Update parquet files (step 5 of 296)

spaces/1doemePnordwo/upscale/README.md

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----
-title: UPSCALE
-emoji: 📷
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: cvsys/upscale
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Android TV 12 ISO Everything You Need to Know Before You Download.md

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-<p>Android TV is a smart TV platform that runs on the Android operating system. It allows you to access a variety of apps, games, and streaming services on your TV. Android TV also supports Google Assistant, Chromecast, and other Google features.</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Brawlhalla for Mac How to Download and Play the Free-to-Play Platform Fighter.md

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-<p>If you are looking for a fun and exciting fighting game that you can play on your Mac, you might want to check out Brawlhalla. Brawlhalla is a free 2D platform fighting game that supports up to 8 players online or local, with full cross-play for PC, PS5, PS4, Xbox Series X|S, Xbox One, Nintendo Switch, iOS, and Android. In this article, we will tell you what Brawlhalla is, how to download it on your Mac, and some tips and tricks to improve your gameplay.</p>
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-<p>Brawlhalla is a game developed by Blue Mammoth Games and published by Ubisoft. It was released in 2017 and has since gained a huge fan base of over 100 million players. Brawlhalla is inspired by the likes of Super Smash Bros. and features cartoonish graphics and simple controls. Here are some of the main features of Brawlhalla:</p>
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-<p>Brawlhalla is a game that requires skill, strategy, and practice to master. Here are some tips and tricks that can help you improve your gameplay and have more fun in Brawlhalla:</p>
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-<p>Movement is one of the most important aspects of Brawlhalla, as it determines how you position yourself, attack, defend, and survive. You should learn how to use your jumps, dashes, fast falls, wall jumps, gravity cancels, chase dodges, and recovery moves effectively. You should also learn how to dodge your opponent's attacks and punish them accordingly. You can use different types of dodges like spot dodge, directional dodge, speed dodge, and chain dodge depending on the situation.</p>
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- <h2>Conclusion</h2>
-<p>Brawlhalla is a fun and exciting fighting game that you can play on your Mac for free. You can download it through Steam or use other methods if you prefer. You can also improve your gameplay by following some tips and tricks that we have shared in this article. We hope you enjoy playing Brawlhalla on your Mac and have a blast with your friends online or offline.</p>
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-<p>Yes, Brawlhalla is free to play and does not have any pay-to-win elements. You can unlock all the characters by playing the game or buying them with in-game currency. You can also buy cosmetic items with real money or another in-game currency.</p>
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-<p>Pachisi was modified by different cultures and regions over time, giving rise to various versions of the game. Some of these versions are Chaupar, Chausar, Chopad, Chatush Pada, <h2>Rules of Ludo Game</h2>
-<p>Ludo is a game that can be played by two to four players, without partnerships. The objective of the game is to race your four tokens from start to finish according to the rolls of a single die. The game has some basic rules that you need to follow in order to play it properly. Here are the rules of ludo game:</p>
-<ul>
-<li>Each player chooses a colour and places their four tokens in their home base, which is one of the large corner areas of the board in their colour.</li>
-<li>The players take turns rolling the die and moving their tokens. The player who rolls the highest number goes first, and then the turn passes clockwise.</li>
-<li>To enter a token into play from its home base, a player must roll a 6. If the player has no tokens in play and does not roll a 6, the turn passes to the next player.</li>
-<li>Once a player has one or more tokens in play, they can move any token the number of squares indicated by the die. A token can only move forward along the main track, which is the path of squares not part of any player's home column.</li>
-<li>If a player rolls a 6, they get another turn. They can either move the same token or a different token. A player can roll up to three consecutive 6s in a row. If they roll a fourth 6, they must return one of their tokens to their home base and lose their turn.</li>
-<li>A player can capture an opponent's token by landing on the same square as it, unless the square is part of the opponent's home column or is marked with a star. The captured token is returned to its home base and must start over.</li>
-<li>A player cannot land on a square that already has one of their own tokens, unless they are moving along their home column.</li>
-<li>A player can form a block by placing two or more of their tokens on the same square. A block cannot be captured or passed by any opponent's token.</li>
-<li>To move a token into its finishing square, which is at the centre of the board, a player must roll the exact number required to end on that square. The finishing square can only hold one token of each colour.</li>
-<li>The first player to move all four of their tokens into their finishing square wins the game.</li>
-</ul>
- <h2>Benefits of Playing Ludo Game</h2>
-<p>Ludo is not only a fun and exciting board game, but also a beneficial one for your health and well-being. Playing ludo can improve your brain function, give you pleasure and relieve stress, lower your blood pressure and boost your immunity, and more. Here are some of the benefits of playing ludo game:</p>
-<ul>
-<li>Ludo improves brain function and cognitive skills. Ludo is a game that requires strategy, tactics, counting, probability, and decision making. These skills help stimulate your brain cells and enhance your mental abilities. Playing ludo can also improve your memory, concentration, and problem-solving skills.</li>
-<li>Ludo gives pleasure and relieves stress. Ludo is a game that brings joy and laughter to you and your friends or family. It is a great way to have fun and relax after a long day. Playing ludo can also release endorphins, which are natural chemicals that make you feel happy and reduce pain. Ludo can also help you cope with anxiety, depression, and loneliness.</li>
-<li>Ludo lowers blood pressure and boosts immunity. Ludo is a game that reduces stress and tension, which are major causes of high blood pressure and heart problems. Playing ludo can also lower your cortisol levels, which are hormones that weaken your immune system and make you more prone to infections and diseases. Ludo can also increase your blood circulation and oxygen supply to your organs.</li>
-</ul> <h2>Tips and Tricks for Playing Ludo Game</h2>
-<p>Ludo is a game that requires both luck and skill. You need to roll the dice well, but you also need to use your brain to make the best moves. Here are some tips and tricks for playing ludo game that can help you win more often:</p>
-<ul>
-<li>Strategize your moves: One of the most important tips to win a game of ludo is to think ahead of the opponent. You can do this by predicting their next moves and preventing their tokens from landing on yours. This step is important as not predicting or analyzing the opponent’s playstyle might end you going back to the home base.</li>
-<li>Play with all your tokens: Do not focus on only one token and neglect the others. Try to move all your tokens out of your base as soon as possible and spread them across the board. This way, you can have more options to choose from and avoid being blocked or captured by the opponent.</li>
-<li>Park the token at the start and capture: A smart trick to play ludo is to park one of your tokens at the start square of your home column and wait for an opportunity to capture an opponent’s token. This way, you can secure your position and also earn an extra turn.</li>
-<li>Utilize the safe boxes: The safe boxes are the squares marked with a star on the board. They are located at the corners of each arm and at the centre of the board. These boxes are safe from being captured by any opponent, so use them wisely to protect your tokens or to plan your next move.</li>
-<li>Know your opponent: Another tip to win ludo is to observe and learn from your opponent’s behaviour and patterns. You can notice their strengths and weaknesses, their preferences and tendencies, their habits and mistakes. By knowing your opponent, you can anticipate their actions and counter them effectively.</li>
-<li>Dice value division: A clever trick to play ludo is to divide the value of the dice by two and use it for two different tokens. For example, if you roll a 6, you can move one token 3 squares and another token 3 squares. This way, you can optimize your moves and cover more ground.</li>
-<li>Rule of 7: A simple rule to remember when playing ludo is that the sum of the opposite sides of a die is always 7. For example, if you roll a 1, the opposite side will be a 6. If you roll a 2, the opposite side will be a 5. This rule can help you predict what number you might get next and plan accordingly.</li>
-<li>Remember your tokens’ positions: A common mistake that players make in ludo is forgetting where their tokens are on the board. This can lead to missing opportunities or making blunders. To avoid this, try to keep track of your tokens’ positions and movements in your mind or on a paper.</li>
-<li>Home > Kill: A golden rule to follow when playing ludo is to always prioritize killing an opponent’s token over moving your own token closer to home. This way, you can eliminate their chances of winning and also gain an extra turn.</li>
-<li>Hone your skills: The best way to improve your ludo game is to practice regularly and learn from your experiences. You can play with different opponents, try different strategies, and experiment with different settings. The more you play, the more you will learn and master the game.</li>
-<li>Choose your format based on risk: Zupee offers four different formats of ludo games: Ludo Turbo, Ludo Supreme, Ludo Ninja, and Ludo Classic. Each format has its own rules, time limit, scoring system, and prize pool. Depending on your risk appetite and skill level, you can choose the format that suits you best.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Ludo is a game that has been enjoyed by millions of people for centuries. It is a game that combines luck and skill, fun and challenge, joy and laughter. It is a game that can be played by anyone, anywhere, anytime.</p>
-<p>Naija ludo pro apk is a game that takes ludo to the next level. It is a game that offers more features, more options, more boards, more levels, more fun. It is a game that lets you play with your friends or family online or offline.</p>
-<p>If you are looking for a professional board game that is made for professionals, then naija ludo pro apk is the game for you. Download it today from <a href="(^1^)">APKCombo</a> or <a href=" ">Google Play Store</a> and enjoy the game of ludo like never before.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about naija ludo pro apk:</p>
-<ol>
-<li><strong>Where can I download naija ludo pro apk?</strong></li>
-<p>You can download naija ludo pro apk from <a href="">APKCombo</a> or <a href="">Google Play Store</a>. These are the official and trusted sources for downloading the game. You can also scan the QR code on the game's website to download it directly to your device.</p>
-<li><strong>Is naija ludo pro apk safe and secure?</strong></li>
-<p>Yes, naija ludo pro apk is safe and secure to use. It does not contain any viruses, malware, or spyware that can harm your device or data. It also does not require any unnecessary permissions or access to your personal information. It is a game that respects your privacy and security.</p>
-<li><strong>Can I play naija ludo pro apk online with other players?</strong></li>
-<p>Yes, you can play naija ludo pro apk online with other players from around the world. You can either join a random match or create a private room and invite your friends or family to join. You can also chat with your opponents and send them emojis during the game.</p>
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-<p>Yes, you can customize the board and pieces in naija ludo pro apk according to your preference. You can choose among three different boards: classic, modern, and Nigerian. You can also choose among four different sets of pieces: standard, premium, deluxe, and royal. You can also change the colour of your pieces if you want.</p>
-<li><strong>What are the differences between naija ludo pro apk and other ludo games?</strong></li>
-<p>Naija ludo pro apk is a game that has many differences from other ludo games. Some of these differences are:</p>
-<ul>
-<li>Naija ludo pro apk has more features and options than other ludo games. You can control the speed, difficulty, dice, capture, play again, barrier, and safe-house options in the game.</li>
-<li>Naija ludo pro apk has more boards and pieces than other ludo games. You can choose among three colourful boards and four sets of pieces in the game.</li>
-<li>Naija ludo pro apk has a visual hand feature that lets you see your opponent's hand and plan your moves accordingly.</li>
-<li>Naija ludo pro apk has a Nigerian theme that gives it a unique flavour and style.</li>
-</ul></p> 197e85843d<br />
-<br />
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spaces/1phancelerku/anime-remove-background/Download Treasure of Montezuma 4 and Experience the Ultimate Match-3 Adventure.md

@@ -1,122 +0,0 @@
-
-<h1>How to Download Treasure of Montezuma 4 and Enjoy a Thrilling Puzzle Adventure</h1>
-<p>If you are looking for a new and exciting puzzle game to play, you should definitely check out Treasure of Montezuma 4. This is the fourth installment of the popular series that has captivated millions of players around the world. In this article, we will tell you what Treasure of Montezuma 4 is, why you should download it, and how to do it from different platforms. Read on and get ready to embark on an amazing journey through the ancient Aztec civilization.</p>
-<h2>download treasure of montezuma 4</h2><br /><p><b><b>Download</b> &mdash; <a href="https://jinyurl.com/2uNMkr">https://jinyurl.com/2uNMkr</a></b></p><br /><br />
- <h2>What is Treasure of Montezuma 4?</h2>
-<p>Treasure of Montezuma 4 is a tile-matching puzzle game that combines elements of adventure, mystery, and magic. You play as Anna, an archaeologist who travels to an Aztec ruin to uncover an ancient secret. Along the way, you will encounter various challenges and surprises that will keep you hooked for hours.</p>
-<h3>A brief introduction to the game and its features</h3>
-<p>The game has three modes: Story Mode, Quest Mode, and Puzzle Mode. In Story Mode, you will follow Anna's story as she explores the ruin and faces an epic boss battle. In Quest Mode, you will complete different tasks and earn rewards. In Puzzle Mode, you will solve tricky puzzles with limited moves.</p>
-<p>The game also has 98 levels in Story Mode and 69 levels in Quest Mode, each with different goals and obstacles. You will need to match three or more tiles of the same color to clear them from the board and create powerful combos. You will also collect crystals and coins that you can use to upgrade your character and build your own Ziggurat.</p>
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-<p>Moreover, the game features seven powerful totems and eight unique bonuses that will help you in your quest. The totems are ancient gods that have special abilities, such as creating explosions, swapping tiles, or freezing time. The bonuses are items that you can activate during the game, such as hammers, bombs, or lightning bolts.</p>
-<p>The game also has stunning graphics and sound effects that create an immersive atmosphere. You will enjoy the colorful animations, the realistic backgrounds, and the authentic music. You will also learn interesting facts about the Aztec culture and history as you play.</p>
- <h2>Why should you download Treasure of Montezuma 4?</h2>
-<p>Treasure of Montezuma 4 is not just another puzzle game. It is a game that offers many benefits for players of all ages and preferences. Here are some of them:</p>
-<h3>The benefits of playing the game, such as fun, challenge, and learning</h3>
-<h4>Fun: How the game offers a variety of modes, levels, and special effects</h4>
-<p>One of the main reasons to download Treasure of Montezuma 4 is that it is fun. The game offers a variety of modes, levels, and special effects that make it entertaining and engaging. You will never get bored with this game because there is always something new and exciting to discover. Whether you want to follow Anna's story, complete quests, or solve puzzles , you will find something that suits your mood and taste.</p>
-<h4>Challenge: How the game tests your skills, strategy, and speed</h4>
-<p>Another reason to download Treasure of Montezuma 4 is that it is challenging. The game tests your skills, strategy, and speed in different ways. You will need to think fast and act faster to clear the board and achieve the goals. You will also need to plan ahead and use the totems and bonuses wisely to overcome the obstacles. The game has different difficulty levels, from easy to hard, so you can adjust the challenge according to your preference and ability.</p>
-<h4>Learning: How the game teaches you about the Aztec culture and history</h4>
-<p>A third reason to download Treasure of Montezuma 4 is that it is educational. The game teaches you about the Aztec culture and history in a fun and interactive way. You will learn about the Aztec gods, symbols, rituals, and architecture as you play. You will also discover the secrets of the Ziggurat, a massive pyramid that was built by the Aztecs to honor their gods. The game has a built-in encyclopedia that provides more information and facts about the topics covered in the game.</p>
- <h2>How to download Treasure of Montezuma 4?</h2>
-<p>Now that you know what Treasure of Montezuma 4 is and why you should download it, you might be wondering how to do it. Well, the good news is that downloading Treasure of Montezuma 4 is easy and convenient. You can download the game from different platforms, such as Steam, PlayStation, and GameHouse. Here are the steps to do it from each platform:</p>
-<h3>The steps to download the game from different platforms, such as Steam, PlayStation, and GameHouse</h3>
-<h4>Steam: How to buy the game for a discounted price and install it on your PC</h4>
-<p>If you want to download Treasure of Montezuma 4 from Steam, you will need to have a Steam account and a compatible PC. You can create a Steam account for free by visiting <a href="">https://store.steampowered.com/join/</a>. Once you have an account, you can buy the game for a discounted price of $2.99 (regular price $9.99) by visiting <a href="">https://store.steampowered.com/app/347400/The_Treasures_of_Montezuma_4/</a>. After you buy the game, you can install it on your PC by following these steps:</p>
-<ol>
-<li>Open Steam and log in with your account.</li>
-<li>Go to Library and find Treasure of Montezuma 4 in your list of games.</li>
-<li>Click on Install and choose a location for the game files.</li>
-<li>Wait for the installation to finish and click on Play.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded Treasure of Montezuma 4 from Steam. Enjoy!</p>
-<h4>PlayStation: How to purchase the game from the PlayStation Store and play it on your PS4 or PS5</h4>
-<p>If you want to download Treasure of Montezuma 4 from PlayStation, you will need to have a PlayStation account and a PS4 or PS5 console. You can create a PlayStation account for free by visiting <a href="">https://www.playstation.com/en-us/support/account/create-account/</a>. Once you have an account, you can purchase the game for $9.99 by visiting <a href="">https://store.playstation.com/en-us/product/UP4151-CUSA01975_00-TREASUREMONTEZUM</a>. After you purchase the game, you can download it on your PS4 or PS5 by following these steps:</p>
-<ol>
-<li>Turn on your PS4 or PS5 and log in with your account.</li>
-<li>Go to Library and find Treasure of Montezuma 4 in your list of games.</li>
-<li>Select Download and wait for the download to finish.</li>
-<li>Select Start to launch the game.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded Treasure of Montezuma 4 from PlayStation. Enjoy!</p>
-<h4>GameHouse: How to sign up for a free trial and access thousands of games, including Treasure of Montezuma 4</h4>
-<p>If you want to download Treasure of Montezuma 4 from GameHouse, you will need to sign up for a free trial and access thousands of games, including Treasure of Montezuma 4. GameHouse is a website that offers unlimited access to over 2,500 games for a monthly fee of $10.99. However, you can try it for free for 14 days by visiting <a href="">https://www.gamehouse.com/</a>. Once you sign up for a free trial, you can download Treasure of Montezuma 4 by following these steps:</p>
-<ol>
-<li>Open GameHouse and log in with your account.</li>
-<li>Go to Puzzle Games and find Treasure of Montezuma 4 in the list of games.</li>
-<li>Select Play Now and wait for the game to load.</li>
-<li>Select Full Screen to enjoy the game in full screen mode.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded Treasure of Montezuma 4 from GameHouse. Enjoy!</p>
- <h2>Conclusion</h2>
-<p>Treasure of Montezuma 4 is a fantastic puzzle game that will keep you entertained and challenged for hours. It has three modes, 98 levels, seven totems, eight bonuses, and stunning graphics and sound effects. It also teaches you about the Aztec culture and history in a fun and interactive way. You can download the game from Steam, PlayStation, or GameHouse, depending on your preference and device. Don't miss this opportunity to experience a thrilling puzzle adventure. Download Treasure of Montezuma 4 today and discover the secrets of the Ziggurat!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Treasure of Montezuma 4:</p>
-<ul>
-<li><b>What are the system requirements for Treasure of Montezuma 4?</b></li>
-<p>The system requirements for Treasure of Montezuma 4 are as follows:</p>
-<table>
-<tr><td>Platform</td><td>Minimum Requirements</td></tr>
-<tr><td>Steam</td><td>OS: Windows XP/Vista/7/8/10<br>CPU: 1.5 GHz<br>RAM: 256 MB<br>Disk Space: 500 MB<br>DirectX: 9.0 or higher</td></tr>
-<tr><td>PlayStation</td><td>PS4 or PS5 console<br>Internet connection<br>PlayStation account<br>Disk Space: 1 GB</td></tr>
-<tr><td>GameHouse</td><td>OS: Windows 7/8/10<br>CPU: 1.6 GHz<br>RAM: 512 MB<br>Disk Space: 500 MB<br>DirectX: 9.0 or higher<br>Internet connection<br>GameHouse account</td></tr>
-</table>
- <li><b>How long does it take to finish Treasure of Montezuma 4?</b></li>
-<p>The time it takes to finish Treasure of Montezuma 4 depends on your skill level and play style. However, on average, it takes about 10 hours to complete Story Mode, 5 hours to complete Quest Mode, and 2 hours to complete Puzzle Mode.</p>
- <li><b>Can I play Treasure of Montezuma 4 offline?</b></li>
-<p>You can play Treasure of Montezuma 4 offline if you download it from Steam or PlayStation. However, you will need an internet connection to activate the game and access some features, such as achievements and leaderboards. If you download it from GameHouse, you will need an internet connection to play the game.</p>
- <li><b>Is Treasure of Montezuma 4 suitable for children?</b></li>
-<p>Treasure of Montezuma 4 is suitable for children aged 7 and above. The game has a rating of E (Everyone) by the ESRB and a rating of PEGI 3 by the PEGI. The game does not contain any violence, blood, gore, or sexual content. However, some parents may find some aspects of the game inappropriate for younger children, such as the depiction of Aztec gods and rituals.</p>
- <li><b>Is there a sequel to Treasure of Montezuma 4?</b></li>
-<p>Treasure of Montezuma 4 is the latest installment of the series as of June 2023. There is no official announcement about a sequel yet. However, you can check out the previous games in the series, such as Treasure of Montezuma, Treasure of Montezuma 2, and Treasure of Montezuma 3.</p>
-</ul></p> 401be4b1e0<br />
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spaces/AI-Zero-to-Hero/02-H5-AR-VR-IOT/style.css

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-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
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-}
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-p {
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spaces/AIFILMS/generate_human_motion/pyrender/pyrender/viewer.py

@@ -1,1160 +0,0 @@
-"""A pyglet-based interactive 3D scene viewer.
-"""
-import copy
-import os
-import sys
-from threading import Thread, RLock
-import time
-
-import imageio
-import numpy as np
-import OpenGL
-import trimesh
-
-try:
-    from Tkinter import Tk, tkFileDialog as filedialog
-except Exception:
-    try:
-        from tkinter import Tk, filedialog as filedialog
-    except Exception:
-        pass
-
-from .constants import (TARGET_OPEN_GL_MAJOR, TARGET_OPEN_GL_MINOR,
-                        MIN_OPEN_GL_MAJOR, MIN_OPEN_GL_MINOR,
-                        TEXT_PADDING, DEFAULT_SCENE_SCALE,
-                        DEFAULT_Z_FAR, DEFAULT_Z_NEAR, RenderFlags, TextAlign)
-from .light import DirectionalLight
-from .node import Node
-from .camera import PerspectiveCamera, OrthographicCamera, IntrinsicsCamera
-from .trackball import Trackball
-from .renderer import Renderer
-from .mesh import Mesh
-
-import pyglet
-from pyglet import clock
-pyglet.options['shadow_window'] = False
-
-
-class Viewer(pyglet.window.Window):
-    """An interactive viewer for 3D scenes.
-
-    The viewer's camera is separate from the scene's, but will take on
-    the parameters of the scene's main view camera and start in the same pose.
-    If the scene does not have a camera, a suitable default will be provided.
-
-    Parameters
-    ----------
-    scene : :class:`Scene`
-        The scene to visualize.
-    viewport_size : (2,) int
-        The width and height of the initial viewing window.
-    render_flags : dict
-        A set of flags for rendering the scene. Described in the note below.
-    viewer_flags : dict
-        A set of flags for controlling the viewer's behavior.
-        Described in the note below.
-    registered_keys : dict
-        A map from ASCII key characters to tuples containing:
-
-        - A function to be called whenever the key is pressed,
-          whose first argument will be the viewer itself.
-        - (Optionally) A list of additional positional arguments
-          to be passed to the function.
-        - (Optionally) A dict of keyword arguments to be passed
-          to the function.
-
-    kwargs : dict
-        Any keyword arguments left over will be interpreted as belonging to
-        either the :attr:`.Viewer.render_flags` or :attr:`.Viewer.viewer_flags`
-        dictionaries. Those flag sets will be updated appropriately.
-
-    Note
-    ----
-    The basic commands for moving about the scene are given as follows:
-
-    - **Rotating about the scene**: Hold the left mouse button and
-      drag the cursor.
-    - **Rotating about the view axis**: Hold ``CTRL`` and the left mouse
-      button and drag the cursor.
-    - **Panning**:
-
-      - Hold SHIFT, then hold the left mouse button and drag the cursor, or
-      - Hold the middle mouse button and drag the cursor.
-
-    - **Zooming**:
-
-      - Scroll the mouse wheel, or
-      - Hold the right mouse button and drag the cursor.
-
-    Other keyboard commands are as follows:
-
-    - ``a``: Toggles rotational animation mode.
-    - ``c``: Toggles backface culling.
-    - ``f``: Toggles fullscreen mode.
-    - ``h``: Toggles shadow rendering.
-    - ``i``: Toggles axis display mode
-      (no axes, world axis, mesh axes, all axes).
-    - ``l``: Toggles lighting mode
-      (scene lighting, Raymond lighting, or direct lighting).
-    - ``m``: Toggles face normal visualization.
-    - ``n``: Toggles vertex normal visualization.
-    - ``o``: Toggles orthographic mode.
-    - ``q``: Quits the viewer.
-    - ``r``: Starts recording a GIF, and pressing again stops recording
-      and opens a file dialog.
-    - ``s``: Opens a file dialog to save the current view as an image.
-    - ``w``: Toggles wireframe mode
-      (scene default, flip wireframes, all wireframe, or all solid).
-    - ``z``: Resets the camera to the initial view.
-
-    Note
-    ----
-    The valid keys for ``render_flags`` are as follows:
-
-    - ``flip_wireframe``: `bool`, If `True`, all objects will have their
-      wireframe modes flipped from what their material indicates.
-      Defaults to `False`.
-    - ``all_wireframe``: `bool`, If `True`, all objects will be rendered
-      in wireframe mode. Defaults to `False`.
-    - ``all_solid``: `bool`, If `True`, all objects will be rendered in
-      solid mode. Defaults to `False`.
-    - ``shadows``: `bool`, If `True`, shadows will be rendered.
-      Defaults to `False`.
-    - ``vertex_normals``: `bool`, If `True`, vertex normals will be
-      rendered as blue lines. Defaults to `False`.
-    - ``face_normals``: `bool`, If `True`, face normals will be rendered as
-      blue lines. Defaults to `False`.
-    - ``cull_faces``: `bool`, If `True`, backfaces will be culled.
-      Defaults to `True`.
-    - ``point_size`` : float, The point size in pixels. Defaults to 1px.
-
-    Note
-    ----
-    The valid keys for ``viewer_flags`` are as follows:
-
-    - ``rotate``: `bool`, If `True`, the scene's camera will rotate
-      about an axis. Defaults to `False`.
-    - ``rotate_rate``: `float`, The rate of rotation in radians per second.
-      Defaults to `PI / 3.0`.
-    - ``rotate_axis``: `(3,) float`, The axis in world coordinates to rotate
-      about. Defaults to ``[0,0,1]``.
-    - ``view_center``: `(3,) float`, The position to rotate the scene about.
-      Defaults to the scene's centroid.
-    - ``use_raymond_lighting``: `bool`, If `True`, an additional set of three
-      directional lights that move with the camera will be added to the scene.
-      Defaults to `False`.
-    - ``use_direct_lighting``: `bool`, If `True`, an additional directional
-      light that moves with the camera and points out of it will be added to
-      the scene. Defaults to `False`.
-    - ``lighting_intensity``: `float`, The overall intensity of the
-      viewer's additional lights (when they're in use). Defaults to 3.0.
-    - ``use_perspective_cam``: `bool`, If `True`, a perspective camera will
-      be used. Otherwise, an orthographic camera is used. Defaults to `True`.
-    - ``save_directory``: `str`, A directory to open the file dialogs in.
-      Defaults to `None`.
-    - ``window_title``: `str`, A title for the viewer's application window.
-      Defaults to `"Scene Viewer"`.
-    - ``refresh_rate``: `float`, A refresh rate for rendering, in Hertz.
-      Defaults to `30.0`.
-    - ``fullscreen``: `bool`, Whether to make viewer fullscreen.
-      Defaults to `False`.
-    - ``show_world_axis``: `bool`, Whether to show the world axis.
-      Defaults to `False`.
-    - ``show_mesh_axes``: `bool`, Whether to show the individual mesh axes.
-      Defaults to `False`.
-    - ``caption``: `list of dict`, Text caption(s) to display on the viewer.
-      Defaults to `None`.
-
-    Note
-    ----
-    Animation can be accomplished by running the viewer with ``run_in_thread``
-    enabled. Then, just run a loop in your main thread, updating the scene as
-    needed. Before updating the scene, be sure to acquire the
-    :attr:`.Viewer.render_lock`, and release it  when your update is done.
-    """
-
-    def __init__(self, scene, viewport_size=None,
-                 render_flags=None, viewer_flags=None,
-                 registered_keys=None, run_in_thread=False,
-                 auto_start=True,
-                 **kwargs):
-
-        #######################################################################
-        # Save attributes and flags
-        #######################################################################
-        if viewport_size is None:
-            viewport_size = (640, 480)
-        self._scene = scene
-        self._viewport_size = viewport_size
-        self._render_lock = RLock()
-        self._is_active = False
-        self._should_close = False
-        self._run_in_thread = run_in_thread
-        self._auto_start = auto_start
-
-        self._default_render_flags = {
-            'flip_wireframe': False,
-            'all_wireframe': False,
-            'all_solid': False,
-            'shadows': False,
-            'vertex_normals': False,
-            'face_normals': False,
-            'cull_faces': True,
-            'point_size': 1.0,
-        }
-        self._default_viewer_flags = {
-            'mouse_pressed': False,
-            'rotate': False,
-            'rotate_rate': np.pi / 3.0,
-            'rotate_axis': np.array([0.0, 0.0, 1.0]),
-            'view_center': None,
-            'record': False,
-            'use_raymond_lighting': False,
-            'use_direct_lighting': False,
-            'lighting_intensity': 3.0,
-            'use_perspective_cam': True,
-            'save_directory': None,
-            'window_title': 'Scene Viewer',
-            'refresh_rate': 30.0,
-            'fullscreen': False,
-            'show_world_axis': False,
-            'show_mesh_axes': False,
-            'caption': None
-        }
-        self._render_flags = self._default_render_flags.copy()
-        self._viewer_flags = self._default_viewer_flags.copy()
-        self._viewer_flags['rotate_axis'] = (
-            self._default_viewer_flags['rotate_axis'].copy()
-        )
-
-        if render_flags is not None:
-            self._render_flags.update(render_flags)
-        if viewer_flags is not None:
-            self._viewer_flags.update(viewer_flags)
-
-        for key in kwargs:
-            if key in self.render_flags:
-                self._render_flags[key] = kwargs[key]
-            elif key in self.viewer_flags:
-                self._viewer_flags[key] = kwargs[key]
-
-        # TODO MAC OS BUG FOR SHADOWS
-        if sys.platform == 'darwin':
-            self._render_flags['shadows'] = False
-
-        self._registered_keys = {}
-        if registered_keys is not None:
-            self._registered_keys = {
-                ord(k.lower()): registered_keys[k] for k in registered_keys
-            }
-
-        #######################################################################
-        # Save internal settings
-        #######################################################################
-
-        # Set up caption stuff
-        self._message_text = None
-        self._ticks_till_fade = 2.0 / 3.0 * self.viewer_flags['refresh_rate']
-        self._message_opac = 1.0 + self._ticks_till_fade
-
-        # Set up raymond lights and direct lights
-        self._raymond_lights = self._create_raymond_lights()
-        self._direct_light = self._create_direct_light()
-
-        # Set up axes
-        self._axes = {}
-        self._axis_mesh = Mesh.from_trimesh(
-            trimesh.creation.axis(origin_size=0.1, axis_radius=0.05,
-                                  axis_length=1.0), smooth=False)
-        if self.viewer_flags['show_world_axis']:
-            self._set_axes(world=self.viewer_flags['show_world_axis'],
-                           mesh=self.viewer_flags['show_mesh_axes'])
-
-        #######################################################################
-        # Set up camera node
-        #######################################################################
-        self._camera_node = None
-        self._prior_main_camera_node = None
-        self._default_camera_pose = None
-        self._default_persp_cam = None
-        self._default_orth_cam = None
-        self._trackball = None
-        self._saved_frames = []
-
-        # Extract main camera from scene and set up our mirrored copy
-        znear = None
-        zfar = None
-        if scene.main_camera_node is not None:
-            n = scene.main_camera_node
-            camera = copy.copy(n.camera)
-            if isinstance(camera, (PerspectiveCamera, IntrinsicsCamera)):
-                self._default_persp_cam = camera
-                znear = camera.znear
-                zfar = camera.zfar
-            elif isinstance(camera, OrthographicCamera):
-                self._default_orth_cam = camera
-                znear = camera.znear
-                zfar = camera.zfar
-            self._default_camera_pose = scene.get_pose(scene.main_camera_node)
-            self._prior_main_camera_node = n
-
-        # Set defaults as needed
-        if zfar is None:
-            zfar = max(scene.scale * 10.0, DEFAULT_Z_FAR)
-        if znear is None or znear == 0:
-            if scene.scale == 0:
-                znear = DEFAULT_Z_NEAR
-            else:
-                znear = min(scene.scale / 10.0, DEFAULT_Z_NEAR)
-
-        if self._default_persp_cam is None:
-            self._default_persp_cam = PerspectiveCamera(
-                yfov=np.pi / 3.0, znear=znear, zfar=zfar
-            )
-        if self._default_orth_cam is None:
-            xmag = ymag = scene.scale
-            if scene.scale == 0:
-                xmag = ymag = 1.0
-            self._default_orth_cam = OrthographicCamera(
-                xmag=xmag, ymag=ymag,
-                znear=znear,
-                zfar=zfar
-            )
-        if self._default_camera_pose is None:
-            self._default_camera_pose = self._compute_initial_camera_pose()
-
-        # Pick camera
-        if self.viewer_flags['use_perspective_cam']:
-            camera = self._default_persp_cam
-        else:
-            camera = self._default_orth_cam
-
-        self._camera_node = Node(
-            matrix=self._default_camera_pose, camera=camera
-        )
-        scene.add_node(self._camera_node)
-        scene.main_camera_node = self._camera_node
-        self._reset_view()
-
-        #######################################################################
-        # Initialize OpenGL context and renderer
-        #######################################################################
-        self._renderer = Renderer(
-            self._viewport_size[0], self._viewport_size[1],
-            self.render_flags['point_size']
-        )
-        self._is_active = True
-
-        if self.run_in_thread:
-            self._thread = Thread(target=self._init_and_start_app)
-            self._thread.start()
-        else:
-            if auto_start:
-                self._init_and_start_app()
-
-    def start(self):
-        self._init_and_start_app()
-
-    @property
-    def scene(self):
-        """:class:`.Scene` : The scene being visualized.
-        """
-        return self._scene
-
-    @property
-    def viewport_size(self):
-        """(2,) int : The width and height of the viewing window.
-        """
-        return self._viewport_size
-
-    @property
-    def render_lock(self):
-        """:class:`threading.RLock` : If acquired, prevents the viewer from
-        rendering until released.
-
-        Run :meth:`.Viewer.render_lock.acquire` before making updates to
-        the scene in a different thread, and run
-        :meth:`.Viewer.render_lock.release` once you're done to let the viewer
-        continue.
-        """
-        return self._render_lock
-
-    @property
-    def is_active(self):
-        """bool : `True` if the viewer is active, or `False` if it has
-        been closed.
-        """
-        return self._is_active
-
-    @property
-    def run_in_thread(self):
-        """bool : Whether the viewer was run in a separate thread.
-        """
-        return self._run_in_thread
-
-    @property
-    def render_flags(self):
-        """dict : Flags for controlling the renderer's behavior.
-
-        - ``flip_wireframe``: `bool`, If `True`, all objects will have their
-          wireframe modes flipped from what their material indicates.
-          Defaults to `False`.
-        - ``all_wireframe``: `bool`, If `True`, all objects will be rendered
-          in wireframe mode. Defaults to `False`.
-        - ``all_solid``: `bool`, If `True`, all objects will be rendered in
-          solid mode. Defaults to `False`.
-        - ``shadows``: `bool`, If `True`, shadows will be rendered.
-          Defaults to `False`.
-        - ``vertex_normals``: `bool`, If `True`, vertex normals will be
-          rendered as blue lines. Defaults to `False`.
-        - ``face_normals``: `bool`, If `True`, face normals will be rendered as
-          blue lines. Defaults to `False`.
-        - ``cull_faces``: `bool`, If `True`, backfaces will be culled.
-          Defaults to `True`.
-        - ``point_size`` : float, The point size in pixels. Defaults to 1px.
-
-        """
-        return self._render_flags
-
-    @render_flags.setter
-    def render_flags(self, value):
-        self._render_flags = value
-
-    @property
-    def viewer_flags(self):
-        """dict : Flags for controlling the viewer's behavior.
-
-        The valid keys for ``viewer_flags`` are as follows:
-
-        - ``rotate``: `bool`, If `True`, the scene's camera will rotate
-          about an axis. Defaults to `False`.
-        - ``rotate_rate``: `float`, The rate of rotation in radians per second.
-          Defaults to `PI / 3.0`.
-        - ``rotate_axis``: `(3,) float`, The axis in world coordinates to
-          rotate about. Defaults to ``[0,0,1]``.
-        - ``view_center``: `(3,) float`, The position to rotate the scene
-          about. Defaults to the scene's centroid.
-        - ``use_raymond_lighting``: `bool`, If `True`, an additional set of
-          three directional lights that move with the camera will be added to
-          the scene. Defaults to `False`.
-        - ``use_direct_lighting``: `bool`, If `True`, an additional directional
-          light that moves with the camera and points out of it will be
-          added to the scene. Defaults to `False`.
-        - ``lighting_intensity``: `float`, The overall intensity of the
-          viewer's additional lights (when they're in use). Defaults to 3.0.
-        - ``use_perspective_cam``: `bool`, If `True`, a perspective camera will
-          be used. Otherwise, an orthographic camera is used. Defaults to
-          `True`.
-        - ``save_directory``: `str`, A directory to open the file dialogs in.
-          Defaults to `None`.
-        - ``window_title``: `str`, A title for the viewer's application window.
-          Defaults to `"Scene Viewer"`.
-        - ``refresh_rate``: `float`, A refresh rate for rendering, in Hertz.
-          Defaults to `30.0`.
-        - ``fullscreen``: `bool`, Whether to make viewer fullscreen.
-          Defaults to `False`.
-        - ``show_world_axis``: `bool`, Whether to show the world axis.
-          Defaults to `False`.
-        - ``show_mesh_axes``: `bool`, Whether to show the individual mesh axes.
-          Defaults to `False`.
-        - ``caption``: `list of dict`, Text caption(s) to display on
-          the viewer. Defaults to `None`.
-
-        """
-        return self._viewer_flags
-
-    @viewer_flags.setter
-    def viewer_flags(self, value):
-        self._viewer_flags = value
-
-    @property
-    def registered_keys(self):
-        """dict : Map from ASCII key character to a handler function.
-
-        This is a map from ASCII key characters to tuples containing:
-
-        - A function to be called whenever the key is pressed,
-          whose first argument will be the viewer itself.
-        - (Optionally) A list of additional positional arguments
-          to be passed to the function.
-        - (Optionally) A dict of keyword arguments to be passed
-          to the function.
-
-        """
-        return self._registered_keys
-
-    @registered_keys.setter
-    def registered_keys(self, value):
-        self._registered_keys = value
-
-    def close_external(self):
-        """Close the viewer from another thread.
-
-        This function will wait for the actual close, so you immediately
-        manipulate the scene afterwards.
-        """
-        self._should_close = True
-        while self.is_active:
-            time.sleep(1.0 / self.viewer_flags['refresh_rate'])
-
-    def save_gif(self, filename=None):
-        """Save the stored GIF frames to a file.
-
-        To use this asynchronously, run the viewer with the ``record``
-        flag and the ``run_in_thread`` flags set.
-        Kill the viewer after your desired time with
-        :meth:`.Viewer.close_external`, and then call :meth:`.Viewer.save_gif`.
-
-        Parameters
-        ----------
-        filename : str
-            The file to save the GIF to. If not specified,
-            a file dialog will be opened to ask the user where
-            to save the GIF file.
-        """
-        if filename is None:
-            filename = self._get_save_filename(['gif', 'all'])
-        if filename is not None:
-            self.viewer_flags['save_directory'] = os.path.dirname(filename)
-            imageio.mimwrite(filename, self._saved_frames,
-                             fps=self.viewer_flags['refresh_rate'],
-                             palettesize=128, subrectangles=True)
-        self._saved_frames = []
-
-    def on_close(self):
-        """Exit the event loop when the window is closed.
-        """
-        # Remove our camera and restore the prior one
-        if self._camera_node is not None:
-            self.scene.remove_node(self._camera_node)
-        if self._prior_main_camera_node is not None:
-            self.scene.main_camera_node = self._prior_main_camera_node
-
-        # Delete any lighting nodes that we've attached
-        if self.viewer_flags['use_raymond_lighting']:
-            for n in self._raymond_lights:
-                if self.scene.has_node(n):
-                    self.scene.remove_node(n)
-        if self.viewer_flags['use_direct_lighting']:
-            if self.scene.has_node(self._direct_light):
-                self.scene.remove_node(self._direct_light)
-
-        # Delete any axis nodes that we've attached
-        self._remove_axes()
-
-        # Delete renderer
-        if self._renderer is not None:
-            self._renderer.delete()
-        self._renderer = None
-
-        # Force clean-up of OpenGL context data
-        try:
-            OpenGL.contextdata.cleanupContext()
-            self.close()
-        except Exception:
-            pass
-        finally:
-            self._is_active = False
-            super(Viewer, self).on_close()
-            pyglet.app.exit()
-
-    def on_draw(self):
-        """Redraw the scene into the viewing window.
-        """
-        if self._renderer is None:
-            return
-
-        if self.run_in_thread or not self._auto_start:
-            self.render_lock.acquire()
-
-        # Make OpenGL context current
-        self.switch_to()
-
-        # Render the scene
-        self.clear()
-        self._render()
-
-        if self._message_text is not None:
-            self._renderer.render_text(
-                self._message_text,
-                self.viewport_size[0] - TEXT_PADDING,
-                TEXT_PADDING,
-                font_pt=20,
-                color=np.array([0.1, 0.7, 0.2,
-                                np.clip(self._message_opac, 0.0, 1.0)]),
-                align=TextAlign.BOTTOM_RIGHT
-            )
-
-        if self.viewer_flags['caption'] is not None:
-            for caption in self.viewer_flags['caption']:
-                xpos, ypos = self._location_to_x_y(caption['location'])
-                self._renderer.render_text(
-                    caption['text'],
-                    xpos,
-                    ypos,
-                    font_name=caption['font_name'],
-                    font_pt=caption['font_pt'],
-                    color=caption['color'],
-                    scale=caption['scale'],
-                    align=caption['location']
-                )
-
-        if self.run_in_thread or not self._auto_start:
-            self.render_lock.release()
-
-    def on_resize(self, width, height):
-        """Resize the camera and trackball when the window is resized.
-        """
-        if self._renderer is None:
-            return
-
-        self._viewport_size = (width, height)
-        self._trackball.resize(self._viewport_size)
-        self._renderer.viewport_width = self._viewport_size[0]
-        self._renderer.viewport_height = self._viewport_size[1]
-        self.on_draw()
-
-    def on_mouse_press(self, x, y, buttons, modifiers):
-        """Record an initial mouse press.
-        """
-        self._trackball.set_state(Trackball.STATE_ROTATE)
-        if (buttons == pyglet.window.mouse.LEFT):
-            ctrl = (modifiers & pyglet.window.key.MOD_CTRL)
-            shift = (modifiers & pyglet.window.key.MOD_SHIFT)
-            if (ctrl and shift):
-                self._trackball.set_state(Trackball.STATE_ZOOM)
-            elif ctrl:
-                self._trackball.set_state(Trackball.STATE_ROLL)
-            elif shift:
-                self._trackball.set_state(Trackball.STATE_PAN)
-        elif (buttons == pyglet.window.mouse.MIDDLE):
-            self._trackball.set_state(Trackball.STATE_PAN)
-        elif (buttons == pyglet.window.mouse.RIGHT):
-            self._trackball.set_state(Trackball.STATE_ZOOM)
-
-        self._trackball.down(np.array([x, y]))
-
-        # Stop animating while using the mouse
-        self.viewer_flags['mouse_pressed'] = True
-
-    def on_mouse_drag(self, x, y, dx, dy, buttons, modifiers):
-        """Record a mouse drag.
-        """
-        self._trackball.drag(np.array([x, y]))
-
-    def on_mouse_release(self, x, y, button, modifiers):
-        """Record a mouse release.
-        """
-        self.viewer_flags['mouse_pressed'] = False
-
-    def on_mouse_scroll(self, x, y, dx, dy):
-        """Record a mouse scroll.
-        """
-        if self.viewer_flags['use_perspective_cam']:
-            self._trackball.scroll(dy)
-        else:
-            spfc = 0.95
-            spbc = 1.0 / 0.95
-            sf = 1.0
-            if dy > 0:
-                sf = spfc * dy
-            elif dy < 0:
-                sf = - spbc * dy
-
-            c = self._camera_node.camera
-            xmag = max(c.xmag * sf, 1e-8)
-            ymag = max(c.ymag * sf, 1e-8 * c.ymag / c.xmag)
-            c.xmag = xmag
-            c.ymag = ymag
-
-    def on_key_press(self, symbol, modifiers):
-        """Record a key press.
-        """
-        # First, check for registered key callbacks
-        if symbol in self.registered_keys:
-            tup = self.registered_keys[symbol]
-            callback = None
-            args = []
-            kwargs = {}
-            if not isinstance(tup, (list, tuple, np.ndarray)):
-                callback = tup
-            else:
-                callback = tup[0]
-                if len(tup) == 2:
-                    args = tup[1]
-                if len(tup) == 3:
-                    kwargs = tup[2]
-            callback(self, *args, **kwargs)
-            return
-
-        # Otherwise, use default key functions
-
-        # A causes the frame to rotate
-        self._message_text = None
-        if symbol == pyglet.window.key.A:
-            self.viewer_flags['rotate'] = not self.viewer_flags['rotate']
-            if self.viewer_flags['rotate']:
-                self._message_text = 'Rotation On'
-            else:
-                self._message_text = 'Rotation Off'
-
-        # C toggles backface culling
-        elif symbol == pyglet.window.key.C:
-            self.render_flags['cull_faces'] = (
-                not self.render_flags['cull_faces']
-            )
-            if self.render_flags['cull_faces']:
-                self._message_text = 'Cull Faces On'
-            else:
-                self._message_text = 'Cull Faces Off'
-
-        # F toggles face normals
-        elif symbol == pyglet.window.key.F:
-            self.viewer_flags['fullscreen'] = (
-                not self.viewer_flags['fullscreen']
-            )
-            self.set_fullscreen(self.viewer_flags['fullscreen'])
-            self.activate()
-            if self.viewer_flags['fullscreen']:
-                self._message_text = 'Fullscreen On'
-            else:
-                self._message_text = 'Fullscreen Off'
-
-        # S toggles shadows
-        elif symbol == pyglet.window.key.H and sys.platform != 'darwin':
-            self.render_flags['shadows'] = not self.render_flags['shadows']
-            if self.render_flags['shadows']:
-                self._message_text = 'Shadows On'
-            else:
-                self._message_text = 'Shadows Off'
-
-        elif symbol == pyglet.window.key.I:
-            if (self.viewer_flags['show_world_axis'] and not
-                    self.viewer_flags['show_mesh_axes']):
-                self.viewer_flags['show_world_axis'] = False
-                self.viewer_flags['show_mesh_axes'] = True
-                self._set_axes(False, True)
-                self._message_text = 'Mesh Axes On'
-            elif (not self.viewer_flags['show_world_axis'] and
-                    self.viewer_flags['show_mesh_axes']):
-                self.viewer_flags['show_world_axis'] = True
-                self.viewer_flags['show_mesh_axes'] = True
-                self._set_axes(True, True)
-                self._message_text = 'All Axes On'
-            elif (self.viewer_flags['show_world_axis'] and
-                    self.viewer_flags['show_mesh_axes']):
-                self.viewer_flags['show_world_axis'] = False
-                self.viewer_flags['show_mesh_axes'] = False
-                self._set_axes(False, False)
-                self._message_text = 'All Axes Off'
-            else:
-                self.viewer_flags['show_world_axis'] = True
-                self.viewer_flags['show_mesh_axes'] = False
-                self._set_axes(True, False)
-                self._message_text = 'World Axis On'
-
-        # L toggles the lighting mode
-        elif symbol == pyglet.window.key.L:
-            if self.viewer_flags['use_raymond_lighting']:
-                self.viewer_flags['use_raymond_lighting'] = False
-                self.viewer_flags['use_direct_lighting'] = True
-                self._message_text = 'Direct Lighting'
-            elif self.viewer_flags['use_direct_lighting']:
-                self.viewer_flags['use_raymond_lighting'] = False
-                self.viewer_flags['use_direct_lighting'] = False
-                self._message_text = 'Default Lighting'
-            else:
-                self.viewer_flags['use_raymond_lighting'] = True
-                self.viewer_flags['use_direct_lighting'] = False
-                self._message_text = 'Raymond Lighting'
-
-        # M toggles face normals
-        elif symbol == pyglet.window.key.M:
-            self.render_flags['face_normals'] = (
-                not self.render_flags['face_normals']
-            )
-            if self.render_flags['face_normals']:
-                self._message_text = 'Face Normals On'
-            else:
-                self._message_text = 'Face Normals Off'
-
-        # N toggles vertex normals
-        elif symbol == pyglet.window.key.N:
-            self.render_flags['vertex_normals'] = (
-                not self.render_flags['vertex_normals']
-            )
-            if self.render_flags['vertex_normals']:
-                self._message_text = 'Vert Normals On'
-            else:
-                self._message_text = 'Vert Normals Off'
-
-        # O toggles orthographic camera mode
-        elif symbol == pyglet.window.key.O:
-            self.viewer_flags['use_perspective_cam'] = (
-                not self.viewer_flags['use_perspective_cam']
-            )
-            if self.viewer_flags['use_perspective_cam']:
-                camera = self._default_persp_cam
-                self._message_text = 'Perspective View'
-            else:
-                camera = self._default_orth_cam
-                self._message_text = 'Orthographic View'
-
-            cam_pose = self._camera_node.matrix.copy()
-            cam_node = Node(matrix=cam_pose, camera=camera)
-            self.scene.remove_node(self._camera_node)
-            self.scene.add_node(cam_node)
-            self.scene.main_camera_node = cam_node
-            self._camera_node = cam_node
-
-        # Q quits the viewer
-        elif symbol == pyglet.window.key.Q:
-            self.on_close()
-
-        # R starts recording frames
-        elif symbol == pyglet.window.key.R:
-            if self.viewer_flags['record']:
-                self.save_gif()
-                self.set_caption(self.viewer_flags['window_title'])
-            else:
-                self.set_caption(
-                    '{} (RECORDING)'.format(self.viewer_flags['window_title'])
-                )
-            self.viewer_flags['record'] = not self.viewer_flags['record']
-
-        # S saves the current frame as an image
-        elif symbol == pyglet.window.key.S:
-            self._save_image()
-
-        # W toggles through wireframe modes
-        elif symbol == pyglet.window.key.W:
-            if self.render_flags['flip_wireframe']:
-                self.render_flags['flip_wireframe'] = False
-                self.render_flags['all_wireframe'] = True
-                self.render_flags['all_solid'] = False
-                self._message_text = 'All Wireframe'
-            elif self.render_flags['all_wireframe']:
-                self.render_flags['flip_wireframe'] = False
-                self.render_flags['all_wireframe'] = False
-                self.render_flags['all_solid'] = True
-                self._message_text = 'All Solid'
-            elif self.render_flags['all_solid']:
-                self.render_flags['flip_wireframe'] = False
-                self.render_flags['all_wireframe'] = False
-                self.render_flags['all_solid'] = False
-                self._message_text = 'Default Wireframe'
-            else:
-                self.render_flags['flip_wireframe'] = True
-                self.render_flags['all_wireframe'] = False
-                self.render_flags['all_solid'] = False
-                self._message_text = 'Flip Wireframe'
-
-        # Z resets the camera viewpoint
-        elif symbol == pyglet.window.key.Z:
-            self._reset_view()
-
-        if self._message_text is not None:
-            self._message_opac = 1.0 + self._ticks_till_fade
-
-    @staticmethod
-    def _time_event(dt, self):
-        """The timer callback.
-        """
-        # Don't run old dead events after we've already closed
-        if not self._is_active:
-            return
-
-        if self.viewer_flags['record']:
-            self._record()
-        if (self.viewer_flags['rotate'] and not
-                self.viewer_flags['mouse_pressed']):
-            self._rotate()
-
-        # Manage message opacity
-        if self._message_text is not None:
-            if self._message_opac > 1.0:
-                self._message_opac -= 1.0
-            else:
-                self._message_opac *= 0.90
-            if self._message_opac < 0.05:
-                self._message_opac = 1.0 + self._ticks_till_fade
-                self._message_text = None
-
-        if self._should_close:
-            self.on_close()
-        else:
-            self.on_draw()
-
-    def _reset_view(self):
-        """Reset the view to a good initial state.
-
-        The view is initially along the positive x-axis at a
-        sufficient distance from the scene.
-        """
-        scale = self.scene.scale
-        if scale == 0.0:
-            scale = DEFAULT_SCENE_SCALE
-        centroid = self.scene.centroid
-
-        if self.viewer_flags['view_center'] is not None:
-            centroid = self.viewer_flags['view_center']
-
-        self._camera_node.matrix = self._default_camera_pose
-        self._trackball = Trackball(
-            self._default_camera_pose, self.viewport_size, scale, centroid
-        )
-
-    def _get_save_filename(self, file_exts):
-        file_types = {
-            'png': ('png files', '*.png'),
-            'jpg': ('jpeg files', '*.jpg'),
-            'gif': ('gif files', '*.gif'),
-            'all': ('all files', '*'),
-        }
-        filetypes = [file_types[x] for x in file_exts]
-        try:
-            root = Tk()
-            save_dir = self.viewer_flags['save_directory']
-            if save_dir is None:
-                save_dir = os.getcwd()
-            filename = filedialog.asksaveasfilename(
-                initialdir=save_dir, title='Select file save location',
-                filetypes=filetypes
-            )
-        except Exception:
-            return None
-
-        root.destroy()
-        if filename == ():
-            return None
-        return filename
-
-    def _save_image(self):
-        filename = self._get_save_filename(['png', 'jpg', 'gif', 'all'])
-        if filename is not None:
-            self.viewer_flags['save_directory'] = os.path.dirname(filename)
-            imageio.imwrite(filename, self._renderer.read_color_buf())
-
-    def _record(self):
-        """Save another frame for the GIF.
-        """
-        data = self._renderer.read_color_buf()
-        if not np.all(data == 0.0):
-            self._saved_frames.append(data)
-
-    def _rotate(self):
-        """Animate the scene by rotating the camera.
-        """
-        az = (self.viewer_flags['rotate_rate'] /
-              self.viewer_flags['refresh_rate'])
-        self._trackball.rotate(az, self.viewer_flags['rotate_axis'])
-
-    def _render(self):
-        """Render the scene into the framebuffer and flip.
-        """
-        scene = self.scene
-        self._camera_node.matrix = self._trackball.pose.copy()
-
-        # Set lighting
-        vli = self.viewer_flags['lighting_intensity']
-        if self.viewer_flags['use_raymond_lighting']:
-            for n in self._raymond_lights:
-                n.light.intensity = vli / 3.0
-                if not self.scene.has_node(n):
-                    scene.add_node(n, parent_node=self._camera_node)
-        else:
-            self._direct_light.light.intensity = vli
-            for n in self._raymond_lights:
-                if self.scene.has_node(n):
-                    self.scene.remove_node(n)
-
-        if self.viewer_flags['use_direct_lighting']:
-            if not self.scene.has_node(self._direct_light):
-                scene.add_node(
-                    self._direct_light, parent_node=self._camera_node
-                )
-        elif self.scene.has_node(self._direct_light):
-            self.scene.remove_node(self._direct_light)
-
-        flags = RenderFlags.NONE
-        if self.render_flags['flip_wireframe']:
-            flags |= RenderFlags.FLIP_WIREFRAME
-        elif self.render_flags['all_wireframe']:
-            flags |= RenderFlags.ALL_WIREFRAME
-        elif self.render_flags['all_solid']:
-            flags |= RenderFlags.ALL_SOLID
-
-        if self.render_flags['shadows']:
-            flags |= RenderFlags.SHADOWS_DIRECTIONAL | RenderFlags.SHADOWS_SPOT
-        if self.render_flags['vertex_normals']:
-            flags |= RenderFlags.VERTEX_NORMALS
-        if self.render_flags['face_normals']:
-            flags |= RenderFlags.FACE_NORMALS
-        if not self.render_flags['cull_faces']:
-            flags |= RenderFlags.SKIP_CULL_FACES
-
-        self._renderer.render(self.scene, flags)
-
-    def _init_and_start_app(self):
-        # Try multiple configs starting with target OpenGL version
-        # and multisampling and removing these options if exception
-        # Note: multisampling not available on all hardware
-        from pyglet.gl import Config
-        confs = [Config(sample_buffers=1, samples=4,
-                        depth_size=24,
-                        double_buffer=True,
-                        major_version=TARGET_OPEN_GL_MAJOR,
-                        minor_version=TARGET_OPEN_GL_MINOR),
-                 Config(depth_size=24,
-                        double_buffer=True,
-                        major_version=TARGET_OPEN_GL_MAJOR,
-                        minor_version=TARGET_OPEN_GL_MINOR),
-                 Config(sample_buffers=1, samples=4,
-                        depth_size=24,
-                        double_buffer=True,
-                        major_version=MIN_OPEN_GL_MAJOR,
-                        minor_version=MIN_OPEN_GL_MINOR),
-                 Config(depth_size=24,
-                        double_buffer=True,
-                        major_version=MIN_OPEN_GL_MAJOR,
-                        minor_version=MIN_OPEN_GL_MINOR)]
-        for conf in confs:
-            try:
-                super(Viewer, self).__init__(config=conf, resizable=True,
-                                             width=self._viewport_size[0],
-                                             height=self._viewport_size[1])
-                break
-            except pyglet.window.NoSuchConfigException:
-                pass
-
-        if not self.context:
-            raise ValueError('Unable to initialize an OpenGL 3+ context')
-        clock.schedule_interval(
-            Viewer._time_event, 1.0 / self.viewer_flags['refresh_rate'], self
-        )
-        self.switch_to()
-        self.set_caption(self.viewer_flags['window_title'])
-        pyglet.app.run()
-
-    def _compute_initial_camera_pose(self):
-        centroid = self.scene.centroid
-        if self.viewer_flags['view_center'] is not None:
-            centroid = self.viewer_flags['view_center']
-        scale = self.scene.scale
-        if scale == 0.0:
-            scale = DEFAULT_SCENE_SCALE
-
-        s2 = 1.0 / np.sqrt(2.0)
-        cp = np.eye(4)
-        cp[:3,:3] = np.array([
-            [0.0, -s2, s2],
-            [1.0, 0.0, 0.0],
-            [0.0, s2, s2]
-        ])
-        hfov = np.pi / 6.0
-        dist = scale / (2.0 * np.tan(hfov))
-        cp[:3,3] = dist * np.array([1.0, 0.0, 1.0]) + centroid
-
-        return cp
-
-    def _create_raymond_lights(self):
-        thetas = np.pi * np.array([1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0])
-        phis = np.pi * np.array([0.0, 2.0 / 3.0, 4.0 / 3.0])
-
-        nodes = []
-
-        for phi, theta in zip(phis, thetas):
-            xp = np.sin(theta) * np.cos(phi)
-            yp = np.sin(theta) * np.sin(phi)
-            zp = np.cos(theta)
-
-            z = np.array([xp, yp, zp])
-            z = z / np.linalg.norm(z)
-            x = np.array([-z[1], z[0], 0.0])
-            if np.linalg.norm(x) == 0:
-                x = np.array([1.0, 0.0, 0.0])
-            x = x / np.linalg.norm(x)
-            y = np.cross(z, x)
-
-            matrix = np.eye(4)
-            matrix[:3,:3] = np.c_[x,y,z]
-            nodes.append(Node(
-                light=DirectionalLight(color=np.ones(3), intensity=1.0),
-                matrix=matrix
-            ))
-
-        return nodes
-
-    def _create_direct_light(self):
-        light = DirectionalLight(color=np.ones(3), intensity=1.0)
-        n = Node(light=light, matrix=np.eye(4))
-        return n
-
-    def _set_axes(self, world, mesh):
-        scale = self.scene.scale
-        if world:
-            if 'scene' not in self._axes:
-                n = Node(mesh=self._axis_mesh, scale=np.ones(3) * scale * 0.3)
-                self.scene.add_node(n)
-                self._axes['scene'] = n
-        else:
-            if 'scene' in self._axes:
-                self.scene.remove_node(self._axes['scene'])
-                self._axes.pop('scene')
-
-        if mesh:
-            old_nodes = []
-            existing_axes = set([self._axes[k] for k in self._axes])
-            for node in self.scene.mesh_nodes:
-                if node not in existing_axes:
-                    old_nodes.append(node)
-
-            for node in old_nodes:
-                if node in self._axes:
-                    continue
-                n = Node(
-                    mesh=self._axis_mesh,
-                    scale=np.ones(3) * node.mesh.scale * 0.5
-                )
-                self.scene.add_node(n, parent_node=node)
-                self._axes[node] = n
-        else:
-            to_remove = set()
-            for main_node in self._axes:
-                if main_node in self.scene.mesh_nodes:
-                    self.scene.remove_node(self._axes[main_node])
-                    to_remove.add(main_node)
-            for main_node in to_remove:
-                self._axes.pop(main_node)
-
-    def _remove_axes(self):
-        for main_node in self._axes:
-            axis_node = self._axes[main_node]
-            self.scene.remove_node(axis_node)
-        self._axes = {}
-
-    def _location_to_x_y(self, location):
-        if location == TextAlign.CENTER:
-            return (self.viewport_size[0] / 2.0, self.viewport_size[1] / 2.0)
-        elif location == TextAlign.CENTER_LEFT:
-            return (TEXT_PADDING, self.viewport_size[1] / 2.0)
-        elif location == TextAlign.CENTER_RIGHT:
-            return (self.viewport_size[0] - TEXT_PADDING,
-                    self.viewport_size[1] / 2.0)
-        elif location == TextAlign.BOTTOM_LEFT:
-            return (TEXT_PADDING, TEXT_PADDING)
-        elif location == TextAlign.BOTTOM_RIGHT:
-            return (self.viewport_size[0] - TEXT_PADDING, TEXT_PADDING)
-        elif location == TextAlign.BOTTOM_CENTER:
-            return (self.viewport_size[0] / 2.0, TEXT_PADDING)
-        elif location == TextAlign.TOP_LEFT:
-            return (TEXT_PADDING, self.viewport_size[1] - TEXT_PADDING)
-        elif location == TextAlign.TOP_RIGHT:
-            return (self.viewport_size[0] - TEXT_PADDING,
-                    self.viewport_size[1] - TEXT_PADDING)
-        elif location == TextAlign.TOP_CENTER:
-            return (self.viewport_size[0] / 2.0,
-                    self.viewport_size[1] - TEXT_PADDING)
-
-
-__all__ = ['Viewer']

spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/hifigan/stft_loss.py

@@ -1,136 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""STFT-based Loss modules."""
-
-import torch
-import torch.nn.functional as F
-
-
-def stft(x, fft_size, hop_size, win_length, window):
-    """Perform STFT and convert to magnitude spectrogram.
-    Args:
-        x (Tensor): Input signal tensor (B, T).
-        fft_size (int): FFT size.
-        hop_size (int): Hop size.
-        win_length (int): Window length.
-        window (str): Window function type.
-    Returns:
-        Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
-    """
-    x_stft = torch.stft(x, fft_size, hop_size, win_length, window)
-    real = x_stft[..., 0]
-    imag = x_stft[..., 1]
-
-    # NOTE(kan-bayashi): clamp is needed to avoid nan or inf
-    return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1)
-
-
-class SpectralConvergengeLoss(torch.nn.Module):
-    """Spectral convergence loss module."""
-
-    def __init__(self):
-        """Initilize spectral convergence loss module."""
-        super(SpectralConvergengeLoss, self).__init__()
-
-    def forward(self, x_mag, y_mag):
-        """Calculate forward propagation.
-        Args:
-            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
-            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
-        Returns:
-            Tensor: Spectral convergence loss value.
-        """
-        return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro")
-
-
-class LogSTFTMagnitudeLoss(torch.nn.Module):
-    """Log STFT magnitude loss module."""
-
-    def __init__(self):
-        """Initilize los STFT magnitude loss module."""
-        super(LogSTFTMagnitudeLoss, self).__init__()
-
-    def forward(self, x_mag, y_mag):
-        """Calculate forward propagation.
-        Args:
-            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
-            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
-        Returns:
-            Tensor: Log STFT magnitude loss value.
-        """
-        return F.l1_loss(torch.log(y_mag), torch.log(x_mag))
-
-
-class STFTLoss(torch.nn.Module):
-    """STFT loss module."""
-
-    def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"):
-        """Initialize STFT loss module."""
-        super(STFTLoss, self).__init__()
-        self.fft_size = fft_size
-        self.shift_size = shift_size
-        self.win_length = win_length
-        self.window = getattr(torch, window)(win_length)
-        self.spectral_convergenge_loss = SpectralConvergengeLoss()
-        self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
-
-    def forward(self, x, y):
-        """Calculate forward propagation.
-        Args:
-            x (Tensor): Predicted signal (B, T).
-            y (Tensor): Groundtruth signal (B, T).
-        Returns:
-            Tensor: Spectral convergence loss value.
-            Tensor: Log STFT magnitude loss value.
-        """
-        x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window.to(x.get_device()))
-        y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(x.get_device()))
-        sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
-        mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
-
-        return sc_loss, mag_loss
-
-
-class MultiResolutionSTFTLoss(torch.nn.Module):
-    """Multi resolution STFT loss module."""
-
-    def __init__(self,
-                 fft_sizes=[1024, 2048, 512],
-                 hop_sizes=[120, 240, 50],
-                 win_lengths=[600, 1200, 240],
-                 window="hann_window"):
-        """Initialize Multi resolution STFT loss module.
-        Args:
-            fft_sizes (list): List of FFT sizes.
-            hop_sizes (list): List of hop sizes.
-            win_lengths (list): List of window lengths.
-            window (str): Window function type.
-        """
-        super(MultiResolutionSTFTLoss, self).__init__()
-        assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
-        self.stft_losses = torch.nn.ModuleList()
-        for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
-            self.stft_losses += [STFTLoss(fs, ss, wl, window)]
-
-    def forward(self, x, y):
-        """Calculate forward propagation.
-        Args:
-            x (Tensor): Predicted signal (B, T).
-            y (Tensor): Groundtruth signal (B, T).
-        Returns:
-            Tensor: Multi resolution spectral convergence loss value.
-            Tensor: Multi resolution log STFT magnitude loss value.
-        """
-        sc_loss = 0.0
-        mag_loss = 0.0
-        for f in self.stft_losses:
-            sc_l, mag_l = f(x, y)
-            sc_loss += sc_l
-            mag_loss += mag_l
-        sc_loss /= len(self.stft_losses)
-        mag_loss /= len(self.stft_losses)
-
-        return sc_loss, mag_loss

spaces/AIWaves/Debate/src/agents/__init__.py

@@ -1,4 +0,0 @@
-from .evolve import *
-from .SOP import *
-from .State import *
-from .utils import *

spaces/Abhaykoul/HelpingAI-t2/README.md

@@ -1,12 +0,0 @@
----
-title: HelpingAI T2
-emoji: ⚡
-colorFrom: green
-colorTo: blue
-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/AchyuthGamer/OpenGPT/g4f/Provider/helper.py

@@ -1,77 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import sys
-from asyncio import AbstractEventLoop
-from os import path
-from typing import Dict, List
-import browser_cookie3
-
-# Change event loop policy on windows
-if sys.platform == 'win32':
-    if isinstance(
-        asyncio.get_event_loop_policy(), asyncio.WindowsProactorEventLoopPolicy
-    ):
-        asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
-
-# Local Cookie Storage
-_cookies: Dict[str, Dict[str, str]] = {}
-
-# If event loop is already running, handle nested event loops
-# If "nest_asyncio" is installed, patch the event loop.
-def get_event_loop() -> AbstractEventLoop:
-    try:
-        asyncio.get_running_loop()
-    except RuntimeError:
-        try:
-            return asyncio.get_event_loop()
-        except RuntimeError:
-            asyncio.set_event_loop(asyncio.new_event_loop())
-            return asyncio.get_event_loop()
-    try:
-        event_loop = asyncio.get_event_loop()
-        if not hasattr(event_loop.__class__, "_nest_patched"):
-            import nest_asyncio
-            nest_asyncio.apply(event_loop)
-        return event_loop
-    except ImportError:
-        raise RuntimeError(
-            'Use "create_async" instead of "create" function in a running event loop. Or install the "nest_asyncio" package.'
-        )
-
-
-# Load cookies for a domain from all supported browsers.
-# Cache the results in the "_cookies" variable.
-def get_cookies(cookie_domain: str) -> Dict[str, str]:
-    if cookie_domain not in _cookies:
-        _cookies[cookie_domain] = {}
-        try:
-            for cookie in browser_cookie3.load(cookie_domain):
-                _cookies[cookie_domain][cookie.name] = cookie.value
-        except:
-            pass
-    return _cookies[cookie_domain]
-
-
-def format_prompt(messages: List[Dict[str, str]], add_special_tokens=False) -> str:
-    if add_special_tokens or len(messages) > 1:
-        formatted = "\n".join(
-            [
-                "%s: %s" % ((message["role"]).capitalize(), message["content"])
-                for message in messages
-            ]
-        )
-        return f"{formatted}\nAssistant:"
-    else:
-        return messages[0]["content"]
-
-
-def get_browser(user_data_dir: str = None):
-    from undetected_chromedriver import Chrome
-    from platformdirs import user_config_dir
-
-    if not user_data_dir:
-        user_data_dir = user_config_dir("g4f")
-        user_data_dir = path.join(user_data_dir, "Default")
-
-    return Chrome(user_data_dir=user_data_dir)

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/rotate-plugin.d.ts

@@ -1,9 +0,0 @@
-import Rotate from './rotate';
-
-export default class RotatePlugin extends Phaser.Plugins.BasePlugin {
-    add(
-        gameObject: Phaser.GameObjects.GameObject,
-        config?: Rotate.IConfig
-    ): Rotate;
-
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/Bejeweled.js

@@ -1,82 +0,0 @@
-import ComponentBase from '../../plugins/utils/componentbase/ComponentBase.js';
-import MainState from './states/MainState.js';
-import Board from './board/Board.js';
-import Input from './input/Input.js';
-import WaitEvents from '../../plugins/waitevents.js';
-import InputMethods from './methods/InputMethods.js';
-import BoardMethods from './methods/BoardMethods.js';
-import WaitEventMethods from './methods/WaitEventMethods.js';
-import DataManagerMethods from '../../plugins/utils/data/DataManagerMethods.js';
-
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class Bejeweled extends ComponentBase {
-    constructor(scene, config) {
-        super(scene, config);
-        // this.scene
-
-        var rexBoardKey = GetValue(config, 'rexBoard', 'rexBoard');
-        this.rexBoard = scene[rexBoardKey];
-
-        this.board = new Board(this, config);
-
-        var defaultInput = GetValue(config, 'input', true);
-        if (defaultInput) {
-            this.input = new Input(this, config);
-        } else {
-            this.input = undefined;
-        }
-
-        this.waitEvents = new WaitEvents();
-
-        this.mainState = new MainState(this, config);
-
-        this.boot();
-    }
-
-    boot() {
-        this.scene.events.once('shutdown', this.destroy, this);
-    }
-
-    shutdown(fromScene) {
-        super.shutdown(fromScene);
-
-        if (this.input) {
-            this.input.destroy();
-        }
-        this.board.destroy();
-        this.mainState.destroy();
-        this.waitEvents.destroy();
-
-        this.destroyDataManager();
-
-        this.board = undefined;
-        this.mainState = undefined;
-        this.input = undefined;
-        this.waitEvents = undefined;
-
-        return this;
-    }
-
-    destroy(fromScene) {
-        this.emit('destroy');
-        super.destroy(fromScene);
-        return this;
-    }
-
-    start() {
-        this.mainState.goto('START');
-        return this;
-    }
-}
-
-Object.assign(
-    Bejeweled.prototype,
-    InputMethods,
-    BoardMethods,
-    WaitEventMethods,
-    DataManagerMethods
-);
-
-export default Bejeweled;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/inputtext/InputText.d.ts

@@ -1,2 +0,0 @@
-import InputText from '../../../plugins/inputtext';
-export default InputText;

spaces/AkitoP/umamusume_bert_vits2/text/symbols.py

@@ -1,188 +0,0 @@
-punctuation = ["!", "?", "…", ",", ".", "'", "-"]
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = "_"
-
-# chinese
-zh_symbols = [
-    "E",
-    "En",
-    "a",
-    "ai",
-    "an",
-    "ang",
-    "ao",
-    "b",
-    "c",
-    "ch",
-    "d",
-    "e",
-    "ei",
-    "en",
-    "eng",
-    "er",
-    "f",
-    "g",
-    "h",
-    "i",
-    "i0",
-    "ia",
-    "ian",
-    "iang",
-    "iao",
-    "ie",
-    "in",
-    "ing",
-    "iong",
-    "ir",
-    "iu",
-    "j",
-    "k",
-    "l",
-    "m",
-    "n",
-    "o",
-    "ong",
-    "ou",
-    "p",
-    "q",
-    "r",
-    "s",
-    "sh",
-    "t",
-    "u",
-    "ua",
-    "uai",
-    "uan",
-    "uang",
-    "ui",
-    "un",
-    "uo",
-    "v",
-    "van",
-    "ve",
-    "vn",
-    "w",
-    "x",
-    "y",
-    "z",
-    "zh",
-    "AA",
-    "EE",
-    "OO",
-]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = [
-    "N",
-    "a",
-    "a:",
-    "b",
-    "by",
-    "ch",
-    "d",
-    "dy",
-    "e",
-    "e:",
-    "f",
-    "g",
-    "gy",
-    "h",
-    "hy",
-    "i",
-    "i:",
-    "j",
-    "k",
-    "ky",
-    "m",
-    "my",
-    "n",
-    "ny",
-    "o",
-    "o:",
-    "p",
-    "py",
-    "q",
-    "r",
-    "ry",
-    "s",
-    "sh",
-    "t",
-    "ts",
-    "ty",
-    "u",
-    "u:",
-    "w",
-    "y",
-    "z",
-    "zy",
-  #  ":"
-]
-num_ja_tones = 1
-
-# English
-en_symbols = [
-    "aa",
-    "ae",
-    "ah",
-    "ao",
-    "aw",
-    "ay",
-    "b",
-    "ch",
-    "d",
-    "dh",
-    "eh",
-    "er",
-    "ey",
-    "f",
-    "g",
-    "hh",
-    "ih",
-    "iy",
-    "jh",
-    "k",
-    "l",
-    "m",
-    "n",
-    "ng",
-    "ow",
-    "oy",
-    "p",
-    "r",
-    "s",
-    "sh",
-    "t",
-    "th",
-    "uh",
-    "uw",
-    "V",
-    "w",
-    "y",
-    "z",
-    "zh",
-]
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
-    "ZH": 0,
-    "JP": num_zh_tones,
-    "EN": num_zh_tones + num_ja_tones,
-}
-
-if __name__ == "__main__":
-    a = set(zh_symbols)
-    b = set(en_symbols)
-    print(sorted(a & b))

spaces/Akmyradov/TurkmenTTSweSTT/uroman/lib/NLP/UTF8.pm

@@ -1,1404 +0,0 @@
-################################################################
-#                                                              #
-# UTF8                                                         #
-#                                                              #
-################################################################
-
-package NLP::UTF8;
-
-use NLP::utilities;
-$util = NLP::utilities;
-
-%empty_ht = ();
-
-sub new {
-   local($caller) = @_;
-
-   my $object = {};
-   my $class = ref( $caller ) || $caller;
-   bless($object, $class);
-   return $object;
-}
-
-sub unicode_string2string {
-# input: string that might contain unicode sequences such as "U+0627"
-# output: string in pure utf-8
-   local($caller,$s) = @_;
-
-   my $pre; 
-   my $unicode; 
-   my $post; 
-   my $r1; 
-   my $r2; 
-   my $r3;
-
-   ($pre,$unicode,$post) = ($s =~ /^(.*)(?:U\+|\\u)([0-9A-Fa-f][0-9A-Fa-f][0-9A-Fa-f][0-9A-Fa-f])(.*)$/);
-   return $s unless defined($post);
-   $r1 = $caller->unicode_string2string($pre);
-   $r2 = $caller->unicode_hex_string2string($unicode);
-   $r3 = $caller->unicode_string2string($post);
-   $result = $r1 . $r2 . $r3;
-   return $result;
-}
-
-sub unicode_hex_string2string {
-# input: "0627" (interpreted as hex code)
-# output: utf-8 string for Arabic letter alef
-   local($caller,$unicode) = @_;
-   return "" unless defined($unicode);
-   my $d = hex($unicode);
-   return $caller->unicode2string($d);
-}
-
-sub unicode2string {
-# input: non-neg integer, e.g. 0x627
-# output: utf-8 string for Arabic letter alef
-   local($caller,$d) = @_;
-   return "" unless defined($d) && $d >= 0;
-   return sprintf("%c",$d) if $d <= 0x7F;
-
-   my $lastbyte1 = ($d & 0x3F) | 0x80;
-   $d >>= 6;
-   return sprintf("%c%c",$d | 0xC0, $lastbyte1) if $d <= 0x1F;
-
-   my $lastbyte2 = ($d & 0x3F) | 0x80;
-   $d >>= 6;
-   return sprintf("%c%c%c",$d | 0xE0, $lastbyte2, $lastbyte1) if $d <= 0xF;
-
-   my $lastbyte3 = ($d & 0x3F) | 0x80;
-   $d >>= 6;
-   return sprintf("%c%c%c%c",$d | 0xF0, $lastbyte3, $lastbyte2, $lastbyte1) if $d <= 0x7;
-
-   my $lastbyte4 = ($d & 0x3F) | 0x80;
-   $d >>= 6;
-   return sprintf("%c%c%c%c%c",$d | 0xF8, $lastbyte4, $lastbyte3, $lastbyte2, $lastbyte1) if $d <= 0x3;
-
-   my $lastbyte5 = ($d & 0x3F) | 0x80;
-   $d >>= 6;
-   return sprintf("%c%c%c%c%c%c",$d | 0xFC, $lastbyte5, $lastbyte4, $lastbyte3, $lastbyte2, $lastbyte1) if $d <= 0x1;
-   return ""; # bad input
-}
-
-sub html2utf8 {
-   local($caller, $string) = @_;
-
-   return $string unless $string =~ /\&\#\d{3,5};/;
-
-   my $prev = "";
-   my $s = $string;
-   while ($s ne $prev) {
-      $prev = $s;
-      ($pre,$d,$post) = ($s =~ /^(.*)\&\#(\d+);(.*)$/);
-      if (defined($d) && ((($d >= 160) && ($d <= 255))
-                       || (($d >= 1500) && ($d <= 1699))
-                       || (($d >= 19968) && ($d <= 40879)))) {
-         $html_code = "\&\#" . $d . ";";
-         $utf8_code = $caller->unicode2string($d);
-         $s =~ s/$html_code/$utf8_code/;
-      }
-   }
-   return $s;
-}
-
-sub xhtml2utf8 {
-   local($caller, $string) = @_;
-
-   return $string unless $string =~ /\&\#x[0-9a-fA-F]{2,5};/;
-
-   my $prev = "";
-   my $s = $string;
-   while ($s ne $prev) {
-      $prev = $s;
-      if (($pre, $html_code, $x, $post) = ($s =~ /^(.*)(\&\#x([0-9a-fA-F]{2,5});)(.*)$/)) {
-         $utf8_code = $caller->unicode_hex_string2string($x);
-         $s =~ s/$html_code/$utf8_code/;
-      }
-   }
-   return $s;
-}
-
-sub utf8_marker {
-   return sprintf("%c%c%c\n", 0xEF, 0xBB, 0xBF);
-}
-
-sub enforcer {
-# input: string that might not conform to utf-8
-# output: string in pure utf-8, with a few "smart replacements" and possibly "?"
-   local($caller,$s,$no_repair) = @_;
-
-   my $ascii;
-   my $utf8;
-   my $rest;
-
-   return $s if $s =~ /^[\x00-\x7F]*$/;
-
-   $no_repair = 0 unless defined($no_repair);
-   $orig = $s;
-   $result = "";
-
-   while ($s ne "") {
-      ($ascii,$rest) = ($s =~ /^([\x00-\x7F]+)(.*)$/);
-      if (defined($ascii)) {
-	 $result .= $ascii;
-	 $s = $rest;
-	 next;
-      }
-      ($utf8,$rest) = ($s =~ /^([\xC0-\xDF][\x80-\xBF])(.*)$/);
-      ($utf8,$rest) = ($s =~ /^([\xE0-\xEF][\x80-\xBF][\x80-\xBF])(.*)$/) 
-	 unless defined($rest);
-      ($utf8,$rest) = ($s =~ /^([\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF])(.*)$/)
-	 unless defined($rest);
-      ($utf8,$rest) = ($s =~ /^([\xF8-\xFB][\x80-\xBF][\x80-\xBF][\x80-\xBF][\x80-\xBF])(.*)$/) 
-	 unless defined($rest);
-      if (defined($utf8)) {
-	 $result .= $utf8;
-	 $s = $rest;
-	 next;
-      }
-      ($c,$rest) = ($s =~ /^(.)(.*)$/);
-      if (defined($c)) {
-	 if    ($no_repair)   { $result .= "?"; }
-	 elsif ($c =~ /\x85/) { $result .= "..."; }
-	 elsif ($c =~ /\x91/) { $result .= "'"; }
-	 elsif ($c =~ /\x92/) { $result .= "'"; }
-	 elsif ($c =~ /\x93/) { $result .= $caller->unicode2string(0x201C); }
-	 elsif ($c =~ /\x94/) { $result .= $caller->unicode2string(0x201D); }
-	 elsif ($c =~ /[\xC0-\xFF]/) {
-	    $c2 = $c;
-	    $c2 =~ tr/[\xC0-\xFF]/[\x80-\xBF]/;
-	    $result .= "\xC3$c2";
-	 } else {
-	    $result .= "?";
-	 }
-	 $s = $rest;
-	 next;
-      }
-      $s = "";
-   }
-   $result .= "\n" if ($orig =~ /\n$/) && ! ($result =~ /\n$/);
-   return $result;
-}
-
-sub split_into_utf8_characters {
-# input: utf8 string
-# output: list of sub-strings, each representing a utf8 character
-   local($caller,$string,$group_control, *ht) = @_;
-
-   @characters = ();
-   $end_of_token_p_string = "";
-   $skipped_bytes = "";
-   $group_control = "" unless defined($group_control);
-   $group_ascii_numbers = ($group_control =~ /ASCII numbers/);
-   $group_ascii_spaces = ($group_control =~ /ASCII spaces/);
-   $group_ascii_punct = ($group_control =~ /ASCII punct/);
-   $group_ascii_chars = ($group_control =~ /ASCII chars/);
-   $group_xml_chars = ($group_control =~ /XML chars/);
-   $group_xml_tags = ($group_control =~ /XML tags/);
-   $return_only_chars = ($group_control =~ /return only chars/);
-   $return_trailing_whitespaces = ($group_control =~ /return trailing whitespaces/);
-   if ($group_control =~ /ASCII all/) {
-      $group_ascii_numbers = 1;
-      $group_ascii_spaces = 1;
-      $group_ascii_chars = 1;
-      $group_ascii_punct = 1;
-   }
-   if ($group_control =~ /(XML chars and tags|XML tags and chars)/) {
-      $group_xml_chars = 1;
-      $group_xml_tags = 1;
-   }
-   $orig_string = $string;
-   $string .= " ";
-   while ($string =~ /\S/) {
-      # one-character UTF-8 = ASCII
-      if ($string =~ /^[\x00-\x7F]/) {
-	 if ($group_xml_chars
-	  && (($dec_unicode, $rest) = ($string =~ /^&#(\d+);(.*)$/s))
-	  && ($utf8_char = $caller->unicode2string($dec_unicode))) {
-	    push(@characters, $utf8_char);
-	    $string = $rest;
-	 } elsif ($group_xml_chars
-	  && (($hex_unicode, $rest) = ($string =~ /^&#x([0-9a-f]{1,6});(.*)$/is))
-	  && ($utf8_char = $caller->unicode_hex_string2string($hex_unicode))) {
-	    push(@characters, $utf8_char);
-	    $string = $rest;
-	 } elsif ($group_xml_chars
-	  && (($html_entity_name, $rest) = ($string =~ /^&([a-z]{1,6});(.*)$/is))
-	  && ($dec_unicode = $ht{HTML_ENTITY_NAME_TO_DECUNICODE}->{$html_entity_name})
-	  && ($utf8_char = $caller->unicode2string($dec_unicode))
-	  ) {
-	    push(@characters, $utf8_char);
-	    $string = $rest;
-	 } elsif ($group_xml_tags
-	       && (($tag, $rest) = ($string =~ /^(<\/?[a-zA-Z][-_:a-zA-Z0-9]*(\s+[a-zA-Z][-_:a-zA-Z0-9]*=\"[^"]*\")*\s*\/?>)(.*)$/s))) {
-            push(@characters, $tag);
-	    $string = $rest;
-	 } elsif ($group_ascii_numbers && ($string =~ /^[12]\d\d\d\.[01]?\d.[0-3]?\d([^0-9].*)?$/)) {
-	    ($date) = ($string =~ /^(\d\d\d\d\.\d?\d.\d?\d)([^0-9].*)?$/);
-	    push(@characters,$date);
-	    $string = substr($string, length($date));
-	 } elsif ($group_ascii_numbers && ($string =~ /^\d/)) {
-	    ($number) = ($string =~ /^(\d+(,\d\d\d)*(\.\d+)?)/);
-	    push(@characters,$number);
-	    $string = substr($string, length($number));
-	 } elsif ($group_ascii_spaces && ($string =~ /^(\s+)/)) {
-	    ($space) = ($string =~ /^(\s+)/);
-	    $string = substr($string, length($space));
-	 } elsif ($group_ascii_punct && (($punct_seq) = ($string =~ /^(-+|\.+|[:,%()"])/))) {
-	    push(@characters,$punct_seq);
-	    $string = substr($string, length($punct_seq));
-	 } elsif ($group_ascii_chars && (($word) = ($string =~ /^(\$[A-Z]*|[A-Z]{1,3}\$)/))) {
-	    push(@characters,$word);
-	    $string = substr($string, length($word));
-	 } elsif ($group_ascii_chars && (($abbrev) = ($string =~ /^((?:Jan|Feb|Febr|Mar|Apr|Jun|Jul|Aug|Sep|Sept|Oct|Nov|Dec|Mr|Mrs|Dr|a.m|p.m)\.)/))) {
-	    push(@characters,$abbrev);
-	    $string = substr($string, length($abbrev));
-	 } elsif ($group_ascii_chars && (($word) = ($string =~ /^(second|minute|hour|day|week|month|year|inch|foot|yard|meter|kilometer|mile)-(?:long|old)/i))) {
-	    push(@characters,$word);
-	    $string = substr($string, length($word));
-	 } elsif ($group_ascii_chars && (($word) = ($string =~ /^(zero|one|two|three|four|five|six|seven|eight|nine|ten|eleven|twelve|thirteen|fourteen|fifteen|sixteen|seventeen|eighteen|nineteen|twenty|thirty|forty|fifty|sixty|seventy|eighty|ninety|hundred|thousand|million|billion|trillion)-/i))) {
-	    push(@characters,$word);
-	    $string = substr($string, length($word));
-	 } elsif ($group_ascii_chars && (($word) = ($string =~ /^([a-zA-Z]+)(?:[ ,;%?|()"]|'s |' |\. |\d+[:hms][0-9 ])/))) {
-	    push(@characters,$word);
-	    $string = substr($string, length($word));
-	 } elsif ($group_ascii_chars && ($string =~ /^([\x21-\x27\x2A-\x7E]+)/)) { # exclude ()
-	    ($ascii) = ($string =~ /^([\x21-\x27\x2A-\x7E]+)/);  # ASCII black-characters
-	    push(@characters,$ascii);
-	    $string = substr($string, length($ascii));
-	 } elsif ($group_ascii_chars && ($string =~ /^([\x21-\x7E]+)/)) {
-	    ($ascii) = ($string =~ /^([\x21-\x7E]+)/);  # ASCII black-characters
-	    push(@characters,$ascii);
-	    $string = substr($string, length($ascii));
-	 } elsif ($group_ascii_chars && ($string =~ /^([\x00-\x7F]+)/)) {
-	    ($ascii) = ($string =~ /^([\x00-\x7F]+)/);
-	    push(@characters,$ascii);
-	    $string = substr($string, length($ascii));
-	 } else {
-	    push(@characters,substr($string, 0, 1));
-	    $string = substr($string, 1);
-	 }
-
-      # two-character UTF-8
-      } elsif ($string =~ /^[\xC0-\xDF][\x80-\xBF]/) {
-	 push(@characters,substr($string, 0, 2));
-	 $string = substr($string, 2);
-
-      # three-character UTF-8
-      } elsif ($string =~ /^[\xE0-\xEF][\x80-\xBF][\x80-\xBF]/) {
-	 push(@characters,substr($string, 0, 3));
-	 $string = substr($string, 3);
-
-      # four-character UTF-8
-      } elsif ($string =~ /^[\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF]/) {
-	 push(@characters,substr($string, 0, 4));
-	 $string = substr($string, 4);
-
-      # five-character UTF-8
-      } elsif ($string =~ /^[\xF8-\xFB][\x80-\xBF][\x80-\xBF][\x80-\xBF][\x80-\xBF]/) {
-	 push(@characters,substr($string, 0, 5));
-	 $string = substr($string, 5);
-
-      # six-character UTF-8
-      } elsif ($string =~ /^[\xFC-\xFD][\x80-\xBF][\x80-\xBF][\x80-\xBF][\x80-\xBF][\x80-\xBF]/) {
-	 push(@characters,substr($string, 0, 6));
-	 $string = substr($string, 6);
-
-      # not a UTF-8 character
-      } else {
-         $skipped_bytes .= substr($string, 0, 1);
-	 $string = substr($string, 1);
-      }
-      
-      $end_of_token_p_string .= ($string =~ /^\S/) ? "0" : "1" 
-	 if $#characters >= length($end_of_token_p_string);
-   }
-   $string =~ s/ $//; # remove previously added space, but keep original spaces
-   if ($return_trailing_whitespaces) {
-      while ($string =~ /^[ \t]/) {
-         push(@characters,substr($string, 0, 1));
-         $string = substr($string, 1);
-      }
-      push(@characters, "\n") if $orig_string =~ /\n$/;
-   }
-   return ($return_only_chars) ? @characters : ($skipped_bytes, $end_of_token_p_string, @characters);
-}
-
-sub max_substring_info {
-   local($caller,$s1,$s2,$info_type) = @_;
-
-   ($skipped_bytes1, $end_of_token_p_string1, @char_list1) = $caller->split_into_utf8_characters($s1, "", *empty_ht);
-   ($skipped_bytes2, $end_of_token_p_string2, @char_list2) = $caller->split_into_utf8_characters($s2, "", *empty_ht);
-   return 0 if $skipped_bytes1 || $skipped_bytes2;
-
-   $best_substring_start1 = 0;
-   $best_substring_start2 = 0;
-   $best_substring_length = 0;
-
-   foreach $start_pos2 ((0 .. $#char_list2)) {
-      last if $start_pos2 + $best_substring_length > $#char_list2;
-      foreach $start_pos1 ((0 .. $#char_list1)) {
-         last if $start_pos1 + $best_substring_length > $#char_list1;
-         $matching_length = 0;
-         while (($start_pos1 + $matching_length <= $#char_list1)
-	     && ($start_pos2 + $matching_length <= $#char_list2)
-	     && ($char_list1[$start_pos1+$matching_length] eq $char_list2[$start_pos2+$matching_length])) {
-	    $matching_length++;
-	 }
-	 if ($matching_length > $best_substring_length) {
-	    $best_substring_length = $matching_length;
-	    $best_substring_start1 = $start_pos1;
-	    $best_substring_start2 = $start_pos2;
-	 }
-      }
-   }
-   if ($info_type =~ /^max-ratio1$/) {
-      $length1 = $#char_list1 + 1;
-      return ($length1 > 0) ? ($best_substring_length / $length1) : 0;
-   } elsif ($info_type =~ /^max-ratio2$/) {
-      $length2 = $#char_list2 + 1;
-      return ($length2 > 0) ? ($best_substring_length / $length2) : 0;
-   } elsif ($info_type =~ /^substring$/) {
-      return join("", @char_list1[$best_substring_start1 .. $best_substring_start1+$best_substring_length-1]);
-   } else {
-      $length1 = $#char_list1 + 1;
-      $length2 = $#char_list2 + 1;
-      $info = "s1=$s1;s2=$s2";
-      $info .= ";best_substring_length=$best_substring_length";
-      $info .= ";best_substring_start1=$best_substring_start1";
-      $info .= ";best_substring_start2=$best_substring_start2";
-      $info .= ";length1=$length1";
-      $info .= ";length2=$length2";
-      return $info;
-   }
-}
-
-sub n_shared_chars_at_start {
-   local($caller,$s1,$s2) = @_;
-
-   my $n = 0;
-   while (($s1 ne "") && ($s2 ne "")) {
-      ($c1, $rest1) = ($s1 =~ /^(.[\x80-\xBF]*)(.*)$/);
-      ($c2, $rest2) = ($s2 =~ /^(.[\x80-\xBF]*)(.*)$/);
-      if ($c1 eq $c2) {
-	 $n++;
-	 $s1 = $rest1;
-	 $s2 = $rest2;
-      } else {
-	 last;
-      }
-   }
-   return $n;
-}
-
-sub char_length {
-   local($caller,$string,$byte_offset) = @_;
-
-   my $char = ($byte_offset) ? substr($string, $byte_offset) : $string;
-   return 1 if $char =~ /^[\x00-\x7F]/;
-   return 2 if $char =~ /^[\xC0-\xDF]/;
-   return 3 if $char =~ /^[\xE0-\xEF]/;
-   return 4 if $char =~ /^[\xF0-\xF7]/;
-   return 5 if $char =~ /^[\xF8-\xFB]/;
-   return 6 if $char =~ /^[\xFC-\xFD]/;
-   return 0;
-}
-
-sub length_in_utf8_chars {
-   local($caller,$s) = @_;
-
-   $s =~ s/[\x80-\xBF]//g;
-   $s =~ s/[\x00-\x7F\xC0-\xFF]/c/g;
-   return length($s);
-}
-
-sub byte_length_of_n_chars {
-   local($caller,$char_length,$string,$byte_offset,$undef_return_value) = @_;
-
-   $byte_offset = 0 unless defined($byte_offset);
-   $undef_return_value = -1 unless defined($undef_return_value);
-   my $result = 0;
-   my $len;
-   foreach $i ((1 .. $char_length)) {
-      $len = $caller->char_length($string,($byte_offset+$result));
-      return $undef_return_value unless $len;
-      $result += $len;
-   }
-   return $result;
-}
-
-sub replace_non_ASCII_bytes {
-   local($caller,$string,$replacement) = @_;
-
-   $replacement = "HEX" unless defined($replacement);
-   if ($replacement =~ /^(Unicode|U\+4|\\u|HEX)$/) {
-      $new_string = "";
-      while (($pre,$utf8_char, $post) = ($string =~ /^([\x09\x0A\x20-\x7E]*)([\x00-\x08\x0B-\x1F\x7F]|[\xC0-\xDF][\x80-\xBF]|[\xE0-\xEF][\x80-\xBF][\x80-\xBF]|[\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF]|[\xF8-\xFF][\x80-\xBF]+|[\x80-\xBF])(.*)$/s)) {
-	 if ($replacement =~ /Unicode/) {
-	    $new_string .= $pre . "<U" . (uc $caller->utf8_to_unicode($utf8_char)) . ">";
-	 } elsif ($replacement =~ /\\u/) {
-	    $new_string .= $pre . "\\u" . (uc sprintf("%04x", $caller->utf8_to_unicode($utf8_char)));
-	 } elsif ($replacement =~ /U\+4/) {
-	    $new_string .= $pre . "<U+" . (uc $caller->utf8_to_4hex_unicode($utf8_char)) . ">";
-	 } else {
-	    $new_string .= $pre . "<HEX-" . $caller->utf8_to_hex($utf8_char) . ">";
-	 }
-	 $string = $post;
-      }
-      $new_string .= $string;
-   } else {
-      $new_string = $string;
-      $new_string =~ s/[\x80-\xFF]/$replacement/g;
-   }
-   return $new_string;
-}
-
-sub valid_utf8_string_p {
-   local($caller,$string) = @_;
-
-   return $string =~ /^(?:[\x09\x0A\x20-\x7E]|[\xC0-\xDF][\x80-\xBF]|[\xE0-\xEF][\x80-\xBF][\x80-\xBF]|[\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF])*$/;
-}
-
-sub valid_utf8_string_incl_ascii_control_p {
-   local($caller,$string) = @_;
-
-   return $string =~ /^(?:[\x00-\x7F]|[\xC0-\xDF][\x80-\xBF]|[\xE0-\xEF][\x80-\xBF][\x80-\xBF]|[\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF])*$/;
-}
-
-sub utf8_to_hex {
-   local($caller,$s) = @_;
-
-   $hex = "";
-   foreach $i ((0 .. length($s)-1)) {
-      $hex .= uc sprintf("%2.2x",ord(substr($s, $i, 1)));
-   }
-   return $hex;
-}
-
-sub hex_to_utf8 {
-   local($caller,$s) = @_;
-   # surface string \xE2\x80\xBA to UTF8
-
-   my $utf8 = "";
-   while (($hex, $rest) = ($s =~ /^(?:\\x)?([0-9A-Fa-f]{2,2})(.*)$/)) {
-      $utf8 .= sprintf("%c", hex($hex));
-      $s = $rest;
-   }
-   return $utf8;
-}
-
-sub utf8_to_4hex_unicode {
-   local($caller,$s) = @_;
-
-   return sprintf("%4.4x", $caller->utf8_to_unicode($s));
-}
-
-sub utf8_to_unicode {
-   local($caller,$s) = @_;
-
-   $unicode = 0;
-   foreach $i ((0 .. length($s)-1)) {
-      $c = substr($s, $i, 1);
-      if ($c =~ /^[\x80-\xBF]$/) {
-	 $unicode = $unicode * 64 + (ord($c) & 0x3F);
-      } elsif ($c =~ /^[\xC0-\xDF]$/) {
-	 $unicode = $unicode * 32 + (ord($c) & 0x1F);
-      } elsif ($c =~ /^[\xE0-\xEF]$/) {
-	 $unicode = $unicode * 16 + (ord($c) & 0x0F);
-      } elsif ($c =~ /^[\xF0-\xF7]$/) {
-	 $unicode = $unicode * 8 + (ord($c) & 0x07);
-      } elsif ($c =~ /^[\xF8-\xFB]$/) {
-	 $unicode = $unicode * 4 + (ord($c) & 0x03);
-      } elsif ($c =~ /^[\xFC-\xFD]$/) {
-	 $unicode = $unicode * 2 + (ord($c) & 0x01);
-      }
-   }
-   return $unicode;
-}
-
-sub charhex {
-   local($caller,$string) = @_;
-
-   my $result = "";
-   while ($string ne "") {
-      $char = substr($string, 0, 1);
-      $string = substr($string, 1);
-      if ($char =~ /^[ -~]$/) {
-         $result .= $char;
-      } else {
-	 $hex = sprintf("%2.2x",ord($char));
-	 $hex =~ tr/a-f/A-F/;
-         $result .= "<HEX-$hex>";
-      }
-   }
-   return $result;
-}
-
-sub windows1252_to_utf8 {
-   local($caller,$s, $norm_to_ascii_p, $preserve_potential_utf8s_p) = @_;
-
-   return $s if $s =~ /^[\x00-\x7F]*$/; # all ASCII
-
-   $norm_to_ascii_p = 1 unless defined($norm_to_ascii_p);
-   $preserve_potential_utf8s_p = 1 unless defined($preserve_potential_utf8s_p);
-   my $result = "";
-   my $c = "";
-   while ($s ne "") {
-      $n_bytes = 1;
-      if ($s =~ /^[\x00-\x7F]/) {
-	 $result .= substr($s, 0, 1);  # ASCII
-      } elsif ($preserve_potential_utf8s_p && ($s =~ /^[\xC0-\xDF][\x80-\xBF]/)) {
-	 $result .= substr($s, 0, 2);  # valid 2-byte UTF8
-         $n_bytes = 2;
-      } elsif ($preserve_potential_utf8s_p && ($s =~ /^[\xE0-\xEF][\x80-\xBF][\x80-\xBF]/)) {
-	 $result .= substr($s, 0, 3);  # valid 3-byte UTF8
-         $n_bytes = 3;
-      } elsif ($preserve_potential_utf8s_p && ($s =~ /^[\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF]/)) {
-	 $result .= substr($s, 0, 4);  # valid 4-byte UTF8
-         $n_bytes = 4;
-      } elsif ($preserve_potential_utf8s_p && ($s =~ /^[\xF8-\xFB][\x80-\xBF][\x80-\xBF][\x80-\xBF][\x80-\xBF]/)) {
-	 $result .= substr($s, 0, 5);  # valid 5-byte UTF8
-         $n_bytes = 5;
-      } elsif ($s =~ /^[\xA0-\xBF]/) {
-	 $c = substr($s, 0, 1);
-	 $result .= "\xC2$c";
-      } elsif ($s =~ /^[\xC0-\xFF]/) {
-	 $c = substr($s, 0, 1);
-	 $c =~ tr/[\xC0-\xFF]/[\x80-\xBF]/;
-	 $result .= "\xC3$c";
-      } elsif ($s =~ /^\x80/) {
-	 $result .= "\xE2\x82\xAC";  # Euro sign
-      } elsif ($s =~ /^\x82/) {
-	 $result .= "\xE2\x80\x9A";  # single low quotation mark
-      } elsif ($s =~ /^\x83/) {
-	 $result .= "\xC6\x92";      # Latin small letter f with hook
-      } elsif ($s =~ /^\x84/) {
-	 $result .= "\xE2\x80\x9E";  # double low quotation mark
-      } elsif ($s =~ /^\x85/) {
-	 $result .= ($norm_to_ascii_p) ? "..." : "\xE2\x80\xA6";  # horizontal ellipsis (three dots)
-      } elsif ($s =~ /^\x86/) {
-	 $result .= "\xE2\x80\xA0";  # dagger
-      } elsif ($s =~ /^\x87/) {
-	 $result .= "\xE2\x80\xA1";  # double dagger
-      } elsif ($s =~ /^\x88/) {
-	 $result .= "\xCB\x86";      # circumflex
-      } elsif ($s =~ /^\x89/) {
-	 $result .= "\xE2\x80\xB0";  # per mille sign
-      } elsif ($s =~ /^\x8A/) {
-	 $result .= "\xC5\xA0";      # Latin capital letter S with caron
-      } elsif ($s =~ /^\x8B/) {
-	 $result .= "\xE2\x80\xB9";  # single left-pointing angle quotation mark
-      } elsif ($s =~ /^\x8C/) {
-	 $result .= "\xC5\x92";      # OE ligature
-      } elsif ($s =~ /^\x8E/) {
-	 $result .= "\xC5\xBD";      # Latin capital letter Z with caron
-      } elsif ($s =~ /^\x91/) {
-	 $result .= ($norm_to_ascii_p) ? "`" : "\xE2\x80\x98";  # left single quotation mark
-      } elsif ($s =~ /^\x92/) {
-	 $result .= ($norm_to_ascii_p) ? "'" : "\xE2\x80\x99";  # right single quotation mark
-      } elsif ($s =~ /^\x93/) {
-	 $result .= "\xE2\x80\x9C";  # left double quotation mark
-      } elsif ($s =~ /^\x94/) {
-	 $result .= "\xE2\x80\x9D";  # right double quotation mark
-      } elsif ($s =~ /^\x95/) {
-	 $result .= "\xE2\x80\xA2";  # bullet
-      } elsif ($s =~ /^\x96/) {
-	 $result .= ($norm_to_ascii_p) ? "-" : "\xE2\x80\x93";  # n dash
-      } elsif ($s =~ /^\x97/) {
-	 $result .= ($norm_to_ascii_p) ? "-" : "\xE2\x80\x94";  # m dash
-      } elsif ($s =~ /^\x98/) {
-	 $result .= ($norm_to_ascii_p) ? "~" : "\xCB\x9C";      # small tilde
-      } elsif ($s =~ /^\x99/) {
-	 $result .= "\xE2\x84\xA2";  # trade mark sign
-      } elsif ($s =~ /^\x9A/) {
-	 $result .= "\xC5\xA1";      # Latin small letter s with caron
-      } elsif ($s =~ /^\x9B/) {
-	 $result .= "\xE2\x80\xBA";  # single right-pointing angle quotation mark
-      } elsif ($s =~ /^\x9C/) {
-	 $result .= "\xC5\x93";      # oe ligature
-      } elsif ($s =~ /^\x9E/) {
-	 $result .= "\xC5\xBE";      # Latin small letter z with caron
-      } elsif ($s =~ /^\x9F/) {
-	 $result .= "\xC5\xB8";      # Latin capital letter Y with diaeresis
-      } else {
-	 $result .= "?";
-      }
-      $s = substr($s, $n_bytes);
-   }
-   return $result;
-}
-
-sub delete_weird_stuff {
-   local($caller, $s) = @_;
-
-   # delete control chacters (except tab and linefeed), zero-width characters, byte order mark,
-   # directional marks, join marks, variation selectors, Arabic tatweel
-   $s =~ s/([\x00-\x08\x0B-\x1F\x7F]|\xC2[\x80-\x9F]|\xD9\x80|\xE2\x80[\x8B-\x8F]|\xEF\xB8[\x80-\x8F]|\xEF\xBB\xBF|\xF3\xA0[\x84-\x87][\x80-\xBF])//g;
-   return $s;
-}
-
-sub number_of_utf8_character {
-   local($caller, $s) = @_;
-
-   $s2 = $s;
-   $s2 =~ s/[\x80-\xBF]//g;
-   return length($s2);
-}
-
-sub cap_letter_reg_exp {
-   # includes A-Z and other Latin-based capital letters with accents, umlauts and other decorations etc.
-   return "[A-Z]|\xC3[\x80-\x96\x98-\x9E]|\xC4[\x80\x82\x84\x86\x88\x8A\x8C\x8E\x90\x94\x964\x98\x9A\x9C\x9E\xA0\xA2\xA4\xA6\xA8\xAA\xAC\xAE\xB0\xB2\xB4\xB6\xB9\xBB\xBD\xBF]|\xC5[\x81\x83\x85\x87\x8A\x8C\x8E\x90\x92\x96\x98\x9A\x9C\x9E\xA0\xA2\xA4\xA6\xA8\xAA\xAC\xB0\xB2\xB4\xB6\xB8\xB9\xBB\xBD]";
-}
-
-sub regex_extended_case_expansion {
-   local($caller, $s) = @_;
-
-   if ($s =~ /\xC3/) {
-   $s =~ s/\xC3\xA0/\xC3\[\x80\xA0\]/g;
-   $s =~ s/\xC3\xA1/\xC3\[\x81\xA1\]/g;
-   $s =~ s/\xC3\xA2/\xC3\[\x82\xA2\]/g;
-   $s =~ s/\xC3\xA3/\xC3\[\x83\xA3\]/g;
-   $s =~ s/\xC3\xA4/\xC3\[\x84\xA4\]/g;
-   $s =~ s/\xC3\xA5/\xC3\[\x85\xA5\]/g;
-   $s =~ s/\xC3\xA6/\xC3\[\x86\xA6\]/g;
-   $s =~ s/\xC3\xA7/\xC3\[\x87\xA7\]/g;
-   $s =~ s/\xC3\xA8/\xC3\[\x88\xA8\]/g;
-   $s =~ s/\xC3\xA9/\xC3\[\x89\xA9\]/g;
-   $s =~ s/\xC3\xAA/\xC3\[\x8A\xAA\]/g;
-   $s =~ s/\xC3\xAB/\xC3\[\x8B\xAB\]/g;
-   $s =~ s/\xC3\xAC/\xC3\[\x8C\xAC\]/g;
-   $s =~ s/\xC3\xAD/\xC3\[\x8D\xAD\]/g;
-   $s =~ s/\xC3\xAE/\xC3\[\x8E\xAE\]/g;
-   $s =~ s/\xC3\xAF/\xC3\[\x8F\xAF\]/g;
-   $s =~ s/\xC3\xB0/\xC3\[\x90\xB0\]/g;
-   $s =~ s/\xC3\xB1/\xC3\[\x91\xB1\]/g;
-   $s =~ s/\xC3\xB2/\xC3\[\x92\xB2\]/g;
-   $s =~ s/\xC3\xB3/\xC3\[\x93\xB3\]/g;
-   $s =~ s/\xC3\xB4/\xC3\[\x94\xB4\]/g;
-   $s =~ s/\xC3\xB5/\xC3\[\x95\xB5\]/g;
-   $s =~ s/\xC3\xB6/\xC3\[\x96\xB6\]/g;
-   $s =~ s/\xC3\xB8/\xC3\[\x98\xB8\]/g;
-   $s =~ s/\xC3\xB9/\xC3\[\x99\xB9\]/g;
-   $s =~ s/\xC3\xBA/\xC3\[\x9A\xBA\]/g;
-   $s =~ s/\xC3\xBB/\xC3\[\x9B\xBB\]/g;
-   $s =~ s/\xC3\xBC/\xC3\[\x9C\xBC\]/g;
-   $s =~ s/\xC3\xBD/\xC3\[\x9D\xBD\]/g;
-   $s =~ s/\xC3\xBE/\xC3\[\x9E\xBE\]/g;
-   }
-   if ($s =~ /\xC5/) {
-   $s =~ s/\xC5\x91/\xC5\[\x90\x91\]/g;
-   $s =~ s/\xC5\xA1/\xC5\[\xA0\xA1\]/g;
-   $s =~ s/\xC5\xB1/\xC5\[\xB0\xB1\]/g;
-   }
-
-   return $s;
-}
-
-sub extended_lower_case {
-   local($caller, $s) = @_;
-
-   $s =~ tr/A-Z/a-z/;
-
-      # Latin-1
-      if ($s =~ /\xC3[\x80-\x9F]/) {
-         $s =~ s/À/à/g;
-         $s =~ s/Á/á/g;
-         $s =~ s/Â/â/g;
-         $s =~ s/Ã/ã/g;
-         $s =~ s/Ä/ä/g;
-         $s =~ s/Å/å/g;
-         $s =~ s/Æ/æ/g;
-         $s =~ s/Ç/ç/g;
-         $s =~ s/È/è/g;
-         $s =~ s/É/é/g;
-         $s =~ s/Ê/ê/g;
-         $s =~ s/Ë/ë/g;
-         $s =~ s/Ì/ì/g;
-         $s =~ s/Í/í/g;
-         $s =~ s/Î/î/g;
-         $s =~ s/Ï/ï/g;
-         $s =~ s/Ð/ð/g;
-         $s =~ s/Ñ/ñ/g;
-         $s =~ s/Ò/ò/g;
-         $s =~ s/Ó/ó/g;
-         $s =~ s/Ô/ô/g;
-         $s =~ s/Õ/õ/g;
-         $s =~ s/Ö/ö/g;
-         $s =~ s/Ø/ø/g;
-         $s =~ s/Ù/ù/g;
-         $s =~ s/Ú/ú/g;
-         $s =~ s/Û/û/g;
-         $s =~ s/Ü/ü/g;
-         $s =~ s/Ý/ý/g;
-         $s =~ s/Þ/þ/g;
-      }
-      # Latin Extended-A
-      if ($s =~ /[\xC4-\xC5][\x80-\xBF]/) {
-         $s =~ s/Ā/ā/g;
-         $s =~ s/Ă/ă/g;
-         $s =~ s/Ą/ą/g;
-         $s =~ s/Ć/ć/g;
-         $s =~ s/Ĉ/ĉ/g;
-         $s =~ s/Ċ/ċ/g;
-         $s =~ s/Č/č/g;
-         $s =~ s/Ď/ď/g;
-         $s =~ s/Đ/đ/g;
-         $s =~ s/Ē/ē/g;
-         $s =~ s/Ĕ/ĕ/g;
-         $s =~ s/Ė/ė/g;
-         $s =~ s/Ę/ę/g;
-         $s =~ s/Ě/ě/g;
-         $s =~ s/Ĝ/ĝ/g;
-         $s =~ s/Ğ/ğ/g;
-         $s =~ s/Ġ/ġ/g;
-         $s =~ s/Ģ/ģ/g;
-         $s =~ s/Ĥ/ĥ/g;
-         $s =~ s/Ħ/ħ/g;
-         $s =~ s/Ĩ/ĩ/g;
-         $s =~ s/Ī/ī/g;
-         $s =~ s/Ĭ/ĭ/g;
-         $s =~ s/Į/į/g;
-         $s =~ s/İ/ı/g;
-         $s =~ s/IJ/ij/g;
-         $s =~ s/Ĵ/ĵ/g;
-         $s =~ s/Ķ/ķ/g;
-         $s =~ s/Ĺ/ĺ/g;
-         $s =~ s/Ļ/ļ/g;
-         $s =~ s/Ľ/ľ/g;
-         $s =~ s/Ŀ/ŀ/g;
-         $s =~ s/Ł/ł/g;
-         $s =~ s/Ń/ń/g;
-         $s =~ s/Ņ/ņ/g;
-         $s =~ s/Ň/ň/g;
-         $s =~ s/Ŋ/ŋ/g;
-         $s =~ s/Ō/ō/g;
-         $s =~ s/Ŏ/ŏ/g;
-         $s =~ s/Ő/ő/g;
-         $s =~ s/Œ/œ/g;
-         $s =~ s/Ŕ/ŕ/g;
-         $s =~ s/Ŗ/ŗ/g;
-         $s =~ s/Ř/ř/g;
-         $s =~ s/Ś/ś/g;
-         $s =~ s/Ŝ/ŝ/g;
-         $s =~ s/Ş/ş/g;
-         $s =~ s/Š/š/g;
-         $s =~ s/Ţ/ţ/g;
-         $s =~ s/Ť/ť/g;
-         $s =~ s/Ŧ/ŧ/g;
-         $s =~ s/Ũ/ũ/g;
-         $s =~ s/Ū/ū/g;
-         $s =~ s/Ŭ/ŭ/g;
-         $s =~ s/Ů/ů/g;
-         $s =~ s/Ű/ű/g;
-         $s =~ s/Ų/ų/g;
-         $s =~ s/Ŵ/ŵ/g;
-         $s =~ s/Ŷ/ŷ/g;
-         $s =~ s/Ź/ź/g;
-         $s =~ s/Ż/ż/g;
-         $s =~ s/Ž/ž/g;
-      }
-      # Greek letters
-      if ($s =~ /\xCE[\x86-\xAB]/) {
-         $s =~ s/Α/α/g;
-         $s =~ s/Β/β/g;
-         $s =~ s/Γ/γ/g;
-         $s =~ s/Δ/δ/g;
-         $s =~ s/Ε/ε/g;
-         $s =~ s/Ζ/ζ/g;
-         $s =~ s/Η/η/g;
-         $s =~ s/Θ/θ/g;
-         $s =~ s/Ι/ι/g;
-         $s =~ s/Κ/κ/g;
-         $s =~ s/Λ/λ/g;
-         $s =~ s/Μ/μ/g;
-         $s =~ s/Ν/ν/g;
-         $s =~ s/Ξ/ξ/g;
-         $s =~ s/Ο/ο/g;
-         $s =~ s/Π/π/g;
-         $s =~ s/Ρ/ρ/g;
-         $s =~ s/Σ/σ/g;
-         $s =~ s/Τ/τ/g;
-         $s =~ s/Υ/υ/g;
-         $s =~ s/Φ/φ/g;
-         $s =~ s/Χ/χ/g;
-         $s =~ s/Ψ/ψ/g;
-         $s =~ s/Ω/ω/g;
-         $s =~ s/Ϊ/ϊ/g;
-         $s =~ s/Ϋ/ϋ/g;
-         $s =~ s/Ά/ά/g;
-         $s =~ s/Έ/έ/g;
-         $s =~ s/Ή/ή/g;
-         $s =~ s/Ί/ί/g;
-         $s =~ s/Ό/ό/g;
-         $s =~ s/Ύ/ύ/g;
-         $s =~ s/Ώ/ώ/g;
-      }
-      # Cyrillic letters
-      if ($s =~ /\xD0[\x80-\xAF]/) {
-         $s =~ s/А/а/g;
-         $s =~ s/Б/б/g;
-         $s =~ s/В/в/g;
-         $s =~ s/Г/г/g;
-         $s =~ s/Д/д/g;
-         $s =~ s/Е/е/g;
-         $s =~ s/Ж/ж/g;
-         $s =~ s/З/з/g;
-         $s =~ s/И/и/g;
-         $s =~ s/Й/й/g;
-         $s =~ s/К/к/g;
-         $s =~ s/Л/л/g;
-         $s =~ s/М/м/g;
-         $s =~ s/Н/н/g;
-         $s =~ s/О/о/g;
-         $s =~ s/П/п/g;
-         $s =~ s/Р/р/g;
-         $s =~ s/С/с/g;
-         $s =~ s/Т/т/g;
-         $s =~ s/У/у/g;
-         $s =~ s/Ф/ф/g;
-         $s =~ s/Х/х/g;
-         $s =~ s/Ц/ц/g;
-         $s =~ s/Ч/ч/g;
-         $s =~ s/Ш/ш/g;
-         $s =~ s/Щ/щ/g;
-         $s =~ s/Ъ/ъ/g;
-         $s =~ s/Ы/ы/g;
-         $s =~ s/Ь/ь/g;
-         $s =~ s/Э/э/g;
-         $s =~ s/Ю/ю/g;
-         $s =~ s/Я/я/g;
-         $s =~ s/Ѐ/ѐ/g;
-         $s =~ s/Ё/ё/g;
-         $s =~ s/Ђ/ђ/g;
-         $s =~ s/Ѓ/ѓ/g;
-         $s =~ s/Є/є/g;
-         $s =~ s/Ѕ/ѕ/g;
-         $s =~ s/І/і/g;
-         $s =~ s/Ї/ї/g;
-         $s =~ s/Ј/ј/g;
-         $s =~ s/Љ/љ/g;
-         $s =~ s/Њ/њ/g;
-         $s =~ s/Ћ/ћ/g;
-         $s =~ s/Ќ/ќ/g;
-         $s =~ s/Ѝ/ѝ/g;
-         $s =~ s/Ў/ў/g;
-         $s =~ s/Џ/џ/g;
-      }
-      # Fullwidth A-Z
-      if ($s =~ /\xEF\xBC[\xA1-\xBA]/) {
-         $s =~ s/Ａ/ａ/g;
-         $s =~ s/Ｂ/ｂ/g;
-         $s =~ s/Ｃ/ｃ/g;
-         $s =~ s/Ｄ/ｄ/g;
-         $s =~ s/Ｅ/ｅ/g;
-         $s =~ s/Ｆ/ｆ/g;
-         $s =~ s/Ｇ/ｇ/g;
-         $s =~ s/Ｈ/ｈ/g;
-         $s =~ s/Ｉ/ｉ/g;
-         $s =~ s/Ｊ/ｊ/g;
-         $s =~ s/Ｋ/ｋ/g;
-         $s =~ s/Ｌ/ｌ/g;
-         $s =~ s/Ｍ/ｍ/g;
-         $s =~ s/Ｎ/ｎ/g;
-         $s =~ s/Ｏ/ｏ/g;
-         $s =~ s/Ｐ/ｐ/g;
-         $s =~ s/Ｑ/ｑ/g;
-         $s =~ s/Ｒ/ｒ/g;
-         $s =~ s/Ｓ/ｓ/g;
-         $s =~ s/Ｔ/ｔ/g;
-         $s =~ s/Ｕ/ｕ/g;
-         $s =~ s/Ｖ/ｖ/g;
-         $s =~ s/Ｗ/ｗ/g;
-         $s =~ s/Ｘ/ｘ/g;
-         $s =~ s/Ｙ/ｙ/g;
-         $s =~ s/Ｚ/ｚ/g;
-      }
-
-   return $s;
-}
-
-sub extended_upper_case {
-   local($caller, $s) = @_;
-
-   $s =~ tr/a-z/A-Z/;
-   return $s unless $s =~ /[\xC3-\xC5][\x80-\xBF]/;
-
-   $s =~ s/\xC3\xA0/\xC3\x80/g;
-   $s =~ s/\xC3\xA1/\xC3\x81/g;
-   $s =~ s/\xC3\xA2/\xC3\x82/g;
-   $s =~ s/\xC3\xA3/\xC3\x83/g;
-   $s =~ s/\xC3\xA4/\xC3\x84/g;
-   $s =~ s/\xC3\xA5/\xC3\x85/g;
-   $s =~ s/\xC3\xA6/\xC3\x86/g;
-   $s =~ s/\xC3\xA7/\xC3\x87/g;
-   $s =~ s/\xC3\xA8/\xC3\x88/g;
-   $s =~ s/\xC3\xA9/\xC3\x89/g;
-   $s =~ s/\xC3\xAA/\xC3\x8A/g;
-   $s =~ s/\xC3\xAB/\xC3\x8B/g;
-   $s =~ s/\xC3\xAC/\xC3\x8C/g;
-   $s =~ s/\xC3\xAD/\xC3\x8D/g;
-   $s =~ s/\xC3\xAE/\xC3\x8E/g;
-   $s =~ s/\xC3\xAF/\xC3\x8F/g;
-   $s =~ s/\xC3\xB0/\xC3\x90/g;
-   $s =~ s/\xC3\xB1/\xC3\x91/g;
-   $s =~ s/\xC3\xB2/\xC3\x92/g;
-   $s =~ s/\xC3\xB3/\xC3\x93/g;
-   $s =~ s/\xC3\xB4/\xC3\x94/g;
-   $s =~ s/\xC3\xB5/\xC3\x95/g;
-   $s =~ s/\xC3\xB6/\xC3\x96/g;
-   $s =~ s/\xC3\xB8/\xC3\x98/g;
-   $s =~ s/\xC3\xB9/\xC3\x99/g;
-   $s =~ s/\xC3\xBA/\xC3\x9A/g;
-   $s =~ s/\xC3\xBB/\xC3\x9B/g;
-   $s =~ s/\xC3\xBC/\xC3\x9C/g;
-   $s =~ s/\xC3\xBD/\xC3\x9D/g;
-   $s =~ s/\xC3\xBE/\xC3\x9E/g;
-
-   $s =~ s/\xC5\x91/\xC5\x90/g;
-   $s =~ s/\xC5\xA1/\xC5\xA0/g;
-   $s =~ s/\xC5\xB1/\xC5\xB0/g;
-   return $s unless $s =~ /[\xC3-\xC5][\x80-\xBF]/;
-
-   return $s;
-}
-
-sub extended_first_upper_case {
-   local($caller, $s) = @_;
-
-   if (($first_char, $rest) = ($s =~ /^([\x00-\x7F]|[\xC0-\xDF][\x80-\xBF]|[\xE0-\xEF][\x80-\xBF][\x80-\xBF])(.*)$/)) {
-      return $caller->extended_upper_case($first_char) . $rest;
-   } else {
-      return $s;
-   }
-}
-
-sub repair_doubly_converted_utf8_strings {
-   local($caller, $s) = @_;
-
-   if ($s =~ /\xC3[\x82-\x85]\xC2[\x80-\xBF]/) {
-      $s =~ s/\xC3\x82\xC2([\x80-\xBF])/\xC2$1/g;
-      $s =~ s/\xC3\x83\xC2([\x80-\xBF])/\xC3$1/g;
-      $s =~ s/\xC3\x84\xC2([\x80-\xBF])/\xC4$1/g;
-      $s =~ s/\xC3\x85\xC2([\x80-\xBF])/\xC5$1/g;
-   }
-   return $s;
-}
-
-sub repair_misconverted_windows_to_utf8_strings {
-   local($caller, $s) = @_;
-
-   # correcting conversions of UTF8 using Latin1-to-UTF converter
-   if ($s =~ /\xC3\xA2\xC2\x80\xC2[\x90-\xEF]/) {
-      my $result = "";
-      while (($pre,$last_c,$post) = ($s =~ /^(.*?)\xC3\xA2\xC2\x80\xC2([\x90-\xEF])(.*)$/s)) {
-         $result .= "$pre\xE2\x80$last_c";
-         $s = $post;
-      }
-      $result .= $s;
-      $s = $result;
-   }
-   # correcting conversions of Windows1252-to-UTF8 using Latin1-to-UTF converter
-   if ($s =~ /\xC2[\x80-\x9F]/) {
-      my $result = "";
-      while (($pre,$c_windows,$post) = ($s =~ /^(.*?)\xC2([\x80-\x9F])(.*)$/s)) {
-	 $c_utf8 = $caller->windows1252_to_utf8($c_windows, 0);
-         $result .= ($c_utf8 eq "?") ? ($pre . "\xC2" . $c_windows) : "$pre$c_utf8";
-         $s = $post;
-      }
-      $result .= $s;
-      $s = $result;
-   }
-   if ($s =~ /\xC3/) {
-      $s =~ s/\xC3\xA2\xE2\x80\x9A\xC2\xAC/\xE2\x82\xAC/g;     # x80 -> Euro sign
-                                                               # x81 codepoint undefined in Windows 1252
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xC5\xA1/\xE2\x80\x9A/g;     # x82 -> single low-9 quotation mark
-      $s =~ s/\xC3\x86\xE2\x80\x99/\xC6\x92/g;                 # x83 -> Latin small letter f with hook
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xC5\xBE/\xE2\x80\x9E/g;     # x84 -> double low-9 quotation mark
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xA6/\xE2\x80\xA6/g;     # x85 -> horizontal ellipsis
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xA0/\xE2\x80\xA0/g;     # x86 -> dagger
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xA1/\xE2\x80\xA1/g;     # x87 -> double dagger
-      $s =~ s/\xC3\x8B\xE2\x80\xA0/\xCB\x86/g;                 # x88 -> modifier letter circumflex accent
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xB0/\xE2\x80\xB0/g;     # x89 -> per mille sign
-      $s =~ s/\xC3\x85\xC2\xA0/\xC5\xA0/g;                     # x8A -> Latin capital letter S with caron
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xB9/\xE2\x80\xB9/g;     # x8B -> single left-pointing angle quotation mark
-      $s =~ s/\xC3\x85\xE2\x80\x99/\xC5\x92/g;                 # x8C -> Latin capital ligature OE
-                                                               # x8D codepoint undefined in Windows 1252
-      $s =~ s/\xC3\x85\xC2\xBD/\xC5\xBD/g;                     # x8E -> Latin capital letter Z with caron
-                                                               # x8F codepoint undefined in Windows 1252
-                                                               # x90 codepoint undefined in Windows 1252
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xCB\x9C/\xE2\x80\x98/g;     # x91 a-circumflex+euro+small tilde -> left single quotation mark
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xE2\x84\xA2/\xE2\x80\x99/g; # x92 a-circumflex+euro+trademark -> right single quotation mark
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xC5\x93/\xE2\x80\x9C/g;     # x93 a-circumflex+euro+Latin small ligature oe -> left double quotation mark
-							       # x94 maps through undefined intermediate code point
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xA2/\xE2\x80\xA2/g;     # x95 a-circumflex+euro+cent sign -> bullet
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xE2\x80\x9C/\xE2\x80\x93/g; # x96 a-circumflex+euro+left double quotation mark -> en dash
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xE2\x80\x9D/\xE2\x80\x94/g; # x97 a-circumflex+euro+right double quotation mark -> em dash
-      $s =~ s/\xC3\x8B\xC5\x93/\xCB\x9C/g;                     # x98 Latin capital e diaeresis+Latin small ligature oe -> small tilde
-      $s =~ s/\xC3\xA2\xE2\x80\x9E\xC2\xA2/\xE2\x84\xA2/g;     # x99 -> trade mark sign
-      $s =~ s/\xC3\x85\xC2\xA1/\xC5\xA1/g;                     # x9A -> Latin small letter s with caron
-      $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xBA/\xE2\x80\xBA/g;     # x9B -> single right-pointing angle quotation mark
-      $s =~ s/\xC3\x85\xE2\x80\x9C/\xC5\x93/g;                 # x9C -> Latin small ligature oe
-							       # x9D codepoint undefined in Windows 1252
-      $s =~ s/\xC3\x85\xC2\xBE/\xC5\xBE/g;                     # x9E -> Latin small letter z with caron
-      $s =~ s/\xC3\x85\xC2\xB8/\xC5\xB8/g;                     # x9F -> Latin capital letter Y with diaeresis 
-      $s =~ s/\xC3\xAF\xC2\xBF\xC2\xBD/\xEF\xBF\xBD/g;         # replacement character
-   }
-
-   return $s;
-}
-
-sub latin1_to_utf {
-   local($caller, $s) = @_;
-
-   my $result = "";
-   while (($pre,$c,$post) = ($s =~ /^(.*?)([\x80-\xFF])(.*)$/s)) {
-      $result .= $pre;
-      if ($c =~ /^[\x80-\xBF]$/) {
-         $result .= "\xC2$c";
-      } elsif ($c =~ /^[\xC0-\xFF]$/) {
-         $c =~ tr/[\xC0-\xFF]/[\x80-\xBF]/;
-         $result .= "\xC3$c";
-      }
-      $s = $post;
-   }
-   $result .= $s;
-   return $result;
-}
-
-sub character_type_is_letter_type {
-   local($caller, $char_type) = @_;
-
-   return ($char_type =~ /\b((CJK|hiragana|kana|katakana)\s+character|diacritic|letter|syllable)\b/);
-}
-
-sub character_type {
-   local($caller, $c) = @_;
-
-   if ($c =~ /^[\x00-\x7F]/) {
-      return "XML tag" if $c =~ /^<.*>$/;
-      return "ASCII Latin letter" if $c =~ /^[a-z]$/i;
-      return "ASCII digit" if $c =~ /^[0-9]$/i;
-      return "ASCII whitespace" if $c =~ /^[\x09-\x0D\x20]$/;
-      return "ASCII control-character" if $c =~ /^[\x00-\x1F\x7F]$/;
-      return "ASCII currency" if $c eq "\$";
-      return "ASCII punctuation";
-   } elsif ($c =~ /^[\xC0-\xDF]/) {
-      return "non-UTF8 (invalid)" unless $c =~ /^[\xC0-\xDF][\x80-\xBF]$/;
-      return "non-shortest-UTF8 (invalid)" if $c =~ /[\xC0-\xC1]/;
-      return "non-ASCII control-character" if $c =~ /\xC2[\x80-\x9F]/;
-      return "non-ASCII whitespace" if $c =~ /\xC2\xA0/;
-      return "non-ASCII currency" if $c =~ /\xC2[\xA2-\xA5]/;
-      return "fraction" if $c =~ /\xC2[\xBC-\xBE]/; # NEW
-      return "superscript digit"  if $c =~ /\xC2[\xB2\xB3\xB9]/;
-      return "non-ASCII Latin letter" if $c =~ /\xC2\xB5/; # micro sign
-      return "non-ASCII punctuation" if $c =~ /\xC2[\xA0-\xBF]/;
-      return "non-ASCII punctuation" if $c =~ /\xC3[\x97\xB7]/;
-      return "non-ASCII Latin letter" if $c =~ /\xC3[\x80-\xBF]/;
-      return "Latin ligature letter" if $c =~ /\xC4[\xB2\xB3]/;
-      return "Latin ligature letter" if $c =~ /\xC5[\x92\x93]/;
-      return "non-ASCII Latin letter" if $c =~ /[\xC4-\xC8]/;
-      return "non-ASCII Latin letter" if $c =~ /\xC9[\x80-\x8F]/;
-      return "IPA" if $c =~ /\xC9[\x90-\xBF]/;
-      return "IPA" if $c =~ /\xCA[\x80-\xBF]/;
-      return "IPA" if $c =~ /\xCB[\x80-\xBF]/;
-      return "combining-diacritic" if $c =~ /\xCC[\x80-\xBF]/;
-      return "combining-diacritic" if $c =~ /\xCD[\x80-\xAF]/;
-      return "Greek punctuation" if $c =~ /\xCD[\xBE]/; # Greek question mark
-      return "Greek punctuation" if $c =~ /\xCE[\x87]/; # Greek semicolon
-      return "Greek letter" if $c =~ /\xCD[\xB0-\xBF]/;
-      return "Greek letter" if $c =~ /\xCE/;
-      return "Greek letter" if $c =~ /\xCF[\x80-\xA1\xB3\xB7\xB8\xBA\xBB]/;
-      return "Coptic letter" if $c =~ /\xCF[\xA2-\xAF]/;
-      return "Cyrillic letter" if $c =~ /[\xD0-\xD3]/;
-      return "Cyrillic letter" if $c =~ /\xD4[\x80-\xAF]/;
-      return "Armenian punctuation" if $c =~ /\xD5[\x9A-\x9F]/;
-      return "Armenian punctuation" if $c =~ /\xD6[\x89-\x8F]/;
-      return "Armenian letter" if $c =~ /\xD4[\xB0-\xBF]/;
-      return "Armenian letter" if $c =~ /\xD5/;
-      return "Armenian letter" if $c =~ /\xD6[\x80-\x8F]/;
-      return "Hebrew accent" if $c =~ /\xD6[\x91-\xAE]/;
-      return "Hebrew punctuation" if $c =~ /\xD6\xBE/;
-      return "Hebrew punctuation" if $c =~ /\xD7[\x80\x83\x86\xB3\xB4]/;
-      return "Hebrew point" if $c =~ /\xD6[\xB0-\xBF]/;
-      return "Hebrew point" if $c =~ /\xD7[\x81\x82\x87]/;
-      return "Hebrew letter" if $c =~ /\xD7[\x90-\xB2]/;
-      return "other Hebrew" if $c =~ /\xD6[\x90-\xBF]/;
-      return "other Hebrew" if $c =~ /\xD7/;
-      return "Arabic currency" if $c =~ /\xD8\x8B/; # Afghani sign
-      return "Arabic punctuation" if $c =~ /\xD8[\x89-\x8D\x9B\x9E\x9F]/;
-      return "Arabic punctuation" if $c =~ /\xD9[\xAA-\xAD]/;
-      return "Arabic punctuation" if $c =~ /\xDB[\x94]/;
-      return "Arabic tatweel" if $c =~ /\xD9\x80/;
-      return "Arabic letter"  if $c =~ /\xD8[\xA0-\xBF]/;
-      return "Arabic letter"  if $c =~ /\xD9[\x81-\x9F]/;
-      return "Arabic letter"  if $c =~ /\xD9[\xAE-\xBF]/;
-      return "Arabic letter"  if $c =~ /\xDA[\x80-\xBF]/;
-      return "Arabic letter"  if $c =~ /\xDB[\x80-\x95]/;
-      return "Arabic Indic digit" if $c =~ /\xD9[\xA0-\xA9]/;
-      return "Arabic Indic digit" if $c =~ /\xDB[\xB0-\xB9]/;
-      return "other Arabic" if $c =~ /[\xD8-\xDB]/;
-      return "Syriac punctuation" if $c =~ /\xDC[\x80-\x8F]/;
-      return "Syriac letter" if $c =~ /\xDC[\x90-\xAF]/;
-      return "Syriac diacritic" if $c =~ /\xDC[\xB0-\xBF]/;
-      return "Syriac diacritic" if $c =~ /\xDD[\x80-\x8A]/;
-      return "Thaana letter" if $c =~ /\xDE/;
-   } elsif ($c =~ /^[\xE0-\xEF]/) {
-      return "non-UTF8 (invalid)" unless $c =~ /^[\xE0-\xEF][\x80-\xBF]{2,2}$/;
-      return "non-shortest-UTF8 (invalid)" if $c =~ /\xE0[\x80-\x9F]/;
-      return "Arabic letter"     if $c =~ /\xE0\xA2[\xA0-\xBF]/; # extended letters
-      return "other Arabic"      if $c =~ /\xE0\xA3/; # extended characters
-      return "Devanagari punctuation" if $c =~ /\xE0\xA5[\xA4\xA5]/; # danda, double danda
-      return "Devanagari digit" if $c =~ /\xE0\xA5[\xA6-\xAF]/;
-      return "Devanagari letter" if $c =~ /\xE0[\xA4-\xA5]/;
-      return "Bengali digit" if $c =~ /\xE0\xA7[\xA6-\xAF]/;
-      return "Bengali currency" if $c =~ /\xE0\xA7[\xB2-\xB9]/;
-      return "Bengali letter" if $c =~ /\xE0[\xA6-\xA7]/;
-      return "Gurmukhi digit" if $c =~ /\xE0\xA9[\xA6-\xAF]/;
-      return "Gurmukhi letter" if $c =~ /\xE0[\xA8-\xA9]/;
-      return "Gujarati digit" if $c =~ /\xE0\xAB[\xA6-\xAF]/;
-      return "Gujarati letter" if $c =~ /\xE0[\xAA-\xAB]/;
-      return "Oriya digit" if $c =~ /\xE0\xAD[\xA6-\xAF]/;
-      return "Oriya fraction" if $c =~ /\xE0\xAD[\xB2-\xB7]/;
-      return "Oriya letter" if $c =~ /\xE0[\xAC-\xAD]/;
-      return "Tamil digit" if $c =~ /\xE0\xAF[\xA6-\xAF]/;
-      return "Tamil number" if $c =~ /\xE0\xAF[\xB0-\xB2]/; # number (10, 100, 1000)
-      return "Tamil letter" if $c =~ /\xE0[\xAE-\xAF]/;
-      return "Telegu digit" if $c =~ /\xE0\xB1[\xA6-\xAF]/;
-      return "Telegu fraction" if $c =~ /\xE0\xB1[\xB8-\xBE]/;
-      return "Telegu letter" if $c =~ /\xE0[\xB0-\xB1]/;
-      return "Kannada digit" if $c =~ /\xE0\xB3[\xA6-\xAF]/;
-      return "Kannada letter" if $c =~ /\xE0[\xB2-\xB3]/;
-      return "Malayalam digit" if $c =~ /\xE0\xB5[\x98-\x9E\xA6-\xB8]/;
-      return "Malayalam punctuation" if $c =~ /\xE0\xB5\xB9/; # date mark
-      return "Malayalam letter" if $c =~ /\xE0[\xB4-\xB5]/;
-      return "Sinhala digit" if $c =~ /\xE0\xB7[\xA6-\xAF]/;
-      return "Sinhala punctuation" if $c =~ /\xE0\xB7\xB4/;
-      return "Sinhala letter" if $c =~ /\xE0[\xB6-\xB7]/;
-      return "Thai currency" if $c =~ /\xE0\xB8\xBF/;
-      return "Thai digit" if $c =~ /\xE0\xB9[\x90-\x99]/;
-      return "Thai character" if $c =~ /\xE0[\xB8-\xB9]/;
-      return "Lao punctuation" if $c =~ /\xE0\xBA\xAF/; # Lao ellipsis
-      return "Lao digit" if $c =~ /\xE0\xBB[\x90-\x99]/;
-      return "Lao character" if $c =~ /\xE0[\xBA-\xBB]/;
-      return "Tibetan punctuation" if $c =~ /\xE0\xBC[\x81-\x94]/;
-      return "Tibetan sign"        if $c =~ /\xE0\xBC[\x95-\x9F]/;
-      return "Tibetan digit"       if $c =~ /\xE0\xBC[\xA0-\xB3]/;
-      return "Tibetan punctuation" if $c =~ /\xE0\xBC[\xB4-\xBD]/;
-      return "Tibetan letter" if $c =~ /\xE0[\xBC-\xBF]/;
-      return "Myanmar digit" if $c =~ /\xE1\x81[\x80-\x89]/;
-      return "Myanmar digit" if $c =~ /\xE1\x82[\x90-\x99]/; # Myanmar Shan digits
-      return "Myanmar punctuation" if $c =~ /\xE1\x81[\x8A-\x8B]/;
-      return "Myanmar letter" if $c =~ /\xE1[\x80-\x81]/;
-      return "Myanmar letter" if $c =~ /\xE1\x82[\x80-\x9F]/;
-      return "Georgian punctuation" if $c =~ /\xE1\x83\xBB/;
-      return "Georgian letter" if $c =~ /\xE1\x82[\xA0-\xBF]/;
-      return "Georgian letter" if $c =~ /\xE1\x83/;
-      return "Georgian letter" if $c =~ /\xE1\xB2[\x90-\xBF]/; # Georgian Mtavruli capital letters
-      return "Georgian letter" if $c =~ /\xE2\xB4[\x80-\xAF]/; # Georgian small letters (Khutsuri)
-      return "Korean Hangul letter" if $c =~ /\xE1[\x84-\x87]/;
-      return "Ethiopic punctuation" if $c =~ /\xE1\x8D[\xA0-\xA8]/;
-      return "Ethiopic digit" if $c =~ /\xE1\x8D[\xA9-\xB1]/;
-      return "Ethiopic number" if $c =~ /\xE1\x8D[\xB2-\xBC]/;
-      return "Ethiopic syllable" if $c =~ /\xE1[\x88-\x8D]/;
-      return "Cherokee letter" if $c =~ /\xE1\x8E[\xA0-\xBF]/;
-      return "Cherokee letter" if $c =~ /\xE1\x8F/;
-      return "Canadian punctuation" if $c =~ /\xE1\x90\x80/; # Canadian Syllabics hyphen
-      return "Canadian punctuation" if $c =~ /\xE1\x99\xAE/; # Canadian Syllabics full stop
-      return "Canadian syllable" if $c =~ /\xE1[\x90-\x99]/;
-      return "Canadian syllable" if $c =~ /\xE1\xA2[\xB0-\xBF]/;
-      return "Canadian syllable" if $c =~ /\xE1\xA3/;
-      return "Ogham whitespace" if $c =~ /\xE1\x9A\x80/;
-      return "Ogham letter" if $c =~ /\xE1\x9A[\x81-\x9A]/;
-      return "Ogham punctuation" if $c =~ /\xE1\x9A[\x9B-\x9C]/;
-      return "Runic punctuation" if $c =~ /\xE1\x9B[\xAB-\xAD]/;
-      return "Runic letter" if $c =~ /\xE1\x9A[\xA0-\xBF]/;
-      return "Runic letter" if $c =~ /\xE1\x9B/;
-      return "Khmer currency" if $c =~ /\xE1\x9F\x9B/;
-      return "Khmer digit" if $c =~ /\xE1\x9F[\xA0-\xA9]/;
-      return "Khmer letter" if $c =~ /\xE1[\x9E-\x9F]/;
-      return "Mongolian punctuation" if $c =~ /\xE1\xA0[\x80-\x8A]/;
-      return "Mongolian digit"       if $c =~ /\xE1\xA0[\x90-\x99]/;
-      return "Mongolian letter" if $c =~ /\xE1[\xA0-\xA1]/;
-      return "Mongolian letter" if $c =~ /\xE1\xA2[\x80-\xAF]/;
-      return "Buginese letter" if $c =~ /\xE1\xA8[\x80-\x9B]/;
-      return "Buginese punctuation" if $c =~ /\xE1\xA8[\x9E-\x9F]/;
-      return "Balinese letter" if $c =~ /\xE1\xAC/;
-      return "Balinese letter" if $c =~ /\xE1\xAD[\x80-\x8F]/;
-      return "Balinese digit" if $c =~ /\xE1\xAD[\x90-\x99]/;
-      return "Balinese puncutation" if $c =~ /\xE1\xAD[\x9A-\xA0]/;
-      return "Balinese symbol" if $c =~ /\xE1\xAD[\xA1-\xBF]/;
-      return "Sundanese digit" if $c =~ /\xE1\xAE[\xB0-\xB9]/;
-      return "Sundanese letter" if $c =~ /\xE1\xAE/;
-      return "Cyrillic letter" if $c =~ /\xE1\xB2[\x80-\x8F]/;
-      return "Sundanese punctuation" if $c =~ /\xE1\xB3[\x80-\x8F]/;
-      return "IPA" if $c =~ /\xE1[\xB4-\xB6]/;
-      return "non-ASCII Latin letter" if $c =~ /\xE1[\xB8-\xBB]/;
-      return "Greek letter" if $c =~ /\xE1[\xBC-\xBF]/;
-      return "non-ASCII whitespace"  if $c =~ /\xE2\x80[\x80-\x8A\xAF]/;
-      return "zero-width space"      if $c =~ /\xE2\x80\x8B/;
-      return "zero-width non-space"  if $c =~ /\xE2\x80\x8C/;
-      return "zero-width joiner"     if $c =~ /\xE2\x80\x8D/;
-      return "directional mark"      if $c =~ /\xE2\x80[\x8E-\x8F\xAA-\xAE]/;
-      return "non-ASCII punctuation" if $c =~ /\xE2\x80[\x90-\xBF]/;
-      return "non-ASCII punctuation" if $c =~ /\xE2\x81[\x80-\x9E]/;
-      return "superscript letter"    if $c =~ /\xE2\x81[\xB1\xBF]/;
-      return "superscript digit"     if $c =~ /\xE2\x81[\xB0-\xB9]/;
-      return "superscript punctuation" if $c =~ /\xE2\x81[\xBA-\xBE]/;
-      return "subscript digit"       if $c =~ /\xE2\x82[\x80-\x89]/;
-      return "subscript punctuation" if $c =~ /\xE2\x82[\x8A-\x8E]/;
-      return "non-ASCII currency"    if $c =~ /\xE2\x82[\xA0-\xBF]/;
-      return "letterlike symbol"     if $c =~ /\xE2\x84/;
-      return "letterlike symbol"     if $c =~ /\xE2\x85[\x80-\x8F]/;
-      return "fraction"              if $c =~ /\xE2\x85[\x90-\x9E]/; # NEW
-      return "Roman number"          if $c =~ /\xE2\x85[\xA0-\xBF]/; # NEW
-      return "arrow symbol"          if $c =~ /\xE2\x86[\x90-\xBF]/;
-      return "arrow symbol"          if $c =~ /\xE2\x87/;
-      return "mathematical operator" if $c =~ /\xE2[\x88-\x8B]/;
-      return "technical symbol"      if $c =~ /\xE2[\x8C-\x8F]/;
-      return "enclosed alphanumeric" if $c =~ /\xE2\x91[\xA0-\xBF]/;
-      return "enclosed alphanumeric" if $c =~ /\xE2[\x92-\x93]/;
-      return "box drawing" if $c =~ /\xE2[\x94-\x95]/;
-      return "geometric shape" if $c =~ /\xE2\x96[\xA0-\xBF]/;
-      return "geometric shape" if $c =~ /\xE2\x97/;
-      return "pictograph" if $c =~ /\xE2[\x98-\x9E]/;
-      return "arrow symbol"          if $c =~ /\xE2\xAC[\x80-\x91\xB0-\xBF]/;
-      return "geometric shape"       if $c =~ /\xE2\xAC[\x92-\xAF]/;         
-      return "arrow symbol"          if $c =~ /\xE2\xAD[\x80-\x8F\x9A-\xBF]/;
-      return "geometric shape"       if $c =~ /\xE2\xAD[\x90-\x99]/;        
-      return "arrow symbol"          if $c =~ /\xE2\xAE[\x80-\xB9]/;       
-      return "geometric shape"       if $c =~ /\xE2\xAE[\xBA-\xBF]/;      
-      return "geometric shape"       if $c =~ /\xE2\xAF[\x80-\x88\x8A-\x8F]/;
-      return "symbol"                if $c =~ /\xE2[\xAC-\xAF]/;
-      return "Coptic fraction" if $c =~ /\xE2\xB3\xBD/;
-      return "Coptic punctuation" if $c =~ /\xE2\xB3[\xB9-\xBF]/;
-      return "Coptic letter" if $c =~ /\xE2[\xB2-\xB3]/;
-      return "Georgian letter" if $c =~ /\xE2\xB4[\x80-\xAF]/;
-      return "Tifinagh punctuation" if $c =~ /\xE2\xB5\xB0/;
-      return "Tifinagh letter" if $c =~ /\xE2\xB4[\xB0-\xBF]/;
-      return "Tifinagh letter" if $c =~ /\xE2\xB5/;
-      return "Ethiopic syllable" if $c =~ /\xE2\xB6/;
-      return "Ethiopic syllable" if $c =~ /\xE2\xB7[\x80-\x9F]/;
-      return "non-ASCII punctuation" if $c =~ /\xE3\x80[\x80-\x91\x94-\x9F\xB0\xBB-\xBD]/;
-      return "symbol" if $c =~ /\xE3\x80[\x91\x92\xA0\xB6\xB7]/;
-      return "Japanese hiragana character" if $c =~ /\xE3\x81/;
-      return "Japanese hiragana character" if $c =~ /\xE3\x82[\x80-\x9F]/;
-      return "Japanese katakana character" if $c =~ /\xE3\x82[\xA0-\xBF]/;
-      return "Japanese katakana character" if $c =~ /\xE3\x83/;
-      return "Bopomofo letter" if $c =~ /\xE3\x84[\x80-\xAF]/;
-      return "Korean Hangul letter" if $c =~ /\xE3\x84[\xB0-\xBF]/;
-      return "Korean Hangul letter" if $c =~ /\xE3\x85/;
-      return "Korean Hangul letter" if $c =~ /\xE3\x86[\x80-\x8F]/;
-      return "Bopomofo letter" if $c =~ /\xE3\x86[\xA0-\xBF]/;
-      return "CJK stroke" if $c =~ /\xE3\x87[\x80-\xAF]/;
-      return "Japanese kana character" if $c =~ /\xE3\x87[\xB0-\xBF]/;
-      return "CJK symbol" if $c =~ /\xE3[\x88-\x8B]/;
-      return "CJK square Latin abbreviation" if $c =~ /\xE3\x8D[\xB1-\xBA]/;
-      return "CJK square Latin abbreviation" if $c =~ /\xE3\x8E/;
-      return "CJK square Latin abbreviation" if $c =~ /\xE3\x8F[\x80-\x9F\xBF]/;
-      return "CJK character" if $c =~ /\xE4[\xB8-\xBF]/;
-      return "CJK character" if $c =~ /[\xE5-\xE9]/;
-      return "Yi syllable" if $c =~ /\xEA[\x80-\x92]/;
-      return "Lisu letter"      if $c =~ /\xEA\x93[\x90-\xBD]/;
-      return "Lisu punctuation" if $c =~ /\xEA\x93[\xBE-\xBF]/;
-      return "Cyrillic letter" if $c =~ /\xEA\x99/;
-      return "Cyrillic letter" if $c =~ /\xEA\x9A[\x80-\x9F]/;
-      return "modifier tone" if $c =~ /\xEA\x9C[\x80-\xA1]/;
-      return "Javanese punctuation" if $c =~ /\xEA\xA7[\x81-\x8D\x9E-\x9F]/;
-      return "Javanese digit" if $c =~ /\xEA\xA7[\x90-\x99]/;
-      return "Javanese letter" if $c =~ /\xEA\xA6/;
-      return "Javanese letter" if $c =~ /\xEA\xA7[\x80-\x9F]/;
-      return "Ethiopic syllable" if $c =~ /\xEA\xAC[\x80-\xAF]/;
-      return "Cherokee letter" if $c =~ /\xEA\xAD[\xB0-\xBF]/;
-      return "Cherokee letter" if $c =~ /\xEA\xAE/;
-      return "Meetai Mayek digit" if $c =~ /\xEA\xAF[\xB0-\xB9]/;
-      return "Meetai Mayek letter" if $c =~ /\xEA\xAF/;
-      return "Korean Hangul syllable" if $c =~ /\xEA[\xB0-\xBF]/;
-      return "Korean Hangul syllable" if $c =~ /[\xEB-\xEC]/;
-      return "Korean Hangul syllable" if $c =~ /\xED[\x80-\x9E]/;
-      return "Klingon letter" if $c =~ /\xEF\xA3[\x90-\xA9]/;
-      return "Klingon digit" if $c =~ /\xEF\xA3[\xB0-\xB9]/;
-      return "Klingon punctuation" if $c =~ /\xEF\xA3[\xBD-\xBE]/;
-      return "Klingon symbol" if $c =~ /\xEF\xA3\xBF/;
-      return "private use character" if $c =~ /\xEE/;
-      return "Latin typographic ligature" if $c =~ /\xEF\xAC[\x80-\x86]/;
-      return "Hebrew presentation letter" if $c =~ /\xEF\xAC[\x9D-\xBF]/;
-      return "Hebrew presentation letter" if $c =~ /\xEF\xAD[\x80-\x8F]/;
-      return "Arabic presentation letter" if $c =~ /\xEF\xAD[\x90-\xBF]/;
-      return "Arabic presentation letter" if $c =~ /\xEF[\xAE-\xB7]/;
-      return "non-ASCII punctuation" if $c =~ /\xEF\xB8[\x90-\x99]/;
-      return "non-ASCII punctuation" if $c =~ /\xEF\xB8[\xB0-\xBF]/;
-      return "non-ASCII punctuation" if $c =~ /\xEF\xB9[\x80-\xAB]/;
-      return "Arabic presentation letter" if $c =~ /\xEF\xB9[\xB0-\xBF]/;
-      return "Arabic presentation letter" if $c =~ /\xEF\xBA/;
-      return "Arabic presentation letter" if $c =~ /\xEF\xBB[\x80-\xBC]/;
-      return "byte-order mark/zero-width no-break space" if $c eq "\xEF\xBB\xBF";
-      return "fullwidth currency" if $c =~ /\xEF\xBC\x84/;
-      return "fullwidth digit" if $c =~ /\xEF\xBC[\x90-\x99]/;
-      return "fullwidth Latin letter" if $c =~ /\xEF\xBC[\xA1-\xBA]/;
-      return "fullwidth Latin letter" if $c =~ /\xEF\xBD[\x81-\x9A]/;
-      return "fullwidth punctuation" if $c =~ /\xEF\xBC/;
-      return "fullwidth punctuation" if $c =~ /\xEF\xBD[\x9B-\xA4]/;
-      return "halfwidth Japanese punctuation" if $c =~ /\xEF\xBD[\xA1-\xA4]/;
-      return "halfwidth Japanese katakana character" if $c =~ /\xEF\xBD[\xA5-\xBF]/;
-      return "halfwidth Japanese katakana character" if $c =~ /\xEF\xBE[\x80-\x9F]/;
-      return "fullwidth currency" if $c =~ /\xEF\xBF[\xA0-\xA6]/;
-      return "replacement character" if $c eq "\xEF\xBF\xBD";
-   } elsif ($c =~ /[\xF0-\xF7]/) {
-      return "non-UTF8 (invalid)" unless $c =~ /[\xF0-\xF7][\x80-\xBF]{3,3}$/;
-      return "non-shortest-UTF8 (invalid)" if $c =~ /\xF0[\x80-\x8F]/;
-      return "Linear B syllable" if $c =~ /\xF0\x90\x80/;
-      return "Linear B syllable" if $c =~ /\xF0\x90\x81[\x80-\x8F]/;
-      return "Linear B symbol"   if $c =~ /\xF0\x90\x81[\x90-\x9F]/;
-      return "Linear B ideogram" if $c =~ /\xF0\x90[\x82-\x83]/;
-      return "Gothic letter" if $c =~ /\xF0\x90\x8C[\xB0-\xBF]/;
-      return "Gothic letter" if $c =~ /\xF0\x90\x8D[\x80-\x8F]/;
-      return "Phoenician letter" if $c =~ /\xF0\x90\xA4[\x80-\x95]/;
-      return "Phoenician number" if $c =~ /\xF0\x90\xA4[\x96-\x9B]/;
-      return "Phoenician punctuation" if $c =~ /\xF0\x90\xA4\x9F/; # word separator
-      return "Old Hungarian number" if $c =~ /\xF0\x90\xB3[\xBA-\xBF]/;
-      return "Old Hungarian letter" if $c =~ /\xF0\x90[\xB2-\xB3]/;
-      return "Cuneiform digit" if $c =~ /\xF0\x92\x90/; # numberic sign
-      return "Cuneiform digit" if $c =~ /\xF0\x92\x91[\x80-\xAF]/; # numberic sign
-      return "Cuneiform punctuation" if $c =~ /\xF0\x92\x91[\xB0-\xBF]/;
-      return "Cuneiform sign" if $c =~ /\xF0\x92[\x80-\x95]/;
-      return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x81\xA8/;
-      return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x82[\xAD-\xB6]/;
-      return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x86[\x90\xBC-\xBF]/;
-      return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x87[\x80-\x84]/;
-      return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x8D[\xA2-\xAB]/;
-      return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x8E[\x86-\x92]/;
-      return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x8F[\xBA-\xBF]/;
-      return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x90[\x80-\x83]/;
-      return "Egyptian hieroglyph" if $c =~ /\xF0\x93[\x80-\x90]/;
-      return "enclosed alphanumeric" if $c =~ /\xF0\x9F[\x84-\x87]/;
-      return "Mahjong symbol" if $c =~ /\xF0\x9F\x80[\x80-\xAF]/;
-      return "Domino symbol" if $c =~ /\xF0\x9F\x80[\xB0-\xBF]/;
-      return "Domino symbol" if $c =~ /\xF0\x9F\x81/;
-      return "Domino symbol" if $c =~ /\xF0\x9F\x82[\x80-\x9F]/;
-      return "Playing card symbol" if $c =~ /\xF0\x9F\x82[\xA0-\xBF]/;
-      return "Playing card symbol" if $c =~ /\xF0\x9F\x83/;
-      return "CJK symbol" if $c =~ /\xF0\x9F[\x88-\x8B]/;
-      return "pictograph" if $c =~ /\xF0\x9F[\x8C-\x9B]/;
-      return "geometric shape" if $c =~ /\xF0\x9F[\x9E-\x9F]/;
-      return "non-ASCII punctuation" if $c =~ /\xF0\x9F[\xA0-\xA3]/;
-      return "pictograph" if $c =~ /\xF0\x9F[\xA4-\xAB]/;
-      return "CJK character" if $c =~ /\xF0[\xA0-\xAF]/;
-      return "tag" if $c =~ /\xF3\xA0[\x80-\x81]/;
-      return "variation selector" if $c =~ /\xF3\xA0[\x84-\x87]/;
-      return "private use character" if $c =~ /\xF3[\xB0-\xBF]/;
-      return "private use character" if $c =~ /\xF4[\x80-\x8F]/;
-      # ...
-   } elsif ($c =~ /[\xF8-\xFB]/) {
-      return "non-UTF8 (invalid)" unless $c =~ /[\xF8-\xFB][\x80-\xBF]{4,4}$/;
-   } elsif ($c =~ /[\xFC-\xFD]/) {
-      return "non-UTF8 (invalid)" unless $c =~ /[\xFC-\xFD][\x80-\xBF]{5,5}$/;
-   } elsif ($c =~ /\xFE/) {
-      return "non-UTF8 (invalid)" unless $c =~ /\xFE][\x80-\xBF]{6,6}$/;
-   } else {
-      return "non-UTF8 (invalid)";
-   }
-   return "other character";
-}
-
-1;
-
-

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

@@ -1,12 +0,0 @@
-from torch import nn
-
-
-def get_activation(act_fn):
-    if act_fn in ["swish", "silu"]:
-        return nn.SiLU()
-    elif act_fn == "mish":
-        return nn.Mish()
-    elif act_fn == "gelu":
-        return nn.GELU()
-    else:
-        raise ValueError(f"Unsupported activation function: {act_fn}")

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

@@ -1,1680 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import Callable, Optional, Union
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from ..utils import deprecate, logging, maybe_allow_in_graph
-from ..utils.import_utils import is_xformers_available
-from .lora import LoRALinearLayer
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-if is_xformers_available():
-    import xformers
-    import xformers.ops
-else:
-    xformers = None
-
-
-@maybe_allow_in_graph
-class Attention(nn.Module):
-    r"""
-    A cross attention layer.
-
-    Parameters:
-        query_dim (`int`): The number of channels in the query.
-        cross_attention_dim (`int`, *optional*):
-            The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
-        heads (`int`,  *optional*, defaults to 8): The number of heads to use for multi-head attention.
-        dim_head (`int`,  *optional*, defaults to 64): The number of channels in each head.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        bias (`bool`, *optional*, defaults to False):
-            Set to `True` for the query, key, and value linear layers to contain a bias parameter.
-    """
-
-    def __init__(
-        self,
-        query_dim: int,
-        cross_attention_dim: Optional[int] = None,
-        heads: int = 8,
-        dim_head: int = 64,
-        dropout: float = 0.0,
-        bias=False,
-        upcast_attention: bool = False,
-        upcast_softmax: bool = False,
-        cross_attention_norm: Optional[str] = None,
-        cross_attention_norm_num_groups: int = 32,
-        added_kv_proj_dim: Optional[int] = None,
-        norm_num_groups: Optional[int] = None,
-        spatial_norm_dim: Optional[int] = None,
-        out_bias: bool = True,
-        scale_qk: bool = True,
-        only_cross_attention: bool = False,
-        eps: float = 1e-5,
-        rescale_output_factor: float = 1.0,
-        residual_connection: bool = False,
-        _from_deprecated_attn_block=False,
-        processor: Optional["AttnProcessor"] = None,
-    ):
-        super().__init__()
-        inner_dim = dim_head * heads
-        cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
-        self.upcast_attention = upcast_attention
-        self.upcast_softmax = upcast_softmax
-        self.rescale_output_factor = rescale_output_factor
-        self.residual_connection = residual_connection
-        self.dropout = dropout
-
-        # we make use of this private variable to know whether this class is loaded
-        # with an deprecated state dict so that we can convert it on the fly
-        self._from_deprecated_attn_block = _from_deprecated_attn_block
-
-        self.scale_qk = scale_qk
-        self.scale = dim_head**-0.5 if self.scale_qk else 1.0
-
-        self.heads = heads
-        # for slice_size > 0 the attention score computation
-        # is split across the batch axis to save memory
-        # You can set slice_size with `set_attention_slice`
-        self.sliceable_head_dim = heads
-
-        self.added_kv_proj_dim = added_kv_proj_dim
-        self.only_cross_attention = only_cross_attention
-
-        if self.added_kv_proj_dim is None and self.only_cross_attention:
-            raise ValueError(
-                "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
-            )
-
-        if norm_num_groups is not None:
-            self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True)
-        else:
-            self.group_norm = None
-
-        if spatial_norm_dim is not None:
-            self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim)
-        else:
-            self.spatial_norm = None
-
-        if cross_attention_norm is None:
-            self.norm_cross = None
-        elif cross_attention_norm == "layer_norm":
-            self.norm_cross = nn.LayerNorm(cross_attention_dim)
-        elif cross_attention_norm == "group_norm":
-            if self.added_kv_proj_dim is not None:
-                # The given `encoder_hidden_states` are initially of shape
-                # (batch_size, seq_len, added_kv_proj_dim) before being projected
-                # to (batch_size, seq_len, cross_attention_dim). The norm is applied
-                # before the projection, so we need to use `added_kv_proj_dim` as
-                # the number of channels for the group norm.
-                norm_cross_num_channels = added_kv_proj_dim
-            else:
-                norm_cross_num_channels = cross_attention_dim
-
-            self.norm_cross = nn.GroupNorm(
-                num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True
-            )
-        else:
-            raise ValueError(
-                f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
-            )
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)
-
-        if not self.only_cross_attention:
-            # only relevant for the `AddedKVProcessor` classes
-            self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
-            self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
-        else:
-            self.to_k = None
-            self.to_v = None
-
-        if self.added_kv_proj_dim is not None:
-            self.add_k_proj = nn.Linear(added_kv_proj_dim, inner_dim)
-            self.add_v_proj = nn.Linear(added_kv_proj_dim, inner_dim)
-
-        self.to_out = nn.ModuleList([])
-        self.to_out.append(nn.Linear(inner_dim, query_dim, bias=out_bias))
-        self.to_out.append(nn.Dropout(dropout))
-
-        # set attention processor
-        # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
-        # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
-        # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
-        if processor is None:
-            processor = (
-                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
-            )
-        self.set_processor(processor)
-
-    def set_use_memory_efficient_attention_xformers(
-        self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
-    ):
-        is_lora = hasattr(self, "processor") and isinstance(
-            self.processor,
-            LORA_ATTENTION_PROCESSORS,
-        )
-        is_custom_diffusion = hasattr(self, "processor") and isinstance(
-            self.processor, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor)
-        )
-        is_added_kv_processor = hasattr(self, "processor") and isinstance(
-            self.processor,
-            (
-                AttnAddedKVProcessor,
-                AttnAddedKVProcessor2_0,
-                SlicedAttnAddedKVProcessor,
-                XFormersAttnAddedKVProcessor,
-                LoRAAttnAddedKVProcessor,
-            ),
-        )
-
-        if use_memory_efficient_attention_xformers:
-            if is_added_kv_processor and (is_lora or is_custom_diffusion):
-                raise NotImplementedError(
-                    f"Memory efficient attention is currently not supported for LoRA or custom diffuson for attention processor type {self.processor}"
-                )
-            if not is_xformers_available():
-                raise ModuleNotFoundError(
-                    (
-                        "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
-                        " xformers"
-                    ),
-                    name="xformers",
-                )
-            elif not torch.cuda.is_available():
-                raise ValueError(
-                    "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
-                    " only available for GPU "
-                )
-            else:
-                try:
-                    # Make sure we can run the memory efficient attention
-                    _ = xformers.ops.memory_efficient_attention(
-                        torch.randn((1, 2, 40), device="cuda"),
-                        torch.randn((1, 2, 40), device="cuda"),
-                        torch.randn((1, 2, 40), device="cuda"),
-                    )
-                except Exception as e:
-                    raise e
-
-            if is_lora:
-                # TODO (sayakpaul): should we throw a warning if someone wants to use the xformers
-                # variant when using PT 2.0 now that we have LoRAAttnProcessor2_0?
-                processor = LoRAXFormersAttnProcessor(
-                    hidden_size=self.processor.hidden_size,
-                    cross_attention_dim=self.processor.cross_attention_dim,
-                    rank=self.processor.rank,
-                    attention_op=attention_op,
-                )
-                processor.load_state_dict(self.processor.state_dict())
-                processor.to(self.processor.to_q_lora.up.weight.device)
-            elif is_custom_diffusion:
-                processor = CustomDiffusionXFormersAttnProcessor(
-                    train_kv=self.processor.train_kv,
-                    train_q_out=self.processor.train_q_out,
-                    hidden_size=self.processor.hidden_size,
-                    cross_attention_dim=self.processor.cross_attention_dim,
-                    attention_op=attention_op,
-                )
-                processor.load_state_dict(self.processor.state_dict())
-                if hasattr(self.processor, "to_k_custom_diffusion"):
-                    processor.to(self.processor.to_k_custom_diffusion.weight.device)
-            elif is_added_kv_processor:
-                # TODO(Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP
-                # which uses this type of cross attention ONLY because the attention mask of format
-                # [0, ..., -10.000, ..., 0, ...,] is not supported
-                # throw warning
-                logger.info(
-                    "Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation."
-                )
-                processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)
-            else:
-                processor = XFormersAttnProcessor(attention_op=attention_op)
-        else:
-            if is_lora:
-                attn_processor_class = (
-                    LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
-                )
-                processor = attn_processor_class(
-                    hidden_size=self.processor.hidden_size,
-                    cross_attention_dim=self.processor.cross_attention_dim,
-                    rank=self.processor.rank,
-                )
-                processor.load_state_dict(self.processor.state_dict())
-                processor.to(self.processor.to_q_lora.up.weight.device)
-            elif is_custom_diffusion:
-                processor = CustomDiffusionAttnProcessor(
-                    train_kv=self.processor.train_kv,
-                    train_q_out=self.processor.train_q_out,
-                    hidden_size=self.processor.hidden_size,
-                    cross_attention_dim=self.processor.cross_attention_dim,
-                )
-                processor.load_state_dict(self.processor.state_dict())
-                if hasattr(self.processor, "to_k_custom_diffusion"):
-                    processor.to(self.processor.to_k_custom_diffusion.weight.device)
-            else:
-                # set attention processor
-                # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
-                # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
-                # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
-                processor = (
-                    AttnProcessor2_0()
-                    if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
-                    else AttnProcessor()
-                )
-
-        self.set_processor(processor)
-
-    def set_attention_slice(self, slice_size):
-        if slice_size is not None and slice_size > self.sliceable_head_dim:
-            raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")
-
-        if slice_size is not None and self.added_kv_proj_dim is not None:
-            processor = SlicedAttnAddedKVProcessor(slice_size)
-        elif slice_size is not None:
-            processor = SlicedAttnProcessor(slice_size)
-        elif self.added_kv_proj_dim is not None:
-            processor = AttnAddedKVProcessor()
-        else:
-            # set attention processor
-            # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
-            # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
-            # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
-            processor = (
-                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
-            )
-
-        self.set_processor(processor)
-
-    def set_processor(self, processor: "AttnProcessor"):
-        # if current processor is in `self._modules` and if passed `processor` is not, we need to
-        # pop `processor` from `self._modules`
-        if (
-            hasattr(self, "processor")
-            and isinstance(self.processor, torch.nn.Module)
-            and not isinstance(processor, torch.nn.Module)
-        ):
-            logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
-            self._modules.pop("processor")
-
-        self.processor = processor
-
-    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, **cross_attention_kwargs):
-        # The `Attention` class can call different attention processors / attention functions
-        # here we simply pass along all tensors to the selected processor class
-        # For standard processors that are defined here, `**cross_attention_kwargs` is empty
-        return self.processor(
-            self,
-            hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            attention_mask=attention_mask,
-            **cross_attention_kwargs,
-        )
-
-    def batch_to_head_dim(self, tensor):
-        head_size = self.heads
-        batch_size, seq_len, dim = tensor.shape
-        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
-        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
-        return tensor
-
-    def head_to_batch_dim(self, tensor, out_dim=3):
-        head_size = self.heads
-        batch_size, seq_len, dim = tensor.shape
-        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
-        tensor = tensor.permute(0, 2, 1, 3)
-
-        if out_dim == 3:
-            tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
-
-        return tensor
-
-    def get_attention_scores(self, query, key, attention_mask=None):
-        dtype = query.dtype
-        if self.upcast_attention:
-            query = query.float()
-            key = key.float()
-
-        if attention_mask is None:
-            baddbmm_input = torch.empty(
-                query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
-            )
-            beta = 0
-        else:
-            baddbmm_input = attention_mask
-            beta = 1
-
-        attention_scores = torch.baddbmm(
-            baddbmm_input,
-            query,
-            key.transpose(-1, -2),
-            beta=beta,
-            alpha=self.scale,
-        )
-        del baddbmm_input
-
-        if self.upcast_softmax:
-            attention_scores = attention_scores.float()
-
-        attention_probs = attention_scores.softmax(dim=-1)
-        del attention_scores
-
-        attention_probs = attention_probs.to(dtype)
-
-        return attention_probs
-
-    def prepare_attention_mask(self, attention_mask, target_length, batch_size=None, out_dim=3):
-        if batch_size is None:
-            deprecate(
-                "batch_size=None",
-                "0.0.15",
-                (
-                    "Not passing the `batch_size` parameter to `prepare_attention_mask` can lead to incorrect"
-                    " attention mask preparation and is deprecated behavior. Please make sure to pass `batch_size` to"
-                    " `prepare_attention_mask` when preparing the attention_mask."
-                ),
-            )
-            batch_size = 1
-
-        head_size = self.heads
-        if attention_mask is None:
-            return attention_mask
-
-        current_length: int = attention_mask.shape[-1]
-        if current_length != target_length:
-            if attention_mask.device.type == "mps":
-                # HACK: MPS: Does not support padding by greater than dimension of input tensor.
-                # Instead, we can manually construct the padding tensor.
-                padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length)
-                padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device)
-                attention_mask = torch.cat([attention_mask, padding], dim=2)
-            else:
-                # TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
-                #       we want to instead pad by (0, remaining_length), where remaining_length is:
-                #       remaining_length: int = target_length - current_length
-                # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
-                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
-
-        if out_dim == 3:
-            if attention_mask.shape[0] < batch_size * head_size:
-                attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
-        elif out_dim == 4:
-            attention_mask = attention_mask.unsqueeze(1)
-            attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
-
-        return attention_mask
-
-    def norm_encoder_hidden_states(self, encoder_hidden_states):
-        assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states"
-
-        if isinstance(self.norm_cross, nn.LayerNorm):
-            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
-        elif isinstance(self.norm_cross, nn.GroupNorm):
-            # Group norm norms along the channels dimension and expects
-            # input to be in the shape of (N, C, *). In this case, we want
-            # to norm along the hidden dimension, so we need to move
-            # (batch_size, sequence_length, hidden_size) ->
-            # (batch_size, hidden_size, sequence_length)
-            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
-            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
-            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
-        else:
-            assert False
-
-        return encoder_hidden_states
-
-
-class AttnProcessor:
-    r"""
-    Default processor for performing attention-related computations.
-    """
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        query = attn.head_to_batch_dim(query)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class LoRAAttnProcessor(nn.Module):
-    r"""
-    Processor for implementing the LoRA attention mechanism.
-
-    Args:
-        hidden_size (`int`, *optional*):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`, *optional*):
-            The number of channels in the `encoder_hidden_states`.
-        rank (`int`, defaults to 4):
-            The dimension of the LoRA update matrices.
-        network_alpha (`int`, *optional*):
-            Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
-    """
-
-    def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None, **kwargs):
-        super().__init__()
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.rank = rank
-
-        q_rank = kwargs.pop("q_rank", None)
-        q_hidden_size = kwargs.pop("q_hidden_size", None)
-        q_rank = q_rank if q_rank is not None else rank
-        q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size
-
-        v_rank = kwargs.pop("v_rank", None)
-        v_hidden_size = kwargs.pop("v_hidden_size", None)
-        v_rank = v_rank if v_rank is not None else rank
-        v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size
-
-        out_rank = kwargs.pop("out_rank", None)
-        out_hidden_size = kwargs.pop("out_hidden_size", None)
-        out_rank = out_rank if out_rank is not None else rank
-        out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size
-
-        self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)
-        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
-        self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)
-        self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)
-
-    def __call__(
-        self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0, temb=None
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
-        query = attn.head_to_batch_dim(query)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
-
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class CustomDiffusionAttnProcessor(nn.Module):
-    r"""
-    Processor for implementing attention for the Custom Diffusion method.
-
-    Args:
-        train_kv (`bool`, defaults to `True`):
-            Whether to newly train the key and value matrices corresponding to the text features.
-        train_q_out (`bool`, defaults to `True`):
-            Whether to newly train query matrices corresponding to the latent image features.
-        hidden_size (`int`, *optional*, defaults to `None`):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`, *optional*, defaults to `None`):
-            The number of channels in the `encoder_hidden_states`.
-        out_bias (`bool`, defaults to `True`):
-            Whether to include the bias parameter in `train_q_out`.
-        dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability to use.
-    """
-
-    def __init__(
-        self,
-        train_kv=True,
-        train_q_out=True,
-        hidden_size=None,
-        cross_attention_dim=None,
-        out_bias=True,
-        dropout=0.0,
-    ):
-        super().__init__()
-        self.train_kv = train_kv
-        self.train_q_out = train_q_out
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-
-        # `_custom_diffusion` id for easy serialization and loading.
-        if self.train_kv:
-            self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-            self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-        if self.train_q_out:
-            self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
-            self.to_out_custom_diffusion = nn.ModuleList([])
-            self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
-            self.to_out_custom_diffusion.append(nn.Dropout(dropout))
-
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
-        batch_size, sequence_length, _ = hidden_states.shape
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-        if self.train_q_out:
-            query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype)
-        else:
-            query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype))
-
-        if encoder_hidden_states is None:
-            crossattn = False
-            encoder_hidden_states = hidden_states
-        else:
-            crossattn = True
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        if self.train_kv:
-            key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))
-            value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))
-            key = key.to(attn.to_q.weight.dtype)
-            value = value.to(attn.to_q.weight.dtype)
-        else:
-            key = attn.to_k(encoder_hidden_states)
-            value = attn.to_v(encoder_hidden_states)
-
-        if crossattn:
-            detach = torch.ones_like(key)
-            detach[:, :1, :] = detach[:, :1, :] * 0.0
-            key = detach * key + (1 - detach) * key.detach()
-            value = detach * value + (1 - detach) * value.detach()
-
-        query = attn.head_to_batch_dim(query)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        if self.train_q_out:
-            # linear proj
-            hidden_states = self.to_out_custom_diffusion[0](hidden_states)
-            # dropout
-            hidden_states = self.to_out_custom_diffusion[1](hidden_states)
-        else:
-            # linear proj
-            hidden_states = attn.to_out[0](hidden_states)
-            # dropout
-            hidden_states = attn.to_out[1](hidden_states)
-
-        return hidden_states
-
-
-class AttnAddedKVProcessor:
-    r"""
-    Processor for performing attention-related computations with extra learnable key and value matrices for the text
-    encoder.
-    """
-
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
-        residual = hidden_states
-        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
-        batch_size, sequence_length, _ = hidden_states.shape
-
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-        query = attn.head_to_batch_dim(query)
-
-        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
-        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
-        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
-        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)
-
-        if not attn.only_cross_attention:
-            key = attn.to_k(hidden_states)
-            value = attn.to_v(hidden_states)
-            key = attn.head_to_batch_dim(key)
-            value = attn.head_to_batch_dim(value)
-            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
-            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
-        else:
-            key = encoder_hidden_states_key_proj
-            value = encoder_hidden_states_value_proj
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
-        hidden_states = hidden_states + residual
-
-        return hidden_states
-
-
-class AttnAddedKVProcessor2_0:
-    r"""
-    Processor for performing scaled dot-product attention (enabled by default if you're using PyTorch 2.0), with extra
-    learnable key and value matrices for the text encoder.
-    """
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError(
-                "AttnAddedKVProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
-            )
-
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
-        residual = hidden_states
-        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
-        batch_size, sequence_length, _ = hidden_states.shape
-
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, out_dim=4)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-        query = attn.head_to_batch_dim(query, out_dim=4)
-
-        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
-        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
-        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj, out_dim=4)
-        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4)
-
-        if not attn.only_cross_attention:
-            key = attn.to_k(hidden_states)
-            value = attn.to_v(hidden_states)
-            key = attn.head_to_batch_dim(key, out_dim=4)
-            value = attn.head_to_batch_dim(value, out_dim=4)
-            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
-            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
-        else:
-            key = encoder_hidden_states_key_proj
-            value = encoder_hidden_states_value_proj
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, residual.shape[1])
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
-        hidden_states = hidden_states + residual
-
-        return hidden_states
-
-
-class LoRAAttnAddedKVProcessor(nn.Module):
-    r"""
-    Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text
-    encoder.
-
-    Args:
-        hidden_size (`int`, *optional*):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`, *optional*, defaults to `None`):
-            The number of channels in the `encoder_hidden_states`.
-        rank (`int`, defaults to 4):
-            The dimension of the LoRA update matrices.
-
-    """
-
-    def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None):
-        super().__init__()
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.rank = rank
-
-        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-        self.add_k_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
-        self.add_v_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
-        self.to_k_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-        self.to_v_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0):
-        residual = hidden_states
-        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
-        batch_size, sequence_length, _ = hidden_states.shape
-
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
-        query = attn.head_to_batch_dim(query)
-
-        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) + scale * self.add_k_proj_lora(
-            encoder_hidden_states
-        )
-        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) + scale * self.add_v_proj_lora(
-            encoder_hidden_states
-        )
-        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
-        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)
-
-        if not attn.only_cross_attention:
-            key = attn.to_k(hidden_states) + scale * self.to_k_lora(hidden_states)
-            value = attn.to_v(hidden_states) + scale * self.to_v_lora(hidden_states)
-            key = attn.head_to_batch_dim(key)
-            value = attn.head_to_batch_dim(value)
-            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
-            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
-        else:
-            key = encoder_hidden_states_key_proj
-            value = encoder_hidden_states_value_proj
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
-        hidden_states = hidden_states + residual
-
-        return hidden_states
-
-
-class XFormersAttnAddedKVProcessor:
-    r"""
-    Processor for implementing memory efficient attention using xFormers.
-
-    Args:
-        attention_op (`Callable`, *optional*, defaults to `None`):
-            The base
-            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
-            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
-            operator.
-    """
-
-    def __init__(self, attention_op: Optional[Callable] = None):
-        self.attention_op = attention_op
-
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
-        residual = hidden_states
-        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
-        batch_size, sequence_length, _ = hidden_states.shape
-
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-        query = attn.head_to_batch_dim(query)
-
-        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
-        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
-        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
-        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)
-
-        if not attn.only_cross_attention:
-            key = attn.to_k(hidden_states)
-            value = attn.to_v(hidden_states)
-            key = attn.head_to_batch_dim(key)
-            value = attn.head_to_batch_dim(value)
-            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
-            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
-        else:
-            key = encoder_hidden_states_key_proj
-            value = encoder_hidden_states_value_proj
-
-        hidden_states = xformers.ops.memory_efficient_attention(
-            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
-        )
-        hidden_states = hidden_states.to(query.dtype)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
-        hidden_states = hidden_states + residual
-
-        return hidden_states
-
-
-class XFormersAttnProcessor:
-    r"""
-    Processor for implementing memory efficient attention using xFormers.
-
-    Args:
-        attention_op (`Callable`, *optional*, defaults to `None`):
-            The base
-            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
-            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
-            operator.
-    """
-
-    def __init__(self, attention_op: Optional[Callable] = None):
-        self.attention_op = attention_op
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.FloatTensor,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        temb: Optional[torch.FloatTensor] = None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, key_tokens, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)
-        if attention_mask is not None:
-            # expand our mask's singleton query_tokens dimension:
-            #   [batch*heads,            1, key_tokens] ->
-            #   [batch*heads, query_tokens, key_tokens]
-            # so that it can be added as a bias onto the attention scores that xformers computes:
-            #   [batch*heads, query_tokens, key_tokens]
-            # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
-            _, query_tokens, _ = hidden_states.shape
-            attention_mask = attention_mask.expand(-1, query_tokens, -1)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        query = attn.head_to_batch_dim(query).contiguous()
-        key = attn.head_to_batch_dim(key).contiguous()
-        value = attn.head_to_batch_dim(value).contiguous()
-
-        hidden_states = xformers.ops.memory_efficient_attention(
-            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
-        )
-        hidden_states = hidden_states.to(query.dtype)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class AttnProcessor2_0:
-    r"""
-    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
-    """
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class LoRAXFormersAttnProcessor(nn.Module):
-    r"""
-    Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers.
-
-    Args:
-        hidden_size (`int`, *optional*):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`, *optional*):
-            The number of channels in the `encoder_hidden_states`.
-        rank (`int`, defaults to 4):
-            The dimension of the LoRA update matrices.
-        attention_op (`Callable`, *optional*, defaults to `None`):
-            The base
-            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
-            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
-            operator.
-        network_alpha (`int`, *optional*):
-            Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
-
-    """
-
-    def __init__(
-        self,
-        hidden_size,
-        cross_attention_dim,
-        rank=4,
-        attention_op: Optional[Callable] = None,
-        network_alpha=None,
-        **kwargs,
-    ):
-        super().__init__()
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.rank = rank
-        self.attention_op = attention_op
-
-        q_rank = kwargs.pop("q_rank", None)
-        q_hidden_size = kwargs.pop("q_hidden_size", None)
-        q_rank = q_rank if q_rank is not None else rank
-        q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size
-
-        v_rank = kwargs.pop("v_rank", None)
-        v_hidden_size = kwargs.pop("v_hidden_size", None)
-        v_rank = v_rank if v_rank is not None else rank
-        v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size
-
-        out_rank = kwargs.pop("out_rank", None)
-        out_hidden_size = kwargs.pop("out_hidden_size", None)
-        out_rank = out_rank if out_rank is not None else rank
-        out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size
-
-        self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)
-        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
-        self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)
-        self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)
-
-    def __call__(
-        self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0, temb=None
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
-        query = attn.head_to_batch_dim(query).contiguous()
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
-
-        key = attn.head_to_batch_dim(key).contiguous()
-        value = attn.head_to_batch_dim(value).contiguous()
-
-        hidden_states = xformers.ops.memory_efficient_attention(
-            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
-        )
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class LoRAAttnProcessor2_0(nn.Module):
-    r"""
-    Processor for implementing the LoRA attention mechanism using PyTorch 2.0's memory-efficient scaled dot-product
-    attention.
-
-    Args:
-        hidden_size (`int`):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`, *optional*):
-            The number of channels in the `encoder_hidden_states`.
-        rank (`int`, defaults to 4):
-            The dimension of the LoRA update matrices.
-        network_alpha (`int`, *optional*):
-            Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
-    """
-
-    def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None, **kwargs):
-        super().__init__()
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.rank = rank
-
-        q_rank = kwargs.pop("q_rank", None)
-        q_hidden_size = kwargs.pop("q_hidden_size", None)
-        q_rank = q_rank if q_rank is not None else rank
-        q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size
-
-        v_rank = kwargs.pop("v_rank", None)
-        v_hidden_size = kwargs.pop("v_hidden_size", None)
-        v_rank = v_rank if v_rank is not None else rank
-        v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size
-
-        out_rank = kwargs.pop("out_rank", None)
-        out_hidden_size = kwargs.pop("out_hidden_size", None)
-        out_rank = out_rank if out_rank is not None else rank
-        out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size
-
-        self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)
-        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
-        self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)
-        self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)
-
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0):
-        residual = hidden_states
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        inner_dim = hidden_states.shape[-1]
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
-
-        head_dim = inner_dim // attn.heads
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class CustomDiffusionXFormersAttnProcessor(nn.Module):
-    r"""
-    Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method.
-
-    Args:
-    train_kv (`bool`, defaults to `True`):
-        Whether to newly train the key and value matrices corresponding to the text features.
-    train_q_out (`bool`, defaults to `True`):
-        Whether to newly train query matrices corresponding to the latent image features.
-    hidden_size (`int`, *optional*, defaults to `None`):
-        The hidden size of the attention layer.
-    cross_attention_dim (`int`, *optional*, defaults to `None`):
-        The number of channels in the `encoder_hidden_states`.
-    out_bias (`bool`, defaults to `True`):
-        Whether to include the bias parameter in `train_q_out`.
-    dropout (`float`, *optional*, defaults to 0.0):
-        The dropout probability to use.
-    attention_op (`Callable`, *optional*, defaults to `None`):
-        The base
-        [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to use
-        as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best operator.
-    """
-
-    def __init__(
-        self,
-        train_kv=True,
-        train_q_out=False,
-        hidden_size=None,
-        cross_attention_dim=None,
-        out_bias=True,
-        dropout=0.0,
-        attention_op: Optional[Callable] = None,
-    ):
-        super().__init__()
-        self.train_kv = train_kv
-        self.train_q_out = train_q_out
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.attention_op = attention_op
-
-        # `_custom_diffusion` id for easy serialization and loading.
-        if self.train_kv:
-            self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-            self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-        if self.train_q_out:
-            self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
-            self.to_out_custom_diffusion = nn.ModuleList([])
-            self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
-            self.to_out_custom_diffusion.append(nn.Dropout(dropout))
-
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if self.train_q_out:
-            query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype)
-        else:
-            query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype))
-
-        if encoder_hidden_states is None:
-            crossattn = False
-            encoder_hidden_states = hidden_states
-        else:
-            crossattn = True
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        if self.train_kv:
-            key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))
-            value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))
-            key = key.to(attn.to_q.weight.dtype)
-            value = value.to(attn.to_q.weight.dtype)
-        else:
-            key = attn.to_k(encoder_hidden_states)
-            value = attn.to_v(encoder_hidden_states)
-
-        if crossattn:
-            detach = torch.ones_like(key)
-            detach[:, :1, :] = detach[:, :1, :] * 0.0
-            key = detach * key + (1 - detach) * key.detach()
-            value = detach * value + (1 - detach) * value.detach()
-
-        query = attn.head_to_batch_dim(query).contiguous()
-        key = attn.head_to_batch_dim(key).contiguous()
-        value = attn.head_to_batch_dim(value).contiguous()
-
-        hidden_states = xformers.ops.memory_efficient_attention(
-            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
-        )
-        hidden_states = hidden_states.to(query.dtype)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        if self.train_q_out:
-            # linear proj
-            hidden_states = self.to_out_custom_diffusion[0](hidden_states)
-            # dropout
-            hidden_states = self.to_out_custom_diffusion[1](hidden_states)
-        else:
-            # linear proj
-            hidden_states = attn.to_out[0](hidden_states)
-            # dropout
-            hidden_states = attn.to_out[1](hidden_states)
-        return hidden_states
-
-
-class SlicedAttnProcessor:
-    r"""
-    Processor for implementing sliced attention.
-
-    Args:
-        slice_size (`int`, *optional*):
-            The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
-            `attention_head_dim` must be a multiple of the `slice_size`.
-    """
-
-    def __init__(self, slice_size):
-        self.slice_size = slice_size
-
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
-        residual = hidden_states
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-        dim = query.shape[-1]
-        query = attn.head_to_batch_dim(query)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        batch_size_attention, query_tokens, _ = query.shape
-        hidden_states = torch.zeros(
-            (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
-        )
-
-        for i in range(batch_size_attention // self.slice_size):
-            start_idx = i * self.slice_size
-            end_idx = (i + 1) * self.slice_size
-
-            query_slice = query[start_idx:end_idx]
-            key_slice = key[start_idx:end_idx]
-            attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
-
-            attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
-
-            attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
-
-            hidden_states[start_idx:end_idx] = attn_slice
-
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class SlicedAttnAddedKVProcessor:
-    r"""
-    Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.
-
-    Args:
-        slice_size (`int`, *optional*):
-            The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
-            `attention_head_dim` must be a multiple of the `slice_size`.
-    """
-
-    def __init__(self, slice_size):
-        self.slice_size = slice_size
-
-    def __call__(self, attn: "Attention", hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
-
-        batch_size, sequence_length, _ = hidden_states.shape
-
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-        dim = query.shape[-1]
-        query = attn.head_to_batch_dim(query)
-
-        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
-        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
-
-        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
-        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)
-
-        if not attn.only_cross_attention:
-            key = attn.to_k(hidden_states)
-            value = attn.to_v(hidden_states)
-            key = attn.head_to_batch_dim(key)
-            value = attn.head_to_batch_dim(value)
-            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
-            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
-        else:
-            key = encoder_hidden_states_key_proj
-            value = encoder_hidden_states_value_proj
-
-        batch_size_attention, query_tokens, _ = query.shape
-        hidden_states = torch.zeros(
-            (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
-        )
-
-        for i in range(batch_size_attention // self.slice_size):
-            start_idx = i * self.slice_size
-            end_idx = (i + 1) * self.slice_size
-
-            query_slice = query[start_idx:end_idx]
-            key_slice = key[start_idx:end_idx]
-            attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
-
-            attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
-
-            attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
-
-            hidden_states[start_idx:end_idx] = attn_slice
-
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
-        hidden_states = hidden_states + residual
-
-        return hidden_states
-
-
-AttentionProcessor = Union[
-    AttnProcessor,
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-    SlicedAttnProcessor,
-    AttnAddedKVProcessor,
-    SlicedAttnAddedKVProcessor,
-    AttnAddedKVProcessor2_0,
-    XFormersAttnAddedKVProcessor,
-    LoRAAttnProcessor,
-    LoRAXFormersAttnProcessor,
-    LoRAAttnProcessor2_0,
-    LoRAAttnAddedKVProcessor,
-    CustomDiffusionAttnProcessor,
-    CustomDiffusionXFormersAttnProcessor,
-]
-
-LORA_ATTENTION_PROCESSORS = (
-    LoRAAttnProcessor,
-    LoRAAttnProcessor2_0,
-    LoRAXFormersAttnProcessor,
-    LoRAAttnAddedKVProcessor,
-)
-
-
-class SpatialNorm(nn.Module):
-    """
-    Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002
-    """
-
-    def __init__(
-        self,
-        f_channels,
-        zq_channels,
-    ):
-        super().__init__()
-        self.norm_layer = nn.GroupNorm(num_channels=f_channels, num_groups=32, eps=1e-6, affine=True)
-        self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
-        self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, f, zq):
-        f_size = f.shape[-2:]
-        zq = F.interpolate(zq, size=f_size, mode="nearest")
-        norm_f = self.norm_layer(f)
-        new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
-        return new_f

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/__init__.py

@@ -1,122 +0,0 @@
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import os
-
-from packaging import version
-
-from .. import __version__
-from .accelerate_utils import apply_forward_hook
-from .constants import (
-    CONFIG_NAME,
-    DEPRECATED_REVISION_ARGS,
-    DIFFUSERS_CACHE,
-    DIFFUSERS_DYNAMIC_MODULE_NAME,
-    FLAX_WEIGHTS_NAME,
-    HF_MODULES_CACHE,
-    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
-    ONNX_EXTERNAL_WEIGHTS_NAME,
-    ONNX_WEIGHTS_NAME,
-    SAFETENSORS_WEIGHTS_NAME,
-    WEIGHTS_NAME,
-)
-from .deprecation_utils import deprecate
-from .doc_utils import replace_example_docstring
-from .dynamic_modules_utils import get_class_from_dynamic_module
-from .hub_utils import (
-    HF_HUB_OFFLINE,
-    _add_variant,
-    _get_model_file,
-    extract_commit_hash,
-    http_user_agent,
-)
-from .import_utils import (
-    BACKENDS_MAPPING,
-    ENV_VARS_TRUE_AND_AUTO_VALUES,
-    ENV_VARS_TRUE_VALUES,
-    USE_JAX,
-    USE_TF,
-    USE_TORCH,
-    DummyObject,
-    OptionalDependencyNotAvailable,
-    is_accelerate_available,
-    is_accelerate_version,
-    is_bs4_available,
-    is_flax_available,
-    is_ftfy_available,
-    is_inflect_available,
-    is_invisible_watermark_available,
-    is_k_diffusion_available,
-    is_k_diffusion_version,
-    is_librosa_available,
-    is_note_seq_available,
-    is_omegaconf_available,
-    is_onnx_available,
-    is_safetensors_available,
-    is_scipy_available,
-    is_tensorboard_available,
-    is_tf_available,
-    is_torch_available,
-    is_torch_version,
-    is_torchsde_available,
-    is_transformers_available,
-    is_transformers_version,
-    is_unidecode_available,
-    is_wandb_available,
-    is_xformers_available,
-    requires_backends,
-)
-from .logging import get_logger
-from .outputs import BaseOutput
-from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
-from .torch_utils import is_compiled_module, randn_tensor
-
-
-if is_torch_available():
-    from .testing_utils import (
-        floats_tensor,
-        load_hf_numpy,
-        load_image,
-        load_numpy,
-        load_pt,
-        nightly,
-        parse_flag_from_env,
-        print_tensor_test,
-        require_torch_2,
-        require_torch_gpu,
-        skip_mps,
-        slow,
-        torch_all_close,
-        torch_device,
-    )
-    from .torch_utils import maybe_allow_in_graph
-
-from .testing_utils import export_to_gif, export_to_obj, export_to_ply, export_to_video
-
-
-logger = get_logger(__name__)
-
-
-def check_min_version(min_version):
-    if version.parse(__version__) < version.parse(min_version):
-        if "dev" in min_version:
-            error_message = (
-                "This example requires a source install from HuggingFace diffusers (see "
-                "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
-            )
-        else:
-            error_message = f"This example requires a minimum version of {min_version},"
-        error_message += f" but the version found is {__version__}.\n"
-        raise ImportError(error_message)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_kdpm2_discrete.py

@@ -1,132 +0,0 @@
-import torch
-
-from diffusers import KDPM2DiscreteScheduler
-from diffusers.utils import torch_device
-
-from .test_schedulers import SchedulerCommonTest
-
-
-class KDPM2DiscreteSchedulerTest(SchedulerCommonTest):
-    scheduler_classes = (KDPM2DiscreteScheduler,)
-    num_inference_steps = 10
-
-    def get_scheduler_config(self, **kwargs):
-        config = {
-            "num_train_timesteps": 1100,
-            "beta_start": 0.0001,
-            "beta_end": 0.02,
-            "beta_schedule": "linear",
-        }
-
-        config.update(**kwargs)
-        return config
-
-    def test_timesteps(self):
-        for timesteps in [10, 50, 100, 1000]:
-            self.check_over_configs(num_train_timesteps=timesteps)
-
-    def test_betas(self):
-        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
-            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
-
-    def test_schedules(self):
-        for schedule in ["linear", "scaled_linear"]:
-            self.check_over_configs(beta_schedule=schedule)
-
-    def test_prediction_type(self):
-        for prediction_type in ["epsilon", "v_prediction"]:
-            self.check_over_configs(prediction_type=prediction_type)
-
-    def test_full_loop_with_v_prediction(self):
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
-        scheduler = scheduler_class(**scheduler_config)
-
-        scheduler.set_timesteps(self.num_inference_steps)
-
-        model = self.dummy_model()
-        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
-        sample = sample.to(torch_device)
-
-        for i, t in enumerate(scheduler.timesteps):
-            sample = scheduler.scale_model_input(sample, t)
-
-            model_output = model(sample, t)
-
-            output = scheduler.step(model_output, t, sample)
-            sample = output.prev_sample
-
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        if torch_device in ["cpu", "mps"]:
-            assert abs(result_sum.item() - 4.6934e-07) < 1e-2
-            assert abs(result_mean.item() - 6.1112e-10) < 1e-3
-        else:
-            # CUDA
-            assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2
-            assert abs(result_mean.item() - 0.0002) < 1e-3
-
-    def test_full_loop_no_noise(self):
-        if torch_device == "mps":
-            return
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config()
-        scheduler = scheduler_class(**scheduler_config)
-
-        scheduler.set_timesteps(self.num_inference_steps)
-
-        model = self.dummy_model()
-        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
-        sample = sample.to(torch_device)
-
-        for i, t in enumerate(scheduler.timesteps):
-            sample = scheduler.scale_model_input(sample, t)
-
-            model_output = model(sample, t)
-
-            output = scheduler.step(model_output, t, sample)
-            sample = output.prev_sample
-
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        if torch_device in ["cpu", "mps"]:
-            assert abs(result_sum.item() - 20.4125) < 1e-2
-            assert abs(result_mean.item() - 0.0266) < 1e-3
-        else:
-            # CUDA
-            assert abs(result_sum.item() - 20.4125) < 1e-2
-            assert abs(result_mean.item() - 0.0266) < 1e-3
-
-    def test_full_loop_device(self):
-        if torch_device == "mps":
-            return
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config()
-        scheduler = scheduler_class(**scheduler_config)
-
-        scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
-
-        model = self.dummy_model()
-        sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
-
-        for t in scheduler.timesteps:
-            sample = scheduler.scale_model_input(sample, t)
-
-            model_output = model(sample, t)
-
-            output = scheduler.step(model_output, t, sample)
-            sample = output.prev_sample
-
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        if str(torch_device).startswith("cpu"):
-            # The following sum varies between 148 and 156 on mps. Why?
-            assert abs(result_sum.item() - 20.4125) < 1e-2
-            assert abs(result_mean.item() - 0.0266) < 1e-3
-        else:
-            # CUDA
-            assert abs(result_sum.item() - 20.4125) < 1e-2
-            assert abs(result_mean.item() - 0.0266) < 1e-3

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/utils/check_inits.py

@@ -1,299 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# 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 collections
-import importlib.util
-import os
-import re
-from pathlib import Path
-
-
-PATH_TO_TRANSFORMERS = "src/transformers"
-
-
-# Matches is_xxx_available()
-_re_backend = re.compile(r"is\_([a-z_]*)_available()")
-# Catches a one-line _import_struct = {xxx}
-_re_one_line_import_struct = re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
-# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
-_re_import_struct_key_value = re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
-# Catches a line if not is_foo_available
-_re_test_backend = re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
-# Catches a line _import_struct["bla"].append("foo")
-_re_import_struct_add_one = re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
-# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
-_re_import_struct_add_many = re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
-# Catches a line with an object between quotes and a comma:     "MyModel",
-_re_quote_object = re.compile('^\s+"([^"]+)",')
-# Catches a line with objects between brackets only:    ["foo", "bar"],
-_re_between_brackets = re.compile("^\s+\[([^\]]+)\]")
-# Catches a line with from foo import bar, bla, boo
-_re_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
-# Catches a line with try:
-_re_try = re.compile(r"^\s*try:")
-# Catches a line with else:
-_re_else = re.compile(r"^\s*else:")
-
-
-def find_backend(line):
-    """Find one (or multiple) backend in a code line of the init."""
-    if _re_test_backend.search(line) is None:
-        return None
-    backends = [b[0] for b in _re_backend.findall(line)]
-    backends.sort()
-    return "_and_".join(backends)
-
-
-def parse_init(init_file):
-    """
-    Read an init_file and parse (per backend) the _import_structure objects defined and the TYPE_CHECKING objects
-    defined
-    """
-    with open(init_file, "r", encoding="utf-8", newline="\n") as f:
-        lines = f.readlines()
-
-    line_index = 0
-    while line_index < len(lines) and not lines[line_index].startswith("_import_structure = {"):
-        line_index += 1
-
-    # If this is a traditional init, just return.
-    if line_index >= len(lines):
-        return None
-
-    # First grab the objects without a specific backend in _import_structure
-    objects = []
-    while not lines[line_index].startswith("if TYPE_CHECKING") and find_backend(lines[line_index]) is None:
-        line = lines[line_index]
-        # If we have everything on a single line, let's deal with it.
-        if _re_one_line_import_struct.search(line):
-            content = _re_one_line_import_struct.search(line).groups()[0]
-            imports = re.findall("\[([^\]]+)\]", content)
-            for imp in imports:
-                objects.extend([obj[1:-1] for obj in imp.split(", ")])
-            line_index += 1
-            continue
-        single_line_import_search = _re_import_struct_key_value.search(line)
-        if single_line_import_search is not None:
-            imports = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", ") if len(obj) > 0]
-            objects.extend(imports)
-        elif line.startswith(" " * 8 + '"'):
-            objects.append(line[9:-3])
-        line_index += 1
-
-    import_dict_objects = {"none": objects}
-    # Let's continue with backend-specific objects in _import_structure
-    while not lines[line_index].startswith("if TYPE_CHECKING"):
-        # If the line is an if not is_backend_available, we grab all objects associated.
-        backend = find_backend(lines[line_index])
-        # Check if the backend declaration is inside a try block:
-        if _re_try.search(lines[line_index - 1]) is None:
-            backend = None
-
-        if backend is not None:
-            line_index += 1
-
-            # Scroll until we hit the else block of try-except-else
-            while _re_else.search(lines[line_index]) is None:
-                line_index += 1
-
-            line_index += 1
-
-            objects = []
-            # Until we unindent, add backend objects to the list
-            while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 4):
-                line = lines[line_index]
-                if _re_import_struct_add_one.search(line) is not None:
-                    objects.append(_re_import_struct_add_one.search(line).groups()[0])
-                elif _re_import_struct_add_many.search(line) is not None:
-                    imports = _re_import_struct_add_many.search(line).groups()[0].split(", ")
-                    imports = [obj[1:-1] for obj in imports if len(obj) > 0]
-                    objects.extend(imports)
-                elif _re_between_brackets.search(line) is not None:
-                    imports = _re_between_brackets.search(line).groups()[0].split(", ")
-                    imports = [obj[1:-1] for obj in imports if len(obj) > 0]
-                    objects.extend(imports)
-                elif _re_quote_object.search(line) is not None:
-                    objects.append(_re_quote_object.search(line).groups()[0])
-                elif line.startswith(" " * 8 + '"'):
-                    objects.append(line[9:-3])
-                elif line.startswith(" " * 12 + '"'):
-                    objects.append(line[13:-3])
-                line_index += 1
-
-            import_dict_objects[backend] = objects
-        else:
-            line_index += 1
-
-    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
-    objects = []
-    while (
-        line_index < len(lines)
-        and find_backend(lines[line_index]) is None
-        and not lines[line_index].startswith("else")
-    ):
-        line = lines[line_index]
-        single_line_import_search = _re_import.search(line)
-        if single_line_import_search is not None:
-            objects.extend(single_line_import_search.groups()[0].split(", "))
-        elif line.startswith(" " * 8):
-            objects.append(line[8:-2])
-        line_index += 1
-
-    type_hint_objects = {"none": objects}
-    # Let's continue with backend-specific objects
-    while line_index < len(lines):
-        # If the line is an if is_backend_available, we grab all objects associated.
-        backend = find_backend(lines[line_index])
-        # Check if the backend declaration is inside a try block:
-        if _re_try.search(lines[line_index - 1]) is None:
-            backend = None
-
-        if backend is not None:
-            line_index += 1
-
-            # Scroll until we hit the else block of try-except-else
-            while _re_else.search(lines[line_index]) is None:
-                line_index += 1
-
-            line_index += 1
-
-            objects = []
-            # Until we unindent, add backend objects to the list
-            while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8):
-                line = lines[line_index]
-                single_line_import_search = _re_import.search(line)
-                if single_line_import_search is not None:
-                    objects.extend(single_line_import_search.groups()[0].split(", "))
-                elif line.startswith(" " * 12):
-                    objects.append(line[12:-2])
-                line_index += 1
-
-            type_hint_objects[backend] = objects
-        else:
-            line_index += 1
-
-    return import_dict_objects, type_hint_objects
-
-
-def analyze_results(import_dict_objects, type_hint_objects):
-    """
-    Analyze the differences between _import_structure objects and TYPE_CHECKING objects found in an init.
-    """
-
-    def find_duplicates(seq):
-        return [k for k, v in collections.Counter(seq).items() if v > 1]
-
-    if list(import_dict_objects.keys()) != list(type_hint_objects.keys()):
-        return ["Both sides of the init do not have the same backends!"]
-
-    errors = []
-    for key in import_dict_objects.keys():
-        duplicate_imports = find_duplicates(import_dict_objects[key])
-        if duplicate_imports:
-            errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}")
-        duplicate_type_hints = find_duplicates(type_hint_objects[key])
-        if duplicate_type_hints:
-            errors.append(f"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}")
-
-        if sorted(set(import_dict_objects[key])) != sorted(set(type_hint_objects[key])):
-            name = "base imports" if key == "none" else f"{key} backend"
-            errors.append(f"Differences for {name}:")
-            for a in type_hint_objects[key]:
-                if a not in import_dict_objects[key]:
-                    errors.append(f"  {a} in TYPE_HINT but not in _import_structure.")
-            for a in import_dict_objects[key]:
-                if a not in type_hint_objects[key]:
-                    errors.append(f"  {a} in _import_structure but not in TYPE_HINT.")
-    return errors
-
-
-def check_all_inits():
-    """
-    Check all inits in the transformers repo and raise an error if at least one does not define the same objects in
-    both halves.
-    """
-    failures = []
-    for root, _, files in os.walk(PATH_TO_TRANSFORMERS):
-        if "__init__.py" in files:
-            fname = os.path.join(root, "__init__.py")
-            objects = parse_init(fname)
-            if objects is not None:
-                errors = analyze_results(*objects)
-                if len(errors) > 0:
-                    errors[0] = f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
-                    failures.append("\n".join(errors))
-    if len(failures) > 0:
-        raise ValueError("\n\n".join(failures))
-
-
-def get_transformers_submodules():
-    """
-    Returns the list of Transformers submodules.
-    """
-    submodules = []
-    for path, directories, files in os.walk(PATH_TO_TRANSFORMERS):
-        for folder in directories:
-            # Ignore private modules
-            if folder.startswith("_"):
-                directories.remove(folder)
-                continue
-            # Ignore leftovers from branches (empty folders apart from pycache)
-            if len(list((Path(path) / folder).glob("*.py"))) == 0:
-                continue
-            short_path = str((Path(path) / folder).relative_to(PATH_TO_TRANSFORMERS))
-            submodule = short_path.replace(os.path.sep, ".")
-            submodules.append(submodule)
-        for fname in files:
-            if fname == "__init__.py":
-                continue
-            short_path = str((Path(path) / fname).relative_to(PATH_TO_TRANSFORMERS))
-            submodule = short_path.replace(".py", "").replace(os.path.sep, ".")
-            if len(submodule.split(".")) == 1:
-                submodules.append(submodule)
-    return submodules
-
-
-IGNORE_SUBMODULES = [
-    "convert_pytorch_checkpoint_to_tf2",
-    "modeling_flax_pytorch_utils",
-]
-
-
-def check_submodules():
-    # This is to make sure the transformers module imported is the one in the repo.
-    spec = importlib.util.spec_from_file_location(
-        "transformers",
-        os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
-        submodule_search_locations=[PATH_TO_TRANSFORMERS],
-    )
-    transformers = spec.loader.load_module()
-
-    module_not_registered = [
-        module
-        for module in get_transformers_submodules()
-        if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
-    ]
-    if len(module_not_registered) > 0:
-        list_of_modules = "\n".join(f"- {module}" for module in module_not_registered)
-        raise ValueError(
-            "The following submodules are not properly registered in the main init of Transformers:\n"
-            f"{list_of_modules}\n"
-            "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."
-        )
-
-
-if __name__ == "__main__":
-    check_all_inits()
-    check_submodules()

spaces/Andy1621/uniformer_image_detection/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py

@@ -1,28 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='InstaBoost',
-        action_candidate=('normal', 'horizontal', 'skip'),
-        action_prob=(1, 0, 0),
-        scale=(0.8, 1.2),
-        dx=15,
-        dy=15,
-        theta=(-1, 1),
-        color_prob=0.5,
-        hflag=False,
-        aug_ratio=0.5),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-# learning policy
-lr_config = dict(step=[32, 44])
-runner = dict(type='EpochBasedRunner', max_epochs=48)

spaces/Andy1621/uniformer_image_detection/configs/scnet/scnet_r50_fpn_20e_coco.py

@@ -1,4 +0,0 @@
-_base_ = './scnet_r50_fpn_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 19])
-runner = dict(type='EpochBasedRunner', max_epochs=20)

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/README.md

@@ -1,75 +0,0 @@
-# Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-```latex
-@inproceedings{deeplabv3plus2018,
-  title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
-  author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
-  booktitle={ECCV},
-  year={2018}
-}
-```
-
-## Results and models
-
-Note:
-`D-8`/`D-16` here corresponding to the output stride 8/16 setting for DeepLab series.
-`MG-124` stands for multi-grid dilation in the last stage of ResNet.
-
-### Cityscapes
-
-| Method     | Backbone        | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                                                                                           |
-| ---------- | --------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3+ | R-50-D8         | 512x1024  |   40000 | 7.5      | 3.94           | 79.61 |         81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610.log.json)                                 |
-| DeepLabV3+ | R-101-D8        | 512x1024  |   40000 | 11       | 2.60           | 80.21 |         81.82 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614.log.json)                             |
-| DeepLabV3+ | R-50-D8         | 769x769   |   40000 | 8.5      | 1.72           | 78.97 |         80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143.log.json)                                     |
-| DeepLabV3+ | R-101-D8        | 769x769   |   40000 | 12.5     | 1.15           | 79.46 |         80.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304.log.json)                                 |
-| DeepLabV3+ | R-18-D8         | 512x1024  |   80000 | 2.2      | 14.27          | 76.89 |         78.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes-20201226_080942.log.json)                                 |
-| DeepLabV3+ | R-50-D8         | 512x1024  |   80000 | -        | -              | 80.09 |         81.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049.log.json)                                 |
-| DeepLabV3+ | R-101-D8        | 512x1024  |   80000 | -        | -              | 80.97 |         82.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143.log.json)                             |
-| DeepLabV3+ | R-18-D8         | 769x769   |   80000 | 2.5      | 5.74           | 76.26 |         77.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes-20201226_083346.log.json)                                     |
-| DeepLabV3+ | R-50-D8         | 769x769   |   80000 | -        | -              | 79.83 |         81.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233.log.json)                                     |
-| DeepLabV3+ | R-101-D8        | 769x769   |   80000 | -        | -              | 80.98 |         82.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405.log.json)                                 |
-| DeepLabV3+ | R-101-D16-MG124 | 512x1024  |   40000 | 5.8      | 7.48           | 79.09 |         80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) |
-| DeepLabV3+ | R-101-D16-MG124 | 512x1024  |   80000 | 9.9      | -              | 79.90 |         81.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) |
-| DeepLabV3+ | R-18b-D8        | 512x1024  |   80000 | 2.1      | 14.95          | 75.87 |         77.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes-20201226_090828.log.json)                             |
-| DeepLabV3+ | R-50b-D8        | 512x1024  |   80000 | 7.4      | 3.94           | 80.28 |         81.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes-20201225_213645.log.json)                             |
-| DeepLabV3+ | R-101b-D8       | 512x1024  |   80000 | 10.9     | 2.60           | 80.16 |         81.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes-20201226_190843.log.json)                         |
-| DeepLabV3+ | R-18b-D8        | 769x769   |   80000 | 2.4      | 5.96           | 76.36 |         78.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes-20201226_151312.log.json)                                 |
-| DeepLabV3+ | R-50b-D8        | 769x769   |   80000 | 8.4      | 1.72           | 79.41 |         80.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes-20201225_224655.log.json)                                 |
-| DeepLabV3+ | R-101b-D8       | 769x769   |   80000 | 12.3     | 1.10           | 79.88 |         81.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes-20201226_205041.log.json)                             |
-
-### ADE20K
-
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                                               |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 10.6     | 21.01          | 42.72 |         43.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028.log.json)         |
-| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 14.1     | 14.16          | 44.60 |         46.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139.log.json)     |
-| DeepLabV3+ | R-50-D8  | 512x512   |  160000 | -        | -              | 43.95 |         44.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504.log.json)     |
-| DeepLabV3+ | R-101-D8 | 512x512   |  160000 | -        | -              | 45.47 |         46.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232.log.json) |
-
-#### Pascal VOC 2012 + Aug
-
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DeepLabV3+ | R-50-D8  | 512x512   |   20000 | 7.6      | 21             | 75.93 |         77.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323.log.json)     |
-| DeepLabV3+ | R-101-D8 | 512x512   |   20000 | 11       | 13.88          | 77.22 |         78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345.log.json) |
-| DeepLabV3+ | R-50-D8  | 512x512   |   40000 | -        | -              | 76.81 |         77.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759.log.json)     |
-| DeepLabV3+ | R-101-D8 | 512x512   |   40000 | -        | -              | 78.62 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333.log.json) |
-
-#### Pascal Context
-
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                                           |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DeepLabV3+ | R-101-D8 | 480x480   |   40000 | -        | 9.09           | 47.30 |         48.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context-20200911_165459.log.json) |
-| DeepLabV3+ | R-101-D8 | 480x480   |   80000 | -        | -              | 47.23 |         48.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context-20200911_155322.log.json) |
-
-#### Pascal Context 59
-
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                                           |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DeepLabV3+ | R-101-D8 | 480x480   |   40000 | -        | -           | 52.86 |         54.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59-20210416_111233.log.json) |
-| DeepLabV3+ | R-101-D8 | 480x480   |   80000 | -        | -              | 53.2 |         54.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59-20210416_111127.log.json) |

spaces/Annelisseishere/Streamlit_GPT/app.py

@@ -1,142 +0,0 @@
-from dotenv import load_dotenv
-import os
-import streamlit as st
-from PyPDF2 import PdfFileReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.llms import OpenAI as LLMSOpenAI
-from langchain.llms import AzureOpenAI
-from langchain.callbacks import get_openai_callback
-from langchain.chat_models import ChatOpenAI
-from docx import Document
-from openpyxl import load_workbook
-import pdfplumber
-
-
-def extract_text_from_pdf(pdf_file):
-    with pdfplumber.open(pdf_file) as pdf:
-        text = ""
-        for page in pdf.pages:
-            text += page.extract_text()
-    return text
-
-
-def extract_text_from_docx(docx_file):
-    doc = Document(docx_file)
-    paragraphs = [paragraph.text for paragraph in doc.paragraphs]
-    return "\n".join(paragraphs)
-
-
-def extract_text_from_excel(excel_file):
-    workbook = load_workbook(excel_file)
-    text = ""
-    for sheet in workbook.sheetnames:
-        worksheet = workbook[sheet]
-        for row in worksheet.iter_rows():
-            for cell in row:
-                if cell.value:
-                    text += str(cell.value) + "\n"
-    return text
-
-
-def split_text_into_chunks(text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=1000,
-        chunk_overlap=200,
-        length_function=len
-    )
-    return text_splitter.split_text(text)
-
-
-def create_knowledge_base(chunks, api_key=None):
-    embeddings = OpenAIEmbeddings(openai_api_key=api_key)
-    knowledge_base = FAISS.from_texts(chunks, embeddings)
-    return knowledge_base
-
-
-def answer_question(question, knowledge_base, model):
-    docs = knowledge_base.similarity_search(question)
-    llm = model(model_name="gpt-3.5-turbo", openai_api_key=st.session_state.api_key)
-    chain = load_qa_chain(llm, chain_type="stuff")
-    with get_openai_callback() as cb:
-        response = chain.run(input_documents=docs, question=question)
-    return response
-
-
-def save_api_key(api_key):
-    st.session_state.api_key = api_key
-
-
-def main():
-    load_dotenv()
-    st.set_page_config(page_title="Ask Your PDF", layout="wide")
-
-    # Sidebar
-    st.sidebar.title("Settings")
-
-    # API Key input
-    st.sidebar.subheader("API Key")
-    api_key = st.sidebar.text_input("Insert your API Key", type="password")
-    st.sidebar.button("Save API Key", on_click=save_api_key, args=(api_key,))
-
-    model_type = st.sidebar.selectbox("Select Language Model", ["OpenAI", "AzureOpenAI"])
-    if model_type == "AzureOpenAI":
-        model = AzureOpenAI
-    else:
-        model = ChatOpenAI
-
-    chunk_size = st.sidebar.slider("Chunk Size", min_value=500, max_value=2000, value=1000, step=100)
-    chunk_overlap = st.sidebar.slider("Chunk Overlap", min_value=100, max_value=500, value=200, step=50)
-    show_content = st.sidebar.checkbox("Show Document Content")
-    show_answers = st.sidebar.checkbox("Show Previous Answers")
-
-    # Main content
-    st.title("Ask Your Document 💭")
-    file_format = st.selectbox("Select File Format", ["PDF", "docx", "xlsx"])
-    document = st.file_uploader("Upload Document", type=[file_format.lower()])
-
-    if not hasattr(st.session_state, "api_key") or not st.session_state.api_key:
-        st.warning("You need to insert your API Key first.")
-    elif document is not None:
-        if file_format == "PDF":
-            text = extract_text_from_pdf(document)
-        elif file_format == "docx":
-            text = extract_text_from_docx(document)
-        elif file_format == "xlsx":
-            text = extract_text_from_excel(document)
-        else:
-            text = ""
-
-        if show_content:
-            st.subheader("Document Text:")
-            st.text_area("Content", value=text, height=300)
-
-        chunks = split_text_into_chunks(text)
-        knowledge_base = create_knowledge_base(chunks, api_key=st.session_state.api_key)
-
-        user_question = st.text_input("Ask a question based on the document content:")
-
-        if user_question:
-            response = answer_question(user_question, knowledge_base, model)
-            st.subheader("Answer:")
-            st.write(response)
-
-            # Store and display previous answers
-            if "answers" not in st.session_state:
-                st.session_state.answers = []
-            st.session_state.answers.append((user_question, response))
-
-        if show_answers:
-            st.subheader("Previous Answers:")
-            for question, answer in st.session_state.answers:
-                st.write(f"Question: {question}")
-                st.write(f"Answer: {answer}")
-                st.write("------")
-
-
-if __name__ == '__main__':
-    main()
-

spaces/ArchitSharma/Digital-Photo-Color-Restoration/src/deoldify/dataset.py

@@ -1,48 +0,0 @@
-import fastai
-from fastai import *
-from fastai.core import *
-from fastai.vision.transform import get_transforms
-from fastai.vision.data import ImageImageList, ImageDataBunch, imagenet_stats
-from .augs import noisify
-
-
-def get_colorize_data(
-    sz: int,
-    bs: int,
-    crappy_path: Path,
-    good_path: Path,
-    random_seed: int = None,
-    keep_pct: float = 1.0,
-    num_workers: int = 8,
-    stats: tuple = imagenet_stats,
-    xtra_tfms=[],
-) -> ImageDataBunch:
-    
-    src = (
-        ImageImageList.from_folder(crappy_path, convert_mode='RGB')
-        .use_partial_data(sample_pct=keep_pct, seed=random_seed)
-        .split_by_rand_pct(0.1, seed=random_seed)
-    )
-
-    data = (
-        src.label_from_func(lambda x: good_path / x.relative_to(crappy_path))
-        .transform(
-            get_transforms(
-                max_zoom=1.2, max_lighting=0.5, max_warp=0.25, xtra_tfms=xtra_tfms
-            ),
-            size=sz,
-            tfm_y=True,
-        )
-        .databunch(bs=bs, num_workers=num_workers, no_check=True)
-        .normalize(stats, do_y=True)
-    )
-
-    data.c = 3
-    return data
-
-
-def get_dummy_databunch() -> ImageDataBunch:
-    path = Path('./assets/dummy/')
-    return get_colorize_data(
-        sz=1, bs=1, crappy_path=path, good_path=path, keep_pct=0.001
-    )

spaces/Arijit-hazra/my-image-captioner/README.md

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

spaces/Ashwanthram/myGenVoiceBot/app.py

@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-headers = {
-      "accept": "text/event-stream",
-      "content-type": "application/json",
-      "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
-      "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
-  return {
-    "text": text,
-    "voice": PLAY_HT_VOICE_ID,
-    "quality": "medium",
-    "output_format": "mp3",
-    "speed": 1,
-    "sample_rate": 24000,
-    "seed": None,
-    "temperature": None
-  }
-
-def get_generated_audio(text):
-  payload = get_payload(text)
-  generated_response = {}
-  try:
-      response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
-      response.raise_for_status()
-      generated_response["type"]= 'SUCCESS'
-      generated_response["response"] = response.text
-  except requests.exceptions.RequestException as e:
-      generated_response["type"]= 'ERROR'
-      try:
-        response_text = json.loads(response.text)
-        if response_text['error_message']:
-          generated_response["response"] = response_text['error_message']
-        else:
-          generated_response["response"] = response.text
-      except Exception as e:
-        generated_response["response"] = response.text
-  except Exception as e:
-    generated_response["type"]= 'ERROR'
-    generated_response["response"] = response.text
-  return generated_response
-
-def extract_urls(text):
-    # Define the regex pattern for URLs
-    url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
-    # Find all occurrences of URLs in the text
-    urls = re.findall(url_pattern, text)
-
-    return urls
-
-def get_audio_reply_for_question(text):
-  generated_audio_event = get_generated_audio(text)
-  #From get_generated_audio, you will get events in a string format, from that we need to extract the url
-  final_response = {
-      "audio_url": '',
-      "message": ''
-  }
-  if generated_audio_event["type"] == 'SUCCESS':
-    audio_urls = extract_urls(generated_audio_event["response"])
-    if len(audio_urls) == 0:
-      final_response['message'] = "No audio file link found in generated event"
-    else:
-      final_response['audio_url'] = audio_urls[-1]
-  else:
-    final_response['message'] = generated_audio_event['response']
-  return final_response
-
-def download_url(url):
-    try:
-        # Send a GET request to the URL to fetch the content
-        final_response = {
-            'content':'',
-            'error':''
-        }
-        response = requests.get(url)
-        # Check if the request was successful (status code 200)
-        if response.status_code == 200:
-            final_response['content'] = response.content
-        else:
-            final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
-    except Exception as e:
-        final_response['error'] = f"Failed to download the URL. Error: {e}"
-    return final_response
-
-def get_filename_from_url(url):
-    # Use os.path.basename() to extract the file name from the URL
-    file_name = os.path.basename(url)
-    return file_name
-
-def get_text_response(user_message):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-def get_text_response_and_audio_response(user_message):
-    response = get_text_response(user_message) # Getting the reply from Open AI
-    audio_reply_for_question_response = get_audio_reply_for_question(response)
-    final_response = {
-        'output_file_path': '',
-        'message':''
-    }
-    audio_url = audio_reply_for_question_response['audio_url']
-    if audio_url:
-      output_file_path=get_filename_from_url(audio_url)
-      download_url_response = download_url(audio_url)
-      audio_content = download_url_response['content']
-      if audio_content:
-        with open(output_file_path, "wb") as audio_file:
-          audio_file.write(audio_content)
-          final_response['output_file_path'] = output_file_path
-      else:
-          final_response['message'] = download_url_response['error']
-    else:
-      final_response['message'] = audio_reply_for_question_response['message']
-    return final_response
-
-def chat_bot_response(message, history):
-    text_and_audio_response = get_text_response_and_audio_response(message)
-    output_file_path = text_and_audio_response['output_file_path']
-    if output_file_path:
-      return (text_and_audio_response['output_file_path'],)
-    else:
-      return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/pyparsing/unicode.py

@@ -1,352 +0,0 @@
-# unicode.py
-
-import sys
-from itertools import filterfalse
-from typing import List, Tuple, Union
-
-
-class _lazyclassproperty:
-    def __init__(self, fn):
-        self.fn = fn
-        self.__doc__ = fn.__doc__
-        self.__name__ = fn.__name__
-
-    def __get__(self, obj, cls):
-        if cls is None:
-            cls = type(obj)
-        if not hasattr(cls, "_intern") or any(
-            cls._intern is getattr(superclass, "_intern", [])
-            for superclass in cls.__mro__[1:]
-        ):
-            cls._intern = {}
-        attrname = self.fn.__name__
-        if attrname not in cls._intern:
-            cls._intern[attrname] = self.fn(cls)
-        return cls._intern[attrname]
-
-
-UnicodeRangeList = List[Union[Tuple[int, int], Tuple[int]]]
-
-
-class unicode_set:
-    """
-    A set of Unicode characters, for language-specific strings for
-    ``alphas``, ``nums``, ``alphanums``, and ``printables``.
-    A unicode_set is defined by a list of ranges in the Unicode character
-    set, in a class attribute ``_ranges``. Ranges can be specified using
-    2-tuples or a 1-tuple, such as::
-
-        _ranges = [
-            (0x0020, 0x007e),
-            (0x00a0, 0x00ff),
-            (0x0100,),
-            ]
-
-    Ranges are left- and right-inclusive. A 1-tuple of (x,) is treated as (x, x).
-
-    A unicode set can also be defined using multiple inheritance of other unicode sets::
-
-        class CJK(Chinese, Japanese, Korean):
-            pass
-    """
-
-    _ranges: UnicodeRangeList = []
-
-    @_lazyclassproperty
-    def _chars_for_ranges(cls):
-        ret = []
-        for cc in cls.__mro__:
-            if cc is unicode_set:
-                break
-            for rr in getattr(cc, "_ranges", ()):
-                ret.extend(range(rr[0], rr[-1] + 1))
-        return [chr(c) for c in sorted(set(ret))]
-
-    @_lazyclassproperty
-    def printables(cls):
-        "all non-whitespace characters in this range"
-        return "".join(filterfalse(str.isspace, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def alphas(cls):
-        "all alphabetic characters in this range"
-        return "".join(filter(str.isalpha, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def nums(cls):
-        "all numeric digit characters in this range"
-        return "".join(filter(str.isdigit, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def alphanums(cls):
-        "all alphanumeric characters in this range"
-        return cls.alphas + cls.nums
-
-    @_lazyclassproperty
-    def identchars(cls):
-        "all characters in this range that are valid identifier characters, plus underscore '_'"
-        return "".join(
-            sorted(
-                set(
-                    "".join(filter(str.isidentifier, cls._chars_for_ranges))
-                    + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzªµº"
-                    + "ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿ"
-                    + "_"
-                )
-            )
-        )
-
-    @_lazyclassproperty
-    def identbodychars(cls):
-        """
-        all characters in this range that are valid identifier body characters,
-        plus the digits 0-9
-        """
-        return "".join(
-            sorted(
-                set(
-                    cls.identchars
-                    + "0123456789"
-                    + "".join(
-                        [c for c in cls._chars_for_ranges if ("_" + c).isidentifier()]
-                    )
-                )
-            )
-        )
-
-
-class pyparsing_unicode(unicode_set):
-    """
-    A namespace class for defining common language unicode_sets.
-    """
-
-    # fmt: off
-
-    # define ranges in language character sets
-    _ranges: UnicodeRangeList = [
-        (0x0020, sys.maxunicode),
-    ]
-
-    class BasicMultilingualPlane(unicode_set):
-        "Unicode set for the Basic Multilingual Plane"
-        _ranges: UnicodeRangeList = [
-            (0x0020, 0xFFFF),
-        ]
-
-    class Latin1(unicode_set):
-        "Unicode set for Latin-1 Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0020, 0x007E),
-            (0x00A0, 0x00FF),
-        ]
-
-    class LatinA(unicode_set):
-        "Unicode set for Latin-A Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0100, 0x017F),
-        ]
-
-    class LatinB(unicode_set):
-        "Unicode set for Latin-B Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0180, 0x024F),
-        ]
-
-    class Greek(unicode_set):
-        "Unicode set for Greek Unicode Character Ranges"
-        _ranges: UnicodeRangeList = [
-            (0x0342, 0x0345),
-            (0x0370, 0x0377),
-            (0x037A, 0x037F),
-            (0x0384, 0x038A),
-            (0x038C,),
-            (0x038E, 0x03A1),
-            (0x03A3, 0x03E1),
-            (0x03F0, 0x03FF),
-            (0x1D26, 0x1D2A),
-            (0x1D5E,),
-            (0x1D60,),
-            (0x1D66, 0x1D6A),
-            (0x1F00, 0x1F15),
-            (0x1F18, 0x1F1D),
-            (0x1F20, 0x1F45),
-            (0x1F48, 0x1F4D),
-            (0x1F50, 0x1F57),
-            (0x1F59,),
-            (0x1F5B,),
-            (0x1F5D,),
-            (0x1F5F, 0x1F7D),
-            (0x1F80, 0x1FB4),
-            (0x1FB6, 0x1FC4),
-            (0x1FC6, 0x1FD3),
-            (0x1FD6, 0x1FDB),
-            (0x1FDD, 0x1FEF),
-            (0x1FF2, 0x1FF4),
-            (0x1FF6, 0x1FFE),
-            (0x2129,),
-            (0x2719, 0x271A),
-            (0xAB65,),
-            (0x10140, 0x1018D),
-            (0x101A0,),
-            (0x1D200, 0x1D245),
-            (0x1F7A1, 0x1F7A7),
-        ]
-
-    class Cyrillic(unicode_set):
-        "Unicode set for Cyrillic Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0400, 0x052F),
-            (0x1C80, 0x1C88),
-            (0x1D2B,),
-            (0x1D78,),
-            (0x2DE0, 0x2DFF),
-            (0xA640, 0xA672),
-            (0xA674, 0xA69F),
-            (0xFE2E, 0xFE2F),
-        ]
-
-    class Chinese(unicode_set):
-        "Unicode set for Chinese Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x2E80, 0x2E99),
-            (0x2E9B, 0x2EF3),
-            (0x31C0, 0x31E3),
-            (0x3400, 0x4DB5),
-            (0x4E00, 0x9FEF),
-            (0xA700, 0xA707),
-            (0xF900, 0xFA6D),
-            (0xFA70, 0xFAD9),
-            (0x16FE2, 0x16FE3),
-            (0x1F210, 0x1F212),
-            (0x1F214, 0x1F23B),
-            (0x1F240, 0x1F248),
-            (0x20000, 0x2A6D6),
-            (0x2A700, 0x2B734),
-            (0x2B740, 0x2B81D),
-            (0x2B820, 0x2CEA1),
-            (0x2CEB0, 0x2EBE0),
-            (0x2F800, 0x2FA1D),
-        ]
-
-    class Japanese(unicode_set):
-        "Unicode set for Japanese Unicode Character Range, combining Kanji, Hiragana, and Katakana ranges"
-        _ranges: UnicodeRangeList = []
-
-        class Kanji(unicode_set):
-            "Unicode set for Kanji Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x4E00, 0x9FBF),
-                (0x3000, 0x303F),
-            ]
-
-        class Hiragana(unicode_set):
-            "Unicode set for Hiragana Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x3041, 0x3096),
-                (0x3099, 0x30A0),
-                (0x30FC,),
-                (0xFF70,),
-                (0x1B001,),
-                (0x1B150, 0x1B152),
-                (0x1F200,),
-            ]
-
-        class Katakana(unicode_set):
-            "Unicode set for Katakana  Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x3099, 0x309C),
-                (0x30A0, 0x30FF),
-                (0x31F0, 0x31FF),
-                (0x32D0, 0x32FE),
-                (0xFF65, 0xFF9F),
-                (0x1B000,),
-                (0x1B164, 0x1B167),
-                (0x1F201, 0x1F202),
-                (0x1F213,),
-            ]
-
-    class Hangul(unicode_set):
-        "Unicode set for Hangul (Korean) Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x1100, 0x11FF),
-            (0x302E, 0x302F),
-            (0x3131, 0x318E),
-            (0x3200, 0x321C),
-            (0x3260, 0x327B),
-            (0x327E,),
-            (0xA960, 0xA97C),
-            (0xAC00, 0xD7A3),
-            (0xD7B0, 0xD7C6),
-            (0xD7CB, 0xD7FB),
-            (0xFFA0, 0xFFBE),
-            (0xFFC2, 0xFFC7),
-            (0xFFCA, 0xFFCF),
-            (0xFFD2, 0xFFD7),
-            (0xFFDA, 0xFFDC),
-        ]
-
-    Korean = Hangul
-
-    class CJK(Chinese, Japanese, Hangul):
-        "Unicode set for combined Chinese, Japanese, and Korean (CJK) Unicode Character Range"
-
-    class Thai(unicode_set):
-        "Unicode set for Thai Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0E01, 0x0E3A),
-            (0x0E3F, 0x0E5B)
-        ]
-
-    class Arabic(unicode_set):
-        "Unicode set for Arabic Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0600, 0x061B),
-            (0x061E, 0x06FF),
-            (0x0700, 0x077F),
-        ]
-
-    class Hebrew(unicode_set):
-        "Unicode set for Hebrew Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0591, 0x05C7),
-            (0x05D0, 0x05EA),
-            (0x05EF, 0x05F4),
-            (0xFB1D, 0xFB36),
-            (0xFB38, 0xFB3C),
-            (0xFB3E,),
-            (0xFB40, 0xFB41),
-            (0xFB43, 0xFB44),
-            (0xFB46, 0xFB4F),
-        ]
-
-    class Devanagari(unicode_set):
-        "Unicode set for Devanagari Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0900, 0x097F),
-            (0xA8E0, 0xA8FF)
-        ]
-
-    # fmt: on
-
-
-pyparsing_unicode.Japanese._ranges = (
-    pyparsing_unicode.Japanese.Kanji._ranges
-    + pyparsing_unicode.Japanese.Hiragana._ranges
-    + pyparsing_unicode.Japanese.Katakana._ranges
-)
-
-pyparsing_unicode.BMP = pyparsing_unicode.BasicMultilingualPlane
-
-# add language identifiers using language Unicode
-pyparsing_unicode.العربية = pyparsing_unicode.Arabic
-pyparsing_unicode.中文 = pyparsing_unicode.Chinese
-pyparsing_unicode.кириллица = pyparsing_unicode.Cyrillic
-pyparsing_unicode.Ελληνικά = pyparsing_unicode.Greek
-pyparsing_unicode.עִברִית = pyparsing_unicode.Hebrew
-pyparsing_unicode.日本語 = pyparsing_unicode.Japanese
-pyparsing_unicode.Japanese.漢字 = pyparsing_unicode.Japanese.Kanji
-pyparsing_unicode.Japanese.カタカナ = pyparsing_unicode.Japanese.Katakana
-pyparsing_unicode.Japanese.ひらがな = pyparsing_unicode.Japanese.Hiragana
-pyparsing_unicode.한국어 = pyparsing_unicode.Korean
-pyparsing_unicode.ไทย = pyparsing_unicode.Thai
-pyparsing_unicode.देवनागरी = pyparsing_unicode.Devanagari

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

@@ -1,42 +0,0 @@
-import unicodedata
-import sys
-
-
-# HFS Plus uses decomposed UTF-8
-def decompose(path):
-    if isinstance(path, str):
-        return unicodedata.normalize('NFD', path)
-    try:
-        path = path.decode('utf-8')
-        path = unicodedata.normalize('NFD', path)
-        path = path.encode('utf-8')
-    except UnicodeError:
-        pass  # Not UTF-8
-    return path
-
-
-def filesys_decode(path):
-    """
-    Ensure that the given path is decoded,
-    NONE when no expected encoding works
-    """
-
-    if isinstance(path, str):
-        return path
-
-    fs_enc = sys.getfilesystemencoding() or 'utf-8'
-    candidates = fs_enc, 'utf-8'
-
-    for enc in candidates:
-        try:
-            return path.decode(enc)
-        except UnicodeDecodeError:
-            continue
-
-
-def try_encode(string, enc):
-    "turn unicode encoding into a functional routine"
-    try:
-        return string.encode(enc)
-    except UnicodeEncodeError:
-        return None

spaces/Audio-AGI/AudioSep/gradio_examples.py

@@ -1,16 +0,0 @@
-from pathlib import Path
-
-CURR_DIR = Path(__file__).resolve().parent
-
-EXAMPLES_DIR = CURR_DIR / "examples"
-
-EXAMPLES = [
-    [EXAMPLES_DIR / "acoustic_guitar.wav", "acoustic guitar"],
-    [EXAMPLES_DIR / "laughing.wav", "laughing"],
-    [
-        EXAMPLES_DIR / "ticktok_piano.wav",
-        "A ticktock sound playing at the same rhythm with piano.",
-    ],
-    [EXAMPLES_DIR / "water_drops.wav", "water drops"],
-    [EXAMPLES_DIR / "noisy_speech.wav", "speech"],
-]

spaces/Awesimo/jojogan/op/fused_act.py

@@ -1,127 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-
-module_path = os.path.dirname(__file__)
-fused = load(
-    "fused",
-    sources=[
-        os.path.join(module_path, "fused_bias_act.cpp"),
-        os.path.join(module_path, "fused_bias_act_kernel.cu"),
-    ],
-)
-
-
-class FusedLeakyReLUFunctionBackward(Function):
-    @staticmethod
-    def forward(ctx, grad_output, out, bias, negative_slope, scale):
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        empty = grad_output.new_empty(0)
-
-        grad_input = fused.fused_bias_act(
-            grad_output.contiguous(), empty, out, 3, 1, negative_slope, scale
-        )
-
-        dim = [0]
-
-        if grad_input.ndim > 2:
-            dim += list(range(2, grad_input.ndim))
-
-        if bias:
-            grad_bias = grad_input.sum(dim).detach()
-
-        else:
-            grad_bias = empty
-
-        return grad_input, grad_bias
-
-    @staticmethod
-    def backward(ctx, gradgrad_input, gradgrad_bias):
-        out, = ctx.saved_tensors
-        gradgrad_out = fused.fused_bias_act(
-            gradgrad_input.contiguous(),
-            gradgrad_bias,
-            out,
-            3,
-            1,
-            ctx.negative_slope,
-            ctx.scale,
-        )
-
-        return gradgrad_out, None, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
-    @staticmethod
-    def forward(ctx, input, bias, negative_slope, scale):
-        empty = input.new_empty(0)
-
-        ctx.bias = bias is not None
-
-        if bias is None:
-            bias = empty
-
-        out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        out, = ctx.saved_tensors
-
-        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
-            grad_output, out, ctx.bias, ctx.negative_slope, ctx.scale
-        )
-
-        if not ctx.bias:
-            grad_bias = None
-
-        return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
-    def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5):
-        super().__init__()
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(channel))
-
-        else:
-            self.bias = None
-
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
-    if input.device.type == "cpu":
-        if bias is not None:
-            rest_dim = [1] * (input.ndim - bias.ndim - 1)
-            return (
-                F.leaky_relu(
-                    input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
-                )
-                * scale
-            )
-
-        else:
-            return F.leaky_relu(input, negative_slope=0.2) * scale
-
-    else:
-        return FusedLeakyReLUFunction.apply(
-            input.contiguous(), bias, negative_slope, scale
-        )

spaces/BaddaAshok0265/AshokGenAI/app.py

@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """ Hello, meet Ashok Badda, your youthful and witty personal assistant! At 20 years old, He's full of energy and always eager to help. Riya's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.

spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/lib_v5/nets_537238KB.py

@@ -1,123 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_537238KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 64)
-        self.stg1_high_band_net = BaseASPPNet(2, 64)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(32, 64)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(64, 128)
-
-        self.out = nn.Conv2d(128, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(64, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h

spaces/Benson/text-generation/Examples/Candy Crush Soda Saga No Download.md

@@ -1,145 +0,0 @@
-
-<h1>Candy Crush Soda Saga: Un juego de puzzle dulce y efervescente</h1>
-<p>Si eres un fan de los juegos de puzzle de match-3, probablemente hayas escuchado o jugado a Candy Crush Saga, uno de los juegos más populares y adictivos de este género. Pero ¿sabías que hay una secuela de este juego que ofrece aún más diversión y desafíos? Se llama Candy Crush Soda Saga, y es un juego que puedes jugar online gratis en tu PC o dispositivo móvil, sin descargar nada. En este artículo, te contaremos todo lo que necesitas saber sobre Candy Crush Soda Saga, cómo jugarlo online y cuáles son algunas de las alternativas a este juego. </p>
- <h2>¿Qué es Candy Crush Soda Saga? </h2>
-<p>Candy Crush Soda Saga es un juego de puzzle desarrollado por King, la misma compañía que creó Candy Crush Saga, Diamond Digger Saga, Farm Heroes Saga y muchos otros juegos populares. Fue lanzado en 2014 como un spin-off de Candy Crush Saga, y desde entonces se ha convertido en uno de los juegos más jugados en Facebook, Android, iOS, Windows Phone y Windows 10. </p>
-<h2>candy crush soda saga no download</h2><br /><p><b><b>Download File</b> &ndash;&ndash;&ndash;&ndash;&ndash;>>> <a href="https://bltlly.com/2v6JBZ">https://bltlly.com/2v6JBZ</a></b></p><br /><br />
- <h3>La secuela de la popular Candy Crush Saga</h3>
-<p>Candy Crush Soda Saga es una secuela de Candy Crush Saga, lo que significa que sigue el mismo juego básico de combinar tres o más caramelos del mismo color para eliminarlos del tablero y completar varios objetivos. Sin embargo, Candy Crush Soda Saga también introduce algunos nuevos elementos y giros que lo hacen diferente de su predecesor. Por ejemplo, en Candy Crush Soda Saga, encontrarás nuevos tipos de dulces, como botellas de refresco, caramelos de pescado, caramelos para colorear, caramelos de panal y dulces de mermelada. También explorarás nuevos mundos y niveles con diferentes temas y orígenes, como lagos de soda, nubes de algodón de azúcar, islas de glaseado, jardines de miel y fábricas de mermelada. También conocerás nuevos personajes y amigos a lo largo de tu viaje, como Kimmy, la hermana de Tiffi que está buscando a su hermano perdido. </p>
- <h3>El juego y las características de Candy Crush Soda Saga</h3>
-
-<ul>
-<li>Más de 10000 niveles de Sodalicious que pondrán a prueba tus habilidades y estrategia. </li>
-<li>Temporadas mensuales durante todo el año, llenas de misiones desafiantes y un pase de temporada alimentado por recompensas.</li>
-<li>Modos de juego burbujeando con diversión y dulces únicos:   <ul>
-  <li>Soda - Cambiar las botellas y hacer coincidir los caramelos para liberar la soda púrpura y guardar los Candy Bears.</li>
-  <li>Glaseado - Partido de dulces para romper el hielo y establecer los osos de caramelo libre. </li>
-  <li>Honeycomb - Coincidir con los dulces al lado de nido de abeja para liberar los osos de caramelo atrapados.</li>
-  <li>Jam - Difundir la mermelada en todo el tablero. </li>
-  </ul>
-</li>
-<li>Dulces únicos y deliciosas nuevas combinaciones a juego:   <ul>
-  <li>Partido 4 dulces en un cuadrado para hacer un pescado sueco! </li>
-  <li>Partido 7 caramelos para el todo nuevo caramelo para colorear! </li>
-  </ul>
-</li>
-<li>¡Explora mundos y niveles jugosos con aún más personajes! </li>
-<h3>Los beneficios de jugar Candy Crush Soda Saga en línea</h3>
-<p>Una de las mejores cosas acerca de Candy Crush Soda Saga es que se puede jugar en línea de forma gratuita en su PC o dispositivo móvil, sin descargar nada. Esto significa que puede disfrutar del juego en cualquier momento y en cualquier lugar, siempre y cuando tenga una conexión a Internet. Jugar a Candy Crush Soda Saga en línea también tiene algunos otros beneficios, como:</p>
-<ul>
-<li>Puedes sincronizar el progreso del juego en todos tus dispositivos, para que puedas continuar donde lo dejaste. </li>
-<li>Puedes conectarte con tus amigos de Facebook y ver sus puntajes y progreso en las tablas de clasificación. </li>
-<li>Puedes enviar y recibir vidas y refuerzos de tus amigos para ayudarse mutuamente. </li>
-<li>Puedes unirte a un equipo o crear el tuyo propio y chatear con otros jugadores. </li>
-<li>Puedes participar en eventos especiales y desafíos y ganar recompensas exclusivas. </li>
-</ul>
- <h2>Cómo jugar Candy Crush Soda Saga en línea gratis en PC y móvil</h2>
-
- <h3>El sitio web oficial de King.com</h3>
-<p>El sitio web oficial de King.com es el mejor lugar para jugar Candy Crush Soda Saga en línea, ya que es la fuente oficial del juego. Puede acceder al sitio web desde cualquier navegador de su PC o dispositivo móvil, y puede jugar el juego en modo de pantalla completa. También puedes iniciar sesión con tu cuenta de Facebook o crear una cuenta de King para sincronizar tu progreso y acceder a todas las funciones del juego. Para jugar Candy Crush Soda Saga online en King.com, sigue estos pasos:</p>
-<ol>
-<li>Visite <a href=">https://king.com/game/candycrushsoda</a> desde su navegador. </li>
-<li>Haga clic en el botón "Jugar ahora" para iniciar el juego. </li>
-<li>Si desea iniciar sesión con su cuenta de Facebook o crear una cuenta de King, haga clic en el botón "Conectar" en la esquina superior derecha de la pantalla. </li>
-<li>Disfruta jugando Candy Crush Soda Saga en línea! </li>
-</ol>
- <h3>Las plataformas de juego en línea de Y8.com, ahora.gg, y Games.lol</h3>
-<p>Si quieres jugar Candy Crush Soda Saga en línea en otros sitios web, también puedes probar algunas de las plataformas de juegos en línea que ofrecen el juego, como Y8.com, now.gg y Games.lol. Estos sitios web le permiten jugar Candy Crush Soda Saga en línea sin iniciar sesión o crear una cuenta, pero pueden no tener todas las características y actualizaciones del sitio web oficial. Para jugar Candy Crush Soda Saga en línea en estos sitios web, siga estos pasos:</p>
-<ol>
-<li>Visite uno de estos sitios web desde su navegador:   <ul>
-  <li><a href=">https://www.y8.com/games/candy_crush_soda_saga</a></li>
-  <li><a href=">https://www.now.gg/play/candy-crush-soda-saga</a></li>
-  <li><a href="">https://games.lol/candy-crush-soda-saga/</a></li>
-  </ul>
-</li>
-<li>Haga clic en el botón "Play" para iniciar el juego. </li>
-<li>Disfruta jugando Candy Crush Soda Saga en línea! </li>
-</ol>
- <h3>Los consejos y trucos para dominar Candy Crush Soda Saga</h3>
-
-<ul>
-<li>Presta atención al objetivo de cada nivel y planifica tus movimientos en consecuencia. </li>
-<li>Combina dulces cerca de la parte inferior del tablero para crear cascadas y limpiar más dulces. </li>
-<li>Usa dulces especiales y potenciadores sabiamente y guárdalos para niveles difíciles. </li>
-<li>Aprende a hacer diferentes combinaciones de dulces especiales, como rayas + envuelto, rayas + pescado, envuelto + pescado, colorante + pescado, etc.</li>
-<li>Sepa cómo tratar con diferentes tipos de bloqueadores, como chocolate, regaliz, hielo, panal, etc.</li>
-<li>Mantén un ojo en el nivel de soda y trata de llenarlo o bajarlo dependiendo del modo. </li>
-<li>No malgastes movimientos y trata de obtener tantas estrellas como sea posible. </li>
-arriba. Siempre puedes reproducir los niveles o pedir ayuda a tus amigos. </li>
-</ul>
- <h2>¿Cuáles son las alternativas a Candy Crush Soda Saga? </h2>
-<p>Candy Crush Soda Saga es un gran juego, pero no es el único de su tipo. Si quieres probar otros juegos similares a Candy Crush Soda Saga, tienes muchas opciones para elegir. Estas son algunas de las alternativas a Candy Crush Soda Saga que te pueden gustar:</p>
- <h3>Los otros juegos de la franquicia Candy Crush</h3>
-<p>Si te gusta Candy Crush Soda Saga, también te pueden encantar los otros juegos de la misma franquicia, como:</p>
-<p></p>
-<ul>
-<li>Candy Crush Saga: El original y clásico juego de puzzle match-3 que comenzó todo. </li>
-<li>Candy Crush Jelly Saga: La tercera entrega de la franquicia, donde tienes que untar jalea y competir con la Jelly Queen.</li>
-<li>Candy Crush Friends Saga: La cuarta y última entrega de la franquicia, donde tienes que combinar dulces y recoger a tus amigos. </li>
-</ul>
-<p>Todos estos juegos son gratis para jugar en línea en King.com o en sus dispositivos móviles, y tienen un juego similar y características como Candy Crush Soda Saga, pero con diferentes giros y desafíos. </p>
- <h3>Los juegos de rompecabezas de match-3 similares de otros desarrolladores</h3>
-
-<ul>
-<li>Bejeweled: El clásico y original juego de puzzle match-3 que inspiró a muchos otros. </li>
-<li>Cookie Jam: Un juego de puzzle de partido 3 delicioso y colorido donde tienes que hornear galletas y pasteles. </li>
-<li>Gummy Drop: Un dulce y aventurero juego de puzzle match-3 donde tienes que viajar alrededor del mundo y reconstruir puntos de referencia. </li>
-<li>Homescapes: Un relajante y divertido juego de puzzle match-3 donde tienes que renovar una mansión y ayudar a una familia. </li>
-<li>Toon Blast: Un juego de rompecabezas de dibujos animados y explosivos match-3 donde tienes que destruir cubos y crear combos. </li>
-</ul>
-<p>Todos estos juegos son gratis para jugar en línea en varios sitios web o en sus dispositivos móviles, y tienen un juego similar y características como Candy Crush Soda Saga, pero con diferentes temas e historias. </p>
- <h3>Los pros y los contras de jugar alternativas a Candy Crush Soda Saga</h3>
-<p>Jugar alternativas a Candy Crush Soda Saga puede ser una buena manera de darle vida a tu experiencia de juego y probar algo nuevo. Sin embargo, también hay algunos pros y contras de jugar alternativas a Candy Crush Soda Saga, como:</p>
- <tabla>
-<tr><th>Pros</th><th>Contras</th></tr>
-<tr><td>Puedes descubrir nuevos juegos y géneros que puedes disfrutar. </td><td>Puedes confundirte o sentirte abrumado por demasiadas opciones. </td></tr>
-<tr><td>Puedes comparar y contrastar diferentes juegos y encontrar tu favorito. </td><td>Usted puede perder interés o motivación en jugar Candy Crush Soda Saga.</td></tr>
-<tr><td>Puede desafiarse a sí mismo con diferentes niveles y modos. </td><td>Puede encontrar algunos juegos demasiado fáciles o demasiado difíciles para su gusto. </td></tr>
-<tr><td>Puedes tener más diversión y variedad en tu tiempo de juego. </td><td>Puedes gastar demasiado tiempo o dinero en juegos. </td></tr>
-</tabla>
- <h2>Conclusión</h2>
-
- <h3>Preguntas frecuentes</h3>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Candy Crush Soda Saga:</p>
- <ol>
-<li>¿Cómo puedo obtener más vidas en Candy Crush Soda Saga? </li>
-<p>Puedes obtener más vidas en Candy Crush Soda Saga haciendo una de las siguientes:   <ul>
-  <li>Espera 30 minutos para que cada vida se regenere automáticamente. </li>
-  <li>Comprar más vidas con barras de oro, la moneda premium del juego. </li>
-  <li>Cambiar la configuración de fecha y hora en su dispositivo para engañar al juego para que le dé más vidas. </li>
-  </ul>
-</p>
- <li>¿Cómo puedo obtener más barras de oro en Candy Crush Soda Saga? </li>
-<p>Puede obtener más barras de oro en Candy Crush Soda Saga haciendo una de las siguientes:   <ul>
-  <li>Completa las misiones y desafíos diarios y gana recompensas. </li>
-  <li>Subir de nivel su pase de temporada y desbloquear barras de oro y otras ventajas. </li>
-  <li>Únete a un equipo o crea tu propio equipo y gana eventos y competiciones. </li>
-  <li>Conecte su juego a su cuenta de Facebook y obtener barras de oro gratis. </li>
-  <li>Comprar más barras de oro con dinero real a través de compras en la aplicación. </li>
-  </ul>
-</p>
- <li>¿Cómo puedo obtener más potenciadores en Candy Crush Soda Saga? </li>
-<p>Puede obtener más potenciadores en Candy Crush Soda Saga haciendo uno de los siguientes:   <ul>
-  <li> Girar la rueda de refuerzo diario y ganar un refuerzo al azar todos los días. </li>
-  <li>Juega el evento de Bubblegum Hill y gana boosters y otros premios. </li>
-  <li>Recoger estrellas y llenar el medidor Star Chaser para obtener boosters gratis. </li>
-  <li>Ver anuncios de vídeo y obtener refuerzos gratis. </li>
-  <li>Comprar más potenciadores con barras de oro o dinero real a través de compras en la aplicación. </li>
-  </ul>
-</p>
- <li>¿Cómo puedo desbloquear nuevos episodios en Candy Crush Soda Saga? </li>
-<p>Puede desbloquear nuevos episodios en Candy Crush Soda Saga haciendo uno de los siguientes:   <ul>
-  <li>Completa todos los niveles en el episodio anterior. </li>
-  <li>Pídele a tus amigos o miembros del equipo de Facebook que te envíen entradas. </li>
-
-  </ul>
-</p>
- <li>¿Cómo puedo contactar al equipo de soporte de Candy Crush Soda Saga? </li>
-<p>Puede ponerse en contacto con el equipo de soporte de Candy Crush Soda Saga haciendo una de las siguientes:   <ul>
-  <li>Visite el sitio web oficial de King.com y haga clic en el botón "Contáctenos" en la parte inferior de la página. </li>
-  <li>Visita la página oficial de Facebook de Candy Crush Soda Saga y envía un mensaje a la página. </li>
-  <li>Visite el foro oficial de King.com y publique su pregunta o problema en la sección correspondiente. </li>
-  </ul>
-</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BertChristiaens/blip-diffusion/README.md

@@ -1,8 +0,0 @@
----
-license: openrail
-title: Blip Diffusion
-sdk: streamlit
-emoji: 🚀
-colorFrom: yellow
-colorTo: green
----

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/docs/bcdoc/__init__.py

@@ -1,13 +0,0 @@
-# Copyright 2013 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-#     http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-__version__ = '0.16.0'

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/resolvelib/resolvers.py

@@ -1,547 +0,0 @@
-import collections
-import itertools
-import operator
-
-from .providers import AbstractResolver
-from .structs import DirectedGraph, IteratorMapping, build_iter_view
-
-RequirementInformation = collections.namedtuple(
-    "RequirementInformation", ["requirement", "parent"]
-)
-
-
-class ResolverException(Exception):
-    """A base class for all exceptions raised by this module.
-
-    Exceptions derived by this class should all be handled in this module. Any
-    bubbling pass the resolver should be treated as a bug.
-    """
-
-
-class RequirementsConflicted(ResolverException):
-    def __init__(self, criterion):
-        super(RequirementsConflicted, self).__init__(criterion)
-        self.criterion = criterion
-
-    def __str__(self):
-        return "Requirements conflict: {}".format(
-            ", ".join(repr(r) for r in self.criterion.iter_requirement()),
-        )
-
-
-class InconsistentCandidate(ResolverException):
-    def __init__(self, candidate, criterion):
-        super(InconsistentCandidate, self).__init__(candidate, criterion)
-        self.candidate = candidate
-        self.criterion = criterion
-
-    def __str__(self):
-        return "Provided candidate {!r} does not satisfy {}".format(
-            self.candidate,
-            ", ".join(repr(r) for r in self.criterion.iter_requirement()),
-        )
-
-
-class Criterion(object):
-    """Representation of possible resolution results of a package.
-
-    This holds three attributes:
-
-    * `information` is a collection of `RequirementInformation` pairs.
-      Each pair is a requirement contributing to this criterion, and the
-      candidate that provides the requirement.
-    * `incompatibilities` is a collection of all known not-to-work candidates
-      to exclude from consideration.
-    * `candidates` is a collection containing all possible candidates deducted
-      from the union of contributing requirements and known incompatibilities.
-      It should never be empty, except when the criterion is an attribute of a
-      raised `RequirementsConflicted` (in which case it is always empty).
-
-    .. note::
-        This class is intended to be externally immutable. **Do not** mutate
-        any of its attribute containers.
-    """
-
-    def __init__(self, candidates, information, incompatibilities):
-        self.candidates = candidates
-        self.information = information
-        self.incompatibilities = incompatibilities
-
-    def __repr__(self):
-        requirements = ", ".join(
-            "({!r}, via={!r})".format(req, parent)
-            for req, parent in self.information
-        )
-        return "Criterion({})".format(requirements)
-
-    def iter_requirement(self):
-        return (i.requirement for i in self.information)
-
-    def iter_parent(self):
-        return (i.parent for i in self.information)
-
-
-class ResolutionError(ResolverException):
-    pass
-
-
-class ResolutionImpossible(ResolutionError):
-    def __init__(self, causes):
-        super(ResolutionImpossible, self).__init__(causes)
-        # causes is a list of RequirementInformation objects
-        self.causes = causes
-
-
-class ResolutionTooDeep(ResolutionError):
-    def __init__(self, round_count):
-        super(ResolutionTooDeep, self).__init__(round_count)
-        self.round_count = round_count
-
-
-# Resolution state in a round.
-State = collections.namedtuple("State", "mapping criteria backtrack_causes")
-
-
-class Resolution(object):
-    """Stateful resolution object.
-
-    This is designed as a one-off object that holds information to kick start
-    the resolution process, and holds the results afterwards.
-    """
-
-    def __init__(self, provider, reporter):
-        self._p = provider
-        self._r = reporter
-        self._states = []
-
-    @property
-    def state(self):
-        try:
-            return self._states[-1]
-        except IndexError:
-            raise AttributeError("state")
-
-    def _push_new_state(self):
-        """Push a new state into history.
-
-        This new state will be used to hold resolution results of the next
-        coming round.
-        """
-        base = self._states[-1]
-        state = State(
-            mapping=base.mapping.copy(),
-            criteria=base.criteria.copy(),
-            backtrack_causes=base.backtrack_causes[:],
-        )
-        self._states.append(state)
-
-    def _add_to_criteria(self, criteria, requirement, parent):
-        self._r.adding_requirement(requirement=requirement, parent=parent)
-
-        identifier = self._p.identify(requirement_or_candidate=requirement)
-        criterion = criteria.get(identifier)
-        if criterion:
-            incompatibilities = list(criterion.incompatibilities)
-        else:
-            incompatibilities = []
-
-        matches = self._p.find_matches(
-            identifier=identifier,
-            requirements=IteratorMapping(
-                criteria,
-                operator.methodcaller("iter_requirement"),
-                {identifier: [requirement]},
-            ),
-            incompatibilities=IteratorMapping(
-                criteria,
-                operator.attrgetter("incompatibilities"),
-                {identifier: incompatibilities},
-            ),
-        )
-
-        if criterion:
-            information = list(criterion.information)
-            information.append(RequirementInformation(requirement, parent))
-        else:
-            information = [RequirementInformation(requirement, parent)]
-
-        criterion = Criterion(
-            candidates=build_iter_view(matches),
-            information=information,
-            incompatibilities=incompatibilities,
-        )
-        if not criterion.candidates:
-            raise RequirementsConflicted(criterion)
-        criteria[identifier] = criterion
-
-    def _remove_information_from_criteria(self, criteria, parents):
-        """Remove information from parents of criteria.
-
-        Concretely, removes all values from each criterion's ``information``
-        field that have one of ``parents`` as provider of the requirement.
-
-        :param criteria: The criteria to update.
-        :param parents: Identifiers for which to remove information from all criteria.
-        """
-        if not parents:
-            return
-        for key, criterion in criteria.items():
-            criteria[key] = Criterion(
-                criterion.candidates,
-                [
-                    information
-                    for information in criterion.information
-                    if (
-                        information.parent is None
-                        or self._p.identify(information.parent) not in parents
-                    )
-                ],
-                criterion.incompatibilities,
-            )
-
-    def _get_preference(self, name):
-        return self._p.get_preference(
-            identifier=name,
-            resolutions=self.state.mapping,
-            candidates=IteratorMapping(
-                self.state.criteria,
-                operator.attrgetter("candidates"),
-            ),
-            information=IteratorMapping(
-                self.state.criteria,
-                operator.attrgetter("information"),
-            ),
-            backtrack_causes=self.state.backtrack_causes,
-        )
-
-    def _is_current_pin_satisfying(self, name, criterion):
-        try:
-            current_pin = self.state.mapping[name]
-        except KeyError:
-            return False
-        return all(
-            self._p.is_satisfied_by(requirement=r, candidate=current_pin)
-            for r in criterion.iter_requirement()
-        )
-
-    def _get_updated_criteria(self, candidate):
-        criteria = self.state.criteria.copy()
-        for requirement in self._p.get_dependencies(candidate=candidate):
-            self._add_to_criteria(criteria, requirement, parent=candidate)
-        return criteria
-
-    def _attempt_to_pin_criterion(self, name):
-        criterion = self.state.criteria[name]
-
-        causes = []
-        for candidate in criterion.candidates:
-            try:
-                criteria = self._get_updated_criteria(candidate)
-            except RequirementsConflicted as e:
-                self._r.rejecting_candidate(e.criterion, candidate)
-                causes.append(e.criterion)
-                continue
-
-            # Check the newly-pinned candidate actually works. This should
-            # always pass under normal circumstances, but in the case of a
-            # faulty provider, we will raise an error to notify the implementer
-            # to fix find_matches() and/or is_satisfied_by().
-            satisfied = all(
-                self._p.is_satisfied_by(requirement=r, candidate=candidate)
-                for r in criterion.iter_requirement()
-            )
-            if not satisfied:
-                raise InconsistentCandidate(candidate, criterion)
-
-            self._r.pinning(candidate=candidate)
-            self.state.criteria.update(criteria)
-
-            # Put newly-pinned candidate at the end. This is essential because
-            # backtracking looks at this mapping to get the last pin.
-            self.state.mapping.pop(name, None)
-            self.state.mapping[name] = candidate
-
-            return []
-
-        # All candidates tried, nothing works. This criterion is a dead
-        # end, signal for backtracking.
-        return causes
-
-    def _backjump(self, causes):
-        """Perform backjumping.
-
-        When we enter here, the stack is like this::
-
-            [ state Z ]
-            [ state Y ]
-            [ state X ]
-            .... earlier states are irrelevant.
-
-        1. No pins worked for Z, so it does not have a pin.
-        2. We want to reset state Y to unpinned, and pin another candidate.
-        3. State X holds what state Y was before the pin, but does not
-           have the incompatibility information gathered in state Y.
-
-        Each iteration of the loop will:
-
-        1.  Identify Z. The incompatibility is not always caused by the latest
-            state. For example, given three requirements A, B and C, with
-            dependencies A1, B1 and C1, where A1 and B1 are incompatible: the
-            last state might be related to C, so we want to discard the
-            previous state.
-        2.  Discard Z.
-        3.  Discard Y but remember its incompatibility information gathered
-            previously, and the failure we're dealing with right now.
-        4.  Push a new state Y' based on X, and apply the incompatibility
-            information from Y to Y'.
-        5a. If this causes Y' to conflict, we need to backtrack again. Make Y'
-            the new Z and go back to step 2.
-        5b. If the incompatibilities apply cleanly, end backtracking.
-        """
-        incompatible_reqs = itertools.chain(
-            (c.parent for c in causes if c.parent is not None),
-            (c.requirement for c in causes),
-        )
-        incompatible_deps = {self._p.identify(r) for r in incompatible_reqs}
-        while len(self._states) >= 3:
-            # Remove the state that triggered backtracking.
-            del self._states[-1]
-
-            # Ensure to backtrack to a state that caused the incompatibility
-            incompatible_state = False
-            while not incompatible_state:
-                # Retrieve the last candidate pin and known incompatibilities.
-                try:
-                    broken_state = self._states.pop()
-                    name, candidate = broken_state.mapping.popitem()
-                except (IndexError, KeyError):
-                    raise ResolutionImpossible(causes)
-                current_dependencies = {
-                    self._p.identify(d)
-                    for d in self._p.get_dependencies(candidate)
-                }
-                incompatible_state = not current_dependencies.isdisjoint(
-                    incompatible_deps
-                )
-
-            incompatibilities_from_broken = [
-                (k, list(v.incompatibilities))
-                for k, v in broken_state.criteria.items()
-            ]
-
-            # Also mark the newly known incompatibility.
-            incompatibilities_from_broken.append((name, [candidate]))
-
-            # Create a new state from the last known-to-work one, and apply
-            # the previously gathered incompatibility information.
-            def _patch_criteria():
-                for k, incompatibilities in incompatibilities_from_broken:
-                    if not incompatibilities:
-                        continue
-                    try:
-                        criterion = self.state.criteria[k]
-                    except KeyError:
-                        continue
-                    matches = self._p.find_matches(
-                        identifier=k,
-                        requirements=IteratorMapping(
-                            self.state.criteria,
-                            operator.methodcaller("iter_requirement"),
-                        ),
-                        incompatibilities=IteratorMapping(
-                            self.state.criteria,
-                            operator.attrgetter("incompatibilities"),
-                            {k: incompatibilities},
-                        ),
-                    )
-                    candidates = build_iter_view(matches)
-                    if not candidates:
-                        return False
-                    incompatibilities.extend(criterion.incompatibilities)
-                    self.state.criteria[k] = Criterion(
-                        candidates=candidates,
-                        information=list(criterion.information),
-                        incompatibilities=incompatibilities,
-                    )
-                return True
-
-            self._push_new_state()
-            success = _patch_criteria()
-
-            # It works! Let's work on this new state.
-            if success:
-                return True
-
-            # State does not work after applying known incompatibilities.
-            # Try the still previous state.
-
-        # No way to backtrack anymore.
-        return False
-
-    def resolve(self, requirements, max_rounds):
-        if self._states:
-            raise RuntimeError("already resolved")
-
-        self._r.starting()
-
-        # Initialize the root state.
-        self._states = [
-            State(
-                mapping=collections.OrderedDict(),
-                criteria={},
-                backtrack_causes=[],
-            )
-        ]
-        for r in requirements:
-            try:
-                self._add_to_criteria(self.state.criteria, r, parent=None)
-            except RequirementsConflicted as e:
-                raise ResolutionImpossible(e.criterion.information)
-
-        # The root state is saved as a sentinel so the first ever pin can have
-        # something to backtrack to if it fails. The root state is basically
-        # pinning the virtual "root" package in the graph.
-        self._push_new_state()
-
-        for round_index in range(max_rounds):
-            self._r.starting_round(index=round_index)
-
-            unsatisfied_names = [
-                key
-                for key, criterion in self.state.criteria.items()
-                if not self._is_current_pin_satisfying(key, criterion)
-            ]
-
-            # All criteria are accounted for. Nothing more to pin, we are done!
-            if not unsatisfied_names:
-                self._r.ending(state=self.state)
-                return self.state
-
-            # keep track of satisfied names to calculate diff after pinning
-            satisfied_names = set(self.state.criteria.keys()) - set(
-                unsatisfied_names
-            )
-
-            # Choose the most preferred unpinned criterion to try.
-            name = min(unsatisfied_names, key=self._get_preference)
-            failure_causes = self._attempt_to_pin_criterion(name)
-
-            if failure_causes:
-                causes = [i for c in failure_causes for i in c.information]
-                # Backjump if pinning fails. The backjump process puts us in
-                # an unpinned state, so we can work on it in the next round.
-                self._r.resolving_conflicts(causes=causes)
-                success = self._backjump(causes)
-                self.state.backtrack_causes[:] = causes
-
-                # Dead ends everywhere. Give up.
-                if not success:
-                    raise ResolutionImpossible(self.state.backtrack_causes)
-            else:
-                # discard as information sources any invalidated names
-                # (unsatisfied names that were previously satisfied)
-                newly_unsatisfied_names = {
-                    key
-                    for key, criterion in self.state.criteria.items()
-                    if key in satisfied_names
-                    and not self._is_current_pin_satisfying(key, criterion)
-                }
-                self._remove_information_from_criteria(
-                    self.state.criteria, newly_unsatisfied_names
-                )
-                # Pinning was successful. Push a new state to do another pin.
-                self._push_new_state()
-
-            self._r.ending_round(index=round_index, state=self.state)
-
-        raise ResolutionTooDeep(max_rounds)
-
-
-def _has_route_to_root(criteria, key, all_keys, connected):
-    if key in connected:
-        return True
-    if key not in criteria:
-        return False
-    for p in criteria[key].iter_parent():
-        try:
-            pkey = all_keys[id(p)]
-        except KeyError:
-            continue
-        if pkey in connected:
-            connected.add(key)
-            return True
-        if _has_route_to_root(criteria, pkey, all_keys, connected):
-            connected.add(key)
-            return True
-    return False
-
-
-Result = collections.namedtuple("Result", "mapping graph criteria")
-
-
-def _build_result(state):
-    mapping = state.mapping
-    all_keys = {id(v): k for k, v in mapping.items()}
-    all_keys[id(None)] = None
-
-    graph = DirectedGraph()
-    graph.add(None)  # Sentinel as root dependencies' parent.
-
-    connected = {None}
-    for key, criterion in state.criteria.items():
-        if not _has_route_to_root(state.criteria, key, all_keys, connected):
-            continue
-        if key not in graph:
-            graph.add(key)
-        for p in criterion.iter_parent():
-            try:
-                pkey = all_keys[id(p)]
-            except KeyError:
-                continue
-            if pkey not in graph:
-                graph.add(pkey)
-            graph.connect(pkey, key)
-
-    return Result(
-        mapping={k: v for k, v in mapping.items() if k in connected},
-        graph=graph,
-        criteria=state.criteria,
-    )
-
-
-class Resolver(AbstractResolver):
-    """The thing that performs the actual resolution work."""
-
-    base_exception = ResolverException
-
-    def resolve(self, requirements, max_rounds=100):
-        """Take a collection of constraints, spit out the resolution result.
-
-        The return value is a representation to the final resolution result. It
-        is a tuple subclass with three public members:
-
-        * `mapping`: A dict of resolved candidates. Each key is an identifier
-            of a requirement (as returned by the provider's `identify` method),
-            and the value is the resolved candidate.
-        * `graph`: A `DirectedGraph` instance representing the dependency tree.
-            The vertices are keys of `mapping`, and each edge represents *why*
-            a particular package is included. A special vertex `None` is
-            included to represent parents of user-supplied requirements.
-        * `criteria`: A dict of "criteria" that hold detailed information on
-            how edges in the graph are derived. Each key is an identifier of a
-            requirement, and the value is a `Criterion` instance.
-
-        The following exceptions may be raised if a resolution cannot be found:
-
-        * `ResolutionImpossible`: A resolution cannot be found for the given
-            combination of requirements. The `causes` attribute of the
-            exception is a list of (requirement, parent), giving the
-            requirements that could not be satisfied.
-        * `ResolutionTooDeep`: The dependency tree is too deeply nested and
-            the resolver gave up. This is usually caused by a circular
-            dependency, but you can try to resolve this by increasing the
-            `max_rounds` argument.
-        """
-        resolution = Resolution(self.provider, self.reporter)
-        state = resolution.resolve(requirements, max_rounds=max_rounds)
-        return _build_result(state)

spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/zipp.py

@@ -1,329 +0,0 @@
-import io
-import posixpath
-import zipfile
-import itertools
-import contextlib
-import sys
-import pathlib
-
-if sys.version_info < (3, 7):
-    from collections import OrderedDict
-else:
-    OrderedDict = dict
-
-
-__all__ = ['Path']
-
-
-def _parents(path):
-    """
-    Given a path with elements separated by
-    posixpath.sep, generate all parents of that path.
-
-    >>> list(_parents('b/d'))
-    ['b']
-    >>> list(_parents('/b/d/'))
-    ['/b']
-    >>> list(_parents('b/d/f/'))
-    ['b/d', 'b']
-    >>> list(_parents('b'))
-    []
-    >>> list(_parents(''))
-    []
-    """
-    return itertools.islice(_ancestry(path), 1, None)
-
-
-def _ancestry(path):
-    """
-    Given a path with elements separated by
-    posixpath.sep, generate all elements of that path
-
-    >>> list(_ancestry('b/d'))
-    ['b/d', 'b']
-    >>> list(_ancestry('/b/d/'))
-    ['/b/d', '/b']
-    >>> list(_ancestry('b/d/f/'))
-    ['b/d/f', 'b/d', 'b']
-    >>> list(_ancestry('b'))
-    ['b']
-    >>> list(_ancestry(''))
-    []
-    """
-    path = path.rstrip(posixpath.sep)
-    while path and path != posixpath.sep:
-        yield path
-        path, tail = posixpath.split(path)
-
-
-_dedupe = OrderedDict.fromkeys
-"""Deduplicate an iterable in original order"""
-
-
-def _difference(minuend, subtrahend):
-    """
-    Return items in minuend not in subtrahend, retaining order
-    with O(1) lookup.
-    """
-    return itertools.filterfalse(set(subtrahend).__contains__, minuend)
-
-
-class CompleteDirs(zipfile.ZipFile):
-    """
-    A ZipFile subclass that ensures that implied directories
-    are always included in the namelist.
-    """
-
-    @staticmethod
-    def _implied_dirs(names):
-        parents = itertools.chain.from_iterable(map(_parents, names))
-        as_dirs = (p + posixpath.sep for p in parents)
-        return _dedupe(_difference(as_dirs, names))
-
-    def namelist(self):
-        names = super(CompleteDirs, self).namelist()
-        return names + list(self._implied_dirs(names))
-
-    def _name_set(self):
-        return set(self.namelist())
-
-    def resolve_dir(self, name):
-        """
-        If the name represents a directory, return that name
-        as a directory (with the trailing slash).
-        """
-        names = self._name_set()
-        dirname = name + '/'
-        dir_match = name not in names and dirname in names
-        return dirname if dir_match else name
-
-    @classmethod
-    def make(cls, source):
-        """
-        Given a source (filename or zipfile), return an
-        appropriate CompleteDirs subclass.
-        """
-        if isinstance(source, CompleteDirs):
-            return source
-
-        if not isinstance(source, zipfile.ZipFile):
-            return cls(_pathlib_compat(source))
-
-        # Only allow for FastLookup when supplied zipfile is read-only
-        if 'r' not in source.mode:
-            cls = CompleteDirs
-
-        source.__class__ = cls
-        return source
-
-
-class FastLookup(CompleteDirs):
-    """
-    ZipFile subclass to ensure implicit
-    dirs exist and are resolved rapidly.
-    """
-
-    def namelist(self):
-        with contextlib.suppress(AttributeError):
-            return self.__names
-        self.__names = super(FastLookup, self).namelist()
-        return self.__names
-
-    def _name_set(self):
-        with contextlib.suppress(AttributeError):
-            return self.__lookup
-        self.__lookup = super(FastLookup, self)._name_set()
-        return self.__lookup
-
-
-def _pathlib_compat(path):
-    """
-    For path-like objects, convert to a filename for compatibility
-    on Python 3.6.1 and earlier.
-    """
-    try:
-        return path.__fspath__()
-    except AttributeError:
-        return str(path)
-
-
-class Path:
-    """
-    A pathlib-compatible interface for zip files.
-
-    Consider a zip file with this structure::
-
-        .
-        ├── a.txt
-        └── b
-            ├── c.txt
-            └── d
-                └── e.txt
-
-    >>> data = io.BytesIO()
-    >>> zf = zipfile.ZipFile(data, 'w')
-    >>> zf.writestr('a.txt', 'content of a')
-    >>> zf.writestr('b/c.txt', 'content of c')
-    >>> zf.writestr('b/d/e.txt', 'content of e')
-    >>> zf.filename = 'mem/abcde.zip'
-
-    Path accepts the zipfile object itself or a filename
-
-    >>> root = Path(zf)
-
-    From there, several path operations are available.
-
-    Directory iteration (including the zip file itself):
-
-    >>> a, b = root.iterdir()
-    >>> a
-    Path('mem/abcde.zip', 'a.txt')
-    >>> b
-    Path('mem/abcde.zip', 'b/')
-
-    name property:
-
-    >>> b.name
-    'b'
-
-    join with divide operator:
-
-    >>> c = b / 'c.txt'
-    >>> c
-    Path('mem/abcde.zip', 'b/c.txt')
-    >>> c.name
-    'c.txt'
-
-    Read text:
-
-    >>> c.read_text()
-    'content of c'
-
-    existence:
-
-    >>> c.exists()
-    True
-    >>> (b / 'missing.txt').exists()
-    False
-
-    Coercion to string:
-
-    >>> import os
-    >>> str(c).replace(os.sep, posixpath.sep)
-    'mem/abcde.zip/b/c.txt'
-
-    At the root, ``name``, ``filename``, and ``parent``
-    resolve to the zipfile. Note these attributes are not
-    valid and will raise a ``ValueError`` if the zipfile
-    has no filename.
-
-    >>> root.name
-    'abcde.zip'
-    >>> str(root.filename).replace(os.sep, posixpath.sep)
-    'mem/abcde.zip'
-    >>> str(root.parent)
-    'mem'
-    """
-
-    __repr = "{self.__class__.__name__}({self.root.filename!r}, {self.at!r})"
-
-    def __init__(self, root, at=""):
-        """
-        Construct a Path from a ZipFile or filename.
-
-        Note: When the source is an existing ZipFile object,
-        its type (__class__) will be mutated to a
-        specialized type. If the caller wishes to retain the
-        original type, the caller should either create a
-        separate ZipFile object or pass a filename.
-        """
-        self.root = FastLookup.make(root)
-        self.at = at
-
-    def open(self, mode='r', *args, pwd=None, **kwargs):
-        """
-        Open this entry as text or binary following the semantics
-        of ``pathlib.Path.open()`` by passing arguments through
-        to io.TextIOWrapper().
-        """
-        if self.is_dir():
-            raise IsADirectoryError(self)
-        zip_mode = mode[0]
-        if not self.exists() and zip_mode == 'r':
-            raise FileNotFoundError(self)
-        stream = self.root.open(self.at, zip_mode, pwd=pwd)
-        if 'b' in mode:
-            if args or kwargs:
-                raise ValueError("encoding args invalid for binary operation")
-            return stream
-        return io.TextIOWrapper(stream, *args, **kwargs)
-
-    @property
-    def name(self):
-        return pathlib.Path(self.at).name or self.filename.name
-
-    @property
-    def suffix(self):
-        return pathlib.Path(self.at).suffix or self.filename.suffix
-
-    @property
-    def suffixes(self):
-        return pathlib.Path(self.at).suffixes or self.filename.suffixes
-
-    @property
-    def stem(self):
-        return pathlib.Path(self.at).stem or self.filename.stem
-
-    @property
-    def filename(self):
-        return pathlib.Path(self.root.filename).joinpath(self.at)
-
-    def read_text(self, *args, **kwargs):
-        with self.open('r', *args, **kwargs) as strm:
-            return strm.read()
-
-    def read_bytes(self):
-        with self.open('rb') as strm:
-            return strm.read()
-
-    def _is_child(self, path):
-        return posixpath.dirname(path.at.rstrip("/")) == self.at.rstrip("/")
-
-    def _next(self, at):
-        return self.__class__(self.root, at)
-
-    def is_dir(self):
-        return not self.at or self.at.endswith("/")
-
-    def is_file(self):
-        return self.exists() and not self.is_dir()
-
-    def exists(self):
-        return self.at in self.root._name_set()
-
-    def iterdir(self):
-        if not self.is_dir():
-            raise ValueError("Can't listdir a file")
-        subs = map(self._next, self.root.namelist())
-        return filter(self._is_child, subs)
-
-    def __str__(self):
-        return posixpath.join(self.root.filename, self.at)
-
-    def __repr__(self):
-        return self.__repr.format(self=self)
-
-    def joinpath(self, *other):
-        next = posixpath.join(self.at, *map(_pathlib_compat, other))
-        return self._next(self.root.resolve_dir(next))
-
-    __truediv__ = joinpath
-
-    @property
-    def parent(self):
-        if not self.at:
-            return self.filename.parent
-        parent_at = posixpath.dirname(self.at.rstrip('/'))
-        if parent_at:
-            parent_at += '/'
-        return self._next(parent_at)

spaces/BillBojangeles2000/bart-large-cnn-samsum/README.md

@@ -1,13 +0,0 @@
----
-title: Bart Large Cnn Samsum
-emoji: 🏢
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
-license: bigcode-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Brasd99/TTS-Voice-Cloner/app.py

@@ -1,101 +0,0 @@
-from TTS.api import TTS
-from bs4 import BeautifulSoup
-import requests
-import streamlit as st
-import tempfile
-import os
-import json
-import datetime
-
-with open('config.json', 'r') as f:
-    config = json.load(f)
-
-APP_NAME = config['APP_NAME']
-APP_LOGO = config['APP_LOGO']
-APP_DESCRIPTION = config['APP_DESCRIPTION']
-LANGUAGES_URL = config['LANGUAGES_URL']
-
-def contains_only_ascii(input_string):
-    return all(ord(char) < 128 for char in input_string)
-
-def get_iso_languages():
-    response = requests.get(LANGUAGES_URL)
-    soup = BeautifulSoup(response.text, 'html.parser')
-
-    p_tags = soup.find_all('p')
-
-    iso_language_dict = {}
-
-    for p_tag in p_tags[1:]:  # Skipping the first <p> which contains the header
-        parts = p_tag.get_text().split()
-        if len(parts) == 2:
-            iso_code, language_name = parts
-            if contains_only_ascii(language_name):
-                iso_language_dict[language_name] = iso_code
-
-    return iso_language_dict
-
-def create_temp_file(input_wav):
-    temp_file = tempfile.NamedTemporaryFile(delete=False)
-    temp_file.write(input_wav.read())
-    return temp_file
-
-def remove_temp_file(temp_file):
-    temp_file.close()
-    os.remove(temp_file.name)
-
-def update_progress(percent, text):
-    progress_bar.progress(percent)
-    status_text.text(text)
-
-iso_languages = get_iso_languages()
-languages = list(iso_languages.keys())
-
-st.set_page_config(page_title=APP_NAME)
-st.title(APP_NAME)
-st.image(APP_LOGO, use_column_width=True)
-st.markdown(APP_DESCRIPTION)
-
-language = st.selectbox('Select a language', languages)
-prompt = st.text_input('Enter your prompt')
-input_wav = st.file_uploader("Upload a WAV file", type=["wav"])
-
-if input_wav:
-    if not input_wav or input_wav is None:
-        st.error('Please upload wav input audio')
-    elif not prompt:
-        st.error('Please write prompt')
-    else:
-        progress_bar = st.progress(0)
-        status_text = st.empty()
-
-        current_datetime = datetime.datetime.now()
-        formatted_datetime = current_datetime.strftime("%Y-%m-%d_%H%M%S")
-        output_filename = f"recording_{formatted_datetime}.wav"
-
-        temp_file = create_temp_file(input_wav)
-
-        iso_code = iso_languages[language]
-
-        print(f'Language: {language}, prompt: {prompt}')
-
-        update_progress(0, 'Loading TTS model...')
-        api = TTS(f"tts_models/{iso_code}/fairseq/vits")
-        
-        update_progress(50, 'Generating audio...')
-        api.tts_with_vc_to_file(
-            prompt,
-            speaker_wav=temp_file.name,
-            file_path=output_filename
-        )
-
-        remove_temp_file(temp_file)
-
-        audio_file = open(output_filename, 'rb')
-        audio_bytes = audio_file.read()
-
-        update_progress(100, 'Audio generated successfully!')
-
-        st.audio(audio_bytes, format='audio/wav')
-
-        st.download_button('Download WAV', data=audio_bytes, file_name='output.wav')

spaces/CMU-80100/80-100-Pre-Writing-Chatbot-Section-H/hf_streaming_chatbot.py

@@ -1,112 +0,0 @@
-import openai
-import os 
-import os.path
-import gradio
-from datetime import date
-from datetime import datetime
-import _thread 
-
-# import the prompts here:
-from prompts import debate_prompt_1
-
-
-#########################################
-
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-print("OPENAI_API_KEY Working...\n")
-
-users = {(os.getenv("user1"), os.getenv("PASSWORD")),(os.getenv("user2"), os.getenv("PASSWORD")), 
-        (os.getenv("user3"), os.getenv("PASSWORD")),(os.getenv("user4"), os.getenv("PASSWORD")),
-        (os.getenv("user5"), os.getenv("PASSWORD")),(os.getenv("user6"), os.getenv("PASSWORD")), 
-        (os.getenv("user7"), os.getenv("PASSWORD")),(os.getenv("user8"), os.getenv("PASSWORD")),
-        (os.getenv("user9"), os.getenv("PASSWORD")),(os.getenv("user10"), os.getenv("PASSWORD")), 
-        (os.getenv("user11"), os.getenv("PASSWORD")),(os.getenv("user12"), os.getenv("PASSWORD")),
-        (os.getenv("user13"), os.getenv("PASSWORD")),(os.getenv("user14"), os.getenv("PASSWORD")), 
-        (os.getenv("user15"), os.getenv("PASSWORD")),(os.getenv("user16"), os.getenv("PASSWORD")),
-        (os.getenv("user17"), os.getenv("PASSWORD")),(os.getenv("user18"), os.getenv("PASSWORD"))}
-
-currentUsers = []
-user_num = -1
-
-def authorization(username, password):
-    if (username, password) in users: 
-        currentUsers.append(username)
-        global user_num 
-        user_num += 1
-        print(currentUsers, user_num)
-        return True
-    else:
-        return False 
-
-
-# now = datetime.now()
-today = date.today()
-# start_time = now.strftime("%H:%M:%S")
-
-output = []
-
-############## STREAMING VERSION W/O FLAGGING ##################################
-
-def predict(message, history):
-    history_openai_format = [{"role": "system", "content": debate_prompt_1}]
-
-    for human, assistant in history:
-        history_openai_format.append({"role": "user", "content": human })
-        history_openai_format.append({"role": "assistant", "content":assistant})
-        output.append(f"{currentUsers[0]}: {human}\n\n")
-        output.append(f"gpt-4: {assistant}\n\n")
-    history_openai_format.append({"role": "user", "content": message})
-
-    # print(currentUsers[user_num])
-    # with open(f'activity/{currentUsers[user_num]}_({today}).txt', 'w') as f:
-    #     if (len(output) > 2): 
-    #         f.write(f"{output[-2]}\n\n")
-    #         f.write(f"{output[-1]}\n\n")
-
-    response = openai.ChatCompletion.create(
-        model='gpt-4',
-        messages= history_openai_format,
-        temperature=0.8,
-        max_tokens=512,
-        top_p=1,
-        stream=True
-    )
-
-    partial_message = ""
-    for chunk in response:
-        if len(chunk['choices'][0]['delta']) != 0:
-            partial_message = partial_message + chunk['choices'][0]['delta']['content']
-            yield partial_message
-
-    # if message == 'exit': 
-    #     _thread.interrupt_main()
-
-gradio.ChatInterface(fn = predict, 
-                     title = "80-100 Pre-Writing AI Assistant Chatbot", 
-                     description = "Welcome to the 80-100 Pre-Writing AI Chatbot.\n This bot is designed to discuss the readings, create outlines, and a variety of pre-writing tasks.\nRemember to copy and paste your interaction to a document. Conversations are not saved.\n Please start the discussion by asking: What is your job?",
-                     
-).queue().launch(auth = authorization)
-
-################################################################################
-
-# today = date.today()
-# now2 = datetime.now()
-# end_time = now2.strftime("%H:%M:%S")
-
-# addition = ""
-
-# if (os.path.isfile(f'activity/{currentUsers[0]}_({today}).txt')):
-#     counter = 1 
-#     addition = f"-{counter}"
-#     while(os.path.isfile(f'activity/{currentUsers[0]}_({today}){addition}.txt')):
-#         counter += 1
-#         addition = f"-{counter}"
-
-# with open(f'activity/{currentUsers[0]}_({today}){addition}.txt', 'w') as f:
-#     f.write(f"Start of Session: {start_time} \n")
-#     f.write(f"End of Session: {end_time} \n\n")
-#     f.writelines(output)
-#     f.write('------End of Session------')
-
-# print("Activity has been logged in the history folder. Have a nice day!")

spaces/CVPR/LIVE/thrust/testing/cuda/stream_per_thread.cmake

@@ -1,11 +0,0 @@
-# This test should always use per-thread streams on NVCC.
-set_target_properties(${test_target} PROPERTIES
-  COMPILE_OPTIONS
-    $<$<AND:$<COMPILE_LANGUAGE:CUDA>,$<CUDA_COMPILER_ID:NVIDIA>>:--default-stream=per-thread>
-)
-
-# NVC++ does not have an equivalent option, and will always
-# use the global stream by default.
-if (CMAKE_CUDA_COMPILER_ID STREQUAL "Feta")
-  set_tests_properties(${test_target} PROPERTIES WILL_FAIL ON)
-endif()

spaces/CVPR/regionclip-demo/detectron2/evaluation/fast_eval_api.py

@@ -1,121 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import logging
-import numpy as np
-import time
-from pycocotools.cocoeval import COCOeval
-
-from detectron2 import _C
-
-logger = logging.getLogger(__name__)
-
-
-class COCOeval_opt(COCOeval):
-    """
-    This is a slightly modified version of the original COCO API, where the functions evaluateImg()
-    and accumulate() are implemented in C++ to speedup evaluation
-    """
-
-    def evaluate(self):
-        """
-        Run per image evaluation on given images and store results in self.evalImgs_cpp, a
-        datastructure that isn't readable from Python but is used by a c++ implementation of
-        accumulate().  Unlike the original COCO PythonAPI, we don't populate the datastructure
-        self.evalImgs because this datastructure is a computational bottleneck.
-        :return: None
-        """
-        tic = time.time()
-
-        p = self.params
-        # add backward compatibility if useSegm is specified in params
-        if p.useSegm is not None:
-            p.iouType = "segm" if p.useSegm == 1 else "bbox"
-        logger.info("Evaluate annotation type *{}*".format(p.iouType))
-        p.imgIds = list(np.unique(p.imgIds))
-        if p.useCats:
-            p.catIds = list(np.unique(p.catIds))
-        p.maxDets = sorted(p.maxDets)
-        self.params = p
-
-        self._prepare()  # bottleneck
-
-        # loop through images, area range, max detection number
-        catIds = p.catIds if p.useCats else [-1]
-
-        if p.iouType == "segm" or p.iouType == "bbox":
-            computeIoU = self.computeIoU
-        elif p.iouType == "keypoints":
-            computeIoU = self.computeOks
-        self.ious = {
-            (imgId, catId): computeIoU(imgId, catId) for imgId in p.imgIds for catId in catIds
-        }  # bottleneck
-
-        maxDet = p.maxDets[-1]
-
-        # <<<< Beginning of code differences with original COCO API
-        def convert_instances_to_cpp(instances, is_det=False):
-            # Convert annotations for a list of instances in an image to a format that's fast
-            # to access in C++
-            instances_cpp = []
-            for instance in instances:
-                instance_cpp = _C.InstanceAnnotation(
-                    int(instance["id"]),
-                    instance["score"] if is_det else instance.get("score", 0.0),
-                    instance["area"],
-                    bool(instance.get("iscrowd", 0)),
-                    bool(instance.get("ignore", 0)),
-                )
-                instances_cpp.append(instance_cpp)
-            return instances_cpp
-
-        # Convert GT annotations, detections, and IOUs to a format that's fast to access in C++
-        ground_truth_instances = [
-            [convert_instances_to_cpp(self._gts[imgId, catId]) for catId in p.catIds]
-            for imgId in p.imgIds
-        ]
-        detected_instances = [
-            [convert_instances_to_cpp(self._dts[imgId, catId], is_det=True) for catId in p.catIds]
-            for imgId in p.imgIds
-        ]
-        ious = [[self.ious[imgId, catId] for catId in catIds] for imgId in p.imgIds]
-
-        if not p.useCats:
-            # For each image, flatten per-category lists into a single list
-            ground_truth_instances = [[[o for c in i for o in c]] for i in ground_truth_instances]
-            detected_instances = [[[o for c in i for o in c]] for i in detected_instances]
-
-        # Call C++ implementation of self.evaluateImgs()
-        self._evalImgs_cpp = _C.COCOevalEvaluateImages(
-            p.areaRng, maxDet, p.iouThrs, ious, ground_truth_instances, detected_instances
-        )
-        self._evalImgs = None
-
-        self._paramsEval = copy.deepcopy(self.params)
-        toc = time.time()
-        logger.info("COCOeval_opt.evaluate() finished in {:0.2f} seconds.".format(toc - tic))
-        # >>>> End of code differences with original COCO API
-
-    def accumulate(self):
-        """
-        Accumulate per image evaluation results and store the result in self.eval.  Does not
-        support changing parameter settings from those used by self.evaluate()
-        """
-        logger.info("Accumulating evaluation results...")
-        tic = time.time()
-        assert hasattr(
-            self, "_evalImgs_cpp"
-        ), "evaluate() must be called before accmulate() is called."
-
-        self.eval = _C.COCOevalAccumulate(self._paramsEval, self._evalImgs_cpp)
-
-        # recall is num_iou_thresholds X num_categories X num_area_ranges X num_max_detections
-        self.eval["recall"] = np.array(self.eval["recall"]).reshape(
-            self.eval["counts"][:1] + self.eval["counts"][2:]
-        )
-
-        # precision and scores are num_iou_thresholds X num_recall_thresholds X num_categories X
-        # num_area_ranges X num_max_detections
-        self.eval["precision"] = np.array(self.eval["precision"]).reshape(self.eval["counts"])
-        self.eval["scores"] = np.array(self.eval["scores"]).reshape(self.eval["counts"])
-        toc = time.time()
-        logger.info("COCOeval_opt.accumulate() finished in {:0.2f} seconds.".format(toc - tic))

spaces/CVPR/regionclip-demo/detectron2/modeling/roi_heads/fast_rcnn.py

@@ -1,1086 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-from typing import Dict, List, Tuple, Union
-import torch
-from fvcore.nn import giou_loss, smooth_l1_loss
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import ShapeSpec, batched_nms, cat, cross_entropy, nonzero_tuple
-from detectron2.layers.soft_nms import batched_soft_nms
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.structures import Boxes, Instances
-from detectron2.utils.events import get_event_storage
-
-__all__ = ["fast_rcnn_inference", "FastRCNNOutputLayers", "CLIPFastRCNNOutputLayers"]
-
-
-logger = logging.getLogger(__name__)
-
-"""
-Shape shorthand in this module:
-
-    N: number of images in the minibatch
-    R: number of ROIs, combined over all images, in the minibatch
-    Ri: number of ROIs in image i
-    K: number of foreground classes. E.g.,there are 80 foreground classes in COCO.
-
-Naming convention:
-
-    deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box
-    transform (see :class:`box_regression.Box2BoxTransform`).
-
-    pred_class_logits: predicted class scores in [-inf, +inf]; use
-        softmax(pred_class_logits) to estimate P(class).
-
-    gt_classes: ground-truth classification labels in [0, K], where [0, K) represent
-        foreground object classes and K represents the background class.
-
-    pred_proposal_deltas: predicted box2box transform deltas for transforming proposals
-        to detection box predictions.
-
-    gt_proposal_deltas: ground-truth box2box transform deltas
-"""
-
-
-def fast_rcnn_inference(
-    boxes: List[torch.Tensor],
-    scores: List[torch.Tensor],
-    image_shapes: List[Tuple[int, int]],
-    score_thresh: float,
-    nms_thresh: float,
-    soft_nms_enabled,
-    soft_nms_method,
-    soft_nms_sigma,
-    soft_nms_prune,
-    topk_per_image: int,
-    scores_bf_multiply,
-):
-    """
-    Call `fast_rcnn_inference_single_image` for all images.
-
-    Args:
-        boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic
-            boxes for each image. Element i has shape (Ri, K * 4) if doing
-            class-specific regression, or (Ri, 4) if doing class-agnostic
-            regression, where Ri is the number of predicted objects for image i.
-            This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`.
-        scores (list[Tensor]): A list of Tensors of predicted class scores for each image.
-            Element i has shape (Ri, K + 1), where Ri is the number of predicted objects
-            for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`.
-        image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch.
-        score_thresh (float): Only return detections with a confidence score exceeding this
-            threshold.
-        nms_thresh (float):  The threshold to use for box non-maximum suppression. Value in [0, 1].
-        soft_nms_enabled (bool): Indicate to use soft non-maximum suppression.
-        soft_nms_method: (str): One of ['gaussian', 'linear', 'hard']
-        soft_nms_sigma: (float): Sigma for gaussian soft nms. Value in (0, inf)
-        soft_nms_prune: (float): Threshold for pruning during soft nms. Value in [0, 1]
-        topk_per_image (int): The number of top scoring detections to return. Set < 0 to return
-            all detections.
-
-    Returns:
-        instances: (list[Instances]): A list of N instances, one for each image in the batch,
-            that stores the topk most confidence detections.
-        kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates
-            the corresponding boxes/scores index in [0, Ri) from the input, for image i.
-    """
-    result_per_image = [
-        fast_rcnn_inference_single_image(
-            boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, 
-            soft_nms_enabled, soft_nms_method, soft_nms_sigma, soft_nms_prune, topk_per_image, s_bf_per_img
-        )
-        for scores_per_image, boxes_per_image, image_shape, s_bf_per_img in zip(scores, boxes, image_shapes, scores_bf_multiply)
-    ]
-    return [x[0] for x in result_per_image], [x[1] for x in result_per_image]
-
-
-def _log_classification_stats(pred_logits, gt_classes, prefix="fast_rcnn"):
-    """
-    Log the classification metrics to EventStorage.
-
-    Args:
-        pred_logits: Rx(K+1) logits. The last column is for background class.
-        gt_classes: R labels
-    """
-    num_instances = gt_classes.numel()
-    if num_instances == 0:
-        return
-    pred_classes = pred_logits.argmax(dim=1)
-    bg_class_ind = pred_logits.shape[1] - 1
-
-    fg_inds = (gt_classes >= 0) & (gt_classes < bg_class_ind)
-    num_fg = fg_inds.nonzero().numel()
-    fg_gt_classes = gt_classes[fg_inds]
-    fg_pred_classes = pred_classes[fg_inds]
-
-    num_false_negative = (fg_pred_classes == bg_class_ind).nonzero().numel()
-    num_accurate = (pred_classes == gt_classes).nonzero().numel()
-    fg_num_accurate = (fg_pred_classes == fg_gt_classes).nonzero().numel()
-
-    storage = get_event_storage()
-    storage.put_scalar(f"{prefix}/cls_accuracy", num_accurate / num_instances)
-    if num_fg > 0:
-        storage.put_scalar(f"{prefix}/fg_cls_accuracy", fg_num_accurate / num_fg)
-        storage.put_scalar(f"{prefix}/false_negative", num_false_negative / num_fg)
-        #print("cls_accuracy {:.2f}; fg_cls_accuracy {:.2f}; false_negative {:.2f}".format(num_accurate / num_instances, fg_num_accurate / num_fg, num_false_negative / num_fg))
-
-
-def fast_rcnn_inference_single_image(
-    boxes,
-    scores,
-    image_shape: Tuple[int, int],
-    score_thresh: float,
-    nms_thresh: float,
-    soft_nms_enabled,
-    soft_nms_method,
-    soft_nms_sigma,
-    soft_nms_prune,
-    topk_per_image: int,
-    scores_bf_multiply: None,
-):
-    """
-    Single-image inference. Return bounding-box detection results by thresholding
-    on scores and applying non-maximum suppression (NMS).
-
-    Args:
-        Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes
-        per image.
-
-    Returns:
-        Same as `fast_rcnn_inference`, but for only one image.
-    """
-    valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
-    if not valid_mask.all():
-        boxes = boxes[valid_mask]
-        scores = scores[valid_mask]
-        scores_bf_multiply = scores_bf_multiply[valid_mask]
-
-    # scores = scores[:, :-1]
-    # scores_bf_multiply = scores_bf_multiply[:, :-1]
-    num_bbox_reg_classes = boxes.shape[1] // 4
-    # Convert to Boxes to use the `clip` function ...
-    boxes = Boxes(boxes.reshape(-1, 4))
-    boxes.clip(image_shape)
-    boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4)  # R x C x 4
-
-    # 1. Filter results based on detection scores. It can make NMS more efficient
-    #    by filtering out low-confidence detections.
-    filter_mask = scores > score_thresh  # R x K
-    # R' x 2. First column contains indices of the R predictions;
-    # Second column contains indices of classes.
-    filter_inds = filter_mask.nonzero()
-    if num_bbox_reg_classes == 1:
-        boxes = boxes[filter_inds[:, 0], 0]
-    else:
-        boxes = boxes[filter_mask]
-    scores = scores[filter_mask]
-    scores_bf_multiply = scores_bf_multiply[filter_mask]
-
-    # 2. Apply NMS for each class independently.
-    if not soft_nms_enabled:
-        keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh)
-    else:
-        keep, soft_nms_scores = batched_soft_nms(
-            boxes,
-            scores,
-            filter_inds[:, 1],
-            soft_nms_method,
-            soft_nms_sigma,
-            nms_thresh,
-            soft_nms_prune,
-        )
-        scores[keep] = soft_nms_scores   
-        # scores_bf_multiply? (TBD)
-        scores_bf_multiply = scores
-    if topk_per_image >= 0:
-        keep = keep[:topk_per_image]
-    boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]
-    scores_bf_multiply = scores_bf_multiply[keep]
-
-    result = Instances(image_shape)
-    result.pred_boxes = Boxes(boxes)
-    result.scores = scores
-    result.scores = scores_bf_multiply # convert to the original scores before multiplying RPN scores
-    result.pred_classes = filter_inds[:, 1]
-    return result, filter_inds[:, 0]
-
-
-class FastRCNNOutputs:
-    """
-    An internal implementation that stores information about outputs of a Fast R-CNN head,
-    and provides methods that are used to decode the outputs of a Fast R-CNN head.
-    """
-
-    def __init__(
-        self,
-        box2box_transform,
-        pred_class_logits,
-        pred_proposal_deltas,
-        proposals,
-        smooth_l1_beta=0.0,
-        box_reg_loss_type="smooth_l1",
-    ):
-        """
-        Args:
-            box2box_transform (Box2BoxTransform/Box2BoxTransformRotated):
-                box2box transform instance for proposal-to-detection transformations.
-            pred_class_logits (Tensor): A tensor of shape (R, K + 1) storing the predicted class
-                logits for all R predicted object instances.
-                Each row corresponds to a predicted object instance.
-            pred_proposal_deltas (Tensor): A tensor of shape (R, K * B) or (R, B) for
-                class-specific or class-agnostic regression. It stores the predicted deltas that
-                transform proposals into final box detections.
-                B is the box dimension (4 or 5).
-                When B is 4, each row is [dx, dy, dw, dh (, ....)].
-                When B is 5, each row is [dx, dy, dw, dh, da (, ....)].
-            proposals (list[Instances]): A list of N Instances, where Instances i stores the
-                proposals for image i, in the field "proposal_boxes".
-                When training, each Instances must have ground-truth labels
-                stored in the field "gt_classes" and "gt_boxes".
-                The total number of all instances must be equal to R.
-            smooth_l1_beta (float): The transition point between L1 and L2 loss in
-                the smooth L1 loss function. When set to 0, the loss becomes L1. When
-                set to +inf, the loss becomes constant 0.
-            box_reg_loss_type (str): Box regression loss type. One of: "smooth_l1", "giou"
-        """
-        self.box2box_transform = box2box_transform
-        self.num_preds_per_image = [len(p) for p in proposals]
-        self.pred_class_logits = pred_class_logits
-        self.pred_proposal_deltas = pred_proposal_deltas
-        self.smooth_l1_beta = smooth_l1_beta
-        self.box_reg_loss_type = box_reg_loss_type
-
-        self.image_shapes = [x.image_size for x in proposals]
-
-        if len(proposals):
-            box_type = type(proposals[0].proposal_boxes)
-            # cat(..., dim=0) concatenates over all images in the batch
-            self.proposals = box_type.cat([p.proposal_boxes for p in proposals])
-            assert (
-                not self.proposals.tensor.requires_grad
-            ), "Proposals should not require gradients!"
-
-            # "gt_classes" exists if and only if training. But other gt fields may
-            # not necessarily exist in training for images that have no groundtruth.
-            if proposals[0].has("gt_classes"):
-                self.gt_classes = cat([p.gt_classes for p in proposals], dim=0)
-
-                # If "gt_boxes" does not exist, the proposals must be all negative and
-                # should not be included in regression loss computation.
-                # Here we just use proposal_boxes as an arbitrary placeholder because its
-                # value won't be used in self.box_reg_loss().
-                gt_boxes = [
-                    p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes for p in proposals
-                ]
-                self.gt_boxes = box_type.cat(gt_boxes)
-        else:
-            self.proposals = Boxes(torch.zeros(0, 4, device=self.pred_proposal_deltas.device))
-        self._no_instances = len(self.proposals) == 0  # no instances found
-
-    def softmax_cross_entropy_loss(self):
-        """
-        Deprecated
-        """
-        _log_classification_stats(self.pred_class_logits, self.gt_classes)
-        return cross_entropy(self.pred_class_logits, self.gt_classes, reduction="mean")
-
-    def box_reg_loss(self):
-        """
-        Deprecated
-        """
-        if self._no_instances:
-            return 0.0 * self.pred_proposal_deltas.sum()
-
-        box_dim = self.proposals.tensor.size(1)  # 4 or 5
-        cls_agnostic_bbox_reg = self.pred_proposal_deltas.size(1) == box_dim
-        device = self.pred_proposal_deltas.device
-
-        bg_class_ind = self.pred_class_logits.shape[1] - 1
-        # Box delta loss is only computed between the prediction for the gt class k
-        # (if 0 <= k < bg_class_ind) and the target; there is no loss defined on predictions
-        # for non-gt classes and background.
-        # Empty fg_inds should produce a valid loss of zero because reduction=sum.
-        fg_inds = nonzero_tuple((self.gt_classes >= 0) & (self.gt_classes < bg_class_ind))[0]
-
-        if cls_agnostic_bbox_reg:
-            # pred_proposal_deltas only corresponds to foreground class for agnostic
-            gt_class_cols = torch.arange(box_dim, device=device)
-        else:
-            # pred_proposal_deltas for class k are located in columns [b * k : b * k + b],
-            # where b is the dimension of box representation (4 or 5)
-            # Note that compared to Detectron1,
-            # we do not perform bounding box regression for background classes.
-            gt_class_cols = box_dim * self.gt_classes[fg_inds, None] + torch.arange(
-                box_dim, device=device
-            )
-
-        if self.box_reg_loss_type == "smooth_l1":
-            gt_proposal_deltas = self.box2box_transform.get_deltas(
-                self.proposals.tensor, self.gt_boxes.tensor
-            )
-            loss_box_reg = smooth_l1_loss(
-                self.pred_proposal_deltas[fg_inds[:, None], gt_class_cols],
-                gt_proposal_deltas[fg_inds],
-                self.smooth_l1_beta,
-                reduction="sum",
-            )
-        elif self.box_reg_loss_type == "giou":
-            fg_pred_boxes = self.box2box_transform.apply_deltas(
-                self.pred_proposal_deltas[fg_inds[:, None], gt_class_cols],
-                self.proposals.tensor[fg_inds],
-            )
-            loss_box_reg = giou_loss(
-                fg_pred_boxes,
-                self.gt_boxes.tensor[fg_inds],
-                reduction="sum",
-            )
-        else:
-            raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'")
-
-        loss_box_reg = loss_box_reg / self.gt_classes.numel()
-        return loss_box_reg
-
-    def losses(self):
-        """
-        Deprecated
-        """
-        return {"loss_cls": self.softmax_cross_entropy_loss(), "loss_box_reg": self.box_reg_loss()}
-
-    def predict_boxes(self):
-        """
-        Deprecated
-        """
-        pred = self.box2box_transform.apply_deltas(self.pred_proposal_deltas, self.proposals.tensor)
-        return pred.split(self.num_preds_per_image, dim=0)
-
-    def predict_probs(self):
-        """
-        Deprecated
-        """
-        probs = F.softmax(self.pred_class_logits, dim=-1)
-        return probs.split(self.num_preds_per_image, dim=0)
-
-
-class FastRCNNOutputLayers(nn.Module):
-    """
-    Two linear layers for predicting Fast R-CNN outputs:
-
-    1. proposal-to-detection box regression deltas
-    2. classification scores
-    """
-
-    @configurable
-    def __init__(
-        self,
-        input_shape: ShapeSpec,
-        *,
-        box2box_transform,
-        num_classes: int,
-        test_score_thresh: float = 0.0,
-        test_nms_thresh: float = 0.5,
-        soft_nms_enabled=False,
-        soft_nms_method="gaussian",
-        soft_nms_sigma=0.5,
-        soft_nms_prune=0.001,
-        test_topk_per_image: int = 100,
-        cls_agnostic_bbox_reg: bool = False,
-        smooth_l1_beta: float = 0.0,
-        box_reg_loss_type: str = "smooth_l1",
-        loss_weight: Union[float, Dict[str, float]] = 1.0,
-        clip_cls_emb: tuple = (False, None),
-        no_box_delta: bool = False,
-        bg_cls_loss_weight: None,
-        multiply_rpn_score: False,
-        openset_test: None,
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            input_shape (ShapeSpec): shape of the input feature to this module
-            box2box_transform (Box2BoxTransform or Box2BoxTransformRotated):
-            num_classes (int): number of foreground classes
-            test_score_thresh (float): threshold to filter predictions results.
-            test_nms_thresh (float): NMS threshold for prediction results.
-            test_topk_per_image (int): number of top predictions to produce per image.
-            cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression
-            smooth_l1_beta (float): transition point from L1 to L2 loss. Only used if
-                `box_reg_loss_type` is "smooth_l1"
-            box_reg_loss_type (str): Box regression loss type. One of: "smooth_l1", "giou"
-            loss_weight (float|dict): weights to use for losses. Can be single float for weighting
-                all losses, or a dict of individual weightings. Valid dict keys are:
-                    * "loss_cls": applied to classification loss
-                    * "loss_box_reg": applied to box regression loss
-        """
-        super().__init__()
-        if isinstance(input_shape, int):  # some backward compatibility
-            input_shape = ShapeSpec(channels=input_shape)
-        self.num_classes = num_classes
-        input_size = input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1)
-        if clip_cls_emb[0]:  # if combine {C4, text emb as classifier}, then has to use att_pool to match dimension
-            input_size = clip_cls_emb[3] if clip_cls_emb[2] in ['CLIPRes5ROIHeads', 'CLIPStandardROIHeads'] else input_size
-        # prediction layer for num_classes foreground classes and one background class (hence + 1)
-        self.cls_score = nn.Linear(input_size, num_classes + 1)
-        num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes
-        box_dim = len(box2box_transform.weights)
-        self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim)
-
-        nn.init.normal_(self.cls_score.weight, std=0.01)
-        nn.init.normal_(self.bbox_pred.weight, std=0.001)
-        for l in [self.cls_score, self.bbox_pred]:
-            nn.init.constant_(l.bias, 0)
-
-        self.box2box_transform = box2box_transform
-        self.smooth_l1_beta = smooth_l1_beta
-        self.test_score_thresh = test_score_thresh
-        self.test_nms_thresh = test_nms_thresh
-        self.soft_nms_enabled = soft_nms_enabled
-        self.soft_nms_method = soft_nms_method
-        self.soft_nms_sigma = soft_nms_sigma
-        self.soft_nms_prune = soft_nms_prune
-        self.test_topk_per_image = test_topk_per_image
-        self.box_reg_loss_type = box_reg_loss_type
-        if isinstance(loss_weight, float):
-            loss_weight = {"loss_cls": loss_weight, "loss_box_reg": loss_weight}
-        self.loss_weight = loss_weight
-
-        # use clip text embeddings as classifier's weights
-        self.use_clip_cls_emb = clip_cls_emb[0]
-        if self.use_clip_cls_emb:
-            ######### V2L projection layer in CVPR OVR model #########
-            if openset_test[3]: # run CVPR model
-                self.emb_pred = nn.Linear(input_size, 768)
-                self.emb_pred.weight.requires_grad = False
-                self.emb_pred.bias.requires_grad = False
-                input_size = 768
-            else:
-                self.emb_pred = None
-            ######### V2L projection layer in CVPR OVR model #########
-            text_emb_require_grad = False
-            self.use_bias = False
-            self.tempurature = openset_test[2] # 0.01 # the smaller, the bigger difference among probs after softmax
-        self.no_box_delta = no_box_delta
-        if bg_cls_loss_weight is not None: # loss weigh for bg regions
-            self.cls_loss_weight = torch.ones(num_classes + 1)
-            self.cls_loss_weight[-1] = bg_cls_loss_weight
-        else:
-            self.cls_loss_weight = None
-        self.multiply_rpn_score = multiply_rpn_score
-        self.focal_scaled_loss = openset_test[4]
-            
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        # if cfg.MODEL.CLIP.CROP_REGION_TYPE == "RPN":
-        #     assert cfg.MODEL.CLIP.NO_BOX_DELTA is False
-        return {
-            "input_shape": input_shape,
-            "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS),
-            # fmt: off
-            "num_classes"           : cfg.MODEL.ROI_HEADS.NUM_CLASSES,
-            "cls_agnostic_bbox_reg" : cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG,
-            "smooth_l1_beta"        : cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA,
-            "test_score_thresh"     : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST,
-            "test_nms_thresh"       : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,
-            "soft_nms_enabled"      : cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED,
-            "soft_nms_method"       : cfg.MODEL.ROI_HEADS.SOFT_NMS_METHOD,
-            "soft_nms_sigma"        : cfg.MODEL.ROI_HEADS.SOFT_NMS_SIGMA,
-            "soft_nms_prune"        : cfg.MODEL.ROI_HEADS.SOFT_NMS_PRUNE,
-            "test_topk_per_image"   : cfg.TEST.DETECTIONS_PER_IMAGE,
-            "box_reg_loss_type"     : cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE,
-            "loss_weight"           : {"loss_box_reg": cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT},
-            "clip_cls_emb"          : (cfg.MODEL.CLIP.USE_TEXT_EMB_CLASSIFIER, cfg.MODEL.CLIP.TEXT_EMB_PATH, cfg.MODEL.ROI_HEADS.NAME, cfg.MODEL.CLIP.TEXT_EMB_DIM),
-            "no_box_delta"          : cfg.MODEL.CLIP.NO_BOX_DELTA or cfg.MODEL.CLIP.CROP_REGION_TYPE == 'GT',
-            "bg_cls_loss_weight"    : cfg.MODEL.CLIP.BG_CLS_LOSS_WEIGHT,
-            "multiply_rpn_score"    : cfg.MODEL.CLIP.MULTIPLY_RPN_SCORE,
-            "openset_test"          : (cfg.MODEL.CLIP.OPENSET_TEST_NUM_CLASSES, cfg.MODEL.CLIP.OPENSET_TEST_TEXT_EMB_PATH, \
-                                       cfg.MODEL.CLIP.CLSS_TEMP, cfg.MODEL.CLIP.RUN_CVPR_OVR, cfg.MODEL.CLIP.FOCAL_SCALED_LOSS)
-            # fmt: on
-        }
-
-    def forward(self, x, queries):
-        """
-        Args:
-            x: per-region features of shape (N, ...) for N bounding boxes to predict.
-
-        Returns:
-            (Tensor, Tensor):
-            First tensor: shape (N,K+1), scores for each of the N box. Each row contains the
-            scores for K object categories and 1 background class.
-
-            Second tensor: bounding box regression deltas for each box. Shape is shape (N,Kx4),
-            or (N,4) for class-agnostic regression.
-        """
-        if x.dim() > 2:
-            x = torch.flatten(x, start_dim=1)
-        if self.use_clip_cls_emb: # use clip text embeddings as classifier's weights
-            normalized_x = F.normalize(x, p=2.0, dim=1)
-            cls_scores = normalized_x @ queries.t()            
-            bg_cls_scores = cls_scores.new(cls_scores.shape[0], 1).fill_(0.3)
-            scores = cls_scores # torch.cat((cls_scores, bg_cls_scores), 1)
-        else:  # default setting
-            scores = self.cls_score(x)
-        proposal_deltas = scores.new(scores.shape[0], 4).fill_(0) # self.bbox_pred(x)
-        return scores, proposal_deltas
-
-    def losses(self, predictions, proposals):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features that were used
-                to compute predictions. The fields ``proposal_boxes``, ``gt_boxes``,
-                ``gt_classes`` are expected.
-
-        Returns:
-            Dict[str, Tensor]: dict of losses
-        """
-        scores, proposal_deltas = predictions
-
-        # parse classification outputs
-        gt_classes = (
-            cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0)
-        )
-        _log_classification_stats(scores, gt_classes)
-
-        # parse box regression outputs
-        if len(proposals):
-            proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)  # Nx4
-            assert not proposal_boxes.requires_grad, "Proposals should not require gradients!"
-            # If "gt_boxes" does not exist, the proposals must be all negative and
-            # should not be included in regression loss computation.
-            # Here we just use proposal_boxes as an arbitrary placeholder because its
-            # value won't be used in self.box_reg_loss().
-            gt_boxes = cat(
-                [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals],
-                dim=0,
-            )
-        else:
-            proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device)
-        
-        # loss weights
-        if self.cls_loss_weight is not None and self.cls_loss_weight.device != scores.device:
-            self.cls_loss_weight = self.cls_loss_weight.to(scores.device)
-        if self.focal_scaled_loss is not None:
-            loss_cls = self.focal_loss(scores, gt_classes, gamma=self.focal_scaled_loss)
-        else:    
-            loss_cls = cross_entropy(scores, gt_classes, reduction="mean") if self.cls_loss_weight is None else \
-                       cross_entropy(scores, gt_classes, reduction="mean", weight=self.cls_loss_weight)
-        losses = {
-            "loss_cls": loss_cls,
-            "loss_box_reg": self.box_reg_loss(
-                proposal_boxes, gt_boxes, proposal_deltas, gt_classes
-            ),
-        }
-        return {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()}
-
-    def focal_loss(self, inputs, targets, alpha=0.25, gamma=0.5, reduction="mean", mode='softmax'):
-        """Inspired by RetinaNet implementation"""
-        if mode == 'sigmoid': # original focal loss implementation, except we include bg loss
-            targets = F.one_hot(targets, num_classes=self.num_classes + 1).to(inputs.dtype)  # create binary label for each logit entry, including bg loss
-            p = torch.sigmoid(inputs)
-            ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
-            p_t = p * targets + (1 - p) * (1 - targets)
-            loss = ce_loss * ((1 - p_t) ** gamma)
-
-            if alpha >= 0:
-                alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
-                loss = alpha_t * loss
-        elif mode == 'softmax':
-            only_fg = False # if True, only fg rois are attached the focal loss scaling
-            #gamma = 0.3 # 0.5 # 0.8 # 1.5 # 1.0
-            alpha = -1  #  no binary target in this case; instead, we can use bg loss weight
-            if targets.numel() == 0 and reduction == "mean":
-                return input.sum() * 0.0  # connect the gradient
-            ce_loss = F.cross_entropy(inputs, targets, reduction="none")
-            p = F.softmax(inputs, dim=-1)
-            p_t = p[torch.arange(p.size(0)).to(p.device), targets]  # get prob of target class
-            if only_fg:  # apply scaling to only fg rois
-                roi_wise_gamma = torch.zeros(p.size(0)).to(p.device)
-                roi_wise_gamma[targets != self.num_classes] = gamma
-                gamma = roi_wise_gamma
-            loss = ce_loss * ((1 - p_t) ** gamma)
-
-            # if alpha >= 0:
-            #     alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
-            #     loss = alpha_t * loss
-            # bg loss weight
-            if self.cls_loss_weight is not None:
-                loss_weight = torch.ones(loss.size(0)).to(p.device)
-                loss_weight[targets == self.num_classes] = self.cls_loss_weight[-1].item()
-                loss = loss * loss_weight
-
-        if reduction == "mean":
-            loss = loss.mean()
-        elif reduction == "sum":
-            loss = loss.sum()
-
-        return loss
-
-    def box_reg_loss(self, proposal_boxes, gt_boxes, pred_deltas, gt_classes):
-        """
-        Args:
-            All boxes are tensors with the same shape Rx(4 or 5).
-            gt_classes is a long tensor of shape R, the gt class label of each proposal.
-            R shall be the number of proposals.
-        """
-        box_dim = proposal_boxes.shape[1]  # 4 or 5
-        # Regression loss is only computed for foreground proposals (those matched to a GT)
-        fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < self.num_classes))[0]
-        if pred_deltas.shape[1] == box_dim:  # cls-agnostic regression
-            fg_pred_deltas = pred_deltas[fg_inds]
-        else:
-            fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[
-                fg_inds, gt_classes[fg_inds]
-            ]
-
-        if self.box_reg_loss_type == "smooth_l1":
-            gt_pred_deltas = self.box2box_transform.get_deltas(
-                proposal_boxes[fg_inds],
-                gt_boxes[fg_inds],
-            )
-            loss_box_reg = smooth_l1_loss(
-                fg_pred_deltas, gt_pred_deltas, self.smooth_l1_beta, reduction="sum"
-            )
-        elif self.box_reg_loss_type == "giou":
-            fg_pred_boxes = self.box2box_transform.apply_deltas(
-                fg_pred_deltas, proposal_boxes[fg_inds]
-            )
-            loss_box_reg = giou_loss(fg_pred_boxes, gt_boxes[fg_inds], reduction="sum")
-        else:
-            raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'")
-        # The reg loss is normalized using the total number of regions (R), not the number
-        # of foreground regions even though the box regression loss is only defined on
-        # foreground regions. Why? Because doing so gives equal training influence to
-        # each foreground example. To see how, consider two different minibatches:
-        #  (1) Contains a single foreground region
-        #  (2) Contains 100 foreground regions
-        # If we normalize by the number of foreground regions, the single example in
-        # minibatch (1) will be given 100 times as much influence as each foreground
-        # example in minibatch (2). Normalizing by the total number of regions, R,
-        # means that the single example in minibatch (1) and each of the 100 examples
-        # in minibatch (2) are given equal influence.
-        return loss_box_reg / max(gt_classes.numel(), 1.0)  # return 0 if empty
-
-    def inference(self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features that were
-                used to compute predictions. The ``proposal_boxes`` field is expected.
-
-        Returns:
-            list[Instances]: same as `fast_rcnn_inference`.
-            list[Tensor]: same as `fast_rcnn_inference`.
-        """        
-        boxes = self.predict_boxes(predictions, proposals)
-        scores = self.predict_probs(predictions, proposals)
-        image_shapes = [x.image_size for x in proposals]
-        scores_bf_multiply = scores  #  as a backup
-        if self.multiply_rpn_score:
-            rpn_scores = [p.get('objectness_logits') for p in proposals]            
-            # filter based on rpn_scores
-            # boxes = (boxes[0][rpn_scores[0] > 0.9],)
-            # scores = (scores[0][rpn_scores[0] > 0.9],)
-            # rpn_scores = [rpn_scores[0][rpn_scores[0] > 0.9]]
-            # scores_bf_multiply = scores  #  as a backup
-            #rpn_scores = [p.get('objectness_logits').sigmoid() for p in proposals]
-            scores = [(torch.sigmoid(s) * torch.sigmoid(rpn_s[:, None])) ** 0.5 for s, rpn_s in zip(scores, rpn_scores)]
-        return fast_rcnn_inference(
-            boxes,
-            scores,
-            image_shapes,
-            self.test_score_thresh,
-            self.test_nms_thresh,
-            self.soft_nms_enabled,
-            self.soft_nms_method,
-            self.soft_nms_sigma,
-            self.soft_nms_prune,
-            self.test_topk_per_image,
-            scores_bf_multiply = scores_bf_multiply if self.multiply_rpn_score else None,
-        )
-
-    def predict_boxes_for_gt_classes(self, predictions, proposals):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features that were used
-                to compute predictions. The fields ``proposal_boxes``, ``gt_classes`` are expected.
-
-        Returns:
-            list[Tensor]:
-                A list of Tensors of predicted boxes for GT classes in case of
-                class-specific box head. Element i of the list has shape (Ri, B), where Ri is
-                the number of proposals for image i and B is the box dimension (4 or 5)
-        """
-        if not len(proposals):
-            return []
-        scores, proposal_deltas = predictions
-        proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)
-        N, B = proposal_boxes.shape
-        predict_boxes = self.box2box_transform.apply_deltas(
-            proposal_deltas, proposal_boxes
-        )  # Nx(KxB)
-
-        K = predict_boxes.shape[1] // B
-        if K > 1:
-            gt_classes = torch.cat([p.gt_classes for p in proposals], dim=0)
-            # Some proposals are ignored or have a background class. Their gt_classes
-            # cannot be used as index.
-            gt_classes = gt_classes.clamp_(0, K - 1)
-
-            predict_boxes = predict_boxes.view(N, K, B)[
-                torch.arange(N, dtype=torch.long, device=predict_boxes.device), gt_classes
-            ]
-        num_prop_per_image = [len(p) for p in proposals]
-        return predict_boxes.split(num_prop_per_image)
-
-    def predict_boxes(
-        self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]
-    ):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features that were
-                used to compute predictions. The ``proposal_boxes`` field is expected.
-
-        Returns:
-            list[Tensor]:
-                A list of Tensors of predicted class-specific or class-agnostic boxes
-                for each image. Element i has shape (Ri, K * B) or (Ri, B), where Ri is
-                the number of proposals for image i and B is the box dimension (4 or 5)
-        """
-        if not len(proposals):
-            return []
-        _, proposal_deltas = predictions
-        num_prop_per_image = [len(p) for p in proposals]
-        proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)
-        if self.no_box_delta:
-            predict_boxes = proposal_boxes
-        else:
-            predict_boxes = self.box2box_transform.apply_deltas(
-                proposal_deltas,
-                proposal_boxes,
-            )  # Nx(KxB)
-        return predict_boxes.split(num_prop_per_image)
-
-    def predict_probs(
-        self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]
-    ):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features that were
-                used to compute predictions.
-
-        Returns:
-            list[Tensor]:
-                A list of Tensors of predicted class probabilities for each image.
-                Element i has shape (Ri, K + 1), where Ri is the number of proposals for image i.
-        """
-        scores, _ = predictions
-        num_inst_per_image = [len(p) for p in proposals]
-        # probs = F.softmax(scores, dim=-1)
-        probs = scores
-        return probs.split(num_inst_per_image, dim=0)
-
-
-class OLDFastRCNNOutputLayers(nn.Module):
-    """
-    Two linear layers for predicting Fast R-CNN outputs:
-
-    1. proposal-to-detection box regression deltas
-    2. classification scores
-    """
-
-    @configurable
-    def __init__(
-        self,
-        input_shape: ShapeSpec,
-        *,
-        box2box_transform,
-        num_classes: int,
-        test_score_thresh: float = 0.0,
-        test_nms_thresh: float = 0.5,
-        test_topk_per_image: int = 100,
-        cls_agnostic_bbox_reg: bool = False,
-        smooth_l1_beta: float = 0.0,
-        box_reg_loss_type: str = "smooth_l1",
-        loss_weight: Union[float, Dict[str, float]] = 1.0,
-        no_box_delta: bool = False,
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            input_shape (ShapeSpec): shape of the input feature to this module
-            box2box_transform (Box2BoxTransform or Box2BoxTransformRotated):
-            num_classes (int): number of foreground classes
-            test_score_thresh (float): threshold to filter predictions results.
-            test_nms_thresh (float): NMS threshold for prediction results.
-            test_topk_per_image (int): number of top predictions to produce per image.
-            cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression
-            smooth_l1_beta (float): transition point from L1 to L2 loss. Only used if
-                `box_reg_loss_type` is "smooth_l1"
-            box_reg_loss_type (str): Box regression loss type. One of: "smooth_l1", "giou"
-            loss_weight (float|dict): weights to use for losses. Can be single float for weighting
-                all losses, or a dict of individual weightings. Valid dict keys are:
-                    * "loss_cls": applied to classification loss
-                    * "loss_box_reg": applied to box regression loss
-        """
-        super().__init__()
-        if isinstance(input_shape, int):  # some backward compatibility
-            input_shape = ShapeSpec(channels=input_shape)
-        self.num_classes = num_classes
-        input_size = input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1)
-        # prediction layer for num_classes foreground classes and one background class (hence + 1)
-        self.cls_score = nn.Linear(input_size, num_classes + 1)
-        num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes
-        box_dim = len(box2box_transform.weights)
-        self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim)
-
-        nn.init.normal_(self.cls_score.weight, std=0.01)
-        nn.init.normal_(self.bbox_pred.weight, std=0.001)
-        for l in [self.cls_score, self.bbox_pred]:
-            nn.init.constant_(l.bias, 0)
-
-        self.box2box_transform = box2box_transform
-        self.smooth_l1_beta = smooth_l1_beta
-        self.test_score_thresh = test_score_thresh
-        self.test_nms_thresh = test_nms_thresh
-        self.test_topk_per_image = test_topk_per_image
-        self.box_reg_loss_type = box_reg_loss_type
-        if isinstance(loss_weight, float):
-            loss_weight = {"loss_cls": loss_weight, "loss_box_reg": loss_weight}
-        self.loss_weight = loss_weight
-        self.no_box_delta = no_box_delta
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        return {
-            "input_shape": input_shape,
-            "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS),
-            # fmt: off
-            "num_classes"           : cfg.MODEL.ROI_HEADS.NUM_CLASSES,
-            "cls_agnostic_bbox_reg" : cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG,
-            "smooth_l1_beta"        : cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA,
-            "test_score_thresh"     : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST,
-            "test_nms_thresh"       : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,
-            "test_topk_per_image"   : cfg.TEST.DETECTIONS_PER_IMAGE,
-            "box_reg_loss_type"     : cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE,
-            "loss_weight"           : {"loss_box_reg": cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT},
-            "no_box_delta"          : cfg.MODEL.CLIP.NO_BOX_DELTA or cfg.MODEL.CLIP.CROP_REGION_TYPE == 'GT',
-            # fmt: on
-        }
-
-    def forward(self, x):
-        """
-        Args:
-            x: per-region features of shape (N, ...) for N bounding boxes to predict.
-
-        Returns:
-            (Tensor, Tensor):
-            First tensor: shape (N,K+1), scores for each of the N box. Each row contains the
-            scores for K object categories and 1 background class.
-
-            Second tensor: bounding box regression deltas for each box. Shape is shape (N,Kx4),
-            or (N,4) for class-agnostic regression.
-        """
-        if x.dim() > 2:
-            x = torch.flatten(x, start_dim=1)
-        scores = self.cls_score(x)
-        proposal_deltas = self.bbox_pred(x)
-        return scores, proposal_deltas
-
-    def losses(self, predictions, proposals):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features that were used
-                to compute predictions. The fields ``proposal_boxes``, ``gt_boxes``,
-                ``gt_classes`` are expected.
-
-        Returns:
-            Dict[str, Tensor]: dict of losses
-        """
-        scores, proposal_deltas = predictions
-
-        # parse classification outputs
-        gt_classes = (
-            cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0)
-        )
-        _log_classification_stats(scores, gt_classes)
-
-        # parse box regression outputs
-        if len(proposals):
-            proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)  # Nx4
-            assert not proposal_boxes.requires_grad, "Proposals should not require gradients!"
-            # If "gt_boxes" does not exist, the proposals must be all negative and
-            # should not be included in regression loss computation.
-            # Here we just use proposal_boxes as an arbitrary placeholder because its
-            # value won't be used in self.box_reg_loss().
-            gt_boxes = cat(
-                [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals],
-                dim=0,
-            )
-        else:
-            proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device)
-
-        losses = {
-            "loss_cls": cross_entropy(scores, gt_classes, reduction="mean"),
-            "loss_box_reg": self.box_reg_loss(
-                proposal_boxes, gt_boxes, proposal_deltas, gt_classes
-            ),
-        }
-        return {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()}
-
-    def box_reg_loss(self, proposal_boxes, gt_boxes, pred_deltas, gt_classes):
-        """
-        Args:
-            All boxes are tensors with the same shape Rx(4 or 5).
-            gt_classes is a long tensor of shape R, the gt class label of each proposal.
-            R shall be the number of proposals.
-        """
-        box_dim = proposal_boxes.shape[1]  # 4 or 5
-        # Regression loss is only computed for foreground proposals (those matched to a GT)
-        fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < self.num_classes))[0]
-        if pred_deltas.shape[1] == box_dim:  # cls-agnostic regression
-            fg_pred_deltas = pred_deltas[fg_inds]
-        else:
-            fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[
-                fg_inds, gt_classes[fg_inds]
-            ]
-
-        if self.box_reg_loss_type == "smooth_l1":
-            gt_pred_deltas = self.box2box_transform.get_deltas(
-                proposal_boxes[fg_inds],
-                gt_boxes[fg_inds],
-            )
-            loss_box_reg = smooth_l1_loss(
-                fg_pred_deltas, gt_pred_deltas, self.smooth_l1_beta, reduction="sum"
-            )
-        elif self.box_reg_loss_type == "giou":
-            fg_pred_boxes = self.box2box_transform.apply_deltas(
-                fg_pred_deltas, proposal_boxes[fg_inds]
-            )
-            loss_box_reg = giou_loss(fg_pred_boxes, gt_boxes[fg_inds], reduction="sum")
-        else:
-            raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'")
-        # The reg loss is normalized using the total number of regions (R), not the number
-        # of foreground regions even though the box regression loss is only defined on
-        # foreground regions. Why? Because doing so gives equal training influence to
-        # each foreground example. To see how, consider two different minibatches:
-        #  (1) Contains a single foreground region
-        #  (2) Contains 100 foreground regions
-        # If we normalize by the number of foreground regions, the single example in
-        # minibatch (1) will be given 100 times as much influence as each foreground
-        # example in minibatch (2). Normalizing by the total number of regions, R,
-        # means that the single example in minibatch (1) and each of the 100 examples
-        # in minibatch (2) are given equal influence.
-        return loss_box_reg / max(gt_classes.numel(), 1.0)  # return 0 if empty
-
-    def inference(self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features that were
-                used to compute predictions. The ``proposal_boxes`` field is expected.
-
-        Returns:
-            list[Instances]: same as `fast_rcnn_inference`.
-            list[Tensor]: same as `fast_rcnn_inference`.
-        """
-        boxes = self.predict_boxes(predictions, proposals)
-        scores = self.predict_probs(predictions, proposals)
-        image_shapes = [x.image_size for x in proposals]
-        return fast_rcnn_inference(
-            boxes,
-            scores,
-            image_shapes,
-            self.test_score_thresh,
-            self.test_nms_thresh,
-            self.test_topk_per_image,
-        )
-
-    def predict_boxes_for_gt_classes(self, predictions, proposals):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features that were used
-                to compute predictions. The fields ``proposal_boxes``, ``gt_classes`` are expected.
-
-        Returns:
-            list[Tensor]:
-                A list of Tensors of predicted boxes for GT classes in case of
-                class-specific box head. Element i of the list has shape (Ri, B), where Ri is
-                the number of proposals for image i and B is the box dimension (4 or 5)
-        """
-        if not len(proposals):
-            return []
-        scores, proposal_deltas = predictions
-        proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)
-        N, B = proposal_boxes.shape
-        predict_boxes = self.box2box_transform.apply_deltas(
-            proposal_deltas, proposal_boxes
-        )  # Nx(KxB)
-
-        K = predict_boxes.shape[1] // B
-        if K > 1:
-            gt_classes = torch.cat([p.gt_classes for p in proposals], dim=0)
-            # Some proposals are ignored or have a background class. Their gt_classes
-            # cannot be used as index.
-            gt_classes = gt_classes.clamp_(0, K - 1)
-
-            predict_boxes = predict_boxes.view(N, K, B)[
-                torch.arange(N, dtype=torch.long, device=predict_boxes.device), gt_classes
-            ]
-        num_prop_per_image = [len(p) for p in proposals]
-        return predict_boxes.split(num_prop_per_image)
-
-    def predict_boxes(
-        self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]
-    ):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features that were
-                used to compute predictions. The ``proposal_boxes`` field is expected.
-
-        Returns:
-            list[Tensor]:
-                A list of Tensors of predicted class-specific or class-agnostic boxes
-                for each image. Element i has shape (Ri, K * B) or (Ri, B), where Ri is
-                the number of proposals for image i and B is the box dimension (4 or 5)
-        """
-        if not len(proposals):
-            return []
-        _, proposal_deltas = predictions
-        num_prop_per_image = [len(p) for p in proposals]
-        proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)
-        if self.no_box_delta:
-            predict_boxes = proposal_boxes
-        else:
-            predict_boxes = self.box2box_transform.apply_deltas(
-                proposal_deltas,
-                proposal_boxes,
-            )  # Nx(KxB)
-        return predict_boxes.split(num_prop_per_image)
-
-    def predict_probs(
-        self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]
-    ):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features that were
-                used to compute predictions.
-
-        Returns:
-            list[Tensor]:
-                A list of Tensors of predicted class probabilities for each image.
-                Element i has shape (Ri, K + 1), where Ri is the number of proposals for image i.
-        """
-        scores, _ = predictions
-        num_inst_per_image = [len(p) for p in proposals]
-        probs = F.softmax(scores, dim=-1)
-        return probs.split(num_inst_per_image, dim=0)

spaces/ChandraMohanNayal/AutoGPT/autogpt/js/overlay.js

@@ -1,29 +0,0 @@
-const overlay = document.createElement('div');
-Object.assign(overlay.style, {
-    position: 'fixed',
-    zIndex: 999999,
-    top: 0,
-    left: 0,
-    width: '100%',
-    height: '100%',
-    background: 'rgba(0, 0, 0, 0.7)',
-    color: '#fff',
-    fontSize: '24px',
-    fontWeight: 'bold',
-    display: 'flex',
-    justifyContent: 'center',
-    alignItems: 'center',
-});
-const textContent = document.createElement('div');
-Object.assign(textContent.style, {
-    textAlign: 'center',
-});
-textContent.textContent = 'AutoGPT Analyzing Page';
-overlay.appendChild(textContent);
-document.body.append(overlay);
-document.body.style.overflow = 'hidden';
-let dotCount = 0;
-setInterval(() => {
-    textContent.textContent = 'AutoGPT Analyzing Page' + '.'.repeat(dotCount);
-    dotCount = (dotCount + 1) % 4;
-}, 1000);

spaces/CikeyQI/meme-api/meme_generator/memes/google/__init__.py

@@ -1,28 +0,0 @@
-from typing import List
-
-from pil_utils import BuildImage, Text2Image
-
-from meme_generator import add_meme
-
-
-def google(images, texts: List[str], args):
-    text = texts[0]
-    text = " ".join(text.splitlines())
-    colors = ["#4285f4", "#db4437", "#f4b400", "#4285f4", "#0f9d58", "#db4437"]
-    t2m = Text2Image.from_text(text, 200)
-    index = 0
-    for char in t2m.lines[0].chars:
-        char.fill = colors[index % len(colors)]
-        if char.char.strip():
-            index += 1
-    return BuildImage(t2m.to_image(bg_color="white", padding=(50, 50))).save_jpg()
-
-
-add_meme(
-    "google",
-    google,
-    min_texts=1,
-    max_texts=1,
-    default_texts=["Google"],
-    keywords=["google"],
-)