Update parquet files (step 63 of 121)

spaces/101-5/gpt4free/g4f/.v1/CONTRIBUTING.md

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-<img alt="gpt4free logo" src="https://user-images.githubusercontent.com/98614666/233799515-1a7cb6a3-b17f-42c4-956d-8d2a0664466f.png">
-
-### Please, follow these steps to contribute:
-1. Reverse a website from this list: [sites-to-reverse](https://github.com/xtekky/gpt4free/issues/40)
-2. Add it to [./testing](https://github.com/xtekky/gpt4free/tree/main/testing)
-3. Refractor it and add it to [./gpt4free](https://github.com/xtekky/gpt4free/tree/main/gpt4free)
-
-### We will be grateful to see you as a contributor!

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Comedy Nights With Kapil 720p 2nd November 2014.md

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-<h1>Comedy Nights with Kapil: Watch the hilarious episode of 2nd November 2014 in HD</h1>
-<p>If you are a fan of comedy shows, you must have watched <strong>Comedy Nights with Kapil</strong>, the popular Indian comedy show hosted by Kapil Sharma. The show features celebrity guests who are interviewed by Kapil and his team of comedians in a humorous way.</p>
-<p>One of the most memorable episodes of the show was aired on <strong>2nd November 2014</strong>, when Kapil invited the cast of <em>Happy New Year</em>, a blockbuster Bollywood movie starring Shah Rukh Khan, Deepika Padukone, Abhishek Bachchan, Sonu Sood, Boman Irani and Vivaan Shah. The episode was full of laughter, fun and entertainment as the stars shared their experiences of making the movie and also participated in some hilarious games and skits with Kapil and his team.</p>
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-<p>The episode was a treat for the fans of both comedy and sports, as they got to see their favorite stars having a blast on the show. The episode also had some hilarious moments, such as when Kapil tried to flirt with Deepika Padukone, when Boman Irani imitated Amitabh Bachchan, when Sonu Sood lifted Kapil in his arms, and when Vivaan Shah danced with Saina Nehwal.</p>
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-<p>You will also get to know more about Saina Nehwal, who is one of the most successful and inspiring sportspersons of India. You will learn how she started playing badminton at a young age, how she trained under different coaches, how she won several national and international tournaments, how she became the world number one in women's singles, how she represented India at the Olympics and other events, and how she balanced her studies and sports.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cyber Chrono Avec Crack Torrent Mega How to Download and Play the Best Trivia Game Ever.md

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-<p>Cyber Chrono is a game that takes place in a futuristic world where time travel is possible. You play as a hacker who can use a device called Chrono to rewind time and change the course of events. You can explore different scenarios based on historical and fictional events, such as World War II, ancient Egypt, medieval Europe, etc.</p>
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-<p>If you want to try Cyber Chrono Avec Crack Torrent Mega, you will need to follow these steps:</p>
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-<p>Playing Cyber Chrono Avec Crack Torrent Mega has some risks and benefits that you should be aware of before deciding whether to try it or not.</p>
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-<li>Can I play Cyber Chrono Avec Crack Torrent Mega online with other players?</li>
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-<p>No, the game does not receive updates or support from the developers. You can only play the version that you downloaded from the torrent site.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Microsoft Word 365 Free Benefits Features and Alternatives.md

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-<h1>How to Download Microsoft Word 365 Free for Windows 10</h1>
-<p>If you are looking for a way to download Microsoft Word 365 free for Windows 10, you are in luck. Microsoft Word 365 is one of the most popular and powerful word processors in the world, and you can get it for free with a few simple steps.</p>
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-<p>In this article, we will show you how to download Microsoft Word 365 free for Windows 10, what are the benefits of using it, and how to activate it with a valid license key.</p>
-<h2>How to Download Microsoft Word 365 Free for Windows 10</h2>
-<p>To download Microsoft Word 365 free for Windows 10, you need to follow these steps:</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Crack Sphinx Iq 2021.md

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-<h1>Crack Sphinx Iq: How to Download and Use the Best Software for Survey and Data Analysis</h1>
-<p>If you are looking for a powerful and reliable software for survey and data analysis, you might have heard of Sphinx iQ. This software is compatible with Windows and Mac (via an emulator software for Mac) and offers a range of features and functions to help you create, manage, and analyze online surveys. But how can you get access to this software without paying a fortune? In this article, we will show you how to crack Sphinx iQ and use it for free.</p>
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-<h2>What is Sphinx iQ?</h2>
-<p>Sphinx iQ is a software developed by Le Sphinx, a French company that has been a reference for 30 years on the survey and data analysis software market. Sphinx iQ allows you to design and administer online surveys, collect and process data, and perform advanced statistical analysis. You can also use Sphinx iQ for implicit learning research, as it can help you encode and store sensorimotor information in your memory. Sphinx iQ has a user-friendly interface and a high customer satisfaction rating of 96%. It is used by 50,000 users in all private and public sectors every day.</p>
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-<p>Cracking Sphinx iQ is not an easy task, as it requires some technical skills and knowledge. However, if you follow these steps carefully, you might be able to crack Sphinx iQ and use it for free.</p>
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-<li>Download the trial version of Sphinx iQ 2 from the official website: https://en.lesphinx-developpement.fr/contact-2-2/telechargement-logiciel/telechargement-sphinx-iq/</li>
-<li>Install the software on your computer and run it.</li>
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-<li>Extract the crack file and copy the file named "sphinx_iq_2.exe" to the installation folder of Sphinx iQ 2. Replace the original file with the cracked one.</li>
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-<h2>What are the Benefits of Cracking Sphinx iQ?</h2>
-<p>By cracking Sphinx iQ, you can enjoy all the benefits of this software without paying anything. You can create unlimited surveys, collect unlimited data, and perform unlimited analysis. You can also access all the features and functions of Sphinx iQ, such as:</p>
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-<p>Cracking Sphinx iQ is not without risks, however. By using a cracked version of this software, you might face some problems, such as:</p>
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-<p>In conclusion, cracking Sphinx iQ is possible but not advisable. While it can save you some money, it can also expose you to many risks and problems. Moreover, cracking Sphinx iQ is unfair and disrespectful to Le Sphinx, who have invested a lot of time and effort in developing this software. Therefore, we recommend that you buy a legitimate license of Sphinx iQ from their official website or authorized resellers. This way, you can enjoy all the benefits of this software without any worries or regrets.</p>
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-- How to use Sphinx iQ for different types of surveys, such as customer satisfaction, employee engagement, market research, etc.
-- How to interpret and present the results of Sphinx iQ analysis, such as using charts, graphs, tables, etc.
-- How to compare and contrast Sphinx iQ with other software for survey and data analysis, such as SurveyMonkey, Qualtrics, SPSS, etc.
-- How to cite and reference Sphinx iQ in your academic or professional work, such as using APA, MLA, Harvard, etc.
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-- How to use Sphinx iQ for different types of surveys, such as customer satisfaction, employee engagement, market research, etc.
-- How to interpret and present the results of Sphinx iQ analysis, such as using charts, graphs, tables, etc.
-- How to compare and contrast Sphinx iQ with other software for survey and data analysis, such as SurveyMonkey, Qualtrics, SPSS, etc.
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-- How to use Sphinx iQ for different types of surveys, such as customer satisfaction, employee engagement, market research, etc.
-- How to interpret and present the results of Sphinx iQ analysis, such as using charts, graphs, tables, etc.
-- How to compare and contrast Sphinx iQ with other software for survey and data analysis, such as SurveyMonkey, Qualtrics, SPSS, etc.
-- How to cite and reference Sphinx iQ in your academic or professional work, such as using APA, MLA, Harvard, etc.
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-<h2>How to Use Sphinx iQ for Different Types of Surveys</h2>
-<p>One of the advantages of Sphinx iQ is that it can help you create and conduct different types of surveys, depending on your needs and objectives. Whether you want to measure customer satisfaction, employee engagement, market research, or any other topic, Sphinx iQ can provide you with the tools and templates to design and administer your surveys. Here are some examples of how to use Sphinx iQ for different types of surveys:</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cut Any YouTube Video and Download It as an APK File The Best Online YouTube Video Cropper.md

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-<p>Do you love watching YouTube videos, but sometimes wish you could only keep the best parts? Do you want to share a funny or interesting clip from a YouTube video with your friends, but don't know how to do it? If you answered yes to any of these questions, then this article is for you. In this article, we will show you how to use YouTube video cut and download apk, a simple and effective way to crop and download your favorite YouTube videos.</p>
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-<p>YouTube video cut and download apk is a term that refers to any app or website that allows you to crop and download YouTube videos. These apps or websites let you enter a YouTube video URL, select the part of the video that you want to cut, and then download or share the cropped video as an mp4 file. You can use these apps or websites on your Android phone, tablet, or computer.</p>
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-<p>There are many reasons why you might want to crop and download YouTube videos. For example, you might want to:</p>
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-<h3>Step 1: Find a suitable app or website</h3>
-<p>The first step is to find an app or website that offers the YouTube video cut and download apk service. There are many options available online, but some of the most popular ones are:</p>
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-<p>VideoCrops is a website that allows you to crop and download YouTube videos in three easy steps. You just need to enter the YouTube video address in the box, select the part that you want to cut, and press the "Crop Selection" button. You can then download your cropped video as an mp4 file or share it on social media.</p>
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-<p>YouTube Trimmer is another website that lets you trim, crop, and share your favorite parts of YouTube videos online. You can enter a YouTube video URL, set the start and end times to select your crop, and then create a custom link to your cropped video. You can also embed your cropped video on your website using HTML code.</p>
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-<p>The next step is to enter the YouTube video URL that you want to crop and download. You can copy and paste the URL from your browser or use the search function on some apps or websites. After entering the URL, you will see a preview of the video on the screen. You can then use the sliders or buttons to select the part of the video that you want to crop. You can also adjust the quality and resolution of your cropped video if needed.</p>
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spaces/1phancelerku/anime-remove-background/Download UC Mini APK Latest Version 2023 for Android 12 Devices.md

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-<p>If you are looking for a lightweight, fast, and reliable browser for your Android 12 device, you might want to try UC Mini APK. UC Mini APK is a modified version of the popular UC Browser that offers a smoother and more enjoyable browsing experience. In this article, we will show you what UC Mini APK is, what features and benefits it has, how to download and install it on your Android 12 device, and how to use it effectively.</p>
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spaces/1phancelerku/anime-remove-background/Enjoy Chess with Friends and Foes with Chess Game Hack APK.md

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- <h2>Features of chess game hack APK</h2>
- <p>Chess game hack APK has many features that make it an attractive option for chess lovers. Here are some of them:</p>
- <h3>Premium unlocked</h3>
- <p>One of the main features of chess game hack APK is that it unlocks all the premium features and content that are normally reserved for paid members. This includes:</p>
- <ul>
-<li>Unlimited access to over 50,000 videos, articles, courses, and books from top coaches and experts.</li>
-<li>Un <p>Unlimited access to advanced analysis tools, such as computer evaluation, opening explorer, game report, and tactics trainer.</li>
-<li>Unlimited access to exclusive tournaments, events, and challenges with prizes and rewards.</li>
-<li>Unlimited access to premium support and customer service.</li>
-</ul>
- <h3>Unlimited puzzles and lessons</h3>
- <p>Another feature of chess game hack APK is that it allows you to solve unlimited puzzles and lessons to improve your chess skills and knowledge. You can choose from different categories, such as tactics, strategy, endgames, openings, and more. You can also adjust the difficulty level and the time limit according to your preference. You can track your progress and performance with statistics and ratings. You can also learn from the detailed explanations and hints provided by the app.</p>
- <h3>Online multiplayer mode</h3>
- <p>Chess game hack APK also enables you to play online multiplayer mode with anyone in the world, regardless of their rating or membership status. You can join or create a game with different time controls, variants, and rules. You can also chat with your opponents and send them emojis and gifts. You can also join or create a club with other players who share your interests and goals. You can participate in club matches, tournaments, and events with your club members.</p>
- <h3>Customizable board and pieces</h3>
- <p>Chess game hack APK also gives you the option to customize your board and pieces with different themes, colors, and styles. You can choose from various options, such as wood, metal, marble, glass, neon, and more. You can also change the size, shape, and design of your pieces. You can also adjust the sound effects, animations, and notifications of your app. You can make your chess experience more fun and personal with chess game hack APK.</p>
- <h2>How to download and install chess game hack APK</h2>
- <p>If you want to try chess game hack APK on your Android device, you have to follow these steps:</p>
- <h3>Step 1: Download the APK file from a trusted source</h3>
- <p>The first step is to download the APK file of chess game hack APK from a trusted source on the internet. You can search for it on Google or use the link provided below. Make sure that the file is safe and virus-free before downloading it. You can also scan it with an antivirus app if you want to be extra careful.</p>
- <p><a href="">Download chess game hack APK here</a></p>
- <h3>Step 2: Enable unknown sources on your device</h3>
- <p>The second step is to enable unknown sources on your device. This is necessary because Android devices do not allow installing apps from sources other than the official Google Play Store by default. To enable unknown sources, you have to go to your device settings > security > unknown sources > toggle on.</p>
- <h3>Step 3: Install the APK file and launch the app</h3>
- <p>The third step is to install the APK file and launch the app. To install the APK file, you have to locate it in your device storage > tap on it > follow the instructions on the screen > wait for the installation to complete. To launch the app, you have to find it in your app drawer > tap on it > enjoy playing and learning chess with unlimited features.</p>
- <h2>Conclusion</h2>
- <p>Chess game hack APK is a modified version of the original Chess - Play and Learn app that gives you access to all the premium features and content for free. It is a great way to play and learn chess with unlimited resources and options. However, it also comes with some risks and drawbacks that you have to consider before using it. We hope that this article has given you enough information about chess game hack APK and how to download and install it on your Android device.</p>
- <p>If you have any questions or feedback about chess game hack APK, feel free to leave a comment below. We would love to hear from you!</p>
- <h2>Frequently Asked Questions</h2>
- <p>Here are some of the most common questions that people ask about chess game hack APK:</p>
- <ol>
-<li><b>Is chess game hack APK legal?</b></li>
-<p>No, chess game hack APK is not legal. It is a modified version of the original app that violates the terms and conditions of Chess.com. Using chess game hack APK may result in legal issues or penalties from Chess.com or other authorities.</p>
-<li><b>Is chess game hack APK safe?</b></li>
-<p>Not necessarily. Chess game hack APK may contain malware or viruses that can harm your device or steal your data. It may also expose you to hackers or scammers who can access your account or personal information. Therefore, you have to be careful about where you download chess game hack APK from and what permissions you grant it to. You should also scan chess game hack APK with an antivirus app before installing it.</p>
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-<li><b>Can I use chess game hack APK on other devices?</b></li>
-<p>No, chess game hack APK is only compatible with Android devices. You cannot use it on iOS, Windows, Mac, or other platforms. If you want to play and learn chess on other devices, you have to use the official app or the web version of Chess.com.</p>
-<li><b>Can I use chess game hack APK offline?</b></li>
-<p>Yes, you can use chess game hack APK offline for some features, such as puzzles, lessons, and analysis. However, you cannot use it offline for online multiplayer mode, videos, articles, and other content that require an internet connection. You also need an internet connection to download and install chess game hack APK on your device.</p>
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spaces/1phancelerku/anime-remove-background/Enjoy Dragon Ball Legends with Platinmods APK Mod Attack Multiplier All Challenges Completed and No Ads.md

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-
-<h1>Dragon Ball Legends APK Mod Platinmods: How to Download and Install</h1>
- <p>If you are a fan of the Dragon Ball franchise, you might have heard of <strong>Dragon Ball Legends</strong>, a popular mobile game that lets you fight with your favorite characters from the anime and manga series. But did you know that there is a way to make the game even more fun and exciting? In this article, we will show you how to download and install <strong>Dragon Ball Legends APK Mod Platinmods</strong>, a modded version of the game that gives you access to various cheats and hacks. Read on to find out more.</p>
-<h2>dragon ball legends apk mod platinmods</h2><br /><p><b><b>Download File</b> ===> <a href="https://jinyurl.com/2uNMK8">https://jinyurl.com/2uNMK8</a></b></p><br /><br />
- <h2>What is Dragon Ball Legends?</h2>
- <h3>A brief introduction to the game and its features</h3>
- <p>Dragon Ball Legends is a 3D action RPG game that was released in 2018 by Bandai Namco Entertainment. The game features an original story that involves a new character named Shallot, who wakes up from a long sleep and finds himself in a world where different eras of Dragon Ball history are mixed together. He joins forces with other characters from the series to uncover the mystery behind this phenomenon and stop a sinister force that threatens the universe.</p>
- <p>The game allows you to create your own team of fighters from a roster of over 200 characters, each with their own unique skills and abilities. You can also customize your characters with different outfits, accessories, and equipment. The game has various modes, such as story mode, event mode, PvP mode, co-op mode, and raid mode, where you can challenge other players or team up with them to defeat powerful enemies. The game also has stunning graphics, voice acting, and sound effects that make you feel like you are watching an episode of the anime.</p>
- <h3>Why you might want to use a modded version of the game</h3>
- <p>While Dragon Ball Legends is undoubtedly an enjoyable game, it also has some drawbacks that might frustrate some players. For example, the game requires a lot of grinding to level up your characters, unlock new ones, and obtain rare items. The game also has a stamina system that limits how much you can play in a day. Moreover, some players might find the game too easy or too hard depending on their skill level and preferences.</p>
- <p>That's where <strong>Dragon Ball Legends APK Mod Platinmods</strong> comes in handy. This is a modified version of the game that gives you access to a mod menu that lets you activate various cheats and hacks that can enhance your gaming experience. For example, you can increase your attack power, defense power, ki (energy), speed, and critical rate. You can also enable god mode, instant win, all challenges completed, no ads, and more. With these features, you can breeze through the game without any hassle or difficulty.</p>
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- <h2>What is Pl <h2>What is Platinmods?</h2>
- <h3>A website that offers modded APKs for various games</h3>
- <p>Platinmods is a website that provides modded APKs for various Android games, including Dragon Ball Legends. A modded APK is a modified version of the original game file that has been altered to include additional features or functions that are not available in the official version. Platinmods has a team of experienced modders who create and update the modded APKs regularly. You can find a wide range of games on Platinmods, from action to strategy, from casual to RPG, and more.</p>
- <h3>The benefits and risks of using Platinmods</h3>
- <p>Using Platinmods has some benefits and risks that you should be aware of before downloading and installing any modded APK. Some of the benefits are:</p>
- <ul>
-<li>You can enjoy the game with more fun and excitement by using the cheats and hacks that the mod menu offers.</li>
-<li>You can save time and money by skipping the grinding and in-app purchases that the game might require.</li>
-<li>You can explore new features and options that the official version might not have.</li>
-</ul>
- <p>Some of the risks are:</p>
- <ul>
-<li>You might violate the terms of service or the privacy policy of the game developer or publisher by using a modded APK.</li>
-<li>You might get banned or suspended from the game if the game detects that you are using a modded APK.</li>
-<li>You might expose your device to malware or viruses that might be hidden in the modded APK.</li>
-</ul>
- <p>Therefore, you should use Platinmods at your own risk and discretion. We are not responsible for any consequences that might arise from using Platinmods.</p>
- <h2>How to download and install Dragon Ball Legends APK Mod Platinmods</h2>
- <h3>The steps to follow to get the modded version of the game</h3>
- <p>If you want to download and install Dragon Ball Legends APK Mod Platinmods, you need to follow these steps:</p>
- <ol>
-<li>Go to <a href="">Platinmods.com</a> and register an account if you don't have one already.</li>
-<li>Search for Dragon Ball Legends in the search bar and click on the result.</li>
-<li>Read the description and the instructions carefully and make sure you meet the requirements for using the modded APK.</li>
-<li>Click on the download link and wait for the file to be downloaded to your device.</li>
-<li>Uninstall the original version of Dragon Ball Legends if you have it installed on your device.</li>
-<li>Enable the installation of unknown sources on your device settings if you haven't done so already.</li>
-<li>Locate the downloaded file on your device and tap on it to install it.</li>
-<li>Launch the game and enjoy the mod menu.</li>
-</ol>
- <h3>The features and options of the mod menu</h3>
- <p>Once you launch the game, you will see a floating icon on your screen that represents the mod menu. You can tap on it to open or close it. The mod menu has various features and options that you can enable or disable according to your preference. Some of them are:</p>
- <table>
-<tr><th>Feature</th><th>Description</th></tr>
-<tr><td>Attack Multiplier</td><td>This feature allows you to increase or decrease your attack power by a certain factor.</td></tr>
-<tr><td>Defense Multiplier</td><td>This feature allows you to increase or decrease your defense power by a certain factor.</td></tr>
-<tr><td>Ki Multiplier</td><td>This feature allows you to increase or decrease your ki (energy) by a certain factor.</td></tr>
-<tr><td>Speed Multiplier</td><td>This feature allows you to increase or decrease your speed by a certain factor.</td></tr>
-<tr><td>Critical Rate Multiplier</td><td>This feature allows you to increase or decrease your critical rate by a certain factor.</td></tr>
-<tr><td>God Mode</td><td>This feature makes you invincible and immune to any damage.</td></tr>
-<tr><td>Instant Win</td><td>This feature allows you to win any battle instantly without fighting.</td></tr>
-<tr><td>All Challenges Completed</td><td>This feature allows you to complete all the challenges in any battle without fulfilling them.</td></tr>
-<tr><td>No Ads</td><td>This feature removes all the ads from the game.</td></tr>
-<tr><td>No Root Detection</td><td>This feature prevents the game from detecting if your device is rooted or not.</td></tr>
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spaces/1toTree/lora_test/ppdiffusers/pipelines/versatile_diffusion/modeling_text_unet.py

@@ -1,1366 +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.
-
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import paddle
-import paddle.nn as nn
-from paddle.distributed.fleet.utils import recompute
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...modeling_utils import ModelMixin
-from ...models.attention import DualTransformer2DModel, Transformer2DModel
-from ...models.cross_attention import (
-    AttnProcessor,
-    CrossAttention,
-    CrossAttnAddedKVProcessor,
-)
-from ...models.embeddings import TimestepEmbedding, Timesteps
-from ...models.unet_2d_condition import UNet2DConditionOutput
-from ...utils import logging
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def get_down_block(
-    down_block_type,
-    num_layers,
-    in_channels,
-    out_channels,
-    temb_channels,
-    add_downsample,
-    resnet_eps,
-    resnet_act_fn,
-    attn_num_head_channels,
-    resnet_groups=None,
-    cross_attention_dim=None,
-    downsample_padding=None,
-    dual_cross_attention=False,
-    use_linear_projection=False,
-    only_cross_attention=False,
-    upcast_attention=False,
-    resnet_time_scale_shift="default",
-):
-    down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
-    if down_block_type == "DownBlockFlat":
-        return DownBlockFlat(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif down_block_type == "CrossAttnDownBlockFlat":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockFlat")
-        return CrossAttnDownBlockFlat(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            cross_attention_dim=cross_attention_dim,
-            attn_num_head_channels=attn_num_head_channels,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    raise ValueError(f"{down_block_type} is not supported.")
-
-
-def get_up_block(
-    up_block_type,
-    num_layers,
-    in_channels,
-    out_channels,
-    prev_output_channel,
-    temb_channels,
-    add_upsample,
-    resnet_eps,
-    resnet_act_fn,
-    attn_num_head_channels,
-    resnet_groups=None,
-    cross_attention_dim=None,
-    dual_cross_attention=False,
-    use_linear_projection=False,
-    only_cross_attention=False,
-    upcast_attention=False,
-    resnet_time_scale_shift="default",
-):
-    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
-    if up_block_type == "UpBlockFlat":
-        return UpBlockFlat(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif up_block_type == "CrossAttnUpBlockFlat":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockFlat")
-        return CrossAttnUpBlockFlat(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            cross_attention_dim=cross_attention_dim,
-            attn_num_head_channels=attn_num_head_channels,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    raise ValueError(f"{up_block_type} is not supported.")
-
-
-# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel with UNet2DConditionModel->UNetFlatConditionModel, nn.Conv2d->LinearMultiDim, Block2D->BlockFlat
-class UNetFlatConditionModel(ModelMixin, ConfigMixin):
-    r"""
-    UNetFlatConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a
-    timestep and returns sample shaped output.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
-    implements for all the models (such as downloading or saving, etc.)
-
-    Parameters:
-        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
-            Height and width of input/output sample.
-        in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
-        out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.
-        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
-        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
-            Whether to flip the sin to cos in the time embedding.
-        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "DownBlockFlat")`):
-            The tuple of downsample blocks to use.
-        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlockFlatCrossAttn"`):
-            The mid block type. Choose from `UNetMidBlockFlatCrossAttn` or `UNetMidBlockFlatSimpleCrossAttn`.
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat",)`):
-            The tuple of upsample blocks to use.
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
-        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
-        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
-        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
-        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
-        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
-        cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
-        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
-        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
-            for resnet blocks, see [`~models.resnet.ResnetBlockFlat`]. Choose from `default` or `scale_shift`.
-        class_embed_type (`str`, *optional*, defaults to None): The type of class embedding to use which is ultimately
-            summed with the time embeddings. Choose from `None`, `"timestep"`, or `"identity"`.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        sample_size: Optional[int] = None,
-        in_channels: int = 4,
-        out_channels: int = 4,
-        center_input_sample: bool = False,
-        flip_sin_to_cos: bool = True,
-        freq_shift: int = 0,
-        down_block_types: Tuple[str] = (
-            "CrossAttnDownBlockFlat",
-            "CrossAttnDownBlockFlat",
-            "CrossAttnDownBlockFlat",
-            "DownBlockFlat",
-        ),
-        mid_block_type: str = "UNetMidBlockFlatCrossAttn",
-        up_block_types: Tuple[str] = (
-            "UpBlockFlat",
-            "CrossAttnUpBlockFlat",
-            "CrossAttnUpBlockFlat",
-            "CrossAttnUpBlockFlat",
-        ),
-        only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
-        layers_per_block: int = 2,
-        downsample_padding: int = 1,
-        mid_block_scale_factor: float = 1,
-        act_fn: str = "silu",
-        norm_num_groups: int = 32,
-        norm_eps: float = 1e-5,
-        cross_attention_dim: int = 1280,
-        attention_head_dim: Union[int, Tuple[int]] = 8,
-        dual_cross_attention: bool = False,
-        use_linear_projection: bool = False,
-        class_embed_type: Optional[str] = None,
-        num_class_embeds: Optional[int] = None,
-        upcast_attention: bool = False,
-        resnet_time_scale_shift: str = "default",
-    ):
-        super().__init__()
-
-        self.sample_size = sample_size
-        time_embed_dim = block_out_channels[0] * 4
-
-        # input
-        self.conv_in = LinearMultiDim(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
-
-        # time
-        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
-        timestep_input_dim = block_out_channels[0]
-
-        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
-
-        # class embedding
-        if class_embed_type is None and num_class_embeds is not None:
-            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
-        elif class_embed_type == "timestep":
-            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
-        elif class_embed_type == "identity":
-            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
-        else:
-            self.class_embedding = None
-
-        self.down_blocks = nn.LayerList([])
-        self.mid_block = None
-        self.up_blocks = nn.LayerList([])
-
-        if isinstance(only_cross_attention, bool):
-            only_cross_attention = [only_cross_attention] * len(down_block_types)
-
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(down_block_types)
-
-        # down
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = get_down_block(
-                down_block_type,
-                num_layers=layers_per_block,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=time_embed_dim,
-                add_downsample=not is_final_block,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim,
-                attn_num_head_channels=attention_head_dim[i],
-                downsample_padding=downsample_padding,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        if mid_block_type == "UNetMidBlockFlatCrossAttn":
-            self.mid_block = UNetMidBlockFlatCrossAttn(
-                in_channels=block_out_channels[-1],
-                temb_channels=time_embed_dim,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                cross_attention_dim=cross_attention_dim,
-                attn_num_head_channels=attention_head_dim[-1],
-                resnet_groups=norm_num_groups,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                upcast_attention=upcast_attention,
-            )
-        elif mid_block_type == "UNetMidBlockFlatSimpleCrossAttn":
-            self.mid_block = UNetMidBlockFlatSimpleCrossAttn(
-                in_channels=block_out_channels[-1],
-                temb_channels=time_embed_dim,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                cross_attention_dim=cross_attention_dim,
-                attn_num_head_channels=attention_head_dim[-1],
-                resnet_groups=norm_num_groups,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-            )
-        else:
-            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
-
-        # count how many layers upsample the images
-        self.num_upsamplers = 0
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        reversed_attention_head_dim = list(reversed(attention_head_dim))
-        reversed_only_cross_attention = list(reversed(only_cross_attention))
-
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(up_block_types):
-            is_final_block = i == len(block_out_channels) - 1
-
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            # add upsample block for all BUT final layer
-            if not is_final_block:
-                add_upsample = True
-                self.num_upsamplers += 1
-            else:
-                add_upsample = False
-
-            up_block = get_up_block(
-                up_block_type,
-                num_layers=layers_per_block + 1,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                prev_output_channel=prev_output_channel,
-                temb_channels=time_embed_dim,
-                add_upsample=add_upsample,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim,
-                attn_num_head_channels=reversed_attention_head_dim[i],
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=reversed_only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        # out
-        self.conv_norm_out = nn.GroupNorm(
-            num_channels=block_out_channels[0], num_groups=norm_num_groups, epsilon=norm_eps
-        )
-        self.conv_act = nn.Silu()
-        self.conv_out = LinearMultiDim(block_out_channels[0], out_channels, kernel_size=3, padding=1)
-
-    @property
-    def attn_processors(self) -> Dict[str, AttnProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: nn.Layer, processors: Dict[str, AttnProcessor]):
-            if hasattr(module, "set_processor"):
-                processors[f"{name}.processor"] = module.processor
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    def set_attn_processor(self, processor: Union[AttnProcessor, Dict[str, AttnProcessor]]):
-        r"""
-        Parameters:
-            `processor (`dict` of `AttnProcessor` or `AttnProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                of **all** `CrossAttention` layers.
-            In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors.:
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: nn.Layer, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    def set_attention_slice(self, slice_size):
-        r"""
-        Enable sliced attention computation.
-
-        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
-        in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
-        Args:
-            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
-                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
-                must be a multiple of `slice_size`.
-        """
-        sliceable_head_dims = []
-
-        def fn_recursive_retrieve_slicable_dims(module: nn.Layer):
-            if hasattr(module, "set_attention_slice"):
-                sliceable_head_dims.append(module.sliceable_head_dim)
-
-            for child in module.children():
-                fn_recursive_retrieve_slicable_dims(child)
-
-        # retrieve number of attention layers
-        for module in self.children():
-            fn_recursive_retrieve_slicable_dims(module)
-
-        num_slicable_layers = len(sliceable_head_dims)
-
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = [dim // 2 for dim in sliceable_head_dims]
-        elif slice_size == "max":
-            # make smallest slice possible
-            slice_size = num_slicable_layers * [1]
-
-        slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
-
-        if len(slice_size) != len(sliceable_head_dims):
-            raise ValueError(
-                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
-                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
-            )
-
-        for i in range(len(slice_size)):
-            size = slice_size[i]
-            dim = sliceable_head_dims[i]
-            if size is not None and size > dim:
-                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
-
-        # Recursively walk through all the children.
-        # Any children which exposes the set_attention_slice method
-        # gets the message
-        def fn_recursive_set_attention_slice(module: nn.Layer, slice_size: List[int]):
-            if hasattr(module, "set_attention_slice"):
-                module.set_attention_slice(slice_size.pop())
-
-            for child in module.children():
-                fn_recursive_set_attention_slice(child, slice_size)
-
-        reversed_slice_size = list(reversed(slice_size))
-        for module in self.children():
-            fn_recursive_set_attention_slice(module, reversed_slice_size)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (CrossAttnDownBlockFlat, DownBlockFlat, CrossAttnUpBlockFlat, UpBlockFlat)):
-            module.gradient_checkpointing = value
-
-    def forward(
-        self,
-        sample: paddle.Tensor,
-        timestep: Union[paddle.Tensor, float, int],
-        encoder_hidden_states: paddle.Tensor,
-        class_labels: Optional[paddle.Tensor] = None,
-        attention_mask: Optional[paddle.Tensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
-    ) -> Union[UNet2DConditionOutput, Tuple]:
-        r"""
-        Args:
-            sample (`paddle.Tensor`): (batch, channel, height, width) noisy inputs tensor
-            timestep (`paddle.Tensor` or `float` or `int`): (batch) timesteps
-            encoder_hidden_states (`paddle.Tensor`): (batch, sequence_length, feature_dim) encoder hidden states
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-        """
-        # By default samples have to be AT least a multiple of the overall upsampling factor.
-        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
-        # However, the upsampling interpolation output size can be forced to fit any upsampling size
-        # on the fly if necessary.
-        default_overall_up_factor = 2**self.num_upsamplers
-
-        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
-        forward_upsample_size = False
-        upsample_size = None
-
-        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
-            logger.info("Forward upsample size to force interpolation output size.")
-            forward_upsample_size = True
-
-        # prepare attention_mask
-        if attention_mask is not None:
-            attention_mask = (1 - attention_mask.cast(sample.dtype)) * -10000.0
-            attention_mask = attention_mask.unsqueeze(1)
-
-        # 0. center input if necessary
-        if self.config.center_input_sample:
-            sample = 2 * sample - 1.0
-
-        # 1. time
-        timesteps = timestep
-        if not paddle.is_tensor(timesteps):
-            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
-            timesteps = paddle.to_tensor([timesteps], dtype="int64")
-        elif paddle.is_tensor(timesteps) and len(timesteps.shape) == 0:
-            timesteps = timesteps[None]
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timesteps = timesteps.expand(
-            [
-                sample.shape[0],
-            ]
-        )
-
-        t_emb = self.time_proj(timesteps)
-
-        # timesteps does not contain any weights and will always return f32 tensors
-        # but time_embedding might actually be running in fp16. so we need to cast here.
-        # there might be better ways to encapsulate this.
-        t_emb = t_emb.cast(self.dtype)
-        emb = self.time_embedding(t_emb)
-
-        if self.class_embedding is not None:
-            if class_labels is None:
-                raise ValueError("class_labels should be provided when num_class_embeds > 0")
-
-            if self.config.class_embed_type == "timestep":
-                class_labels = self.time_proj(class_labels)
-
-            class_emb = self.class_embedding(class_labels).cast(self.dtype)
-            emb = emb + class_emb
-
-        # 2. pre-process
-        sample = self.conv_in(sample)
-
-        # 3. down
-        down_block_res_samples = (sample,)
-        for downsample_block in self.down_blocks:
-            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
-                sample, res_samples = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                )
-            else:
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
-
-            down_block_res_samples += res_samples
-
-        # 4. mid
-        sample = self.mid_block(
-            sample,
-            emb,
-            encoder_hidden_states=encoder_hidden_states,
-            attention_mask=attention_mask,
-            cross_attention_kwargs=cross_attention_kwargs,
-        )
-
-        # 5. up
-        for i, upsample_block in enumerate(self.up_blocks):
-            is_final_block = i == len(self.up_blocks) - 1
-
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
-            # if we have not reached the final block and need to forward the
-            # upsample size, we do it here
-            if not is_final_block and forward_upsample_size:
-                upsample_size = down_block_res_samples[-1].shape[2:]
-
-            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    upsample_size=upsample_size,
-                    attention_mask=attention_mask,
-                )
-            else:
-                sample = upsample_block(
-                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
-                )
-        # 6. post-process
-        sample = self.conv_norm_out(sample)
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        if not return_dict:
-            return (sample,)
-
-        return UNet2DConditionOutput(sample=sample)
-
-
-class LinearMultiDim(nn.Linear):
-    def __init__(self, in_features, out_features=None, second_dim=4, *args, **kwargs):
-        in_features = [in_features, second_dim, 1] if isinstance(in_features, int) else list(in_features)
-        if out_features is None:
-            out_features = in_features
-        out_features = [out_features, second_dim, 1] if isinstance(out_features, int) else list(out_features)
-        self.in_features_multidim = in_features
-        self.out_features_multidim = out_features
-        super().__init__(np.array(in_features).prod(), np.array(out_features).prod())
-
-    def forward(self, input_tensor, *args, **kwargs):
-        shape = input_tensor.shape
-        n_dim = len(self.in_features_multidim)
-        input_tensor = input_tensor.reshape([*shape[0:-n_dim], self.in_features])
-        output_tensor = super().forward(input_tensor)
-        output_tensor = output_tensor.reshape([*shape[0:-n_dim], *self.out_features_multidim])
-        return output_tensor
-
-
-class ResnetBlockFlat(nn.Layer):
-    def __init__(
-        self,
-        *,
-        in_channels,
-        out_channels=None,
-        dropout=0.0,
-        temb_channels=512,
-        groups=32,
-        groups_out=None,
-        pre_norm=True,
-        eps=1e-6,
-        time_embedding_norm="default",
-        use_in_shortcut=None,
-        second_dim=4,
-        **kwargs,
-    ):
-        super().__init__()
-        self.pre_norm = pre_norm
-        self.pre_norm = True
-
-        in_channels = [in_channels, second_dim, 1] if isinstance(in_channels, int) else list(in_channels)
-        self.in_channels_prod = np.array(in_channels).prod()
-        self.channels_multidim = in_channels
-
-        if out_channels is not None:
-            out_channels = [out_channels, second_dim, 1] if isinstance(out_channels, int) else list(out_channels)
-            out_channels_prod = np.array(out_channels).prod()
-            self.out_channels_multidim = out_channels
-        else:
-            out_channels_prod = self.in_channels_prod
-            self.out_channels_multidim = self.channels_multidim
-        self.time_embedding_norm = time_embedding_norm
-
-        if groups_out is None:
-            groups_out = groups
-
-        self.norm1 = nn.GroupNorm(num_groups=groups, num_channels=self.in_channels_prod, epsilon=eps)
-        self.conv1 = nn.Conv2D(self.in_channels_prod, out_channels_prod, kernel_size=1, padding=0)
-
-        if temb_channels is not None:
-            self.time_emb_proj = nn.Linear(temb_channels, out_channels_prod)
-        else:
-            self.time_emb_proj = None
-
-        self.norm2 = nn.GroupNorm(num_groups=groups_out, num_channels=out_channels_prod, epsilon=eps)
-        self.dropout = nn.Dropout(dropout)
-        self.conv2 = nn.Conv2D(out_channels_prod, out_channels_prod, kernel_size=1, padding=0)
-
-        self.nonlinearity = nn.Silu()
-
-        self.use_in_shortcut = (
-            self.in_channels_prod != out_channels_prod if use_in_shortcut is None else use_in_shortcut
-        )
-
-        self.conv_shortcut = None
-        if self.use_in_shortcut:
-            self.conv_shortcut = nn.Conv2D(
-                self.in_channels_prod, out_channels_prod, kernel_size=1, stride=1, padding=0
-            )
-
-    def forward(self, input_tensor, temb):
-        shape = input_tensor.shape
-        n_dim = len(self.channels_multidim)
-        input_tensor = input_tensor.reshape([*shape[0:-n_dim], self.in_channels_prod, 1, 1])
-        input_tensor = input_tensor.reshape([-1, self.in_channels_prod, 1, 1])
-
-        hidden_states = input_tensor
-
-        hidden_states = self.norm1(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-        hidden_states = self.conv1(hidden_states)
-
-        if temb is not None:
-            temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
-            hidden_states = hidden_states + temb
-
-        hidden_states = self.norm2(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.conv2(hidden_states)
-
-        if self.conv_shortcut is not None:
-            input_tensor = self.conv_shortcut(input_tensor)
-
-        output_tensor = input_tensor + hidden_states
-
-        output_tensor = output_tensor.reshape([*shape[0:-n_dim], -1])
-        output_tensor = output_tensor.reshape([*shape[0:-n_dim], *self.out_channels_multidim])
-
-        return output_tensor
-
-
-# Copied from diffusers.models.unet_2d_blocks.DownBlock2D with DownBlock2D->DownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
-class DownBlockFlat(nn.Layer):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_downsample=True,
-        downsample_padding=1,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.LayerList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.LayerList(
-                [
-                    LinearMultiDim(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states, temb=None):
-        output_states = ()
-
-        for resnet in self.resnets:
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = recompute(create_custom_forward(resnet), hidden_states, temb)
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states
-
-
-# Copied from diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D with CrossAttnDownBlock2D->CrossAttnDownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
-class CrossAttnDownBlockFlat(nn.Layer):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        cross_attention_dim=1280,
-        output_scale_factor=1.0,
-        downsample_padding=1,
-        add_downsample=True,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        only_cross_attention=False,
-        upcast_attention=False,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.attn_num_head_channels = attn_num_head_channels
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        attn_num_head_channels,
-                        out_channels // attn_num_head_channels,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        only_cross_attention=only_cross_attention,
-                        upcast_attention=upcast_attention,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        attn_num_head_channels,
-                        out_channels // attn_num_head_channels,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-        self.attentions = nn.LayerList(attentions)
-        self.resnets = nn.LayerList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.LayerList(
-                [
-                    LinearMultiDim(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None
-    ):
-        output_states = ()
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)[0]  # move [0]
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = recompute(create_custom_forward(resnet), hidden_states, temb)
-                hidden_states = recompute(
-                    create_custom_forward(attn, return_dict=False),
-                    hidden_states,
-                    encoder_hidden_states,
-                    cross_attention_kwargs,
-                )  # [0]
-            else:
-                hidden_states = resnet(hidden_states, temb)
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                ).sample
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states
-
-
-# Copied from diffusers.models.unet_2d_blocks.UpBlock2D with UpBlock2D->UpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim
-class UpBlockFlat(nn.Layer):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_upsample=True,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.LayerList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.LayerList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = paddle.concat([hidden_states, res_hidden_states], axis=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = recompute(create_custom_forward(resnet), hidden_states, temb)
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-        return hidden_states
-
-
-# Copied from diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D with CrossAttnUpBlock2D->CrossAttnUpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim
-class CrossAttnUpBlockFlat(nn.Layer):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        prev_output_channel: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        cross_attention_dim=1280,
-        output_scale_factor=1.0,
-        add_upsample=True,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        only_cross_attention=False,
-        upcast_attention=False,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.attn_num_head_channels = attn_num_head_channels
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        attn_num_head_channels,
-                        out_channels // attn_num_head_channels,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        only_cross_attention=only_cross_attention,
-                        upcast_attention=upcast_attention,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        attn_num_head_channels,
-                        out_channels // attn_num_head_channels,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-        self.attentions = nn.LayerList(attentions)
-        self.resnets = nn.LayerList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.LayerList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states,
-        res_hidden_states_tuple,
-        temb=None,
-        encoder_hidden_states=None,
-        cross_attention_kwargs=None,
-        upsample_size=None,
-        attention_mask=None,
-    ):
-        # TODO(Patrick, William) - attention mask is not used
-        for resnet, attn in zip(self.resnets, self.attentions):
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = paddle.concat([hidden_states, res_hidden_states], axis=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)[0]  # move [0]
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = recompute(create_custom_forward(resnet), hidden_states, temb)
-                hidden_states = recompute(
-                    create_custom_forward(attn, return_dict=False),
-                    hidden_states,
-                    encoder_hidden_states,
-                    cross_attention_kwargs,
-                )  # [0]
-            else:
-                hidden_states = resnet(hidden_states, temb)
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                ).sample
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-        return hidden_states
-
-
-# Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DCrossAttn with UNetMidBlock2DCrossAttn->UNetMidBlockFlatCrossAttn, ResnetBlock2D->ResnetBlockFlat
-class UNetMidBlockFlatCrossAttn(nn.Layer):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        output_scale_factor=1.0,
-        cross_attention_dim=1280,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        upcast_attention=False,
-    ):
-        super().__init__()
-
-        self.has_cross_attention = True
-        self.attn_num_head_channels = attn_num_head_channels
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlockFlat(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-            )
-        ]
-        attentions = []
-
-        for _ in range(num_layers):
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        attn_num_head_channels,
-                        in_channels // attn_num_head_channels,
-                        in_channels=in_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        upcast_attention=upcast_attention,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        attn_num_head_channels,
-                        in_channels // attn_num_head_channels,
-                        in_channels=in_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.attentions = nn.LayerList(attentions)
-        self.resnets = nn.LayerList(resnets)
-
-    def forward(
-        self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None
-    ):
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            hidden_states = attn(
-                hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                cross_attention_kwargs=cross_attention_kwargs,
-            ).sample
-            hidden_states = resnet(hidden_states, temb)
-
-        return hidden_states
-
-
-# Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DSimpleCrossAttn with UNetMidBlock2DSimpleCrossAttn->UNetMidBlockFlatSimpleCrossAttn, ResnetBlock2D->ResnetBlockFlat
-class UNetMidBlockFlatSimpleCrossAttn(nn.Layer):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        output_scale_factor=1.0,
-        cross_attention_dim=1280,
-    ):
-        super().__init__()
-
-        self.has_cross_attention = True
-
-        self.attn_num_head_channels = attn_num_head_channels
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        self.num_heads = in_channels // self.attn_num_head_channels
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlockFlat(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-            )
-        ]
-        attentions = []
-
-        for _ in range(num_layers):
-            attentions.append(
-                CrossAttention(
-                    query_dim=in_channels,
-                    cross_attention_dim=in_channels,
-                    heads=self.num_heads,
-                    dim_head=attn_num_head_channels,
-                    added_kv_proj_dim=cross_attention_dim,
-                    norm_num_groups=resnet_groups,
-                    bias=True,
-                    upcast_softmax=True,
-                    processor=CrossAttnAddedKVProcessor(),
-                )
-            )
-            resnets.append(
-                ResnetBlockFlat(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.attentions = nn.LayerList(attentions)
-        self.resnets = nn.LayerList(resnets)
-
-    def forward(
-        self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None
-    ):
-        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            # attn
-            hidden_states = attn(
-                hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                attention_mask=attention_mask,
-                **cross_attention_kwargs,
-            )
-
-            # resnet
-            hidden_states = resnet(hidden_states, temb)
-
-        return hidden_states

spaces/3druga/ae-6/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Virus561/anytig").launch()

spaces/801artistry/RVC801/infer/modules/train/preprocess.py

@@ -1,147 +0,0 @@
-import multiprocessing
-import os
-import sys
-
-from scipy import signal
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-print(sys.argv)
-inp_root = sys.argv[1]
-sr = int(sys.argv[2])
-n_p = int(sys.argv[3])
-exp_dir = sys.argv[4]
-noparallel = sys.argv[5] == "True"
-per = float(sys.argv[6])
-import multiprocessing
-import os
-import traceback
-
-import librosa
-import numpy as np
-from scipy.io import wavfile
-
-from infer.lib.audio import load_audio
-from infer.lib.slicer2 import Slicer
-
-mutex = multiprocessing.Lock()
-f = open("%s/preprocess.log" % exp_dir, "a+")
-
-
-def println(strr):
-    mutex.acquire()
-    print(strr)
-    f.write("%s\n" % strr)
-    f.flush()
-    mutex.release()
-
-
-class PreProcess:
-    def __init__(self, sr, exp_dir, per=3.7):
-        self.slicer = Slicer(
-            sr=sr,
-            threshold=-42,
-            min_length=1500,
-            min_interval=400,
-            hop_size=15,
-            max_sil_kept=500,
-        )
-        self.sr = sr
-        self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
-        self.per = per
-        self.overlap = 0.3
-        self.tail = self.per + self.overlap
-        self.max = 0.9
-        self.alpha = 0.75
-        self.exp_dir = exp_dir
-        self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
-        self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
-        os.makedirs(self.exp_dir, exist_ok=True)
-        os.makedirs(self.gt_wavs_dir, exist_ok=True)
-        os.makedirs(self.wavs16k_dir, exist_ok=True)
-
-    def norm_write(self, tmp_audio, idx0, idx1):
-        tmp_max = np.abs(tmp_audio).max()
-        if tmp_max > 2.5:
-            print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
-            return
-        tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + (
-            1 - self.alpha
-        ) * tmp_audio
-        wavfile.write(
-            "%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1),
-            self.sr,
-            tmp_audio.astype(np.float32),
-        )
-        tmp_audio = librosa.resample(
-            tmp_audio, orig_sr=self.sr, target_sr=16000
-        )  # , res_type="soxr_vhq"
-        wavfile.write(
-            "%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1),
-            16000,
-            tmp_audio.astype(np.float32),
-        )
-
-    def pipeline(self, path, idx0):
-        try:
-            audio = load_audio(path, self.sr)
-            # zero phased digital filter cause pre-ringing noise...
-            # audio = signal.filtfilt(self.bh, self.ah, audio)
-            audio = signal.lfilter(self.bh, self.ah, audio)
-
-            idx1 = 0
-            for audio in self.slicer.slice(audio):
-                i = 0
-                while 1:
-                    start = int(self.sr * (self.per - self.overlap) * i)
-                    i += 1
-                    if len(audio[start:]) > self.tail * self.sr:
-                        tmp_audio = audio[start : start + int(self.per * self.sr)]
-                        self.norm_write(tmp_audio, idx0, idx1)
-                        idx1 += 1
-                    else:
-                        tmp_audio = audio[start:]
-                        idx1 += 1
-                        break
-                self.norm_write(tmp_audio, idx0, idx1)
-            println("%s->Suc." % path)
-        except:
-            println("%s->%s" % (path, traceback.format_exc()))
-
-    def pipeline_mp(self, infos):
-        for path, idx0 in infos:
-            self.pipeline(path, idx0)
-
-    def pipeline_mp_inp_dir(self, inp_root, n_p):
-        try:
-            infos = [
-                ("%s/%s" % (inp_root, name), idx)
-                for idx, name in enumerate(sorted(list(os.listdir(inp_root))))
-            ]
-            if noparallel:
-                for i in range(n_p):
-                    self.pipeline_mp(infos[i::n_p])
-            else:
-                ps = []
-                for i in range(n_p):
-                    p = multiprocessing.Process(
-                        target=self.pipeline_mp, args=(infos[i::n_p],)
-                    )
-                    ps.append(p)
-                    p.start()
-                for i in range(n_p):
-                    ps[i].join()
-        except:
-            println("Fail. %s" % traceback.format_exc())
-
-
-def preprocess_trainset(inp_root, sr, n_p, exp_dir, per):
-    pp = PreProcess(sr, exp_dir, per)
-    println("start preprocess")
-    println(sys.argv)
-    pp.pipeline_mp_inp_dir(inp_root, n_p)
-    println("end preprocess")
-
-
-if __name__ == "__main__":
-    preprocess_trainset(inp_root, sr, n_p, exp_dir, per)

spaces/AI-Hobbyist/Hoyo-RVC/go-realtime-gui.bat

@@ -1,2 +0,0 @@
-runtime\python.exe gui.py
-pause

spaces/AI-Zero-to-Hero/04-GR-Seq-2-Seq-QA-Auto-Gen/app.py

@@ -1,51 +0,0 @@
-import gradio as gr
-from qasrl_model_pipeline import QASRL_Pipeline
-
-models = ["kleinay/qanom-seq2seq-model-baseline", 
-          "kleinay/qanom-seq2seq-model-joint"]
-pipelines = {model: QASRL_Pipeline(model) for model in models}
-
-
-description = f"""Using Seq2Seq T5 model which takes a sequence of items and outputs another sequence this model generates Questions and Answers (QA) with focus on Semantic Role Labeling (SRL)""" 
-title="Seq2Seq T5 Questions and Answers (QA) with Semantic Role Labeling (SRL)"
-examples = [[models[0], "In March and April the patient <p> had two falls.  One was related to asthma, heart palpitations.  The second was due to syncope and post covid vaccination dizziness during exercise.  The patient is now getting an EKG.  Former EKG had shown that there was a bundle branch block.  Patient had some uncontrolled immune system reactions like anaphylaxis and shortness of breath.", True, "fall"],
-            [models[1], "In March and April the patient had two falls.  One was related to asthma, heart palpitations.  The second was due to syncope and post covid vaccination dizziness during exercise.  The patient is now getting an EKG.  Former EKG had shown that there was a bundle branch block.  Patient had some uncontrolled immune system reactions <p> like anaphylaxis and shortness of breath.", True, "reactions"],
-            [models[0], "In March and April the patient had two falls.  One was related <p> to asthma, heart palpitations.  The second was due to syncope and post covid vaccination dizziness during exercise.  The patient is now getting an EKG.  Former EKG had shown that there was a bundle branch block.  Patient had some uncontrolled immune system reactions like anaphylaxis and shortness of breath.", True, "relate"],
-            [models[1], "In March and April the patient <p> had two falls.  One was related to asthma, heart palpitations.  The second was due to syncope and post covid vaccination dizziness during exercise.  The patient is now getting an EKG.  Former EKG had shown that there was a bundle branch block.  Patient had some uncontrolled immune system reactions like anaphylaxis and shortness of breath.", False, "fall"]]
-
-input_sent_box_label = "Insert sentence here. Mark the predicate by adding the token '<p>' before it."
-verb_form_inp_placeholder = "e.g. 'decide' for the nominalization 'decision', 'teach' for 'teacher', etc."
-links = """<p style='text-align: center'>
-<a href='https://www.qasrl.org' target='_blank'>QASRL Website</a>  |  <a href='https://huggingface.co/kleinay/qanom-seq2seq-model-baseline' target='_blank'>Model Repo at Huggingface Hub</a>
-</p>"""
-def call(model_name, sentence, is_nominal, verb_form):
-    predicate_marker="<p>"
-    if predicate_marker not in sentence:
-        raise ValueError("You must highlight one word of the sentence as a predicate using preceding '<p>'.")
-        
-    if not verb_form:
-        if is_nominal:
-            raise ValueError("You should provide the verbal form of the nominalization")
-            
-        toks = sentence.split(" ")
-        pred_idx = toks.index(predicate_marker)
-        predicate = toks(pred_idx+1)
-        verb_form=predicate
-    pipeline = pipelines[model_name]
-    pipe_out = pipeline([sentence], 
-                    predicate_marker=predicate_marker, 
-                    predicate_type="nominal" if is_nominal else "verbal",
-                    verb_form=verb_form)[0]
-    return pipe_out["QAs"], pipe_out["generated_text"]
-iface = gr.Interface(fn=call, 
-                     inputs=[gr.inputs.Radio(choices=models, default=models[0], label="Model"), 
-                             gr.inputs.Textbox(placeholder=input_sent_box_label, label="Sentence", lines=4), 
-                             gr.inputs.Checkbox(default=True, label="Is Nominalization?"),
-                             gr.inputs.Textbox(placeholder=verb_form_inp_placeholder, label="Verbal form (for nominalizations)", default='')], 
-                     outputs=[gr.outputs.JSON(label="Model Output - QASRL"), gr.outputs.Textbox(label="Raw output sequence")],
-                     title=title,
-                     description=description,
-                     article=links,
-                     examples=examples )
-                     
-iface.launch()

spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/modules/codebooks_patterns.py

@@ -1,539 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import namedtuple
-from dataclasses import dataclass
-from functools import lru_cache
-import logging
-import typing as tp
-
-from abc import ABC, abstractmethod
-import torch
-
-LayoutCoord = namedtuple('LayoutCoord', ['t', 'q'])  # (timestep, codebook index)
-PatternLayout = tp.List[tp.List[LayoutCoord]]  # Sequence of coordinates
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Pattern:
-    """Base implementation of a pattern over a sequence with multiple codebooks.
-
-    The codebook pattern consists in a layout, defining for each sequence step
-    the list of coordinates of each codebook timestep in the resulting interleaved sequence.
-    The first item of the pattern is always an empty list in order to properly insert a special token
-    to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
-    and ``timesteps`` the number of timesteps corresponding to the original sequence.
-
-    The pattern provides convenient methods to build and revert interleaved sequences from it:
-    ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
-        to the interleaved sequence of shape [B, K, S] applying the pattern, with S being the batch size,
-        K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
-        for the output sequence. The unfilled positions are replaced with a special token and the built sequence
-        is returned along with a mask indicating valid tokens.
-    ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
-        of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
-        to fill and specify invalid positions if needed.
-    See the dedicated methods for more details.
-    """
-    # Pattern layout, for each sequence step, we have a list of coordinates
-    # corresponding to the original codebook timestep and position.
-    # The first list is always an empty list in order to properly insert
-    # a special token to start with.
-    layout: PatternLayout
-    timesteps: int
-    n_q: int
-
-    def __post_init__(self):
-        assert len(self.layout) > 0
-        assert self.layout[0] == []
-        self._validate_layout()
-        self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
-        self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
-        logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
-
-    def _validate_layout(self):
-        """Runs checks on the layout to ensure a valid pattern is defined.
-        A pattern is considered invalid if:
-            - Multiple timesteps for a same codebook are defined in the same sequence step
-            - The timesteps for a given codebook are not in ascending order as we advance in the sequence
-              (this would mean that we have future timesteps before past timesteps).
-        """
-        q_timesteps = {q: 0 for q in range(self.n_q)}
-        for s, seq_coords in enumerate(self.layout):
-            if len(seq_coords) > 0:
-                qs = set()
-                for coord in seq_coords:
-                    qs.add(coord.q)
-                    last_q_timestep = q_timesteps[coord.q]
-                    assert coord.t >= last_q_timestep, \
-                        f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
-                    q_timesteps[coord.q] = coord.t
-                # each sequence step contains at max 1 coordinate per codebook
-                assert len(qs) == len(seq_coords), \
-                    f"Multiple entries for a same codebook are found at step {s}"
-
-    @property
-    def num_sequence_steps(self):
-        return len(self.layout) - 1
-
-    @property
-    def max_delay(self):
-        max_t_in_seq_coords = 0
-        for seq_coords in self.layout[1:]:
-            for coords in seq_coords:
-                max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
-        return max_t_in_seq_coords - self.timesteps
-
-    @property
-    def valid_layout(self):
-        valid_step = len(self.layout) - self.max_delay
-        return self.layout[:valid_step]
-
-    def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
-        """Get codebook coordinates in the layout that corresponds to the specified timestep t
-        and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
-        and the actual codebook coordinates.
-        """
-        assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
-        if q is not None:
-            assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
-        coords = []
-        for s, seq_codes in enumerate(self.layout):
-            for code in seq_codes:
-                if code.t == t and (q is None or code.q == q):
-                    coords.append((s, code))
-        return coords
-
-    def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
-        return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
-
-    def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
-        steps_with_timesteps = self.get_steps_with_timestep(t, q)
-        return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
-
-    def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
-                                                device: tp.Union[torch.device, str] = 'cpu'):
-        """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
-
-        Args:
-            timesteps (int): Maximum number of timesteps steps to consider.
-            keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
-            device (Union[torch.device, str]): Device for created tensors.
-        Returns:
-            indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
-        """
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
-        # use the proper layout based on whether we limit ourselves to valid steps only or not,
-        # note that using the valid_layout will result in a truncated sequence up to the valid steps
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        # the last value is n_q * timesteps as we have flattened z and append special token as the last token
-        # which will correspond to the index: n_q * timesteps
-        indexes[:] = n_q * timesteps
-        # iterate over the pattern and fill scattered indexes and mask
-        for s, sequence_coords in enumerate(ref_layout):
-            for coords in sequence_coords:
-                if coords.t < timesteps:
-                    indexes[coords.q, s] = coords.t + coords.q * timesteps
-                    mask[coords.q, s] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Build sequence corresponding to the pattern from the input tensor z.
-        The sequence is built using up to sequence_steps if specified, and non-pattern
-        coordinates are filled with the special token.
-
-        Args:
-            z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
-            special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
-                corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
-        """
-        B, K, T = z.shape
-        indexes, mask = self._build_pattern_sequence_scatter_indexes(
-            T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
-        )
-        z = z.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
-        values = z[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
-                                                 keep_only_valid_steps: bool = False,
-                                                 is_model_output: bool = False,
-                                                 device: tp.Union[torch.device, str] = 'cpu'):
-        """Builds scatter indexes required to retrieve the original multi-codebook sequence
-        from interleaving pattern.
-
-        Args:
-            sequence_steps (int): Sequence steps.
-            n_q (int): Number of codebooks.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-            is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
-            device (Union[torch.device, str]): Device for created tensors.
-        Returns:
-            torch.Tensor: Indexes for reconstructing the output, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
-        timesteps = self.timesteps
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert sequence_steps <= len(ref_layout), \
-            f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
-
-        # ensure we take the appropriate indexes to keep the model output from the first special token as well
-        if is_model_output:
-            ref_layout = ref_layout[1:]
-
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        indexes[:] = n_q * sequence_steps
-        for s, sequence_codes in enumerate(ref_layout):
-            if s < sequence_steps:
-                for code in sequence_codes:
-                    if code.t < timesteps:
-                        indexes[code.q, code.t] = s + code.q * sequence_steps
-                        mask[code.q, code.t] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
-        The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
-        are filled with the special token.
-
-        Args:
-            s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
-            special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
-                corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        B, K, S = s.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
-        )
-        s = s.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
-        values = s[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
-        """Revert model logits obtained on a sequence built from the pattern
-        back to a tensor matching the original sequence.
-
-        This method is similar to ``revert_pattern_sequence`` with the following specificities:
-        1. It is designed to work with the extra cardinality dimension
-        2. We return the logits for the first sequence item that matches the special_token and
-        which matching target in the original sequence is the first item of the sequence,
-        while we skip the last logits as there is no matching target
-        """
-        B, card, K, S = logits.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
-        )
-        logits = logits.reshape(B, card, -1)
-        # we append the special token as the last index of our flattened z tensor
-        logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1)  # [B, card, K x S]
-        values = logits[:, :, indexes.view(-1)]
-        values = values.view(B, card, K, indexes.shape[-1])
-        return values, indexes, mask
-
-
-class CodebooksPatternProvider(ABC):
-    """Abstraction around providing pattern for interleaving codebooks.
-
-    The CodebooksPatternProvider abstraction allows to implement various strategies to
-    define interleaving pattern of sequences composed of multiple codebooks. For a given
-    number of codebooks `n_q`, the pattern provider can generate a specified pattern
-    corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
-    can be used to construct a new sequence from the original codes respecting the specified
-    pattern. The pattern is defined as a list of list of code coordinates, code coordinate
-    being a tuple with the original timestep and codebook to build the new sequence.
-    Note that all patterns must start with an empty list that is then used to insert a first
-    sequence step of special tokens in the newly generated sequence.
-
-    Args:
-        n_q (int): number of codebooks.
-        cached (bool): if True, patterns for a given length are cached. In general
-            that should be true for efficiency reason to avoid synchronization points.
-    """
-    def __init__(self, n_q: int, cached: bool = True):
-        assert n_q > 0
-        self.n_q = n_q
-        self.get_pattern = lru_cache(100)(self.get_pattern)  # type: ignore
-
-    @abstractmethod
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern with specific interleaving between codebooks.
-
-        Args:
-            timesteps (int): Total numer of timesteps.
-        """
-        raise NotImplementedError()
-
-
-class DelayedPatternProvider(CodebooksPatternProvider):
-    """Provider for delayed pattern across delayed codebooks.
-    Codebooks are delayed in the sequence and sequence steps will contain codebooks
-    from different timesteps.
-
-    Example:
-        Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
-        [[1, 2, 3, 4],
-        [1, 2, 3, 4],
-        [1, 2, 3, 4]]
-        The resulting sequence obtained from the returned pattern is:
-        [[S, 1, 2, 3, 4],
-        [S, S, 1, 2, 3],
-        [S, S, S, 1, 2]]
-        (with S being a special token)
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (Optional[List[int]]): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-        flatten_first (int): Flatten the first N timesteps.
-        empty_initial (int): Prepend with N empty list of coordinates.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None,
-                 flatten_first: int = 0, empty_initial: int = 0):
-        super().__init__(n_q)
-        if delays is None:
-            delays = list(range(n_q))
-        self.delays = delays
-        self.flatten_first = flatten_first
-        self.empty_initial = empty_initial
-        assert len(self.delays) == self.n_q
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        max_delay = max(self.delays)
-        if self.empty_initial:
-            out += [[] for _ in range(self.empty_initial)]
-        if self.flatten_first:
-            for t in range(min(timesteps, self.flatten_first)):
-                for q in range(self.n_q):
-                    out.append([LayoutCoord(t, q)])
-        for t in range(self.flatten_first, timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= self.flatten_first:
-                    v.append(LayoutCoord(t_for_q, q))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class ParallelPatternProvider(DelayedPatternProvider):
-    """Provider for parallel pattern across codebooks.
-    This pattern provider is a special case of the delayed pattern with actually no delay,
-    hence delays=repeat(0, n_q).
-
-    Args:
-        n_q (int): Number of codebooks.
-    """
-    def __init__(self, n_q: int):
-        super().__init__(n_q, [0] * n_q)
-
-
-class UnrolledPatternProvider(CodebooksPatternProvider):
-    """Provider for unrolling codebooks pattern.
-    This pattern provider enables to represent the codebook flattened completely or only to some extend
-    while also specifying a given delay between the flattened codebooks representation, allowing to
-    unroll the codebooks in the sequence.
-
-    Example:
-        1. Flattening of the codebooks.
-        By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
-        taking n_q = 3 and timesteps = 4:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
-        for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
-        taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
-        allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
-        same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
-        and delays = [0, 3, 3]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, S, 1, S, 2, S, 3, S, 4],
-         [S, S, S, 1, S, 2, S, 3, S, 4],
-         [1, 2, 3, S, 4, S, 5, S, 6, S]]
-
-    Args:
-        n_q (int): Number of codebooks.
-        flattening (Optional[List[int]]): Flattening schema over the codebooks. If not defined,
-            the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
-            have n_q extra steps for each timestep.
-        delays (Optional[List[int]]): Delay for each of the codebooks. If not defined,
-            no delay is added and therefore will default to [0] * ``n_q``.
-            Note that two codebooks that will be flattened to the same inner step
-            should have the same delay, otherwise the pattern is considered as invalid.
-    """
-    FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
-
-    def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
-                 delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if flattening is None:
-            flattening = list(range(n_q))
-        if delays is None:
-            delays = [0] * n_q
-        assert len(flattening) == n_q
-        assert len(delays) == n_q
-        assert sorted(flattening) == flattening
-        assert sorted(delays) == delays
-        self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
-        self.max_delay = max(delays)
-
-    def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
-        """Build a flattened codebooks representation as a dictionary of inner step
-        and the actual codebook indices corresponding to the flattened codebook. For convenience, we
-        also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
-        """
-        flattened_codebooks: dict = {}
-        for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
-            if inner_step not in flattened_codebooks:
-                flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
-            else:
-                flat_codebook = flattened_codebooks[inner_step]
-                assert flat_codebook.delay == delay, (
-                    "Delay and flattening between codebooks is inconsistent: ",
-                    "two codebooks flattened to the same position should have the same delay."
-                )
-                flat_codebook.codebooks.append(q)
-            flattened_codebooks[inner_step] = flat_codebook
-        return flattened_codebooks
-
-    @property
-    def _num_inner_steps(self):
-        """Number of inner steps to unroll between timesteps in order to flatten the codebooks.
-        """
-        return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
-
-    def num_virtual_steps(self, timesteps: int) -> int:
-        return timesteps * self._num_inner_steps + 1
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern for delay across codebooks.
-
-        Args:
-            timesteps (int): Total numer of timesteps.
-        """
-        # the PatternLayout is built as a tuple of sequence position and list of coordinates
-        # so that it can be reordered properly given the required delay between codebooks of given timesteps
-        indexed_out: list = [(-1, [])]
-        max_timesteps = timesteps + self.max_delay
-        for t in range(max_timesteps):
-            # for each timestep, we unroll the flattened codebooks,
-            # emitting the sequence step with the corresponding delay
-            for step in range(self._num_inner_steps):
-                if step in self._flattened_codebooks:
-                    # we have codebooks at this virtual step to emit
-                    step_codebooks = self._flattened_codebooks[step]
-                    t_for_q = t + step_codebooks.delay
-                    coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
-                    if t_for_q < max_timesteps and t < max_timesteps:
-                        indexed_out.append((t_for_q, coords))
-                else:
-                    # there is no codebook in this virtual step so we emit an empty list
-                    indexed_out.append((t, []))
-        out = [coords for _, coords in sorted(indexed_out)]
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class VALLEPattern(CodebooksPatternProvider):
-    """Almost VALL-E style pattern. We futher allow some delays for the
-    codebooks other than the first one.
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (Optional[List[int]]): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if delays is None:
-            delays = [0] * (n_q - 1)
-        self.delays = delays
-        assert len(self.delays) == self.n_q - 1
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for t in range(timesteps):
-            out.append([LayoutCoord(t, 0)])
-        max_delay = max(self.delays)
-        for t in range(timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= 0:
-                    v.append(LayoutCoord(t_for_q, q + 1))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class MusicLMPattern(CodebooksPatternProvider):
-    """Almost MusicLM style pattern. This is equivalent to full flattening
-    but in a different order.
-
-    Args:
-        n_q (int): Number of codebooks.
-        group_by (int): Number of codebooks to group together.
-    """
-    def __init__(self, n_q: int, group_by: int = 2):
-        super().__init__(n_q)
-        self.group_by = group_by
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for offset in range(0, self.n_q, self.group_by):
-            for t in range(timesteps):
-                for q in range(offset, offset + self.group_by):
-                    out.append([LayoutCoord(t, q)])
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/hooks.server.ts

@@ -1,107 +0,0 @@
-import { COOKIE_NAME, MESSAGES_BEFORE_LOGIN } from "$env/static/private";
-import type { Handle } from "@sveltejs/kit";
-import {
-	PUBLIC_GOOGLE_ANALYTICS_ID,
-	PUBLIC_DEPRECATED_GOOGLE_ANALYTICS_ID,
-	PUBLIC_ORIGIN,
-	PUBLIC_APP_DISCLAIMER,
-} from "$env/static/public";
-import { collections } from "$lib/server/database";
-import { base } from "$app/paths";
-import { refreshSessionCookie, requiresUser } from "$lib/server/auth";
-import { ERROR_MESSAGES } from "$lib/stores/errors";
-
-export const handle: Handle = async ({ event, resolve }) => {
-	const token = event.cookies.get(COOKIE_NAME);
-
-	event.locals.sessionId = token || crypto.randomUUID();
-
-	function errorResponse(status: number, message: string) {
-		const sendJson =
-			event.request.headers.get("accept")?.includes("application/json") ||
-			event.request.headers.get("content-type")?.includes("application/json");
-		return new Response(sendJson ? JSON.stringify({ error: message }) : message, {
-			status,
-			headers: {
-				"content-type": sendJson ? "application/json" : "text/plain",
-			},
-		});
-	}
-
-	// CSRF protection
-	const requestContentType = event.request.headers.get("content-type")?.split(";")[0] ?? "";
-	/** https://developer.mozilla.org/en-US/docs/Web/HTML/Element/form#attr-enctype */
-	const nativeFormContentTypes = [
-		"multipart/form-data",
-		"application/x-www-form-urlencoded",
-		"text/plain",
-	];
-	if (event.request.method === "POST" && nativeFormContentTypes.includes(requestContentType)) {
-		const referer = event.request.headers.get("referer");
-
-		if (!referer) {
-			return errorResponse(403, "Non-JSON form requests need to have a referer");
-		}
-
-		const validOrigins = [
-			new URL(event.request.url).origin,
-			...(PUBLIC_ORIGIN ? [new URL(PUBLIC_ORIGIN).origin] : []),
-		];
-
-		if (!validOrigins.includes(new URL(referer).origin)) {
-			return errorResponse(403, "Invalid referer for POST request");
-		}
-	}
-
-	// if (
-	// 	!event.url.pathname.startsWith(`${base}/login`) &&
-	// 	!event.url.pathname.startsWith(`${base}/admin`) &&
-	// 	!["GET", "OPTIONS", "HEAD"].includes(event.request.method)
-	// ) {
-	// 	if (
-	// 		!user &&
-	// 		requiresUser &&
-	// 		!((MESSAGES_BEFORE_LOGIN ? parseInt(MESSAGES_BEFORE_LOGIN) : 0) > 0)
-	// 	) {
-	// 		return errorResponse(401, ERROR_MESSAGES.authOnly);
-	// 	}
-
-	// 	// if login is not required and the call is not from /settings and we display the ethics modal with PUBLIC_APP_DISCLAIMER
-	// 	//  we check if the user has accepted the ethics modal first.
-	// 	// If login is required, `ethicsModalAcceptedAt` is already true at this point, so do not pass this condition. This saves a DB call.
-	// 	if (
-	// 		!requiresUser &&
-	// 		!event.url.pathname.startsWith(`${base}/settings`) &&
-	// 		!!PUBLIC_APP_DISCLAIMER
-	// 	) {
-	// 		const hasAcceptedEthicsModal = await collections.settings.countDocuments({
-	// 			sessionId: event.locals.sessionId,
-	// 			ethicsModalAcceptedAt: { $exists: true },
-	// 		});
-
-	// 		if (!hasAcceptedEthicsModal) {
-	// 			return errorResponse(405, "You need to accept the welcome modal first");
-	// 		}
-	// 	}
-	// }
-
-	refreshSessionCookie(event.cookies, event.locals.sessionId);
-
-	let replaced = false;
-
-	const response = await resolve(event, {
-		transformPageChunk: (chunk) => {
-			// For some reason, Sveltekit doesn't let us load env variables from .env in the app.html template
-			if (replaced || !chunk.html.includes("%gaId%") || !chunk.html.includes("%gaIdDeprecated%")) {
-				return chunk.html;
-			}
-			replaced = true;
-
-			return chunk.html
-				.replace("%gaId%", PUBLIC_GOOGLE_ANALYTICS_ID)
-				.replace("%gaIdDeprecated%", PUBLIC_DEPRECATED_GOOGLE_ANALYTICS_ID);
-		},
-	});
-
-	return response;
-};

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

@@ -1,5 +0,0 @@
-import CustomShapes from "./CustomShapes";
-
-export default function (
-    config?: CustomShapes.IConfig
-): CustomShapes;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthbuttons/FixWidthButtons.d.ts

@@ -1,89 +0,0 @@
-// import * as Phaser from 'phaser';
-import FixWidthSizer from '../fixwidthsizer/FixWidthSizer';
-import { IConfig as IConfigButtons } from '../utils/buttongroup/Buttons';
-
-
-export default FixWidthButtons;
-
-declare namespace FixWidthButtons {
-
-    interface IConfig extends FixWidthSizer.IConfig, IConfigButtons {
-        background?: Phaser.GameObjects.GameObject,
-
-        buttons?: Phaser.GameObjects.GameObject[],
-    }
-
-}
-
-declare class FixWidthButtons extends FixWidthSizer {
-    constructor(
-        scene: Phaser.Scene,
-        config?: FixWidthButtons.IConfig
-    );
-
-    emitButtonClick(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    setButtonEnable(
-        index?: number | Phaser.GameObjects.GameObject | boolean,
-        enable?: boolean
-    ): this;
-
-    toggleButtonEnable(
-        index?: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    getButtonEnable(
-        index: number | Phaser.GameObjects.GameObject
-    ): boolean;
-
-    getButton(
-        index: number
-    ): Phaser.GameObjects.GameObject | null;
-
-    addButton(
-        gameObject: Phaser.GameObjects.GameObject
-    ): this;
-
-    removeButton(
-        gameObject: Phaser.GameObjects.GameObject,
-        destroyChild?: boolean
-    ): this;
-
-    clearButtons(
-        destroyChild?: boolean
-    ): this;
-
-    showButton(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    hideButton(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    forEachButtton(
-        callback: (button: Phaser.GameObjects.GameObject, index: number, buttons: Phaser.GameObjects.GameObject[]) => void,
-        scop?: unknown
-    ): this;
-
-    readonly buttons: Phaser.GameObjects.GameObject[];
-
-    value: unknown;
-
-    setSelectedButtonName(
-        name: string
-    ): this;
-
-    getSelectedButtonName(): string;
-
-    setButtonState(
-        name: string,
-        state?: boolean
-    ): this;
-
-    getButtonState(
-        name: string
-    ): boolean;
-}

spaces/AlexWelcing/MusicLM/musiclm_pytorch.py

@@ -1,559 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-
-from torchaudio.transforms import Spectrogram, TimeStretch, FrequencyMasking, TimeMasking
-
-from audiolm_pytorch import AudioLM
-from audiolm_pytorch.utils import AudioConditionerBase
-
-from x_clip.tokenizer import tokenizer
-from vector_quantize_pytorch import ResidualVQ
-
-from einops import rearrange, repeat, reduce, pack, unpack
-
-from beartype.typing import List, Optional, Tuple
-from beartype import beartype
-
-# functions
-
-def exists(val):
-    return val is not None
-
-def default(val, d):
-    return val if exists(val) else d
-
-def round_down_nearest_multiple(n, divisor):
-    return n // divisor * divisor
-
-# tensor functions
-
-def log(t, eps = 1e-20):
-    return torch.log(t.clamp(min = eps))
-
-def l2norm(t):
-    return F.normalize(t, p = 2, dim = -1)
-
-# 2d sinusoidal positional embedding
-# simple vit paper shows it is good enough compared to learned
-
-def posemb_sincos_2d(patches, temperature = 10000, dtype = torch.float32):
-    _, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype
-
-    y, x = torch.meshgrid(torch.arange(h, device = device), torch.arange(w, device = device), indexing = 'ij')
-    assert (dim % 4) == 0, 'feature dimension must be multiple of 4 for sincos emb'
-
-    omega = torch.arange(dim // 4, device = device) / (dim // 4 - 1)
-    omega = 1. / (temperature ** omega)
-
-    y = y.flatten()[:, None] * omega[None, :]
-    x = x.flatten()[:, None] * omega[None, :] 
-
-    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim = 1)
-    pe = pe.type(dtype)
-
-    return rearrange(pe, '(h w) d -> h w d', h = h, w = w)
-
-# biasless layernorm
-
-class LayerNorm(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.gamma = nn.Parameter(torch.ones(dim))
-        self.register_buffer('beta', torch.zeros(dim))
-
-    def forward(self, x):
-        return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta)
-
-# feedforward
-
-class GEGLU(nn.Module):
-    def forward(self, x):
-        x, gate = x.chunk(2, dim = -1)
-        return F.gelu(gate) * x
-
-def FeedForward(dim, mult = 4, dropout = 0.):
-    dim_hidden = int(dim * mult * 2 / 3)
-
-    return nn.Sequential(
-        LayerNorm(dim),
-        nn.Linear(dim, dim_hidden * 2, bias = False),
-        GEGLU(),
-        nn.Dropout(dropout),
-        nn.Linear(dim_hidden, dim, bias = False)
-    )
-
-# attention
-
-class Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        causal = False,
-        dim_head = 64,
-        heads = 8,
-        dropout = 0.
-    ):
-        super().__init__()
-        self.heads = heads
-        self.scale = dim_head ** -0.5
-        self.causal = causal
-        inner_dim = dim_head * heads
-
-        self.norm = LayerNorm(dim)
-
-        self.attn_dropout = nn.Dropout(dropout)
-
-        self.to_q = nn.Linear(dim, inner_dim, bias = False)
-        self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
-
-        self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, dim, bias = False),
-            nn.Dropout(dropout)
-        )
-
-    def forward(
-        self,
-        x,
-        mask = None
-    ):
-        b, n, _, device = *x.shape, x.device
-
-        # prenorm
-
-        x = self.norm(x)
-
-        # project for queries, keys, values
-
-        q, k, v = self.to_q(x), *self.to_kv(x).chunk(2, dim = -1)
-
-        # split for multi-headed attention
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), (q, k, v))
-
-        q = q * self.scale
-
-        # similarities
-
-        sim = einsum('b h i d, b h j d -> b h i j', q, k)
-
-        if exists(mask):
-            mask = rearrange(mask, 'b j -> b 1 1 j')
-            sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max)
-
-        if self.causal:
-            i, j = sim.shape[-2:]
-            causal_mask = torch.ones((i, j), dtype = torch.bool, device = x.device).triu(j - i + 1)
-            sim = sim.masked_fill(causal_mask, -torch.finfo(sim.dtype).max)
-
-        # attention
-
-        attn = sim.softmax(dim = -1)
-        attn = self.attn_dropout(attn)
-
-        # aggregate
-
-        out = einsum('b h i j, b h j d -> b h i d', attn, v)
-
-        # merge heads
-
-        out = rearrange(out, 'b h n d -> b n (h d)')
-        return self.to_out(out)
-
-# transformer
-
-class Transformer(nn.Module):
-    def __init__(
-        self,
-        dim,
-        depth,
-        dim_head = 64,
-        heads = 8,
-        attn_dropout = 0.,
-        ff_mult = 4,
-        ff_dropout = 0.
-    ):
-        super().__init__()
-        self.layers = nn.ModuleList([])
-        for _ in range(depth):
-            self.layers.append(nn.ModuleList([
-                Attention(dim = dim, dim_head = dim_head, heads = heads, dropout = attn_dropout),
-                FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout),
-            ]))
-
-    def forward(self, x, mask = None):
-
-        for attn, ff in self.layers:
-            x = attn(x, mask = mask) + x
-            x = ff(x) + x
-
-        return x
-
-# Audio Spectrogram Transformer - https://arxiv.org/abs/2104.01778
-
-def pair(t):
-    return (t, t) if not isinstance(t, tuple) else t
-
-class AudioSpectrogramTransformer(nn.Module):
-    def __init__(
-        self,
-        dim,
-        depth,
-        patch_size = 16,
-        dim_head = 64,
-        heads = 8,
-        attn_dropout = 0.,
-        ff_mult = 4,
-        ff_dropout = 0.,
-        spec_n_fft = 128,
-        spec_power = 2,
-        spec_win_length = 24,
-        spec_hop_length = None,
-        spec_pad = 0,
-        spec_center = True,
-        spec_pad_mode = 'reflect',
-        spec_aug_stretch_factor = 0.8,
-        spec_aug_freq_mask = 80,
-        spec_aug_time_mask = 80
-    ):
-        super().__init__()
-        self.dim = dim
-
-        self.patch_size = pair(patch_size)
-        self.to_patch_tokens = nn.Conv2d(self.patch_size[0] * self.patch_size[1], dim, 1)
-
-        self.spec = Spectrogram(
-            n_fft = spec_n_fft,
-            power = spec_power,
-            win_length = spec_win_length,
-            hop_length = spec_hop_length,
-            pad = spec_pad,
-            center = spec_center,
-            pad_mode = spec_pad_mode
-        )
-
-        # SpecAugment - seems to be widely used in audio field https://arxiv.org/abs/1904.08779
-
-        self.aug = torch.nn.Sequential(
-            TimeStretch(spec_aug_stretch_factor, fixed_rate=True),
-            FrequencyMasking(freq_mask_param = spec_aug_freq_mask),
-            TimeMasking(time_mask_param = spec_aug_time_mask),
-        )
-
-        self.transformer = Transformer(
-            dim = dim,
-            depth = depth,
-            dim_head = dim_head,
-            heads = heads,
-            attn_dropout = attn_dropout,
-            ff_mult = ff_mult,
-            ff_dropout = ff_dropout
-        )
-
-        self.norm = LayerNorm(dim)
-
-    def forward(self, x):
-        x = self.spec(x)
-
-        if self.training:
-            x = self.aug(x)
-
-        # automatically crop if audio does not yield a 2d spectrogram that is divisible by patch sizes
-
-        height, width = x.shape[-2:]
-        patch_height, patch_width = self.patch_size
-
-        rounded_height, rounded_width = map(lambda args: round_down_nearest_multiple(*args), ((height, patch_height), (width, patch_width)))
-
-        if (height, width) != (rounded_height, rounded_width): # just keep printing to be annoying until it is fixed
-            print(f'spectrogram yielded shape of {(height, width)}, but had to be cropped to {(rounded_height, rounded_width)} to be patchified for transformer')
-
-        x = x[..., :rounded_height, :rounded_width]
-
-        # to patches
-
-        x = rearrange(x, 'b (h p1) (w p2) -> b (p1 p2) h w', p1 = patch_height, p2 = patch_width)
-        x = self.to_patch_tokens(x)
-
-        # 2d sinusoidal positional embedding
-
-        x = rearrange(x, 'b c h w -> b h w c')
-        x = x + posemb_sincos_2d(x)
-
-        # attention, what else
-
-        x = rearrange(x, 'b ... c -> b (...) c')
-
-        x = self.transformer(x)
-
-        # final global average and norm (most recent papers show this is superior to CLS token)
-
-        x = reduce(x, 'b n d -> b d', 'mean')
-
-        return self.norm(x)
-
-# text transformer
-
-@beartype
-class TextTransformer(nn.Module):
-    def __init__(
-        self,
-        dim,
-        depth,
-        num_tokens = tokenizer.vocab_size,
-        max_seq_len = 256,
-        dim_head = 64,
-        heads = 8,
-        attn_dropout = 0.,
-        ff_dropout = 0.,
-        ff_mult = 4,
-        pad_id = 0
-    ):
-        super().__init__()
-        self.dim = dim
-
-        self.token_emb = nn.Embedding(num_tokens, dim)
-        self.pos_emb = nn.Embedding(max_seq_len, dim)
-
-        self.cls_token = nn.Parameter(torch.randn(dim))
-
-        self.transformer = Transformer(
-            dim = dim,
-            depth = depth,
-            dim_head = dim_head,
-            heads = heads,
-            attn_dropout = attn_dropout,
-            ff_dropout = ff_dropout,
-            ff_mult = ff_mult
-        )
-
-        self.pad_id = pad_id
-        self.norm = LayerNorm(dim)
-
-    def forward(
-        self,
-        x = None,
-        raw_texts: Optional[List[str]] = None,
-        mask = None
-    ):
-        assert exists(x) ^ exists(raw_texts)
-
-        if exists(raw_texts):
-            x = tokenizer.tokenize(raw_texts)
-
-        if not exists(mask):
-            mask = x != self.pad_id
-
-        b, n, device = *x.shape, x.device
-
-        # token embedding + positional embedding
-
-        x = self.token_emb(x)
-        x = x + self.pos_emb(torch.arange(n, device = device))
-
-        # cls tokens, as in bert
-
-        cls_tokens = repeat(self.cls_token, 'd -> b d', b = b)
-        x, ps = pack([cls_tokens, x], 'b * d')
-
-        # account for attending to cls token with self attention mask
-
-        mask = F.pad(mask, (1, 0), value = True)
-
-        # attention
-
-        x = self.transformer(x, mask = mask)
-
-        # unpack the cls tokens
-
-        cls_tokens, _ = unpack(x, ps, 'b * d')
-
-        return self.norm(cls_tokens)
-
-# main classes
-
-@beartype
-class MuLaN(nn.Module):
-    def __init__(
-        self,
-        audio_transformer: AudioSpectrogramTransformer,
-        text_transformer: TextTransformer,
-        dim_latent = 128,                       # they use 128
-        decoupled_contrastive_learning = True,  # think this was used, make it optional
-    ):
-        super().__init__()
-        self.dim_latent = dim_latent
-
-        self.audio = audio_transformer
-        self.text = text_transformer
-
-        self.temperature = nn.Parameter(torch.tensor(1.))
-
-        self.text_to_latents = nn.Linear(self.text.dim, dim_latent)
-        self.audio_to_latents = nn.Linear(self.audio.dim, dim_latent)
-
-        self.decoupled_contrastive_learning = decoupled_contrastive_learning
-
-    def get_audio_latents(
-        self,
-        wavs
-    ):
-        audio_embeds = self.audio(wavs)
-        audio_latents = self.audio_to_latents(audio_embeds)
-        return l2norm(audio_latents)
-
-    def get_text_latents(
-        self,
-        texts = None,
-        raw_texts: Optional[List[str]] = None
-    ):
-        text_embeds = self.text(texts)
-        text_latents = self.text_to_latents(text_embeds)
-        return l2norm(text_latents)
-
-    def forward(
-        self,
-        wavs,
-        texts = None,
-        raw_texts: Optional[List[str]] = None,
-        return_similarities = False
-    ):
-        batch, device = wavs.shape[0], wavs.device
-
-        audio_latents = self.get_audio_latents(wavs)
-        text_latents = self.get_text_latents(texts, raw_texts = raw_texts)
-
-        cosine_sim = einsum('i d, j d -> i j', audio_latents, text_latents)
-
-        assert cosine_sim.shape[0] == cosine_sim.shape[1], 'batch sizes for audio and text are not equal'
-
-        if return_similarities:
-            return cosine_sim
-
-        cosine_sim = cosine_sim * self.temperature.exp()
-
-        cosine_sim_exp = cosine_sim.exp()
-
-        numerator = cosine_sim_exp.diag()
-
-        if self.decoupled_contrastive_learning:
-            eye = torch.eye(batch, device = device)
-            cosine_sim_exp = cosine_sim_exp.masked_fill(eye, 0.)
-
-        denominator = reduce(cosine_sim_exp, 'i j -> i', 'sum')
-
-        contrastive_loss = -log(numerator / denominator)
-        return contrastive_loss.mean()
-
-# music lm
-
-@beartype
-class MuLaNEmbedQuantizer(AudioConditionerBase):
-    def __init__(
-        self,
-        mulan: MuLaN,
-        conditioning_dims: Tuple[int, ...],
-        rq_num_quantizers = 8,
-        rq_ema_decay = 0.9,
-        codebook_size = 1024,
-        namespaces: Tuple[str, ...] = ('semantic', 'coarse', 'fine'),
-    ):
-        super().__init__()
-        self.mulan = mulan
-
-        assert len(namespaces) > 0
-        self.namespaces = namespaces
-        self.conditioning_dims = conditioning_dims
-
-        assert len(conditioning_dims) == len(namespaces), 'number of conditioning dimensions must be equal to number of namespaces'
-
-        dim = mulan.dim_latent
-
-        self.rq = ResidualVQ(
-            dim = dim,
-            num_quantizers = rq_num_quantizers,
-            codebook_size = codebook_size,
-            decay = rq_ema_decay,
-            commitment_weight = 0,    # only use EMA to update codebooks
-            kmeans_init = True,
-            threshold_ema_dead_code = 2,
-            quantize_dropout = False  # no quantize dropout
-        )
-
-        self.dim = dim
-        self.num_codebooks = rq_num_quantizers
-
-        self.cond_embeddings = nn.ParameterDict({})
-
-        for namespace, conditioning_dim in zip(namespaces, conditioning_dims):
-            cond_embeddings = nn.Parameter(torch.randn(rq_num_quantizers, codebook_size, conditioning_dim))
-            nn.init.normal_(cond_embeddings, std = 0.02)
-
-            self.cond_embeddings[namespace] = cond_embeddings
-
-        self.set_default_namespace(namespaces[0])
-
-    def parameters(self):
-        return self.cond_embeddings.parameters()
-
-    def set_default_namespace(self, namespace):
-        self._default_namespace = namespace
-
-    def forward(
-        self,
-        wavs = None,
-        texts = None,
-        namespace = None
-    ):
-        assert exists(wavs) ^ exists(texts)
-
-        namespace = default(namespace, self._default_namespace)
-        assert namespace in self.namespaces, f'namespace {namespace} not found'
-        cond_embeddings = self.cond_embeddings[namespace]
-
-        with torch.no_grad():
-            self.mulan.eval()
-
-            # sound and language live in joint embedding space because of contrastive learning
-
-            if exists(wavs):
-                latents = self.mulan.get_audio_latents(wavs)
-            elif exists(texts):
-                latents = self.mulan.get_text_latents(texts)
-
-        _, indices, _ = self.rq(latents)
-
-        batch, num_codebooks, dim = indices.shape[0], self.num_codebooks, cond_embeddings.shape[-1]
-
-        cond_embeddings = repeat(cond_embeddings, 'q c d -> b q c d', b = batch)
-        indices = repeat(indices, 'b q -> b q 1 d', q = num_codebooks, d = dim)
-
-        cond_embeddings = cond_embeddings.gather(2, indices)
-        return rearrange(cond_embeddings, 'b q 1 d -> b q d')
-
-@beartype
-class MusicLM(nn.Module):
-    def __init__(
-        self,
-        audio_lm: AudioLM,
-        mulan_embed_quantizer: MuLaNEmbedQuantizer
-    ):
-        super().__init__()
-        assert not exists(audio_lm.audio_conditioner), 'mulan must not have been passed into AudioLM. it will be managed externally now, embedding the text into the joint embedding space for text-to-audio synthesis'
-
-        self.mulan_embed_quantizer = mulan_embed_quantizer
-        self.audio_lm = audio_lm
-
-    @torch.no_grad()
-    def forward(
-        self,
-        raw_texts: List[str],
-        **audio_lm_kwargs
-    ):
-        self.eval()
-
-        texts = tokenizer.tokenize(raw_texts)
-
-        text_embeds = self.mulan_embed_quantizer(texts = texts)
-
-        return self.audio_lm(text_embeds = text_embeds, **audio_lm_kwargs)

spaces/AliUsama98/Usama_TextClassifier/README.md

@@ -1,12 +0,0 @@
----
-title: Usama TextClassifier
-emoji: 📈
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlterM/Zaglyt2-transformer-test/m_conf.py

@@ -1,3 +0,0 @@
-input_length = 20
-emb_dim = 128
-emb_o_dim = 256

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/audio_diffusion/test_audio_diffusion.py

@@ -1,204 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import unittest
-
-import numpy as np
-import torch
-
-from diffusers import (
-    AudioDiffusionPipeline,
-    AutoencoderKL,
-    DDIMScheduler,
-    DDPMScheduler,
-    DiffusionPipeline,
-    Mel,
-    UNet2DConditionModel,
-    UNet2DModel,
-)
-from diffusers.utils import slow, torch_device
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
-
-
-enable_full_determinism()
-
-
-class PipelineFastTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    @property
-    def dummy_unet(self):
-        torch.manual_seed(0)
-        model = UNet2DModel(
-            sample_size=(32, 64),
-            in_channels=1,
-            out_channels=1,
-            layers_per_block=2,
-            block_out_channels=(128, 128),
-            down_block_types=("AttnDownBlock2D", "DownBlock2D"),
-            up_block_types=("UpBlock2D", "AttnUpBlock2D"),
-        )
-        return model
-
-    @property
-    def dummy_unet_condition(self):
-        torch.manual_seed(0)
-        model = UNet2DConditionModel(
-            sample_size=(64, 32),
-            in_channels=1,
-            out_channels=1,
-            layers_per_block=2,
-            block_out_channels=(128, 128),
-            down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
-            up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),
-            cross_attention_dim=10,
-        )
-        return model
-
-    @property
-    def dummy_vqvae_and_unet(self):
-        torch.manual_seed(0)
-        vqvae = AutoencoderKL(
-            sample_size=(128, 64),
-            in_channels=1,
-            out_channels=1,
-            latent_channels=1,
-            layers_per_block=2,
-            block_out_channels=(128, 128),
-            down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D"),
-            up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D"),
-        )
-        unet = UNet2DModel(
-            sample_size=(64, 32),
-            in_channels=1,
-            out_channels=1,
-            layers_per_block=2,
-            block_out_channels=(128, 128),
-            down_block_types=("AttnDownBlock2D", "DownBlock2D"),
-            up_block_types=("UpBlock2D", "AttnUpBlock2D"),
-        )
-        return vqvae, unet
-
-    @slow
-    def test_audio_diffusion(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        mel = Mel(
-            x_res=self.dummy_unet.config.sample_size[1],
-            y_res=self.dummy_unet.config.sample_size[0],
-        )
-
-        scheduler = DDPMScheduler()
-        pipe = AudioDiffusionPipeline(vqvae=None, unet=self.dummy_unet, mel=mel, scheduler=scheduler)
-        pipe = pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device=device).manual_seed(42)
-        output = pipe(generator=generator, steps=4)
-        audio = output.audios[0]
-        image = output.images[0]
-
-        generator = torch.Generator(device=device).manual_seed(42)
-        output = pipe(generator=generator, steps=4, return_dict=False)
-        image_from_tuple = output[0][0]
-
-        assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
-        assert (
-            image.height == self.dummy_unet.config.sample_size[0]
-            and image.width == self.dummy_unet.config.sample_size[1]
-        )
-        image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
-        image_from_tuple_slice = np.frombuffer(image_from_tuple.tobytes(), dtype="uint8")[:10]
-        expected_slice = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() == 0
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() == 0
-
-        mel = Mel(
-            x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1],
-            y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0],
-        )
-
-        scheduler = DDIMScheduler()
-        dummy_vqvae_and_unet = self.dummy_vqvae_and_unet
-        pipe = AudioDiffusionPipeline(
-            vqvae=self.dummy_vqvae_and_unet[0], unet=dummy_vqvae_and_unet[1], mel=mel, scheduler=scheduler
-        )
-        pipe = pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        np.random.seed(0)
-        raw_audio = np.random.uniform(-1, 1, ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,))
-        generator = torch.Generator(device=device).manual_seed(42)
-        output = pipe(raw_audio=raw_audio, generator=generator, start_step=5, steps=10)
-        image = output.images[0]
-
-        assert (
-            image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
-            and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
-        )
-        image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
-        expected_slice = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() == 0
-
-        dummy_unet_condition = self.dummy_unet_condition
-        pipe = AudioDiffusionPipeline(
-            vqvae=self.dummy_vqvae_and_unet[0], unet=dummy_unet_condition, mel=mel, scheduler=scheduler
-        )
-        pipe = pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        np.random.seed(0)
-        encoding = torch.rand((1, 1, 10))
-        output = pipe(generator=generator, encoding=encoding)
-        image = output.images[0]
-        image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
-        expected_slice = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() == 0
-
-
-@slow
-@require_torch_gpu
-class PipelineIntegrationTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_audio_diffusion(self):
-        device = torch_device
-
-        pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256")
-        pipe = pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device=device).manual_seed(42)
-        output = pipe(generator=generator)
-        audio = output.audios[0]
-        image = output.images[0]
-
-        assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
-        assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
-        image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
-        expected_slice = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() == 0

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

@@ -1,201 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import unittest
-from collections import OrderedDict
-
-import torch
-
-from diffusers import (
-    AutoPipelineForImage2Image,
-    AutoPipelineForInpainting,
-    AutoPipelineForText2Image,
-    ControlNetModel,
-)
-from diffusers.pipelines.auto_pipeline import (
-    AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
-    AUTO_INPAINT_PIPELINES_MAPPING,
-    AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
-)
-from diffusers.utils import slow
-
-
-PRETRAINED_MODEL_REPO_MAPPING = OrderedDict(
-    [
-        ("stable-diffusion", "runwayml/stable-diffusion-v1-5"),
-        ("if", "DeepFloyd/IF-I-XL-v1.0"),
-        ("kandinsky", "kandinsky-community/kandinsky-2-1"),
-        ("kandinsky22", "kandinsky-community/kandinsky-2-2-decoder"),
-    ]
-)
-
-
-class AutoPipelineFastTest(unittest.TestCase):
-    def test_from_pipe_consistent(self):
-        pipe = AutoPipelineForText2Image.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-pipe", requires_safety_checker=False
-        )
-        original_config = dict(pipe.config)
-
-        pipe = AutoPipelineForImage2Image.from_pipe(pipe)
-        assert dict(pipe.config) == original_config
-
-        pipe = AutoPipelineForText2Image.from_pipe(pipe)
-        assert dict(pipe.config) == original_config
-
-    def test_from_pipe_override(self):
-        pipe = AutoPipelineForText2Image.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-pipe", requires_safety_checker=False
-        )
-
-        pipe = AutoPipelineForImage2Image.from_pipe(pipe, requires_safety_checker=True)
-        assert pipe.config.requires_safety_checker is True
-
-        pipe = AutoPipelineForText2Image.from_pipe(pipe, requires_safety_checker=True)
-        assert pipe.config.requires_safety_checker is True
-
-    def test_from_pipe_consistent_sdxl(self):
-        pipe = AutoPipelineForImage2Image.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-xl-pipe",
-            requires_aesthetics_score=True,
-            force_zeros_for_empty_prompt=False,
-        )
-
-        original_config = dict(pipe.config)
-
-        pipe = AutoPipelineForText2Image.from_pipe(pipe)
-        pipe = AutoPipelineForImage2Image.from_pipe(pipe)
-
-        assert dict(pipe.config) == original_config
-
-
-@slow
-class AutoPipelineIntegrationTest(unittest.TestCase):
-    def test_pipe_auto(self):
-        for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items():
-            # test txt2img
-            pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
-                model_repo, variant="fp16", torch_dtype=torch.float16
-            )
-            self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
-
-            pipe_to = AutoPipelineForText2Image.from_pipe(pipe_txt2img)
-            self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
-
-            pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_txt2img)
-            self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
-
-            if "kandinsky" not in model_name:
-                pipe_to = AutoPipelineForInpainting.from_pipe(pipe_txt2img)
-                self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
-
-            del pipe_txt2img, pipe_to
-            gc.collect()
-
-            # test img2img
-
-            pipe_img2img = AutoPipelineForImage2Image.from_pretrained(
-                model_repo, variant="fp16", torch_dtype=torch.float16
-            )
-            self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
-
-            pipe_to = AutoPipelineForText2Image.from_pipe(pipe_img2img)
-            self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
-
-            pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_img2img)
-            self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
-
-            if "kandinsky" not in model_name:
-                pipe_to = AutoPipelineForInpainting.from_pipe(pipe_img2img)
-                self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
-
-            del pipe_img2img, pipe_to
-            gc.collect()
-
-            # test inpaint
-
-            if "kandinsky" not in model_name:
-                pipe_inpaint = AutoPipelineForInpainting.from_pretrained(
-                    model_repo, variant="fp16", torch_dtype=torch.float16
-                )
-                self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
-
-                pipe_to = AutoPipelineForText2Image.from_pipe(pipe_inpaint)
-                self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
-
-                pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_inpaint)
-                self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
-
-                pipe_to = AutoPipelineForInpainting.from_pipe(pipe_inpaint)
-                self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
-
-                del pipe_inpaint, pipe_to
-                gc.collect()
-
-    def test_from_pipe_consistent(self):
-        for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items():
-            if model_name in ["kandinsky", "kandinsky22"]:
-                auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image]
-            else:
-                auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image, AutoPipelineForInpainting]
-
-            # test from_pretrained
-            for pipe_from_class in auto_pipes:
-                pipe_from = pipe_from_class.from_pretrained(model_repo, variant="fp16", torch_dtype=torch.float16)
-                pipe_from_config = dict(pipe_from.config)
-
-                for pipe_to_class in auto_pipes:
-                    pipe_to = pipe_to_class.from_pipe(pipe_from)
-                    self.assertEqual(dict(pipe_to.config), pipe_from_config)
-
-                del pipe_from, pipe_to
-                gc.collect()
-
-    def test_controlnet(self):
-        # test from_pretrained
-        model_repo = "runwayml/stable-diffusion-v1-5"
-        controlnet_repo = "lllyasviel/sd-controlnet-canny"
-
-        controlnet = ControlNetModel.from_pretrained(controlnet_repo, torch_dtype=torch.float16)
-
-        pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
-            model_repo, controlnet=controlnet, torch_dtype=torch.float16
-        )
-        self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
-
-        pipe_img2img = AutoPipelineForImage2Image.from_pretrained(
-            model_repo, controlnet=controlnet, torch_dtype=torch.float16
-        )
-        self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
-
-        pipe_inpaint = AutoPipelineForInpainting.from_pretrained(
-            model_repo, controlnet=controlnet, torch_dtype=torch.float16
-        )
-        self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING["stable-diffusion-controlnet"])
-
-        # test from_pipe
-        for pipe_from in [pipe_txt2img, pipe_img2img, pipe_inpaint]:
-            pipe_to = AutoPipelineForText2Image.from_pipe(pipe_from)
-            self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
-            self.assertEqual(dict(pipe_to.config), dict(pipe_txt2img.config))
-
-            pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_from)
-            self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
-            self.assertEqual(dict(pipe_to.config), dict(pipe_img2img.config))
-
-            pipe_to = AutoPipelineForInpainting.from_pipe(pipe_from)
-            self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING["stable-diffusion-controlnet"])
-            self.assertEqual(dict(pipe_to.config), dict(pipe_inpaint.config))

spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/balanced_l1_loss.py

@@ -1,120 +0,0 @@
-import mmcv
-import numpy as np
-import torch
-import torch.nn as nn
-
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def balanced_l1_loss(pred,
-                     target,
-                     beta=1.0,
-                     alpha=0.5,
-                     gamma=1.5,
-                     reduction='mean'):
-    """Calculate balanced L1 loss.
-
-    Please see the `Libra R-CNN <https://arxiv.org/pdf/1904.02701.pdf>`_
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, 4).
-        target (torch.Tensor): The learning target of the prediction with
-            shape (N, 4).
-        beta (float): The loss is a piecewise function of prediction and target
-            and ``beta`` serves as a threshold for the difference between the
-            prediction and target. Defaults to 1.0.
-        alpha (float): The denominator ``alpha`` in the balanced L1 loss.
-            Defaults to 0.5.
-        gamma (float): The ``gamma`` in the balanced L1 loss.
-            Defaults to 1.5.
-        reduction (str, optional): The method that reduces the loss to a
-            scalar. Options are "none", "mean" and "sum".
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    assert beta > 0
-    assert pred.size() == target.size() and target.numel() > 0
-
-    diff = torch.abs(pred - target)
-    b = np.e**(gamma / alpha) - 1
-    loss = torch.where(
-        diff < beta, alpha / b *
-        (b * diff + 1) * torch.log(b * diff / beta + 1) - alpha * diff,
-        gamma * diff + gamma / b - alpha * beta)
-
-    return loss
-
-
-@LOSSES.register_module()
-class BalancedL1Loss(nn.Module):
-    """Balanced L1 Loss.
-
-    arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)
-
-    Args:
-        alpha (float): The denominator ``alpha`` in the balanced L1 loss.
-            Defaults to 0.5.
-        gamma (float): The ``gamma`` in the balanced L1 loss. Defaults to 1.5.
-        beta (float, optional): The loss is a piecewise function of prediction
-            and target. ``beta`` serves as a threshold for the difference
-            between the prediction and target. Defaults to 1.0.
-        reduction (str, optional): The method that reduces the loss to a
-            scalar. Options are "none", "mean" and "sum".
-        loss_weight (float, optional): The weight of the loss. Defaults to 1.0
-    """
-
-    def __init__(self,
-                 alpha=0.5,
-                 gamma=1.5,
-                 beta=1.0,
-                 reduction='mean',
-                 loss_weight=1.0):
-        super(BalancedL1Loss, self).__init__()
-        self.alpha = alpha
-        self.gamma = gamma
-        self.beta = beta
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function of loss.
-
-        Args:
-            pred (torch.Tensor): The prediction with shape (N, 4).
-            target (torch.Tensor): The learning target of the prediction with
-                shape (N, 4).
-            weight (torch.Tensor, optional): Sample-wise loss weight with
-                shape (N, ).
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Options are "none", "mean" and "sum".
-
-        Returns:
-            torch.Tensor: The calculated loss
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        loss_bbox = self.loss_weight * balanced_l1_loss(
-            pred,
-            target,
-            weight,
-            alpha=self.alpha,
-            gamma=self.gamma,
-            beta=self.beta,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss_bbox

spaces/Andy1621/uniformer_image_segmentation/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py

@@ -1,2 +0,0 @@
-_base_ = './apcnet_r50-d8_512x512_80k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/point_rend/pointrend_r50_512x512_160k_ade20k.py

@@ -1,32 +0,0 @@
-_base_ = [
-    '../_base_/models/pointrend_r50.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
-]
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(decode_head=[
-    dict(
-        type='FPNHead',
-        in_channels=[256, 256, 256, 256],
-        in_index=[0, 1, 2, 3],
-        feature_strides=[4, 8, 16, 32],
-        channels=128,
-        dropout_ratio=-1,
-        num_classes=150,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    dict(
-        type='PointHead',
-        in_channels=[256],
-        in_index=[0],
-        channels=256,
-        num_fcs=3,
-        coarse_pred_each_layer=True,
-        dropout_ratio=-1,
-        num_classes=150,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
-])
-lr_config = dict(warmup='linear', warmup_iters=200)

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/whisper_stt/script.py

@@ -1,71 +0,0 @@
-import gradio as gr
-import speech_recognition as sr
-
-from modules import shared
-
-input_hijack = {
-    'state': False,
-    'value': ["", ""]
-}
-
-# parameters which can be customized in settings.json of webui
-params = {
-    'whipser_language': 'english',
-    'whipser_model': 'small.en',
-    'auto_submit': True
-}
-
-
-def chat_input_modifier(text, visible_text, state):
-    global input_hijack
-    if input_hijack['state']:
-        input_hijack['state'] = False
-        return input_hijack['value']
-    else:
-        return text, visible_text
-
-
-def do_stt(audio, whipser_model, whipser_language):
-    transcription = ""
-    r = sr.Recognizer()
-
-    # Convert to AudioData
-    audio_data = sr.AudioData(sample_rate=audio[0], frame_data=audio[1], sample_width=4)
-
-    try:
-        transcription = r.recognize_whisper(audio_data, language=whipser_language, model=whipser_model)
-    except sr.UnknownValueError:
-        print("Whisper could not understand audio")
-    except sr.RequestError as e:
-        print("Could not request results from Whisper", e)
-
-    return transcription
-
-
-def auto_transcribe(audio, auto_submit, whipser_model, whipser_language):
-    if audio is None:
-        return "", ""
-    transcription = do_stt(audio, whipser_model, whipser_language)
-    if auto_submit:
-        input_hijack.update({"state": True, "value": [transcription, transcription]})
-
-    return transcription, None
-
-
-def ui():
-    with gr.Accordion("Whisper STT", open=True):
-        with gr.Row():
-            audio = gr.Audio(source="microphone")
-        with gr.Row():
-            with gr.Accordion("Settings", open=False):
-                auto_submit = gr.Checkbox(label='Submit the transcribed audio automatically', value=params['auto_submit'])
-                whipser_model = gr.Dropdown(label='Whisper Model', value=params['whipser_model'], choices=["tiny.en", "base.en", "small.en", "medium.en", "tiny", "base", "small", "medium", "large"])
-                whipser_language = gr.Dropdown(label='Whisper Language', value=params['whipser_language'], choices=["chinese", "german", "spanish", "russian", "korean", "french", "japanese", "portuguese", "turkish", "polish", "catalan", "dutch", "arabic", "swedish", "italian", "indonesian", "hindi", "finnish", "vietnamese", "hebrew", "ukrainian", "greek", "malay", "czech", "romanian", "danish", "hungarian", "tamil", "norwegian", "thai", "urdu", "croatian", "bulgarian", "lithuanian", "latin", "maori", "malayalam", "welsh", "slovak", "telugu", "persian", "latvian", "bengali", "serbian", "azerbaijani", "slovenian", "kannada", "estonian", "macedonian", "breton", "basque", "icelandic", "armenian", "nepali", "mongolian", "bosnian", "kazakh", "albanian", "swahili", "galician", "marathi", "punjabi", "sinhala", "khmer", "shona", "yoruba", "somali", "afrikaans", "occitan", "georgian", "belarusian", "tajik", "sindhi", "gujarati", "amharic", "yiddish", "lao", "uzbek", "faroese", "haitian creole", "pashto", "turkmen", "nynorsk", "maltese", "sanskrit", "luxembourgish", "myanmar", "tibetan", "tagalog", "malagasy", "assamese", "tatar", "hawaiian", "lingala", "hausa", "bashkir", "javanese", "sundanese"])
-
-    audio.change(
-        auto_transcribe, [audio, auto_submit, whipser_model, whipser_language], [shared.gradio['textbox'], audio]).then(
-        None, auto_submit, None, _js="(check) => {if (check) { document.getElementById('Generate').click() }}")
-
-    whipser_model.change(lambda x: params.update({"whipser_model": x}), whipser_model, None)
-    whipser_language.change(lambda x: params.update({"whipser_language": x}), whipser_language, None)
-    auto_submit.change(lambda x: params.update({"auto_submit": x}), auto_submit, None)

spaces/Artrajz/vits-simple-api/bert_vits2/models.py

@@ -1,686 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from bert_vits2 import commons
-from bert_vits2 import modules
-from bert_vits2 import attentions
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-from bert_vits2.commons import init_weights, get_padding
-from bert_vits2.text import num_tones, num_languages
-
-
-class DurationDiscriminator(nn.Module):  # vits2
-    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.gin_channels = gin_channels
-
-        self.drop = nn.Dropout(p_dropout)
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_1 = modules.LayerNorm(filter_channels)
-        self.conv_2 = nn.Conv1d(
-            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
-        )
-        self.norm_2 = modules.LayerNorm(filter_channels)
-        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
-
-        self.pre_out_conv_1 = nn.Conv1d(2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
-        self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-        self.output_layer = nn.Sequential(
-            nn.Linear(filter_channels, 1),
-            nn.Sigmoid()
-        )
-
-    def forward_probability(self, x, x_mask, dur, g=None):
-        dur = self.dur_proj(dur)
-        x = torch.cat([x, dur], dim=1)
-        x = self.pre_out_conv_1(x * x_mask)
-        x = torch.relu(x)
-        x = self.pre_out_norm_1(x)
-        x = self.drop(x)
-        x = self.pre_out_conv_2(x * x_mask)
-        x = torch.relu(x)
-        x = self.pre_out_norm_2(x)
-        x = self.drop(x)
-        x = x * x_mask
-        x = x.transpose(1, 2)
-        output_prob = self.output_layer(x)
-        return output_prob
-
-    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
-        x = torch.detach(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.conv_1(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_1(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_2(x)
-        x = self.drop(x)
-
-        output_probs = []
-        for dur in [dur_r, dur_hat]:
-            output_prob = self.forward_probability(x, x_mask, dur, g)
-            output_probs.append(output_prob)
-
-        return output_probs
-
-
-class TransformerCouplingBlock(nn.Module):
-    def __init__(self,
-                 channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 n_flows=4,
-                 gin_channels=0,
-                 share_parameter=False
-                 ):
-
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-
-        self.wn = attentions.FFT(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout,
-                                 isflow=True, gin_channels=self.gin_channels) if share_parameter else None
-
-        for i in range(n_flows):
-            self.flows.append(
-                modules.TransformerCouplingLayer(channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout,
-                                                 filter_channels, mean_only=True, wn_sharing_parameter=self.wn,
-                                                 gin_channels=self.gin_channels))
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-
-class StochasticDurationPredictor(nn.Module):
-    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-        super().__init__()
-        filter_channels = in_channels  # it needs to be removed from future version.
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.log_flow = modules.Log()
-        self.flows = nn.ModuleList()
-        self.flows.append(modules.ElementwiseAffine(2))
-        for i in range(n_flows):
-            self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-            self.flows.append(modules.Flip())
-
-        self.post_pre = nn.Conv1d(1, filter_channels, 1)
-        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-        self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-        self.post_flows = nn.ModuleList()
-        self.post_flows.append(modules.ElementwiseAffine(2))
-        for i in range(4):
-            self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-            self.post_flows.append(modules.Flip())
-
-        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-        self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-        x = torch.detach(x)
-        x = self.pre(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.convs(x, x_mask)
-        x = self.proj(x) * x_mask
-
-        if not reverse:
-            flows = self.flows
-            assert w is not None
-
-            logdet_tot_q = 0
-            h_w = self.post_pre(w)
-            h_w = self.post_convs(h_w, x_mask)
-            h_w = self.post_proj(h_w) * x_mask
-            e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-            z_q = e_q
-            for flow in self.post_flows:
-                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-                logdet_tot_q += logdet_q
-            z_u, z1 = torch.split(z_q, [1, 1], 1)
-            u = torch.sigmoid(z_u) * x_mask
-            z0 = (w - u) * x_mask
-            logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
-            logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
-
-            logdet_tot = 0
-            z0, logdet = self.log_flow(z0, x_mask)
-            logdet_tot += logdet
-            z = torch.cat([z0, z1], 1)
-            for flow in flows:
-                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-                logdet_tot = logdet_tot + logdet
-            nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
-            return nll + logq  # [b]
-        else:
-            flows = list(reversed(self.flows))
-            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
-            z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-            for flow in flows:
-                z = flow(z, x_mask, g=x, reverse=reverse)
-            z0, z1 = torch.split(z, [1, 1], 1)
-            logw = z0
-            return logw
-
-
-class DurationPredictor(nn.Module):
-    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.gin_channels = gin_channels
-
-        self.drop = nn.Dropout(p_dropout)
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_1 = modules.LayerNorm(filter_channels)
-        self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_2 = modules.LayerNorm(filter_channels)
-        self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-    def forward(self, x, x_mask, g=None):
-        x = torch.detach(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.conv_1(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_1(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_2(x)
-        x = self.drop(x)
-        x = self.proj(x * x_mask)
-        return x * x_mask
-
-
-class TextEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 gin_channels=0,
-                 symbols=None):
-        super().__init__()
-        self.n_vocab = n_vocab
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.gin_channels = gin_channels
-        self.emb = nn.Embedding(len(symbols), hidden_channels)
-        nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
-        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
-        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5)
-        self.language_emb = nn.Embedding(num_languages, hidden_channels)
-        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5)
-        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
-        self.ja_bert_proj = nn.Conv1d(768, hidden_channels, 1)
-
-        self.encoder = attentions.Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            gin_channels=self.gin_channels)
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, tone, language, bert, ja_bert, g=None):
-        bert_emb = self.bert_proj(bert).transpose(1, 2)
-        ja_bert_emb = self.ja_bert_proj(ja_bert).transpose(1, 2)
-        x = (self.emb(x) + self.tone_emb(tone) + self.language_emb(language) + bert_emb + ja_bert_emb) * math.sqrt(
-            self.hidden_channels)  # [b, t, h]
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-        x = self.encoder(x * x_mask, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(self,
-                 channels,
-                 hidden_channels,
-                 kernel_size,
-                 dilation_rate,
-                 n_layers,
-                 n_flows=4,
-                 gin_channels=0):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
-                                              gin_channels=gin_channels, mean_only=True))
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 hidden_channels,
-                 kernel_size,
-                 dilation_rate,
-                 n_layers,
-                 gin_channels=0):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
-                 upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)),
-                                k, u, padding=(k - u) // 2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class ReferenceEncoder(nn.Module):
-    '''
-    inputs --- [N, Ty/r, n_mels*r]  mels
-    outputs --- [N, ref_enc_gru_size]
-    '''
-
-    def __init__(self, spec_channels, gin_channels=0):
-
-        super().__init__()
-        self.spec_channels = spec_channels
-        ref_enc_filters = [32, 32, 64, 64, 128, 128]
-        K = len(ref_enc_filters)
-        filters = [1] + ref_enc_filters
-        convs = [weight_norm(nn.Conv2d(in_channels=filters[i],
-                                       out_channels=filters[i + 1],
-                                       kernel_size=(3, 3),
-                                       stride=(2, 2),
-                                       padding=(1, 1))) for i in range(K)]
-        self.convs = nn.ModuleList(convs)
-        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
-
-        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
-        self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels,
-                          hidden_size=256 // 2,
-                          batch_first=True)
-        self.proj = nn.Linear(128, gin_channels)
-
-    def forward(self, inputs, mask=None):
-        N = inputs.size(0)
-        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
-        for conv in self.convs:
-            out = conv(out)
-            # out = wn(out)
-            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
-
-        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
-        T = out.size(1)
-        N = out.size(0)
-        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
-
-        self.gru.flatten_parameters()
-        memory, out = self.gru(out)  # out --- [1, N, 128]
-
-        return self.proj(out.squeeze(0))
-
-    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
-        for i in range(n_convs):
-            L = (L - kernel_size + 2 * pad) // stride + 1
-        return L
-
-
-class SynthesizerTrn(nn.Module):
-    """
-      Synthesizer for Training
-      """
-
-    def __init__(self,
-                 n_vocab,
-                 spec_channels,
-                 segment_size,
-                 inter_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 resblock,
-                 resblock_kernel_sizes,
-                 resblock_dilation_sizes,
-                 upsample_rates,
-                 upsample_initial_channel,
-                 upsample_kernel_sizes,
-                 n_speakers=256,
-                 gin_channels=256,
-                 use_sdp=True,
-                 n_flow_layer=4,
-                 n_layers_trans_flow=6,
-                 flow_share_parameter=False,
-                 use_transformer_flow=True,
-                 **kwargs):
-
-        super().__init__()
-        self.n_vocab = n_vocab
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.n_speakers = n_speakers
-        self.gin_channels = gin_channels
-        self.n_layers_trans_flow = n_layers_trans_flow
-        self.use_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", True)
-        self.use_sdp = use_sdp
-        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
-        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
-        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
-        self.current_mas_noise_scale = self.mas_noise_scale_initial
-        if self.use_spk_conditioned_encoder and gin_channels > 0:
-            self.enc_gin_channels = gin_channels
-        symbols = kwargs.get("symbols")
-        self.enc_p = TextEncoder(n_vocab,
-                                 inter_channels,
-                                 hidden_channels,
-                                 filter_channels,
-                                 n_heads,
-                                 n_layers,
-                                 kernel_size,
-                                 p_dropout,
-                                 gin_channels=self.enc_gin_channels,
-                                 symbols=symbols,
-                                 )
-        self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
-                             upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-        self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
-                                      gin_channels=gin_channels)
-        if use_transformer_flow:
-            self.flow = TransformerCouplingBlock(inter_channels, hidden_channels, filter_channels, n_heads,
-                                                 n_layers_trans_flow, 5, p_dropout, n_flow_layer,
-                                                 gin_channels=gin_channels, share_parameter=flow_share_parameter)
-        else:
-            self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer,
-                                              gin_channels=gin_channels)
-        self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-        self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-        if self.n_speakers > 0:
-            self.emb_g = nn.Embedding(self.n_speakers, gin_channels)
-        else:
-            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
-
-    def infer(self, x, x_lengths, sid, tone, language, bert, ja_bert, noise_scale=.667, length_scale=1,
-              noise_scale_w=0.8,
-              max_len=None, sdp_ratio=0, y=None):
-        # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
-        # g = self.gst(y)
-        if self.n_speakers > 0:
-            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-        else:
-            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
-        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert, ja_bert, g=g)
-        logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (sdp_ratio) + self.dp(x, x_mask,
-                                                                                                         g=g) * (
-                       1 - sdp_ratio)
-        w = torch.exp(logw) * x_mask * length_scale
-        w_ceil = torch.ceil(w)
-        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-        attn = commons.generate_path(w_ceil, attn_mask)
-
-        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
-                                                                                 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-
-        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-        z = self.flow(z_p, y_mask, g=g, reverse=True)
-        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
-        return o, attn, y_mask, (z, z_p, m_p, logs_p)

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

@@ -1,54 +0,0 @@
-from typing import Optional, TYPE_CHECKING
-
-from .segment import Segment
-from .style import StyleType
-from ._loop import loop_last
-
-
-if TYPE_CHECKING:
-    from .console import (
-        Console,
-        ConsoleOptions,
-        RenderResult,
-        RenderableType,
-        Group,
-    )
-
-
-class Screen:
-    """A renderable that fills the terminal screen and crops excess.
-
-    Args:
-        renderable (RenderableType): Child renderable.
-        style (StyleType, optional): Optional background style. Defaults to None.
-    """
-
-    renderable: "RenderableType"
-
-    def __init__(
-        self,
-        *renderables: "RenderableType",
-        style: Optional[StyleType] = None,
-        application_mode: bool = False,
-    ) -> None:
-        from pip._vendor.rich.console import Group
-
-        self.renderable = Group(*renderables)
-        self.style = style
-        self.application_mode = application_mode
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        width, height = options.size
-        style = console.get_style(self.style) if self.style else None
-        render_options = options.update(width=width, height=height)
-        lines = console.render_lines(
-            self.renderable or "", render_options, style=style, pad=True
-        )
-        lines = Segment.set_shape(lines, width, height, style=style)
-        new_line = Segment("\n\r") if self.application_mode else Segment.line()
-        for last, line in loop_last(lines):
-            yield from line
-            if not last:
-                yield new_line

spaces/Awesimo/jojogan/e4e/scripts/inference.py

@@ -1,133 +0,0 @@
-import argparse
-
-import torch
-import numpy as np
-import sys
-import os
-import dlib
-
-sys.path.append(".")
-sys.path.append("..")
-
-from configs import data_configs, paths_config
-from datasets.inference_dataset import InferenceDataset
-from torch.utils.data import DataLoader
-from utils.model_utils import setup_model
-from utils.common import tensor2im
-from utils.alignment import align_face
-from PIL import Image
-
-
-def main(args):
-    net, opts = setup_model(args.ckpt, device)
-    is_cars = 'cars_' in opts.dataset_type
-    generator = net.decoder
-    generator.eval()
-    args, data_loader = setup_data_loader(args, opts)
-
-    # Check if latents exist
-    latents_file_path = os.path.join(args.save_dir, 'latents.pt')
-    if os.path.exists(latents_file_path):
-        latent_codes = torch.load(latents_file_path).to(device)
-    else:
-        latent_codes = get_all_latents(net, data_loader, args.n_sample, is_cars=is_cars)
-        torch.save(latent_codes, latents_file_path)
-
-    if not args.latents_only:
-        generate_inversions(args, generator, latent_codes, is_cars=is_cars)
-
-
-def setup_data_loader(args, opts):
-    dataset_args = data_configs.DATASETS[opts.dataset_type]
-    transforms_dict = dataset_args['transforms'](opts).get_transforms()
-    images_path = args.images_dir if args.images_dir is not None else dataset_args['test_source_root']
-    print(f"images path: {images_path}")
-    align_function = None
-    if args.align:
-        align_function = run_alignment
-    test_dataset = InferenceDataset(root=images_path,
-                                    transform=transforms_dict['transform_test'],
-                                    preprocess=align_function,
-                                    opts=opts)
-
-    data_loader = DataLoader(test_dataset,
-                             batch_size=args.batch,
-                             shuffle=False,
-                             num_workers=2,
-                             drop_last=True)
-
-    print(f'dataset length: {len(test_dataset)}')
-
-    if args.n_sample is None:
-        args.n_sample = len(test_dataset)
-    return args, data_loader
-
-
-def get_latents(net, x, is_cars=False):
-    codes = net.encoder(x)
-    if net.opts.start_from_latent_avg:
-        if codes.ndim == 2:
-            codes = codes + net.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :]
-        else:
-            codes = codes + net.latent_avg.repeat(codes.shape[0], 1, 1)
-    if codes.shape[1] == 18 and is_cars:
-        codes = codes[:, :16, :]
-    return codes
-
-
-def get_all_latents(net, data_loader, n_images=None, is_cars=False):
-    all_latents = []
-    i = 0
-    with torch.no_grad():
-        for batch in data_loader:
-            if n_images is not None and i > n_images:
-                break
-            x = batch
-            inputs = x.to(device).float()
-            latents = get_latents(net, inputs, is_cars)
-            all_latents.append(latents)
-            i += len(latents)
-    return torch.cat(all_latents)
-
-
-def save_image(img, save_dir, idx):
-    result = tensor2im(img)
-    im_save_path = os.path.join(save_dir, f"{idx:05d}.jpg")
-    Image.fromarray(np.array(result)).save(im_save_path)
-
-
-@torch.no_grad()
-def generate_inversions(args, g, latent_codes, is_cars):
-    print('Saving inversion images')
-    inversions_directory_path = os.path.join(args.save_dir, 'inversions')
-    os.makedirs(inversions_directory_path, exist_ok=True)
-    for i in range(args.n_sample):
-        imgs, _ = g([latent_codes[i].unsqueeze(0)], input_is_latent=True, randomize_noise=False, return_latents=True)
-        if is_cars:
-            imgs = imgs[:, :, 64:448, :]
-        save_image(imgs[0], inversions_directory_path, i + 1)
-
-
-def run_alignment(image_path):
-    predictor = dlib.shape_predictor(paths_config.model_paths['shape_predictor'])
-    aligned_image = align_face(filepath=image_path, predictor=predictor)
-    print("Aligned image has shape: {}".format(aligned_image.size))
-    return aligned_image
-
-
-if __name__ == "__main__":
-    device = "cuda"
-
-    parser = argparse.ArgumentParser(description="Inference")
-    parser.add_argument("--images_dir", type=str, default=None,
-                        help="The directory of the images to be inverted")
-    parser.add_argument("--save_dir", type=str, default=None,
-                        help="The directory to save the latent codes and inversion images. (default: images_dir")
-    parser.add_argument("--batch", type=int, default=1, help="batch size for the generator")
-    parser.add_argument("--n_sample", type=int, default=None, help="number of the samples to infer.")
-    parser.add_argument("--latents_only", action="store_true", help="infer only the latent codes of the directory")
-    parser.add_argument("--align", action="store_true", help="align face images before inference")
-    parser.add_argument("ckpt", metavar="CHECKPOINT", help="path to generator checkpoint")
-
-    args = parser.parse_args()
-    main(args)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/res2net.py

@@ -1,802 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# This file is modified from https://github.com/Res2Net/Res2Net-detectron2/blob/master/detectron2/modeling/backbone/resnet.py
-# The original file is under Apache-2.0 License
-import numpy as np
-import fvcore.nn.weight_init as weight_init
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.layers import (
-    CNNBlockBase,
-    Conv2d,
-    DeformConv,
-    ModulatedDeformConv,
-    ShapeSpec,
-    get_norm,
-)
-
-from detectron2.modeling.backbone import Backbone
-from detectron2.modeling.backbone.fpn import FPN 
-from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
-from .fpn_p5 import LastLevelP6P7_P5
-from .bifpn import BiFPN
-
-__all__ = [
-    "ResNetBlockBase",
-    "BasicBlock",
-    "BottleneckBlock",
-    "DeformBottleneckBlock",
-    "BasicStem",
-    "ResNet",
-    "make_stage",
-    "build_res2net_backbone",
-]
-
-
-ResNetBlockBase = CNNBlockBase
-"""
-Alias for backward compatibiltiy.
-"""
-
-
-class BasicBlock(CNNBlockBase):
-    """
-    The basic residual block for ResNet-18 and ResNet-34, with two 3x3 conv layers
-    and a projection shortcut if needed.
-    """
-
-    def __init__(self, in_channels, out_channels, *, stride=1, norm="BN"):
-        """
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            stride (int): Stride for the first conv.
-            norm (str or callable): normalization for all conv layers.
-                See :func:`layers.get_norm` for supported format.
-        """
-        super().__init__(in_channels, out_channels, stride)
-
-        if in_channels != out_channels:
-            self.shortcut = Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size=1,
-                stride=stride,
-                bias=False,
-                norm=get_norm(norm, out_channels),
-            )
-        else:
-            self.shortcut = None
-
-        self.conv1 = Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size=3,
-            stride=stride,
-            padding=1,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-
-        self.conv2 = Conv2d(
-            out_channels,
-            out_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-
-        for layer in [self.conv1, self.conv2, self.shortcut]:
-            if layer is not None:  # shortcut can be None
-                weight_init.c2_msra_fill(layer)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = F.relu_(out)
-        out = self.conv2(out)
-
-        if self.shortcut is not None:
-            shortcut = self.shortcut(x)
-        else:
-            shortcut = x
-
-        out += shortcut
-        out = F.relu_(out)
-        return out
-
-
-class BottleneckBlock(CNNBlockBase):
-    """
-    The standard bottle2neck residual block used by Res2Net-50, 101 and 152.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        *,
-        bottleneck_channels,
-        stride=1,
-        num_groups=1,
-        norm="BN",
-        stride_in_1x1=False,
-        dilation=1,
-        basewidth=26, 
-        scale=4,
-    ):
-        """
-        Args:
-            bottleneck_channels (int): number of output channels for the 3x3
-                "bottleneck" conv layers.
-            num_groups (int): number of groups for the 3x3 conv layer.
-            norm (str or callable): normalization for all conv layers.
-                See :func:`layers.get_norm` for supported format.
-            stride_in_1x1 (bool): when stride>1, whether to put stride in the
-                first 1x1 convolution or the bottleneck 3x3 convolution.
-            dilation (int): the dilation rate of the 3x3 conv layer.
-        """
-        super().__init__(in_channels, out_channels, stride)
-
-        if in_channels != out_channels:
-            self.shortcut = nn.Sequential(
-                nn.AvgPool2d(kernel_size=stride, stride=stride, 
-                    ceil_mode=True, count_include_pad=False),
-                Conv2d(
-                    in_channels,
-                    out_channels,
-                    kernel_size=1,
-                    stride=1,
-                    bias=False,
-                    norm=get_norm(norm, out_channels),
-                )
-            )
-        else:
-            self.shortcut = None
-
-        # The original MSRA ResNet models have stride in the first 1x1 conv
-        # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have
-        # stride in the 3x3 conv
-        stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
-        width = bottleneck_channels//scale
-
-        self.conv1 = Conv2d(
-            in_channels,
-            bottleneck_channels,
-            kernel_size=1,
-            stride=stride_1x1,
-            bias=False,
-            norm=get_norm(norm, bottleneck_channels),
-        )
-        if scale == 1:
-          self.nums = 1
-        else:
-          self.nums = scale -1
-        if self.in_channels!=self.out_channels and stride_3x3!=2:
-            self.pool = nn.AvgPool2d(kernel_size=3, stride = stride_3x3, padding=1)
-
-        convs = []
-        bns = []
-        for i in range(self.nums):
-            convs.append(nn.Conv2d(
-                            width, 
-                            width, 
-                            kernel_size=3, 
-                            stride=stride_3x3, 
-                            padding=1 * dilation, 
-                            bias=False,
-                            groups=num_groups,
-                            dilation=dilation,
-                            ))
-            bns.append(get_norm(norm, width))
-        self.convs = nn.ModuleList(convs)
-        self.bns = nn.ModuleList(bns)
-
-        self.conv3 = Conv2d(
-            bottleneck_channels,
-            out_channels,
-            kernel_size=1,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-        self.scale = scale
-        self.width = width
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.stride_3x3 = stride_3x3
-        for layer in [self.conv1, self.conv3]:
-            if layer is not None:  # shortcut can be None
-                weight_init.c2_msra_fill(layer)
-        if self.shortcut is not None:
-            for layer in self.shortcut.modules():
-                if isinstance(layer, Conv2d):
-                    weight_init.c2_msra_fill(layer)
-                
-        for layer in self.convs:
-            if layer is not None:  # shortcut can be None
-                weight_init.c2_msra_fill(layer)
-
-        # Zero-initialize the last normalization in each residual branch,
-        # so that at the beginning, the residual branch starts with zeros,
-        # and each residual block behaves like an identity.
-        # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
-        # "For BN layers, the learnable scaling coefficient γ is initialized
-        # to be 1, except for each residual block's last BN
-        # where γ is initialized to be 0."
-
-        # nn.init.constant_(self.conv3.norm.weight, 0)
-        # TODO this somehow hurts performance when training GN models from scratch.
-        # Add it as an option when we need to use this code to train a backbone.
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = F.relu_(out)
-
-        spx = torch.split(out, self.width, 1)
-        for i in range(self.nums):
-            if i==0 or self.in_channels!=self.out_channels:
-                sp = spx[i]
-            else:
-                sp = sp + spx[i]
-            sp = self.convs[i](sp)
-            sp = F.relu_(self.bns[i](sp))
-            if i==0:
-                out = sp
-            else:
-                out = torch.cat((out, sp), 1)
-        if self.scale!=1 and self.stride_3x3==1:
-            out = torch.cat((out, spx[self.nums]), 1)
-        elif self.scale != 1 and self.stride_3x3==2:
-            out = torch.cat((out, self.pool(spx[self.nums])), 1)
-
-        out = self.conv3(out)
-
-        if self.shortcut is not None:
-            shortcut = self.shortcut(x)
-        else:
-            shortcut = x
-
-        out += shortcut
-        out = F.relu_(out)
-        return out
-
-
-class DeformBottleneckBlock(ResNetBlockBase):
-    """
-    Not implemented for res2net yet.
-    Similar to :class:`BottleneckBlock`, but with deformable conv in the 3x3 convolution.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        *,
-        bottleneck_channels,
-        stride=1,
-        num_groups=1,
-        norm="BN",
-        stride_in_1x1=False,
-        dilation=1,
-        deform_modulated=False,
-        deform_num_groups=1,
-        basewidth=26, 
-        scale=4,
-    ):
-        super().__init__(in_channels, out_channels, stride)
-        self.deform_modulated = deform_modulated
-
-        if in_channels != out_channels:
-            # self.shortcut = Conv2d(
-            #     in_channels,
-            #     out_channels,
-            #     kernel_size=1,
-            #     stride=stride,
-            #     bias=False,
-            #     norm=get_norm(norm, out_channels),
-            # )
-            self.shortcut = nn.Sequential(
-                nn.AvgPool2d(kernel_size=stride, stride=stride, 
-                    ceil_mode=True, count_include_pad=False),
-                Conv2d(
-                    in_channels,
-                    out_channels,
-                    kernel_size=1,
-                    stride=1,
-                    bias=False,
-                    norm=get_norm(norm, out_channels),
-                )
-            )
-        else:
-            self.shortcut = None
-
-        stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
-        width = bottleneck_channels//scale
-
-        self.conv1 = Conv2d(
-            in_channels,
-            bottleneck_channels,
-            kernel_size=1,
-            stride=stride_1x1,
-            bias=False,
-            norm=get_norm(norm, bottleneck_channels),
-        )
-
-        if scale == 1:
-          self.nums = 1
-        else:
-          self.nums = scale -1
-        if self.in_channels!=self.out_channels and stride_3x3!=2:
-            self.pool = nn.AvgPool2d(kernel_size=3, stride = stride_3x3, padding=1)
-
-        if deform_modulated:
-            deform_conv_op = ModulatedDeformConv
-            # offset channels are 2 or 3 (if with modulated) * kernel_size * kernel_size
-            offset_channels = 27
-        else:
-            deform_conv_op = DeformConv
-            offset_channels = 18
-
-        # self.conv2_offset = Conv2d(
-        #     bottleneck_channels,
-        #     offset_channels * deform_num_groups,
-        #     kernel_size=3,
-        #     stride=stride_3x3,
-        #     padding=1 * dilation,
-        #     dilation=dilation,
-        # )
-        # self.conv2 = deform_conv_op(
-        #     bottleneck_channels,
-        #     bottleneck_channels,
-        #     kernel_size=3,
-        #     stride=stride_3x3,
-        #     padding=1 * dilation,
-        #     bias=False,
-        #     groups=num_groups,
-        #     dilation=dilation,
-        #     deformable_groups=deform_num_groups,
-        #     norm=get_norm(norm, bottleneck_channels),
-        # )
-
-        conv2_offsets = []
-        convs = []
-        bns = []
-        for i in range(self.nums):
-            conv2_offsets.append(Conv2d(
-                            width, 
-                            offset_channels * deform_num_groups, 
-                            kernel_size=3, 
-                            stride=stride_3x3, 
-                            padding=1 * dilation, 
-                            bias=False,
-                            groups=num_groups,
-                            dilation=dilation,
-                            ))
-            convs.append(deform_conv_op(
-                            width, 
-                            width, 
-                            kernel_size=3, 
-                            stride=stride_3x3, 
-                            padding=1 * dilation, 
-                            bias=False,
-                            groups=num_groups,
-                            dilation=dilation,
-                            deformable_groups=deform_num_groups,
-                            ))
-            bns.append(get_norm(norm, width))
-        self.conv2_offsets = nn.ModuleList(conv2_offsets)
-        self.convs = nn.ModuleList(convs)
-        self.bns = nn.ModuleList(bns)
-
-        self.conv3 = Conv2d(
-            bottleneck_channels,
-            out_channels,
-            kernel_size=1,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-        self.scale = scale
-        self.width = width
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.stride_3x3 = stride_3x3
-        # for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]:
-        #     if layer is not None:  # shortcut can be None
-        #         weight_init.c2_msra_fill(layer)
-
-        # nn.init.constant_(self.conv2_offset.weight, 0)
-        # nn.init.constant_(self.conv2_offset.bias, 0)
-        for layer in [self.conv1, self.conv3]:
-            if layer is not None:  # shortcut can be None
-                weight_init.c2_msra_fill(layer)
-        if self.shortcut is not None:
-            for layer in self.shortcut.modules():
-                if isinstance(layer, Conv2d):
-                    weight_init.c2_msra_fill(layer)
-                
-        for layer in self.convs:
-            if layer is not None:  # shortcut can be None
-                weight_init.c2_msra_fill(layer)
-
-        for layer in self.conv2_offsets:
-            if layer.weight is not None:
-                nn.init.constant_(layer.weight, 0)
-            if layer.bias is not None:
-                nn.init.constant_(layer.bias, 0)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = F.relu_(out)
-
-        # if self.deform_modulated:
-        #     offset_mask = self.conv2_offset(out)
-        #     offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1)
-        #     offset = torch.cat((offset_x, offset_y), dim=1)
-        #     mask = mask.sigmoid()
-        #     out = self.conv2(out, offset, mask)
-        # else:
-        #     offset = self.conv2_offset(out)
-        #     out = self.conv2(out, offset)
-        # out = F.relu_(out)
-
-        spx = torch.split(out, self.width, 1)
-        for i in range(self.nums):
-            if i==0 or self.in_channels!=self.out_channels:
-                sp = spx[i].contiguous()
-            else:
-                sp = sp + spx[i].contiguous()
-            
-            # sp = self.convs[i](sp)
-            if self.deform_modulated:
-                offset_mask = self.conv2_offsets[i](sp)
-                offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1)
-                offset = torch.cat((offset_x, offset_y), dim=1)
-                mask = mask.sigmoid()
-                sp = self.convs[i](sp, offset, mask)
-            else:
-                offset = self.conv2_offsets[i](sp)
-                sp = self.convs[i](sp, offset)
-            sp = F.relu_(self.bns[i](sp))
-            if i==0:
-                out = sp
-            else:
-                out = torch.cat((out, sp), 1)
-        if self.scale!=1 and self.stride_3x3==1:
-            out = torch.cat((out, spx[self.nums]), 1)
-        elif self.scale != 1 and self.stride_3x3==2:
-            out = torch.cat((out, self.pool(spx[self.nums])), 1)
-
-        out = self.conv3(out)
-
-        if self.shortcut is not None:
-            shortcut = self.shortcut(x)
-        else:
-            shortcut = x
-
-        out += shortcut
-        out = F.relu_(out)
-        return out
-
-
-def make_stage(block_class, num_blocks, first_stride, *, in_channels, out_channels, **kwargs):
-    """
-    Create a list of blocks just like those in a ResNet stage.
-    Args:
-        block_class (type): a subclass of ResNetBlockBase
-        num_blocks (int):
-        first_stride (int): the stride of the first block. The other blocks will have stride=1.
-        in_channels (int): input channels of the entire stage.
-        out_channels (int): output channels of **every block** in the stage.
-        kwargs: other arguments passed to the constructor of every block.
-    Returns:
-        list[nn.Module]: a list of block module.
-    """
-    assert "stride" not in kwargs, "Stride of blocks in make_stage cannot be changed."
-    blocks = []
-    for i in range(num_blocks):
-        blocks.append(
-            block_class(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                stride=first_stride if i == 0 else 1,
-                **kwargs,
-            )
-        )
-        in_channels = out_channels
-    return blocks
-
-
-class BasicStem(CNNBlockBase):
-    """
-    The standard ResNet stem (layers before the first residual block).
-    """
-
-    def __init__(self, in_channels=3, out_channels=64, norm="BN"):
-        """
-        Args:
-            norm (str or callable): norm after the first conv layer.
-                See :func:`layers.get_norm` for supported format.
-        """
-        super().__init__(in_channels, out_channels, 4)
-        self.in_channels = in_channels
-        self.conv1 = nn.Sequential(
-            Conv2d(
-                in_channels,
-                32,
-                kernel_size=3,
-                stride=2,
-                padding=1,
-                bias=False,
-                ),
-            get_norm(norm, 32),
-            nn.ReLU(inplace=True),
-            Conv2d(
-                32,
-                32,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=False,
-                ),
-            get_norm(norm, 32),
-            nn.ReLU(inplace=True),
-            Conv2d(
-                32,
-                out_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=False,
-                ),
-        )
-        self.bn1 = get_norm(norm, out_channels)
-
-        for layer in self.conv1:
-            if isinstance(layer, Conv2d):
-                weight_init.c2_msra_fill(layer)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = F.relu_(x)
-        x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
-        return x
-
-
-class ResNet(Backbone):
-    def __init__(self, stem, stages, num_classes=None, out_features=None):
-        """
-        Args:
-            stem (nn.Module): a stem module
-            stages (list[list[CNNBlockBase]]): several (typically 4) stages,
-                each contains multiple :class:`CNNBlockBase`.
-            num_classes (None or int): if None, will not perform classification.
-                Otherwise, will create a linear layer.
-            out_features (list[str]): name of the layers whose outputs should
-                be returned in forward. Can be anything in "stem", "linear", or "res2" ...
-                If None, will return the output of the last layer.
-        """
-        super(ResNet, self).__init__()
-        self.stem = stem
-        self.num_classes = num_classes
-
-        current_stride = self.stem.stride
-        self._out_feature_strides = {"stem": current_stride}
-        self._out_feature_channels = {"stem": self.stem.out_channels}
-
-        self.stages_and_names = []
-        for i, blocks in enumerate(stages):
-            assert len(blocks) > 0, len(blocks)
-            for block in blocks:
-                assert isinstance(block, CNNBlockBase), block
-
-            name = "res" + str(i + 2)
-            stage = nn.Sequential(*blocks)
-
-            self.add_module(name, stage)
-            self.stages_and_names.append((stage, name))
-
-            self._out_feature_strides[name] = current_stride = int(
-                current_stride * np.prod([k.stride for k in blocks])
-            )
-            self._out_feature_channels[name] = curr_channels = blocks[-1].out_channels
-
-        if num_classes is not None:
-            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-            self.linear = nn.Linear(curr_channels, num_classes)
-
-            # Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
-            # "The 1000-way fully-connected layer is initialized by
-            # drawing weights from a zero-mean Gaussian with standard deviation of 0.01."
-            nn.init.normal_(self.linear.weight, std=0.01)
-            name = "linear"
-
-        if out_features is None:
-            out_features = [name]
-        self._out_features = out_features
-        assert len(self._out_features)
-        children = [x[0] for x in self.named_children()]
-        for out_feature in self._out_features:
-            assert out_feature in children, "Available children: {}".format(", ".join(children))
-
-    def forward(self, x):
-        outputs = {}
-        x = self.stem(x)
-        if "stem" in self._out_features:
-            outputs["stem"] = x
-        for stage, name in self.stages_and_names:
-            x = stage(x)
-            if name in self._out_features:
-                outputs[name] = x
-        if self.num_classes is not None:
-            x = self.avgpool(x)
-            x = torch.flatten(x, 1)
-            x = self.linear(x)
-            if "linear" in self._out_features:
-                outputs["linear"] = x
-        return outputs
-
-    def output_shape(self):
-        return {
-            name: ShapeSpec(
-                channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
-            )
-            for name in self._out_features
-        }
-
-    def freeze(self, freeze_at=0):
-        """
-        Freeze the first several stages of the ResNet. Commonly used in
-        fine-tuning.
-        Args:
-            freeze_at (int): number of stem and stages to freeze.
-                `1` means freezing the stem. `2` means freezing the stem and
-                the first stage, etc.
-        Returns:
-            nn.Module: this ResNet itself
-        """
-        if freeze_at >= 1:
-            self.stem.freeze()
-        for idx, (stage, _) in enumerate(self.stages_and_names, start=2):
-            if freeze_at >= idx:
-                for block in stage.children():
-                    block.freeze()
-        return self
-
-
-@BACKBONE_REGISTRY.register()
-def build_res2net_backbone(cfg, input_shape):
-    """
-    Create a Res2Net instance from config.
-    Returns:
-        ResNet: a :class:`ResNet` instance.
-    """
-    # need registration of new blocks/stems?
-    norm = cfg.MODEL.RESNETS.NORM
-    stem = BasicStem(
-        in_channels=input_shape.channels,
-        out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS,
-        norm=norm,
-    )
-
-    # fmt: off
-    freeze_at           = cfg.MODEL.BACKBONE.FREEZE_AT
-    out_features        = cfg.MODEL.RESNETS.OUT_FEATURES
-    depth               = cfg.MODEL.RESNETS.DEPTH
-    num_groups          = cfg.MODEL.RESNETS.NUM_GROUPS
-    width_per_group     = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
-    scale              = 4
-    bottleneck_channels = num_groups * width_per_group * scale
-    in_channels         = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
-    out_channels        = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
-    stride_in_1x1       = cfg.MODEL.RESNETS.STRIDE_IN_1X1
-    res5_dilation       = cfg.MODEL.RESNETS.RES5_DILATION
-    deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE
-    deform_modulated    = cfg.MODEL.RESNETS.DEFORM_MODULATED
-    deform_num_groups   = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS
-    # fmt: on
-    assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation)
-
-    num_blocks_per_stage = {
-        18: [2, 2, 2, 2],
-        34: [3, 4, 6, 3],
-        50: [3, 4, 6, 3],
-        101: [3, 4, 23, 3],
-        152: [3, 8, 36, 3],
-    }[depth]
-
-    if depth in [18, 34]:
-        assert out_channels == 64, "Must set MODEL.RESNETS.RES2_OUT_CHANNELS = 64 for R18/R34"
-        assert not any(
-            deform_on_per_stage
-        ), "MODEL.RESNETS.DEFORM_ON_PER_STAGE unsupported for R18/R34"
-        assert res5_dilation == 1, "Must set MODEL.RESNETS.RES5_DILATION = 1 for R18/R34"
-        assert num_groups == 1, "Must set MODEL.RESNETS.NUM_GROUPS = 1 for R18/R34"
-
-    stages = []
-
-    # Avoid creating variables without gradients
-    # It consumes extra memory and may cause allreduce to fail
-    out_stage_idx = [{"res2": 2, "res3": 3, "res4": 4, "res5": 5}[f] for f in out_features]
-    max_stage_idx = max(out_stage_idx)
-    for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)):
-        dilation = res5_dilation if stage_idx == 5 else 1
-        first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2
-        stage_kargs = {
-            "num_blocks": num_blocks_per_stage[idx],
-            "first_stride": first_stride,
-            "in_channels": in_channels,
-            "out_channels": out_channels,
-            "norm": norm,
-        }
-        # Use BasicBlock for R18 and R34.
-        if depth in [18, 34]:
-            stage_kargs["block_class"] = BasicBlock
-        else:
-            stage_kargs["bottleneck_channels"] = bottleneck_channels
-            stage_kargs["stride_in_1x1"] = stride_in_1x1
-            stage_kargs["dilation"] = dilation
-            stage_kargs["num_groups"] = num_groups
-            stage_kargs["scale"] = scale
-
-            if deform_on_per_stage[idx]:
-                stage_kargs["block_class"] = DeformBottleneckBlock
-                stage_kargs["deform_modulated"] = deform_modulated
-                stage_kargs["deform_num_groups"] = deform_num_groups
-            else:
-                stage_kargs["block_class"] = BottleneckBlock
-        blocks = make_stage(**stage_kargs)
-        in_channels = out_channels
-        out_channels *= 2
-        bottleneck_channels *= 2
-        stages.append(blocks)
-    return ResNet(stem, stages, out_features=out_features).freeze(freeze_at)
-
-
-@BACKBONE_REGISTRY.register()
-def build_p67_res2net_fpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Args:
-        cfg: a detectron2 CfgNode
-
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = build_res2net_backbone(cfg, input_shape)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.FPN.OUT_CHANNELS
-    backbone = FPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        norm=cfg.MODEL.FPN.NORM,
-        top_block=LastLevelP6P7_P5(out_channels, out_channels),
-        fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
-    )
-    return backbone
-
-
-@BACKBONE_REGISTRY.register()
-def build_res2net_bifpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Args:
-        cfg: a detectron2 CfgNode
-
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = build_res2net_backbone(cfg, input_shape)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    backbone = BiFPN(
-        cfg=cfg,
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=cfg.MODEL.BIFPN.OUT_CHANNELS,
-        norm=cfg.MODEL.BIFPN.NORM,
-        num_levels=cfg.MODEL.BIFPN.NUM_LEVELS,
-        num_bifpn=cfg.MODEL.BIFPN.NUM_BIFPN,
-        separable_conv=cfg.MODEL.BIFPN.SEPARABLE_CONV,
-    )
-    return backbone

spaces/Bart92/RVC_HF/demucs/utils.py

@@ -1,323 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import errno
-import functools
-import hashlib
-import inspect
-import io
-import os
-import random
-import socket
-import tempfile
-import warnings
-import zlib
-from contextlib import contextmanager
-
-from diffq import UniformQuantizer, DiffQuantizer
-import torch as th
-import tqdm
-from torch import distributed
-from torch.nn import functional as F
-
-
-def center_trim(tensor, reference):
-    """
-    Center trim `tensor` with respect to `reference`, along the last dimension.
-    `reference` can also be a number, representing the length to trim to.
-    If the size difference != 0 mod 2, the extra sample is removed on the right side.
-    """
-    if hasattr(reference, "size"):
-        reference = reference.size(-1)
-    delta = tensor.size(-1) - reference
-    if delta < 0:
-        raise ValueError("tensor must be larger than reference. " f"Delta is {delta}.")
-    if delta:
-        tensor = tensor[..., delta // 2:-(delta - delta // 2)]
-    return tensor
-
-
-def average_metric(metric, count=1.):
-    """
-    Average `metric` which should be a float across all hosts. `count` should be
-    the weight for this particular host (i.e. number of examples).
-    """
-    metric = th.tensor([count, count * metric], dtype=th.float32, device='cuda')
-    distributed.all_reduce(metric, op=distributed.ReduceOp.SUM)
-    return metric[1].item() / metric[0].item()
-
-
-def free_port(host='', low=20000, high=40000):
-    """
-    Return a port number that is most likely free.
-    This could suffer from a race condition although
-    it should be quite rare.
-    """
-    sock = socket.socket()
-    while True:
-        port = random.randint(low, high)
-        try:
-            sock.bind((host, port))
-        except OSError as error:
-            if error.errno == errno.EADDRINUSE:
-                continue
-            raise
-        return port
-
-
-def sizeof_fmt(num, suffix='B'):
-    """
-    Given `num` bytes, return human readable size.
-    Taken from https://stackoverflow.com/a/1094933
-    """
-    for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
-        if abs(num) < 1024.0:
-            return "%3.1f%s%s" % (num, unit, suffix)
-        num /= 1024.0
-    return "%.1f%s%s" % (num, 'Yi', suffix)
-
-
-def human_seconds(seconds, display='.2f'):
-    """
-    Given `seconds` seconds, return human readable duration.
-    """
-    value = seconds * 1e6
-    ratios = [1e3, 1e3, 60, 60, 24]
-    names = ['us', 'ms', 's', 'min', 'hrs', 'days']
-    last = names.pop(0)
-    for name, ratio in zip(names, ratios):
-        if value / ratio < 0.3:
-            break
-        value /= ratio
-        last = name
-    return f"{format(value, display)} {last}"
-
-
-class TensorChunk:
-    def __init__(self, tensor, offset=0, length=None):
-        total_length = tensor.shape[-1]
-        assert offset >= 0
-        assert offset < total_length
-
-        if length is None:
-            length = total_length - offset
-        else:
-            length = min(total_length - offset, length)
-
-        self.tensor = tensor
-        self.offset = offset
-        self.length = length
-        self.device = tensor.device
-
-    @property
-    def shape(self):
-        shape = list(self.tensor.shape)
-        shape[-1] = self.length
-        return shape
-
-    def padded(self, target_length):
-        delta = target_length - self.length
-        total_length = self.tensor.shape[-1]
-        assert delta >= 0
-
-        start = self.offset - delta // 2
-        end = start + target_length
-
-        correct_start = max(0, start)
-        correct_end = min(total_length, end)
-
-        pad_left = correct_start - start
-        pad_right = end - correct_end
-
-        out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))
-        assert out.shape[-1] == target_length
-        return out
-
-
-def tensor_chunk(tensor_or_chunk):
-    if isinstance(tensor_or_chunk, TensorChunk):
-        return tensor_or_chunk
-    else:
-        assert isinstance(tensor_or_chunk, th.Tensor)
-        return TensorChunk(tensor_or_chunk)
-
-
-def apply_model(model, mix, shifts=None, split=False,
-                overlap=0.25, transition_power=1., progress=False):
-    """
-    Apply model to a given mixture.
-
-    Args:
-        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
-            and apply the oppositve shift to the output. This is repeated `shifts` time and
-            all predictions are averaged. This effectively makes the model time equivariant
-            and improves SDR by up to 0.2 points.
-        split (bool): if True, the input will be broken down in 8 seconds extracts
-            and predictions will be performed individually on each and concatenated.
-            Useful for model with large memory footprint like Tasnet.
-        progress (bool): if True, show a progress bar (requires split=True)
-    """
-    assert transition_power >= 1, "transition_power < 1 leads to weird behavior."
-    device = mix.device
-    channels, length = mix.shape
-    if split:
-        out = th.zeros(len(model.sources), channels, length, device=device)
-        sum_weight = th.zeros(length, device=device)
-        segment = model.segment_length
-        stride = int((1 - overlap) * segment)
-        offsets = range(0, length, stride)
-        scale = stride / model.samplerate
-        if progress:
-            offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds')
-        # We start from a triangle shaped weight, with maximal weight in the middle
-        # of the segment. Then we normalize and take to the power `transition_power`.
-        # Large values of transition power will lead to sharper transitions.
-        weight = th.cat([th.arange(1, segment // 2 + 1),
-                         th.arange(segment - segment // 2, 0, -1)]).to(device)
-        assert len(weight) == segment
-        # If the overlap < 50%, this will translate to linear transition when
-        # transition_power is 1.
-        weight = (weight / weight.max())**transition_power
-        for offset in offsets:
-            chunk = TensorChunk(mix, offset, segment)
-            chunk_out = apply_model(model, chunk, shifts=shifts)
-            chunk_length = chunk_out.shape[-1]
-            out[..., offset:offset + segment] += weight[:chunk_length] * chunk_out
-            sum_weight[offset:offset + segment] += weight[:chunk_length]
-            offset += segment
-        assert sum_weight.min() > 0
-        out /= sum_weight
-        return out
-    elif shifts:
-        max_shift = int(0.5 * model.samplerate)
-        mix = tensor_chunk(mix)
-        padded_mix = mix.padded(length + 2 * max_shift)
-        out = 0
-        for _ in range(shifts):
-            offset = random.randint(0, max_shift)
-            shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)
-            shifted_out = apply_model(model, shifted)
-            out += shifted_out[..., max_shift - offset:]
-        out /= shifts
-        return out
-    else:
-        valid_length = model.valid_length(length)
-        mix = tensor_chunk(mix)
-        padded_mix = mix.padded(valid_length)
-        with th.no_grad():
-            out = model(padded_mix.unsqueeze(0))[0]
-        return center_trim(out, length)
-
-
-@contextmanager
-def temp_filenames(count, delete=True):
-    names = []
-    try:
-        for _ in range(count):
-            names.append(tempfile.NamedTemporaryFile(delete=False).name)
-        yield names
-    finally:
-        if delete:
-            for name in names:
-                os.unlink(name)
-
-
-def get_quantizer(model, args, optimizer=None):
-    quantizer = None
-    if args.diffq:
-        quantizer = DiffQuantizer(
-            model, min_size=args.q_min_size, group_size=8)
-        if optimizer is not None:
-            quantizer.setup_optimizer(optimizer)
-    elif args.qat:
-        quantizer = UniformQuantizer(
-                model, bits=args.qat, min_size=args.q_min_size)
-    return quantizer
-
-
-def load_model(path, strict=False):
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        load_from = path
-        package = th.load(load_from, 'cpu')
-
-    klass = package["klass"]
-    args = package["args"]
-    kwargs = package["kwargs"]
-
-    if strict:
-        model = klass(*args, **kwargs)
-    else:
-        sig = inspect.signature(klass)
-        for key in list(kwargs):
-            if key not in sig.parameters:
-                warnings.warn("Dropping inexistant parameter " + key)
-                del kwargs[key]
-        model = klass(*args, **kwargs)
-
-    state = package["state"]
-    training_args = package["training_args"]
-    quantizer = get_quantizer(model, training_args)
-
-    set_state(model, quantizer, state)
-    return model
-
-
-def get_state(model, quantizer):
-    if quantizer is None:
-        state = {k: p.data.to('cpu') for k, p in model.state_dict().items()}
-    else:
-        state = quantizer.get_quantized_state()
-        buf = io.BytesIO()
-        th.save(state, buf)
-        state = {'compressed': zlib.compress(buf.getvalue())}
-    return state
-
-
-def set_state(model, quantizer, state):
-    if quantizer is None:
-        model.load_state_dict(state)
-    else:
-        buf = io.BytesIO(zlib.decompress(state["compressed"]))
-        state = th.load(buf, "cpu")
-        quantizer.restore_quantized_state(state)
-
-    return state
-
-
-def save_state(state, path):
-    buf = io.BytesIO()
-    th.save(state, buf)
-    sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]
-
-    path = path.parent / (path.stem + "-" + sig + path.suffix)
-    path.write_bytes(buf.getvalue())
-
-
-def save_model(model, quantizer, training_args, path):
-    args, kwargs = model._init_args_kwargs
-    klass = model.__class__
-
-    state = get_state(model, quantizer)
-
-    save_to = path
-    package = {
-        'klass': klass,
-        'args': args,
-        'kwargs': kwargs,
-        'state': state,
-        'training_args': training_args,
-    }
-    th.save(package, save_to)
-
-
-def capture_init(init):
-    @functools.wraps(init)
-    def __init__(self, *args, **kwargs):
-        self._init_args_kwargs = (args, kwargs)
-        init(self, *args, **kwargs)
-
-    return __init__

spaces/Benson/text-generation/Examples/Bosque Isla Relajante Juego Mod Apk.md

@@ -1,49 +0,0 @@
-<br />
-<h1>Isla del bosque: Juego relajante Mod APK - Una revisión</h1>
-<p>¿Te gusta la naturaleza y los animales? ¿Quieres escapar del estrés y el ruido de la ciudad? ¿Desea relajarse y disfrutar de un juego tranquilo y relajante? Si respondiste sí a cualquiera de estas preguntas, entonces deberías probar Forest Island: Relaxing Game. Este es un juego que le permite crear su propia isla del bosque con animales lindos, aves, plantas y hábitats naturales. También puede escuchar música relajante y sonidos que calman su mente y alma. En este artículo, vamos a revisar Forest Island: Relaxing Game y decirle por qué debe descargar la versión apk mod de este juego. </p>
- <h2>¿Qué es Forest Island: Juego relajante? </h2>
-<p>Forest Island: Relaxing Game es un juego de simulación desarrollado por Nanali Studios. Está disponible para dispositivos Android y tiene más de 100,000 descargas en Google Play Store. El juego está clasificado 4.5 de 5 estrellas por los usuarios que lo han jugado. </p>
-<h2>bosque isla relajante juego mod apk</h2><br /><p><b><b>Download Zip</b> &#10031; <a href="https://bltlly.com/2v6IXk">https://bltlly.com/2v6IXk</a></b></p><br /><br />
-<p>El juego es simple y fácil de jugar. Solo tienes que tocar en la pantalla para crear tu propia isla forestal. Puedes elegir entre diferentes tipos de animales, aves, plantas y hábitats naturales para decorar tu isla. También puedes interactuar con los animales y las aves alimentándolos, jugando con ellos y tomando fotos de ellos. También puede cambiar entre los modos día y noche para ver cómo cambia su isla con la hora del día. </p>
- <h3>Características de Forest Island: Juego relajante</h3>
-<p>Forest Island: Relaxing Game tiene muchas características que lo convierten en un juego divertido y relajante para jugar. Aquí están algunas de ellas:</p>
- <h4>Animales y pájaros lindos</h4>
-
- <h4>Varios hábitats naturales</h4>
-<p>El juego tiene más de 20 tipos de hábitats naturales que puede utilizar para crear su propia isla bosque. Puede elegir entre bosques, lagos, praderas, grandes rocas, costas, mesetas, acantilados, selvas, desiertos, campos de nieve, volcanes, cuevas, cascadas, islas, arrecifes de coral y más. Cada hábitat tiene su propio paisaje y atmósfera. Puedes mezclar y combinar diferentes hábitats para crear tu propia isla única. </p>
- <h4>Música y sonidos relajantes</h4>
-<p>El juego tiene música relajante que calma tu mente y alma. También puedes escuchar varios sonidos de la naturaleza en modo descanso. Puedes escuchar el viento soplando, el agua fluyendo, los pájaros cantando, los animales rugiendo, y más. Puede ajustar el volumen de la música y los sonidos según su preferencia. </p>
- <h3> ¿Por qué descargar Forest Island: Relajante juego Mod APK? </h3>
-<p>Forest Island: Relaxing Game es un juego gratuito que puedes descargar desde Google Play Store. Sin embargo, si quieres disfrutar de más características y beneficios de este juego, usted debe descargar la versión apk mod de este juego. Aquí hay algunas razones por las que:</p>
- <h4>Monedas y gemas ilimitadas</h4>
-<p>En la versión original del juego, necesitas monedas y gemas para comprar nuevos animales, aves, plantas y hábitats. También necesitas monedas y gemas para desbloquear el modo de descanso y el modo nocturno. Sin embargo, en la versión apk mod del juego, obtienes monedas y gemas ilimitadas gratis. Puedes comprar lo que quieras sin preocuparte por quedarte sin dinero. También puedes disfrutar del modo de descanso y el modo nocturno en cualquier momento. </p>
- <h4>No hay anuncios y ventanas emergentes</h4>
-<p>En la versión original del juego, tienes que ver anuncios y ventanas emergentes para ganar monedas y gemas. Estos anuncios y ventanas emergentes pueden ser molestos y distraer. También pueden interrumpir el juego y arruinar tu estado de ánimo. Sin embargo, en la versión apk mod del juego, no tienes que ver ningún anuncio o pop-ups. Puedes jugar el juego sin interrupciones ni distracciones. </p>
- <h4>Fácil instalación y compatibilidad</h4>
-
- <h2>Cómo descargar e instalar Forest Island: Relajante juego Mod APK? </h2>
-<p>Si está interesado en descargar e instalar Forest Island: Relajante Game Mod APK, puede seguir estos pasos:</p>
-<p></p>
- <h3>Paso 1: Descargar el archivo apk mod de una fuente de confianza</h3>
-<p>El primer paso es descargar el archivo apk mod de una fuente de confianza. Puede utilizar el siguiente enlace para descargar la última versión de Forest Island: Relajante Game Mod APK. El tamaño del archivo es de unos 100 MB, así que asegúrate de tener suficiente espacio en tu dispositivo. </p>
-<p><a href="">Isla del bosque: Juego relajante Mod APK Enlace de descarga</a></p>
- <h3>Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo</h3>
-<p>El segundo paso es habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y enciéndala. </p>
- <h3>Paso 3: Instalar el archivo apk mod y lanzar el juego</h3>
-<p>El tercer paso es instalar el archivo apk mod y lanzar el juego. Para hacer esto, localizar el archivo apk mod descargado en el almacenamiento del dispositivo, a continuación, toque en él para iniciar el proceso de instalación. Siga las instrucciones en la pantalla para completar la instalación. Una vez realizada la instalación, puedes iniciar el juego desde el cajón de la app o la pantalla de inicio. </p>
- <h2>Conclusión</h2>
-
-<p>Esperamos que haya disfrutado de este artículo y lo encontró útil. Si usted tiene alguna pregunta o retroalimentación acerca de Forest Island: Relajante Game o su versión apk mod, no dude en dejar un comentario a continuación. Nos encantaría saber de ti. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Forest Island: Relajante Game y su versión mod apk:</p>
- <h4>P: ¿Es seguro jugar a Forest Island: Juego relajante? </h4>
-<p>A: Sí, Forest Island: Relaxing Game es seguro jugar. El juego no contiene ningún virus, malware o spyware que pueda dañar su dispositivo o datos. El juego tampoco requiere ninguna información personal o permisos que puedan comprometer su privacidad o seguridad. </p>
- <h4>Q: ¿Es Forest Island: Relajante juego Mod APK legal? </h4>
-<p>A: Sí, Forest Island: Relaxing Game Mod APK es legal. El archivo mod apk no es una versión hackeada o agrietada del juego. Es una versión modificada del juego que proporciona algunas características y beneficios adicionales para los usuarios. El archivo apk mod no viola ninguna ley o reglamento que regule el uso de aplicaciones y juegos. </p>
- <h4>Q: ¿Puedo jugar a Forest Island: Juego relajante sin conexión? </h4>
-<p>A: Sí, puedes jugar sin conexión a Forest Island: Relaxing Game. El juego no requiere una conexión a Internet para funcionar o funcionar correctamente. Puedes jugar el juego en cualquier momento y en cualquier lugar que quieras sin limitaciones o restricciones. </p>
- <h4>Q: ¿Puedo actualizar Forest Island: Relajante juego Mod APK? </h4>
-<p>A: Sí, puede actualizar Forest Island: Relaxing Game Mod APK. El archivo mod apk se actualiza regularmente para que coincida con la última versión del juego. Puede comprobar si hay actualizaciones desde el siguiente enlace o desde la propia aplicación. También puede habilitar las actualizaciones automáticas en la configuración de su dispositivo para obtener las últimas actualizaciones tan pronto como estén disponibles. </p>
- <h4>Q: ¿Puedo compartir Forest Island: Relajante juego Mod APK con mis amigos? </h4> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Gratis De Backgammon Para Android.md

@@ -1,85 +0,0 @@
-
-<h1>Descarga gratuita de backgammon para Android: Cómo jugar el juego de mesa clásico en su teléfono</h1>
- <h2>Introducción</h2>
- <p>El backgammon es uno de los juegos de mesa más antiguos y populares del mundo. Es un juego de habilidad y estrategia, donde dos jugadores compiten para mover sus piezas alrededor de un tablero y fuera de él, mientras intentan evitar que su oponente haga lo mismo. Backgammon tiene una rica historia y cultura, que se remonta a miles de años a la antigua Mesopotamia, Egipto, Roma, India y China. También es un juego de diversión y emoción, ya que el resultado puede cambiar con cada tirada de dados. </p>
- <p>Pero no necesitas un tablero físico y piezas para disfrutar del backgammon. Usted puede jugar en su teléfono, en cualquier momento y en cualquier lugar, con una aplicación gratuita de backgammon. Jugar al backgammon en tu teléfono tiene muchos beneficios, como comodidad, variedad, desafío y entretenimiento. También puede jugar contra otros jugadores en línea, o contra un oponente de la computadora con diferentes niveles de dificultad. También puedes personalizar tu experiencia de backgammon con diferentes tableros, piezas, dados y configuraciones. </p>
-<h2>descargar gratis de backgammon para android</h2><br /><p><b><b>DOWNLOAD</b> &rarr; <a href="https://bltlly.com/2v6KHx">https://bltlly.com/2v6KHx</a></b></p><br /><br />
- <p>En este artículo, le mostraremos cómo descargar e instalar backgammon gratis en su dispositivo Android. También le explicaremos cómo jugar al backgammon en su teléfono, y le daremos algunos consejos y trucos para ganar más juegos. Si usted es un principiante o un experto, usted encontrará algo útil e interesante en este artículo. Así que vamos a empezar! </p>
- <h2>Cómo descargar e instalar Backgammon gratis en tu dispositivo Android</h2>
- <p>Hay muchas aplicaciones de backgammon disponibles para dispositivos Android, pero no todas valen la pena descargarse. Algunos pueden tener gráficos pobres, anuncios molestos o un juego injusto. Para ayudarle a elegir la mejor aplicación de backgammon para su teléfono, hemos seleccionado tres de los más populares y altamente calificados. Aquí están:</p>
- <ul>
-
-<li><strong>Backgammon Plus by Zynga</strong>: Esta es otra gran aplicación de backgammon gratuito que ofrece modos individuales y multijugador. Usted puede jugar al backgammon clásico por sí mismo o contra amigos en línea. También puedes unirte a torneos y ligas para competir con otros jugadores de todo el mundo. Puede personalizar su experiencia de backgammon con diferentes diseños de dados y tableros. También puedes recoger recompensas completando desafíos diarios y haciendo girar la rueda. </li>
-<li><strong>Backgammon por mvsvnx-dev</strong>: Esta es una aplicación de backgammon gratis simple pero elegante que ofrece modos individuales y multijugador. Puedes jugar contra el ordenador o contra otro jugador online o offline. También puedes ajustar la velocidad y el sonido del juego según tus preferencias. La aplicación tiene un diseño minimalista que se centra en la jugabilidad. </li>
-</ul>
- <p>Para descargar cualquiera de estas aplicaciones <p>Ahora sabes cómo jugar al backgammon en tu teléfono. Pero ¿cómo puedes ganar más juegos? Aquí hay algunos consejos y trucos que te ayudarán a mejorar tus habilidades de backgammon y vencer a tus oponentes. </p>
- <h2>Consejos y trucos para ganar juegos de backgammon en tu teléfono</h2>
- <p>Backgammon es un juego de habilidad y estrategia, pero también de suerte y azar. No puedes controlar los dados, pero puedes controlar cómo los usas. Aquí hay algunos consejos y trucos que te ayudarán a hacer los mejores movimientos y ganar más juegos:</p>
- <h3>Cómo usar estrategia y tácticas en Backgammon</h3>
- <p>La estrategia es el plan o objetivo general de tu juego, mientras que las tácticas son los movimientos o acciones específicas que tomas para lograr tu estrategia. En el backgammon, hay dos estrategias principales: competir y golpear. Carreras significa tratar de mover las fichas más rápido que su oponente, mientras que golpear significa tratar de bloquear o capturar las fichas de su oponente. Dependiendo de la situación, puede optar por utilizar una o ambas de estas estrategias. </p>
- <p>Algunos consejos generales para el uso de estrategias y tácticas en el backgammon son:</p>
-<p></p>
- <ul>
-
-<li>Trata de evitar dejar manchas (fichas individuales) en el tablero, especialmente en el tablero de tu oponente. Esto hará que sea menos probable que te golpeen y pierdas el ritmo (la ventaja de estar por delante en la carrera). </li>
-<li>Intenta crear números primos (seis puntos consecutivos) o números primos parciales (cuatro o cinco puntos consecutivos) frente a las fichas de tu oponente. Esto evitará que avancen y los obligará a quedarse atrás. </li>
-<li>Trate de usar el cubo de doblar sabiamente. Solo ofrezca un doble cuando tenga una clara ventaja o una buena oportunidad de ganar. Solo acepta un doble cuando tengas una probabilidad razonable de ganar o perder por un pequeño margen. </li>
-</ul>
- <h3>Cómo evitar errores y errores comunes en Backgammon</h3>
- <p>Errores y errores son movimientos que te cuestan el juego o una cantidad significativa de puntos. Pueden ser causados por falta de conocimiento, mal juicio o factores emocionales. Para evitar cometer errores y errores en el backgammon, necesitas aprender de ellos y evitar repetirlos. Aquí hay algunos errores y errores comunes que debes evitar:</p>
- <ul>
-<li>Moverse demasiado rápido o demasiado lento. Moverse demasiado rápido puede llevar a errores descuidados, mientras que moverse demasiado lento puede llevar a pensar demasiado y perder oportunidades. Necesitas encontrar el equilibrio correcto entre velocidad y precisión. </li>
-<li>Ignorar la posición de las fichas en el tablero. Necesitas prestar atención a todo el tablero, no solo a tus propias fichas. Necesitas considerar cómo tus movimientos afectan las opciones de tu oponente y viceversa. </li>
-<li>Ignorar las probabilidades de los dados. Necesitas saber las probabilidades de lanzar ciertos números y combinaciones, y cómo afectan tus movimientos. Necesitas usar matemáticas y lógica, no intuición o superstición. </li>
-<li>Ignorar el valor del juego. Necesitas saber cuánto vale cada juego, dependiendo de la puntuación, el cubo y las apuestas. Necesitas ajustar tu estrategia y tácticas en consecuencia. </li>
-</ul>
-
- <p>La mejor manera de mejorar tus habilidades de backgammon es practicar regularmente y aprender de tu experiencia. Jugar al backgammon en tu teléfono es una gran manera de practicar, ya que puedes jugar en cualquier momento y en cualquier lugar, contra diferentes oponentes y niveles de dificultad. Aquí hay algunas maneras de practicar y mejorar tus habilidades de backgammon en tu teléfono:</p>
- <ul>
-<li>Juega contra la computadora o contra otros jugadores en línea. Prueba diferentes modos, configuraciones y desafíos. Aprende de tus ganancias y pérdidas. </li>
-<li>Utilice las características de sugerencia y estadísticas de la aplicación. Vea qué mueve la aplicación sugiere y por qué. Analiza tu desempeño e identifica tus fortalezas y debilidades. </li>
-<li>Lee libros, artículos, blogs, foros o videos sobre backgammon. Aprende de expertos y otros jugadores que comparten sus consejos, trucos, estrategias, tácticas, análisis e historias. </li>
-<li>Únete a un club de backgammon o comunidad online o offline. Conoce a otros jugadores que comparten tu pasión por el backgammon. Intercambiar ideas, opiniones, comentarios, consejos y apoyo. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <p>Aquí hay algunas preguntas frecuentes sobre el backgammon y su reproducción en el teléfono:</p>
- <ol>
-<li><strong>¿Cuál es la mejor aplicación gratuita de backgammon para Android? </strong></li>
-<p>No hay una respuesta definitiva a esta pregunta, ya que diferentes aplicaciones pueden adaptarse a diferentes preferencias y gustos. Sin embargo, algunas de las aplicaciones gratuitas de backgammon más populares y altamente calificadas para Android son Backgammon by AI Factory Limited, Backgammon Plus by Zynga y Backgammon by mvsvnx-dev. Puedes probar cualquiera de estas aplicaciones o explorar otras opciones en Google Play Store.</p>
-<li><strong>¿Cómo puedo jugar al backgammon online con otros jugadores? </strong></li>
-<p>La mayoría de las aplicaciones gratuitas de backgammon ofrecen un modo multijugador en línea, donde puedes jugar contra otros jugadores de todo el mundo. Para jugar en línea, es necesario tener una conexión a Internet y una cuenta válida en la aplicación. A continuación, puede optar por unirse a un juego al azar o crear su propio juego con ajustes específicos. También puede invitar a sus amigos a jugar con usted en línea. </p>
-<li><strong>¿Cómo puedo mejorar mis habilidades de backgammon? </strong></li>
-<p>La mejor manera de mejorar tus habilidades de backgammon es practicar regularmente y aprender de tu experiencia. También puedes usar las funciones de sugerencias y estadísticas de la aplicación para ver qué mueve la aplicación y por qué. También puedes leer libros, artículos, blogs, foros o videos sobre backgammon para aprender de expertos y otros jugadores. También puede unirse a un club de backgammon o comunidad en línea o fuera de línea para conocer a otros jugadores que comparten su pasión por el backgammon. </p>
-<li><strong>¿Cuáles son algunos términos y abreviaturas comunes de backgammon? </strong></li>
-<p>Aquí hay algunos términos y abreviaturas comunes de backgammon que puedes encontrar mientras juegas o lees sobre backgammon:</p>
- <ul>
-<li><strong>Pip</strong>: Un punto en el tablero o una unidad de distancia entre dos puntos. </li>
-<li><strong>Blot</strong>: Un solo verificador en un punto que puede ser golpeado por un oponente. </li>
-
-<li><strong>Bar</strong>: El centro del tablero donde se colocan las fichas. </li>
-<li><strong>Bear off</strong>: Para quitar una ficha del tablero cuando llega al tablero. </li>
-<li><strong>Gammon</strong>: Una victoria quitando todas las fichas antes de que el oponente se lleve cualquier ficha. </li>
-<li><strong>Backgammon</strong>: Una victoria al quitar todas las fichas mientras el oponente todavía tiene una o más fichas en la barra o en su tablero. </li>
-<li><strong>Cube</strong>: El cubo de duplicación que se utiliza para aumentar el valor del juego. </li>
-<li><strong>Duplicar</strong>: Para ofrecer o aceptar un doble del valor del juego usando el cubo. </li>
-<li><strong>BG</strong>: Abreviatura para backgammon. </li>
-<li><strong>DMP</strong>: Abreviatura para punto de partido doble, el último juego de un partido donde ambos jugadores necesitan un punto para ganar. </li>
-<li><strong>GG</strong>: Abreviatura para un buen juego, una forma educada de terminar un juego o un partido. </li>
-</ul>
- <li><strong>¿Dónde puedo encontrar más información sobre backgammon? </strong></li>
-<p>Si quieres aprender más sobre el backgammon, hay muchos recursos disponibles online y offline. Algunos de los mejores sitios web para el backgammon son:</p>
- <ul>
-<li>[Backgammon Galore]: Un sitio web completo que cubre todo sobre el backgammon, desde reglas y estrategias y tácticas a la historia y la cultura. También tiene un foro, un glosario, un cuestionario y una colección de enlaces. </li>
-<li>[Backgammon.org]: Un sitio web que ofrece juegos de backgammon en línea, torneos y lecciones. También tiene un blog, una revista, un podcast y una tienda. </li>
-<li>[GammonVillage]: Un sitio web que proporciona noticias, artículos, comentarios, videos y libros sobre backgammon. También tiene una tienda, un foro y un directorio de clubes. </li>
-</ul>
- <p>Algunos de los mejores libros para backgammon son:</p>
- <ul>
-
-<li><strong>Backgammon por Paul Magriel</strong>: Un libro clásico que cubre la teoría y la práctica del backgammon, desde los movimientos de apertura y el juego posicional hasta la duplicación y los finales. También incluye diagramas, ejemplos y ejercicios. </li>
-<li><strong>Backgammon Boot Camp por Walter Trice</strong>: Un libro completo que cubre todos los aspectos del backgammon, desde fundamentos y conceptos hasta análisis y evaluación. También incluye problemas, soluciones, exámenes y pruebas. </li>
-</ul>
- <p>Estos son solo algunos de los muchos recursos disponibles para los entusiastas del backgammon. También puedes encontrar más información en las redes sociales, como Facebook, Twitter, YouTube o Instagram.</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/dynamodb/transform.py

@@ -1,343 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import copy
-
-from boto3.compat import collections_abc
-from boto3.docs.utils import DocumentModifiedShape
-from boto3.dynamodb.conditions import ConditionBase, ConditionExpressionBuilder
-from boto3.dynamodb.types import TypeDeserializer, TypeSerializer
-
-
-def register_high_level_interface(base_classes, **kwargs):
-    base_classes.insert(0, DynamoDBHighLevelResource)
-
-
-class _ForgetfulDict(dict):
-    """A dictionary that discards any items set on it. For use as `memo` in
-    `copy.deepcopy()` when every instance of a repeated object in the deepcopied
-    data structure should result in a separate copy.
-    """
-
-    def __setitem__(self, key, value):
-        pass
-
-
-def copy_dynamodb_params(params, **kwargs):
-    return copy.deepcopy(params, memo=_ForgetfulDict())
-
-
-class DynamoDBHighLevelResource:
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # Apply handler that creates a copy of the user provided dynamodb
-        # item such that it can be modified.
-        self.meta.client.meta.events.register(
-            'provide-client-params.dynamodb',
-            copy_dynamodb_params,
-            unique_id='dynamodb-create-params-copy',
-        )
-
-        self._injector = TransformationInjector()
-        # Apply the handler that generates condition expressions including
-        # placeholders.
-        self.meta.client.meta.events.register(
-            'before-parameter-build.dynamodb',
-            self._injector.inject_condition_expressions,
-            unique_id='dynamodb-condition-expression',
-        )
-
-        # Apply the handler that serializes the request from python
-        # types to dynamodb types.
-        self.meta.client.meta.events.register(
-            'before-parameter-build.dynamodb',
-            self._injector.inject_attribute_value_input,
-            unique_id='dynamodb-attr-value-input',
-        )
-
-        # Apply the handler that deserializes the response from dynamodb
-        # types to python types.
-        self.meta.client.meta.events.register(
-            'after-call.dynamodb',
-            self._injector.inject_attribute_value_output,
-            unique_id='dynamodb-attr-value-output',
-        )
-
-        # Apply the documentation customizations to account for
-        # the transformations.
-        attr_value_shape_docs = DocumentModifiedShape(
-            'AttributeValue',
-            new_type='valid DynamoDB type',
-            new_description=(
-                '- The value of the attribute. The valid value types are '
-                'listed in the '
-                ':ref:`DynamoDB Reference Guide<ref_valid_dynamodb_types>`.'
-            ),
-            new_example_value=(
-                '\'string\'|123|Binary(b\'bytes\')|True|None|set([\'string\'])'
-                '|set([123])|set([Binary(b\'bytes\')])|[]|{}'
-            ),
-        )
-
-        key_expression_shape_docs = DocumentModifiedShape(
-            'KeyExpression',
-            new_type=(
-                'condition from :py:class:`boto3.dynamodb.conditions.Key` '
-                'method'
-            ),
-            new_description=(
-                'The condition(s) a key(s) must meet. Valid conditions are '
-                'listed in the '
-                ':ref:`DynamoDB Reference Guide<ref_dynamodb_conditions>`.'
-            ),
-            new_example_value='Key(\'mykey\').eq(\'myvalue\')',
-        )
-
-        con_expression_shape_docs = DocumentModifiedShape(
-            'ConditionExpression',
-            new_type=(
-                'condition from :py:class:`boto3.dynamodb.conditions.Attr` '
-                'method'
-            ),
-            new_description=(
-                'The condition(s) an attribute(s) must meet. Valid conditions '
-                'are listed in the '
-                ':ref:`DynamoDB Reference Guide<ref_dynamodb_conditions>`.'
-            ),
-            new_example_value='Attr(\'myattribute\').eq(\'myvalue\')',
-        )
-
-        self.meta.client.meta.events.register(
-            'docs.*.dynamodb.*.complete-section',
-            attr_value_shape_docs.replace_documentation_for_matching_shape,
-            unique_id='dynamodb-attr-value-docs',
-        )
-
-        self.meta.client.meta.events.register(
-            'docs.*.dynamodb.*.complete-section',
-            key_expression_shape_docs.replace_documentation_for_matching_shape,
-            unique_id='dynamodb-key-expression-docs',
-        )
-
-        self.meta.client.meta.events.register(
-            'docs.*.dynamodb.*.complete-section',
-            con_expression_shape_docs.replace_documentation_for_matching_shape,
-            unique_id='dynamodb-cond-expression-docs',
-        )
-
-
-class TransformationInjector:
-    """Injects the transformations into the user provided parameters."""
-
-    def __init__(
-        self,
-        transformer=None,
-        condition_builder=None,
-        serializer=None,
-        deserializer=None,
-    ):
-        self._transformer = transformer
-        if transformer is None:
-            self._transformer = ParameterTransformer()
-
-        self._condition_builder = condition_builder
-        if condition_builder is None:
-            self._condition_builder = ConditionExpressionBuilder()
-
-        self._serializer = serializer
-        if serializer is None:
-            self._serializer = TypeSerializer()
-
-        self._deserializer = deserializer
-        if deserializer is None:
-            self._deserializer = TypeDeserializer()
-
-    def inject_condition_expressions(self, params, model, **kwargs):
-        """Injects the condition expression transformation into the parameters
-
-        This injection includes transformations for ConditionExpression shapes
-        and KeyExpression shapes. It also handles any placeholder names and
-        values that are generated when transforming the condition expressions.
-        """
-        self._condition_builder.reset()
-        generated_names = {}
-        generated_values = {}
-
-        # Create and apply the Condition Expression transformation.
-        transformation = ConditionExpressionTransformation(
-            self._condition_builder,
-            placeholder_names=generated_names,
-            placeholder_values=generated_values,
-            is_key_condition=False,
-        )
-        self._transformer.transform(
-            params, model.input_shape, transformation, 'ConditionExpression'
-        )
-
-        # Create and apply the Key Condition Expression transformation.
-        transformation = ConditionExpressionTransformation(
-            self._condition_builder,
-            placeholder_names=generated_names,
-            placeholder_values=generated_values,
-            is_key_condition=True,
-        )
-        self._transformer.transform(
-            params, model.input_shape, transformation, 'KeyExpression'
-        )
-
-        expr_attr_names_input = 'ExpressionAttributeNames'
-        expr_attr_values_input = 'ExpressionAttributeValues'
-
-        # Now that all of the condition expression transformation are done,
-        # update the placeholder dictionaries in the request.
-        if expr_attr_names_input in params:
-            params[expr_attr_names_input].update(generated_names)
-        else:
-            if generated_names:
-                params[expr_attr_names_input] = generated_names
-
-        if expr_attr_values_input in params:
-            params[expr_attr_values_input].update(generated_values)
-        else:
-            if generated_values:
-                params[expr_attr_values_input] = generated_values
-
-    def inject_attribute_value_input(self, params, model, **kwargs):
-        """Injects DynamoDB serialization into parameter input"""
-        self._transformer.transform(
-            params,
-            model.input_shape,
-            self._serializer.serialize,
-            'AttributeValue',
-        )
-
-    def inject_attribute_value_output(self, parsed, model, **kwargs):
-        """Injects DynamoDB deserialization into responses"""
-        if model.output_shape is not None:
-            self._transformer.transform(
-                parsed,
-                model.output_shape,
-                self._deserializer.deserialize,
-                'AttributeValue',
-            )
-
-
-class ConditionExpressionTransformation:
-    """Provides a transformation for condition expressions
-
-    The ``ParameterTransformer`` class can call this class directly
-    to transform the condition expressions in the parameters provided.
-    """
-
-    def __init__(
-        self,
-        condition_builder,
-        placeholder_names,
-        placeholder_values,
-        is_key_condition=False,
-    ):
-        self._condition_builder = condition_builder
-        self._placeholder_names = placeholder_names
-        self._placeholder_values = placeholder_values
-        self._is_key_condition = is_key_condition
-
-    def __call__(self, value):
-        if isinstance(value, ConditionBase):
-            # Create a conditional expression string with placeholders
-            # for the provided condition.
-            built_expression = self._condition_builder.build_expression(
-                value, is_key_condition=self._is_key_condition
-            )
-
-            self._placeholder_names.update(
-                built_expression.attribute_name_placeholders
-            )
-            self._placeholder_values.update(
-                built_expression.attribute_value_placeholders
-            )
-
-            return built_expression.condition_expression
-        # Use the user provided value if it is not a ConditonBase object.
-        return value
-
-
-class ParameterTransformer:
-    """Transforms the input to and output from botocore based on shape"""
-
-    def transform(self, params, model, transformation, target_shape):
-        """Transforms the dynamodb input to or output from botocore
-
-        It applies a specified transformation whenever a specific shape name
-        is encountered while traversing the parameters in the dictionary.
-
-        :param params: The parameters structure to transform.
-        :param model: The operation model.
-        :param transformation: The function to apply the parameter
-        :param target_shape: The name of the shape to apply the
-            transformation to
-        """
-        self._transform_parameters(model, params, transformation, target_shape)
-
-    def _transform_parameters(
-        self, model, params, transformation, target_shape
-    ):
-        type_name = model.type_name
-        if type_name in ('structure', 'map', 'list'):
-            getattr(self, f'_transform_{type_name}')(
-                model, params, transformation, target_shape
-            )
-
-    def _transform_structure(
-        self, model, params, transformation, target_shape
-    ):
-        if not isinstance(params, collections_abc.Mapping):
-            return
-        for param in params:
-            if param in model.members:
-                member_model = model.members[param]
-                member_shape = member_model.name
-                if member_shape == target_shape:
-                    params[param] = transformation(params[param])
-                else:
-                    self._transform_parameters(
-                        member_model,
-                        params[param],
-                        transformation,
-                        target_shape,
-                    )
-
-    def _transform_map(self, model, params, transformation, target_shape):
-        if not isinstance(params, collections_abc.Mapping):
-            return
-        value_model = model.value
-        value_shape = value_model.name
-        for key, value in params.items():
-            if value_shape == target_shape:
-                params[key] = transformation(value)
-            else:
-                self._transform_parameters(
-                    value_model, params[key], transformation, target_shape
-                )
-
-    def _transform_list(self, model, params, transformation, target_shape):
-        if not isinstance(params, collections_abc.MutableSequence):
-            return
-        member_model = model.member
-        member_shape = member_model.name
-        for i, item in enumerate(params):
-            if member_shape == target_shape:
-                params[i] = transformation(item)
-            else:
-                self._transform_parameters(
-                    member_model, params[i], transformation, target_shape
-                )

spaces/Blockinger/OVAChatGPT/app.py

@@ -1,49 +0,0 @@
-import whisper
-import gradio as gr
-import time
-from pyChatGPT import ChatGPT
-import warnings
-
-warnings.filterwarnings("ignore")
-secret_token = "eyJhbGciOiJkaXIiLCJlbmMiOiJBMjU2R0NNIn0..7mhijdQ19ze0tuj2.sjEQP7Gi4PYli8CUWMGC0GOj4tVDlZOmU8DWlCtRO-uUrkvPLn0hbhHpg0RMERCbBORAmHZ-wawqvZMmiZ2rfxBuaNw0aGp5bZ1IR2N3Na1qyTHuZ29D2TS287MJcsmfiXJaYTb_sxwxi8d9_uY4HEv6aseywDplaGQFmrLnRSKy2kbFXoYXmfXwolIm9miO68JXlYm4tRig2gNf5YsfEvd0rEBf7q7NzdSUud2DVYu7Q-qq6h_AVDCFFgJ7Y3zzhGSJ0c8DPHnynxBzv5KaB94GA05oqWtdqYxZGJ9xLaZOa-KFPGVRK7flzY3jCa8QKxHz8v-bFjdfb8jqU3cGjgyU47_B-P_aLaZ_K2ZFksjImgGzYvOGA1DIk9twImBpDwWtJwZuFifrsIL-d51D33xJJkP4LS_WKjRJJBkS2BPR2A8-NFJ3KaizZLiUdYa1pDlvTHpmd51QwJ7i4cjr41G4VY4O1n-sLgyFcoEbYKsIpuaiFffjyVg1WNJNLl-qOOzkslNjE-xcx5Y6Uo5fEurRfp1vEFlb2PXycaRRUT4r95_39PhSNZciHHFGkNSOoc7Zk90DtrJIcRvFA61YdgSo4_g5H90qSpDL_JvZIuAut58hWbBdxrZDV5c1GIgotX5ZmSyN9DuqRBTwEqdenqo1krMdZc6B1epHV0fON6Ur7PI_CgmIjBWclLc2uxSmnlvBJTvsDp-xOaJty6wFtkyui_QiLXZ0SzJAAOmDgXem91MtjfFF5h0Kc2AviplqyOw6BIFp7i5-oDEBI6FEpDyPNnSHJiMGP8HDV0RIqZs2RK4xUb1FolSqP-DfpZ7gikpcE_Gr-wSM8daHU65g--RxhtgJXi12pR0hz4io5qCfneF__D81Y6tc6x1T79ezPJh9lFRMxYO8G8tJHbUHioHmt4zt-NNy8o0h_fVD3qfRE2w46nju0DGLCw_A4VVL_gTxurVRutvVXj3mgVVEtWNG74jcPSR6jIwJTpmRl7pmj94pQqINJH_-365E2OG55HdZnFhil05_n3uN_0ZZIsaGbznmDvUaGSuwdrhnbnbLSMrAVC9CYs8Euovgh6hQd9aAo4vaboOOECZOeyTbyBmvaOqzHev0GMoXdeUXXCOJAlUQ3wYnFFZ27gr5NNAMqC7uSo0fVEupyop8m3hjjDrO4kVBarCF8IvA-hM2DZ-AUMWuyjcRn4gmJAJMLNxiBM6wPG_Y4HUuYTHZMW_-f5Id0csjCqo9f4TkMDZj6-h3CR6F-Uis80A0HzKSI9jIedrnkS6VfiZw-ZP4T9Ef3AemSqRvhgLN8kcfRwGcB9zotrKDHfrJJQHT4tIEzE5P6JC0KUiObId9nIGAe2IPCed66DbkLF8V_iiQ-ttuQ8ID6gTdSAo7Fl2iV140EbATlNRYCRjrcfFq_Vz2rSNoFJg3r-iE0xh4kmnYTkf7C8NUWY56-r39PP4qRaNbAIEePOBqBa7NKbUem9BSJDa9HZEL3HZ8Xo91yCNOexm-T_MW7_0QgsMbzaZhe3A9KxQjd-Iebn6NIANYqYZ-XubKL_S2OkUCfx_DCCsKFUhsjl1RMF3e-QwX-W2NeTtfpv-7XEe-SgyJsL9eXwYhdZioETLe0McvPNTA3MaZJ068AvWEPsh3NIt5TFIRFhwekGI6wbFIP_amtRSczhGIVmjZblSgvg7XL1V42_vTERz3yHkZmcF7-_T0OV08fOfDQmmU1Q8jUq-v3psPhpORhhzRgNl3gkBeXatDOTKAn6MrGz-EMb44_LVFcqelPABM0Y2H8-h8Yz2d5gdykEyLw6fMF0RCQ8P_OG2AFJMCEqcupzezuf5Q-bzHCZ0yIfksCOX_cMFqVJ2IJTn1SKljgpQ29Bm0IH7zdKWUiu54fVkW9Ie8OpnF6EckCatioCWY3tuA0A0mK2eob4Rn7RnC_KynIOoWjc7SOAjHX42g0rjKqPNk4eQGa7n0QZ5q1tuP2qtvQOhX5ai0QVj_zsGfFYVHtJ8mpA8Mq4fR_BZZ5XdHJF2PpFSXxsvfnZKK_Dz_UMjhwqUtezlxy7aQTr_Bs0JzVMfjfx7Y7sbM_ZymFmcUIq9QzYViMudwpESUjAKTQU_Aw9xIrNxzp-ZZA7EztXIrP5XQSEE0PTVutubJqXDKvTbxSA00c0zs0IF8yVXqi3tsIqsEmM2RFHkDjxwf-U6-ac3rsYtqoTC5nnj6dojw71bgDpwiFBvsrlIDKSgPu2yRM-EXwQFkLOttS4YAWu89yoJDaThRsDOP93wWaAmH1QQc8-kbWfbgOo2pREMUQBNw8pCZEUDMDm1TzNjfpuI8cABMtMu6AjUoesp1sXaa5ZLAeNvuraJ0dDQAvi8.4lEJ1vUP4kQSy_8xka6HOQ"
-model = whisper.load_model("base")
-model.device
-
-def transcribe(audio) :
-
-    # load audio and pad/trim it to fit 30 seconds 
-    audio = whisper.load_audio(audio)
-    audio = whisper.pad_or_trim(audio)
-
-    # make log-Mel spectogram and move to the same device as the model
-    mel = whisper.log_mel_spectogram(audio).to(model.device)
-
-    # detect the spoken language 
-    _, probs = model.detect_language(mel)
-
-    # decode audio
-    options = whisper.DecodingOptions()
-    result = whisper.decode(model, mel, options)
-    result_text = result.text
-
-    # Pass the generated text to audio
-    chatgpt_api = ChatGPT(secret_token)
-    resp = chatgpt_api.send_message(result_text)
-    out_result = resp['message']
-
-    return [result_text, out_result]
-
-output_1 = gr.Textbox(label="Speech to Text")
-output_2 = gr.Textbox(label="ChatGPT Output")
-
-gr.Interface(
-    title = 'OpenAI Whisper and ChatGPT ASR Gradio Web UI',
-    fn=transcribe, 
-    inputs=[ 
-        gr.inputs.Audio(source="microphone", type ="filepath")
-    ], 
-
-    outputs= [ 
-        output_1, output_2
-    ],
-    live=True).launch()

spaces/CALM/Dashboard/streamlit_observable/frontend/src/react-app-env.d.ts

@@ -1 +0,0 @@
-/// <reference types="react-scripts" />

spaces/CVPR/LIVE/ptr.h

@@ -1,23 +0,0 @@
-#pragma once
-
-#include <cstddef>
-
-/**
- * Python doesn't have a pointer type, therefore we create a pointer wrapper
- * see https://stackoverflow.com/questions/48982143/returning-and-passing-around-raw-pod-pointers-arrays-with-python-c-and-pyb?rq=1
- */
-template <typename T>
-class ptr {
-public:
-    ptr() : p(nullptr) {}
-    ptr(T* p) : p(p) {}
-    ptr(std::size_t p) : p((T*)p) {}
-    ptr(const ptr& other) : ptr(other.p) {}
-    T* operator->() const { return p; }
-    T* get() const { return p; }
-    void destroy() { delete p; }
-    bool is_null() const { return p == nullptr; }
-    size_t as_size_t() const {return (size_t)p;}
-private:
-    T* p;
-};

spaces/CVPR/WALT/mmdet/core/bbox/assigners/assign_result.py

@@ -1,204 +0,0 @@
-import torch
-
-from mmdet.utils import util_mixins
-
-
-class AssignResult(util_mixins.NiceRepr):
-    """Stores assignments between predicted and truth boxes.
-
-    Attributes:
-        num_gts (int): the number of truth boxes considered when computing this
-            assignment
-
-        gt_inds (LongTensor): for each predicted box indicates the 1-based
-            index of the assigned truth box. 0 means unassigned and -1 means
-            ignore.
-
-        max_overlaps (FloatTensor): the iou between the predicted box and its
-            assigned truth box.
-
-        labels (None | LongTensor): If specified, for each predicted box
-            indicates the category label of the assigned truth box.
-
-    Example:
-        >>> # An assign result between 4 predicted boxes and 9 true boxes
-        >>> # where only two boxes were assigned.
-        >>> num_gts = 9
-        >>> max_overlaps = torch.LongTensor([0, .5, .9, 0])
-        >>> gt_inds = torch.LongTensor([-1, 1, 2, 0])
-        >>> labels = torch.LongTensor([0, 3, 4, 0])
-        >>> self = AssignResult(num_gts, gt_inds, max_overlaps, labels)
-        >>> print(str(self))  # xdoctest: +IGNORE_WANT
-        <AssignResult(num_gts=9, gt_inds.shape=(4,), max_overlaps.shape=(4,),
-                      labels.shape=(4,))>
-        >>> # Force addition of gt labels (when adding gt as proposals)
-        >>> new_labels = torch.LongTensor([3, 4, 5])
-        >>> self.add_gt_(new_labels)
-        >>> print(str(self))  # xdoctest: +IGNORE_WANT
-        <AssignResult(num_gts=9, gt_inds.shape=(7,), max_overlaps.shape=(7,),
-                      labels.shape=(7,))>
-    """
-
-    def __init__(self, num_gts, gt_inds, max_overlaps, labels=None):
-        self.num_gts = num_gts
-        self.gt_inds = gt_inds
-        self.max_overlaps = max_overlaps
-        self.labels = labels
-        # Interface for possible user-defined properties
-        self._extra_properties = {}
-
-    @property
-    def num_preds(self):
-        """int: the number of predictions in this assignment"""
-        return len(self.gt_inds)
-
-    def set_extra_property(self, key, value):
-        """Set user-defined new property."""
-        assert key not in self.info
-        self._extra_properties[key] = value
-
-    def get_extra_property(self, key):
-        """Get user-defined property."""
-        return self._extra_properties.get(key, None)
-
-    @property
-    def info(self):
-        """dict: a dictionary of info about the object"""
-        basic_info = {
-            'num_gts': self.num_gts,
-            'num_preds': self.num_preds,
-            'gt_inds': self.gt_inds,
-            'max_overlaps': self.max_overlaps,
-            'labels': self.labels,
-        }
-        basic_info.update(self._extra_properties)
-        return basic_info
-
-    def __nice__(self):
-        """str: a "nice" summary string describing this assign result"""
-        parts = []
-        parts.append(f'num_gts={self.num_gts!r}')
-        if self.gt_inds is None:
-            parts.append(f'gt_inds={self.gt_inds!r}')
-        else:
-            parts.append(f'gt_inds.shape={tuple(self.gt_inds.shape)!r}')
-        if self.max_overlaps is None:
-            parts.append(f'max_overlaps={self.max_overlaps!r}')
-        else:
-            parts.append('max_overlaps.shape='
-                         f'{tuple(self.max_overlaps.shape)!r}')
-        if self.labels is None:
-            parts.append(f'labels={self.labels!r}')
-        else:
-            parts.append(f'labels.shape={tuple(self.labels.shape)!r}')
-        return ', '.join(parts)
-
-    @classmethod
-    def random(cls, **kwargs):
-        """Create random AssignResult for tests or debugging.
-
-        Args:
-            num_preds: number of predicted boxes
-            num_gts: number of true boxes
-            p_ignore (float): probability of a predicted box assinged to an
-                ignored truth
-            p_assigned (float): probability of a predicted box not being
-                assigned
-            p_use_label (float | bool): with labels or not
-            rng (None | int | numpy.random.RandomState): seed or state
-
-        Returns:
-            :obj:`AssignResult`: Randomly generated assign results.
-
-        Example:
-            >>> from mmdet.core.bbox.assigners.assign_result import *  # NOQA
-            >>> self = AssignResult.random()
-            >>> print(self.info)
-        """
-        from mmdet.core.bbox import demodata
-        rng = demodata.ensure_rng(kwargs.get('rng', None))
-
-        num_gts = kwargs.get('num_gts', None)
-        num_preds = kwargs.get('num_preds', None)
-        p_ignore = kwargs.get('p_ignore', 0.3)
-        p_assigned = kwargs.get('p_assigned', 0.7)
-        p_use_label = kwargs.get('p_use_label', 0.5)
-        num_classes = kwargs.get('p_use_label', 3)
-
-        if num_gts is None:
-            num_gts = rng.randint(0, 8)
-        if num_preds is None:
-            num_preds = rng.randint(0, 16)
-
-        if num_gts == 0:
-            max_overlaps = torch.zeros(num_preds, dtype=torch.float32)
-            gt_inds = torch.zeros(num_preds, dtype=torch.int64)
-            if p_use_label is True or p_use_label < rng.rand():
-                labels = torch.zeros(num_preds, dtype=torch.int64)
-            else:
-                labels = None
-        else:
-            import numpy as np
-            # Create an overlap for each predicted box
-            max_overlaps = torch.from_numpy(rng.rand(num_preds))
-
-            # Construct gt_inds for each predicted box
-            is_assigned = torch.from_numpy(rng.rand(num_preds) < p_assigned)
-            # maximum number of assignments constraints
-            n_assigned = min(num_preds, min(num_gts, is_assigned.sum()))
-
-            assigned_idxs = np.where(is_assigned)[0]
-            rng.shuffle(assigned_idxs)
-            assigned_idxs = assigned_idxs[0:n_assigned]
-            assigned_idxs.sort()
-
-            is_assigned[:] = 0
-            is_assigned[assigned_idxs] = True
-
-            is_ignore = torch.from_numpy(
-                rng.rand(num_preds) < p_ignore) & is_assigned
-
-            gt_inds = torch.zeros(num_preds, dtype=torch.int64)
-
-            true_idxs = np.arange(num_gts)
-            rng.shuffle(true_idxs)
-            true_idxs = torch.from_numpy(true_idxs)
-            gt_inds[is_assigned] = true_idxs[:n_assigned]
-
-            gt_inds = torch.from_numpy(
-                rng.randint(1, num_gts + 1, size=num_preds))
-            gt_inds[is_ignore] = -1
-            gt_inds[~is_assigned] = 0
-            max_overlaps[~is_assigned] = 0
-
-            if p_use_label is True or p_use_label < rng.rand():
-                if num_classes == 0:
-                    labels = torch.zeros(num_preds, dtype=torch.int64)
-                else:
-                    labels = torch.from_numpy(
-                        # remind that we set FG labels to [0, num_class-1]
-                        # since mmdet v2.0
-                        # BG cat_id: num_class
-                        rng.randint(0, num_classes, size=num_preds))
-                    labels[~is_assigned] = 0
-            else:
-                labels = None
-
-        self = cls(num_gts, gt_inds, max_overlaps, labels)
-        return self
-
-    def add_gt_(self, gt_labels):
-        """Add ground truth as assigned results.
-
-        Args:
-            gt_labels (torch.Tensor): Labels of gt boxes
-        """
-        self_inds = torch.arange(
-            1, len(gt_labels) + 1, dtype=torch.long, device=gt_labels.device)
-        self.gt_inds = torch.cat([self_inds, self.gt_inds])
-
-        self.max_overlaps = torch.cat(
-            [self.max_overlaps.new_ones(len(gt_labels)), self.max_overlaps])
-
-        if self.labels is not None:
-            self.labels = torch.cat([gt_labels, self.labels])

spaces/CVPR/WALT/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py

@@ -1,215 +0,0 @@
-import mmcv
-import numpy as np
-import torch
-
-from ..builder import BBOX_CODERS
-from .base_bbox_coder import BaseBBoxCoder
-
-
-@BBOX_CODERS.register_module()
-class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder):
-    """Legacy Delta XYWH BBox coder used in MMDet V1.x.
-
-    Following the practice in R-CNN [1]_, this coder encodes bbox (x1, y1, x2,
-    y2) into delta (dx, dy, dw, dh) and decodes delta (dx, dy, dw, dh)
-    back to original bbox (x1, y1, x2, y2).
-
-    Note:
-        The main difference between :class`LegacyDeltaXYWHBBoxCoder` and
-        :class:`DeltaXYWHBBoxCoder` is whether ``+ 1`` is used during width and
-        height calculation. We suggest to only use this coder when testing with
-        MMDet V1.x models.
-
-    References:
-        .. [1] https://arxiv.org/abs/1311.2524
-
-    Args:
-        target_means (Sequence[float]): denormalizing means of target for
-            delta coordinates
-        target_stds (Sequence[float]): denormalizing standard deviation of
-            target for delta coordinates
-    """
-
-    def __init__(self,
-                 target_means=(0., 0., 0., 0.),
-                 target_stds=(1., 1., 1., 1.)):
-        super(BaseBBoxCoder, self).__init__()
-        self.means = target_means
-        self.stds = target_stds
-
-    def encode(self, bboxes, gt_bboxes):
-        """Get box regression transformation deltas that can be used to
-        transform the ``bboxes`` into the ``gt_bboxes``.
-
-        Args:
-            bboxes (torch.Tensor): source boxes, e.g., object proposals.
-            gt_bboxes (torch.Tensor): target of the transformation, e.g.,
-                ground-truth boxes.
-
-        Returns:
-            torch.Tensor: Box transformation deltas
-        """
-        assert bboxes.size(0) == gt_bboxes.size(0)
-        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
-        encoded_bboxes = legacy_bbox2delta(bboxes, gt_bboxes, self.means,
-                                           self.stds)
-        return encoded_bboxes
-
-    def decode(self,
-               bboxes,
-               pred_bboxes,
-               max_shape=None,
-               wh_ratio_clip=16 / 1000):
-        """Apply transformation `pred_bboxes` to `boxes`.
-
-        Args:
-            boxes (torch.Tensor): Basic boxes.
-            pred_bboxes (torch.Tensor): Encoded boxes with shape
-            max_shape (tuple[int], optional): Maximum shape of boxes.
-                Defaults to None.
-            wh_ratio_clip (float, optional): The allowed ratio between
-                width and height.
-
-        Returns:
-            torch.Tensor: Decoded boxes.
-        """
-        assert pred_bboxes.size(0) == bboxes.size(0)
-        decoded_bboxes = legacy_delta2bbox(bboxes, pred_bboxes, self.means,
-                                           self.stds, max_shape, wh_ratio_clip)
-
-        return decoded_bboxes
-
-
-@mmcv.jit(coderize=True)
-def legacy_bbox2delta(proposals,
-                      gt,
-                      means=(0., 0., 0., 0.),
-                      stds=(1., 1., 1., 1.)):
-    """Compute deltas of proposals w.r.t. gt in the MMDet V1.x manner.
-
-    We usually compute the deltas of x, y, w, h of proposals w.r.t ground
-    truth bboxes to get regression target.
-    This is the inverse function of `delta2bbox()`
-
-    Args:
-        proposals (Tensor): Boxes to be transformed, shape (N, ..., 4)
-        gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4)
-        means (Sequence[float]): Denormalizing means for delta coordinates
-        stds (Sequence[float]): Denormalizing standard deviation for delta
-            coordinates
-
-    Returns:
-        Tensor: deltas with shape (N, 4), where columns represent dx, dy,
-            dw, dh.
-    """
-    assert proposals.size() == gt.size()
-
-    proposals = proposals.float()
-    gt = gt.float()
-    px = (proposals[..., 0] + proposals[..., 2]) * 0.5
-    py = (proposals[..., 1] + proposals[..., 3]) * 0.5
-    pw = proposals[..., 2] - proposals[..., 0] + 1.0
-    ph = proposals[..., 3] - proposals[..., 1] + 1.0
-
-    gx = (gt[..., 0] + gt[..., 2]) * 0.5
-    gy = (gt[..., 1] + gt[..., 3]) * 0.5
-    gw = gt[..., 2] - gt[..., 0] + 1.0
-    gh = gt[..., 3] - gt[..., 1] + 1.0
-
-    dx = (gx - px) / pw
-    dy = (gy - py) / ph
-    dw = torch.log(gw / pw)
-    dh = torch.log(gh / ph)
-    deltas = torch.stack([dx, dy, dw, dh], dim=-1)
-
-    means = deltas.new_tensor(means).unsqueeze(0)
-    stds = deltas.new_tensor(stds).unsqueeze(0)
-    deltas = deltas.sub_(means).div_(stds)
-
-    return deltas
-
-
-@mmcv.jit(coderize=True)
-def legacy_delta2bbox(rois,
-                      deltas,
-                      means=(0., 0., 0., 0.),
-                      stds=(1., 1., 1., 1.),
-                      max_shape=None,
-                      wh_ratio_clip=16 / 1000):
-    """Apply deltas to shift/scale base boxes in the MMDet V1.x manner.
-
-    Typically the rois are anchor or proposed bounding boxes and the deltas are
-    network outputs used to shift/scale those boxes.
-    This is the inverse function of `bbox2delta()`
-
-    Args:
-        rois (Tensor): Boxes to be transformed. Has shape (N, 4)
-        deltas (Tensor): Encoded offsets with respect to each roi.
-            Has shape (N, 4 * num_classes). Note N = num_anchors * W * H when
-            rois is a grid of anchors. Offset encoding follows [1]_.
-        means (Sequence[float]): Denormalizing means for delta coordinates
-        stds (Sequence[float]): Denormalizing standard deviation for delta
-            coordinates
-        max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W)
-        wh_ratio_clip (float): Maximum aspect ratio for boxes.
-
-    Returns:
-        Tensor: Boxes with shape (N, 4), where columns represent
-            tl_x, tl_y, br_x, br_y.
-
-    References:
-        .. [1] https://arxiv.org/abs/1311.2524
-
-    Example:
-        >>> rois = torch.Tensor([[ 0.,  0.,  1.,  1.],
-        >>>                      [ 0.,  0.,  1.,  1.],
-        >>>                      [ 0.,  0.,  1.,  1.],
-        >>>                      [ 5.,  5.,  5.,  5.]])
-        >>> deltas = torch.Tensor([[  0.,   0.,   0.,   0.],
-        >>>                        [  1.,   1.,   1.,   1.],
-        >>>                        [  0.,   0.,   2.,  -1.],
-        >>>                        [ 0.7, -1.9, -0.5,  0.3]])
-        >>> legacy_delta2bbox(rois, deltas, max_shape=(32, 32))
-        tensor([[0.0000, 0.0000, 1.5000, 1.5000],
-                [0.0000, 0.0000, 5.2183, 5.2183],
-                [0.0000, 0.1321, 7.8891, 0.8679],
-                [5.3967, 2.4251, 6.0033, 3.7749]])
-    """
-    means = deltas.new_tensor(means).repeat(1, deltas.size(1) // 4)
-    stds = deltas.new_tensor(stds).repeat(1, deltas.size(1) // 4)
-    denorm_deltas = deltas * stds + means
-    dx = denorm_deltas[:, 0::4]
-    dy = denorm_deltas[:, 1::4]
-    dw = denorm_deltas[:, 2::4]
-    dh = denorm_deltas[:, 3::4]
-    max_ratio = np.abs(np.log(wh_ratio_clip))
-    dw = dw.clamp(min=-max_ratio, max=max_ratio)
-    dh = dh.clamp(min=-max_ratio, max=max_ratio)
-    # Compute center of each roi
-    px = ((rois[:, 0] + rois[:, 2]) * 0.5).unsqueeze(1).expand_as(dx)
-    py = ((rois[:, 1] + rois[:, 3]) * 0.5).unsqueeze(1).expand_as(dy)
-    # Compute width/height of each roi
-    pw = (rois[:, 2] - rois[:, 0] + 1.0).unsqueeze(1).expand_as(dw)
-    ph = (rois[:, 3] - rois[:, 1] + 1.0).unsqueeze(1).expand_as(dh)
-    # Use exp(network energy) to enlarge/shrink each roi
-    gw = pw * dw.exp()
-    gh = ph * dh.exp()
-    # Use network energy to shift the center of each roi
-    gx = px + pw * dx
-    gy = py + ph * dy
-    # Convert center-xy/width/height to top-left, bottom-right
-
-    # The true legacy box coder should +- 0.5 here.
-    # However, current implementation improves the performance when testing
-    # the models trained in MMDetection 1.X (~0.5 bbox AP, 0.2 mask AP)
-    x1 = gx - gw * 0.5
-    y1 = gy - gh * 0.5
-    x2 = gx + gw * 0.5
-    y2 = gy + gh * 0.5
-    if max_shape is not None:
-        x1 = x1.clamp(min=0, max=max_shape[1] - 1)
-        y1 = y1.clamp(min=0, max=max_shape[0] - 1)
-        x2 = x2.clamp(min=0, max=max_shape[1] - 1)
-        y2 = y2.clamp(min=0, max=max_shape[0] - 1)
-    bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view_as(deltas)
-    return bboxes

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

@@ -1,7 +0,0 @@
-from .builder import build_match_cost
-from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost
-
-__all__ = [
-    'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
-    'FocalLossCost'
-]

spaces/CVPR/drawings-to-human/frontend/svelte.config.js

@@ -1,32 +0,0 @@
-import adapter from '@sveltejs/adapter-static';
-import preprocess from 'svelte-preprocess';
-
-const dev = process.env.NODE_ENV === 'development';
-
-console.log('dev', dev);
-/** @type {import('@sveltejs/kit').Config} */
-const config = {
-	// Consult https://github.com/sveltejs/svelte-preprocess
-	// for more information about preprocessors
-	preprocess: preprocess({
-		postcss: true
-	}),
-
-	kit: {
-		paths: {
-			base: '/static'
-		},
-		adapter: adapter({
-			pages: 'build',
-			assets: 'build',
-			fallback: null,
-			precompress: false
-		}),
-
-		prerender: {
-			default: true
-		}
-	}
-};
-
-export default config;

spaces/CVPR/regionclip-demo/detectron2/layers/csrc/README.md

@@ -1,7 +0,0 @@
-
-
-To add a new Op:
-
-1. Create a new directory
-2. Implement new ops there
-3. Delcare its Python interface in `vision.cpp`.

spaces/CikeyQI/Yunzai/Yunzai/plugins/other/setPubCk.js

@@ -1,167 +0,0 @@
-import plugin from '../../lib/plugins/plugin.js'
-import GsCfg from '../genshin/model/gsCfg.js'
-import fs from 'node:fs'
-import lodash from 'lodash'
-import fetch from 'node-fetch'
-import YAML from 'yaml'
-import MysInfo from '../genshin/model/mys/mysInfo.js'
-import common from '../../lib/common/common.js'
-
-export class setPubCk extends plugin {
-  constructor (e) {
-    super({
-      name: '配置',
-      dsc: '#配置ck',
-      event: 'message',
-      priority: 700,
-      rule: [
-        {
-          reg: '^#配置(ck|cookie)$|^#*配置公共查询ck$',
-          fnc: 'setPubCk',
-          permission: 'master'
-        },
-        {
-          reg: '^#使用(全部|用户)ck$',
-          fnc: 'setUserCk',
-          permission: 'master'
-        }
-      ]
-    })
-
-    this.file = './plugins/genshin/config/mys.pubCk.yaml'
-  }
-
-  /** 配置公共ck */
-  async setPubCk () {
-    /** 设置上下文，后续接收到内容会执行doRep方法 */
-    this.setContext('pubCk')
-    /** 回复 */
-    await this.reply('请发送米游社cookie......\n配置后该ck将会加入公共查询池')
-  }
-
-  async pubCk () {
-    let msg = this.e.msg
-
-    if (!(/(ltoken|ltoken_v2)/.test(this.e.msg) && /(ltuid|ltmid_v2|account_mid_v2)/.test(this.e.msg))) {
-      this.e.reply('cookie错误，请发送正确的cookie')
-      return true
-    }
-
-    this.finish('pubCk')
-
-    let ck = msg.replace(/#|'|"/g, '')
-    let param = {}
-    ck.split(';').forEach((v) => {
-      // cookie_token_v2,ltoken_v2值也可能有=
-      // let tmp = lodash.trim(v).split('=')
-      let tmp = lodash.trim(v);
-      let index = tmp.indexOf("=");
-      param[tmp.slice(0,index)] = tmp.slice(index+1);
-    })
-
-    this.ck = ''
-    lodash.forEach(param, (v, k) => {
-      if (['ltoken', 'ltuid', 'cookie_token', 'account_id', 'cookie_token_v2', 'account_mid_v2', 'ltmid_v2', 'ltoken_v2'].includes(k)) {
-        this.ck += `${k}=${v};`
-      }
-    })
-
-    /** 检查ck是否失效 */
-    if (!await this.checkCk()) {
-      logger.mark(`配置公共cookie错误：${this.checkMsg || 'cookie错误'}`)
-      await this.e.reply(`配置公共cookie错误：${this.checkMsg || 'cookie错误'}`)
-      return
-    }
-
-    this.ltuid = param.ltuid
-    // 判断是否是v2版ck
-    if (param.cookie_token_v2 && (param.account_mid_v2 || param.ltoken_v2) && !(/(\d{4,9})/g).test(this.ltuid)) {
-      // 获取米游社通行证id
-      let userFullInfo = await this.getUserInfo()
-      if (userFullInfo?.data?.user_info) {
-        let userInfo = userFullInfo?.data?.user_info
-        this.ltuid = userInfo.uid
-        this.ck = `${this.ck}ltuid=${this.ltuid};`
-      } else {
-        logger.mark(`配置公共cookie错误：${userFullInfo.message || 'cookie错误'}`)
-        await this.e.reply(`配置公共cookie错误：${userFullInfo.message || 'cookie错误'}`)
-        return
-      }
-    }
-
-    let ckArr = GsCfg.getConfig('mys', 'pubCk') || []
-
-    /** 判断是否重复 */
-    for (let ck of ckArr) {
-      if (ck.includes(this.ltuid)) {
-        await this.e.reply('配置公共cookie错误：该ck已配置')
-        return
-      }
-    }
-
-    ckArr.push(this.ck)
-    this.save(ckArr)
-    GsCfg.change_myspubCk()
-
-    await this.e.reply(`配置公共ck成功：第${ckArr.length}个`)
-  }
-
-  /** 检查ck是否可用 */
-  async checkCk () {
-    let url = 'https://api-takumi.mihoyo.com/binding/api/getUserGameRolesByCookie?game_biz=hk4e_cn'
-    let res = await fetch(url, { method: 'get', headers: { Cookie: this.ck } })
-    if (!res.ok) return false
-    res = await res.json()
-    if (res.retcode != 0) {
-      this.checkMsg = res.message
-      return false
-    }
-
-    return true
-  }
-
-  // 获取米游社通行证id
-  async getUserInfo (server = 'mys') {
-    try {
-      const that = this
-      let url = {
-        mys: 'https://bbs-api.mihoyo.com/user/wapi/getUserFullInfo?gids=2',
-        hoyolab: ''
-      }
-      let res = await fetch(url[server], {
-        method: 'get',
-        headers: {
-          Cookie: that.ck,
-          Accept: 'application/json, text/plain, */*',
-          Connection: 'keep-alive',
-          Host: 'bbs-api.mihoyo.com',
-          Origin: 'https://m.bbs.mihoyo.com',
-          Referer: ' https://m.bbs.mihoyo.com/'
-        }
-      })
-      if (!res.ok) return res
-      res = await res.json()
-      return res
-    } catch (e) {
-      return null
-    }
-  }
-
-  save (data) {
-    data = YAML.stringify(data)
-    fs.writeFileSync(this.file, data)
-  }
-
-  async setUserCk () {
-    let set = './plugins/genshin/config/mys.set.yaml'
-
-    let config = fs.readFileSync(set, 'utf8')
-    config = config.replace(/allowUseCookie: [0-1]/g, 'allowUseCookie: 1')
-    fs.writeFileSync(set, config, 'utf8')
-
-    await common.sleep(500)
-    await MysInfo.initCache(true)
-
-    await this.reply('开启成功，用户ck已加入公共查询ck池')
-  }
-}

spaces/Cloudyy/bark-voice-cloning/README.md

@@ -1,16 +0,0 @@
----
-title: Bark Voice Cloning
-emoji: 🐶
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.29.0
-python_version: 3.10.11
-app_file: app.py
-models:
-- facebook/hubert-base-ls960
-- GitMylo/bark-voice-cloning
-pinned: false
-license: mit
-duplicated_from: GitMylo/bark-voice-cloning
----

spaces/CofAI/chat.v1/temp.py

@@ -1,4 +0,0 @@
-import pandas as pd
-
-pd = pd.DataFrame({'address':[], 'car_num': [], 'lat': [], 'long': [], 'time': [], 'date': []})
-pd.to_csv('data.csv', index=False)

spaces/CosmoAI/ChitChat/app.py

@@ -1,110 +0,0 @@
-import gradio
-from transformers import pipeline
-
-# Initialize the Hugging Face model
-model = pipeline(model='google/flan-t5-base')
-
-
-# Define the chatbot function
-def chatbot(input_text):
-  # Generate a response from the Hugging Face model
-  response = model(input_text, max_length=250, do_sample=True)[0]['generated_text'].strip()
-  
-  # Return the bot response
-  return response
-
-# Define the Gradio interface
-gradio_interface = gradio.Interface(
-    fn=chatbot,
-    inputs='text',
-    outputs='text',
-    title='Chatbot',
-    description='A weird chatbot conversations experience.',
-    examples=[
-        ['Hi, how are you?']
-    ]
-)
-
-# Launch the Gradio interface
-gradio_interface.launch()
-
-
-
-
-
-# from dotenv import load_dotenv
-# from langchain import HuggingFaceHub, LLMChain
-# from langchain import PromptTemplates
-# import gradio
-
-# load_dotenv()
-# os.getenv('HF_API')
-
-# hub_llm = HuggingFaceHub(repo_id='facebook/blenderbot-400M-distill')
-
-# prompt = prompt_templates(
-#     input_variable = ["question"],
-#     template = "Answer is: {question}"
-# )
-
-# hub_chain = LLMChain(prompt=prompt, llm=hub_llm, verbose=True)
-
-
-
-
-
-# Sample code for AI language model interaction
-# from transformers import GPT2Tokenizer, GPT2LMHeadModel
-# import gradio
-
-
-# def simptok(data):
-#     # Load pre-trained model and tokenizer (using the transformers library)
-#     model_name = "gpt2"
-#     tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-#     model = GPT2LMHeadModel.from_pretrained(model_name)
-    
-#     # User input
-#     user_input = data
-    
-#     # Tokenize input
-#     input_ids = tokenizer.encode(user_input, return_tensors="pt")
-    
-#     # Generate response
-#     output = model.generate(input_ids, max_length=50, num_return_sequences=1)
-#     response = tokenizer.decode(output[0], skip_special_tokens=True)
-#     return response
-
-
-# def responsenew(data):
-#     return simptok(data)
-
-
-# from hugchat import hugchat
-# import gradio as gr
-# import time
-
-# # Create a chatbot connection
-# chatbot = hugchat.ChatBot(cookie_path="cookies.json")
-
-# # New a conversation (ignore error)
-# id = chatbot.new_conversation()
-# chatbot.change_conversation(id)
-
-
-# def get_answer(data):
-#     return chatbot.chat(data)
-
-# gradio_interface = gr.Interface(
-#   fn = get_answer,
-#   inputs = "text",
-#   outputs = "text"
-# )
-# gradio_interface.launch()
-
-# gradio_interface = gradio.Interface(
-#   fn = responsenew,
-#   inputs = "text",
-#   outputs = "text"
-# )
-# gradio_interface.launch()

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/cldm/ddim_hacked.py

@@ -1,317 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-
-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.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-
-    def register_buffer(self, name, attr):
-        # Do not force attr to CUDA device by default.  It may not exist.
-        #if type(attr) == torch.Tensor:
-        #    if attr.device != torch.device("cuda"):
-        #        attr = attr.to(torch.device("cuda"))
-        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, ...
-               dynamic_threshold=None,
-               ucg_schedule=None,
-               **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}")
-
-            elif isinstance(conditioning, list):
-                for ctmp in conditioning:
-                    if ctmp.shape[0] != 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,
-                                                    dynamic_threshold=dynamic_threshold,
-                                                    ucg_schedule=ucg_schedule
-                                                    )
-        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, dynamic_threshold=None,
-                      ucg_schedule=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]
-        print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
-
-        for i, step in enumerate(iterator):
-            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
-
-            if ucg_schedule is not None:
-                assert len(ucg_schedule) == len(time_range)
-                unconditional_guidance_scale = ucg_schedule[i]
-
-            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,
-                                      dynamic_threshold=dynamic_threshold)
-            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,
-                      dynamic_threshold=None):
-        b, *_, device = *x.shape, x.device
-
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            model_output = self.model.apply_model(x, t, c)
-        else:
-            model_t = self.model.apply_model(x, t, c)
-            model_uncond = self.model.apply_model(x, t, unconditional_conditioning)
-            model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
-
-        if self.model.parameterization == "v":
-            e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
-        else:
-            e_t = model_output
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps", 'not implemented'
-            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
-        if self.model.parameterization != "v":
-            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        else:
-            pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
-
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-
-        if dynamic_threshold is not None:
-            raise NotImplementedError()
-
-        # 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 encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
-               unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
-        num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
-
-        assert t_enc <= num_reference_steps
-        num_steps = t_enc
-
-        if use_original_steps:
-            alphas_next = self.alphas_cumprod[:num_steps]
-            alphas = self.alphas_cumprod_prev[:num_steps]
-        else:
-            alphas_next = self.ddim_alphas[:num_steps]
-            alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
-
-        x_next = x0
-        intermediates = []
-        inter_steps = []
-        for i in tqdm(range(num_steps), desc='Encoding Image'):
-            t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
-            if unconditional_guidance_scale == 1.:
-                noise_pred = self.model.apply_model(x_next, t, c)
-            else:
-                assert unconditional_conditioning is not None
-                e_t_uncond, noise_pred = torch.chunk(
-                    self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
-                                           torch.cat((unconditional_conditioning, c))), 2)
-                noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
-
-            xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
-            weighted_noise_pred = alphas_next[i].sqrt() * (
-                    (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
-            x_next = xt_weighted + weighted_noise_pred
-            if return_intermediates and i % (
-                    num_steps // return_intermediates) == 0 and i < num_steps - 1:
-                intermediates.append(x_next)
-                inter_steps.append(i)
-            elif return_intermediates and i >= num_steps - 2:
-                intermediates.append(x_next)
-                inter_steps.append(i)
-            if callback: callback(i)
-
-        out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
-        if return_intermediates:
-            out.update({'intermediates': intermediates})
-        return x_next, out
-
-    @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, callback=None):
-
-        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(iterator):
-            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)
-            if callback: callback(i)
-        return x_dec