-how-to-remove-duplicate-valuescase-insensitive-from-a-list-with-same-o
-        def transform(L):
-            S = set(L)
-            return [item.title() for item in L if item.lower() not in S and not S.add(item.lower())]
-
-
-        clean_cand_step = list(set([game[0] for game in list(zip(candidates[0],[len(self.game_df[self.game_df.name.isin([x])]) for x in candidates[0]])) if game[1]==0]))
-        clean_cand_step = transform(clean_cand_step)
-
-        clean_cand_step = [re.sub(re.compile("(?<=[ ])And(?=[ ])"),'and',
-                                  re.sub(re.compile('(?<=\S) (([(]|\b)[Ss]econd [Ee]dition([)]|\b)|[Ss]econd [Ee]dition|2[Nn][Dd] [Ee]dition|([(]|\b)[Tt]hird [Ee]dition([)]|\b)|3[Rr][Dd] [Ee]dition)|["]Second Edition["]'),"",
-                                  re.sub(re.compile("(?<=[a-z])'S"),"'s",
-                                  re.sub(re.compile("(?<=[ ])Of(?=[ ])"),"of",x)))) 
-                                  for x in clean_cand_step]
-
-        
-        clean_cand = []
-        for cand in clean_cand_step:
-            try: 
-                inter = cand.split(":")
-                if inter[0].lower()==inter[1].lower():
-                    clean_cand.append(inter[0])
-                else:
-                    clean_cand.append(cand)
-            except:
-                clean_cand.append(cand)
-
-        #text processing
-        token_cand = doc_text_preprocessing(pd.Series(clean_cand))
-        token_art = doc_text_preprocessing(pd.Series([candidates[1]]))
-        sim = [self.nlp(title) for title in [" ".join(title) for title in token_cand]]
-        doc = self.nlp(" ".join(token_art[0]))
-
-        #scores cosine similarity between generated titles and body text, if the word is unknown (i.e. generator knows it but spacy doesn't)
-        #it assigns a random probability to populate
-
-        scores = [x if x !=0 else random.uniform(.3, .7) for x in [tok.similarity(doc) for tok in sim]]
-        
-        out_titles = sorted(list(zip(clean_cand,scores)),key=itemgetter(1),reverse=True)
-   
-        pat = re.compile("(?<=[!.?])(?=[^\s])")
-        pat2 = re.compile("([Ff]rom the [Pp]ublisher[: ]|[Ff]rom the [Dd]esigner[: ]|[Gg]ame [Dd]escription)")
-        pat3 = re.compile(": [Tt]he [Gg]ame: [Tt]he [Gg]ame|: [Tt]he [Gg]ame")
-        pat4 = re.compile("[Tt]he __")
-        pat5 = re.compile("__ [Gg]ame")
-        pat6 = re.compile("[Tt]he [Gg]ame [Oo]f __")
-        
-        desc = re.sub(pat," ",candidates[1])   
-        desc = re.sub(pat2,"",desc)
-        desc = re.sub(pat3,"",desc)
-        desc = re.sub(pat4,"__",desc)
-        desc = re.sub(pat5,"__",desc)
-        desc = re.sub(pat6,"__",desc)
-
-        return {'text':desc,'titles':out_titles}

spaces/AyameYODAYO/xijinpingx/README.md

@@ -1,10 +0,0 @@
----
-title: Xijinpingx
-emoji: 😻
-colorFrom: gray
-colorTo: purple
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/BLACKHOST/Banner/banner.py

@@ -1,4 +0,0 @@
-import pyfiglet
-text="H O S T 1 L E T"
-
-print('\033[31m'+pyfiglet.figlet_format(text,font='slant')+"\n"+'\033[34m'+"_"*60+'\033[00m')

spaces/Benson/text-generation/Examples/Apktime V2 2 Apk.md

@@ -1,41 +0,0 @@
-<br />
-<h1>Qué es APKTime y por qué lo necesitas</h1>
-<p>Si usted está buscando una manera de acceder a una amplia gama de aplicaciones de Android que no están disponibles en el oficial de Google Play Store, entonces es posible que desee comprobar APKTime. APKTime es una tienda de aplicaciones gratuita que ofrece todos los archivos APK más recientes y populares para varias categorías, como entretenimiento, deportes, juegos, elementos esenciales, animación y contenido para adultos. Puede encontrar y descargar aplicaciones que no se encuentran en otras tiendas de aplicaciones, como aplicaciones de streaming, aplicaciones modificadas, aplicaciones hackeadas y más. </p>
-<h2>apktime v2 2 apk</h2><br /><p><b><b>DOWNLOAD</b> &mdash; <a href="https://bltlly.com/2v6KQV">https://bltlly.com/2v6KQV</a></b></p><br /><br />
-<p>APKTime es fácil de usar y tiene una interfaz fácil de usar. Puede navegar a través de diferentes secciones y sub-secciones, o utilizar la función de búsqueda para encontrar la aplicación que desea. También puedes ver las calificaciones, reseñas y capturas de pantalla de cada aplicación antes de descargarla. APKTime también actualiza sus aplicaciones regularmente, por lo que siempre puede obtener las últimas versiones. Por otra parte, APKTime comprueba sus aplicaciones para los permisos y elimina muchos no deseados, por lo que es más seguro y fácil de instalar en su dispositivo. </p>
- <h2>Cómo descargar e instalar APKTime v2 2 apk en su dispositivo Android</h2>
-<p>Para descargar e instalar APKTime v2 2 apk en su dispositivo Android, es necesario seguir estos sencillos pasos:</p>
-<ol>
-<li>Vaya al sitio web oficial de APKTime ([4](https://apktime.com/)) y haga clic en el botón de descarga. Alternativamente, puede utilizar este enlace ([3](https://filehippo.com/android/download_apktime/)) para descargar el archivo apk directamente desde Filehippo.</li>
-<li>Una vez completada la descarga, vaya a la configuración del dispositivo y habilite la opción de instalar aplicaciones de fuentes desconocidas. Esto le permitirá instalar archivos APK que no son de Google Play Store.</li>
-<li>Busque el archivo apk descargado en su administrador de archivos y toque en él para iniciar el proceso de instalación. Siga las instrucciones en pantalla y conceda los permisos necesarios. </li>
-
-</ol>
-<p>También puede ver este video tutorial ([1](https://archive.org/details/apktime-v-2.2-original_20200614)) para obtener más detalles sobre cómo descargar e instalar APKTime v2 apk en su dispositivo Android. </p>
- <h2>Cómo usar APKTime para encontrar e instalar aplicaciones de terceros</h2>
-<p>Una vez que haya instalado APKTime en su dispositivo, puede usarlo para encontrar e instalar aplicaciones de terceros que se adapten a sus necesidades y preferencias. Aquí hay algunos consejos y trucos para usar APKTime:</p>
-<ul>
-<li>Para encontrar una aplicación, puede navegar a través de las diferentes categorías y subcategorías, o utilizar la función de búsqueda en la esquina superior derecha de la pantalla. También puedes ordenar las aplicaciones por popularidad, calificación o fecha. </li>
-<li>Para descargar una aplicación, simplemente toque en su nombre o icono y luego toque en el botón de descarga. Verá una barra de progreso que muestra el estado de la descarga. También puede pausar o reanudar la descarga en cualquier momento. </li>
-<li>Para instalar una aplicación, toque en el botón de instalación después de que se complete la descarga. Es posible que necesite conceder algunos permisos o habilitar algunos ajustes para que la aplicación funcione correctamente. </li>
-<li>Para actualizar una aplicación, vaya a la sección de actualizaciones en la barra de menú y toque en el botón de actualización junto al nombre de la aplicación. También puede habilitar actualizaciones automáticas para todas las aplicaciones en la sección de configuración. </li>
-<li>Para desinstalar una aplicación, vaya a la sección instalada en la barra de menú y toque en el botón de desinstalación junto al nombre de la aplicación. También puede desinstalar una aplicación de la configuración del dispositivo o archivo li> Utilice una fuente confiable y confiable para descargar APKTime y las aplicaciones que desee de ella. No utilice ningún sitio web o enlace no oficial o no verificado que pueda contener versiones falsas o modificadas de APKTime o las aplicaciones. Utilice siempre el sitio web oficial de APKTime ([4](https://apktime.com/)) o un sitio web de terceros de confianza como Filehippo ([3](https://filehippo.com/android/download_apktime/)) para descargar los archivos apk. </li>
-</ul>
- <h2>Conclusión</h2>
-
-<p>Sin embargo, el uso de APKTime también viene con algunos riesgos y desafíos, como malware, virus, spyware, problemas legales, problemas de compatibilidad, errores, errores, consumo de batería, consumo de almacenamiento, consumo de datos, consumo de ancho de banda, etc. Por lo tanto, debe tomar algunas precauciones y medidas para mantenerse seguro mientras usa APKTime. Necesita usar un servicio VPN, una aplicación antivirus, una aplicación de copia de seguridad, sentido común y precaución al usar APKTime.</p>
-<p></p>
-<p>Esperamos que este artículo te haya ayudado a entender qué es APKTime y cómo usarlo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer! </p>
- <h3>Preguntas frecuentes</h3>
-<h4>¿Cuál es la diferencia entre APKTime y Aptoide? </h4>
-<p>APKTime y Aptoide son tiendas de aplicaciones gratuitas que ofrecen aplicaciones de terceros para dispositivos Android. Sin embargo, hay algunas diferencias entre ellos. APKTime tiene una interfaz más organizada y fácil de usar que Aptoide. APKTime también tiene más categorías y subcategorías que Aptoide. Aptoide tiene más aplicaciones que APKTime, pero algunas de ellas pueden ser obsoletas o poco fiables. Aptoide también requiere que cree una cuenta e inicie sesión para usarla, mientras que APKTime no lo hace. </p>
- <h4>¿APKTime es legal? </h4>
-<p>APKTime en sí es legal, ya que no aloja ni distribuye ninguna aplicación en su plataforma. Solo proporciona enlaces para descargar los archivos apk de otras fuentes. Sin embargo, algunas de las aplicaciones que puedes encontrar y descargar de APKTime pueden no ser legales, ya que pueden violar los términos y condiciones de algunas aplicaciones o servicios, o infringir los derechos de propiedad intelectual de algunos desarrolladores o editores. Por lo tanto, siempre debes comprobar la legalidad de las aplicaciones antes de descargarlas e instalarlas desde APKTime.</p>
- <h4> ¿APKTime es seguro? </h4>
-
- <h4>¿Cómo puedo actualizar APKTime? </h4>
-<p>Para actualizar APKTime, puede ir al sitio web oficial de APKTime ([4](https://apktime.com/)) y descargar la última versión del archivo apk, o ir a la sección de actualizaciones en la barra de menú de la tienda de aplicaciones y pulse en el botón de actualización junto al nombre de la aplicación. También puede habilitar actualizaciones automáticas para todas las aplicaciones en la sección de configuración de la tienda de aplicaciones. </p>
- <h4>¿Cómo puedo desinstalar APKTime? </h4>
-<p>Para desinstalar APKTime, puede ir a la configuración de su dispositivo y toque en aplicaciones o administrador de aplicaciones y encontrar y toque en APKTime y luego toque en desinstalar, o ir a su aplicación de administrador de archivos y localizar y eliminar el archivo apk de APKTime.</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Brbaro Vieja Escuela Accin Rpg Apk.md

@@ -1,50 +0,0 @@
-
-<h1>Bárbaro: Old School Action RPG APK - Un juego de aventura no lineal para Android</h1>
-<p>Si usted está buscando un juego que le ofrece mucha libertad, desafío, y la inmersión, es posible que desee echa un vistazo a <strong>Barbarian: Old School Action RPG APK</strong>. Este es un juego que le permite explorar un mundo vivo, participar en el combate dinámico, y dar forma a su propio destino. En este artículo, te contaremos de qué se trata este juego, cuáles son sus características principales y cómo descargarlo e instalarlo en tu dispositivo Android. </p>
-<h2>Introducción</h2>
-<p><strong>Barbarian: Old School Action RPG APK</strong> es un juego desarrollado por Barbar Games, un estudio independiente que tiene como objetivo crear juegos únicos y originales. El juego está inspirado en juegos de rol clásicos como The Elder Scrolls, Gothic y Fallout. Se encuentra en un mundo de fantasía medieval donde puedes elegir tu propio camino y papel. Puedes ser un héroe o un villano, un guerrero o un mago, un cazador o un comerciante. El juego te ofrece muchas opciones y posibilidades para personalizar a tu personaje e influir en el mundo que te rodea. </p>
-<h2>bárbaro vieja escuela acción rpg apk</h2><br /><p><b><b>Download</b> &#10040;&#10040;&#10040; <a href="https://bltlly.com/2v6Kdx">https://bltlly.com/2v6Kdx</a></b></p><br /><br />
-<p>Algunas de las características principales del juego son:</p>
-<ul>
-<li>Un mundo vivo que reacciona a sus acciones y decisiones. Los NPC tienen sus propias vidas, necesidades y comportamientos. Pueden comer, dormir, trabajar, cazar, comerciar, luchar o incluso traicionarte. El mundo también tiene un paisaje complejo y variado que incluye montañas, bosques, mazmorras, cuevas y más. </li>
-<li>Un sistema de combate dinámico que requiere habilidad y estrategia. Puedes usar diferentes tipos de armas, como espadas, hachas, arcos o ballestas. También puedes bloquear, esquivar, detener o contraatacar. El resultado de la batalla depende de las estadísticas y habilidades de tu personaje, así como de tu propia habilidad. </li>
-<li>Un sistema de desarrollo de personajes que te permite actualizar tu equipo y aprender nuevas habilidades. Puedes encontrar o crear armaduras y armas más poderosas. También puedes entrenar con otros personajes que pueden enseñarte nuevas habilidades o mejorar las existentes. </li>
-</ul>
-
-<ol>
-<li>Ir a [este enlace]( 1 ) o [este enlace]( 2 ) y descargar el archivo APK. </li>
-<li>Habilitar fuentes desconocidas en el dispositivo yendo a Configuración > Seguridad > Fuentes desconocidas.</li>
-<li>Busque el archivo descargado en su dispositivo y toque en él para instalarlo. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
-<h2>Juego</h2>
-<h3>Mundo viviente</h3>
-<p>interactúan entre sí y con usted. Pueden ser amigables, hostiles o neutrales. También pueden unirse o salir de su partido, dependiendo de su reputación y acciones. El mundo tampoco tiene ubicaciones de carga, lo que significa que puede viajar sin problemas de una zona a otra. El juego también cuenta con un avanzado sistema de IA que hace que los PNJ y los enemigos se comporten de forma realista e inteligente. </p>
-<h3>Sistema de combate</h3>
-<p>Otro aspecto importante de <strong>Bárbaro: Old School Action RPG APK</strong> es su sistema de combate. El juego te ofrece una variedad de armas para elegir, como espadas, hachas, arcos o ballestas. Cada arma tiene sus propias ventajas y desventajas, como velocidad, alcance, daño y durabilidad. También puedes cambiar entre armas cuerpo a cuerpo y armas a distancia durante el combate. El juego también te permite bloquear, esquivar, detener o contraatacar a tus enemigos. El sistema de combate no se basa en la suerte o el azar, sino en tu habilidad y las estadísticas de tu personaje. Necesitas prestar atención a tu resistencia, salud y barras de maná, así como a los movimientos y ataques de tus enemigos. </p>
-<h3>Desarrollo de caracteres</h3>
-
-<h2>Parcela</h2>
-<h3>Terminaciones múltiples</h3>
-<p>La trama de <strong>Barbarian: Old School Action RPG APK</strong> no es lineal o predeterminado. El juego te permite elegir tu propio camino y papel en el mundo. Puedes ser un héroe o un villano, un salvador o un destructor, un líder o un seguidor. El juego tiene múltiples finales que dependen de sus acciones y decisiones a lo largo del juego. Puedes elegir ponerte del lado de las fuerzas del mal o del bien, o crear tu propia facción. También puedes establecer el orden en el mundo completando misiones, resolviendo problemas o conquistando territorios. El juego tiene un sistema moral que refleja tu reputación y alineación. </p>
-<h3>Historia de ramificación</h3>
-<p>El juego también tiene una historia ramificada que te desafía y te mantiene interesado. El juego tiene un alto nivel de complejidad y profundidad que requiere que pienses y planees con anticipación. El juego tiene muchos personajes, misiones y secretos que descubrir. Cada personaje tiene su propia historia, personalidad y objetivos. Cada misión tiene múltiples formas de completarla, con diferentes consecuencias y recompensas. Cada secreto tiene su propio misterio y recompensa. El juego también tiene eventos aleatorios que pueden cambiar el curso de la historia o crear nuevas oportunidades. </p>
-<h2>Conclusión</h2>
-<p>En conclusión, <strong>Barbarian: Old School Action RPG APK</strong> es un juego que le ofrece una aventura no lineal en un mundo vivo. El juego tiene un sistema de combate dinámico que requiere habilidad y estrategia, un sistema de desarrollo de personajes que le permite personalizar sus habilidades y equipos, y una trama que tiene múltiples finales y una historia ramificada. El juego es adecuado para los fanáticos de los RPG clásicos que disfrutan de libertad, desafío e inmersión. </p>
-<p>Si estás interesado en jugar a este juego, puedes descargarlo desde [este enlace] o [este enlace]. También puedes visitar el sitio web oficial del juego [aquí] o seguir al desarrollador en Twitter [aquí]. ¡Esperamos que disfrutes de este juego tanto como nosotros! </p>
-<p></p>
-<h2>Preguntas frecuentes</h2>
-<ul>
-
-<li>A: La duración del juego depende de cómo lo juegues y qué decisiones tomes. Se puede tomar en cualquier lugar de 20 a 40 horas para completar la historia principal, pero también hay muchas misiones secundarias y actividades que pueden extender el juego. </li>
-<li><strong>Q: ¿El juego está en línea o fuera de línea? </strong></li>
-<li>A: El juego es solo offline. No necesitas una conexión a Internet para jugarlo. </li>
-<li><strong>Q: ¿El juego es gratis o pagado? </strong></li>
-<li>A: El juego es gratis para descargar y jugar. Sin embargo, contiene anuncios y compras en la aplicación que pueden mejorar su experiencia. </li>
-<li>para jugar el juego? </strong></li>
-<li>A: El juego requiere Android 4.4 o superior y al menos 2 GB de RAM y 500 MB de espacio de almacenamiento. </li>
-<li><strong>Q: ¿Cómo puedo contactar al desarrollador o reportar un error? </strong></li>
-<li>A: Puede ponerse en contacto con el desarrollador enviando un correo electrónico a [esta dirección] o rellenando [este formulario]. También puedes reportar un error o dar retroalimentación usando el menú del juego. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Chica Del Siglo 21.md

@@ -1,87 +0,0 @@
-<br />
-<h1>Descargar 21st Century Girl: Una guía para disfrutar de la última película romántica coreana</h1>
-<p>¿Estás buscando una película dulce y encantadora para ver este fin de semana? ¿Te gustan los dramas coreanos y las películas que te hacen reír, llorar y desmayarse? Si respondiste sí, entonces definitivamente deberías ver <strong>20th Century Girl</strong>, una nueva película romántica coreana que se está transmitiendo en Netflix ahora mismo. </p>
-<h2>¿Qué es 20th Century Girl y por qué usted debe verlo</h2>
-<p><strong>20th Century Girl</strong> es una película coreana de 2022 que sigue el primer amor y amistades de un estudiante de secundaria llamado Bo-Ra en 1999. Bo-Ra es una chica brillante y positiva que es buena en taekwondo y miembro del club de radiodifusión en su escuela. También es la mejor amiga de Yeon-Du, que está enamorada de Hyun-Jin, un chico popular en su clase. Yeon-Du le pide a Bo-Ra que averigüe todo sobre Hyun-Jin antes de ir a los EE.UU. para una cirugía cardíaca. Sin embargo, a medida que Bo-Ra se acerca a Hyun-Jin, ella comienza a enamorarse de él también. </p>
-<h2>descargar chica del siglo 21</h2><br /><p><b><b>DOWNLOAD</b> &#10145; <a href="https://bltlly.com/2v6LVX">https://bltlly.com/2v6LVX</a></b></p><br /><br />
-<h3>La trama de la película</h3>
-
-<h3>El reparto y el equipo de la película</h3>
-<p>La película cuenta con un talentoso elenco de jóvenes actores que dan vida a sus personajes. Kim You-jung interpreta a Bo-Ra, la heroína de la historia. Kim You-jung es una actriz famosa que ha protagonizado muchos dramas y películas, como <em>Moon Embracing the Sun</em>, <em>Love in the Moonlight</em>, <em>Clean with Passion for Now</em>, y <em>Tune in for Love</em>. Byeon Woo-seok interpreta a Hyun-Jin, el chico guapo pero problemático que llama la atención de Bo-Ra. Byeon Woo-seok es una estrella emergente que ha aparecido en <em>Flower Crew: Joseon Marriage Agency</em>, <em>Record of Youth</em>, <em>Buscar: WWW</em>, and <em>Dear. M</em>. Park Jung-woo interpreta a Woon-Ho, el dulce y leal amigo al que le gusta Bo-Ra. Park Jung-woo es un recién llegado que ha hecho su debut en esta película. Lee Na-eun interpreta a Yeon-Du, el mejor amigo de Bo-Ra que tiene una enfermedad cardíaca. Lee Na-eun es miembro del grupo de chicas April y una actriz que ha actuado en <em>A-Teen</em>, <em>Extraordinary You</em>, y <em>Taxi Driver</em>. La película también cuenta con otros actores de reparto, como Kim Sun-young, Kim Mi-kyung, Kim Sang-ho y Lee Jong-won, que interpretan los papeles de la familia de Bo-Ra, maestra y directora. La película está dirigida por Lee Dong-eun, quien es conocido por sus trabajos anteriores <em>Mother’s Job</em> y <em>In Between Seasons</em>. La película está escrita por Kim Min-jung y Lee Dong-eun, basada en el webtoon del mismo nombre de Yoon Yi-soo. La película es producida por Lotte Entertainment y distribuida por Netflix. <h3>Las críticas y valoraciones de la película</h3>
-
-<h2>Cómo descargar 20th Century Girl de forma legal y segura</h2>
-<p>Si estás interesado en ver <strong>20th Century Girl</strong>, es posible que te estés preguntando cómo descargarlo de forma legal y segura. La buena noticia es que puede transmitir y descargar fácilmente la película en Netflix, el servicio de transmisión líder en el mundo que ofrece una amplia gama de películas, programas, documentales y más. Estos son algunos de los beneficios de la transmisión en Netflix, los pasos para descargar la película en Netflix y algunos consejos y trucos para optimizar su experiencia de visualización. </p>
-<h3>Los beneficios de la transmisión en Netflix</h3>
-<p>Streaming en Netflix tiene muchas ventajas que hacen que valga la pena su tiempo y dinero. Algunos de estos beneficios son:</p>
-<ul>
-<li>Puedes ver la película en cualquier momento, en cualquier lugar, en cualquier dispositivo. Puede transmitir la película en su televisor inteligente, computadora portátil, tableta, teléfono inteligente o consola de juegos. También puede descargar la película en su dispositivo y verla sin conexión cuando no tiene acceso a Internet o desea guardar datos. </li>
-<li>Puedes disfrutar de la película en alta calidad y con subtítulos. Puede elegir entre diferentes resoluciones, como HD o 4K, dependiendo de su dispositivo y la velocidad de Internet. También puede seleccionar entre varios idiomas de subtítulos, como inglés, español, francés o coreano.</li>
-<li>Puedes acceder a otro contenido relacionado con la película. Puedes ver videos entre bastidores, entrevistas con el reparto y el equipo, trailers, teasers y más. También puedes ver otras películas y programas coreanos similares a <strong>20th Century Girl</strong>, como <em>Crash Landing on You</em>, <em>Itaewon Class</em>, <em>Start-Up</em>, y <em>Sweet Home</em>. </li>
-<li>Puede compartir sus opiniones y recomendaciones con otros espectadores. Puede calificar la película, escribir una reseña o dejar un comentario en Netflix. También puede unirse a comunidades y foros en línea donde puede discutir la película con otros fans. </li>
-
-</ul>
-<h3>Los pasos para descargar la película en Netflix</h3>
-<p>Para descargar <strong>20th Century Girl</strong> en Netflix, necesitas tener una cuenta de Netflix y un dispositivo compatible. Si no tiene uno, puede registrarse para una prueba gratuita o un plan de suscripción mensual en el sitio web o la aplicación de Netflix. Los planes varían en precio y características, como el número de pantallas que puede ver al mismo tiempo, la calidad del video y la disponibilidad de descargas. Una vez que tenga una cuenta, puede seguir estos pasos para descargar la película en Netflix:</p>
-<ol>
-<li>Abra la aplicación de Netflix en su dispositivo e inicie sesión con su cuenta. </li>
-<li>Busque <strong>20th Century Girl</strong> en la barra de búsqueda o busque las categorías hasta encontrarla. </li>
-<li>Selecciona la película y toca el botón <strong>Descargar</strong> que aparece debajo del título. También puedes tocar el botón <strong>More</strong> y luego seleccionar <strong>Download</strong> desde el menú. </li>
-<li>Espere a que se complete la descarga. Puede comprobar el progreso de la descarga en la pestaña <strong>Downloads</strong> en la parte inferior de la pantalla. </li>
-<li>Una vez finalizada la descarga, puede ver la película sin conexión pulsando en la pestaña <strong>Descargas</strong> y seleccionando la película. También puedes acceder a tus descargas desde el botón <strong>Menú</strong> en la parte superior izquierda de la pantalla y elegir <strong>Mis descargas</strong>. </li>
-</ol>
-<h3>Los consejos y trucos para optimizar tu experiencia de visualización</h3>
-<p>Para aprovechar al máximo tu experiencia de streaming y descarga, aquí hay algunos consejos y trucos que puedes probar:</p>
-<ul>
-<li>Asegúrese de tener una conexión a Internet estable y rápida. Si su Internet es lento o poco confiable, puede experimentar almacenamiento en búfer, retraso o video de baja calidad. Para evitar esto, puede usar una conexión por cable en lugar de Wi-Fi, cerrar cualquier otra aplicación o programa que use ancho de banda o actualizar su plan de Internet si es posible. </li>
-
-<li>Elimina cualquier descarga que ya no necesites. Si has visto la película o no quieres verla de nuevo, puedes borrarla de tu dispositivo para liberar espacio. Puedes hacer esto tocando el botón <strong>Editar</strong> en la pestaña <strong>Descargas</strong> y seleccionando la película. También puede eliminar todas las descargas a la vez pulsando en el botón <strong>Eliminar todas las descargas</strong> en el menú <strong>/strong>. </li>
-<li>Póngase en contacto con el servicio al cliente de Netflix si encuentra algún problema o problema. Si tiene preguntas, quejas o comentarios sobre la transmisión o descarga en Netflix, puede ponerse en contacto con el servicio de atención al cliente de Netflix a través del teléfono, el chat o el correo electrónico. También puede visitar el Centro de ayuda de Netflix para obtener más información y soluciones. </li>
-</ul>
-<h2>Cómo disfrutar de 20th Century Girl con tus amigos y familiares</h2>
-<p><strong>20th Century Girl</strong> es una película que se disfruta mejor con tus amigos y familiares. Es una película que te hará reír, llorar y sentir nostalgia por tu primer amor y amistades. Aquí hay algunas maneras de disfrutar de 20th Century Girl con sus seres queridos:</p>
-<h3>Los mejores aperitivos y bebidas para preparar la noche de cine</h3>
-<p>Ninguna noche de cine está completa sin algunos deliciosos aperitivos y bebidas para comer mientras observa. Estos son algunos de los mejores aperitivos y bebidas que van bien con 20th Century Girl:</p>
-<tabla>
-<tr><th>Snacks</th><th>Bebidas</th></tr>
-
-</tabla>
-<h3>Los divertidos juegos y actividades para hacer antes y después de la película</h3>
-<p>Además de ver la película, también puede tener algunos juegos divertidos y actividades para hacer con sus amigos y familiares antes y después de la película. Aquí hay algunas ideas:</p>
-<p></p>
-<ul>
-<li>Antes de la película, puede jugar un juego de preguntas sobre 1999, el año en que se desarrolla la película. Puede hacerse preguntas sobre los eventos, tendencias, celebridades, música, películas y programas que ocurrieron o fueron populares en 1999. También puede utilizar cuestionarios o aplicaciones en línea para poner a prueba sus conocimientos. </li>
-<li>Después de la película, puede jugar un juego de karaoke con las canciones de la banda sonora de la película. La película presenta algunas de las canciones más icónicas de los 90 y 2000, como <em>I Want It That Way</em> de Backstreet Boys, <em>... Baby One More Time</em> por Britney Spears, <em>Mi corazón seguirá adelante</em> por Celine Dion, y <em>Mientras me ames</em> por Justin Bieber. Puedes cantar estas canciones usando una máquina de karaoke, un micrófono o una aplicación para smartphone. </li>
-<li>Otra actividad que puedes hacer después de la película es hacer una cápsula del tiempo con tus amigos y familiares. Pueden escribir cartas a sus seres futuros, tomar fotos o videos de ustedes mismos, o recoger artículos que representan sus vidas actuales, como recuerdos, boletos, revistas o juguetes. A continuación, puede poner estas cosas en una caja o un recipiente y sellarlo con una fecha. Usted puede decidir cuándo abrir la cápsula del tiempo en el futuro, como en 10 años, 20 años, o en una ocasión especial. </li>
-</ul>
-<h3>Las preguntas de discusión y los temas para compartir sus pensamientos y sentimientos sobre la película</h3>
-<p>Una de las mejores maneras de disfrutar de <strong>20th Century Girl</strong> es compartir tus pensamientos y sentimientos sobre la película con tus amigos y familiares. Puede tener una discusión significativa y animada sobre la película haciéndose algunas preguntas y temas, como:</p>
-<ul>
-<li>¿Qué te gustó o no de la película? </li>
-
-<li> ¿Cuál fue tu escena o momento favorito en la película y por qué? </li>
-<li>¿Cómo te hizo sentir la película? ¿Te hizo reír, llorar o ambos? </li>
-<li>¿Qué aprendiste de la película? ¿Te enseñó algo sobre ti mismo, amor, amistad o vida? </li>
-<li>¿Cómo se relacionó la película con tus propias experiencias? ¿Te recordó tu primer amor o amistades? </li>
-<li>¿Cómo retrató la película la era de 1999? ¿Capturó la esencia de ese período de tiempo? </li>
-<li>¿Qué te pareció el final de la película? ¿Estabas satisfecho o decepcionado? </li>
-<li>Si pudieras cambiar algo sobre la película, ¿qué sería? </li>
-<li>Si hubiera una secuela de la película, ¿qué querrías que pasara? </li>
-</ul>
-<h2>Conclusión</h2>
-<p><strong>20th Century Girl</strong> es una película romántica coreana que te llevará a un viaje nostálgico y conmovedor de primer amor y amistades. Es una película que puedes ver con tus amigos y familiares y pasar un rato divertido y memorable juntos. Puede transmitir y descargar fácilmente la película en Netflix, donde también puede encontrar otro contenido relacionado con la película. También puede preparar algunos aperitivos y bebidas, jugar algunos juegos y actividades, y tener algunas discusiones sobre la película para mejorar su experiencia de visualización. Si estás buscando una película dulce y encantadora para ver este fin de semana, no te pierdas <strong>20th Century Girl</strong>. ¡Descárgalo ahora en Netflix y disfruta! </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí están algunas de las preguntas más frecuentes sobre <strong>20th Century Girl</strong>:</p>
-<ol>
-<li><strong>¿Está la chica del siglo XX basada en una historia real? </strong></li>
-<p>No, 20th Century Girl no se basa en una historia real. Se basa en un webtoon del mismo nombre de Yoon Yi-soo. </p>
-<li><strong>¿Dónde fue filmada la chica del siglo 20? </strong></li>
-<p>La película fue filmada en varios lugares de Corea del Sur, como Seúl, Busan, la isla de Jeju y la provincia de Gyeonggi.</p>
-
-<p>Las canciones de 20 Century Girl son cantadas por varios artistas, como Backstreet Boys, Britney Spears, Celine Dion, Justin Bieber e IU. La banda sonora original de la película está compuesta por Kim Jun-seok y Park Se-jun.</p>
-<li><strong>¿Cuánto tiempo es la chica del siglo 20? </strong></li>
-<p>La película tiene un tiempo de ejecución de 115 minutos. </p>
-<li><strong>Es la chica del siglo XX adecuada para los niños? </strong></li>
-<p>La película tiene una calificación PG-13 para algunas referencias de lenguaje, violencia y sexuales. Es adecuada para adolescentes y adultos, pero no para niños pequeños. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/__version__.py

@@ -1,14 +0,0 @@
-# .-. .-. .-. . . .-. .-. .-. .-.
-# |(  |-  |.| | | |-  `-.  |  `-.
-# ' ' `-' `-`.`-' `-' `-'  '  `-'
-
-__title__ = "requests"
-__description__ = "Python HTTP for Humans."
-__url__ = "https://requests.readthedocs.io"
-__version__ = "2.28.2"
-__build__ = 0x022802
-__author__ = "Kenneth Reitz"
-__author_email__ = "[email protected]"
-__license__ = "Apache 2.0"
-__copyright__ = "Copyright Kenneth Reitz"
-__cake__ = "\u2728 \U0001f370 \u2728"

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

@@ -1,1076 +0,0 @@
-# Copyright (c) 2010-2020 Benjamin Peterson
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-"""Utilities for writing code that runs on Python 2 and 3"""
-
-from __future__ import absolute_import
-
-import functools
-import itertools
-import operator
-import sys
-import types
-
-__author__ = "Benjamin Peterson <[email protected]>"
-__version__ = "1.16.0"
-
-
-# Useful for very coarse version differentiation.
-PY2 = sys.version_info[0] == 2
-PY3 = sys.version_info[0] == 3
-PY34 = sys.version_info[0:2] >= (3, 4)
-
-if PY3:
-    string_types = (str,)
-    integer_types = (int,)
-    class_types = (type,)
-    text_type = str
-    binary_type = bytes
-
-    MAXSIZE = sys.maxsize
-else:
-    string_types = (basestring,)
-    integer_types = (int, long)
-    class_types = (type, types.ClassType)
-    text_type = unicode
-    binary_type = str
-
-    if sys.platform.startswith("java"):
-        # Jython always uses 32 bits.
-        MAXSIZE = int((1 << 31) - 1)
-    else:
-        # It's possible to have sizeof(long) != sizeof(Py_ssize_t).
-        class X(object):
-            def __len__(self):
-                return 1 << 31
-
-        try:
-            len(X())
-        except OverflowError:
-            # 32-bit
-            MAXSIZE = int((1 << 31) - 1)
-        else:
-            # 64-bit
-            MAXSIZE = int((1 << 63) - 1)
-        del X
-
-if PY34:
-    from importlib.util import spec_from_loader
-else:
-    spec_from_loader = None
-
-
-def _add_doc(func, doc):
-    """Add documentation to a function."""
-    func.__doc__ = doc
-
-
-def _import_module(name):
-    """Import module, returning the module after the last dot."""
-    __import__(name)
-    return sys.modules[name]
-
-
-class _LazyDescr(object):
-    def __init__(self, name):
-        self.name = name
-
-    def __get__(self, obj, tp):
-        result = self._resolve()
-        setattr(obj, self.name, result)  # Invokes __set__.
-        try:
-            # This is a bit ugly, but it avoids running this again by
-            # removing this descriptor.
-            delattr(obj.__class__, self.name)
-        except AttributeError:
-            pass
-        return result
-
-
-class MovedModule(_LazyDescr):
-    def __init__(self, name, old, new=None):
-        super(MovedModule, self).__init__(name)
-        if PY3:
-            if new is None:
-                new = name
-            self.mod = new
-        else:
-            self.mod = old
-
-    def _resolve(self):
-        return _import_module(self.mod)
-
-    def __getattr__(self, attr):
-        _module = self._resolve()
-        value = getattr(_module, attr)
-        setattr(self, attr, value)
-        return value
-
-
-class _LazyModule(types.ModuleType):
-    def __init__(self, name):
-        super(_LazyModule, self).__init__(name)
-        self.__doc__ = self.__class__.__doc__
-
-    def __dir__(self):
-        attrs = ["__doc__", "__name__"]
-        attrs += [attr.name for attr in self._moved_attributes]
-        return attrs
-
-    # Subclasses should override this
-    _moved_attributes = []
-
-
-class MovedAttribute(_LazyDescr):
-    def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):
-        super(MovedAttribute, self).__init__(name)
-        if PY3:
-            if new_mod is None:
-                new_mod = name
-            self.mod = new_mod
-            if new_attr is None:
-                if old_attr is None:
-                    new_attr = name
-                else:
-                    new_attr = old_attr
-            self.attr = new_attr
-        else:
-            self.mod = old_mod
-            if old_attr is None:
-                old_attr = name
-            self.attr = old_attr
-
-    def _resolve(self):
-        module = _import_module(self.mod)
-        return getattr(module, self.attr)
-
-
-class _SixMetaPathImporter(object):
-
-    """
-    A meta path importer to import six.moves and its submodules.
-
-    This class implements a PEP302 finder and loader. It should be compatible
-    with Python 2.5 and all existing versions of Python3
-    """
-
-    def __init__(self, six_module_name):
-        self.name = six_module_name
-        self.known_modules = {}
-
-    def _add_module(self, mod, *fullnames):
-        for fullname in fullnames:
-            self.known_modules[self.name + "." + fullname] = mod
-
-    def _get_module(self, fullname):
-        return self.known_modules[self.name + "." + fullname]
-
-    def find_module(self, fullname, path=None):
-        if fullname in self.known_modules:
-            return self
-        return None
-
-    def find_spec(self, fullname, path, target=None):
-        if fullname in self.known_modules:
-            return spec_from_loader(fullname, self)
-        return None
-
-    def __get_module(self, fullname):
-        try:
-            return self.known_modules[fullname]
-        except KeyError:
-            raise ImportError("This loader does not know module " + fullname)
-
-    def load_module(self, fullname):
-        try:
-            # in case of a reload
-            return sys.modules[fullname]
-        except KeyError:
-            pass
-        mod = self.__get_module(fullname)
-        if isinstance(mod, MovedModule):
-            mod = mod._resolve()
-        else:
-            mod.__loader__ = self
-        sys.modules[fullname] = mod
-        return mod
-
-    def is_package(self, fullname):
-        """
-        Return true, if the named module is a package.
-
-        We need this method to get correct spec objects with
-        Python 3.4 (see PEP451)
-        """
-        return hasattr(self.__get_module(fullname), "__path__")
-
-    def get_code(self, fullname):
-        """Return None
-
-        Required, if is_package is implemented"""
-        self.__get_module(fullname)  # eventually raises ImportError
-        return None
-
-    get_source = get_code  # same as get_code
-
-    def create_module(self, spec):
-        return self.load_module(spec.name)
-
-    def exec_module(self, module):
-        pass
-
-
-_importer = _SixMetaPathImporter(__name__)
-
-
-class _MovedItems(_LazyModule):
-
-    """Lazy loading of moved objects"""
-
-    __path__ = []  # mark as package
-
-
-_moved_attributes = [
-    MovedAttribute("cStringIO", "cStringIO", "io", "StringIO"),
-    MovedAttribute("filter", "itertools", "builtins", "ifilter", "filter"),
-    MovedAttribute(
-        "filterfalse", "itertools", "itertools", "ifilterfalse", "filterfalse"
-    ),
-    MovedAttribute("input", "__builtin__", "builtins", "raw_input", "input"),
-    MovedAttribute("intern", "__builtin__", "sys"),
-    MovedAttribute("map", "itertools", "builtins", "imap", "map"),
-    MovedAttribute("getcwd", "os", "os", "getcwdu", "getcwd"),
-    MovedAttribute("getcwdb", "os", "os", "getcwd", "getcwdb"),
-    MovedAttribute("getoutput", "commands", "subprocess"),
-    MovedAttribute("range", "__builtin__", "builtins", "xrange", "range"),
-    MovedAttribute(
-        "reload_module", "__builtin__", "importlib" if PY34 else "imp", "reload"
-    ),
-    MovedAttribute("reduce", "__builtin__", "functools"),
-    MovedAttribute("shlex_quote", "pipes", "shlex", "quote"),
-    MovedAttribute("StringIO", "StringIO", "io"),
-    MovedAttribute("UserDict", "UserDict", "collections"),
-    MovedAttribute("UserList", "UserList", "collections"),
-    MovedAttribute("UserString", "UserString", "collections"),
-    MovedAttribute("xrange", "__builtin__", "builtins", "xrange", "range"),
-    MovedAttribute("zip", "itertools", "builtins", "izip", "zip"),
-    MovedAttribute(
-        "zip_longest", "itertools", "itertools", "izip_longest", "zip_longest"
-    ),
-    MovedModule("builtins", "__builtin__"),
-    MovedModule("configparser", "ConfigParser"),
-    MovedModule(
-        "collections_abc",
-        "collections",
-        "collections.abc" if sys.version_info >= (3, 3) else "collections",
-    ),
-    MovedModule("copyreg", "copy_reg"),
-    MovedModule("dbm_gnu", "gdbm", "dbm.gnu"),
-    MovedModule("dbm_ndbm", "dbm", "dbm.ndbm"),
-    MovedModule(
-        "_dummy_thread",
-        "dummy_thread",
-        "_dummy_thread" if sys.version_info < (3, 9) else "_thread",
-    ),
-    MovedModule("http_cookiejar", "cookielib", "http.cookiejar"),
-    MovedModule("http_cookies", "Cookie", "http.cookies"),
-    MovedModule("html_entities", "htmlentitydefs", "html.entities"),
-    MovedModule("html_parser", "HTMLParser", "html.parser"),
-    MovedModule("http_client", "httplib", "http.client"),
-    MovedModule("email_mime_base", "email.MIMEBase", "email.mime.base"),
-    MovedModule("email_mime_image", "email.MIMEImage", "email.mime.image"),
-    MovedModule("email_mime_multipart", "email.MIMEMultipart", "email.mime.multipart"),
-    MovedModule(
-        "email_mime_nonmultipart", "email.MIMENonMultipart", "email.mime.nonmultipart"
-    ),
-    MovedModule("email_mime_text", "email.MIMEText", "email.mime.text"),
-    MovedModule("BaseHTTPServer", "BaseHTTPServer", "http.server"),
-    MovedModule("CGIHTTPServer", "CGIHTTPServer", "http.server"),
-    MovedModule("SimpleHTTPServer", "SimpleHTTPServer", "http.server"),
-    MovedModule("cPickle", "cPickle", "pickle"),
-    MovedModule("queue", "Queue"),
-    MovedModule("reprlib", "repr"),
-    MovedModule("socketserver", "SocketServer"),
-    MovedModule("_thread", "thread", "_thread"),
-    MovedModule("tkinter", "Tkinter"),
-    MovedModule("tkinter_dialog", "Dialog", "tkinter.dialog"),
-    MovedModule("tkinter_filedialog", "FileDialog", "tkinter.filedialog"),
-    MovedModule("tkinter_scrolledtext", "ScrolledText", "tkinter.scrolledtext"),
-    MovedModule("tkinter_simpledialog", "SimpleDialog", "tkinter.simpledialog"),
-    MovedModule("tkinter_tix", "Tix", "tkinter.tix"),
-    MovedModule("tkinter_ttk", "ttk", "tkinter.ttk"),
-    MovedModule("tkinter_constants", "Tkconstants", "tkinter.constants"),
-    MovedModule("tkinter_dnd", "Tkdnd", "tkinter.dnd"),
-    MovedModule("tkinter_colorchooser", "tkColorChooser", "tkinter.colorchooser"),
-    MovedModule("tkinter_commondialog", "tkCommonDialog", "tkinter.commondialog"),
-    MovedModule("tkinter_tkfiledialog", "tkFileDialog", "tkinter.filedialog"),
-    MovedModule("tkinter_font", "tkFont", "tkinter.font"),
-    MovedModule("tkinter_messagebox", "tkMessageBox", "tkinter.messagebox"),
-    MovedModule("tkinter_tksimpledialog", "tkSimpleDialog", "tkinter.simpledialog"),
-    MovedModule("urllib_parse", __name__ + ".moves.urllib_parse", "urllib.parse"),
-    MovedModule("urllib_error", __name__ + ".moves.urllib_error", "urllib.error"),
-    MovedModule("urllib", __name__ + ".moves.urllib", __name__ + ".moves.urllib"),
-    MovedModule("urllib_robotparser", "robotparser", "urllib.robotparser"),
-    MovedModule("xmlrpc_client", "xmlrpclib", "xmlrpc.client"),
-    MovedModule("xmlrpc_server", "SimpleXMLRPCServer", "xmlrpc.server"),
-]
-# Add windows specific modules.
-if sys.platform == "win32":
-    _moved_attributes += [
-        MovedModule("winreg", "_winreg"),
-    ]
-
-for attr in _moved_attributes:
-    setattr(_MovedItems, attr.name, attr)
-    if isinstance(attr, MovedModule):
-        _importer._add_module(attr, "moves." + attr.name)
-del attr
-
-_MovedItems._moved_attributes = _moved_attributes
-
-moves = _MovedItems(__name__ + ".moves")
-_importer._add_module(moves, "moves")
-
-
-class Module_six_moves_urllib_parse(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_parse"""
-
-
-_urllib_parse_moved_attributes = [
-    MovedAttribute("ParseResult", "urlparse", "urllib.parse"),
-    MovedAttribute("SplitResult", "urlparse", "urllib.parse"),
-    MovedAttribute("parse_qs", "urlparse", "urllib.parse"),
-    MovedAttribute("parse_qsl", "urlparse", "urllib.parse"),
-    MovedAttribute("urldefrag", "urlparse", "urllib.parse"),
-    MovedAttribute("urljoin", "urlparse", "urllib.parse"),
-    MovedAttribute("urlparse", "urlparse", "urllib.parse"),
-    MovedAttribute("urlsplit", "urlparse", "urllib.parse"),
-    MovedAttribute("urlunparse", "urlparse", "urllib.parse"),
-    MovedAttribute("urlunsplit", "urlparse", "urllib.parse"),
-    MovedAttribute("quote", "urllib", "urllib.parse"),
-    MovedAttribute("quote_plus", "urllib", "urllib.parse"),
-    MovedAttribute("unquote", "urllib", "urllib.parse"),
-    MovedAttribute("unquote_plus", "urllib", "urllib.parse"),
-    MovedAttribute(
-        "unquote_to_bytes", "urllib", "urllib.parse", "unquote", "unquote_to_bytes"
-    ),
-    MovedAttribute("urlencode", "urllib", "urllib.parse"),
-    MovedAttribute("splitquery", "urllib", "urllib.parse"),
-    MovedAttribute("splittag", "urllib", "urllib.parse"),
-    MovedAttribute("splituser", "urllib", "urllib.parse"),
-    MovedAttribute("splitvalue", "urllib", "urllib.parse"),
-    MovedAttribute("uses_fragment", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_netloc", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_params", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_query", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_relative", "urlparse", "urllib.parse"),
-]
-for attr in _urllib_parse_moved_attributes:
-    setattr(Module_six_moves_urllib_parse, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_parse._moved_attributes = _urllib_parse_moved_attributes
-
-_importer._add_module(
-    Module_six_moves_urllib_parse(__name__ + ".moves.urllib_parse"),
-    "moves.urllib_parse",
-    "moves.urllib.parse",
-)
-
-
-class Module_six_moves_urllib_error(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_error"""
-
-
-_urllib_error_moved_attributes = [
-    MovedAttribute("URLError", "urllib2", "urllib.error"),
-    MovedAttribute("HTTPError", "urllib2", "urllib.error"),
-    MovedAttribute("ContentTooShortError", "urllib", "urllib.error"),
-]
-for attr in _urllib_error_moved_attributes:
-    setattr(Module_six_moves_urllib_error, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_error._moved_attributes = _urllib_error_moved_attributes
-
-_importer._add_module(
-    Module_six_moves_urllib_error(__name__ + ".moves.urllib.error"),
-    "moves.urllib_error",
-    "moves.urllib.error",
-)
-
-
-class Module_six_moves_urllib_request(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_request"""
-
-
-_urllib_request_moved_attributes = [
-    MovedAttribute("urlopen", "urllib2", "urllib.request"),
-    MovedAttribute("install_opener", "urllib2", "urllib.request"),
-    MovedAttribute("build_opener", "urllib2", "urllib.request"),
-    MovedAttribute("pathname2url", "urllib", "urllib.request"),
-    MovedAttribute("url2pathname", "urllib", "urllib.request"),
-    MovedAttribute("getproxies", "urllib", "urllib.request"),
-    MovedAttribute("Request", "urllib2", "urllib.request"),
-    MovedAttribute("OpenerDirector", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPDefaultErrorHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPRedirectHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPCookieProcessor", "urllib2", "urllib.request"),
-    MovedAttribute("ProxyHandler", "urllib2", "urllib.request"),
-    MovedAttribute("BaseHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPPasswordMgr", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPPasswordMgrWithDefaultRealm", "urllib2", "urllib.request"),
-    MovedAttribute("AbstractBasicAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPBasicAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("ProxyBasicAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("AbstractDigestAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPDigestAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("ProxyDigestAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPSHandler", "urllib2", "urllib.request"),
-    MovedAttribute("FileHandler", "urllib2", "urllib.request"),
-    MovedAttribute("FTPHandler", "urllib2", "urllib.request"),
-    MovedAttribute("CacheFTPHandler", "urllib2", "urllib.request"),
-    MovedAttribute("UnknownHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPErrorProcessor", "urllib2", "urllib.request"),
-    MovedAttribute("urlretrieve", "urllib", "urllib.request"),
-    MovedAttribute("urlcleanup", "urllib", "urllib.request"),
-    MovedAttribute("URLopener", "urllib", "urllib.request"),
-    MovedAttribute("FancyURLopener", "urllib", "urllib.request"),
-    MovedAttribute("proxy_bypass", "urllib", "urllib.request"),
-    MovedAttribute("parse_http_list", "urllib2", "urllib.request"),
-    MovedAttribute("parse_keqv_list", "urllib2", "urllib.request"),
-]
-for attr in _urllib_request_moved_attributes:
-    setattr(Module_six_moves_urllib_request, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_request._moved_attributes = _urllib_request_moved_attributes
-
-_importer._add_module(
-    Module_six_moves_urllib_request(__name__ + ".moves.urllib.request"),
-    "moves.urllib_request",
-    "moves.urllib.request",
-)
-
-
-class Module_six_moves_urllib_response(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_response"""
-
-
-_urllib_response_moved_attributes = [
-    MovedAttribute("addbase", "urllib", "urllib.response"),
-    MovedAttribute("addclosehook", "urllib", "urllib.response"),
-    MovedAttribute("addinfo", "urllib", "urllib.response"),
-    MovedAttribute("addinfourl", "urllib", "urllib.response"),
-]
-for attr in _urllib_response_moved_attributes:
-    setattr(Module_six_moves_urllib_response, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_response._moved_attributes = _urllib_response_moved_attributes
-
-_importer._add_module(
-    Module_six_moves_urllib_response(__name__ + ".moves.urllib.response"),
-    "moves.urllib_response",
-    "moves.urllib.response",
-)
-
-
-class Module_six_moves_urllib_robotparser(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_robotparser"""
-
-
-_urllib_robotparser_moved_attributes = [
-    MovedAttribute("RobotFileParser", "robotparser", "urllib.robotparser"),
-]
-for attr in _urllib_robotparser_moved_attributes:
-    setattr(Module_six_moves_urllib_robotparser, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_robotparser._moved_attributes = (
-    _urllib_robotparser_moved_attributes
-)
-
-_importer._add_module(
-    Module_six_moves_urllib_robotparser(__name__ + ".moves.urllib.robotparser"),
-    "moves.urllib_robotparser",
-    "moves.urllib.robotparser",
-)
-
-
-class Module_six_moves_urllib(types.ModuleType):
-
-    """Create a six.moves.urllib namespace that resembles the Python 3 namespace"""
-
-    __path__ = []  # mark as package
-    parse = _importer._get_module("moves.urllib_parse")
-    error = _importer._get_module("moves.urllib_error")
-    request = _importer._get_module("moves.urllib_request")
-    response = _importer._get_module("moves.urllib_response")
-    robotparser = _importer._get_module("moves.urllib_robotparser")
-
-    def __dir__(self):
-        return ["parse", "error", "request", "response", "robotparser"]
-
-
-_importer._add_module(
-    Module_six_moves_urllib(__name__ + ".moves.urllib"), "moves.urllib"
-)
-
-
-def add_move(move):
-    """Add an item to six.moves."""
-    setattr(_MovedItems, move.name, move)
-
-
-def remove_move(name):
-    """Remove item from six.moves."""
-    try:
-        delattr(_MovedItems, name)
-    except AttributeError:
-        try:
-            del moves.__dict__[name]
-        except KeyError:
-            raise AttributeError("no such move, %r" % (name,))
-
-
-if PY3:
-    _meth_func = "__func__"
-    _meth_self = "__self__"
-
-    _func_closure = "__closure__"
-    _func_code = "__code__"
-    _func_defaults = "__defaults__"
-    _func_globals = "__globals__"
-else:
-    _meth_func = "im_func"
-    _meth_self = "im_self"
-
-    _func_closure = "func_closure"
-    _func_code = "func_code"
-    _func_defaults = "func_defaults"
-    _func_globals = "func_globals"
-
-
-try:
-    advance_iterator = next
-except NameError:
-
-    def advance_iterator(it):
-        return it.next()
-
-
-next = advance_iterator
-
-
-try:
-    callable = callable
-except NameError:
-
-    def callable(obj):
-        return any("__call__" in klass.__dict__ for klass in type(obj).__mro__)
-
-
-if PY3:
-
-    def get_unbound_function(unbound):
-        return unbound
-
-    create_bound_method = types.MethodType
-
-    def create_unbound_method(func, cls):
-        return func
-
-    Iterator = object
-else:
-
-    def get_unbound_function(unbound):
-        return unbound.im_func
-
-    def create_bound_method(func, obj):
-        return types.MethodType(func, obj, obj.__class__)
-
-    def create_unbound_method(func, cls):
-        return types.MethodType(func, None, cls)
-
-    class Iterator(object):
-        def next(self):
-            return type(self).__next__(self)
-
-    callable = callable
-_add_doc(
-    get_unbound_function, """Get the function out of a possibly unbound function"""
-)
-
-
-get_method_function = operator.attrgetter(_meth_func)
-get_method_self = operator.attrgetter(_meth_self)
-get_function_closure = operator.attrgetter(_func_closure)
-get_function_code = operator.attrgetter(_func_code)
-get_function_defaults = operator.attrgetter(_func_defaults)
-get_function_globals = operator.attrgetter(_func_globals)
-
-
-if PY3:
-
-    def iterkeys(d, **kw):
-        return iter(d.keys(**kw))
-
-    def itervalues(d, **kw):
-        return iter(d.values(**kw))
-
-    def iteritems(d, **kw):
-        return iter(d.items(**kw))
-
-    def iterlists(d, **kw):
-        return iter(d.lists(**kw))
-
-    viewkeys = operator.methodcaller("keys")
-
-    viewvalues = operator.methodcaller("values")
-
-    viewitems = operator.methodcaller("items")
-else:
-
-    def iterkeys(d, **kw):
-        return d.iterkeys(**kw)
-
-    def itervalues(d, **kw):
-        return d.itervalues(**kw)
-
-    def iteritems(d, **kw):
-        return d.iteritems(**kw)
-
-    def iterlists(d, **kw):
-        return d.iterlists(**kw)
-
-    viewkeys = operator.methodcaller("viewkeys")
-
-    viewvalues = operator.methodcaller("viewvalues")
-
-    viewitems = operator.methodcaller("viewitems")
-
-_add_doc(iterkeys, "Return an iterator over the keys of a dictionary.")
-_add_doc(itervalues, "Return an iterator over the values of a dictionary.")
-_add_doc(iteritems, "Return an iterator over the (key, value) pairs of a dictionary.")
-_add_doc(
-    iterlists, "Return an iterator over the (key, [values]) pairs of a dictionary."
-)
-
-
-if PY3:
-
-    def b(s):
-        return s.encode("latin-1")
-
-    def u(s):
-        return s
-
-    unichr = chr
-    import struct
-
-    int2byte = struct.Struct(">B").pack
-    del struct
-    byte2int = operator.itemgetter(0)
-    indexbytes = operator.getitem
-    iterbytes = iter
-    import io
-
-    StringIO = io.StringIO
-    BytesIO = io.BytesIO
-    del io
-    _assertCountEqual = "assertCountEqual"
-    if sys.version_info[1] <= 1:
-        _assertRaisesRegex = "assertRaisesRegexp"
-        _assertRegex = "assertRegexpMatches"
-        _assertNotRegex = "assertNotRegexpMatches"
-    else:
-        _assertRaisesRegex = "assertRaisesRegex"
-        _assertRegex = "assertRegex"
-        _assertNotRegex = "assertNotRegex"
-else:
-
-    def b(s):
-        return s
-
-    # Workaround for standalone backslash
-
-    def u(s):
-        return unicode(s.replace(r"\\", r"\\\\"), "unicode_escape")
-
-    unichr = unichr
-    int2byte = chr
-
-    def byte2int(bs):
-        return ord(bs[0])
-
-    def indexbytes(buf, i):
-        return ord(buf[i])
-
-    iterbytes = functools.partial(itertools.imap, ord)
-    import StringIO
-
-    StringIO = BytesIO = StringIO.StringIO
-    _assertCountEqual = "assertItemsEqual"
-    _assertRaisesRegex = "assertRaisesRegexp"
-    _assertRegex = "assertRegexpMatches"
-    _assertNotRegex = "assertNotRegexpMatches"
-_add_doc(b, """Byte literal""")
-_add_doc(u, """Text literal""")
-
-
-def assertCountEqual(self, *args, **kwargs):
-    return getattr(self, _assertCountEqual)(*args, **kwargs)
-
-
-def assertRaisesRegex(self, *args, **kwargs):
-    return getattr(self, _assertRaisesRegex)(*args, **kwargs)
-
-
-def assertRegex(self, *args, **kwargs):
-    return getattr(self, _assertRegex)(*args, **kwargs)
-
-
-def assertNotRegex(self, *args, **kwargs):
-    return getattr(self, _assertNotRegex)(*args, **kwargs)
-
-
-if PY3:
-    exec_ = getattr(moves.builtins, "exec")
-
-    def reraise(tp, value, tb=None):
-        try:
-            if value is None:
-                value = tp()
-            if value.__traceback__ is not tb:
-                raise value.with_traceback(tb)
-            raise value
-        finally:
-            value = None
-            tb = None
-
-else:
-
-    def exec_(_code_, _globs_=None, _locs_=None):
-        """Execute code in a namespace."""
-        if _globs_ is None:
-            frame = sys._getframe(1)
-            _globs_ = frame.f_globals
-            if _locs_ is None:
-                _locs_ = frame.f_locals
-            del frame
-        elif _locs_ is None:
-            _locs_ = _globs_
-        exec ("""exec _code_ in _globs_, _locs_""")
-
-    exec_(
-        """def reraise(tp, value, tb=None):
-    try:
-        raise tp, value, tb
-    finally:
-        tb = None
-"""
-    )
-
-
-if sys.version_info[:2] > (3,):
-    exec_(
-        """def raise_from(value, from_value):
-    try:
-        raise value from from_value
-    finally:
-        value = None
-"""
-    )
-else:
-
-    def raise_from(value, from_value):
-        raise value
-
-
-print_ = getattr(moves.builtins, "print", None)
-if print_ is None:
-
-    def print_(*args, **kwargs):
-        """The new-style print function for Python 2.4 and 2.5."""
-        fp = kwargs.pop("file", sys.stdout)
-        if fp is None:
-            return
-
-        def write(data):
-            if not isinstance(data, basestring):
-                data = str(data)
-            # If the file has an encoding, encode unicode with it.
-            if (
-                isinstance(fp, file)
-                and isinstance(data, unicode)
-                and fp.encoding is not None
-            ):
-                errors = getattr(fp, "errors", None)
-                if errors is None:
-                    errors = "strict"
-                data = data.encode(fp.encoding, errors)
-            fp.write(data)
-
-        want_unicode = False
-        sep = kwargs.pop("sep", None)
-        if sep is not None:
-            if isinstance(sep, unicode):
-                want_unicode = True
-            elif not isinstance(sep, str):
-                raise TypeError("sep must be None or a string")
-        end = kwargs.pop("end", None)
-        if end is not None:
-            if isinstance(end, unicode):
-                want_unicode = True
-            elif not isinstance(end, str):
-                raise TypeError("end must be None or a string")
-        if kwargs:
-            raise TypeError("invalid keyword arguments to print()")
-        if not want_unicode:
-            for arg in args:
-                if isinstance(arg, unicode):
-                    want_unicode = True
-                    break
-        if want_unicode:
-            newline = unicode("\n")
-            space = unicode(" ")
-        else:
-            newline = "\n"
-            space = " "
-        if sep is None:
-            sep = space
-        if end is None:
-            end = newline
-        for i, arg in enumerate(args):
-            if i:
-                write(sep)
-            write(arg)
-        write(end)
-
-
-if sys.version_info[:2] < (3, 3):
-    _print = print_
-
-    def print_(*args, **kwargs):
-        fp = kwargs.get("file", sys.stdout)
-        flush = kwargs.pop("flush", False)
-        _print(*args, **kwargs)
-        if flush and fp is not None:
-            fp.flush()
-
-
-_add_doc(reraise, """Reraise an exception.""")
-
-if sys.version_info[0:2] < (3, 4):
-    # This does exactly the same what the :func:`py3:functools.update_wrapper`
-    # function does on Python versions after 3.2. It sets the ``__wrapped__``
-    # attribute on ``wrapper`` object and it doesn't raise an error if any of
-    # the attributes mentioned in ``assigned`` and ``updated`` are missing on
-    # ``wrapped`` object.
-    def _update_wrapper(
-        wrapper,
-        wrapped,
-        assigned=functools.WRAPPER_ASSIGNMENTS,
-        updated=functools.WRAPPER_UPDATES,
-    ):
-        for attr in assigned:
-            try:
-                value = getattr(wrapped, attr)
-            except AttributeError:
-                continue
-            else:
-                setattr(wrapper, attr, value)
-        for attr in updated:
-            getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
-        wrapper.__wrapped__ = wrapped
-        return wrapper
-
-    _update_wrapper.__doc__ = functools.update_wrapper.__doc__
-
-    def wraps(
-        wrapped,
-        assigned=functools.WRAPPER_ASSIGNMENTS,
-        updated=functools.WRAPPER_UPDATES,
-    ):
-        return functools.partial(
-            _update_wrapper, wrapped=wrapped, assigned=assigned, updated=updated
-        )
-
-    wraps.__doc__ = functools.wraps.__doc__
-
-else:
-    wraps = functools.wraps
-
-
-def with_metaclass(meta, *bases):
-    """Create a base class with a metaclass."""
-    # This requires a bit of explanation: the basic idea is to make a dummy
-    # metaclass for one level of class instantiation that replaces itself with
-    # the actual metaclass.
-    class metaclass(type):
-        def __new__(cls, name, this_bases, d):
-            if sys.version_info[:2] >= (3, 7):
-                # This version introduced PEP 560 that requires a bit
-                # of extra care (we mimic what is done by __build_class__).
-                resolved_bases = types.resolve_bases(bases)
-                if resolved_bases is not bases:
-                    d["__orig_bases__"] = bases
-            else:
-                resolved_bases = bases
-            return meta(name, resolved_bases, d)
-
-        @classmethod
-        def __prepare__(cls, name, this_bases):
-            return meta.__prepare__(name, bases)
-
-    return type.__new__(metaclass, "temporary_class", (), {})
-
-
-def add_metaclass(metaclass):
-    """Class decorator for creating a class with a metaclass."""
-
-    def wrapper(cls):
-        orig_vars = cls.__dict__.copy()
-        slots = orig_vars.get("__slots__")
-        if slots is not None:
-            if isinstance(slots, str):
-                slots = [slots]
-            for slots_var in slots:
-                orig_vars.pop(slots_var)
-        orig_vars.pop("__dict__", None)
-        orig_vars.pop("__weakref__", None)
-        if hasattr(cls, "__qualname__"):
-            orig_vars["__qualname__"] = cls.__qualname__
-        return metaclass(cls.__name__, cls.__bases__, orig_vars)
-
-    return wrapper
-
-
-def ensure_binary(s, encoding="utf-8", errors="strict"):
-    """Coerce **s** to six.binary_type.
-
-    For Python 2:
-      - `unicode` -> encoded to `str`
-      - `str` -> `str`
-
-    For Python 3:
-      - `str` -> encoded to `bytes`
-      - `bytes` -> `bytes`
-    """
-    if isinstance(s, binary_type):
-        return s
-    if isinstance(s, text_type):
-        return s.encode(encoding, errors)
-    raise TypeError("not expecting type '%s'" % type(s))
-
-
-def ensure_str(s, encoding="utf-8", errors="strict"):
-    """Coerce *s* to `str`.
-
-    For Python 2:
-      - `unicode` -> encoded to `str`
-      - `str` -> `str`
-
-    For Python 3:
-      - `str` -> `str`
-      - `bytes` -> decoded to `str`
-    """
-    # Optimization: Fast return for the common case.
-    if type(s) is str:
-        return s
-    if PY2 and isinstance(s, text_type):
-        return s.encode(encoding, errors)
-    elif PY3 and isinstance(s, binary_type):
-        return s.decode(encoding, errors)
-    elif not isinstance(s, (text_type, binary_type)):
-        raise TypeError("not expecting type '%s'" % type(s))
-    return s
-
-
-def ensure_text(s, encoding="utf-8", errors="strict"):
-    """Coerce *s* to six.text_type.
-
-    For Python 2:
-      - `unicode` -> `unicode`
-      - `str` -> `unicode`
-
-    For Python 3:
-      - `str` -> `str`
-      - `bytes` -> decoded to `str`
-    """
-    if isinstance(s, binary_type):
-        return s.decode(encoding, errors)
-    elif isinstance(s, text_type):
-        return s
-    else:
-        raise TypeError("not expecting type '%s'" % type(s))
-
-
-def python_2_unicode_compatible(klass):
-    """
-    A class decorator that defines __unicode__ and __str__ methods under Python 2.
-    Under Python 3 it does nothing.
-
-    To support Python 2 and 3 with a single code base, define a __str__ method
-    returning text and apply this decorator to the class.
-    """
-    if PY2:
-        if "__str__" not in klass.__dict__:
-            raise ValueError(
-                "@python_2_unicode_compatible cannot be applied "
-                "to %s because it doesn't define __str__()." % klass.__name__
-            )
-        klass.__unicode__ = klass.__str__
-        klass.__str__ = lambda self: self.__unicode__().encode("utf-8")
-    return klass
-
-
-# Complete the moves implementation.
-# This code is at the end of this module to speed up module loading.
-# Turn this module into a package.
-__path__ = []  # required for PEP 302 and PEP 451
-__package__ = __name__  # see PEP 366 @ReservedAssignment
-if globals().get("__spec__") is not None:
-    __spec__.submodule_search_locations = []  # PEP 451 @UndefinedVariable
-# Remove other six meta path importers, since they cause problems. This can
-# happen if six is removed from sys.modules and then reloaded. (Setuptools does
-# this for some reason.)
-if sys.meta_path:
-    for i, importer in enumerate(sys.meta_path):
-        # Here's some real nastiness: Another "instance" of the six module might
-        # be floating around. Therefore, we can't use isinstance() to check for
-        # the six meta path importer, since the other six instance will have
-        # inserted an importer with different class.
-        if (
-            type(importer).__name__ == "_SixMetaPathImporter"
-            and importer.name == __name__
-        ):
-            del sys.meta_path[i]
-            break
-    del i, importer
-# Finally, add the importer to the meta path import hook.
-sys.meta_path.append(_importer)

spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/contrib/_securetransport/low_level.py

@@ -1,397 +0,0 @@
-"""
-Low-level helpers for the SecureTransport bindings.
-
-These are Python functions that are not directly related to the high-level APIs
-but are necessary to get them to work. They include a whole bunch of low-level
-CoreFoundation messing about and memory management. The concerns in this module
-are almost entirely about trying to avoid memory leaks and providing
-appropriate and useful assistance to the higher-level code.
-"""
-import base64
-import ctypes
-import itertools
-import os
-import re
-import ssl
-import struct
-import tempfile
-
-from .bindings import CFConst, CoreFoundation, Security
-
-# This regular expression is used to grab PEM data out of a PEM bundle.
-_PEM_CERTS_RE = re.compile(
-    b"-----BEGIN CERTIFICATE-----\n(.*?)\n-----END CERTIFICATE-----", re.DOTALL
-)
-
-
-def _cf_data_from_bytes(bytestring):
-    """
-    Given a bytestring, create a CFData object from it. This CFData object must
-    be CFReleased by the caller.
-    """
-    return CoreFoundation.CFDataCreate(
-        CoreFoundation.kCFAllocatorDefault, bytestring, len(bytestring)
-    )
-
-
-def _cf_dictionary_from_tuples(tuples):
-    """
-    Given a list of Python tuples, create an associated CFDictionary.
-    """
-    dictionary_size = len(tuples)
-
-    # We need to get the dictionary keys and values out in the same order.
-    keys = (t[0] for t in tuples)
-    values = (t[1] for t in tuples)
-    cf_keys = (CoreFoundation.CFTypeRef * dictionary_size)(*keys)
-    cf_values = (CoreFoundation.CFTypeRef * dictionary_size)(*values)
-
-    return CoreFoundation.CFDictionaryCreate(
-        CoreFoundation.kCFAllocatorDefault,
-        cf_keys,
-        cf_values,
-        dictionary_size,
-        CoreFoundation.kCFTypeDictionaryKeyCallBacks,
-        CoreFoundation.kCFTypeDictionaryValueCallBacks,
-    )
-
-
-def _cfstr(py_bstr):
-    """
-    Given a Python binary data, create a CFString.
-    The string must be CFReleased by the caller.
-    """
-    c_str = ctypes.c_char_p(py_bstr)
-    cf_str = CoreFoundation.CFStringCreateWithCString(
-        CoreFoundation.kCFAllocatorDefault,
-        c_str,
-        CFConst.kCFStringEncodingUTF8,
-    )
-    return cf_str
-
-
-def _create_cfstring_array(lst):
-    """
-    Given a list of Python binary data, create an associated CFMutableArray.
-    The array must be CFReleased by the caller.
-
-    Raises an ssl.SSLError on failure.
-    """
-    cf_arr = None
-    try:
-        cf_arr = CoreFoundation.CFArrayCreateMutable(
-            CoreFoundation.kCFAllocatorDefault,
-            0,
-            ctypes.byref(CoreFoundation.kCFTypeArrayCallBacks),
-        )
-        if not cf_arr:
-            raise MemoryError("Unable to allocate memory!")
-        for item in lst:
-            cf_str = _cfstr(item)
-            if not cf_str:
-                raise MemoryError("Unable to allocate memory!")
-            try:
-                CoreFoundation.CFArrayAppendValue(cf_arr, cf_str)
-            finally:
-                CoreFoundation.CFRelease(cf_str)
-    except BaseException as e:
-        if cf_arr:
-            CoreFoundation.CFRelease(cf_arr)
-        raise ssl.SSLError("Unable to allocate array: %s" % (e,))
-    return cf_arr
-
-
-def _cf_string_to_unicode(value):
-    """
-    Creates a Unicode string from a CFString object. Used entirely for error
-    reporting.
-
-    Yes, it annoys me quite a lot that this function is this complex.
-    """
-    value_as_void_p = ctypes.cast(value, ctypes.POINTER(ctypes.c_void_p))
-
-    string = CoreFoundation.CFStringGetCStringPtr(
-        value_as_void_p, CFConst.kCFStringEncodingUTF8
-    )
-    if string is None:
-        buffer = ctypes.create_string_buffer(1024)
-        result = CoreFoundation.CFStringGetCString(
-            value_as_void_p, buffer, 1024, CFConst.kCFStringEncodingUTF8
-        )
-        if not result:
-            raise OSError("Error copying C string from CFStringRef")
-        string = buffer.value
-    if string is not None:
-        string = string.decode("utf-8")
-    return string
-
-
-def _assert_no_error(error, exception_class=None):
-    """
-    Checks the return code and throws an exception if there is an error to
-    report
-    """
-    if error == 0:
-        return
-
-    cf_error_string = Security.SecCopyErrorMessageString(error, None)
-    output = _cf_string_to_unicode(cf_error_string)
-    CoreFoundation.CFRelease(cf_error_string)
-
-    if output is None or output == u"":
-        output = u"OSStatus %s" % error
-
-    if exception_class is None:
-        exception_class = ssl.SSLError
-
-    raise exception_class(output)
-
-
-def _cert_array_from_pem(pem_bundle):
-    """
-    Given a bundle of certs in PEM format, turns them into a CFArray of certs
-    that can be used to validate a cert chain.
-    """
-    # Normalize the PEM bundle's line endings.
-    pem_bundle = pem_bundle.replace(b"\r\n", b"\n")
-
-    der_certs = [
-        base64.b64decode(match.group(1)) for match in _PEM_CERTS_RE.finditer(pem_bundle)
-    ]
-    if not der_certs:
-        raise ssl.SSLError("No root certificates specified")
-
-    cert_array = CoreFoundation.CFArrayCreateMutable(
-        CoreFoundation.kCFAllocatorDefault,
-        0,
-        ctypes.byref(CoreFoundation.kCFTypeArrayCallBacks),
-    )
-    if not cert_array:
-        raise ssl.SSLError("Unable to allocate memory!")
-
-    try:
-        for der_bytes in der_certs:
-            certdata = _cf_data_from_bytes(der_bytes)
-            if not certdata:
-                raise ssl.SSLError("Unable to allocate memory!")
-            cert = Security.SecCertificateCreateWithData(
-                CoreFoundation.kCFAllocatorDefault, certdata
-            )
-            CoreFoundation.CFRelease(certdata)
-            if not cert:
-                raise ssl.SSLError("Unable to build cert object!")
-
-            CoreFoundation.CFArrayAppendValue(cert_array, cert)
-            CoreFoundation.CFRelease(cert)
-    except Exception:
-        # We need to free the array before the exception bubbles further.
-        # We only want to do that if an error occurs: otherwise, the caller
-        # should free.
-        CoreFoundation.CFRelease(cert_array)
-        raise
-
-    return cert_array
-
-
-def _is_cert(item):
-    """
-    Returns True if a given CFTypeRef is a certificate.
-    """
-    expected = Security.SecCertificateGetTypeID()
-    return CoreFoundation.CFGetTypeID(item) == expected
-
-
-def _is_identity(item):
-    """
-    Returns True if a given CFTypeRef is an identity.
-    """
-    expected = Security.SecIdentityGetTypeID()
-    return CoreFoundation.CFGetTypeID(item) == expected
-
-
-def _temporary_keychain():
-    """
-    This function creates a temporary Mac keychain that we can use to work with
-    credentials. This keychain uses a one-time password and a temporary file to
-    store the data. We expect to have one keychain per socket. The returned
-    SecKeychainRef must be freed by the caller, including calling
-    SecKeychainDelete.
-
-    Returns a tuple of the SecKeychainRef and the path to the temporary
-    directory that contains it.
-    """
-    # Unfortunately, SecKeychainCreate requires a path to a keychain. This
-    # means we cannot use mkstemp to use a generic temporary file. Instead,
-    # we're going to create a temporary directory and a filename to use there.
-    # This filename will be 8 random bytes expanded into base64. We also need
-    # some random bytes to password-protect the keychain we're creating, so we
-    # ask for 40 random bytes.
-    random_bytes = os.urandom(40)
-    filename = base64.b16encode(random_bytes[:8]).decode("utf-8")
-    password = base64.b16encode(random_bytes[8:])  # Must be valid UTF-8
-    tempdirectory = tempfile.mkdtemp()
-
-    keychain_path = os.path.join(tempdirectory, filename).encode("utf-8")
-
-    # We now want to create the keychain itself.
-    keychain = Security.SecKeychainRef()
-    status = Security.SecKeychainCreate(
-        keychain_path, len(password), password, False, None, ctypes.byref(keychain)
-    )
-    _assert_no_error(status)
-
-    # Having created the keychain, we want to pass it off to the caller.
-    return keychain, tempdirectory
-
-
-def _load_items_from_file(keychain, path):
-    """
-    Given a single file, loads all the trust objects from it into arrays and
-    the keychain.
-    Returns a tuple of lists: the first list is a list of identities, the
-    second a list of certs.
-    """
-    certificates = []
-    identities = []
-    result_array = None
-
-    with open(path, "rb") as f:
-        raw_filedata = f.read()
-
-    try:
-        filedata = CoreFoundation.CFDataCreate(
-            CoreFoundation.kCFAllocatorDefault, raw_filedata, len(raw_filedata)
-        )
-        result_array = CoreFoundation.CFArrayRef()
-        result = Security.SecItemImport(
-            filedata,  # cert data
-            None,  # Filename, leaving it out for now
-            None,  # What the type of the file is, we don't care
-            None,  # what's in the file, we don't care
-            0,  # import flags
-            None,  # key params, can include passphrase in the future
-            keychain,  # The keychain to insert into
-            ctypes.byref(result_array),  # Results
-        )
-        _assert_no_error(result)
-
-        # A CFArray is not very useful to us as an intermediary
-        # representation, so we are going to extract the objects we want
-        # and then free the array. We don't need to keep hold of keys: the
-        # keychain already has them!
-        result_count = CoreFoundation.CFArrayGetCount(result_array)
-        for index in range(result_count):
-            item = CoreFoundation.CFArrayGetValueAtIndex(result_array, index)
-            item = ctypes.cast(item, CoreFoundation.CFTypeRef)
-
-            if _is_cert(item):
-                CoreFoundation.CFRetain(item)
-                certificates.append(item)
-            elif _is_identity(item):
-                CoreFoundation.CFRetain(item)
-                identities.append(item)
-    finally:
-        if result_array:
-            CoreFoundation.CFRelease(result_array)
-
-        CoreFoundation.CFRelease(filedata)
-
-    return (identities, certificates)
-
-
-def _load_client_cert_chain(keychain, *paths):
-    """
-    Load certificates and maybe keys from a number of files. Has the end goal
-    of returning a CFArray containing one SecIdentityRef, and then zero or more
-    SecCertificateRef objects, suitable for use as a client certificate trust
-    chain.
-    """
-    # Ok, the strategy.
-    #
-    # This relies on knowing that macOS will not give you a SecIdentityRef
-    # unless you have imported a key into a keychain. This is a somewhat
-    # artificial limitation of macOS (for example, it doesn't necessarily
-    # affect iOS), but there is nothing inside Security.framework that lets you
-    # get a SecIdentityRef without having a key in a keychain.
-    #
-    # So the policy here is we take all the files and iterate them in order.
-    # Each one will use SecItemImport to have one or more objects loaded from
-    # it. We will also point at a keychain that macOS can use to work with the
-    # private key.
-    #
-    # Once we have all the objects, we'll check what we actually have. If we
-    # already have a SecIdentityRef in hand, fab: we'll use that. Otherwise,
-    # we'll take the first certificate (which we assume to be our leaf) and
-    # ask the keychain to give us a SecIdentityRef with that cert's associated
-    # key.
-    #
-    # We'll then return a CFArray containing the trust chain: one
-    # SecIdentityRef and then zero-or-more SecCertificateRef objects. The
-    # responsibility for freeing this CFArray will be with the caller. This
-    # CFArray must remain alive for the entire connection, so in practice it
-    # will be stored with a single SSLSocket, along with the reference to the
-    # keychain.
-    certificates = []
-    identities = []
-
-    # Filter out bad paths.
-    paths = (path for path in paths if path)
-
-    try:
-        for file_path in paths:
-            new_identities, new_certs = _load_items_from_file(keychain, file_path)
-            identities.extend(new_identities)
-            certificates.extend(new_certs)
-
-        # Ok, we have everything. The question is: do we have an identity? If
-        # not, we want to grab one from the first cert we have.
-        if not identities:
-            new_identity = Security.SecIdentityRef()
-            status = Security.SecIdentityCreateWithCertificate(
-                keychain, certificates[0], ctypes.byref(new_identity)
-            )
-            _assert_no_error(status)
-            identities.append(new_identity)
-
-            # We now want to release the original certificate, as we no longer
-            # need it.
-            CoreFoundation.CFRelease(certificates.pop(0))
-
-        # We now need to build a new CFArray that holds the trust chain.
-        trust_chain = CoreFoundation.CFArrayCreateMutable(
-            CoreFoundation.kCFAllocatorDefault,
-            0,
-            ctypes.byref(CoreFoundation.kCFTypeArrayCallBacks),
-        )
-        for item in itertools.chain(identities, certificates):
-            # ArrayAppendValue does a CFRetain on the item. That's fine,
-            # because the finally block will release our other refs to them.
-            CoreFoundation.CFArrayAppendValue(trust_chain, item)
-
-        return trust_chain
-    finally:
-        for obj in itertools.chain(identities, certificates):
-            CoreFoundation.CFRelease(obj)
-
-
-TLS_PROTOCOL_VERSIONS = {
-    "SSLv2": (0, 2),
-    "SSLv3": (3, 0),
-    "TLSv1": (3, 1),
-    "TLSv1.1": (3, 2),
-    "TLSv1.2": (3, 3),
-}
-
-
-def _build_tls_unknown_ca_alert(version):
-    """
-    Builds a TLS alert record for an unknown CA.
-    """
-    ver_maj, ver_min = TLS_PROTOCOL_VERSIONS[version]
-    severity_fatal = 0x02
-    description_unknown_ca = 0x30
-    msg = struct.pack(">BB", severity_fatal, description_unknown_ca)
-    msg_len = len(msg)
-    record_type_alert = 0x15
-    record = struct.pack(">BBBH", record_type_alert, ver_maj, ver_min, msg_len) + msg
-    return record

spaces/Bijoy2001/real-time-voice-recognition/README.md

@@ -1,12 +0,0 @@
----
-title: Real Time Voice Recognition
-emoji: 👀
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/CC123123/blip2_t/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;
-}