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-<h2>What is Attack on Titan AOT Mobile Fan Game V3.0 APK Offline?</h2>
-<h3>A fan-made game based on the popular anime and manga series</h3>
-<p>Attack on Titan AOT Mobile Fan Game V3.0 APK Offline is a game created by Julhiecio, a fan of the series who wanted to make a game that captures the essence of the original story. The game is set in a world where humanity lives inside walls to protect themselves from giant humanoid creatures called Titans, who devour humans for no apparent reason. The game follows the adventures of Eren Yeager, Mikasa Ackerman, Armin Arlert, and other members of the Survey Corps, who fight against the Titans using special equipment called Vertical Maneuvering Equipment (VME), which allows them to move around using grappling hooks and blades.</p>
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-<h3>Features of the game</h3>
-<h4>Offline multiplayer mode</h4>
-<p>One of the main features of Attack on Titan AOT Mobile Fan Game V3.0 APK Offline is that it can be played offline with up to four players using a local Wi-Fi network. This means that you don't need an internet connection to enjoy the game with your friends or family. You can choose to cooperate or compete with each other in various modes, such as survival, capture the flag, or deathmatch.</p>
-<h4>Large map with various locations</h4>
-<p>The game also features a large map that is based on the anime and manga series, with various locations that you can explore and interact with. You can visit places like Shiganshina District, Trost District, Forest of Giant Trees, Utgard Castle, and more. You can also find resources like gas, blades, food, and water that you can use to replenish your equipment and health.</p>
-<h4>Customizable characters and weapons</h4>
-<p>Another feature of Attack on Titan AOT Mobile Fan Game V3.0 APK Offline is that you can customize your character and weapons according to your preferences. You can choose from different hairstyles, outfits, accessories, and skins for your character, as well as different types of blades, guns, and bombs for your weapons. You can also unlock new items by completing missions or collecting coins.</p>
-<h4>Smooth graphics and animations</h4>
-<p>The game also boasts smooth graphics and animations that make the game look realistic and immersive. The game uses 3D models and textures that are faithful to the original series, as well as dynamic lighting and shadows that create a realistic atmosphere. The game also uses fluid animations that make the movement and combat of the characters smooth and responsive.</p>
-<h4>Easy controls and interface</h4>
-<p>The game also has easy controls and interface that make the game easy to play and navigate. The game uses touch-screen controls that are intuitive and simple to use. You can move your character using a virtual joystick, aim and shoot using buttons, and switch between weapons using icons. You can also access menus and options using a hamburger button. The game also has a clear and simple interface that shows your health, gas, blades, and coins, as well as a mini-map and a mission log.</p>
-<h2>How to download and install Attack on Titan AOT Mobile Fan Game V3.0 APK Offline?</h2>
-<h3>Download the APK file from a trusted source</h3>
-<p>To download and install Attack on Titan AOT Mobile Fan Game V3.0 APK Offline, you need to get the APK file from a trusted source. You can find the link to the official website of the game developer in the description below. Alternatively, you can search for the game on Google Play Store or other third-party websites that offer APK files. However, be careful of fake or malicious links that may harm your device or steal your data.</p>
-<h3>Enable unknown sources on your device</h3>
-<p>Before you can install the APK file, you need to enable unknown sources on your device. This is because the game is not available on the official app store and you need to allow your device to install apps from other sources. To do this, go to your device settings and look for security or privacy options. Then, find and enable the option that says unknown sources or allow installation of apps from unknown sources.</p>
-<h3>Install the APK file and launch the game</h3>
-<p>After you have enabled unknown sources, you can install the APK file by tapping on it and following the instructions. It may take a few minutes for the installation to complete. Once it is done, you can launch the game by tapping on its icon on your home screen or app drawer. You can also create a shortcut for the game on your desktop for easier access.</p>
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-<h2>Tips and tricks for playing Attack on Titan AOT Mobile Fan Game V3.0 APK Offline</h2>
-<h3>Learn the basics of movement and combat</h3>
-<p>The first thing you need to do when playing Attack on Titan AOT Mobile Fan Game V3.0 APK Offline is to learn the basics of movement and combat. The game has a tutorial mode that teaches you how to use the VME, how to attack and dodge Titans, how to reload and change weapons, and how to use items. You can also practice your skills in training mode or in offline mode with bots.</p>
-<h3>Explore the map and find resources</h3>
-<p>The next thing you need to do is to explore the map and find resources that can help you survive and fight better. The map has various locations that have different advantages and disadvantages. For example, some places have more gas stations or supply depots, while others have more Titans or enemies. You can also find hidden items or secrets that can give you extra coins or bonuses.</p>
-<h3>Upgrade your equipment and skills</h3>
-<p>The third thing you need to do is to upgrade your equipment and skills as you progress in the game. You can use coins that you earn from missions or collect from the map to buy new items or upgrade existing ones. You can also use skill points that you gain from leveling up to improve your attributes or unlock new abilities. You can access the shop and the skill tree from the main menu or from checkpoints in the map.</p>
-<h3>Team up with other players or play solo</h3>
-<p>The last thing you need to do is to decide whether you want to team up with other players or play solo in offline mode. Both options have their pros and cons, depending on your preference and play style. If you team up with other players, you can cooperate and communicate with them using voice chat or text chat, as well as share resources and items. However, you may also encounter problems such as lag, disconnects, trolls, or cheaters. If you play solo, you can enjoy the game at your own pace and without any distractions or interruptions. However, you may also face more challenges and difficulties, especially against stronger Titans or enemies.</p>
-<h2>Conclusion</h2>
-<p>Attack on Titan AOT Mobile Fan Game V3.0 APK Offline is a fan-made game that lets you experience the thrill and excitement of the anime and manga series on your mobile device. The game has offline multiplayer mode, large map with various locations, customizable characters and weapons, smooth graphics and animations, easy controls and interface, and more features that make it fun and enjoyable. The game is free to download and install, but you need to follow some steps to get it safely and securely. The game also has some tips and tricks that can help you play better and have more fun.</p>
-<p>If you are looking for a game that is based on Attack on Titan and can be played offline with your friends or alone, then Attack on Titan AOT Mobile Fan Game V3.0 APK Offline is a great choice for you.</p>
- FAQs Q: Is Attack on Titan A OT Mobile Fan Game V3.0 APK Offline an official game? A: No, it is not an official game. It is a fan-made game created by Julhiecio, a fan of the series who wanted to make a game that captures the essence of the original story. Q: How can I play Attack on Titan AOT Mobile Fan Game V3.0 APK Offline with my friends? A: You can play the game with your friends using a local Wi-Fi network. You can choose to cooperate or compete with each other in various modes, such as survival, capture the flag, or deathmatch. Q: What are the requirements to play Attack on Titan AOT Mobile Fan Game V3.0 APK Offline? A: You need an Android device that has at least 2 GB of RAM and 500 MB of free storage space. You also need to enable unknown sources on your device to install the APK file. Q: Where can I get more information about Attack on Titan AOT Mobile Fan Game V3.0 APK Offline? A: You can get more information about the game from the official website of the game developer, which is linked in the description below. You can also follow the game developer on social media platforms, such as Facebook, Twitter, Instagram, and YouTube. Q: How can I support the game developer of Attack on Titan AOT Mobile Fan Game V3.0 APK Offline? A: You can support the game developer by giving feedback, suggestions, or bug reports on the official website or social media platforms. You can also donate to the game developer via PayPal or Patreon.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Enjoy Stumble Guys in Your Browser - No APK Required.md

@@ -1,130 +0,0 @@
-<br />
-<h1>How to Download Stumble Guys Without APK</h1>
-<p>If you are looking for a fun and addictive game to play with your friends or strangers online, you might want to check out <strong>Stumble Guys</strong>. It is a massive multiplayer party knockout game that will make you laugh, scream, and stumble your way to victory. But how can you download Stumble Guys without APK? In this article, we will explain what Stumble Guys is, what an APK file is, and how you can download Stumble Guys without APK on your device.</p>
- <h2>What is Stumble Guys?</h2>
-<h3>A fun and chaotic multiplayer party game</h3>
-<p>Stumble Guys is a game that was inspired by popular TV shows like Wipeout and Takeshi's Castle. The game involves racing through obstacle courses against up to 32 players online. You have to run, jump, dash, slide, and dodge your way to the finish line while avoiding being eliminated by other players or the environment. The game features 17 unique obstacle courses that are randomly selected each round, so you never know what to expect. The game also has colorful, whacky graphics and hilarious sound effects that add to the fun.</p>
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- <h3>Available on different platforms and devices</h3>
-<p>Stumble Guys was originally released as a mobile game for Android devices in August 2020. Since then, it has gained millions of downloads and positive reviews from players. In October 2021, the game was also released on Steam for Windows PC users. The game supports cross-play between Android and PC users, so you can play with anyone regardless of their device. The game also has a party mode that allows you to invite your friends and create private matches. You can also customize your Stumble Guy with different outfits and emotes that you can unlock by playing the game or purchasing them from the store.</p>
- <h2>What is an APK file?</h2>
-<h3>A package file format for Android apps</h3>
-<p>An APK file stands for Android Package Kit. It is a file format that Android uses to distribute and install apps. An APK file contains all the code, resources, assets, certificates, and manifest file that an app needs to run properly on an Android device. An APK file can be downloaded from various sources, such as Google Play Store, third-party websites, or directly from the app developer.</p>
- <h3>The pros and cons of using APK files</h3>
-<p>There are some advantages and disadvantages of using APK files to install apps on your Android device. Some of the pros are:</p>
-<ul>
-<li>You can access apps that are not available in your region or on Google Play Store.</li>
-<li>You can install older versions of apps that may have features or compatibility that you prefer.</li>
-<li>You can update apps faster than waiting for the official update from Google Play Store.</li>
-</ul>
-<p>Some of the cons are:</p>
-<ul>
-<li>You may expose your device to malware or viruses that may harm your data or system.</li>
-<li>You may violate the terms of service or privacy policy of the app developer or Google Play Store.</li>
-<li>You may encounter compatibility or performance issues with your device or other apps.</li>
-</ul>
- <h2>How to download Stumble Guys without APK</h2>
-<h3>Download from Google Play Store or Steam</h3>
-<p>The easiest and safest way to download Stumble Guys without APK is to get it from the official sources, such as Google Play Store or Steam. Here are the steps to do so:</p>
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-<table>
-<tr>
-<th>Platform</th>
-<th>Steps</th>
-</tr>
-<tr>
-<td>Android</td>
-<td>
-<ol>
-<li>Open Google Play Store on your device.</li>
-<li>Search for Stumble Guys or use this link: <a href="">Stumble Guys - Apps on Google Play</a>.</li>
-<li>Tap on Install and wait for the download to finish.</li>
-<li>Launch the game and enjoy.</li>
-</ol>
-</td>
-</tr>
-<tr>
-<td>PC</td>
-<td>
-<ol>
-<li>Open Steam on your PC or download it from <a href="">Steam, The Ultimate Online Game Platform</a>.</li>
-<li>Search for Stumble Guys or use this link: <a href="">Stumble Guys on Steam</a>.</li>
-<li>Click on Add to Cart and purchase the game.</li>
-<li>Download and install the game from your Steam library.</li>
-<li>Launch the game and enjoy.</li>
-</ol>
-</td>
-</tr>
-</table>
- <h3>Use an Android emulator on PC or Mac</h3>
-<p>If you don't have an Android device or a PC that can run Steam, you can still play Stumble Guys without APK by using an Android emulator. An Android emulator is a software that simulates an Android device on your PC or Mac. You can use it to run Android apps and games on your computer. There are many Android emulators available online, such as BlueStacks, NoxPlayer, LDPlayer, etc. Here are the general steps to use an Android emulator to play Stumble Guys:</p>
-<ol>
-<li>Download and install an Android emulator of your choice from its official website.</li>
-<li>Launch the emulator and sign in with your Google account.</li>
-<li>Open Google Play Store on the emulator and search for Stumble Guys or use this link: <a href="">Stumble Guys - Apps on Google Play</a>.</li>
-<li>Install and launch the game on the emulator.</li>
-<li>Enjoy playing Stumble Guys on your PC or Mac.</li>
-</ol>
- <h2>Conclusion</h2>
-<h3>Summarize the main points of the article</h3>
-<p>In conclusion, Stumble Guys is a fun and chaotic multiplayer party game that you can play with up to 32 players online. You can download Stumble Guys without APK by getting it from Google Play Store or Steam, or by using an Android emulator on your PC or Mac. By doing so, you can avoid the risks of using APK files and enjoy the game safely and smoothly.</p>
- <h3>Provide a call to action for the readers</h3>
-<p>If you are ready to join the fun and stumble your way to victory, download Stumble Guys today and invite your friends to play with you. You will have a blast competing with other players in hilarious obstacle courses. Don't forget to customize your Stumble Guy with cool outfits and emotes. Have fun and good luck!</p>
- <h2>FAQs</h2>
- <h3>Is Stumble Guys free to play?</h3>
-<p>Yes, Stumble Guys is free to play on Android devices. However, you can purchase in-game items such as outfits, emotes, coins, and gems with real money. On PC, you have to buy the game from Steam for $4.99.</p>
- <h3>Can I play Stumble Guys with my friends?</h3>
-<p>Yes, you can play Stumble Guys with your friends by using the party mode. You can invite up to 32 friends to join your private match. You can also chat with them using voice or text messages.</p>
- <h3>How many players can join a Stumble Guys match?</h3>
-<p>A Stumble Guys match can have up to 32 players online. The match consists of multiple rounds of obstacle courses that eliminate players until one winner remains.</p>
- <h3>What are the system requirements for Stumble Guys?</h3>
-<p>The minimum system requirements for Stumble Guys are:</p>
- <table><tr><th>Platform</th><th>Requirements</th></tr><tr><td>Android</td><td><ul><li>Android 5.0 or higher</li><li>2 GB of RAM or more</li><li>100 MB of free storage space or more</li></ul></td></tr><tr><td>PC</td><td><ul><li>Windows 7 or higher (64-bit)</li><li>Dual core CPU 2.4 GHz or faster</li><li>NVIDIA GeForce 8600/9600GT or equivalent GPU</li <li>4 GB of RAM or more</li>
-<li>1 GB of free storage space or more</li>
-</ul>
-</td>
-</tr>
-</table>
- <h3>How can I customize my Stumble Guy?</h3>
-<p>You can customize your Stumble Guy by changing its outfit and emote. You can unlock new outfits and emotes by playing the game, completing missions, or buying them from the store. You can also mix and match different parts of the outfits to create your own unique look.</p> 401be4b1e0<br />
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spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Engineering Interviews 4be8039581d04456b0151f2cc4b22130/Questions ede8818b3a0e447f80145905690eb3f6/FizzBuzz 70828a5e5e6846a48686f66bb9ccc8b6.md

@@ -1,46 +0,0 @@
-# FizzBuzz
-
-Difficulty: Easy
-Skills: Algorithms, Front end
-
-<aside>
-💡 Create a new question in this database and choose `Interview Question` from the list of templates to automatically generate the format below.
-
-</aside>
-
-# Description
-
-Write a description for the interview question here.
-
-# Sample Inputs
-
-Give some valid inputs the candidate can expect to test their solution with.
-
-- ...
-- ...
-
-# Expected Outputs
-
-For each sample input above, list the expected output. 
-
-- ...
-- ...
-
-# Solutions
-
-Provide possible solutions in common languages to this problem.
-
-### Javascript
-
-```jsx
-function solution() {
-	
-}
-```
-
-### Python
-
-```python
-def solution():
-	pass
-```

spaces/AIFILMS/ControlNet-Video/style.css

@@ -1,105 +0,0 @@
-#col-container {max-width: 820px; margin-left: auto; margin-right: auto;}
-#duplicate-container{
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  line-height: 1em;
-  flex-direction: row-reverse;
-  font-size:1em;
-}
-a, a:hover, a:visited {
-  text-decoration-line: underline; 
-  font-weight: 600;
-  color: #1f2937 !important;
-}
-
-.dark a, .dark a:hover, .dark a:visited {
-  color: #f3f4f6 !important;
-}
-
-.label-wrap {
-  margin-bottom: 12px;
-}
-
-.footer {
-  margin-bottom: 45px;
-  margin-top: 10px;
-  text-align: center;
-  border-bottom: 1px solid #e5e5e5;
-}
-
-.footer>p {
-  font-size: .8rem!important;
-  display: inline-block;
-  padding: 0 10px;
-  transform: translateY(26px);
-  background: white;
-}
-.dark .footer {
-  border-color: #303030;
-}
-.dark .footer>p {
-  background: #0b0f19;
-}
-
-div#may-like-container > p {
-  font-size: .8em;
-  margin-bottom: 4px;
-}
-
-.animate-spin {
-  animation: spin 1s linear infinite;
-}
-
-@keyframes spin {
-  from {
-      transform: rotate(0deg);
-  }
-  to {
-      transform: rotate(360deg);
-  }
-}
-
-#share-btn-container {
-  display: flex; 
-  padding-left: 0.5rem !important; 
-  padding-right: 0.5rem !important; 
-  background-color: #000000; 
-  justify-content: center; 
-  align-items: center; 
-  border-radius: 9999px !important; 
-  max-width: 13rem;
-}
-
-#share-btn-container:hover {
-  background-color: #060606;
-}
-
-#share-btn {
-  all: initial; 
-  color: #ffffff;
-  font-weight: 600; 
-  cursor:pointer; 
-  font-family: 'IBM Plex Sans', sans-serif; 
-  margin-left: 0.5rem !important; 
-  padding-top: 0.5rem !important; 
-  padding-bottom: 0.5rem !important;
-  right:0;
-}
-
-#share-btn * {
-  all: unset;
-}
-
-#share-btn-container div:nth-child(-n+2){
-  width: auto !important;
-  min-height: 0px !important;
-}
-
-#share-btn-container .wrap {
-  display: none !important;
-}
-
-#share-btn-container.hidden {
-  display: none!important;
-}

spaces/AIFILMS/StyleGANEX/models/stylegan2/op/upfirdn2d.py

@@ -1,61 +0,0 @@
-from collections import abc
-
-import torch
-from torch.nn import functional as F
-
-
-def upfirdn2d(inputs, kernel, up=1, down=1, pad=(0, 0)):
-    if not isinstance(up, abc.Iterable):
-        up = (up, up)
-
-    if not isinstance(down, abc.Iterable):
-        down = (down, down)
-
-    if len(pad) == 2:
-        pad = (pad[0], pad[1], pad[0], pad[1])
-
-    return upfirdn2d_native(inputs, kernel, *up, *down, *pad)
-
-
-def upfirdn2d_native(
-    inputs, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-):
-    _, channel, in_h, in_w = inputs.shape
-    inputs = inputs.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = inputs.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = inputs.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(
-        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
-    )
-    out = out[
-          :,
-          max(-pad_y0, 0): out.shape[1] - max(-pad_y1, 0),
-          max(-pad_x0, 0): out.shape[2] - max(-pad_x1, 0),
-          :,
-          ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape(
-        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
-    )
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x
-
-    return out.view(-1, channel, out_h, out_w)

spaces/ASJMO/freegpt/g4f/Provider/Providers/helpers/phind.py

@@ -1,69 +0,0 @@
-import sys
-import json
-import datetime
-import urllib.parse
-
-from curl_cffi import requests
-
-config = json.loads(sys.argv[1])
-prompt = config['messages'][-1]['content']
-
-skill = 'expert' if config['model'] == 'gpt-4' else 'intermediate'
-
-json_data = json.dumps({
-    'question': prompt,
-    'options': {
-        'skill': skill,
-        'date': datetime.datetime.now().strftime('%d/%m/%Y'),
-        'language': 'en',
-        'detailed': True,
-        'creative': True,
-        'customLinks': []}}, separators=(',', ':'))
-
-headers = {
-    'Content-Type': 'application/json',
-    'Pragma': 'no-cache',
-    'Accept': '*/*',
-    'Sec-Fetch-Site': 'same-origin',
-    'Accept-Language': 'en-GB,en;q=0.9',
-    'Cache-Control': 'no-cache',
-    'Sec-Fetch-Mode': 'cors',
-    'Content-Length': str(len(json_data)),
-    'Origin': 'https://www.phind.com',
-    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.4 Safari/605.1.15',
-    'Referer': f'https://www.phind.com/search?q={urllib.parse.quote(prompt)}&source=searchbox',
-    'Connection': 'keep-alive',
-    'Host': 'www.phind.com',
-    'Sec-Fetch-Dest': 'empty'
-}
-
-
-def output(chunk):
-    try:
-        if b'PHIND_METADATA' in chunk:
-            return
-        
-        if chunk == b'data:  \r\ndata: \r\ndata: \r\n\r\n':
-            chunk = b'data:  \n\r\n\r\n'
-
-        chunk = chunk.decode()
-
-        chunk = chunk.replace('data: \r\n\r\ndata: ', 'data: \n')
-        chunk = chunk.replace('\r\ndata: \r\ndata: \r\n\r\n', '\n\r\n\r\n')
-        chunk = chunk.replace('data: ', '').replace('\r\n\r\n', '')
-
-        print(chunk, flush=True, end = '')
-        
-    except json.decoder.JSONDecodeError:
-        pass
-
-while True:
-    try:
-        response = requests.post('https://www.phind.com/api/infer/answer',
-                         headers=headers, data=json_data, content_callback=output, timeout=999999, impersonate='safari15_5')
-        
-        exit(0)
-    
-    except Exception as e:
-        print('an error occured, retrying... |', e, flush=True)
-        continue

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/WebSearch.ts

@@ -1,36 +0,0 @@
-import type { Conversation } from "./Conversation";
-import type { Timestamps } from "./Timestamps";
-
-export interface WebSearch extends Timestamps {
-	prompt: string;
-
-	searchQuery: string;
-	results: string[];
-	knowledgeGraph: string;
-	answerBox: string;
-	summary: string;
-
-	messages: WebSearchMessage[];
-}
-
-export type WebSearchMessageUpdate = {
-	type: "update";
-	message: string;
-	args?: string[];
-};
-
-export type WebSearchMessageError = {
-	type: "error";
-	message: string;
-	args?: string[];
-};
-
-export type WebSearchMessageResult = {
-	type: "result";
-	id: string;
-};
-
-export type WebSearchMessage =
-	| WebSearchMessageUpdate
-	| WebSearchMessageResult
-	| WebSearchMessageError;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/perspective/Perspective.d.ts

@@ -1,2 +0,0 @@
-import { ContainerPerspective } from '../../../plugins/perspectiveimage';
-export default ContainerPerspective;

spaces/AlexWang/lama/bin/gen_mask_dataset_hydra.py

@@ -1,124 +0,0 @@
-#!/usr/bin/env python3
-
-import glob
-import os
-import shutil
-import traceback
-import hydra
-from omegaconf import OmegaConf
-
-import PIL.Image as Image
-import numpy as np
-from joblib import Parallel, delayed
-
-from saicinpainting.evaluation.masks.mask import SegmentationMask, propose_random_square_crop
-from saicinpainting.evaluation.utils import load_yaml, SmallMode
-from saicinpainting.training.data.masks import MixedMaskGenerator
-
-
-class MakeManyMasksWrapper:
-    def __init__(self, impl, variants_n=2):
-        self.impl = impl
-        self.variants_n = variants_n
-
-    def get_masks(self, img):
-        img = np.transpose(np.array(img), (2, 0, 1))
-        return [self.impl(img)[0] for _ in range(self.variants_n)]
-
-
-def process_images(src_images, indir, outdir, config):
-    if config.generator_kind == 'segmentation':
-        mask_generator = SegmentationMask(**config.mask_generator_kwargs)
-    elif config.generator_kind == 'random':
-        mask_generator_kwargs = OmegaConf.to_container(config.mask_generator_kwargs, resolve=True)
-        variants_n = mask_generator_kwargs.pop('variants_n', 2)
-        mask_generator = MakeManyMasksWrapper(MixedMaskGenerator(**mask_generator_kwargs),
-                                              variants_n=variants_n)
-    else:
-        raise ValueError(f'Unexpected generator kind: {config.generator_kind}')
-
-    max_tamper_area = config.get('max_tamper_area', 1)
-
-    for infile in src_images:
-        try:
-            file_relpath = infile[len(indir):]
-            img_outpath = os.path.join(outdir, file_relpath)
-            os.makedirs(os.path.dirname(img_outpath), exist_ok=True)
-
-            image = Image.open(infile).convert('RGB')
-
-            # scale input image to output resolution and filter smaller images
-            if min(image.size) < config.cropping.out_min_size:
-                handle_small_mode = SmallMode(config.cropping.handle_small_mode)
-                if handle_small_mode == SmallMode.DROP:
-                    continue
-                elif handle_small_mode == SmallMode.UPSCALE:
-                    factor = config.cropping.out_min_size / min(image.size)
-                    out_size = (np.array(image.size) * factor).round().astype('uint32')
-                    image = image.resize(out_size, resample=Image.BICUBIC)
-            else:
-                factor = config.cropping.out_min_size / min(image.size)
-                out_size = (np.array(image.size) * factor).round().astype('uint32')
-                image = image.resize(out_size, resample=Image.BICUBIC)
-
-            # generate and select masks
-            src_masks = mask_generator.get_masks(image)
-
-            filtered_image_mask_pairs = []
-            for cur_mask in src_masks:
-                if config.cropping.out_square_crop:
-                    (crop_left,
-                     crop_top,
-                     crop_right,
-                     crop_bottom) = propose_random_square_crop(cur_mask,
-                                                               min_overlap=config.cropping.crop_min_overlap)
-                    cur_mask = cur_mask[crop_top:crop_bottom, crop_left:crop_right]
-                    cur_image = image.copy().crop((crop_left, crop_top, crop_right, crop_bottom))
-                else:
-                    cur_image = image
-
-                if len(np.unique(cur_mask)) == 0 or cur_mask.mean() > max_tamper_area:
-                    continue
-
-                filtered_image_mask_pairs.append((cur_image, cur_mask))
-
-            mask_indices = np.random.choice(len(filtered_image_mask_pairs),
-                                            size=min(len(filtered_image_mask_pairs), config.max_masks_per_image),
-                                            replace=False)
-
-            # crop masks; save masks together with input image
-            mask_basename = os.path.join(outdir, os.path.splitext(file_relpath)[0])
-            for i, idx in enumerate(mask_indices):
-                cur_image, cur_mask = filtered_image_mask_pairs[idx]
-                cur_basename = mask_basename + f'_crop{i:03d}'
-                Image.fromarray(np.clip(cur_mask * 255, 0, 255).astype('uint8'),
-                                mode='L').save(cur_basename + f'_mask{i:03d}.png')
-                cur_image.save(cur_basename + '.png')
-        except KeyboardInterrupt:
-            return
-        except Exception as ex:
-            print(f'Could not make masks for {infile} due to {ex}:\n{traceback.format_exc()}')
-
-
-@hydra.main(config_path='../configs/data_gen/whydra', config_name='random_medium_256.yaml')
-def main(config: OmegaConf):
-    if not config.indir.endswith('/'):
-        config.indir += '/'
-
-    os.makedirs(config.outdir, exist_ok=True)
-
-    in_files = list(glob.glob(os.path.join(config.indir, '**', f'*.{config.location.extension}'),
-                              recursive=True))
-    if config.n_jobs == 0:
-        process_images(in_files, config.indir, config.outdir, config)
-    else:
-        in_files_n = len(in_files)
-        chunk_size = in_files_n // config.n_jobs + (1 if in_files_n % config.n_jobs > 0 else 0)
-        Parallel(n_jobs=config.n_jobs)(
-            delayed(process_images)(in_files[start:start+chunk_size], config.indir, config.outdir, config)
-            for start in range(0, len(in_files), chunk_size)
-        )
-
-
-if __name__ == '__main__':
-    main()

spaces/AlexWang/lama/models/ade20k/segm_lib/utils/data/dataloader.py

@@ -1,425 +0,0 @@
-import torch
-import torch.multiprocessing as multiprocessing
-from torch._C import _set_worker_signal_handlers, \
-    _remove_worker_pids, _error_if_any_worker_fails
-try:
-    from torch._C import _set_worker_pids
-except:
-    from torch._C import _update_worker_pids as _set_worker_pids
-from .sampler import SequentialSampler, RandomSampler, BatchSampler
-import signal
-import collections
-import re
-import sys
-import threading
-import traceback
-from torch._six import string_classes, int_classes
-import numpy as np
-
-if sys.version_info[0] == 2:
-    import Queue as queue
-else:
-    import queue
-
-
-class ExceptionWrapper(object):
-    r"Wraps an exception plus traceback to communicate across threads"
-
-    def __init__(self, exc_info):
-        self.exc_type = exc_info[0]
-        self.exc_msg = "".join(traceback.format_exception(*exc_info))
-
-
-_use_shared_memory = False
-"""Whether to use shared memory in default_collate"""
-
-
-def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id):
-    global _use_shared_memory
-    _use_shared_memory = True
-
-    # Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
-    # module's handlers are executed after Python returns from C low-level
-    # handlers, likely when the same fatal signal happened again already.
-    # https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
-    _set_worker_signal_handlers()
-
-    torch.set_num_threads(1)
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-
-    if init_fn is not None:
-        init_fn(worker_id)
-
-    while True:
-        r = index_queue.get()
-        if r is None:
-            break
-        idx, batch_indices = r
-        try:
-            samples = collate_fn([dataset[i] for i in batch_indices])
-        except Exception:
-            data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
-        else:
-            data_queue.put((idx, samples))
-
-
-def _worker_manager_loop(in_queue, out_queue, done_event, pin_memory, device_id):
-    if pin_memory:
-        torch.cuda.set_device(device_id)
-
-    while True:
-        try:
-            r = in_queue.get()
-        except Exception:
-            if done_event.is_set():
-                return
-            raise
-        if r is None:
-            break
-        if isinstance(r[1], ExceptionWrapper):
-            out_queue.put(r)
-            continue
-        idx, batch = r
-        try:
-            if pin_memory:
-                batch = pin_memory_batch(batch)
-        except Exception:
-            out_queue.put((idx, ExceptionWrapper(sys.exc_info())))
-        else:
-            out_queue.put((idx, batch))
-
-numpy_type_map = {
-    'float64': torch.DoubleTensor,
-    'float32': torch.FloatTensor,
-    'float16': torch.HalfTensor,
-    'int64': torch.LongTensor,
-    'int32': torch.IntTensor,
-    'int16': torch.ShortTensor,
-    'int8': torch.CharTensor,
-    'uint8': torch.ByteTensor,
-}
-
-
-def default_collate(batch):
-    "Puts each data field into a tensor with outer dimension batch size"
-
-    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
-    elem_type = type(batch[0])
-    if torch.is_tensor(batch[0]):
-        out = None
-        if _use_shared_memory:
-            # If we're in a background process, concatenate directly into a
-            # shared memory tensor to avoid an extra copy
-            numel = sum([x.numel() for x in batch])
-            storage = batch[0].storage()._new_shared(numel)
-            out = batch[0].new(storage)
-        return torch.stack(batch, 0, out=out)
-    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
-            and elem_type.__name__ != 'string_':
-        elem = batch[0]
-        if elem_type.__name__ == 'ndarray':
-            # array of string classes and object
-            if re.search('[SaUO]', elem.dtype.str) is not None:
-                raise TypeError(error_msg.format(elem.dtype))
-
-            return torch.stack([torch.from_numpy(b) for b in batch], 0)
-        if elem.shape == ():  # scalars
-            py_type = float if elem.dtype.name.startswith('float') else int
-            return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
-    elif isinstance(batch[0], int_classes):
-        return torch.LongTensor(batch)
-    elif isinstance(batch[0], float):
-        return torch.DoubleTensor(batch)
-    elif isinstance(batch[0], string_classes):
-        return batch
-    elif isinstance(batch[0], collections.Mapping):
-        return {key: default_collate([d[key] for d in batch]) for key in batch[0]}
-    elif isinstance(batch[0], collections.Sequence):
-        transposed = zip(*batch)
-        return [default_collate(samples) for samples in transposed]
-
-    raise TypeError((error_msg.format(type(batch[0]))))
-
-
-def pin_memory_batch(batch):
-    if torch.is_tensor(batch):
-        return batch.pin_memory()
-    elif isinstance(batch, string_classes):
-        return batch
-    elif isinstance(batch, collections.Mapping):
-        return {k: pin_memory_batch(sample) for k, sample in batch.items()}
-    elif isinstance(batch, collections.Sequence):
-        return [pin_memory_batch(sample) for sample in batch]
-    else:
-        return batch
-
-
-_SIGCHLD_handler_set = False
-"""Whether SIGCHLD handler is set for DataLoader worker failures. Only one
-handler needs to be set for all DataLoaders in a process."""
-
-
-def _set_SIGCHLD_handler():
-    # Windows doesn't support SIGCHLD handler
-    if sys.platform == 'win32':
-        return
-    # can't set signal in child threads
-    if not isinstance(threading.current_thread(), threading._MainThread):
-        return
-    global _SIGCHLD_handler_set
-    if _SIGCHLD_handler_set:
-        return
-    previous_handler = signal.getsignal(signal.SIGCHLD)
-    if not callable(previous_handler):
-        previous_handler = None
-
-    def handler(signum, frame):
-        # This following call uses `waitid` with WNOHANG from C side. Therefore,
-        # Python can still get and update the process status successfully.
-        _error_if_any_worker_fails()
-        if previous_handler is not None:
-            previous_handler(signum, frame)
-
-    signal.signal(signal.SIGCHLD, handler)
-    _SIGCHLD_handler_set = True
-
-
-class DataLoaderIter(object):
-    "Iterates once over the DataLoader's dataset, as specified by the sampler"
-
-    def __init__(self, loader):
-        self.dataset = loader.dataset
-        self.collate_fn = loader.collate_fn
-        self.batch_sampler = loader.batch_sampler
-        self.num_workers = loader.num_workers
-        self.pin_memory = loader.pin_memory and torch.cuda.is_available()
-        self.timeout = loader.timeout
-        self.done_event = threading.Event()
-
-        self.sample_iter = iter(self.batch_sampler)
-
-        if self.num_workers > 0:
-            self.worker_init_fn = loader.worker_init_fn
-            self.index_queue = multiprocessing.SimpleQueue()
-            self.worker_result_queue = multiprocessing.SimpleQueue()
-            self.batches_outstanding = 0
-            self.worker_pids_set = False
-            self.shutdown = False
-            self.send_idx = 0
-            self.rcvd_idx = 0
-            self.reorder_dict = {}
-
-            base_seed = torch.LongTensor(1).random_(0, 2**31-1)[0]
-            self.workers = [
-                multiprocessing.Process(
-                    target=_worker_loop,
-                    args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn,
-                          base_seed + i, self.worker_init_fn, i))
-                for i in range(self.num_workers)]
-
-            if self.pin_memory or self.timeout > 0:
-                self.data_queue = queue.Queue()
-                if self.pin_memory:
-                    maybe_device_id = torch.cuda.current_device()
-                else:
-                    # do not initialize cuda context if not necessary
-                    maybe_device_id = None
-                self.worker_manager_thread = threading.Thread(
-                    target=_worker_manager_loop,
-                    args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
-                          maybe_device_id))
-                self.worker_manager_thread.daemon = True
-                self.worker_manager_thread.start()
-            else:
-                self.data_queue = self.worker_result_queue
-
-            for w in self.workers:
-                w.daemon = True  # ensure that the worker exits on process exit
-                w.start()
-
-            _set_worker_pids(id(self), tuple(w.pid for w in self.workers))
-            _set_SIGCHLD_handler()
-            self.worker_pids_set = True
-
-            # prime the prefetch loop
-            for _ in range(2 * self.num_workers):
-                self._put_indices()
-
-    def __len__(self):
-        return len(self.batch_sampler)
-
-    def _get_batch(self):
-        if self.timeout > 0:
-            try:
-                return self.data_queue.get(timeout=self.timeout)
-            except queue.Empty:
-                raise RuntimeError('DataLoader timed out after {} seconds'.format(self.timeout))
-        else:
-            return self.data_queue.get()
-
-    def __next__(self):
-        if self.num_workers == 0:  # same-process loading
-            indices = next(self.sample_iter)  # may raise StopIteration
-            batch = self.collate_fn([self.dataset[i] for i in indices])
-            if self.pin_memory:
-                batch = pin_memory_batch(batch)
-            return batch
-
-        # check if the next sample has already been generated
-        if self.rcvd_idx in self.reorder_dict:
-            batch = self.reorder_dict.pop(self.rcvd_idx)
-            return self._process_next_batch(batch)
-
-        if self.batches_outstanding == 0:
-            self._shutdown_workers()
-            raise StopIteration
-
-        while True:
-            assert (not self.shutdown and self.batches_outstanding > 0)
-            idx, batch = self._get_batch()
-            self.batches_outstanding -= 1
-            if idx != self.rcvd_idx:
-                # store out-of-order samples
-                self.reorder_dict[idx] = batch
-                continue
-            return self._process_next_batch(batch)
-
-    next = __next__  # Python 2 compatibility
-
-    def __iter__(self):
-        return self
-
-    def _put_indices(self):
-        assert self.batches_outstanding < 2 * self.num_workers
-        indices = next(self.sample_iter, None)
-        if indices is None:
-            return
-        self.index_queue.put((self.send_idx, indices))
-        self.batches_outstanding += 1
-        self.send_idx += 1
-
-    def _process_next_batch(self, batch):
-        self.rcvd_idx += 1
-        self._put_indices()
-        if isinstance(batch, ExceptionWrapper):
-            raise batch.exc_type(batch.exc_msg)
-        return batch
-
-    def __getstate__(self):
-        # TODO: add limited pickling support for sharing an iterator
-        # across multiple threads for HOGWILD.
-        # Probably the best way to do this is by moving the sample pushing
-        # to a separate thread and then just sharing the data queue
-        # but signalling the end is tricky without a non-blocking API
-        raise NotImplementedError("DataLoaderIterator cannot be pickled")
-
-    def _shutdown_workers(self):
-        try:
-            if not self.shutdown:
-                self.shutdown = True
-                self.done_event.set()
-                # if worker_manager_thread is waiting to put
-                while not self.data_queue.empty():
-                    self.data_queue.get()
-                for _ in self.workers:
-                    self.index_queue.put(None)
-                # done_event should be sufficient to exit worker_manager_thread,
-                # but be safe here and put another None
-                self.worker_result_queue.put(None)
-        finally:
-            # removes pids no matter what
-            if self.worker_pids_set:
-                _remove_worker_pids(id(self))
-                self.worker_pids_set = False
-
-    def __del__(self):
-        if self.num_workers > 0:
-            self._shutdown_workers()
-
-
-class DataLoader(object):
-    """
-    Data loader. Combines a dataset and a sampler, and provides
-    single- or multi-process iterators over the dataset.
-
-    Arguments:
-        dataset (Dataset): dataset from which to load the data.
-        batch_size (int, optional): how many samples per batch to load
-            (default: 1).
-        shuffle (bool, optional): set to ``True`` to have the data reshuffled
-            at every epoch (default: False).
-        sampler (Sampler, optional): defines the strategy to draw samples from
-            the dataset. If specified, ``shuffle`` must be False.
-        batch_sampler (Sampler, optional): like sampler, but returns a batch of
-            indices at a time. Mutually exclusive with batch_size, shuffle,
-            sampler, and drop_last.
-        num_workers (int, optional): how many subprocesses to use for data
-            loading. 0 means that the data will be loaded in the main process.
-            (default: 0)
-        collate_fn (callable, optional): merges a list of samples to form a mini-batch.
-        pin_memory (bool, optional): If ``True``, the data loader will copy tensors
-            into CUDA pinned memory before returning them.
-        drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
-            if the dataset size is not divisible by the batch size. If ``False`` and
-            the size of dataset is not divisible by the batch size, then the last batch
-            will be smaller. (default: False)
-        timeout (numeric, optional): if positive, the timeout value for collecting a batch
-            from workers. Should always be non-negative. (default: 0)
-        worker_init_fn (callable, optional): If not None, this will be called on each
-            worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
-            input, after seeding and before data loading. (default: None)
-
-    .. note:: By default, each worker will have its PyTorch seed set to
-              ``base_seed + worker_id``, where ``base_seed`` is a long generated
-              by main process using its RNG. You may use ``torch.initial_seed()`` to access
-              this value in :attr:`worker_init_fn`, which can be used to set other seeds
-              (e.g. NumPy) before data loading.
-
-    .. warning:: If ``spawn'' start method is used, :attr:`worker_init_fn` cannot be an
-                 unpicklable object, e.g., a lambda function.
-    """
-
-    def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
-                 num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False,
-                 timeout=0, worker_init_fn=None):
-        self.dataset = dataset
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-        self.collate_fn = collate_fn
-        self.pin_memory = pin_memory
-        self.drop_last = drop_last
-        self.timeout = timeout
-        self.worker_init_fn = worker_init_fn
-
-        if timeout < 0:
-            raise ValueError('timeout option should be non-negative')
-
-        if batch_sampler is not None:
-            if batch_size > 1 or shuffle or sampler is not None or drop_last:
-                raise ValueError('batch_sampler is mutually exclusive with '
-                                 'batch_size, shuffle, sampler, and drop_last')
-
-        if sampler is not None and shuffle:
-            raise ValueError('sampler is mutually exclusive with shuffle')
-
-        if self.num_workers < 0:
-            raise ValueError('num_workers cannot be negative; '
-                             'use num_workers=0 to disable multiprocessing.')
-
-        if batch_sampler is None:
-            if sampler is None:
-                if shuffle:
-                    sampler = RandomSampler(dataset)
-                else:
-                    sampler = SequentialSampler(dataset)
-            batch_sampler = BatchSampler(sampler, batch_size, drop_last)
-
-        self.sampler = sampler
-        self.batch_sampler = batch_sampler
-
-    def __iter__(self):
-        return DataLoaderIter(self)
-
-    def __len__(self):
-        return len(self.batch_sampler)

spaces/Ananthap4/itineraryGenerator/README.md

@@ -1,12 +0,0 @@
----
-title: ItineraryGenerator
-emoji: 🌍
-colorFrom: indigo
-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/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_ddpm_parallel.py

@@ -1,604 +0,0 @@
-# Copyright 2023 ParaDiGMS authors and 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.
-
-# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, randn_tensor
-from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
-
-
-@dataclass
-# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput
-class DDPMParallelSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.FloatTensor
-    pred_original_sample: Optional[torch.FloatTensor] = None
-
-
-# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
-def betas_for_alpha_bar(
-    num_diffusion_timesteps,
-    max_beta=0.999,
-    alpha_transform_type="cosine",
-):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
-                     Choose from `cosine` or `exp`
-
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-    if alpha_transform_type == "cosine":
-
-        def alpha_bar_fn(t):
-            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    elif alpha_transform_type == "exp":
-
-        def alpha_bar_fn(t):
-            return math.exp(t * -12.0)
-
-    else:
-        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
-
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
-    return torch.tensor(betas, dtype=torch.float32)
-
-
-class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
-    """
-    Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
-    Langevin dynamics sampling.
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    For more details, see the original paper: https://arxiv.org/abs/2006.11239
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, `squaredcos_cap_v2` or `sigmoid`.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        variance_type (`str`):
-            options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
-            `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
-        clip_sample (`bool`, default `True`):
-            option to clip predicted sample for numerical stability.
-        clip_sample_range (`float`, default `1.0`):
-            the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
-        prediction_type (`str`, default `epsilon`, optional):
-            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
-            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
-        thresholding (`bool`, default `False`):
-            whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
-            Note that the thresholding method is unsuitable for latent-space diffusion models (such as
-            stable-diffusion).
-        dynamic_thresholding_ratio (`float`, default `0.995`):
-            the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
-            (https://arxiv.org/abs/2205.11487). Valid only when `thresholding=True`.
-        sample_max_value (`float`, default `1.0`):
-            the threshold value for dynamic thresholding. Valid only when `thresholding=True`.
-        timestep_spacing (`str`, default `"leading"`):
-            The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample
-            Steps are Flawed](https://arxiv.org/abs/2305.08891) for more information.
-        steps_offset (`int`, default `0`):
-            an offset added to the inference steps. You can use a combination of `offset=1` and
-            `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
-            stable diffusion.
-    """
-
-    _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    order = 1
-    _is_ode_scheduler = False
-
-    @register_to_config
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.__init__
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        variance_type: str = "fixed_small",
-        clip_sample: bool = True,
-        prediction_type: str = "epsilon",
-        thresholding: bool = False,
-        dynamic_thresholding_ratio: float = 0.995,
-        clip_sample_range: float = 1.0,
-        sample_max_value: float = 1.0,
-        timestep_spacing: str = "leading",
-        steps_offset: int = 0,
-    ):
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
-            )
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        elif beta_schedule == "sigmoid":
-            # GeoDiff sigmoid schedule
-            betas = torch.linspace(-6, 6, num_train_timesteps)
-            self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-        self.one = torch.tensor(1.0)
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.custom_timesteps = False
-        self.num_inference_steps = None
-        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
-
-        self.variance_type = variance_type
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.scale_model_input
-    def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`torch.FloatTensor`): input sample
-            timestep (`int`, optional): current timestep
-
-        Returns:
-            `torch.FloatTensor`: scaled input sample
-        """
-        return sample
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.set_timesteps
-    def set_timesteps(
-        self,
-        num_inference_steps: Optional[int] = None,
-        device: Union[str, torch.device] = None,
-        timesteps: Optional[List[int]] = None,
-    ):
-        """
-        Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`Optional[int]`):
-                the number of diffusion steps used when generating samples with a pre-trained model. If passed, then
-                `timesteps` must be `None`.
-            device (`str` or `torch.device`, optional):
-                the device to which the timesteps are moved to.
-            custom_timesteps (`List[int]`, optional):
-                custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
-                timestep spacing strategy of equal spacing between timesteps is used. If passed, `num_inference_steps`
-                must be `None`.
-
-        """
-        if num_inference_steps is not None and timesteps is not None:
-            raise ValueError("Can only pass one of `num_inference_steps` or `custom_timesteps`.")
-
-        if timesteps is not None:
-            for i in range(1, len(timesteps)):
-                if timesteps[i] >= timesteps[i - 1]:
-                    raise ValueError("`custom_timesteps` must be in descending order.")
-
-            if timesteps[0] >= self.config.num_train_timesteps:
-                raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`:"
-                    f" {self.config.num_train_timesteps}."
-                )
-
-            timesteps = np.array(timesteps, dtype=np.int64)
-            self.custom_timesteps = True
-        else:
-            if num_inference_steps > self.config.num_train_timesteps:
-                raise ValueError(
-                    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
-                    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
-                    f" maximal {self.config.num_train_timesteps} timesteps."
-                )
-
-            self.num_inference_steps = num_inference_steps
-            self.custom_timesteps = False
-
-            # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
-            if self.config.timestep_spacing == "linspace":
-                timesteps = (
-                    np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps)
-                    .round()[::-1]
-                    .copy()
-                    .astype(np.int64)
-                )
-            elif self.config.timestep_spacing == "leading":
-                step_ratio = self.config.num_train_timesteps // self.num_inference_steps
-                # creates integer timesteps by multiplying by ratio
-                # casting to int to avoid issues when num_inference_step is power of 3
-                timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
-                timesteps += self.config.steps_offset
-            elif self.config.timestep_spacing == "trailing":
-                step_ratio = self.config.num_train_timesteps / self.num_inference_steps
-                # creates integer timesteps by multiplying by ratio
-                # casting to int to avoid issues when num_inference_step is power of 3
-                timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64)
-                timesteps -= 1
-            else:
-                raise ValueError(
-                    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
-                )
-
-        self.timesteps = torch.from_numpy(timesteps).to(device)
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._get_variance
-    def _get_variance(self, t, predicted_variance=None, variance_type=None):
-        prev_t = self.previous_timestep(t)
-
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one
-        current_beta_t = 1 - alpha_prod_t / alpha_prod_t_prev
-
-        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
-        # and sample from it to get previous sample
-        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
-        variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * current_beta_t
-
-        # we always take the log of variance, so clamp it to ensure it's not 0
-        variance = torch.clamp(variance, min=1e-20)
-
-        if variance_type is None:
-            variance_type = self.config.variance_type
-
-        # hacks - were probably added for training stability
-        if variance_type == "fixed_small":
-            variance = variance
-        # for rl-diffuser https://arxiv.org/abs/2205.09991
-        elif variance_type == "fixed_small_log":
-            variance = torch.log(variance)
-            variance = torch.exp(0.5 * variance)
-        elif variance_type == "fixed_large":
-            variance = current_beta_t
-        elif variance_type == "fixed_large_log":
-            # Glide max_log
-            variance = torch.log(current_beta_t)
-        elif variance_type == "learned":
-            return predicted_variance
-        elif variance_type == "learned_range":
-            min_log = torch.log(variance)
-            max_log = torch.log(current_beta_t)
-            frac = (predicted_variance + 1) / 2
-            variance = frac * max_log + (1 - frac) * min_log
-
-        return variance
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
-    def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
-        """
-        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
-        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
-        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
-        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
-        photorealism as well as better image-text alignment, especially when using very large guidance weights."
-
-        https://arxiv.org/abs/2205.11487
-        """
-        dtype = sample.dtype
-        batch_size, channels, height, width = sample.shape
-
-        if dtype not in (torch.float32, torch.float64):
-            sample = sample.float()  # upcast for quantile calculation, and clamp not implemented for cpu half
-
-        # Flatten sample for doing quantile calculation along each image
-        sample = sample.reshape(batch_size, channels * height * width)
-
-        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"
-
-        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
-        s = torch.clamp(
-            s, min=1, max=self.config.sample_max_value
-        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
-
-        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
-        sample = torch.clamp(sample, -s, s) / s  # "we threshold xt0 to the range [-s, s] and then divide by s"
-
-        sample = sample.reshape(batch_size, channels, height, width)
-        sample = sample.to(dtype)
-
-        return sample
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timestep: int,
-        sample: torch.FloatTensor,
-        generator=None,
-        return_dict: bool = True,
-    ) -> Union[DDPMParallelSchedulerOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-            generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than DDPMParallelSchedulerOutput class
-
-        Returns:
-            [`~schedulers.scheduling_utils.DDPMParallelSchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.DDPMParallelSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`.
-            When returning a tuple, the first element is the sample tensor.
-
-        """
-        t = timestep
-
-        prev_t = self.previous_timestep(t)
-
-        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
-            model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
-        else:
-            predicted_variance = None
-
-        # 1. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-        current_alpha_t = alpha_prod_t / alpha_prod_t_prev
-        current_beta_t = 1 - current_alpha_t
-
-        # 2. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
-        if self.config.prediction_type == "epsilon":
-            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-        elif self.config.prediction_type == "sample":
-            pred_original_sample = model_output
-        elif self.config.prediction_type == "v_prediction":
-            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-        else:
-            raise ValueError(
-                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or"
-                " `v_prediction`  for the DDPMScheduler."
-            )
-
-        # 3. Clip or threshold "predicted x_0"
-        if self.config.thresholding:
-            pred_original_sample = self._threshold_sample(pred_original_sample)
-        elif self.config.clip_sample:
-            pred_original_sample = pred_original_sample.clamp(
-                -self.config.clip_sample_range, self.config.clip_sample_range
-            )
-
-        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
-        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
-        pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * current_beta_t) / beta_prod_t
-        current_sample_coeff = current_alpha_t ** (0.5) * beta_prod_t_prev / beta_prod_t
-
-        # 5. Compute predicted previous sample µ_t
-        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
-        pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
-
-        # 6. Add noise
-        variance = 0
-        if t > 0:
-            device = model_output.device
-            variance_noise = randn_tensor(
-                model_output.shape, generator=generator, device=device, dtype=model_output.dtype
-            )
-            if self.variance_type == "fixed_small_log":
-                variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise
-            elif self.variance_type == "learned_range":
-                variance = self._get_variance(t, predicted_variance=predicted_variance)
-                variance = torch.exp(0.5 * variance) * variance_noise
-            else:
-                variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * variance_noise
-
-        pred_prev_sample = pred_prev_sample + variance
-
-        if not return_dict:
-            return (pred_prev_sample,)
-
-        return DDPMParallelSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
-
-    def batch_step_no_noise(
-        self,
-        model_output: torch.FloatTensor,
-        timesteps: List[int],
-        sample: torch.FloatTensor,
-    ) -> torch.FloatTensor:
-        """
-        Batched version of the `step` function, to be able to reverse the SDE for multiple samples/timesteps at once.
-        Also, does not add any noise to the predicted sample, which is necessary for parallel sampling where the noise
-        is pre-sampled by the pipeline.
-
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
-            timesteps (`List[int]`):
-                current discrete timesteps in the diffusion chain. This is now a list of integers.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-
-        Returns:
-            `torch.FloatTensor`: sample tensor at previous timestep.
-        """
-        t = timesteps
-        num_inference_steps = self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps
-        prev_t = t - self.config.num_train_timesteps // num_inference_steps
-
-        t = t.view(-1, *([1] * (model_output.ndim - 1)))
-        prev_t = prev_t.view(-1, *([1] * (model_output.ndim - 1)))
-
-        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
-            model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
-        else:
-            pass
-
-        # 1. compute alphas, betas
-        self.alphas_cumprod = self.alphas_cumprod.to(model_output.device)
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[torch.clip(prev_t, min=0)]
-        alpha_prod_t_prev[prev_t < 0] = torch.tensor(1.0)
-
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-        current_alpha_t = alpha_prod_t / alpha_prod_t_prev
-        current_beta_t = 1 - current_alpha_t
-
-        # 2. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
-        if self.config.prediction_type == "epsilon":
-            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-        elif self.config.prediction_type == "sample":
-            pred_original_sample = model_output
-        elif self.config.prediction_type == "v_prediction":
-            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-        else:
-            raise ValueError(
-                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or"
-                " `v_prediction`  for the DDPMParallelScheduler."
-            )
-
-        # 3. Clip or threshold "predicted x_0"
-        if self.config.thresholding:
-            pred_original_sample = self._threshold_sample(pred_original_sample)
-        elif self.config.clip_sample:
-            pred_original_sample = pred_original_sample.clamp(
-                -self.config.clip_sample_range, self.config.clip_sample_range
-            )
-
-        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
-        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
-        pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * current_beta_t) / beta_prod_t
-        current_sample_coeff = current_alpha_t ** (0.5) * beta_prod_t_prev / beta_prod_t
-
-        # 5. Compute predicted previous sample µ_t
-        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
-        pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
-
-        return pred_prev_sample
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
-    def add_noise(
-        self,
-        original_samples: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
-        timesteps = timesteps.to(original_samples.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        return noisy_samples
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
-    def get_velocity(
-        self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as sample
-        alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
-        timesteps = timesteps.to(sample.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(sample.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
-        return velocity
-
-    def __len__(self):
-        return self.config.num_train_timesteps
-
-    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep
-    def previous_timestep(self, timestep):
-        if self.custom_timesteps:
-            index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]
-            if index == self.timesteps.shape[0] - 1:
-                prev_t = torch.tensor(-1)
-            else:
-                prev_t = self.timesteps[index + 1]
-        else:
-            num_inference_steps = (
-                self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps
-            )
-            prev_t = timestep - self.config.num_train_timesteps // num_inference_steps
-
-        return prev_t

spaces/Andy1621/uniformer_image_detection/mmdet/datasets/deepfashion.py

@@ -1,10 +0,0 @@
-from .builder import DATASETS
-from .coco import CocoDataset
-
-
-@DATASETS.register_module()
-class DeepFashionDataset(CocoDataset):
-
-    CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag',
-               'neckwear', 'headwear', 'eyeglass', 'belt', 'footwear', 'hair',
-               'skin', 'face')

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_40k_pascal_context_59.py

@@ -1,10 +0,0 @@
-_base_ = [
-    '../_base_/models/deeplabv3plus_r50-d8.py',
-    '../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=59),
-    auxiliary_head=dict(num_classes=59),
-    test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
-optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://resnet18_v1c',
-    backbone=dict(depth=18),
-    decode_head=dict(
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))

spaces/AnimalEquality/chatbot/lv_recipe_chatbot/ingredient_vision.py

@@ -1,132 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/03_ingredient_vision.ipynb.
-
-# %% auto 0
-__all__ = ['SAMPLE_IMG_DIR', 'format_image', 'BlipImageCaptioning', 'BlipVQA', 'VeganIngredientFinder']
-
-# %% ../nbs/03_ingredient_vision.ipynb 3
-import imghdr
-import os
-import time
-from pathlib import Path
-
-import numpy as np
-import torch
-from PIL import Image
-from transformers import (
-    BlipForConditionalGeneration,
-    BlipForQuestionAnswering,
-    BlipProcessor,
-    pipeline,
-)
-
-import constants
-
-# %% ../nbs/03_ingredient_vision.ipynb 7
-# fmt: off
-def format_image(
-    image: str # Image file path
-):
-    # fmt: on
-    img = Image.open(image)
-    width, height = img.size
-    ratio = min(512 / width, 512 / height)
-    width_new, height_new = (round(width * ratio), round(height * ratio))
-    width_new = int(np.round(width_new / 64.0)) * 64
-    height_new = int(np.round(height_new / 64.0)) * 64
-    img = img.resize((width_new, height_new))
-    img = img.convert("RGB")
-    return img
-
-# %% ../nbs/03_ingredient_vision.ipynb 8
-class BlipImageCaptioning:
-    """
-    Useful when you want to know what is inside the photo.
-    """
-
-    # fmt: off
-    def __init__(self, 
-        device: str
-    ): # pytorch hardware identifier to run model on options: "cpu, cuda_0, cuda_1 ..., cuda_n" 
-    # fmt: on
-        self.device = device
-        self.torch_dtype = torch.float16 if "cuda" in device else torch.float32
-        self.processor = BlipProcessor.from_pretrained(
-            "Salesforce/blip-image-captioning-base"
-        )
-        self.model = BlipForConditionalGeneration.from_pretrained(
-            "Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype
-        ).to(self.device)
-
-    def inference(self, 
-            image: Image
-    ) -> str: # Caption for the image
-        inputs = self.processor(image, return_tensors="pt").to(
-            self.device, self.torch_dtype
-        )
-        out = self.model.generate(**inputs, max_new_tokens=50)
-        captions = self.processor.decode(out[0], skip_special_tokens=True)
-        return captions
-
-# %% ../nbs/03_ingredient_vision.ipynb 10
-class BlipVQA:
-    # fmt: off
-    """
-    BLIP Visual Question Answering
-    Useful when you need an answer for a question based on an image.
-    Examples:
-        what is the background color of this image, how many cats are in this figure, what is in this figure?
-    """
-    # fmt: on
-    def __init__(self, device: str):
-        self.torch_dtype = torch.float16 if "cuda" in device else torch.float32
-        self.device = device
-        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
-        self.model = BlipForQuestionAnswering.from_pretrained(
-            "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype
-        ).to(self.device)
-
-    # fmt: off
-    def inference(self, 
-        image: Image, 
-        question: str
-    ) -> str: # Answer to the query on the image
-    # fmt: on
-        image = image.convert("RGB")
-        inputs = self.processor(image, question, return_tensors="pt").to(
-            self.device, self.torch_dtype
-        )
-        out = self.model.generate(**inputs, max_new_tokens=100)
-        answer = self.processor.decode(out[0], skip_special_tokens=True)
-        return answer
-
-# %% ../nbs/03_ingredient_vision.ipynb 12
-SAMPLE_IMG_DIR = Path(f"{constants.ROOT_DIR}/assets/images/vegan_ingredients")
-
-# %% ../nbs/03_ingredient_vision.ipynb 19
-class VeganIngredientFinder:
-    def __init__(self):
-        self.vqa = BlipVQA("cpu")
-
-    # fmt: off
-    def list_ingredients(self, 
-                         img: str # Image file path
-    ) -> str:
-        #fmt: on
-        img = format_image(img)
-        answer = self.vqa.inference(
-            img, f"What are three of the vegetables seen in the image if any?"
-        )
-        answer += "\n" + self.vqa.inference(
-            img, f"What are three of the fruits seen in the image if any?"
-        )
-        answer += "\n" + self.vqa.inference(
-            img, f"What grains and starches are in the image if any?"
-        )
-        if (
-            "yes"
-            in self.vqa.inference(
-                img, f"Is there plant-based milk in the image?"
-            ).lower()
-        ):
-            answer += "\n" + "plant-based milk"
-        return answer

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/video/optflow.py

@@ -1,254 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
-import cv2
-import numpy as np
-
-from annotator.uniformer.mmcv.arraymisc import dequantize, quantize
-from annotator.uniformer.mmcv.image import imread, imwrite
-from annotator.uniformer.mmcv.utils import is_str
-
-
-def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs):
-    """Read an optical flow map.
-
-    Args:
-        flow_or_path (ndarray or str): A flow map or filepath.
-        quantize (bool): whether to read quantized pair, if set to True,
-            remaining args will be passed to :func:`dequantize_flow`.
-        concat_axis (int): The axis that dx and dy are concatenated,
-            can be either 0 or 1. Ignored if quantize is False.
-
-    Returns:
-        ndarray: Optical flow represented as a (h, w, 2) numpy array
-    """
-    if isinstance(flow_or_path, np.ndarray):
-        if (flow_or_path.ndim != 3) or (flow_or_path.shape[-1] != 2):
-            raise ValueError(f'Invalid flow with shape {flow_or_path.shape}')
-        return flow_or_path
-    elif not is_str(flow_or_path):
-        raise TypeError(f'"flow_or_path" must be a filename or numpy array, '
-                        f'not {type(flow_or_path)}')
-
-    if not quantize:
-        with open(flow_or_path, 'rb') as f:
-            try:
-                header = f.read(4).decode('utf-8')
-            except Exception:
-                raise IOError(f'Invalid flow file: {flow_or_path}')
-            else:
-                if header != 'PIEH':
-                    raise IOError(f'Invalid flow file: {flow_or_path}, '
-                                  'header does not contain PIEH')
-
-            w = np.fromfile(f, np.int32, 1).squeeze()
-            h = np.fromfile(f, np.int32, 1).squeeze()
-            flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))
-    else:
-        assert concat_axis in [0, 1]
-        cat_flow = imread(flow_or_path, flag='unchanged')
-        if cat_flow.ndim != 2:
-            raise IOError(
-                f'{flow_or_path} is not a valid quantized flow file, '
-                f'its dimension is {cat_flow.ndim}.')
-        assert cat_flow.shape[concat_axis] % 2 == 0
-        dx, dy = np.split(cat_flow, 2, axis=concat_axis)
-        flow = dequantize_flow(dx, dy, *args, **kwargs)
-
-    return flow.astype(np.float32)
-
-
-def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
-    """Write optical flow to file.
-
-    If the flow is not quantized, it will be saved as a .flo file losslessly,
-    otherwise a jpeg image which is lossy but of much smaller size. (dx and dy
-    will be concatenated horizontally into a single image if quantize is True.)
-
-    Args:
-        flow (ndarray): (h, w, 2) array of optical flow.
-        filename (str): Output filepath.
-        quantize (bool): Whether to quantize the flow and save it to 2 jpeg
-            images. If set to True, remaining args will be passed to
-            :func:`quantize_flow`.
-        concat_axis (int): The axis that dx and dy are concatenated,
-            can be either 0 or 1. Ignored if quantize is False.
-    """
-    if not quantize:
-        with open(filename, 'wb') as f:
-            f.write('PIEH'.encode('utf-8'))
-            np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
-            flow = flow.astype(np.float32)
-            flow.tofile(f)
-            f.flush()
-    else:
-        assert concat_axis in [0, 1]
-        dx, dy = quantize_flow(flow, *args, **kwargs)
-        dxdy = np.concatenate((dx, dy), axis=concat_axis)
-        imwrite(dxdy, filename)
-
-
-def quantize_flow(flow, max_val=0.02, norm=True):
-    """Quantize flow to [0, 255].
-
-    After this step, the size of flow will be much smaller, and can be
-    dumped as jpeg images.
-
-    Args:
-        flow (ndarray): (h, w, 2) array of optical flow.
-        max_val (float): Maximum value of flow, values beyond
-                        [-max_val, max_val] will be truncated.
-        norm (bool): Whether to divide flow values by image width/height.
-
-    Returns:
-        tuple[ndarray]: Quantized dx and dy.
-    """
-    h, w, _ = flow.shape
-    dx = flow[..., 0]
-    dy = flow[..., 1]
-    if norm:
-        dx = dx / w  # avoid inplace operations
-        dy = dy / h
-    # use 255 levels instead of 256 to make sure 0 is 0 after dequantization.
-    flow_comps = [
-        quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]
-    ]
-    return tuple(flow_comps)
-
-
-def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
-    """Recover from quantized flow.
-
-    Args:
-        dx (ndarray): Quantized dx.
-        dy (ndarray): Quantized dy.
-        max_val (float): Maximum value used when quantizing.
-        denorm (bool): Whether to multiply flow values with width/height.
-
-    Returns:
-        ndarray: Dequantized flow.
-    """
-    assert dx.shape == dy.shape
-    assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)
-
-    dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]
-
-    if denorm:
-        dx *= dx.shape[1]
-        dy *= dx.shape[0]
-    flow = np.dstack((dx, dy))
-    return flow
-
-
-def flow_warp(img, flow, filling_value=0, interpolate_mode='nearest'):
-    """Use flow to warp img.
-
-    Args:
-        img (ndarray, float or uint8): Image to be warped.
-        flow (ndarray, float): Optical Flow.
-        filling_value (int): The missing pixels will be set with filling_value.
-        interpolate_mode (str): bilinear -> Bilinear Interpolation;
-                                nearest -> Nearest Neighbor.
-
-    Returns:
-        ndarray: Warped image with the same shape of img
-    """
-    warnings.warn('This function is just for prototyping and cannot '
-                  'guarantee the computational efficiency.')
-    assert flow.ndim == 3, 'Flow must be in 3D arrays.'
-    height = flow.shape[0]
-    width = flow.shape[1]
-    channels = img.shape[2]
-
-    output = np.ones(
-        (height, width, channels), dtype=img.dtype) * filling_value
-
-    grid = np.indices((height, width)).swapaxes(0, 1).swapaxes(1, 2)
-    dx = grid[:, :, 0] + flow[:, :, 1]
-    dy = grid[:, :, 1] + flow[:, :, 0]
-    sx = np.floor(dx).astype(int)
-    sy = np.floor(dy).astype(int)
-    valid = (sx >= 0) & (sx < height - 1) & (sy >= 0) & (sy < width - 1)
-
-    if interpolate_mode == 'nearest':
-        output[valid, :] = img[dx[valid].round().astype(int),
-                               dy[valid].round().astype(int), :]
-    elif interpolate_mode == 'bilinear':
-        # dirty walkround for integer positions
-        eps_ = 1e-6
-        dx, dy = dx + eps_, dy + eps_
-        left_top_ = img[np.floor(dx[valid]).astype(int),
-                        np.floor(dy[valid]).astype(int), :] * (
-                            np.ceil(dx[valid]) - dx[valid])[:, None] * (
-                                np.ceil(dy[valid]) - dy[valid])[:, None]
-        left_down_ = img[np.ceil(dx[valid]).astype(int),
-                         np.floor(dy[valid]).astype(int), :] * (
-                             dx[valid] - np.floor(dx[valid]))[:, None] * (
-                                 np.ceil(dy[valid]) - dy[valid])[:, None]
-        right_top_ = img[np.floor(dx[valid]).astype(int),
-                         np.ceil(dy[valid]).astype(int), :] * (
-                             np.ceil(dx[valid]) - dx[valid])[:, None] * (
-                                 dy[valid] - np.floor(dy[valid]))[:, None]
-        right_down_ = img[np.ceil(dx[valid]).astype(int),
-                          np.ceil(dy[valid]).astype(int), :] * (
-                              dx[valid] - np.floor(dx[valid]))[:, None] * (
-                                  dy[valid] - np.floor(dy[valid]))[:, None]
-        output[valid, :] = left_top_ + left_down_ + right_top_ + right_down_
-    else:
-        raise NotImplementedError(
-            'We only support interpolation modes of nearest and bilinear, '
-            f'but got {interpolate_mode}.')
-    return output.astype(img.dtype)
-
-
-def flow_from_bytes(content):
-    """Read dense optical flow from bytes.
-
-    .. note::
-        This load optical flow function works for FlyingChairs, FlyingThings3D,
-        Sintel, FlyingChairsOcc datasets, but cannot load the data from
-        ChairsSDHom.
-
-    Args:
-        content (bytes): Optical flow bytes got from files or other streams.
-
-    Returns:
-        ndarray: Loaded optical flow with the shape (H, W, 2).
-    """
-
-    # header in first 4 bytes
-    header = content[:4]
-    if header.decode('utf-8') != 'PIEH':
-        raise Exception('Flow file header does not contain PIEH')
-    # width in second 4 bytes
-    width = np.frombuffer(content[4:], np.int32, 1).squeeze()
-    # height in third 4 bytes
-    height = np.frombuffer(content[8:], np.int32, 1).squeeze()
-    # after first 12 bytes, all bytes are flow
-    flow = np.frombuffer(content[12:], np.float32, width * height * 2).reshape(
-        (height, width, 2))
-
-    return flow
-
-
-def sparse_flow_from_bytes(content):
-    """Read the optical flow in KITTI datasets from bytes.
-
-    This function is modified from RAFT load the `KITTI datasets
-    <https://github.com/princeton-vl/RAFT/blob/224320502d66c356d88e6c712f38129e60661e80/core/utils/frame_utils.py#L102>`_.
-
-    Args:
-        content (bytes): Optical flow bytes got from files or other streams.
-
-    Returns:
-        Tuple(ndarray, ndarray): Loaded optical flow with the shape (H, W, 2)
-            and flow valid mask with the shape (H, W).
-    """  # nopa
-
-    content = np.frombuffer(content, np.uint8)
-    flow = cv2.imdecode(content, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_COLOR)
-    flow = flow[:, :, ::-1].astype(np.float32)
-    # flow shape (H, W, 2) valid shape (H, W)
-    flow, valid = flow[:, :, :2], flow[:, :, 2]
-    flow = (flow - 2**15) / 64.0
-    return flow, valid

spaces/AsakuraMizu/moe-tts/export_model.py

@@ -1,13 +0,0 @@
-import torch
-
-if __name__ == '__main__':
-    model_path = "saved_model/18/model.pth"
-    output_path = "saved_model/18/model1.pth"
-    checkpoint_dict = torch.load(model_path, map_location='cpu')
-    checkpoint_dict_new = {}
-    for k, v in checkpoint_dict.items():
-        if k == "optimizer":
-            print("remove optimizer")
-            continue
-        checkpoint_dict_new[k] = v
-    torch.save(checkpoint_dict_new, output_path)

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

@@ -1,34 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any
-
-
-class Pager(ABC):
-    """Base class for a pager."""
-
-    @abstractmethod
-    def show(self, content: str) -> None:
-        """Show content in pager.
-
-        Args:
-            content (str): Content to be displayed.
-        """
-
-
-class SystemPager(Pager):
-    """Uses the pager installed on the system."""
-
-    def _pager(self, content: str) -> Any:  #  pragma: no cover
-        return __import__("pydoc").pager(content)
-
-    def show(self, content: str) -> None:
-        """Use the same pager used by pydoc."""
-        self._pager(content)
-
-
-if __name__ == "__main__":  # pragma: no cover
-    from .__main__ import make_test_card
-    from .console import Console
-
-    console = Console()
-    with console.pager(styles=True):
-        console.print(make_test_card())

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/deploy/torchscript_mask_rcnn.cpp

@@ -1,187 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-// @lint-ignore-every CLANGTIDY
-// This is an example code that demonstrates how to run inference
-// with a torchscript format Mask R-CNN model exported by ./export_model.py
-// using export method=tracing, caffe2_tracing & scripting.
-
-#include <opencv2/opencv.hpp>
-#include <iostream>
-#include <string>
-
-#include <c10/cuda/CUDAStream.h>
-#include <torch/csrc/autograd/grad_mode.h>
-#include <torch/csrc/jit/runtime/graph_executor.h>
-#include <torch/script.h>
-
-// only needed for export_method=tracing
-#include <torchvision/vision.h> // @oss-only
-// @fb-only: #include <torchvision/csrc/vision.h>
-
-using namespace std;
-
-c10::IValue get_caffe2_tracing_inputs(cv::Mat& img, c10::Device device) {
-  const int height = img.rows;
-  const int width = img.cols;
-  // FPN models require divisibility of 32.
-  // Tracing mode does padding inside the graph, but caffe2_tracing does not.
-  assert(height % 32 == 0 && width % 32 == 0);
-  const int channels = 3;
-
-  auto input =
-      torch::from_blob(img.data, {1, height, width, channels}, torch::kUInt8);
-  // NHWC to NCHW
-  input = input.to(device, torch::kFloat).permute({0, 3, 1, 2}).contiguous();
-
-  std::array<float, 3> im_info_data{height * 1.0f, width * 1.0f, 1.0f};
-  auto im_info =
-      torch::from_blob(im_info_data.data(), {1, 3}).clone().to(device);
-  return std::make_tuple(input, im_info);
-}
-
-c10::IValue get_tracing_inputs(cv::Mat& img, c10::Device device) {
-  const int height = img.rows;
-  const int width = img.cols;
-  const int channels = 3;
-
-  auto input =
-      torch::from_blob(img.data, {height, width, channels}, torch::kUInt8);
-  // HWC to CHW
-  input = input.to(device, torch::kFloat).permute({2, 0, 1}).contiguous();
-  return input;
-}
-
-// create a Tuple[Dict[str, Tensor]] which is the input type of scripted model
-c10::IValue get_scripting_inputs(cv::Mat& img, c10::Device device) {
-  const int height = img.rows;
-  const int width = img.cols;
-  const int channels = 3;
-
-  auto img_tensor =
-      torch::from_blob(img.data, {height, width, channels}, torch::kUInt8);
-  // HWC to CHW
-  img_tensor =
-      img_tensor.to(device, torch::kFloat).permute({2, 0, 1}).contiguous();
-  auto dic = c10::Dict<std::string, torch::Tensor>();
-  dic.insert("image", img_tensor);
-  return std::make_tuple(dic);
-}
-
-c10::IValue
-get_inputs(std::string export_method, cv::Mat& img, c10::Device device) {
-  // Given an image, create inputs in the format required by the model.
-  if (export_method == "tracing")
-    return get_tracing_inputs(img, device);
-  if (export_method == "caffe2_tracing")
-    return get_caffe2_tracing_inputs(img, device);
-  if (export_method == "scripting")
-    return get_scripting_inputs(img, device);
-  abort();
-}
-
-struct MaskRCNNOutputs {
-  at::Tensor pred_boxes, pred_classes, pred_masks, scores;
-  int num_instances() const {
-    return pred_boxes.sizes()[0];
-  }
-};
-
-MaskRCNNOutputs get_outputs(std::string export_method, c10::IValue outputs) {
-  // Given outputs of the model, extract tensors from it to turn into a
-  // common MaskRCNNOutputs format.
-  if (export_method == "tracing") {
-    auto out_tuple = outputs.toTuple()->elements();
-    // They are ordered alphabetically by their field name in Instances
-    return MaskRCNNOutputs{
-        out_tuple[0].toTensor(),
-        out_tuple[1].toTensor(),
-        out_tuple[2].toTensor(),
-        out_tuple[3].toTensor()};
-  }
-  if (export_method == "caffe2_tracing") {
-    auto out_tuple = outputs.toTuple()->elements();
-    // A legacy order used by caffe2 models
-    return MaskRCNNOutputs{
-        out_tuple[0].toTensor(),
-        out_tuple[2].toTensor(),
-        out_tuple[3].toTensor(),
-        out_tuple[1].toTensor()};
-  }
-  if (export_method == "scripting") {
-    // With the ScriptableAdapter defined in export_model.py, the output is
-    // List[Dict[str, Any]].
-    auto out_dict = outputs.toList().get(0).toGenericDict();
-    return MaskRCNNOutputs{
-        out_dict.at("pred_boxes").toTensor(),
-        out_dict.at("pred_classes").toTensor(),
-        out_dict.at("pred_masks").toTensor(),
-        out_dict.at("scores").toTensor()};
-  }
-  abort();
-}
-
-int main(int argc, const char* argv[]) {
-  if (argc != 4) {
-    cerr << R"xx(
-Usage:
-   ./torchscript_mask_rcnn model.ts input.jpg EXPORT_METHOD
-
-   EXPORT_METHOD can be "tracing", "caffe2_tracing" or "scripting".
-)xx";
-    return 1;
-  }
-  std::string image_file = argv[2];
-  std::string export_method = argv[3];
-  assert(
-      export_method == "caffe2_tracing" || export_method == "tracing" ||
-      export_method == "scripting");
-
-  torch::jit::getBailoutDepth() = 1;
-  torch::autograd::AutoGradMode guard(false);
-  auto module = torch::jit::load(argv[1]);
-
-  assert(module.buffers().size() > 0);
-  // Assume that the entire model is on the same device.
-  // We just put input to this device.
-  auto device = (*begin(module.buffers())).device();
-
-  cv::Mat input_img = cv::imread(image_file, cv::IMREAD_COLOR);
-  auto inputs = get_inputs(export_method, input_img, device);
-
-  // Run the network
-  auto output = module.forward({inputs});
-  if (device.is_cuda())
-    c10::cuda::getCurrentCUDAStream().synchronize();
-
-  // run 3 more times to benchmark
-  int N_benchmark = 3, N_warmup = 1;
-  auto start_time = chrono::high_resolution_clock::now();
-  for (int i = 0; i < N_benchmark + N_warmup; ++i) {
-    if (i == N_warmup)
-      start_time = chrono::high_resolution_clock::now();
-    output = module.forward({inputs});
-    if (device.is_cuda())
-      c10::cuda::getCurrentCUDAStream().synchronize();
-  }
-  auto end_time = chrono::high_resolution_clock::now();
-  auto ms = chrono::duration_cast<chrono::microseconds>(end_time - start_time)
-                .count();
-  cout << "Latency (should vary with different inputs): "
-       << ms * 1.0 / 1e6 / N_benchmark << " seconds" << endl;
-
-  // Parse Mask R-CNN outputs
-  auto rcnn_outputs = get_outputs(export_method, output);
-  cout << "Number of detected objects: " << rcnn_outputs.num_instances()
-       << endl;
-
-  cout << "pred_boxes: " << rcnn_outputs.pred_boxes.toString() << " "
-       << rcnn_outputs.pred_boxes.sizes() << endl;
-  cout << "scores: " << rcnn_outputs.scores.toString() << " "
-       << rcnn_outputs.scores.sizes() << endl;
-  cout << "pred_classes: " << rcnn_outputs.pred_classes.toString() << " "
-       << rcnn_outputs.pred_classes.sizes() << endl;
-  cout << "pred_masks: " << rcnn_outputs.pred_masks.toString() << " "
-       << rcnn_outputs.pred_masks.sizes() << endl;
-
-  cout << rcnn_outputs.pred_boxes << endl;
-  return 0;
-}

spaces/AyameYODAYO/xijinpingx/style.css

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

spaces/Aziizzz/ChestXrayClassification/app.py

@@ -1,107 +0,0 @@
-### 1. Imports and class names setup ###
-import gradio as gr
-import os
-import torch
-
-from timeit import default_timer as timer
-from typing import Tuple, Dict
-import torchvision
-
-from torch import nn
-
-
-def create_effnetb2_model(num_classes: int = 1,
-                          seed: int = 42):
-    """Creates an EfficientNetB2 feature extractor model and transforms.
-
-    Args:
-        num_classes (int, optional): number of classes in the classifier head. 
-            Defaults to 3.
-        seed (int, optional): random seed value. Defaults to 42.
-
-    Returns:
-        model (torch.nn.Module): EffNetB2 feature extractor model. 
-        transforms (torchvision.transforms): EffNetB2 image transforms.
-    """
-    # Create EffNetB2 pretrained weights, transforms and model
-    weights = torchvision.models.AlexNet_Weights.DEFAULT
-    transforms = weights.transforms()
-    model = torchvision.models.alexnet(weights=weights)
-
-    # Freeze all layers in base model
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # Change classifier head with random seed for reproducibility
-    torch.manual_seed(seed)
-    model.classifier = nn.Sequential(
-        nn.Dropout(p=0.2,),
-        nn.Linear(in_features=9216, out_features=1),
-    )
-
-    return model, transforms
-
-
-# Setup class names
-class_names = ["Normal", "Pneumonia"]
-
-### 2. Model and transforms preparation ###
-
-# Create EffNetB2 model
-effnetb2, effnetb2_transforms = create_effnetb2_model(
-    num_classes=1,  # len(class_names) would also work
-)
-
-# Load saved weights
-effnetb2.load_state_dict(
-    torch.load(
-        f="alexnet_pretrained.pth",
-        map_location=torch.device("cpu"),  # load to CPU
-    )
-)
-
-
-def predict(img) -> Tuple[Dict, float]:
-    """Transforms and performs a prediction on img and returns prediction and time taken.
-    """
-    # Start the timer
-    start_time = timer()
-
-    # Transform the target image and add a batch dimension
-    img = effnetb2_transforms(img).unsqueeze(0)
-
-    # Put model into evaluation mode and turn on inference mode
-    effnetb2.eval()
-    with torch.inference_mode():
-        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
-        pred_probs = torch.sigmoid(effnetb2(img)).squeeze()
-
-    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
-    pred_labels_and_probs = {
-        'Normal': 1-pred_probs.item(), 'Pneumonia': pred_probs.item()}
-
-    # Calculate the prediction time
-    pred_time = round(timer() - start_time, 5)
-
-    # Return the prediction dictionary and prediction time
-    return pred_labels_and_probs, pred_time
-
-
-example_list = [[f"examples/example{i+1}.jpg"] for i in range(3)]
-# Create title, description and article strings
-title = "ChestXray Classification"
-description = "An Alexnet computer vision model to classify images of Xray Chest images as Normal or Pneumonia."
-article = "Created at (https://github.com/azizche/chest_xray_Classification)."
-
-# Create the Gradio demo
-demo = gr.Interface(fn=predict,  # mapping function from input to output
-                    inputs=gr.Image(type="pil"),  # what are the inputs?
-                    outputs=[gr.Label(num_top_classes=2, label="Predictions"),  # what are the outputs?
-                             gr.Number(label="Prediction time (s)")],  # our fn has two outputs, therefore we have two outputs
-                    examples=example_list,
-                    title=title,
-                    description=description,
-                    article=article)
-
-# Launch the demo!
-demo.launch()

spaces/BenjaminB/pyscript-demo/index.html

@@ -1,57 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <title>PyScript Test</title>
-    <link rel="stylesheet" href="https://pyscript.net/alpha/pyscript.css" />
-    <script defer src="https://pyscript.net/alpha/pyscript.js"></script>
-    <py-env>
-        - scikit-learn
-        - tabulate
-    </py-env>
-
-    <!-- from https://stackoverflow.com/a/62032824 -->
-    <link rel="stylesheet"
-          href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.6.0/styles/default.min.css">
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.6.0/highlight.min.js"
-            integrity="sha512-gU7kztaQEl7SHJyraPfZLQCNnrKdaQi5ndOyt4L4UPL/FHDd/uB9Je6KDARIqwnNNE27hnqoWLBq+Kpe4iHfeQ=="
-            crossorigin="anonymous"
-            referrerpolicy="no-referrer"></script>
-    <script>hljs.initHighlightingOnLoad();</script>
-
-  </head>
-  <body>
-      <p>Define your own sklearn classifier and evaluate it on the toy dataset. An example is shown below:</p>
-      <pre>
-          <code class="python">from sklearn.linear_model import LogisticRegression
-clf = LogisticRegression(random_state=0)
-evaluate(clf)</code>
-      </pre>
-      Try to achieve a test accuracy of 0.85 or better! Get some inspiration for possible classifiers <a href="https://scikit-learn.org/stable/supervised_learning.html" title="List of sklearn estimators">here</a>.
-      <br><br>
-      Enter your code below, then press Shift+Enter:
-      <py-script>
-          from statistics import mean
-          from sklearn.datasets import make_classification
-          from sklearn.model_selection import cross_validate
-          import tabulate
-
-          X, y = make_classification(n_samples=1000, n_informative=10, random_state=0)
-
-          def evaluate(clf):
-              cv_result = cross_validate(clf, X, y, scoring='accuracy', cv=5)
-              time_fit = sum(cv_result['fit_time'])
-              time_score = sum(cv_result['score_time'])
-
-              print(f"Mean test accuracy: {mean(cv_result['test_score']):.3f}")
-              print(f"Total training time: {time_fit:.1f} seconds")
-              print(f"Total time for scoring: {time_score:.1f} seconds")
-
-              show_result = {'split': [1, 2, 3, 4, 5], 'accuracy': cv_result['test_score']}
-              print("Accuracy for each cross validation split:")
-              return tabulate.tabulate(show_result, tablefmt='html', headers='keys', floatfmt='.3')
-      </py-script>
-
-      <py-repl auto-generate="true"></py-repl>
-  </body>
-</html>

spaces/Benson/text-generation/Examples/Descargar Gratis Youtube Apk.md

@@ -1,62 +0,0 @@
-<br />
-<h1>Cómo descargar vídeos de YouTube con Yandex APK</h1>
-<p>YouTube es una de las plataformas para compartir videos más populares del mundo, donde puedes ver millones de videos gratis. Sin embargo, a veces es posible que desee descargar videos de YouTube a su dispositivo Android para su visualización sin conexión, especialmente cuando tiene una conexión a Internet limitada o inestable. En este artículo, le mostraremos cómo descargar videos de YouTube con Yandex APK, una aplicación de navegador potente y versátil que puede ayudarlo a guardar sus videos favoritos de forma fácil y rápida. </p>
-<h2>descargar gratis youtube apk</h2><br /><p><b><b>Download File</b> &#10037;&#10037;&#10037; <a href="https://bltlly.com/2v6Mza">https://bltlly.com/2v6Mza</a></b></p><br /><br />
- <h2>¿Qué es Yandex APK? </h2>
-<p>Yandex APK es una aplicación para Android que le permite acceder al navegador Yandex, un navegador web rápido y seguro desarrollado por Yandex, una empresa de internet rusa. Yandex Browser tiene muchas características que lo hacen destacar de otros navegadores, como:</p>
- <h3>Características de Yandex APK</h3>
-<ul>
-<li>Modo de protección: Esta función le protege de sitios web maliciosos, phishing y malware mediante el bloqueo de anuncios y rastreadores no deseados. </li>
-<li>Modo Turbo: Esta función acelera su experiencia de navegación comprimiendo páginas web y guardando sus datos móviles. </li>
-<li>Modo Zen: Esta función personaliza sus recomendaciones de contenido basadas en sus intereses y preferencias. </li>
-<li>SmartBox: Esta función le permite buscar en la web y acceder a sus aplicaciones favoritas desde la barra de direcciones. </li>
-<li>Gestor de descargas: Esta función le permite gestionar sus descargas de forma fácil y eficiente. </li>
-</ul>
- <h3>Cómo instalar Yandex APK en su dispositivo Android</h3>
-<p>Para instalar Yandex APK en su dispositivo Android, es necesario seguir estos pasos:</p>
-<ol>
-<li>Descargar el archivo Yandex APK de una fuente de confianza, como [APKCombo]( 2 ) o [JalanTikus]( 1 ). </li>
-<li>Abra la aplicación Administrador de archivos en su dispositivo Android y busque el archivo APK descargado. </li>
-<li>Toque en el archivo APK y permitir la instalación de aplicaciones desconocidas desde su configuración. </li>
-<li>Siga las instrucciones en la pantalla para completar el proceso de instalación. </li>
-
-</ol>
- <h2>Cómo descargar vídeos de YouTube con Yandex APK</h2>
-<p>Una vez que haya instalado Yandex APK en su dispositivo Android, puede comenzar a descargar videos de YouTube con él. Estos son los pasos que debes seguir:</p>
- <h3>Paso 1: Abra el navegador Yandex en su dispositivo Android</h3>
-<p>Abra la aplicación Yandex Browser en su dispositivo Android y asegúrese de que tiene una conexión a Internet estable. </p>
- <h3>Paso 2: Ir a YouTube y encontrar el video que desea descargar</h3>
-<p>En la barra de direcciones, escribe youtube.com y pulsa enter. Serás redirigido al sitio web de YouTube. También puede utilizar la función SmartBox para buscar vídeos de YouTube directamente desde la barra de direcciones. Encuentre el video que desea descargar y toque en él para reproducirlo. </p>
-<p></p>
- <h3>Paso 3: Toque en el icono de descarga en la parte inferior del reproductor de vídeo</h3>
-<p>Tan pronto como comience a reproducir un video de YouTube, verá un icono de descarga en la parte inferior del reproductor de video. Toque en el icono de descarga y verá una ventana emergente con diferentes opciones. </p>
- <h3>Paso 4: Elija el formato y la calidad del video</h3>
-<p>En la ventana emergente, puede elegir el formato y la calidad del video que desea descargar. Puede elegir entre formatos MP4, 3GP, WEBM y M4A, y de calidad 144p a 1080p. También puede ver el tamaño del archivo y el tiempo estimado de descarga para cada opción. Elija la opción que se adapte a sus necesidades y toque en el botón de descarga. </p>
- <h3>Paso 5: Espere a que la descarga termine y disfrute de su video sin conexión</h3>
-
- <h2>Beneficios de descargar vídeos de YouTube con Yandex APK</h2>
-<p>Descargar vídeos de YouTube con Yandex APK tiene muchos beneficios, tales como:</p>
- <h3>Guardar datos móviles y espacio de almacenamiento</h3>
-<p>Al descargar videos de YouTube con Yandex APK, puede guardar sus datos móviles y espacio de almacenamiento. Puede utilizar la función de modo Turbo para comprimir páginas web y reducir el consumo de datos. También puede elegir el formato y la calidad del vídeo que se adapte a la capacidad de su dispositivo. Puedes eliminar o mover tus videos descargados cuando quieras. </p>
- <h3>Ver vídeos en cualquier momento y en cualquier lugar sin conexión a Internet</h3>
-<p>Al descargar videos de YouTube con Yandex APK, puede ver videos en cualquier momento y en cualquier lugar sin conexión a Internet. No tiene que preocuparse por el almacenamiento en búfer, la carga o las interrupciones. Puedes ver tus videos favoritos sin conexión en la pantalla de tu dispositivo o en una pantalla más grande con un Chromecast o un televisor inteligente.</p>
- <h3>Compartir vídeos con tus amigos y familiares fácilmente</h3>
-<p>Al descargar videos de YouTube con Yandex APK, puede compartir videos con sus amigos y familiares fácilmente. Puede enviar sus vídeos descargados a través de Bluetooth, Wi-Fi Direct u otras aplicaciones. También puede subirlos a servicios en la nube o plataformas de redes sociales. Puedes compartir tus vídeos con quien quieras sin problemas. </p>
- <h2>Conclusión</h2>
-<p>En conclusión, Yandex APK es una gran aplicación que le permite descargar vídeos de YouTube con facilidad y comodidad. Tiene muchas características que lo convierten en una aplicación de navegador potente y versátil que puede mejorar su experiencia de navegación. Es rápido, seguro y personalizado. También es fácil de instalar y usar. Si quieres descargar vídeos de YouTube con Yandex APK, solo tienes que seguir los pasos que te hemos mostrado en este artículo y disfrutar de tus vídeos sin conexión. </p>
- <h2>Preguntas frecuentes</h2>
-<ul>
-<li><b>Q: ¿Es Yandex APK seguro de usar? </b></li>
-
-<li><b>Q: ¿Yandex APK es libre de usar? </b></li>
-<li>A: Sí, Yandex APK es de uso gratuito. Usted no tiene que pagar nada para descargar o usarlo. Sin embargo, puede ver algunos anuncios o contenido patrocinado en la aplicación, que ayudan a apoyar su desarrollo y mantenimiento. </li>
-<li><b>Q: ¿Puedo descargar vídeos de YouTube con Yandex APK en otros dispositivos? </b></li>
-<li>A: Sí, puede descargar vídeos de YouTube con Yandex APK en otros dispositivos además de los dispositivos Android. También puede usarlo en dispositivos Windows, Mac, Linux, iOS y Smart TV. Solo necesitas descargar la versión apropiada de Yandex Browser para tu dispositivo desde su sitio web oficial o tienda de aplicaciones. </li>
-<li><b>Q: ¿Puedo descargar vídeos de YouTube con Yandex APK en otros idiomas? </b></li>
-<li>A: Sí, puede descargar vídeos de YouTube con Yandex APK en otros idiomas además de Inglés. Puede cambiar el idioma de la aplicación desde el menú de configuración. También puede cambiar el idioma de YouTube desde el menú de configuración. </li>
-<li><b>Q: ¿Puedo descargar vídeos de YouTube con Yandex APK en alta resolución? </b></li>
-<li>A: Sí, puede descargar vídeos de YouTube con Yandex APK en alta resolución hasta 1080p de calidad. Sin embargo, esto puede depender de la disponibilidad de la fuente de vídeo y del rendimiento y el espacio de almacenamiento del dispositivo. También puede utilizar la función de modo Turbo para reducir el tamaño del archivo y el tiempo de descarga de los vídeos de alta resolución.</li>
-</ul>
- <p>Espero que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer y feliz descarga! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/stop-generating/+server.ts

@@ -1,27 +0,0 @@
-import { collections } from "$lib/server/database";
-import { error } from "@sveltejs/kit";
-import { ObjectId } from "mongodb";
-
-/**
- * Ideally, we'd be able to detect the client-side abort, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850
- */
-export async function POST({ params, locals }) {
-	const conversationId = new ObjectId(params.id);
-
-	const conversation = await collections.conversations.findOne({
-		_id: conversationId,
-		sessionId: locals.sessionId,
-	});
-
-	if (!conversation) {
-		throw error(404, "Conversation not found");
-	}
-
-	await collections.abortedGenerations.updateOne(
-		{ conversationId },
-		{ $set: { updatedAt: new Date() }, $setOnInsert: { createdAt: new Date() } },
-		{ upsert: true }
-	);
-
-	return new Response();
-}

spaces/Blessin/movie-poster-generator/README.md

@@ -1,13 +0,0 @@
----
-title: Movie Poster Generator
-emoji: 🐨
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/LIVE/pybind11/tests/test_operator_overloading.py

@@ -1,145 +0,0 @@
-# -*- coding: utf-8 -*-
-import pytest
-from pybind11_tests import operators as m
-from pybind11_tests import ConstructorStats
-
-
-def test_operator_overloading():
-    v1 = m.Vector2(1, 2)
-    v2 = m.Vector(3, -1)
-    v3 = m.Vector2(1, 2)  # Same value as v1, but different instance.
-    assert v1 is not v3
-
-    assert str(v1) == "[1.000000, 2.000000]"
-    assert str(v2) == "[3.000000, -1.000000]"
-
-    assert str(-v2) == "[-3.000000, 1.000000]"
-
-    assert str(v1 + v2) == "[4.000000, 1.000000]"
-    assert str(v1 - v2) == "[-2.000000, 3.000000]"
-    assert str(v1 - 8) == "[-7.000000, -6.000000]"
-    assert str(v1 + 8) == "[9.000000, 10.000000]"
-    assert str(v1 * 8) == "[8.000000, 16.000000]"
-    assert str(v1 / 8) == "[0.125000, 0.250000]"
-    assert str(8 - v1) == "[7.000000, 6.000000]"
-    assert str(8 + v1) == "[9.000000, 10.000000]"
-    assert str(8 * v1) == "[8.000000, 16.000000]"
-    assert str(8 / v1) == "[8.000000, 4.000000]"
-    assert str(v1 * v2) == "[3.000000, -2.000000]"
-    assert str(v2 / v1) == "[3.000000, -0.500000]"
-
-    assert v1 == v3
-    assert v1 != v2
-    assert hash(v1) == 4
-    # TODO(eric.cousineau): Make this work.
-    # assert abs(v1) == "abs(Vector2)"
-
-    v1 += 2 * v2
-    assert str(v1) == "[7.000000, 0.000000]"
-    v1 -= v2
-    assert str(v1) == "[4.000000, 1.000000]"
-    v1 *= 2
-    assert str(v1) == "[8.000000, 2.000000]"
-    v1 /= 16
-    assert str(v1) == "[0.500000, 0.125000]"
-    v1 *= v2
-    assert str(v1) == "[1.500000, -0.125000]"
-    v2 /= v1
-    assert str(v2) == "[2.000000, 8.000000]"
-
-    cstats = ConstructorStats.get(m.Vector2)
-    assert cstats.alive() == 3
-    del v1
-    assert cstats.alive() == 2
-    del v2
-    assert cstats.alive() == 1
-    del v3
-    assert cstats.alive() == 0
-    assert cstats.values() == [
-        '[1.000000, 2.000000]',
-        '[3.000000, -1.000000]',
-        '[1.000000, 2.000000]',
-        '[-3.000000, 1.000000]',
-        '[4.000000, 1.000000]',
-        '[-2.000000, 3.000000]',
-        '[-7.000000, -6.000000]',
-        '[9.000000, 10.000000]',
-        '[8.000000, 16.000000]',
-        '[0.125000, 0.250000]',
-        '[7.000000, 6.000000]',
-        '[9.000000, 10.000000]',
-        '[8.000000, 16.000000]',
-        '[8.000000, 4.000000]',
-        '[3.000000, -2.000000]',
-        '[3.000000, -0.500000]',
-        '[6.000000, -2.000000]',
-    ]
-    assert cstats.default_constructions == 0
-    assert cstats.copy_constructions == 0
-    assert cstats.move_constructions >= 10
-    assert cstats.copy_assignments == 0
-    assert cstats.move_assignments == 0
-
-
-def test_operators_notimplemented():
-    """#393: need to return NotSupported to ensure correct arithmetic operator behavior"""
-
-    c1, c2 = m.C1(), m.C2()
-    assert c1 + c1 == 11
-    assert c2 + c2 == 22
-    assert c2 + c1 == 21
-    assert c1 + c2 == 12
-
-
-def test_nested():
-    """#328: first member in a class can't be used in operators"""
-
-    a = m.NestA()
-    b = m.NestB()
-    c = m.NestC()
-
-    a += 10
-    assert m.get_NestA(a) == 13
-    b.a += 100
-    assert m.get_NestA(b.a) == 103
-    c.b.a += 1000
-    assert m.get_NestA(c.b.a) == 1003
-    b -= 1
-    assert m.get_NestB(b) == 3
-    c.b -= 3
-    assert m.get_NestB(c.b) == 1
-    c *= 7
-    assert m.get_NestC(c) == 35
-
-    abase = a.as_base()
-    assert abase.value == -2
-    a.as_base().value += 44
-    assert abase.value == 42
-    assert c.b.a.as_base().value == -2
-    c.b.a.as_base().value += 44
-    assert c.b.a.as_base().value == 42
-
-    del c
-    pytest.gc_collect()
-    del a  # Shouldn't delete while abase is still alive
-    pytest.gc_collect()
-
-    assert abase.value == 42
-    del abase, b
-    pytest.gc_collect()
-
-
-def test_overriding_eq_reset_hash():
-
-    assert m.Comparable(15) is not m.Comparable(15)
-    assert m.Comparable(15) == m.Comparable(15)
-
-    with pytest.raises(TypeError):
-        hash(m.Comparable(15))  # TypeError: unhashable type: 'm.Comparable'
-
-    for hashable in (m.Hashable, m.Hashable2):
-        assert hashable(15) is not hashable(15)
-        assert hashable(15) == hashable(15)
-
-        assert hash(hashable(15)) == 15
-        assert hash(hashable(15)) == hash(hashable(15))

spaces/CVPR/LIVE/thrust/thrust/equal.h

@@ -1,238 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file equal.h
- *  \brief Equality between ranges
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-
-namespace thrust
-{
-
-
-/*! \addtogroup reductions
- *  \{
- *  \addtogroup comparisons
- *  \ingroup reductions
- *  \{
- */
-
-
-/*! \p equal returns \c true if the two ranges <tt>[first1, last1)</tt>
- *  and <tt>[first2, first2 + (last1 - first1))</tt> are identical when
- *  compared element-by-element, and otherwise returns \c false.
- *
- *  This version of \p equal returns \c true if and only if for every
- *  iterator \c i in <tt>[first1, last1)</tt>, <tt>*i == *(first2 + (i - first1))</tt>.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first1 The beginning of the first sequence.
- *  \param last1  The end of the first sequence.
- *  \param first2 The beginning of the second sequence.
- *  \return \c true, if the sequences are equal; \c false, otherwise.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator1's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>,
- *          and \p InputIterator1's \c value_type can be compared for equality with \c InputIterator2's \c value_type.
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator2's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>,
- *          and \p InputIterator2's \c value_type can be compared for equality with \c InputIterator1's \c value_type.
- *
- *  The following code snippet demonstrates how to use \p equal to test
- *  two ranges for equality using the \p thrust::host execution policy:
- *
- *  \code
- *  #include <thrust/equal.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  int A1[7] = {3, 1, 4, 1, 5, 9, 3};
- *  int A2[7] = {3, 1, 4, 2, 8, 5, 7};
- *  ...
- *  bool result = thrust::equal(thrust::host, A1, A1 + 7, A2);
- *
- *  // result == false
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/equal.html
- */
-template<typename DerivedPolicy, typename InputIterator1, typename InputIterator2>
-__host__ __device__
-bool equal(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, InputIterator1 first1, InputIterator1 last1, InputIterator2 first2);
-
-
-/*! \p equal returns \c true if the two ranges <tt>[first1, last1)</tt>
- *  and <tt>[first2, first2 + (last1 - first1))</tt> are identical when
- *  compared element-by-element, and otherwise returns \c false.
- *
- *  This version of \p equal returns \c true if and only if for every
- *  iterator \c i in <tt>[first1, last1)</tt>, <tt>*i == *(first2 + (i - first1))</tt>.
- *
- *  \param first1 The beginning of the first sequence.
- *  \param last1  The end of the first sequence.
- *  \param first2 The beginning of the second sequence.
- *  \return \c true, if the sequences are equal; \c false, otherwise.
- *
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator1's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>,
- *          and \p InputIterator1's \c value_type can be compared for equality with \c InputIterator2's \c value_type.
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator2's \c value_type is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>,
- *          and \p InputIterator2's \c value_type can be compared for equality with \c InputIterator1's \c value_type.
- *
- *  The following code snippet demonstrates how to use \p equal to test
- *  two ranges for equality.
- *
- *  \code
- *  #include <thrust/equal.h>
- *  ...
- *  int A1[7] = {3, 1, 4, 1, 5, 9, 3};
- *  int A2[7] = {3, 1, 4, 2, 8, 5, 7};
- *  ...
- *  bool result = thrust::equal(A1, A1 + 7, A2);
- *
- *  // result == false
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/equal.html
- */
-template <typename InputIterator1, typename InputIterator2>
-bool equal(InputIterator1 first1, InputIterator1 last1,
-           InputIterator2 first2);
-
-
-/*! \p equal returns \c true if the two ranges <tt>[first1, last1)</tt>
- *  and <tt>[first2, first2 + (last1 - first1))</tt> are identical when
- *  compared element-by-element, and otherwise returns \c false.
- *
- *  This version of \p equal returns \c true if and only if for every
- *  iterator \c i in <tt>[first1, last1)</tt>,
- *  <tt>binary_pred(*i, *(first2 + (i - first1)))</tt> is \c true.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first1 The beginning of the first sequence.
- *  \param last1  The end of the first sequence.
- *  \param first2 The beginning of the second sequence.
- *  \param binary_pred Binary predicate used to test element equality.
- *  \return \c true, if the sequences are equal; \c false, otherwise.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator1's \c value_type is convertible to \p BinaryPredicate's \c first_argument_type.
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator2's \c value_type is convertible to \p BinaryPredicate's \c second_argument_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p equal to compare the
- *  elements in two ranges modulo 2 using the \p thrust::host execution policy.
- *
- *  \code
- *  #include <thrust/equal.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  
- *  struct compare_modulo_two
- *  {
- *    __host__ __device__
- *    bool operator()(int x, int y) const
- *    {
- *      return (x % 2) == (y % 2);
- *    }
- *  };
- *  ...
- *  int x[6] = {0, 2, 4, 6, 8, 10};
- *  int y[6] = {1, 3, 5, 7, 9, 11};
- *
- *  bool result = thrust::equal(x, x + 6, y, compare_modulo_two());
- *
- *  // result is false
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/equal.html
- */
-template<typename DerivedPolicy, typename InputIterator1, typename InputIterator2, typename BinaryPredicate>
-__host__ __device__
-bool equal(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, InputIterator1 first1, InputIterator1 last1, InputIterator2 first2, BinaryPredicate binary_pred);
-
-
-/*! \p equal returns \c true if the two ranges <tt>[first1, last1)</tt>
- *  and <tt>[first2, first2 + (last1 - first1))</tt> are identical when
- *  compared element-by-element, and otherwise returns \c false.
- *
- *  This version of \p equal returns \c true if and only if for every
- *  iterator \c i in <tt>[first1, last1)</tt>,
- *  <tt>binary_pred(*i, *(first2 + (i - first1)))</tt> is \c true.
- *
- *  \param first1 The beginning of the first sequence.
- *  \param last1  The end of the first sequence.
- *  \param first2 The beginning of the second sequence.
- *  \param binary_pred Binary predicate used to test element equality.
- *  \return \c true, if the sequences are equal; \c false, otherwise.
- *
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator1's \c value_type is convertible to \p BinaryPredicate's \c first_argument_type.
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *          and \p InputIterator2's \c value_type is convertible to \p BinaryPredicate's \c second_argument_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p equal to compare the
- *  elements in two ranges modulo 2.
- *
- *  \code
- *  #include <thrust/equal.h>
- *  
- *  struct compare_modulo_two
- *  {
- *    __host__ __device__
- *    bool operator()(int x, int y) const
- *    {
- *      return (x % 2) == (y % 2);
- *    }
- *  };
- *  ...
- *  int x[6] = {0, 2, 4, 6, 8, 10};
- *  int y[6] = {1, 3, 5, 7, 9, 11};
- *
- *  bool result = thrust::equal(x, x + 5, y, compare_modulo_two());
- *
- *  // result is true
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/equal.html
- */
-template <typename InputIterator1, typename InputIterator2, 
-          typename BinaryPredicate>
-bool equal(InputIterator1 first1, InputIterator1 last1,
-           InputIterator2 first2, BinaryPredicate binary_pred);
-
-
-/*! \} // end comparisons
- *  \} // end reductions
- */
-
-} // end namespace thrust
-
-#include <thrust/detail/equal.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/scan.h

@@ -1,23 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system inherits scan
-#include <thrust/system/detail/sequential/scan.h>
-

spaces/CVPR/Text2Human/Text2Human/data/parsing_generation_segm_attr_dataset.py

@@ -1,80 +0,0 @@
-import os
-import os.path
-
-import numpy as np
-import torch
-import torch.utils.data as data
-from PIL import Image
-
-
-class ParsingGenerationDeepFashionAttrSegmDataset(data.Dataset):
-
-    def __init__(self, segm_dir, pose_dir, ann_file, downsample_factor=2):
-        self._densepose_path = pose_dir
-        self._segm_path = segm_dir
-        self._image_fnames = []
-        self.attrs = []
-
-        self.downsample_factor = downsample_factor
-
-        # training, ground-truth available
-        assert os.path.exists(ann_file)
-        for row in open(os.path.join(ann_file), 'r'):
-            annotations = row.split()
-            self._image_fnames.append(annotations[0])
-            self.attrs.append([int(i) for i in annotations[1:]])
-
-    def _open_file(self, path_prefix, fname):
-        return open(os.path.join(path_prefix, fname), 'rb')
-
-    def _load_densepose(self, raw_idx):
-        fname = self._image_fnames[raw_idx]
-        fname = f'{fname[:-4]}_densepose.png'
-        with self._open_file(self._densepose_path, fname) as f:
-            densepose = Image.open(f)
-            if self.downsample_factor != 1:
-                width, height = densepose.size
-                width = width // self.downsample_factor
-                height = height // self.downsample_factor
-                densepose = densepose.resize(
-                    size=(width, height), resample=Image.NEAREST)
-            # channel-wise IUV order, [3, H, W]
-            densepose = np.array(densepose)[:, :, 2:].transpose(2, 0, 1)
-        return densepose.astype(np.float32)
-
-    def _load_segm(self, raw_idx):
-        fname = self._image_fnames[raw_idx]
-        fname = f'{fname[:-4]}_segm.png'
-        with self._open_file(self._segm_path, fname) as f:
-            segm = Image.open(f)
-            if self.downsample_factor != 1:
-                width, height = segm.size
-                width = width // self.downsample_factor
-                height = height // self.downsample_factor
-                segm = segm.resize(
-                    size=(width, height), resample=Image.NEAREST)
-            segm = np.array(segm)
-        return segm.astype(np.float32)
-
-    def __getitem__(self, index):
-        pose = self._load_densepose(index)
-        segm = self._load_segm(index)
-        attr = self.attrs[index]
-
-        pose = torch.from_numpy(pose)
-        segm = torch.LongTensor(segm)
-        attr = torch.LongTensor(attr)
-
-        pose = pose / 12. - 1
-
-        return_dict = {
-            'densepose': pose,
-            'segm': segm,
-            'attr': attr,
-            'img_name': self._image_fnames[index]
-        }
-
-        return return_dict
-
-    def __len__(self):
-        return len(self._image_fnames)

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

@@ -1,18 +0,0 @@
-from ..builder import BBOX_CODERS
-from .base_bbox_coder import BaseBBoxCoder
-
-
-@BBOX_CODERS.register_module()
-class PseudoBBoxCoder(BaseBBoxCoder):
-    """Pseudo bounding box coder."""
-
-    def __init__(self, **kwargs):
-        super(BaseBBoxCoder, self).__init__(**kwargs)
-
-    def encode(self, bboxes, gt_bboxes):
-        """torch.Tensor: return the given ``bboxes``"""
-        return gt_bboxes
-
-    def decode(self, bboxes, pred_bboxes):
-        """torch.Tensor: return the given ``pred_bboxes``"""
-        return pred_bboxes

spaces/CVPR/WALT/mmdet/core/evaluation/__init__.py

@@ -1,15 +0,0 @@
-from .class_names import (cityscapes_classes, coco_classes, dataset_aliases,
-                          get_classes, imagenet_det_classes,
-                          imagenet_vid_classes, voc_classes)
-from .eval_hooks import DistEvalHook, EvalHook
-from .mean_ap import average_precision, eval_map, print_map_summary
-from .recall import (eval_recalls, plot_iou_recall, plot_num_recall,
-                     print_recall_summary)
-
-__all__ = [
-    'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes',
-    'coco_classes', 'cityscapes_classes', 'dataset_aliases', 'get_classes',
-    'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map',
-    'print_map_summary', 'eval_recalls', 'print_recall_summary',
-    'plot_num_recall', 'plot_iou_recall'
-]

spaces/CVPR/WALT/mmdet/models/losses/accuracy.py

@@ -1,78 +0,0 @@
-import mmcv
-import torch.nn as nn
-
-
-@mmcv.jit(coderize=True)
-def accuracy(pred, target, topk=1, thresh=None):
-    """Calculate accuracy according to the prediction and target.
-
-    Args:
-        pred (torch.Tensor): The model prediction, shape (N, num_class)
-        target (torch.Tensor): The target of each prediction, shape (N, )
-        topk (int | tuple[int], optional): If the predictions in ``topk``
-            matches the target, the predictions will be regarded as
-            correct ones. Defaults to 1.
-        thresh (float, optional): If not None, predictions with scores under
-            this threshold are considered incorrect. Default to None.
-
-    Returns:
-        float | tuple[float]: If the input ``topk`` is a single integer,
-            the function will return a single float as accuracy. If
-            ``topk`` is a tuple containing multiple integers, the
-            function will return a tuple containing accuracies of
-            each ``topk`` number.
-    """
-    assert isinstance(topk, (int, tuple))
-    if isinstance(topk, int):
-        topk = (topk, )
-        return_single = True
-    else:
-        return_single = False
-
-    maxk = max(topk)
-    if pred.size(0) == 0:
-        accu = [pred.new_tensor(0.) for i in range(len(topk))]
-        return accu[0] if return_single else accu
-    assert pred.ndim == 2 and target.ndim == 1
-    assert pred.size(0) == target.size(0)
-    assert maxk <= pred.size(1), \
-        f'maxk {maxk} exceeds pred dimension {pred.size(1)}'
-    pred_value, pred_label = pred.topk(maxk, dim=1)
-    pred_label = pred_label.t()  # transpose to shape (maxk, N)
-    correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))
-    if thresh is not None:
-        # Only prediction values larger than thresh are counted as correct
-        correct = correct & (pred_value > thresh).t()
-    res = []
-    for k in topk:
-        correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
-        res.append(correct_k.mul_(100.0 / pred.size(0)))
-    return res[0] if return_single else res
-
-
-class Accuracy(nn.Module):
-
-    def __init__(self, topk=(1, ), thresh=None):
-        """Module to calculate the accuracy.
-
-        Args:
-            topk (tuple, optional): The criterion used to calculate the
-                accuracy. Defaults to (1,).
-            thresh (float, optional): If not None, predictions with scores
-                under this threshold are considered incorrect. Default to None.
-        """
-        super().__init__()
-        self.topk = topk
-        self.thresh = thresh
-
-    def forward(self, pred, target):
-        """Forward function to calculate accuracy.
-
-        Args:
-            pred (torch.Tensor): Prediction of models.
-            target (torch.Tensor): Target for each prediction.
-
-        Returns:
-            tuple[float]: The accuracies under different topk criterions.
-        """
-        return accuracy(pred, target, self.topk, self.thresh)

spaces/CVPR/WALT/mmdet/models/roi_heads/__init__.py

@@ -1,43 +0,0 @@
-'''
-from .base_roi_head import BaseRoIHead
-from .bbox_heads import (BBoxHead, ConvFCBBoxHead, DoubleConvFCBBoxHead,
-                         SCNetBBoxHead, Shared2FCBBoxHead,
-                         Shared4Conv1FCBBoxHead)
-from .cascade_roi_head import CascadeRoIHead
-from .double_roi_head import DoubleHeadRoIHead
-from .dynamic_roi_head import DynamicRoIHead
-from .grid_roi_head import GridRoIHead
-from .htc_roi_head import HybridTaskCascadeRoIHead
-from .mask_heads import (CoarseMaskHead, FCNMaskHead, FeatureRelayHead,
-                         FusedSemanticHead, GlobalContextHead, GridHead,
-                         HTCMaskHead, MaskIoUHead, MaskPointHead,
-                         SCNetMaskHead, SCNetSemanticHead)
-from .mask_scoring_roi_head import MaskScoringRoIHead
-from .pisa_roi_head import PISARoIHead
-from .point_rend_roi_head import PointRendRoIHead
-from .roi_extractors import SingleRoIExtractor
-from .scnet_roi_head import SCNetRoIHead
-from .shared_heads import ResLayer
-from .sparse_roi_head import SparseRoIHead
-from .standard_roi_head import StandardRoIHead
-from .trident_roi_head import TridentRoIHead
-
-__all__ = [
-    'BaseRoIHead', 'CascadeRoIHead', 'DoubleHeadRoIHead', 'MaskScoringRoIHead',
-    'HybridTaskCascadeRoIHead', 'GridRoIHead', 'ResLayer', 'BBoxHead',
-    'ConvFCBBoxHead', 'Shared2FCBBoxHead', 'StandardRoIHead',
-    'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', 'FCNMaskHead',
-    'HTCMaskHead', 'FusedSemanticHead', 'GridHead', 'MaskIoUHead',
-    'SingleRoIExtractor', 'PISARoIHead', 'PointRendRoIHead', 'MaskPointHead',
-    'CoarseMaskHead', 'DynamicRoIHead', 'SparseRoIHead', 'TridentRoIHead',
-    'SCNetRoIHead', 'SCNetMaskHead', 'SCNetSemanticHead', 'SCNetBBoxHead',
-    'FeatureRelayHead', 'GlobalContextHead'
-]
-'''
-from .bbox_heads import (BBoxHead, ConvFCBBoxHead, DoubleConvFCBBoxHead,
-                         SCNetBBoxHead, Shared2FCBBoxHead,
-                         Shared4Conv1FCBBoxHead)
-from .standard_roi_head import StandardRoIHead
-from .roi_extractors import SingleRoIExtractor
-from .mask_heads import FCNMaskHead
-__all__ = ['BBoxHead','StandardRoIHead','SingleRoIExtractor','Shared2FCBBoxHead','FCNMaskHead']

spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/components/WebSocket.js

@@ -1,134 +0,0 @@
-import Client from "./Client.js";
-import { Config, Version } from './index.js'
-import { sleep } from '../model/index.js'
-import { redAdapter } from '../model/red/index.js'
-// import { satoriAdapter } from '../model/satori/index.js'
-
-let sendSocketList = []
-let allSocketList = []
-
-async function createWebSocket(data) {
-    if (typeof data.close != 'undefined' && typeof data.closed == 'undefined') {
-        data.closed = data.close
-        delete data.close
-    }
-    const client = new Client(data)
-    setAllSocketList(client)
-    if (data.address == 'ws_address') return
-    if (data.closed) return
-    sendSocketList = sendSocketList.filter(i => i.name != data.name)
-    switch (Number(data.type)) {
-        case 1:
-            if (!await checkVersion(data)) return
-            client.createWs()
-            sendSocketList.push(client)
-            break;
-        case 2:
-            if (!await checkVersion(data)) return
-            client.createServer()
-            sendSocketList.push(client)
-            break
-        case 3:
-            client.createGSUidWs()
-            sendSocketList.push(client)
-            break
-        case 4:
-            if (Version.isTrss) return
-            // client.createQQNT()
-            redAdapter.connect(client)
-            break
-        case 5:
-            if (!await checkVersion(data)) return
-            client.createHttp()
-            break
-        case 6:
-            if (!await checkVersion(data)) return
-            client.createHttpPost()
-            sendSocketList.push(client)
-            break
-        default:
-            return;
-    }
-}
-
-function setAllSocketList(data) {
-    allSocketList = allSocketList.filter(i => i.name != data.name)
-    allSocketList.push(data)
-}
-
-async function checkVersion(data) {
-    if (Version.isTrss) {
-        if (!data.uin) {
-            logger.warn(`[ws-plugin] ${data.name} 缺少配置项uin 请删除连接后重新#ws添加连接`)
-            return false
-        } else {
-            let log = false
-            for (let i = 0; i < 20; i++) {
-                if (Version.protocol.some(i => i == Bot[data.uin]?.version?.name)) {
-                    return true
-                }
-                if (!log) {
-                    logger.warn(`[ws-plugin] ${data.name} 暂未适配当前协议端或未连接对应协议端,20秒后重新判断`)
-                    log = true
-                }
-                await sleep(1000)
-            }
-            logger.warn(`[ws-plugin] ${data.name} 暂未适配当前协议端或未连接对应协议端 ${data.uin}`)
-            return false
-        }
-    }
-    return true
-}
-
-function modifyWebSocket(target) {
-    // if (Version.isTrss) return
-    switch (target.type) {
-        case 'add':
-        case 'open':
-            if (target.data.type == 4) {
-                const client = new Client(target.data)
-                setAllSocketList(client)
-                redAdapter.connect(client)
-            } else {
-                createWebSocket(target.data)
-            }
-            break;
-        case 'del':
-        case 'close':
-            for (const i of allSocketList) {
-                if (i.name == target.data.name) {
-                    i.close()
-                    break
-                }
-            }
-            break
-        default:
-            return;
-    }
-}
-
-function clearWebSocket() {
-    for (const i of allSocketList) {
-        i.close()
-    }
-}
-
-
-function initWebSocket() {
-    // if (Version.isTrss) return
-    for (const i of Config.servers) {
-        createWebSocket(i)
-    }
-}
-
-
-export {
-    initWebSocket,
-    clearWebSocket,
-    modifyWebSocket,
-    allSocketList,
-    setAllSocketList,
-    sendSocketList,
-    createWebSocket
-}
-

spaces/ClinBAY/Safeterm_Demo/send_email_request.py

@@ -1,98 +0,0 @@
-import os
-from dotenv import load_dotenv
-import msal
-import requests
-# import json
-
-
-def send_email(subject, email, name, organization, meddra_license, agree_terms, save_data) -> None:
-    """
-    Send an email with user settings
-    @param save_data:
-    @type save_data:
-    @param agree_terms:
-    @type agree_terms:
-    @param meddra_license:
-    @type meddra_license:
-    @param organization:
-    @type organization:
-    @param name:
-    @type name:
-    @param email:
-    @type email:
-    @param subject:
-    @type subject:
-    @return:
-    @rtype:
-    """
-
-    body = f"""
-        Request for API Key - Safeterm
-
-        Settings:
-        - Free Demo (30 days, 50 terms limit)
-        - Version: 26.0
-        - Language: English
-
-        Contact Information:
-        - Email: {email}
-        - Full Name: {name}
-        - Organization: {organization}
-
-        Terms of use:
-        - Valid medDRA License: {meddra_license}
-        - Agrees to Safeterm terms: {agree_terms}
-        - Consent to data storage: {save_data}
-        """
-
-    load_dotenv()
-
-    client_id = os.getenv("CLIENT_ID")
-    client_secret = os.getenv("CLIENT_SECRET")
-    tenant_id = os.getenv("TENANT_ID")
-    authority = f"https://login.microsoftonline.com/{tenant_id}"
-    sender = os.getenv("MAIL_SENDER")
-    receiver = os.getenv("MAIL_RECIPIENT")
-    cc_receiver = os.getenv("CC_RECIPIENT")
-
-    app = msal.ConfidentialClientApplication(
-        client_id=client_id,
-        client_credential=client_secret,
-        authority=authority)
-
-    scopes = ["https://graph.microsoft.com/.default"]
-
-    result = app.acquire_token_silent(scopes, account=None)
-
-    if not result:
-        print("No suitable token exists in cache. Let's get a new one from Azure Active Directory.")
-        result = app.acquire_token_for_client(scopes=scopes)
-
-    if "access_token" in result:
-        endpoint = f'https://graph.microsoft.com/v1.0/users/{sender}/sendMail'
-        email_msg = {
-            'Message': {
-                'Subject': subject,
-                'Body': {
-                    'ContentType': 'Text',
-                    'Content': body
-                },
-                'ToRecipients': [{'EmailAddress': {'Address': receiver}}],
-                'CcRecipients': [{'EmailAddress': {'Address': cc_receiver}}]  # Added CcRecipients here
-            },
-            'SaveToSentItems': 'true'
-        }
-
-        r = requests.post(endpoint, headers={'Authorization': 'Bearer ' + result['access_token']}, json=email_msg)
-
-        if r.ok:
-            print('Sent email successfully')
-        else:
-            print(r.json())
-    else:
-        print(result.get("error"))
-        print(result.get("error_description"))
-        print(result.get("correlation_id"))
-
-# Sample usage
-# send_email("Test Email Hugging Face Demo", "This is a test email.")

spaces/CognitiveLabs/GPT-auto-webscraping/chains/output_format/base.py

@@ -1,19 +0,0 @@
-from langchain.chains import LLMChain
-from langchain.memory import ConversationBufferMemory
-from chains.output_format.templates import output_format_chat_prompt
-
-
-def chain_output_format(llm) -> LLMChain:
-    # memory
-    html_memory = ConversationBufferMemory(
-        input_key="html_content", memory_key="chat_history"
-    )
-
-    # chain
-    return LLMChain(
-        llm=llm,
-        prompt=output_format_chat_prompt,
-        verbose=True,
-        output_key="output_format",
-        memory=html_memory,
-    )

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/csrc/ROIAlign.h

@@ -1,46 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-#pragma once
-
-#include "cpu/vision.h"
-
-#ifdef WITH_CUDA
-#include "cuda/vision.h"
-#endif
-
-// Interface for Python
-at::Tensor ROIAlign_forward(const at::Tensor& input,
-                            const at::Tensor& rois,
-                            const float spatial_scale,
-                            const int pooled_height,
-                            const int pooled_width,
-                            const int sampling_ratio) {
-  if (input.type().is_cuda()) {
-#ifdef WITH_CUDA
-    return ROIAlign_forward_cuda(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  return ROIAlign_forward_cpu(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
-}
-
-at::Tensor ROIAlign_backward(const at::Tensor& grad,
-                             const at::Tensor& rois,
-                             const float spatial_scale,
-                             const int pooled_height,
-                             const int pooled_width,
-                             const int batch_size,
-                             const int channels,
-                             const int height,
-                             const int width,
-                             const int sampling_ratio) {
-  if (grad.type().is_cuda()) {
-#ifdef WITH_CUDA
-    return ROIAlign_backward_cuda(grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width, sampling_ratio);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/etree.py

@@ -1,478 +0,0 @@
-"""Shim module exporting the same ElementTree API for lxml and
-xml.etree backends.
-
-When lxml is installed, it is automatically preferred over the built-in
-xml.etree module.
-On Python 2.7, the cElementTree module is preferred over the pure-python
-ElementTree module.
-
-Besides exporting a unified interface, this also defines extra functions
-or subclasses built-in ElementTree classes to add features that are
-only availble in lxml, like OrderedDict for attributes, pretty_print and
-iterwalk.
-"""
-from fontTools.misc.textTools import tostr
-
-
-XML_DECLARATION = """<?xml version='1.0' encoding='%s'?>"""
-
-__all__ = [
-    # public symbols
-    "Comment",
-    "dump",
-    "Element",
-    "ElementTree",
-    "fromstring",
-    "fromstringlist",
-    "iselement",
-    "iterparse",
-    "parse",
-    "ParseError",
-    "PI",
-    "ProcessingInstruction",
-    "QName",
-    "SubElement",
-    "tostring",
-    "tostringlist",
-    "TreeBuilder",
-    "XML",
-    "XMLParser",
-    "register_namespace",
-]
-
-try:
-    from lxml.etree import *
-
-    _have_lxml = True
-except ImportError:
-    try:
-        from xml.etree.cElementTree import *
-
-        # the cElementTree version of XML function doesn't support
-        # the optional 'parser' keyword argument
-        from xml.etree.ElementTree import XML
-    except ImportError:  # pragma: no cover
-        from xml.etree.ElementTree import *
-    _have_lxml = False
-
-    import sys
-
-    # dict is always ordered in python >= 3.6 and on pypy
-    PY36 = sys.version_info >= (3, 6)
-    try:
-        import __pypy__
-    except ImportError:
-        __pypy__ = None
-    _dict_is_ordered = bool(PY36 or __pypy__)
-    del PY36, __pypy__
-
-    if _dict_is_ordered:
-        _Attrib = dict
-    else:
-        from collections import OrderedDict as _Attrib
-
-    if isinstance(Element, type):
-        _Element = Element
-    else:
-        # in py27, cElementTree.Element cannot be subclassed, so
-        # we need to import the pure-python class
-        from xml.etree.ElementTree import Element as _Element
-
-    class Element(_Element):
-        """Element subclass that keeps the order of attributes."""
-
-        def __init__(self, tag, attrib=_Attrib(), **extra):
-            super(Element, self).__init__(tag)
-            self.attrib = _Attrib()
-            if attrib:
-                self.attrib.update(attrib)
-            if extra:
-                self.attrib.update(extra)
-
-    def SubElement(parent, tag, attrib=_Attrib(), **extra):
-        """Must override SubElement as well otherwise _elementtree.SubElement
-        fails if 'parent' is a subclass of Element object.
-        """
-        element = parent.__class__(tag, attrib, **extra)
-        parent.append(element)
-        return element
-
-    def _iterwalk(element, events, tag):
-        include = tag is None or element.tag == tag
-        if include and "start" in events:
-            yield ("start", element)
-        for e in element:
-            for item in _iterwalk(e, events, tag):
-                yield item
-        if include:
-            yield ("end", element)
-
-    def iterwalk(element_or_tree, events=("end",), tag=None):
-        """A tree walker that generates events from an existing tree as
-        if it was parsing XML data with iterparse().
-        Drop-in replacement for lxml.etree.iterwalk.
-        """
-        if iselement(element_or_tree):
-            element = element_or_tree
-        else:
-            element = element_or_tree.getroot()
-        if tag == "*":
-            tag = None
-        for item in _iterwalk(element, events, tag):
-            yield item
-
-    _ElementTree = ElementTree
-
-    class ElementTree(_ElementTree):
-        """ElementTree subclass that adds 'pretty_print' and 'doctype'
-        arguments to the 'write' method.
-        Currently these are only supported for the default XML serialization
-        'method', and not also for "html" or "text", for these are delegated
-        to the base class.
-        """
-
-        def write(
-            self,
-            file_or_filename,
-            encoding=None,
-            xml_declaration=False,
-            method=None,
-            doctype=None,
-            pretty_print=False,
-        ):
-            if method and method != "xml":
-                # delegate to super-class
-                super(ElementTree, self).write(
-                    file_or_filename,
-                    encoding=encoding,
-                    xml_declaration=xml_declaration,
-                    method=method,
-                )
-                return
-
-            if encoding is not None and encoding.lower() == "unicode":
-                if xml_declaration:
-                    raise ValueError(
-                        "Serialisation to unicode must not request an XML declaration"
-                    )
-                write_declaration = False
-                encoding = "unicode"
-            elif xml_declaration is None:
-                # by default, write an XML declaration only for non-standard encodings
-                write_declaration = encoding is not None and encoding.upper() not in (
-                    "ASCII",
-                    "UTF-8",
-                    "UTF8",
-                    "US-ASCII",
-                )
-            else:
-                write_declaration = xml_declaration
-
-            if encoding is None:
-                encoding = "ASCII"
-
-            if pretty_print:
-                # NOTE this will modify the tree in-place
-                _indent(self._root)
-
-            with _get_writer(file_or_filename, encoding) as write:
-                if write_declaration:
-                    write(XML_DECLARATION % encoding.upper())
-                    if pretty_print:
-                        write("\n")
-                if doctype:
-                    write(_tounicode(doctype))
-                    if pretty_print:
-                        write("\n")
-
-                qnames, namespaces = _namespaces(self._root)
-                _serialize_xml(write, self._root, qnames, namespaces)
-
-    import io
-
-    def tostring(
-        element,
-        encoding=None,
-        xml_declaration=None,
-        method=None,
-        doctype=None,
-        pretty_print=False,
-    ):
-        """Custom 'tostring' function that uses our ElementTree subclass, with
-        pretty_print support.
-        """
-        stream = io.StringIO() if encoding == "unicode" else io.BytesIO()
-        ElementTree(element).write(
-            stream,
-            encoding=encoding,
-            xml_declaration=xml_declaration,
-            method=method,
-            doctype=doctype,
-            pretty_print=pretty_print,
-        )
-        return stream.getvalue()
-
-    # serialization support
-
-    import re
-
-    # Valid XML strings can include any Unicode character, excluding control
-    # characters, the surrogate blocks, FFFE, and FFFF:
-    #   Char ::= #x9 | #xA | #xD | [#x20-#xD7FF] | [#xE000-#xFFFD] | [#x10000-#x10FFFF]
-    # Here we reversed the pattern to match only the invalid characters.
-    # For the 'narrow' python builds supporting only UCS-2, which represent
-    # characters beyond BMP as UTF-16 surrogate pairs, we need to pass through
-    # the surrogate block. I haven't found a more elegant solution...
-    UCS2 = sys.maxunicode < 0x10FFFF
-    if UCS2:
-        _invalid_xml_string = re.compile(
-            "[\u0000-\u0008\u000B-\u000C\u000E-\u001F\uFFFE-\uFFFF]"
-        )
-    else:
-        _invalid_xml_string = re.compile(
-            "[\u0000-\u0008\u000B-\u000C\u000E-\u001F\uD800-\uDFFF\uFFFE-\uFFFF]"
-        )
-
-    def _tounicode(s):
-        """Test if a string is valid user input and decode it to unicode string
-        using ASCII encoding if it's a bytes string.
-        Reject all bytes/unicode input that contains non-XML characters.
-        Reject all bytes input that contains non-ASCII characters.
-        """
-        try:
-            s = tostr(s, encoding="ascii", errors="strict")
-        except UnicodeDecodeError:
-            raise ValueError(
-                "Bytes strings can only contain ASCII characters. "
-                "Use unicode strings for non-ASCII characters."
-            )
-        except AttributeError:
-            _raise_serialization_error(s)
-        if s and _invalid_xml_string.search(s):
-            raise ValueError(
-                "All strings must be XML compatible: Unicode or ASCII, "
-                "no NULL bytes or control characters"
-            )
-        return s
-
-    import contextlib
-
-    @contextlib.contextmanager
-    def _get_writer(file_or_filename, encoding):
-        # returns text write method and release all resources after using
-        try:
-            write = file_or_filename.write
-        except AttributeError:
-            # file_or_filename is a file name
-            f = open(
-                file_or_filename,
-                "w",
-                encoding="utf-8" if encoding == "unicode" else encoding,
-                errors="xmlcharrefreplace",
-            )
-            with f:
-                yield f.write
-        else:
-            # file_or_filename is a file-like object
-            # encoding determines if it is a text or binary writer
-            if encoding == "unicode":
-                # use a text writer as is
-                yield write
-            else:
-                # wrap a binary writer with TextIOWrapper
-                detach_buffer = False
-                if isinstance(file_or_filename, io.BufferedIOBase):
-                    buf = file_or_filename
-                elif isinstance(file_or_filename, io.RawIOBase):
-                    buf = io.BufferedWriter(file_or_filename)
-                    detach_buffer = True
-                else:
-                    # This is to handle passed objects that aren't in the
-                    # IOBase hierarchy, but just have a write method
-                    buf = io.BufferedIOBase()
-                    buf.writable = lambda: True
-                    buf.write = write
-                    try:
-                        # TextIOWrapper uses this methods to determine
-                        # if BOM (for UTF-16, etc) should be added
-                        buf.seekable = file_or_filename.seekable
-                        buf.tell = file_or_filename.tell
-                    except AttributeError:
-                        pass
-                wrapper = io.TextIOWrapper(
-                    buf,
-                    encoding=encoding,
-                    errors="xmlcharrefreplace",
-                    newline="\n",
-                )
-                try:
-                    yield wrapper.write
-                finally:
-                    # Keep the original file open when the TextIOWrapper and
-                    # the BufferedWriter are destroyed
-                    wrapper.detach()
-                    if detach_buffer:
-                        buf.detach()
-
-    from xml.etree.ElementTree import _namespace_map
-
-    def _namespaces(elem):
-        # identify namespaces used in this tree
-
-        # maps qnames to *encoded* prefix:local names
-        qnames = {None: None}
-
-        # maps uri:s to prefixes
-        namespaces = {}
-
-        def add_qname(qname):
-            # calculate serialized qname representation
-            try:
-                qname = _tounicode(qname)
-                if qname[:1] == "{":
-                    uri, tag = qname[1:].rsplit("}", 1)
-                    prefix = namespaces.get(uri)
-                    if prefix is None:
-                        prefix = _namespace_map.get(uri)
-                        if prefix is None:
-                            prefix = "ns%d" % len(namespaces)
-                        else:
-                            prefix = _tounicode(prefix)
-                        if prefix != "xml":
-                            namespaces[uri] = prefix
-                    if prefix:
-                        qnames[qname] = "%s:%s" % (prefix, tag)
-                    else:
-                        qnames[qname] = tag  # default element
-                else:
-                    qnames[qname] = qname
-            except TypeError:
-                _raise_serialization_error(qname)
-
-        # populate qname and namespaces table
-        for elem in elem.iter():
-            tag = elem.tag
-            if isinstance(tag, QName):
-                if tag.text not in qnames:
-                    add_qname(tag.text)
-            elif isinstance(tag, str):
-                if tag not in qnames:
-                    add_qname(tag)
-            elif tag is not None and tag is not Comment and tag is not PI:
-                _raise_serialization_error(tag)
-            for key, value in elem.items():
-                if isinstance(key, QName):
-                    key = key.text
-                if key not in qnames:
-                    add_qname(key)
-                if isinstance(value, QName) and value.text not in qnames:
-                    add_qname(value.text)
-            text = elem.text
-            if isinstance(text, QName) and text.text not in qnames:
-                add_qname(text.text)
-        return qnames, namespaces
-
-    def _serialize_xml(write, elem, qnames, namespaces, **kwargs):
-        tag = elem.tag
-        text = elem.text
-        if tag is Comment:
-            write("<!--%s-->" % _tounicode(text))
-        elif tag is ProcessingInstruction:
-            write("<?%s?>" % _tounicode(text))
-        else:
-            tag = qnames[_tounicode(tag) if tag is not None else None]
-            if tag is None:
-                if text:
-                    write(_escape_cdata(text))
-                for e in elem:
-                    _serialize_xml(write, e, qnames, None)
-            else:
-                write("<" + tag)
-                if namespaces:
-                    for uri, prefix in sorted(
-                        namespaces.items(), key=lambda x: x[1]
-                    ):  # sort on prefix
-                        if prefix:
-                            prefix = ":" + prefix
-                        write(' xmlns%s="%s"' % (prefix, _escape_attrib(uri)))
-                attrs = elem.attrib
-                if attrs:
-                    # try to keep existing attrib order
-                    if len(attrs) <= 1 or type(attrs) is _Attrib:
-                        items = attrs.items()
-                    else:
-                        # if plain dict, use lexical order
-                        items = sorted(attrs.items())
-                    for k, v in items:
-                        if isinstance(k, QName):
-                            k = _tounicode(k.text)
-                        else:
-                            k = _tounicode(k)
-                        if isinstance(v, QName):
-                            v = qnames[_tounicode(v.text)]
-                        else:
-                            v = _escape_attrib(v)
-                        write(' %s="%s"' % (qnames[k], v))
-                if text is not None or len(elem):
-                    write(">")
-                    if text:
-                        write(_escape_cdata(text))
-                    for e in elem:
-                        _serialize_xml(write, e, qnames, None)
-                    write("</" + tag + ">")
-                else:
-                    write("/>")
-        if elem.tail:
-            write(_escape_cdata(elem.tail))
-
-    def _raise_serialization_error(text):
-        raise TypeError("cannot serialize %r (type %s)" % (text, type(text).__name__))
-
-    def _escape_cdata(text):
-        # escape character data
-        try:
-            text = _tounicode(text)
-            # it's worth avoiding do-nothing calls for short strings
-            if "&" in text:
-                text = text.replace("&", "&amp;")
-            if "<" in text:
-                text = text.replace("<", "&lt;")
-            if ">" in text:
-                text = text.replace(">", "&gt;")
-            return text
-        except (TypeError, AttributeError):
-            _raise_serialization_error(text)
-
-    def _escape_attrib(text):
-        # escape attribute value
-        try:
-            text = _tounicode(text)
-            if "&" in text:
-                text = text.replace("&", "&amp;")
-            if "<" in text:
-                text = text.replace("<", "&lt;")
-            if ">" in text:
-                text = text.replace(">", "&gt;")
-            if '"' in text:
-                text = text.replace('"', "&quot;")
-            if "\n" in text:
-                text = text.replace("\n", "&#10;")
-            return text
-        except (TypeError, AttributeError):
-            _raise_serialization_error(text)
-
-    def _indent(elem, level=0):
-        # From http://effbot.org/zone/element-lib.htm#prettyprint
-        i = "\n" + level * "  "
-        if len(elem):
-            if not elem.text or not elem.text.strip():
-                elem.text = i + "  "
-            if not elem.tail or not elem.tail.strip():
-                elem.tail = i
-            for elem in elem:
-                _indent(elem, level + 1)
-            if not elem.tail or not elem.tail.strip():
-                elem.tail = i
-        else:
-            if level and (not elem.tail or not elem.tail.strip()):
-                elem.tail = i

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/featureVars.py

@@ -1,605 +0,0 @@
-"""Module to build FeatureVariation tables:
-https://docs.microsoft.com/en-us/typography/opentype/spec/chapter2#featurevariations-table
-
-NOTE: The API is experimental and subject to change.
-"""
-from fontTools.misc.dictTools import hashdict
-from fontTools.misc.intTools import bit_count
-from fontTools.ttLib import newTable
-from fontTools.ttLib.tables import otTables as ot
-from fontTools.ttLib.ttVisitor import TTVisitor
-from fontTools.otlLib.builder import buildLookup, buildSingleSubstSubtable
-from collections import OrderedDict
-
-from .errors import VarLibError, VarLibValidationError
-
-
-def addFeatureVariations(font, conditionalSubstitutions, featureTag="rvrn"):
-    """Add conditional substitutions to a Variable Font.
-
-    The `conditionalSubstitutions` argument is a list of (Region, Substitutions)
-    tuples.
-
-    A Region is a list of Boxes. A Box is a dict mapping axisTags to
-    (minValue, maxValue) tuples. Irrelevant axes may be omitted and they are
-    interpretted as extending to end of axis in each direction.  A Box represents
-    an orthogonal 'rectangular' subset of an N-dimensional design space.
-    A Region represents a more complex subset of an N-dimensional design space,
-    ie. the union of all the Boxes in the Region.
-    For efficiency, Boxes within a Region should ideally not overlap, but
-    functionality is not compromised if they do.
-
-    The minimum and maximum values are expressed in normalized coordinates.
-
-    A Substitution is a dict mapping source glyph names to substitute glyph names.
-
-    Example:
-
-    # >>> f = TTFont(srcPath)
-    # >>> condSubst = [
-    # ...     # A list of (Region, Substitution) tuples.
-    # ...     ([{"wdth": (0.5, 1.0)}], {"cent": "cent.rvrn"}),
-    # ...     ([{"wght": (0.5, 1.0)}], {"dollar": "dollar.rvrn"}),
-    # ... ]
-    # >>> addFeatureVariations(f, condSubst)
-    # >>> f.save(dstPath)
-    """
-
-    processLast = featureTag != "rvrn"
-
-    _checkSubstitutionGlyphsExist(
-        glyphNames=set(font.getGlyphOrder()),
-        substitutions=conditionalSubstitutions,
-    )
-
-    substitutions = overlayFeatureVariations(conditionalSubstitutions)
-
-    # turn substitution dicts into tuples of tuples, so they are hashable
-    conditionalSubstitutions, allSubstitutions = makeSubstitutionsHashable(
-        substitutions
-    )
-    if "GSUB" not in font:
-        font["GSUB"] = buildGSUB()
-
-    # setup lookups
-    lookupMap = buildSubstitutionLookups(
-        font["GSUB"].table, allSubstitutions, processLast
-    )
-
-    # addFeatureVariationsRaw takes a list of
-    #  ( {condition}, [ lookup indices ] )
-    # so rearrange our lookups to match
-    conditionsAndLookups = []
-    for conditionSet, substitutions in conditionalSubstitutions:
-        conditionsAndLookups.append(
-            (conditionSet, [lookupMap[s] for s in substitutions])
-        )
-
-    addFeatureVariationsRaw(font, font["GSUB"].table, conditionsAndLookups, featureTag)
-
-
-def _checkSubstitutionGlyphsExist(glyphNames, substitutions):
-    referencedGlyphNames = set()
-    for _, substitution in substitutions:
-        referencedGlyphNames |= substitution.keys()
-        referencedGlyphNames |= set(substitution.values())
-    missing = referencedGlyphNames - glyphNames
-    if missing:
-        raise VarLibValidationError(
-            "Missing glyphs are referenced in conditional substitution rules:"
-            f" {', '.join(missing)}"
-        )
-
-
-def overlayFeatureVariations(conditionalSubstitutions):
-    """Compute overlaps between all conditional substitutions.
-
-    The `conditionalSubstitutions` argument is a list of (Region, Substitutions)
-    tuples.
-
-    A Region is a list of Boxes. A Box is a dict mapping axisTags to
-    (minValue, maxValue) tuples. Irrelevant axes may be omitted and they are
-    interpretted as extending to end of axis in each direction.  A Box represents
-    an orthogonal 'rectangular' subset of an N-dimensional design space.
-    A Region represents a more complex subset of an N-dimensional design space,
-    ie. the union of all the Boxes in the Region.
-    For efficiency, Boxes within a Region should ideally not overlap, but
-    functionality is not compromised if they do.
-
-    The minimum and maximum values are expressed in normalized coordinates.
-
-    A Substitution is a dict mapping source glyph names to substitute glyph names.
-
-    Returns data is in similar but different format.  Overlaps of distinct
-    substitution Boxes (*not* Regions) are explicitly listed as distinct rules,
-    and rules with the same Box merged.  The more specific rules appear earlier
-    in the resulting list.  Moreover, instead of just a dictionary of substitutions,
-    a list of dictionaries is returned for substitutions corresponding to each
-    unique space, with each dictionary being identical to one of the input
-    substitution dictionaries.  These dictionaries are not merged to allow data
-    sharing when they are converted into font tables.
-
-    Example::
-
-        >>> condSubst = [
-        ...     # A list of (Region, Substitution) tuples.
-        ...     ([{"wght": (0.5, 1.0)}], {"dollar": "dollar.rvrn"}),
-        ...     ([{"wght": (0.5, 1.0)}], {"dollar": "dollar.rvrn"}),
-        ...     ([{"wdth": (0.5, 1.0)}], {"cent": "cent.rvrn"}),
-        ...     ([{"wght": (0.5, 1.0), "wdth": (-1, 1.0)}], {"dollar": "dollar.rvrn"}),
-        ... ]
-        >>> from pprint import pprint
-        >>> pprint(overlayFeatureVariations(condSubst))
-        [({'wdth': (0.5, 1.0), 'wght': (0.5, 1.0)},
-          [{'dollar': 'dollar.rvrn'}, {'cent': 'cent.rvrn'}]),
-         ({'wdth': (0.5, 1.0)}, [{'cent': 'cent.rvrn'}]),
-         ({'wght': (0.5, 1.0)}, [{'dollar': 'dollar.rvrn'}])]
-
-    """
-
-    # Merge same-substitutions rules, as this creates fewer number oflookups.
-    merged = OrderedDict()
-    for value, key in conditionalSubstitutions:
-        key = hashdict(key)
-        if key in merged:
-            merged[key].extend(value)
-        else:
-            merged[key] = value
-    conditionalSubstitutions = [(v, dict(k)) for k, v in merged.items()]
-    del merged
-
-    # Merge same-region rules, as this is cheaper.
-    # Also convert boxes to hashdict()
-    #
-    # Reversing is such that earlier entries win in case of conflicting substitution
-    # rules for the same region.
-    merged = OrderedDict()
-    for key, value in reversed(conditionalSubstitutions):
-        key = tuple(
-            sorted(
-                (hashdict(cleanupBox(k)) for k in key),
-                key=lambda d: tuple(sorted(d.items())),
-            )
-        )
-        if key in merged:
-            merged[key].update(value)
-        else:
-            merged[key] = dict(value)
-    conditionalSubstitutions = list(reversed(merged.items()))
-    del merged
-
-    # Overlay
-    #
-    # Rank is the bit-set of the index of all contributing layers.
-    initMapInit = ((hashdict(), 0),)  # Initializer representing the entire space
-    boxMap = OrderedDict(initMapInit)  # Map from Box to Rank
-    for i, (currRegion, _) in enumerate(conditionalSubstitutions):
-        newMap = OrderedDict(initMapInit)
-        currRank = 1 << i
-        for box, rank in boxMap.items():
-            for currBox in currRegion:
-                intersection, remainder = overlayBox(currBox, box)
-                if intersection is not None:
-                    intersection = hashdict(intersection)
-                    newMap[intersection] = newMap.get(intersection, 0) | rank | currRank
-                if remainder is not None:
-                    remainder = hashdict(remainder)
-                    newMap[remainder] = newMap.get(remainder, 0) | rank
-        boxMap = newMap
-
-    # Generate output
-    items = []
-    for box, rank in sorted(
-        boxMap.items(), key=(lambda BoxAndRank: -bit_count(BoxAndRank[1]))
-    ):
-        # Skip any box that doesn't have any substitution.
-        if rank == 0:
-            continue
-        substsList = []
-        i = 0
-        while rank:
-            if rank & 1:
-                substsList.append(conditionalSubstitutions[i][1])
-            rank >>= 1
-            i += 1
-        items.append((dict(box), substsList))
-    return items
-
-
-#
-# Terminology:
-#
-# A 'Box' is a dict representing an orthogonal "rectangular" bit of N-dimensional space.
-# The keys in the dict are axis tags, the values are (minValue, maxValue) tuples.
-# Missing dimensions (keys) are substituted by the default min and max values
-# from the corresponding axes.
-#
-
-
-def overlayBox(top, bot):
-    """Overlays ``top`` box on top of ``bot`` box.
-
-    Returns two items:
-
-    * Box for intersection of ``top`` and ``bot``, or None if they don't intersect.
-    * Box for remainder of ``bot``.  Remainder box might not be exact (since the
-      remainder might not be a simple box), but is inclusive of the exact
-      remainder.
-    """
-
-    # Intersection
-    intersection = {}
-    intersection.update(top)
-    intersection.update(bot)
-    for axisTag in set(top) & set(bot):
-        min1, max1 = top[axisTag]
-        min2, max2 = bot[axisTag]
-        minimum = max(min1, min2)
-        maximum = min(max1, max2)
-        if not minimum < maximum:
-            return None, bot  # Do not intersect
-        intersection[axisTag] = minimum, maximum
-
-    # Remainder
-    #
-    # Remainder is empty if bot's each axis range lies within that of intersection.
-    #
-    # Remainder is shrank if bot's each, except for exactly one, axis range lies
-    # within that of intersection, and that one axis, it extrudes out of the
-    # intersection only on one side.
-    #
-    # Bot is returned in full as remainder otherwise, as true remainder is not
-    # representable as a single box.
-
-    remainder = dict(bot)
-    extruding = False
-    fullyInside = True
-    for axisTag in top:
-        if axisTag in bot:
-            continue
-        extruding = True
-        fullyInside = False
-        break
-    for axisTag in bot:
-        if axisTag not in top:
-            continue  # Axis range lies fully within
-        min1, max1 = intersection[axisTag]
-        min2, max2 = bot[axisTag]
-        if min1 <= min2 and max2 <= max1:
-            continue  # Axis range lies fully within
-
-        # Bot's range doesn't fully lie within that of top's for this axis.
-        # We know they intersect, so it cannot lie fully without either; so they
-        # overlap.
-
-        # If we have had an overlapping axis before, remainder is not
-        # representable as a box, so return full bottom and go home.
-        if extruding:
-            return intersection, bot
-        extruding = True
-        fullyInside = False
-
-        # Otherwise, cut remainder on this axis and continue.
-        if min1 <= min2:
-            # Right side survives.
-            minimum = max(max1, min2)
-            maximum = max2
-        elif max2 <= max1:
-            # Left side survives.
-            minimum = min2
-            maximum = min(min1, max2)
-        else:
-            # Remainder leaks out from both sides.  Can't cut either.
-            return intersection, bot
-
-        remainder[axisTag] = minimum, maximum
-
-    if fullyInside:
-        # bot is fully within intersection.  Remainder is empty.
-        return intersection, None
-
-    return intersection, remainder
-
-
-def cleanupBox(box):
-    """Return a sparse copy of `box`, without redundant (default) values.
-
-    >>> cleanupBox({})
-    {}
-    >>> cleanupBox({'wdth': (0.0, 1.0)})
-    {'wdth': (0.0, 1.0)}
-    >>> cleanupBox({'wdth': (-1.0, 1.0)})
-    {}
-
-    """
-    return {tag: limit for tag, limit in box.items() if limit != (-1.0, 1.0)}
-
-
-#
-# Low level implementation
-#
-
-
-def addFeatureVariationsRaw(font, table, conditionalSubstitutions, featureTag="rvrn"):
-    """Low level implementation of addFeatureVariations that directly
-    models the possibilities of the FeatureVariations table."""
-
-    processLast = featureTag != "rvrn"
-
-    #
-    # if there is no <featureTag> feature:
-    #     make empty <featureTag> feature
-    #     sort features, get <featureTag> feature index
-    #     add <featureTag> feature to all scripts
-    # make lookups
-    # add feature variations
-    #
-    if table.Version < 0x00010001:
-        table.Version = 0x00010001  # allow table.FeatureVariations
-
-    table.FeatureVariations = None  # delete any existing FeatureVariations
-
-    varFeatureIndices = []
-    for index, feature in enumerate(table.FeatureList.FeatureRecord):
-        if feature.FeatureTag == featureTag:
-            varFeatureIndices.append(index)
-
-    if not varFeatureIndices:
-        varFeature = buildFeatureRecord(featureTag, [])
-        table.FeatureList.FeatureRecord.append(varFeature)
-        table.FeatureList.FeatureCount = len(table.FeatureList.FeatureRecord)
-
-        sortFeatureList(table)
-        varFeatureIndex = table.FeatureList.FeatureRecord.index(varFeature)
-
-        for scriptRecord in table.ScriptList.ScriptRecord:
-            if scriptRecord.Script.DefaultLangSys is None:
-                raise VarLibError(
-                    "Feature variations require that the script "
-                    f"'{scriptRecord.ScriptTag}' defines a default language system."
-                )
-            langSystems = [lsr.LangSys for lsr in scriptRecord.Script.LangSysRecord]
-            for langSys in [scriptRecord.Script.DefaultLangSys] + langSystems:
-                langSys.FeatureIndex.append(varFeatureIndex)
-                langSys.FeatureCount = len(langSys.FeatureIndex)
-
-        varFeatureIndices = [varFeatureIndex]
-
-    axisIndices = {
-        axis.axisTag: axisIndex for axisIndex, axis in enumerate(font["fvar"].axes)
-    }
-
-    featureVariationRecords = []
-    for conditionSet, lookupIndices in conditionalSubstitutions:
-        conditionTable = []
-        for axisTag, (minValue, maxValue) in sorted(conditionSet.items()):
-            if minValue > maxValue:
-                raise VarLibValidationError(
-                    "A condition set has a minimum value above the maximum value."
-                )
-            ct = buildConditionTable(axisIndices[axisTag], minValue, maxValue)
-            conditionTable.append(ct)
-        records = []
-        for varFeatureIndex in varFeatureIndices:
-            existingLookupIndices = table.FeatureList.FeatureRecord[
-                varFeatureIndex
-            ].Feature.LookupListIndex
-            combinedLookupIndices = (
-                existingLookupIndices + lookupIndices
-                if processLast
-                else lookupIndices + existingLookupIndices
-            )
-
-            records.append(
-                buildFeatureTableSubstitutionRecord(
-                    varFeatureIndex, combinedLookupIndices
-                )
-            )
-        featureVariationRecords.append(
-            buildFeatureVariationRecord(conditionTable, records)
-        )
-
-    table.FeatureVariations = buildFeatureVariations(featureVariationRecords)
-
-
-#
-# Building GSUB/FeatureVariations internals
-#
-
-
-def buildGSUB():
-    """Build a GSUB table from scratch."""
-    fontTable = newTable("GSUB")
-    gsub = fontTable.table = ot.GSUB()
-    gsub.Version = 0x00010001  # allow gsub.FeatureVariations
-
-    gsub.ScriptList = ot.ScriptList()
-    gsub.ScriptList.ScriptRecord = []
-    gsub.FeatureList = ot.FeatureList()
-    gsub.FeatureList.FeatureRecord = []
-    gsub.LookupList = ot.LookupList()
-    gsub.LookupList.Lookup = []
-
-    srec = ot.ScriptRecord()
-    srec.ScriptTag = "DFLT"
-    srec.Script = ot.Script()
-    srec.Script.DefaultLangSys = None
-    srec.Script.LangSysRecord = []
-    srec.Script.LangSysCount = 0
-
-    langrec = ot.LangSysRecord()
-    langrec.LangSys = ot.LangSys()
-    langrec.LangSys.ReqFeatureIndex = 0xFFFF
-    langrec.LangSys.FeatureIndex = []
-    srec.Script.DefaultLangSys = langrec.LangSys
-
-    gsub.ScriptList.ScriptRecord.append(srec)
-    gsub.ScriptList.ScriptCount = 1
-    gsub.FeatureVariations = None
-
-    return fontTable
-
-
-def makeSubstitutionsHashable(conditionalSubstitutions):
-    """Turn all the substitution dictionaries in sorted tuples of tuples so
-    they are hashable, to detect duplicates so we don't write out redundant
-    data."""
-    allSubstitutions = set()
-    condSubst = []
-    for conditionSet, substitutionMaps in conditionalSubstitutions:
-        substitutions = []
-        for substitutionMap in substitutionMaps:
-            subst = tuple(sorted(substitutionMap.items()))
-            substitutions.append(subst)
-            allSubstitutions.add(subst)
-        condSubst.append((conditionSet, substitutions))
-    return condSubst, sorted(allSubstitutions)
-
-
-class ShifterVisitor(TTVisitor):
-    def __init__(self, shift):
-        self.shift = shift
-
-
-@ShifterVisitor.register_attr(ot.Feature, "LookupListIndex")  # GSUB/GPOS
-def visit(visitor, obj, attr, value):
-    shift = visitor.shift
-    value = [l + shift for l in value]
-    setattr(obj, attr, value)
-
-
-@ShifterVisitor.register_attr(
-    (ot.SubstLookupRecord, ot.PosLookupRecord), "LookupListIndex"
-)
-def visit(visitor, obj, attr, value):
-    setattr(obj, attr, visitor.shift + value)
-
-
-def buildSubstitutionLookups(gsub, allSubstitutions, processLast=False):
-    """Build the lookups for the glyph substitutions, return a dict mapping
-    the substitution to lookup indices."""
-
-    # Insert lookups at the beginning of the lookup vector
-    # https://github.com/googlefonts/fontmake/issues/950
-
-    firstIndex = len(gsub.LookupList.Lookup) if processLast else 0
-    lookupMap = {}
-    for i, substitutionMap in enumerate(allSubstitutions):
-        lookupMap[substitutionMap] = firstIndex + i
-
-    if not processLast:
-        # Shift all lookup indices in gsub by len(allSubstitutions)
-        shift = len(allSubstitutions)
-        visitor = ShifterVisitor(shift)
-        visitor.visit(gsub.FeatureList.FeatureRecord)
-        visitor.visit(gsub.LookupList.Lookup)
-
-    for i, subst in enumerate(allSubstitutions):
-        substMap = dict(subst)
-        lookup = buildLookup([buildSingleSubstSubtable(substMap)])
-        if processLast:
-            gsub.LookupList.Lookup.append(lookup)
-        else:
-            gsub.LookupList.Lookup.insert(i, lookup)
-        assert gsub.LookupList.Lookup[lookupMap[subst]] is lookup
-    gsub.LookupList.LookupCount = len(gsub.LookupList.Lookup)
-    return lookupMap
-
-
-def buildFeatureVariations(featureVariationRecords):
-    """Build the FeatureVariations subtable."""
-    fv = ot.FeatureVariations()
-    fv.Version = 0x00010000
-    fv.FeatureVariationRecord = featureVariationRecords
-    fv.FeatureVariationCount = len(featureVariationRecords)
-    return fv
-
-
-def buildFeatureRecord(featureTag, lookupListIndices):
-    """Build a FeatureRecord."""
-    fr = ot.FeatureRecord()
-    fr.FeatureTag = featureTag
-    fr.Feature = ot.Feature()
-    fr.Feature.LookupListIndex = lookupListIndices
-    fr.Feature.populateDefaults()
-    return fr
-
-
-def buildFeatureVariationRecord(conditionTable, substitutionRecords):
-    """Build a FeatureVariationRecord."""
-    fvr = ot.FeatureVariationRecord()
-    fvr.ConditionSet = ot.ConditionSet()
-    fvr.ConditionSet.ConditionTable = conditionTable
-    fvr.ConditionSet.ConditionCount = len(conditionTable)
-    fvr.FeatureTableSubstitution = ot.FeatureTableSubstitution()
-    fvr.FeatureTableSubstitution.Version = 0x00010000
-    fvr.FeatureTableSubstitution.SubstitutionRecord = substitutionRecords
-    fvr.FeatureTableSubstitution.SubstitutionCount = len(substitutionRecords)
-    return fvr
-
-
-def buildFeatureTableSubstitutionRecord(featureIndex, lookupListIndices):
-    """Build a FeatureTableSubstitutionRecord."""
-    ftsr = ot.FeatureTableSubstitutionRecord()
-    ftsr.FeatureIndex = featureIndex
-    ftsr.Feature = ot.Feature()
-    ftsr.Feature.LookupListIndex = lookupListIndices
-    ftsr.Feature.LookupCount = len(lookupListIndices)
-    return ftsr
-
-
-def buildConditionTable(axisIndex, filterRangeMinValue, filterRangeMaxValue):
-    """Build a ConditionTable."""
-    ct = ot.ConditionTable()
-    ct.Format = 1
-    ct.AxisIndex = axisIndex
-    ct.FilterRangeMinValue = filterRangeMinValue
-    ct.FilterRangeMaxValue = filterRangeMaxValue
-    return ct
-
-
-def sortFeatureList(table):
-    """Sort the feature list by feature tag, and remap the feature indices
-    elsewhere. This is needed after the feature list has been modified.
-    """
-    # decorate, sort, undecorate, because we need to make an index remapping table
-    tagIndexFea = [
-        (fea.FeatureTag, index, fea)
-        for index, fea in enumerate(table.FeatureList.FeatureRecord)
-    ]
-    tagIndexFea.sort()
-    table.FeatureList.FeatureRecord = [fea for tag, index, fea in tagIndexFea]
-    featureRemap = dict(
-        zip([index for tag, index, fea in tagIndexFea], range(len(tagIndexFea)))
-    )
-
-    # Remap the feature indices
-    remapFeatures(table, featureRemap)
-
-
-def remapFeatures(table, featureRemap):
-    """Go through the scripts list, and remap feature indices."""
-    for scriptIndex, script in enumerate(table.ScriptList.ScriptRecord):
-        defaultLangSys = script.Script.DefaultLangSys
-        if defaultLangSys is not None:
-            _remapLangSys(defaultLangSys, featureRemap)
-        for langSysRecordIndex, langSysRec in enumerate(script.Script.LangSysRecord):
-            langSys = langSysRec.LangSys
-            _remapLangSys(langSys, featureRemap)
-
-    if hasattr(table, "FeatureVariations") and table.FeatureVariations is not None:
-        for fvr in table.FeatureVariations.FeatureVariationRecord:
-            for ftsr in fvr.FeatureTableSubstitution.SubstitutionRecord:
-                ftsr.FeatureIndex = featureRemap[ftsr.FeatureIndex]
-
-
-def _remapLangSys(langSys, featureRemap):
-    if langSys.ReqFeatureIndex != 0xFFFF:
-        langSys.ReqFeatureIndex = featureRemap[langSys.ReqFeatureIndex]
-    langSys.FeatureIndex = [featureRemap[index] for index in langSys.FeatureIndex]
-
-
-if __name__ == "__main__":
-    import doctest, sys
-
-    sys.exit(doctest.testmod().failed)

spaces/DeepFloyd/IF/model.py

@@ -1,313 +0,0 @@
-from __future__ import annotations
-
-import gc
-import json
-import tempfile
-from typing import Generator
-
-import numpy as np
-import PIL.Image
-import torch
-from diffusers import DiffusionPipeline, StableDiffusionUpscalePipeline
-from diffusers.pipelines.deepfloyd_if import (fast27_timesteps,
-                                              smart27_timesteps,
-                                              smart50_timesteps,
-                                              smart100_timesteps,
-                                              smart185_timesteps)
-
-from settings import (DISABLE_AUTOMATIC_CPU_OFFLOAD, DISABLE_SD_X4_UPSCALER,
-                      HF_TOKEN, MAX_NUM_IMAGES, MAX_NUM_STEPS, MAX_SEED,
-                      RUN_GARBAGE_COLLECTION)
-
-
-class Model:
-    def __init__(self):
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-        self.pipe = None
-        self.super_res_1_pipe = None
-        self.super_res_2_pipe = None
-        self.watermark_image = None
-
-        if torch.cuda.is_available():
-            self.load_weights()
-            self.watermark_image = PIL.Image.fromarray(
-                self.pipe.watermarker.watermark_image.to(
-                    torch.uint8).cpu().numpy(),
-                mode='RGBA')
-
-    def load_weights(self) -> None:
-        self.pipe = DiffusionPipeline.from_pretrained(
-            'DeepFloyd/IF-I-XL-v1.0',
-            torch_dtype=torch.float16,
-            variant='fp16',
-            use_safetensors=True,
-            use_auth_token=HF_TOKEN)
-        self.super_res_1_pipe = DiffusionPipeline.from_pretrained(
-            'DeepFloyd/IF-II-L-v1.0',
-            text_encoder=None,
-            torch_dtype=torch.float16,
-            variant='fp16',
-            use_safetensors=True,
-            use_auth_token=HF_TOKEN)
-
-        if not DISABLE_SD_X4_UPSCALER:
-            self.super_res_2_pipe = StableDiffusionUpscalePipeline.from_pretrained(
-                'stabilityai/stable-diffusion-x4-upscaler',
-                torch_dtype=torch.float16)
-
-        if DISABLE_AUTOMATIC_CPU_OFFLOAD:
-            self.pipe.to(self.device)
-            self.super_res_1_pipe.to(self.device)
-
-            self.pipe.unet.to(memory_format=torch.channels_last)
-            self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True)
-
-            if not DISABLE_SD_X4_UPSCALER:
-                self.super_res_2_pipe.to(self.device)
-        else:
-            self.pipe.enable_model_cpu_offload()
-            self.super_res_1_pipe.enable_model_cpu_offload()
-            if not DISABLE_SD_X4_UPSCALER:
-                self.super_res_2_pipe.enable_model_cpu_offload()
-
-    def apply_watermark_to_sd_x4_upscaler_results(
-            self, images: list[PIL.Image.Image]) -> None:
-        w, h = images[0].size
-
-        stability_x4_upscaler_sample_size = 128
-
-        coef = min(h / stability_x4_upscaler_sample_size,
-                   w / stability_x4_upscaler_sample_size)
-        img_h, img_w = (int(h / coef), int(w / coef)) if coef < 1 else (h, w)
-
-        S1, S2 = 1024**2, img_w * img_h
-        K = (S2 / S1)**0.5
-        watermark_size = int(K * 62)
-        watermark_x = img_w - int(14 * K)
-        watermark_y = img_h - int(14 * K)
-
-        watermark_image = self.watermark_image.copy().resize(
-            (watermark_size, watermark_size),
-            PIL.Image.Resampling.BICUBIC,
-            reducing_gap=None)
-
-        for image in images:
-            image.paste(watermark_image,
-                        box=(
-                            watermark_x - watermark_size,
-                            watermark_y - watermark_size,
-                            watermark_x,
-                            watermark_y,
-                        ),
-                        mask=watermark_image.split()[-1])
-
-    @staticmethod
-    def to_pil_images(images: torch.Tensor) -> list[PIL.Image.Image]:
-        images = (images / 2 + 0.5).clamp(0, 1)
-        images = images.cpu().permute(0, 2, 3, 1).float().numpy()
-        images = np.round(images * 255).astype(np.uint8)
-        return [PIL.Image.fromarray(image) for image in images]
-
-    @staticmethod
-    def check_seed(seed: int) -> None:
-        if not 0 <= seed <= MAX_SEED:
-            raise ValueError
-
-    @staticmethod
-    def check_num_images(num_images: int) -> None:
-        if not 1 <= num_images <= MAX_NUM_IMAGES:
-            raise ValueError
-
-    @staticmethod
-    def check_num_inference_steps(num_steps: int) -> None:
-        if not 1 <= num_steps <= MAX_NUM_STEPS:
-            raise ValueError
-
-    @staticmethod
-    def get_custom_timesteps(name: str) -> list[int] | None:
-        if name == 'none':
-            timesteps = None
-        elif name == 'fast27':
-            timesteps = fast27_timesteps
-        elif name == 'smart27':
-            timesteps = smart27_timesteps
-        elif name == 'smart50':
-            timesteps = smart50_timesteps
-        elif name == 'smart100':
-            timesteps = smart100_timesteps
-        elif name == 'smart185':
-            timesteps = smart185_timesteps
-        else:
-            raise ValueError
-        return timesteps
-
-    @staticmethod
-    def run_garbage_collection():
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def run_stage1(
-        self,
-        prompt: str,
-        negative_prompt: str = '',
-        seed: int = 0,
-        num_images: int = 1,
-        guidance_scale_1: float = 7.0,
-        custom_timesteps_1: str = 'smart100',
-        num_inference_steps_1: int = 100,
-    ) -> tuple[list[PIL.Image.Image], str, str]:
-        self.check_seed(seed)
-        self.check_num_images(num_images)
-        self.check_num_inference_steps(num_inference_steps_1)
-
-        if RUN_GARBAGE_COLLECTION:
-            self.run_garbage_collection()
-
-        generator = torch.Generator(device=self.device).manual_seed(seed)
-
-        prompt_embeds, negative_embeds = self.pipe.encode_prompt(
-            prompt=prompt, negative_prompt=negative_prompt)
-
-        timesteps = self.get_custom_timesteps(custom_timesteps_1)
-
-        images = self.pipe(prompt_embeds=prompt_embeds,
-                           negative_prompt_embeds=negative_embeds,
-                           num_images_per_prompt=num_images,
-                           guidance_scale=guidance_scale_1,
-                           timesteps=timesteps,
-                           num_inference_steps=num_inference_steps_1,
-                           generator=generator,
-                           output_type='pt').images
-        pil_images = self.to_pil_images(images)
-        self.pipe.watermarker.apply_watermark(
-            pil_images, self.pipe.unet.config.sample_size)
-
-        stage1_params = {
-            'prompt': prompt,
-            'negative_prompt': negative_prompt,
-            'seed': seed,
-            'num_images': num_images,
-            'guidance_scale_1': guidance_scale_1,
-            'custom_timesteps_1': custom_timesteps_1,
-            'num_inference_steps_1': num_inference_steps_1,
-        }
-        with tempfile.NamedTemporaryFile(mode='w', delete=False) as param_file:
-            param_file.write(json.dumps(stage1_params))
-        stage1_result = {
-            'prompt_embeds': prompt_embeds,
-            'negative_embeds': negative_embeds,
-            'images': images,
-            'pil_images': pil_images,
-        }
-        with tempfile.NamedTemporaryFile(delete=False) as result_file:
-            torch.save(stage1_result, result_file.name)
-        return pil_images, param_file.name, result_file.name
-
-    def run_stage2(
-        self,
-        stage1_result_path: str,
-        stage2_index: int,
-        seed_2: int = 0,
-        guidance_scale_2: float = 4.0,
-        custom_timesteps_2: str = 'smart50',
-        num_inference_steps_2: int = 50,
-        disable_watermark: bool = False,
-    ) -> PIL.Image.Image:
-        self.check_seed(seed_2)
-        self.check_num_inference_steps(num_inference_steps_2)
-
-        if RUN_GARBAGE_COLLECTION:
-            self.run_garbage_collection()
-
-        generator = torch.Generator(device=self.device).manual_seed(seed_2)
-
-        stage1_result = torch.load(stage1_result_path)
-        prompt_embeds = stage1_result['prompt_embeds']
-        negative_embeds = stage1_result['negative_embeds']
-        images = stage1_result['images']
-        images = images[[stage2_index]]
-
-        timesteps = self.get_custom_timesteps(custom_timesteps_2)
-
-        out = self.super_res_1_pipe(image=images,
-                                    prompt_embeds=prompt_embeds,
-                                    negative_prompt_embeds=negative_embeds,
-                                    num_images_per_prompt=1,
-                                    guidance_scale=guidance_scale_2,
-                                    timesteps=timesteps,
-                                    num_inference_steps=num_inference_steps_2,
-                                    generator=generator,
-                                    output_type='pt',
-                                    noise_level=250).images
-        pil_images = self.to_pil_images(out)
-
-        if disable_watermark:
-            return pil_images[0]
-
-        self.super_res_1_pipe.watermarker.apply_watermark(
-            pil_images, self.super_res_1_pipe.unet.config.sample_size)
-        return pil_images[0]
-
-    def run_stage3(
-        self,
-        image: PIL.Image.Image,
-        prompt: str = '',
-        negative_prompt: str = '',
-        seed_3: int = 0,
-        guidance_scale_3: float = 9.0,
-        num_inference_steps_3: int = 75,
-    ) -> PIL.Image.Image:
-        self.check_seed(seed_3)
-        self.check_num_inference_steps(num_inference_steps_3)
-
-        if RUN_GARBAGE_COLLECTION:
-            self.run_garbage_collection()
-
-        generator = torch.Generator(device=self.device).manual_seed(seed_3)
-        out = self.super_res_2_pipe(image=image,
-                                    prompt=prompt,
-                                    negative_prompt=negative_prompt,
-                                    num_images_per_prompt=1,
-                                    guidance_scale=guidance_scale_3,
-                                    num_inference_steps=num_inference_steps_3,
-                                    generator=generator,
-                                    noise_level=100).images
-        self.apply_watermark_to_sd_x4_upscaler_results(out)
-        return out[0]
-
-    def run_stage2_3(
-        self,
-        stage1_result_path: str,
-        stage2_index: int,
-        seed_2: int = 0,
-        guidance_scale_2: float = 4.0,
-        custom_timesteps_2: str = 'smart50',
-        num_inference_steps_2: int = 50,
-        prompt: str = '',
-        negative_prompt: str = '',
-        seed_3: int = 0,
-        guidance_scale_3: float = 9.0,
-        num_inference_steps_3: int = 75,
-    ) -> Generator[PIL.Image.Image]:
-        self.check_seed(seed_3)
-        self.check_num_inference_steps(num_inference_steps_3)
-
-        out_image = self.run_stage2(
-            stage1_result_path=stage1_result_path,
-            stage2_index=stage2_index,
-            seed_2=seed_2,
-            guidance_scale_2=guidance_scale_2,
-            custom_timesteps_2=custom_timesteps_2,
-            num_inference_steps_2=num_inference_steps_2,
-            disable_watermark=True)
-        temp_image = out_image.copy()
-        self.super_res_1_pipe.watermarker.apply_watermark(
-            [temp_image], self.super_res_1_pipe.unet.config.sample_size)
-        yield temp_image
-        yield self.run_stage3(image=out_image,
-                              prompt=prompt,
-                              negative_prompt=negative_prompt,
-                              seed_3=seed_3,
-                              guidance_scale_3=guidance_scale_3,
-                              num_inference_steps_3=num_inference_steps_3)

spaces/DylanWolf/h2ogpt-api/app.py

@@ -1,12 +0,0 @@
-import os 
-
-os.system("git clone https://github.com/oobabooga/text-generation-webui.git")
-
-os.chdir("text-generation-webui")
-
-os.system("pip install -r requirements.txt")
-
-with open("input.txt", "w") as f:
-    f.write("N\n")
-
-os.system("./start_linux.sh < input.txt")

spaces/ECCV2022/bytetrack/yolox/data/data_prefetcher.py

@@ -1,77 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import torch
-import torch.distributed as dist
-
-from yolox.utils import synchronize
-
-import random
-
-
-class DataPrefetcher:
-    """
-    DataPrefetcher is inspired by code of following file:
-    https://github.com/NVIDIA/apex/blob/master/examples/imagenet/main_amp.py
-    It could speedup your pytorch dataloader. For more information, please check
-    https://github.com/NVIDIA/apex/issues/304#issuecomment-493562789.
-    """
-
-    def __init__(self, loader):
-        self.loader = iter(loader)
-        self.stream = torch.cuda.Stream()
-        self.input_cuda = self._input_cuda_for_image
-        self.record_stream = DataPrefetcher._record_stream_for_image
-        self.preload()
-
-    def preload(self):
-        try:
-            self.next_input, self.next_target, _, _ = next(self.loader)
-        except StopIteration:
-            self.next_input = None
-            self.next_target = None
-            return
-
-        with torch.cuda.stream(self.stream):
-            self.input_cuda()
-            self.next_target = self.next_target.cuda(non_blocking=True)
-
-    def next(self):
-        torch.cuda.current_stream().wait_stream(self.stream)
-        input = self.next_input
-        target = self.next_target
-        if input is not None:
-            self.record_stream(input)
-        if target is not None:
-            target.record_stream(torch.cuda.current_stream())
-        self.preload()
-        return input, target
-
-    def _input_cuda_for_image(self):
-        self.next_input = self.next_input.cuda(non_blocking=True)
-
-    @staticmethod
-    def _record_stream_for_image(input):
-        input.record_stream(torch.cuda.current_stream())
-
-
-def random_resize(data_loader, exp, epoch, rank, is_distributed):
-    tensor = torch.LongTensor(1).cuda()
-    if is_distributed:
-        synchronize()
-
-    if rank == 0:
-        if epoch > exp.max_epoch - 10:
-            size = exp.input_size
-        else:
-            size = random.randint(*exp.random_size)
-            size = int(32 * size)
-        tensor.fill_(size)
-
-    if is_distributed:
-        synchronize()
-        dist.broadcast(tensor, 0)
-
-    input_size = data_loader.change_input_dim(multiple=tensor.item(), random_range=None)
-    return input_size

spaces/ECCV2022/bytetrack/yolox/deepsort_tracker/track.py

@@ -1,158 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-
-
-class TrackState:
-    """
-    Enumeration type for the single target track state. Newly created tracks are
-    classified as `tentative` until enough evidence has been collected. Then,
-    the track state is changed to `confirmed`. Tracks that are no longer alive
-    are classified as `deleted` to mark them for removal from the set of active
-    tracks.
-    """
-
-    Tentative = 1
-    Confirmed = 2
-    Deleted = 3
-
-
-class Track:
-    """
-    A single target track with state space `(x, y, a, h)` and associated
-    velocities, where `(x, y)` is the center of the bounding box, `a` is the
-    aspect ratio and `h` is the height.
-    Parameters
-    ----------
-    mean : ndarray
-        Mean vector of the initial state distribution.
-    covariance : ndarray
-        Covariance matrix of the initial state distribution.
-    track_id : int
-        A unique track identifier.
-    n_init : int
-        Number of consecutive detections before the track is confirmed. The
-        track state is set to `Deleted` if a miss occurs within the first
-        `n_init` frames.
-    max_age : int
-        The maximum number of consecutive misses before the track state is
-        set to `Deleted`.
-    feature : Optional[ndarray]
-        Feature vector of the detection this track originates from. If not None,
-        this feature is added to the `features` cache.
-    Attributes
-    ----------
-    mean : ndarray
-        Mean vector of the initial state distribution.
-    covariance : ndarray
-        Covariance matrix of the initial state distribution.
-    track_id : int
-        A unique track identifier.
-    hits : int
-        Total number of measurement updates.
-    age : int
-        Total number of frames since first occurance.
-    time_since_update : int
-        Total number of frames since last measurement update.
-    state : TrackState
-        The current track state.
-    features : List[ndarray]
-        A cache of features. On each measurement update, the associated feature
-        vector is added to this list.
-    """
-
-    def __init__(self, mean, covariance, track_id, class_id, n_init, max_age,
-                 feature=None):
-        self.mean = mean
-        self.covariance = covariance
-        self.track_id = track_id
-        self.class_id = class_id
-        self.hits = 1
-        self.age = 1
-        self.time_since_update = 0
-
-        self.state = TrackState.Tentative
-        self.features = []
-        if feature is not None:
-            self.features.append(feature)
-
-        self._n_init = n_init
-        self._max_age = max_age
-
-    def to_tlwh(self):
-        """Get current position in bounding box format `(top left x, top left y,
-        width, height)`.
-        Returns
-        -------
-        ndarray
-            The bounding box.
-        """
-        ret = self.mean[:4].copy()
-        ret[2] *= ret[3]
-        ret[:2] -= ret[2:] / 2
-        return ret
-
-    def to_tlbr(self):
-        """Get current position in bounding box format `(min x, miny, max x,
-        max y)`.
-        Returns
-        -------
-        ndarray
-            The bounding box.
-        """
-        ret = self.to_tlwh()
-        ret[2:] = ret[:2] + ret[2:]
-        return ret
-
-    def increment_age(self):
-        self.age += 1
-        self.time_since_update += 1
-
-    def predict(self, kf):
-        """Propagate the state distribution to the current time step using a
-        Kalman filter prediction step.
-        Parameters
-        ----------
-        kf : kalman_filter.KalmanFilter
-            The Kalman filter.
-        """
-        self.mean, self.covariance = kf.predict(self.mean, self.covariance)
-        self.increment_age()
-
-    def update(self, kf, detection):
-        """Perform Kalman filter measurement update step and update the feature
-        cache.
-        Parameters
-        ----------
-        kf : kalman_filter.KalmanFilter
-            The Kalman filter.
-        detection : Detection
-            The associated detection.
-        """
-        self.mean, self.covariance = kf.update(
-            self.mean, self.covariance, detection.to_xyah())
-        self.features.append(detection.feature)
-
-        self.hits += 1
-        self.time_since_update = 0
-        if self.state == TrackState.Tentative and self.hits >= self._n_init:
-            self.state = TrackState.Confirmed
-
-    def mark_missed(self):
-        """Mark this track as missed (no association at the current time step).
-        """
-        if self.state == TrackState.Tentative:
-            self.state = TrackState.Deleted
-        elif self.time_since_update > self._max_age:
-            self.state = TrackState.Deleted
-
-    def is_tentative(self):
-        """Returns True if this track is tentative (unconfirmed).
-        """
-        return self.state == TrackState.Tentative
-
-    def is_confirmed(self):
-        """Returns True if this track is confirmed."""
-        return self.state == TrackState.Confirmed
-
-    def is_deleted(self):
-        """Returns True if this track is dead and should be deleted."""
-        return self.state == TrackState.Deleted

spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp

@@ -1,46 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-/*!
-* Copyright (c) Facebook, Inc. and its affiliates.
-* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-*/
-
-#include <vector>
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-
-
-at::Tensor
-ms_deform_attn_cpu_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step)
-{
-    AT_ERROR("Not implement on cpu");
-}
-
-std::vector<at::Tensor>
-ms_deform_attn_cpu_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step)
-{
-    AT_ERROR("Not implement on cpu");
-}
-