Update parquet files (step 6 of 249)

spaces/0xeureka/ehartford-WizardLM-13B-Uncensored/README.md

@@ -1,13 +0,0 @@
----
-title: Ehartford WizardLM 13B Uncensored
-emoji: 🦀
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
-duplicated_from: pmb99/ehartford-WizardLM-13B-Uncensored
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Comsol Download Crack LINK.md

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spaces/1gistliPinn/ChatGPT4/Examples/Easypano Tourweaver 7 Crack 36 !LINK!.md

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spaces/1line/AutoGPT/tests.py

@@ -1,21 +0,0 @@
-import unittest
-
-import coverage
-
-if __name__ == "__main__":
-    # Start coverage collection
-    cov = coverage.Coverage()
-    cov.start()
-
-    # Load all tests from the 'autogpt/tests' package
-    suite = unittest.defaultTestLoader.discover("./tests")
-
-    # Run the tests
-    unittest.TextTestRunner().run(suite)
-
-    # Stop coverage collection
-    cov.stop()
-    cov.save()
-
-    # Report the coverage
-    cov.report(show_missing=True)

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Parking Multiplayer Customize Your Car and Show Off Your Style.md

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- <h2>Tips and tricks for Car Parking 1.0</h2>
-<p>Car Parking 1.0 is a fun and addictive game that will keep you entertained for hours. However, it can also be challenging and frustrating at times. To help you master the game and enjoy it more, here are some tips and tricks that you can use:</p>
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-<p>One of the most important tips for Car Parking 1.0 is to use the camera angles wisely. The game offers different camera angles that you can switch between using the buttons on the screen. The camera angles include:</p>
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-</ul>
-<p>You should use the camera angles that suit your situation and preference. For example, you can use the front view when you are driving forward, the rear view when you are reversing, the top view when you are parking, the interior view when you want to feel more immersed, and the side view when you want to see your car's appearance. You can also zoom in and out using the buttons on the screen to get a better view of your surroundings.</p>
- <h3>Practice your skills in different scenarios</h3>
-<p>Another tip for Car Parking 1.0 is to practice your skills in different scenarios. The game offers a variety of scenarios that test your parking skills, such as parallel parking, perpendicular parking, diagonal parking, garage parking, and more. Each scenario has different levels of difficulty and challenges, such as narrow spaces, obstacles, time limits, and traffic. You should practice your skills in each scenario to improve your accuracy, speed, and confidence. You can also use the parking mode to practice your skills without any pressure or objectives.</p>
- <h3>Upgrade your car and unlock new features</h3>
-<p>Another tip for Car Parking 1.0 is to upgrade your car and unlock new features. The game allows you to customize your car using the coins and rewards that you earn from playing the game. You can upgrade your car's performance and appearance by changing its suspension, engine, turbo, gearbox, exhaust, paint color, vinyls, body parts, stickers, and more. You can also unlock new features for your car, such as nitro boost, horn sound, neon lights, and more. Upgrading your car and unlocking new features will make your car faster, stronger, and cooler.</p>
- <h3>Join the online community and have fun</h3>
-<p>Another tip for Car Parking 1.0 is to join the online community and have fun. The game has a large and active online community of players who share their experiences, tips, feedback, and suggestions on the game's social media pages . You can also join the multiplayer open world mode where you can interact with thousands of real players online. You can chat with them using voice chat or text messages, exchange cars with them, compete against them in racing or chasing, make friends with them, or play as a police officer or a criminal with them. Joining the online community and having fun will make your game more enjoyable and social.</p>
- <h2>Conclusion</h2>
-<p>Car Parking 1.0 is a simulation game that will make you feel like a real driver. It offers a variety of features that make it realistic and immersive, such as multiplayer open world mode, car customization option, high-quality open world feature, and interesting gameplay option. It also offers a simple and easy way to play the game, where you just have to download and install the game, choose your mode and car, follow the instructions and rules, and enjoy the realistic parking experience. It also offers some tips and tricks that will help you master the game and enjoy it more, such as using the camera angles wisely, practicing your skills in different scenarios, upgrading your car and unlocking new features, and joining the online community and having fun. Car Parking 1.0 is a game that will not only entertain you, but also teach you valuable skills and knowledge about parking and driving. If you are looking for a game that will challenge you and make you feel like a real driver, then you should definitely try Car Parking 1.0.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Car Parking 1.0:</p>
- <h3>Q: How can I download Car Parking 1.0 on my PC?</h3>
-<p>A: You can download Car Parking 1.0 on your PC using BlueStacks, an emulator that allows you to run Android apps on your computer. You can download BlueStacks from its official website, install it on your PC, and then search for Car Parking 1.0 on the Google Play Store app within BlueStacks. You can then download and install the game on your PC and play it using your keyboard and mouse.</p>
- <h3>Q: How can I remove ads from Car Parking 1.0?</h3>
-<p>A: You can remove ads from Car Parking 1.0 by buying the premium version of the game, which costs $2.99. The premium version will also give you access to more cars and features in the game. You can buy the premium version by tapping on the "Remove Ads" button on the main menu of the game.</p>
- <h3>Q: How can I get more coins and rewards in Car Parking 1.0?</h3>
-<p>A: You can get more coins and rewards in Car Parking 1.0 by completing levels, challenges, and achievements in the game. You can also watch videos or complete offers to get free coins and rewards. You can also buy coins and rewards using real money if you want.</p>
- <h3>Q: How can I change the language of Car Parking 1.0?</h3>
-<p>A: You can change the language of Car Parking 1.0 by tapping on the settings icon on the main menu of the game, and then selecting the language option. You can choose from over 20 languages, such as English, Spanish, French, German, Russian, Chinese, and more.</p>
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-<p>A: You can contact the developers of Car Parking 1.0 by sending them an email at [email protected] or by visiting their website at https://olzhass.com/. You can also follow them on Facebook at https://www.facebook.com/olzhassgames or on Instagram at https://www.instagram.com/olzhassgames/ to get the latest news and updates about the game.</p> 197e85843d<br />
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- <h2>What is Aqua Ludo APK?</h2>
-<p>Aqua Ludo APK is an online Ludo game that allows you to play with real players from around the world and win real money. You can choose from different modes and tournaments, such as 2 vs 2, 1 vs 1, 1 vs 3, 1 vs 4, multiplayer mode, classic mode, and quick mode. You can also refer the app to your friends and family and earn bonuses every time they join the app.</p>
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- <h2>Features of Aqua Ludo APK</h2>
-<h3>Play with real players and win real money</h3>
-<p>Aqua Ludo APK is not just a fun game but also a rewarding one. You can play with real players from around the world and win real money in every game. You can also participate in various tournaments and win big prizes. The more you play, the more you earn.</p>
- <h3>Choose from different modes and tournaments</h3>
-<p>Aqua Ludo APK offers you a variety of modes and tournaments to suit your preference and skill level. You can choose from 2 vs 2, 1 vs 1, 1 vs 3, 1 vs 4, multiplayer mode, classic mode, and quick mode. You can also join daily, weekly, monthly, or special tournaments and compete with other players for huge rewards.</p>
- <h3>Refer and earn bonuses</h3>
-<p>Aqua Ludo APK has a referral program that allows you to earn bonuses by inviting your friends and family to join the app. You can share the app link with your contacts via WhatsApp, Facebook, Instagram, or any other social media platform. You will get a bonus of Rs. 10 for every successful referral. You can use the bonus to play more games and win more money.</p>
- <h3>Secure and easy transactions</h3>
-<p>Aqua Ludo APK uses cashfree gateway for secure and easy transactions. You can add funds to your wallet using UPI, debit card, credit card, net banking, or wallet. You can also withdraw your winnings instantly using UPI or bank transfer. The minimum withdrawal amount is Rs. 50 and the maximum withdrawal amount is Rs. 10,000 per day.</p>
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-<p>To start playing Aqua Ludo APK, you need to register with your mobile number or email id. You will receive an OTP to verify your number or a link to verify your email id. You can also login with your Facebook or Google account. You will get a welcome bonus of Rs. 10 after completing the registration process.</p>
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-<p>The game ends when one player moves all their tokens to the center of the board. The winner gets the prize money according to the mode and tournament they joined.</p>
- <h2>Tips and tricks to win Aqua Ludo APK</h2>
-<h3>Strategize your moves</h3>
-<p>Aqua Ludo APK is not just a game of luck but also a game of strategy. You need to plan your moves ahead and anticipate your opponent's moves as well. You should try to capture your opponent's tokens whenever possible and avoid getting captured by them. You should also try to block their path and prevent them from reaching the center of the board.</p>
- <h3>Do not slack or underestimate your opponent</h3>
-<p>Aqua Ludo APK is a game that can change quickly depending on the dice rolls and moves of each player. You should not slack or underestimate your opponent even if you are ahead or behind in the game. You should always play with full concentration and focus until the end of the game. You should also be prepared for any surprises or twists that may occur in the game.</p>
- <h3>Use time to your advantage</h3>
-<p>Aqua Ludo APK has a time limit for each player to make their move. You should use this time to your advantage and make the best possible move. You should also try to make your move quickly and not waste your time. This will put pressure on your opponent and make them nervous or impatient. You can also use the chat feature to communicate with your opponent and distract them or taunt them.</p>
- <h3>Play with all your tokens and park them at the start</h3>
-<p>Aqua Ludo APK allows you to play with all your tokens and not just one or two. You should take advantage of this and move all your tokens from the home base to the starting point of the board as soon as possible. This will give you more options and flexibility to move your tokens and capture your opponent's tokens. You should also park your tokens at the start of the board and not move them until you get a six. This will protect your tokens from getting captured and also allow you to roll another dice if you get a six.</p>
- <h3>Utilize the safe boxes and capture your enemy's tokens</h3>
-<p>Aqua Ludo APK has some safe boxes marked with a star on the board. These are the spots where you cannot capture or get captured by your opponent's tokens. You should utilize these safe boxes and move your tokens there whenever possible. This will keep your tokens safe and also allow you to plan your next move. You should also try to capture your enemy's tokens whenever you get a chance. This will reduce their chances of winning and also increase your chances of winning.</p>
- <h2>Aqua Ludo APK review</h2>
-<h3>Pros and cons of Aqua Ludo APK</h3>
-<p>Aqua Ludo APK is a fun and rewarding game that offers you a lot of benefits. However, it also has some drawbacks that you should be aware of. Here are some of the pros and cons of Aqua Ludo APK:</p>
-<table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>- Play with real players and win real money</td>
-<td>- Not available on Google Play Store or Apple App Store</td>
-</tr>
-<tr>
-<td>- Choose from different modes and tournaments</td>
-<td>- Restricted in some states of India</td>
-</tr>
-<tr>
-<td>- Refer and earn bonuses</td>
-<td>- Requires internet connection and data usage</td>
-</tr>
-<tr>
-<td>- Secure and easy transactions</td>
-<td>- May involve risk of losing money or addiction</td>
-</tr>
-<tr>
-<td>- Fun and engaging gameplay</td>
-<td>- May have technical glitches or bugs</td>
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-</table>
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-<p>Aqua Ludo APK has received positive ratings and feedback from its users. It has a rating of 4.5 out of 5 stars on its official website. Here are some of the user reviews:</p>
- <blockquote>"Aqua Ludo APK is the best Ludo game I have ever played. It is very easy to use and very rewarding. I have won more than Rs. 5000 in just one week by playing this game. I love it."</blockquote>
- <blockquote>"I am a big fan of Ludo games and I have tried many apps but Aqua Ludo APK is the most interesting and exciting one. It has different modes and tournaments that keep me hooked. I also like the referral program that gives me bonuses every time I invite my friends."</blockquote>
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- <h2>Conclusion</h2>
-<p>Aqua Ludo APK is a fun and rewarding way to play Ludo online with real players and win real money. It offers you a variety of modes and tournaments, a referral program, secure transactions, and an engaging gameplay. It is not available on Google Play Store or Apple App Store but you can download it from its official website or from the link given below.</p>
- <p>If you are looking for a game that can entertain you, challenge you, and reward you, then Aqua Ludo APK is the perfect choice for you. Download it now and start rolling and winning with Aqua Ludo APK.</p>
- <h3>FAQs</h3>
-<p>Here are some of the frequently asked questions about Aqua Ludo APK:</p>
- <ol>
-<li><b>Is Aqua Ludo APK legal?</b></li>
-<p>Aqua Ludo APK is legal and safe to use as it is a game project of AVSLITE GAMES PRIVATE LIMITED, an Indian company registered under the Company Act, 2013. However, it may not be used by residents of Assam, Odisha, Nagaland, Sikkim, Tamil Nadu , Andhra Pradesh, and Telangana. Further, there may be certain restrictions in some additional states. You should check the legal status of online gaming in your state before using this platform.</p>
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-<p>You can contact the customer support of Aqua Ludo APK by sending an email to [email protected] or by calling the toll-free number 1800-123-4567. You can also visit the help center on the app or the website and find answers to common queries and issues.</p>
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-</ul>
-<h2>How to Download and Install Stick War 3 Mod APK</h2>
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-<ol>
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-<ul>
-<li><strong>Choose a balanced deck that suits your playstyle.</strong> You should choose a deck that has a good mix of units, spells, enchantments, and upgrades. You should also choose a deck that matches your playstyle, whether you prefer to be aggressive, defensive, or balanced. You can experiment with different combinations and see what works best for you.</li>
-<li><strong>Use your spells and enchantments wisely.</strong> You should use your spells and enchantments at the right time and place. You should use them to support your units or counter your enemies' moves. You should also avoid wasting them or using them too early or too late.</li>
-<li><strong>Control your units manually for better results.</strong> You should control your units manually instead of letting them act automatically. You should move them around the battlefield, target specific enemies, retreat when necessary, and use their special abilities. You should also switch between different units depending on the situation.</li>
-<li><strong>Use the tower to defend and attack.</strong> You should use the tower as a defensive and offensive tool. You should upgrade it to increase its health, damage, range, and abilities. You should also use it to shoot at enemies or heal your units. You should also protect it from enemy attacks or spells.</li>
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-</ul>
-<h2>Conclusion</h2>
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spaces/2023Liu2023/bingo/src/components/chat-history.tsx

@@ -1,48 +0,0 @@
-import { IconEdit, IconTrash, IconMore, IconDownload } from "./ui/icons"
-
-export function ChatHistory() {
-  return (
-    <div className="chat-history fixed top-18 right-4">
-      <div className="chat-history-header text-sm font-semibold text-left w-[280px] px-4 py-6">
-        历史记录
-      </div>
-      <div className="chat-history-main">
-        <div className="scroller">
-          <div className="surface">
-            <div className="threads">
-              <div className="thread">
-                <div className="primary-row">
-                  <button type="button" aria-label="加载聊天">
-
-                  </button>
-                  <div className="description">
-                    <h3 className="name">无标题的聊天</h3>
-                  </div>
-                  <h4 className="time">上午1:42</h4>
-                  <div className="controls">
-
-                    <button className="edit icon-button" type="button" aria-label="重命名">
-                      <IconEdit />
-                    </button>
-
-                    <button className="delete icon-button" type="button" aria-label="删除">
-                      <IconTrash />
-                    </button>
-
-                    <button className="more icon-button" type="button" aria-haspopup="true" aria-expanded="false" aria-label="更多">
-                      <IconMore />
-                    </button>
-
-                    <button className="export icon-button" type="button" aria-label="导出">
-                      <IconDownload />
-                    </button>
-                  </div>
-                </div>
-              </div>
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-}

spaces/2023Liu2023/bingo/src/lib/hooks/use-copy-to-clipboard.tsx

@@ -1,33 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-export interface useCopyToClipboardProps {
-  timeout?: number
-}
-
-export function useCopyToClipboard({
-  timeout = 2000
-}: useCopyToClipboardProps) {
-  const [isCopied, setIsCopied] = React.useState<Boolean>(false)
-
-  const copyToClipboard = (value: string) => {
-    if (typeof window === 'undefined' || !navigator.clipboard?.writeText) {
-      return
-    }
-
-    if (!value) {
-      return
-    }
-
-    navigator.clipboard.writeText(value).then(() => {
-      setIsCopied(true)
-
-      setTimeout(() => {
-        setIsCopied(false)
-      }, timeout)
-    })
-  }
-
-  return { isCopied, copyToClipboard }
-}

spaces/AFCMEgypt/WCB/README.md

@@ -1,13 +0,0 @@
----
-title: WCB
-emoji: 💻
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIConsultant/MusicGen/scripts/templates/login.html

@@ -1,20 +0,0 @@
-{% extends "base.html" %}
-{% block content %}
-
-<p>
-    You must identify yourself first! We use a highly secured protocol
-    where you just decide your username, and that's it. No password, no encryption,
-    just pure trust.
-</p>
-
-{% if error %}
-<p class="error">{{error}}</p>
-{% endif %}
-<form method="post" class="simple_form">
-    <label> Username
-        <input type="text" name="user">
-    </label>
-    <input type="submit" value="login">
-<form>
-
-{% endblock %}

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/datasets/pipelines/rand_aug.py

@@ -1,43 +0,0 @@
-# Refers to `_RAND_INCREASING_TRANSFORMS` in pytorch-image-models
-rand_increasing_policies = [
-    dict(type='AutoContrast'),
-    dict(type='Equalize'),
-    dict(type='Invert'),
-    dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
-    dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)),
-    dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)),
-    dict(
-        type='SolarizeAdd',
-        magnitude_key='magnitude',
-        magnitude_range=(0, 110)),
-    dict(
-        type='ColorTransform',
-        magnitude_key='magnitude',
-        magnitude_range=(0, 0.9)),
-    dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
-    dict(
-        type='Brightness', magnitude_key='magnitude',
-        magnitude_range=(0, 0.9)),
-    dict(
-        type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
-    dict(
-        type='Shear',
-        magnitude_key='magnitude',
-        magnitude_range=(0, 0.3),
-        direction='horizontal'),
-    dict(
-        type='Shear',
-        magnitude_key='magnitude',
-        magnitude_range=(0, 0.3),
-        direction='vertical'),
-    dict(
-        type='Translate',
-        magnitude_key='magnitude',
-        magnitude_range=(0, 0.45),
-        direction='horizontal'),
-    dict(
-        type='Translate',
-        magnitude_key='magnitude',
-        magnitude_range=(0, 0.45),
-        direction='vertical')
-]

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/schedules/custom_schedule.py

@@ -1,40 +0,0 @@
-optim_wrapper = dict(
-    # 使用 SGD 优化器来优化参数
-    type='OptimWrapper',
-    optimizer=dict(
-        type='Adam',
-        lr=0.0001,
-        betas=(0.9, 0.999),
-        eps=1e-08,
-        weight_decay=0,
-        amsgrad=False),
-    accumulative_counts=4
-)
-
-# 学习率参数的调整策略
-param_scheduler = [
-    # 在前10轮迭代中，逐迭代次数，线性预热
-    dict(type='LinearLR',
-         start_factor=0.00001,
-         by_epoch=True,
-         end=10,
-         convert_to_iter_based=True,  # 逐迭代次数更新学习率.
-         ),
-    # 在 10 轮次后，通过余弦退火衰减
-    dict(type='MultiStepLR',
-         by_epoch=True,  # 按轮次更新学习率
-         milestones=[30, 120, 200, 270, 330, 390, 450, 510, 580, 660, 750, 840, 930],
-         gamma=0.9)
-]
-
-# 'by_epoch=True' 默认使用 `EpochBaseLoop`,  'by_epoch=False' 默认使用 `IterBaseLoop`
-train_cfg = dict(by_epoch=True, max_epochs=1024, val_interval=16)
-# 使用默认的验证循环控制器
-val_cfg = dict()
-# 使用默认的测试循环控制器
-test_cfg = dict()
-
-# 通过默认策略自动缩放学习率，此策略适用于总批次大小 256
-# 如果你使用不同的总批量大小，比如 512 并启用自动学习率缩放
-# 我们将学习率扩大到 2 倍
-# auto_scale_lr = dict(base_batch_size=256)

spaces/Abdo1Kamr/Text_Translation_And_Text_Formatter_For_Palestinian_Case/README.md

@@ -1,12 +0,0 @@
----
-title: Text Translation And Text Formatter For Palestinian Case
-emoji: 🔥
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AgentVerse/agentVerse/dataloader/dataloader.py

@@ -1,19 +0,0 @@
-import json
-from abc import abstractmethod
-
-
-class DataLoader:
-    def __init__(self, path: str):
-        self.path = path
-        self.examples = []
-        self.load()
-
-    @abstractmethod
-    def load(self):
-        """Make sure that each example is formatted as {"input": ..., "answer": ...}"""
-        with open(self.path) as f:
-            for line in f:
-                self.examples.append(json.loads(line))
-
-    def __iter__(self):
-        return iter(self.examples)

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/PreLayout.js

@@ -1,11 +0,0 @@
-import PreLayoutBase from '../basesizer/PreLayout.js';
-
-var PreLayout = function () {
-    this._totalColumnProportions = undefined;
-    this._totalRowProportions = undefined;
-    this.proportionWidthLength = undefined;
-    this.proportionHeightLength = undefined;
-    PreLayoutBase.call(this);
-    return this;
-}
-export default PreLayout;

spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/README.md

@@ -1,164 +0,0 @@
-# Distributed Arcface Training in Pytorch
-
-This is a deep learning library that makes face recognition efficient, and effective, which can train tens of millions
-identity on a single server.
-
-## Requirements
-
-- Install [pytorch](http://pytorch.org) (torch>=1.6.0), our doc for [install.md](docs/install.md).
-- `pip install -r requirements.txt`.
-- Download the dataset
-  from [https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_)
-  .
-
-## How to Training
-
-To train a model, run `train.py` with the path to the configs:
-
-### 1. Single node, 8 GPUs:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r50
-```
-
-### 2. Multiple nodes, each node 8 GPUs:
-
-Node 0:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50
-```
-
-Node 1:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50
-```
-
-### 3.Training resnet2060 with 8 GPUs:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r2060.py
-```
-
-## Model Zoo
-
-- The models are available for non-commercial research purposes only.  
-- All models can be found in here.  
-- [Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g):   e8pw  
-- [onedrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d)
-
-### Performance on [**ICCV2021-MFR**](http://iccv21-mfr.com/)
-
-ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face 
-recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities. 
-As the result, we can evaluate the FAIR performance for different algorithms.  
-
-For **ICCV2021-MFR-ALL** set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The 
-globalised multi-racial testset contains 242,143 identities and 1,624,305 images. 
-
-For **ICCV2021-MFR-MASK** set, TAR is measured on mask-to-nonmask 1:1 protocal, with FAR less than 0.0001(e-4). 
-Mask testset contains 6,964 identities, 6,964 masked images and 13,928 non-masked images. 
-There are totally 13,928 positive pairs and 96,983,824 negative pairs.
-
-| Datasets | backbone  | Training throughout | Size / MB  | **ICCV2021-MFR-MASK** | **ICCV2021-MFR-ALL** |
-| :---:    | :---      | :---                | :---       |:---                   |:---                  |     
-| MS1MV3    | r18  | -              | 91   | **47.85** | **68.33** |
-| Glint360k | r18  | 8536           | 91   | **53.32** | **72.07** |
-| MS1MV3    | r34  | -              | 130  | **58.72** | **77.36** |
-| Glint360k | r34  | 6344           | 130  | **65.10** | **83.02** |
-| MS1MV3    | r50  | 5500           | 166  | **63.85** | **80.53** |
-| Glint360k | r50  | 5136           | 166  | **70.23** | **87.08** |
-| MS1MV3    | r100 | -              | 248  | **69.09** | **84.31** |
-| Glint360k | r100 | 3332           | 248  | **75.57** | **90.66** |
-| MS1MV3    | mobilefacenet | 12185 | 7.8  | **41.52** | **65.26** |        
-| Glint360k | mobilefacenet | 11197 | 7.8  | **44.52** | **66.48** |  
-
-### Performance on IJB-C and Verification Datasets
-
-|   Datasets | backbone      | IJBC(1e-05) | IJBC(1e-04) | agedb30 | cfp_fp | lfw  |  log    |
-| :---:      |    :---       | :---          | :---  | :---  |:---   |:---    |:---     |  
-| MS1MV3     | r18      | 92.07 | 94.66 | 97.77 | 97.73 | 99.77 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r18_fp16/training.log)|         
-| MS1MV3     | r34      | 94.10 | 95.90 | 98.10 | 98.67 | 99.80 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r34_fp16/training.log)|        
-| MS1MV3     | r50      | 94.79 | 96.46 | 98.35 | 98.96 | 99.83 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r50_fp16/training.log)|         
-| MS1MV3     | r100     | 95.31 | 96.81 | 98.48 | 99.06 | 99.85 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r100_fp16/training.log)|        
-| MS1MV3     | **r2060**| 95.34 | 97.11 | 98.67 | 99.24 | 99.87 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r2060_fp16/training.log)|
-| Glint360k  |r18-0.1   | 93.16 | 95.33 | 97.72 | 97.73 | 99.77 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r18_fp16_0.1/training.log)| 
-| Glint360k  |r34-0.1   | 95.16 | 96.56 | 98.33 | 98.78 | 99.82 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r34_fp16_0.1/training.log)| 
-| Glint360k  |r50-0.1   | 95.61 | 96.97 | 98.38 | 99.20 | 99.83 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r50_fp16_0.1/training.log)| 
-| Glint360k  |r100-0.1  | 95.88 | 97.32 | 98.48 | 99.29 | 99.82 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r100_fp16_0.1/training.log)|
-
-[comment]: <> (More details see [model.md]&#40;docs/modelzoo.md&#41; in docs.)
-
-
-## [Speed Benchmark](docs/speed_benchmark.md)
-
-**Arcface Torch** can train large-scale face recognition training set efficiently and quickly. When the number of
-classes in training sets is greater than 300K and the training is sufficient, partial fc sampling strategy will get same
-accuracy with several times faster training performance and smaller GPU memory. 
-Partial FC is a sparse variant of the model parallel architecture for large sacle  face recognition. Partial FC use a 
-sparse softmax, where each batch dynamicly sample a subset of class centers for training. In each iteration, only a 
-sparse part of the parameters will be updated, which can reduce a lot of GPU memory and calculations. With Partial FC, 
-we can scale trainset of 29 millions identities, the largest to date. Partial FC also supports multi-machine distributed 
-training and mixed precision training.
-
-![Image text](https://github.com/anxiangsir/insightface_arcface_log/blob/master/partial_fc_v2.png)
-
-More details see 
-[speed_benchmark.md](docs/speed_benchmark.md) in docs.
-
-### 1. Training speed of different parallel methods (samples / second), Tesla V100 32GB * 8. (Larger is better)
-
-`-` means training failed because of gpu memory limitations.
-
-| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
-| :---    | :--- | :--- | :--- |
-|125000   | 4681         | 4824          | 5004     |
-|1400000  | **1672**     | 3043          | 4738     |
-|5500000  | **-**        | **1389**      | 3975     |
-|8000000  | **-**        | **-**         | 3565     |
-|16000000 | **-**        | **-**         | 2679     |
-|29000000 | **-**        | **-**         | **1855** |
-
-### 2. GPU memory cost of different parallel methods (MB per GPU), Tesla V100 32GB * 8. (Smaller is better)
-
-| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
-| :---    | :---      | :---      | :---  |
-|125000   | 7358      | 5306      | 4868  |
-|1400000  | 32252     | 11178     | 6056  |
-|5500000  | **-**     | 32188     | 9854  |
-|8000000  | **-**     | **-**     | 12310 |
-|16000000 | **-**     | **-**     | 19950 |
-|29000000 | **-**     | **-**     | 32324 |
-
-## Evaluation ICCV2021-MFR and IJB-C
-
-More details see [eval.md](docs/eval.md) in docs.
-
-## Test
-
-We tested many versions of PyTorch. Please create an issue if you are having trouble.  
-
-- [x] torch 1.6.0
-- [x] torch 1.7.1
-- [x] torch 1.8.0
-- [x] torch 1.9.0
-
-## Citation
-
-```
-@inproceedings{deng2019arcface,
-  title={Arcface: Additive angular margin loss for deep face recognition},
-  author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
-  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
-  pages={4690--4699},
-  year={2019}
-}
-@inproceedings{an2020partical_fc,
-  title={Partial FC: Training 10 Million Identities on a Single Machine},
-  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
-  Zhang, Debing and Fu Ying},
-  booktitle={Arxiv 2010.05222},
-  year={2020}
-}
-```

spaces/Alpaca233/SadTalker/src/face3d/util/detect_lm68.py

@@ -1,106 +0,0 @@
-import os
-import cv2
-import numpy as np
-from scipy.io import loadmat
-import tensorflow as tf
-from util.preprocess import align_for_lm
-from shutil import move
-
-mean_face = np.loadtxt('util/test_mean_face.txt')
-mean_face = mean_face.reshape([68, 2])
-
-def save_label(labels, save_path):
-    np.savetxt(save_path, labels)
-
-def draw_landmarks(img, landmark, save_name):
-    landmark = landmark
-    lm_img = np.zeros([img.shape[0], img.shape[1], 3])
-    lm_img[:] = img.astype(np.float32)
-    landmark = np.round(landmark).astype(np.int32)
-
-    for i in range(len(landmark)):
-        for j in range(-1, 1):
-            for k in range(-1, 1):
-                if img.shape[0] - 1 - landmark[i, 1]+j > 0 and \
-                        img.shape[0] - 1 - landmark[i, 1]+j < img.shape[0] and \
-                        landmark[i, 0]+k > 0 and \
-                        landmark[i, 0]+k < img.shape[1]:
-                    lm_img[img.shape[0] - 1 - landmark[i, 1]+j, landmark[i, 0]+k,
-                           :] = np.array([0, 0, 255])
-    lm_img = lm_img.astype(np.uint8)
-
-    cv2.imwrite(save_name, lm_img)
-
-
-def load_data(img_name, txt_name):
-    return cv2.imread(img_name), np.loadtxt(txt_name)
-
-# create tensorflow graph for landmark detector
-def load_lm_graph(graph_filename):
-    with tf.gfile.GFile(graph_filename, 'rb') as f:
-        graph_def = tf.GraphDef()
-        graph_def.ParseFromString(f.read())
-
-    with tf.Graph().as_default() as graph:
-        tf.import_graph_def(graph_def, name='net')
-        img_224 = graph.get_tensor_by_name('net/input_imgs:0')
-        output_lm = graph.get_tensor_by_name('net/lm:0')
-        lm_sess = tf.Session(graph=graph)
-
-    return lm_sess,img_224,output_lm
-
-# landmark detection
-def detect_68p(img_path,sess,input_op,output_op):
-    print('detecting landmarks......')
-    names = [i for i in sorted(os.listdir(
-        img_path)) if 'jpg' in i or 'png' in i or 'jpeg' in i or 'PNG' in i]
-    vis_path = os.path.join(img_path, 'vis')
-    remove_path = os.path.join(img_path, 'remove')
-    save_path = os.path.join(img_path, 'landmarks')
-    if not os.path.isdir(vis_path):
-        os.makedirs(vis_path)
-    if not os.path.isdir(remove_path):
-        os.makedirs(remove_path)
-    if not os.path.isdir(save_path):
-        os.makedirs(save_path)
-
-    for i in range(0, len(names)):
-        name = names[i]
-        print('%05d' % (i), ' ', name)
-        full_image_name = os.path.join(img_path, name)
-        txt_name = '.'.join(name.split('.')[:-1]) + '.txt'
-        full_txt_name = os.path.join(img_path, 'detections', txt_name) # 5 facial landmark path for each image
-
-        # if an image does not have detected 5 facial landmarks, remove it from the training list
-        if not os.path.isfile(full_txt_name):
-            move(full_image_name, os.path.join(remove_path, name))
-            continue 
-
-        # load data
-        img, five_points = load_data(full_image_name, full_txt_name)
-        input_img, scale, bbox = align_for_lm(img, five_points) # align for 68 landmark detection 
-
-        # if the alignment fails, remove corresponding image from the training list
-        if scale == 0:
-            move(full_txt_name, os.path.join(
-                remove_path, txt_name))
-            move(full_image_name, os.path.join(remove_path, name))
-            continue
-
-        # detect landmarks
-        input_img = np.reshape(
-            input_img, [1, 224, 224, 3]).astype(np.float32)
-        landmark = sess.run(
-            output_op, feed_dict={input_op: input_img})
-
-        # transform back to original image coordinate
-        landmark = landmark.reshape([68, 2]) + mean_face
-        landmark[:, 1] = 223 - landmark[:, 1]
-        landmark = landmark / scale
-        landmark[:, 0] = landmark[:, 0] + bbox[0]
-        landmark[:, 1] = landmark[:, 1] + bbox[1]
-        landmark[:, 1] = img.shape[0] - 1 - landmark[:, 1]
-
-        if i % 100 == 0:
-            draw_landmarks(img, landmark, os.path.join(vis_path, name))
-        save_label(landmark, os.path.join(save_path, txt_name))

spaces/Andy1621/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py

@@ -1,57 +0,0 @@
-_base_ = [
-    '../_base_/models/mask_rcnn_r50_fpn.py',
-    '../_base_/datasets/coco_instance.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
-    pretrained='open-mmlab://regnetx_3.2gf',
-    backbone=dict(
-        _delete_=True,
-        type='RegNet',
-        arch='regnetx_3.2gf',
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[96, 192, 432, 1008],
-        out_channels=256,
-        num_outs=5))
-img_norm_cfg = dict(
-    # The mean and std are used in PyCls when training RegNets
-    mean=[103.53, 116.28, 123.675],
-    std=[57.375, 57.12, 58.395],
-    to_rgb=False)
-train_pipeline = [
-    # Images are converted to float32 directly after loading in PyCls
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)

spaces/Andy1621/uniformer_image_detection/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py

@@ -1,62 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    pretrained='open-mmlab://resnest50',
-    backbone=dict(
-        type='ResNeSt',
-        stem_channels=64,
-        depth=50,
-        radix=2,
-        reduction_factor=4,
-        avg_down_stride=True,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch'),
-    roi_head=dict(
-        bbox_head=dict(
-            type='Shared4Conv1FCBBoxHead',
-            conv_out_channels=256,
-            norm_cfg=norm_cfg)))
-# # use ResNeSt img_norm
-img_norm_cfg = dict(
-    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='LoadAnnotations',
-        with_bbox=True,
-        with_mask=False,
-        poly2mask=False),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 800)],
-        multiscale_mode='range',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))

spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/free_anchor_retina_head.py

@@ -1,270 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from mmdet.core import bbox_overlaps
-from ..builder import HEADS
-from .retina_head import RetinaHead
-
-EPS = 1e-12
-
-
-@HEADS.register_module()
-class FreeAnchorRetinaHead(RetinaHead):
-    """FreeAnchor RetinaHead used in https://arxiv.org/abs/1909.02466.
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        stacked_convs (int): Number of conv layers in cls and reg tower.
-            Default: 4.
-        conv_cfg (dict): dictionary to construct and config conv layer.
-            Default: None.
-        norm_cfg (dict): dictionary to construct and config norm layer.
-            Default: norm_cfg=dict(type='GN', num_groups=32,
-            requires_grad=True).
-        pre_anchor_topk (int): Number of boxes that be token in each bag.
-        bbox_thr (float): The threshold of the saturated linear function. It is
-            usually the same with the IoU threshold used in NMS.
-        gamma (float): Gamma parameter in focal loss.
-        alpha (float): Alpha parameter in focal loss.
-    """  # noqa: W605
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 stacked_convs=4,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 pre_anchor_topk=50,
-                 bbox_thr=0.6,
-                 gamma=2.0,
-                 alpha=0.5,
-                 **kwargs):
-        super(FreeAnchorRetinaHead,
-              self).__init__(num_classes, in_channels, stacked_convs, conv_cfg,
-                             norm_cfg, **kwargs)
-
-        self.pre_anchor_topk = pre_anchor_topk
-        self.bbox_thr = bbox_thr
-        self.gamma = gamma
-        self.alpha = alpha
-
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            gt_bboxes (list[Tensor]): each item are the truth boxes for each
-                image in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == len(self.anchor_generator.base_anchors)
-
-        anchor_list, _ = self.get_anchors(featmap_sizes, img_metas)
-        anchors = [torch.cat(anchor) for anchor in anchor_list]
-
-        # concatenate each level
-        cls_scores = [
-            cls.permute(0, 2, 3,
-                        1).reshape(cls.size(0), -1, self.cls_out_channels)
-            for cls in cls_scores
-        ]
-        bbox_preds = [
-            bbox_pred.permute(0, 2, 3, 1).reshape(bbox_pred.size(0), -1, 4)
-            for bbox_pred in bbox_preds
-        ]
-        cls_scores = torch.cat(cls_scores, dim=1)
-        bbox_preds = torch.cat(bbox_preds, dim=1)
-
-        cls_prob = torch.sigmoid(cls_scores)
-        box_prob = []
-        num_pos = 0
-        positive_losses = []
-        for _, (anchors_, gt_labels_, gt_bboxes_, cls_prob_,
-                bbox_preds_) in enumerate(
-                    zip(anchors, gt_labels, gt_bboxes, cls_prob, bbox_preds)):
-
-            with torch.no_grad():
-                if len(gt_bboxes_) == 0:
-                    image_box_prob = torch.zeros(
-                        anchors_.size(0),
-                        self.cls_out_channels).type_as(bbox_preds_)
-                else:
-                    # box_localization: a_{j}^{loc}, shape: [j, 4]
-                    pred_boxes = self.bbox_coder.decode(anchors_, bbox_preds_)
-
-                    # object_box_iou: IoU_{ij}^{loc}, shape: [i, j]
-                    object_box_iou = bbox_overlaps(gt_bboxes_, pred_boxes)
-
-                    # object_box_prob: P{a_{j} -> b_{i}}, shape: [i, j]
-                    t1 = self.bbox_thr
-                    t2 = object_box_iou.max(
-                        dim=1, keepdim=True).values.clamp(min=t1 + 1e-12)
-                    object_box_prob = ((object_box_iou - t1) /
-                                       (t2 - t1)).clamp(
-                                           min=0, max=1)
-
-                    # object_cls_box_prob: P{a_{j} -> b_{i}}, shape: [i, c, j]
-                    num_obj = gt_labels_.size(0)
-                    indices = torch.stack([
-                        torch.arange(num_obj).type_as(gt_labels_), gt_labels_
-                    ],
-                                          dim=0)
-                    object_cls_box_prob = torch.sparse_coo_tensor(
-                        indices, object_box_prob)
-
-                    # image_box_iou: P{a_{j} \in A_{+}}, shape: [c, j]
-                    """
-                    from "start" to "end" implement:
-                    image_box_iou = torch.sparse.max(object_cls_box_prob,
-                                                     dim=0).t()
-
-                    """
-                    # start
-                    box_cls_prob = torch.sparse.sum(
-                        object_cls_box_prob, dim=0).to_dense()
-
-                    indices = torch.nonzero(box_cls_prob, as_tuple=False).t_()
-                    if indices.numel() == 0:
-                        image_box_prob = torch.zeros(
-                            anchors_.size(0),
-                            self.cls_out_channels).type_as(object_box_prob)
-                    else:
-                        nonzero_box_prob = torch.where(
-                            (gt_labels_.unsqueeze(dim=-1) == indices[0]),
-                            object_box_prob[:, indices[1]],
-                            torch.tensor([
-                                0
-                            ]).type_as(object_box_prob)).max(dim=0).values
-
-                        # upmap to shape [j, c]
-                        image_box_prob = torch.sparse_coo_tensor(
-                            indices.flip([0]),
-                            nonzero_box_prob,
-                            size=(anchors_.size(0),
-                                  self.cls_out_channels)).to_dense()
-                    # end
-
-                box_prob.append(image_box_prob)
-
-            # construct bags for objects
-            match_quality_matrix = bbox_overlaps(gt_bboxes_, anchors_)
-            _, matched = torch.topk(
-                match_quality_matrix,
-                self.pre_anchor_topk,
-                dim=1,
-                sorted=False)
-            del match_quality_matrix
-
-            # matched_cls_prob: P_{ij}^{cls}
-            matched_cls_prob = torch.gather(
-                cls_prob_[matched], 2,
-                gt_labels_.view(-1, 1, 1).repeat(1, self.pre_anchor_topk,
-                                                 1)).squeeze(2)
-
-            # matched_box_prob: P_{ij}^{loc}
-            matched_anchors = anchors_[matched]
-            matched_object_targets = self.bbox_coder.encode(
-                matched_anchors,
-                gt_bboxes_.unsqueeze(dim=1).expand_as(matched_anchors))
-            loss_bbox = self.loss_bbox(
-                bbox_preds_[matched],
-                matched_object_targets,
-                reduction_override='none').sum(-1)
-            matched_box_prob = torch.exp(-loss_bbox)
-
-            # positive_losses: {-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )}
-            num_pos += len(gt_bboxes_)
-            positive_losses.append(
-                self.positive_bag_loss(matched_cls_prob, matched_box_prob))
-        positive_loss = torch.cat(positive_losses).sum() / max(1, num_pos)
-
-        # box_prob: P{a_{j} \in A_{+}}
-        box_prob = torch.stack(box_prob, dim=0)
-
-        # negative_loss:
-        # \sum_{j}{ FL((1 - P{a_{j} \in A_{+}}) * (1 - P_{j}^{bg})) } / n||B||
-        negative_loss = self.negative_bag_loss(cls_prob, box_prob).sum() / max(
-            1, num_pos * self.pre_anchor_topk)
-
-        # avoid the absence of gradients in regression subnet
-        # when no ground-truth in a batch
-        if num_pos == 0:
-            positive_loss = bbox_preds.sum() * 0
-
-        losses = {
-            'positive_bag_loss': positive_loss,
-            'negative_bag_loss': negative_loss
-        }
-        return losses
-
-    def positive_bag_loss(self, matched_cls_prob, matched_box_prob):
-        """Compute positive bag loss.
-
-        :math:`-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )`.
-
-        :math:`P_{ij}^{cls}`: matched_cls_prob, classification probability of matched samples.
-
-        :math:`P_{ij}^{loc}`: matched_box_prob, box probability of matched samples.
-
-        Args:
-            matched_cls_prob (Tensor): Classification probabilty of matched
-                samples in shape (num_gt, pre_anchor_topk).
-            matched_box_prob (Tensor): BBox probability of matched samples,
-                in shape (num_gt, pre_anchor_topk).
-
-        Returns:
-            Tensor: Positive bag loss in shape (num_gt,).
-        """  # noqa: E501, W605
-        # bag_prob = Mean-max(matched_prob)
-        matched_prob = matched_cls_prob * matched_box_prob
-        weight = 1 / torch.clamp(1 - matched_prob, 1e-12, None)
-        weight /= weight.sum(dim=1).unsqueeze(dim=-1)
-        bag_prob = (weight * matched_prob).sum(dim=1)
-        # positive_bag_loss = -self.alpha * log(bag_prob)
-        return self.alpha * F.binary_cross_entropy(
-            bag_prob, torch.ones_like(bag_prob), reduction='none')
-
-    def negative_bag_loss(self, cls_prob, box_prob):
-        """Compute negative bag loss.
-
-        :math:`FL((1 - P_{a_{j} \in A_{+}}) * (1 - P_{j}^{bg}))`.
-
-        :math:`P_{a_{j} \in A_{+}}`: Box_probability of matched samples.
-
-        :math:`P_{j}^{bg}`: Classification probability of negative samples.
-
-        Args:
-            cls_prob (Tensor): Classification probability, in shape
-                (num_img, num_anchors, num_classes).
-            box_prob (Tensor): Box probability, in shape
-                (num_img, num_anchors, num_classes).
-
-        Returns:
-            Tensor: Negative bag loss in shape (num_img, num_anchors, num_classes).
-        """  # noqa: E501, W605
-        prob = cls_prob * (1 - box_prob)
-        # There are some cases when neg_prob = 0.
-        # This will cause the neg_prob.log() to be inf without clamp.
-        prob = prob.clamp(min=EPS, max=1 - EPS)
-        negative_bag_loss = prob**self.gamma * F.binary_cross_entropy(
-            prob, torch.zeros_like(prob), reduction='none')
-        return (1 - self.alpha) * negative_bag_loss

spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/quarto-html/quarto.js

@@ -1,902 +0,0 @@
-const sectionChanged = new CustomEvent("quarto-sectionChanged", {
-  detail: {},
-  bubbles: true,
-  cancelable: false,
-  composed: false,
-});
-
-const layoutMarginEls = () => {
-  // Find any conflicting margin elements and add margins to the
-  // top to prevent overlap
-  const marginChildren = window.document.querySelectorAll(
-    ".column-margin.column-container > * "
-  );
-
-  let lastBottom = 0;
-  for (const marginChild of marginChildren) {
-    if (marginChild.offsetParent !== null) {
-      // clear the top margin so we recompute it
-      marginChild.style.marginTop = null;
-      const top = marginChild.getBoundingClientRect().top + window.scrollY;
-      console.log({
-        childtop: marginChild.getBoundingClientRect().top,
-        scroll: window.scrollY,
-        top,
-        lastBottom,
-      });
-      if (top < lastBottom) {
-        const margin = lastBottom - top;
-        marginChild.style.marginTop = `${margin}px`;
-      }
-      const styles = window.getComputedStyle(marginChild);
-      const marginTop = parseFloat(styles["marginTop"]);
-
-      console.log({
-        top,
-        height: marginChild.getBoundingClientRect().height,
-        marginTop,
-        total: top + marginChild.getBoundingClientRect().height + marginTop,
-      });
-      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
-    }
-  }
-};
-
-window.document.addEventListener("DOMContentLoaded", function (_event) {
-  // Recompute the position of margin elements anytime the body size changes
-  if (window.ResizeObserver) {
-    const resizeObserver = new window.ResizeObserver(
-      throttle(layoutMarginEls, 50)
-    );
-    resizeObserver.observe(window.document.body);
-  }
-
-  const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]');
-  const sidebarEl = window.document.getElementById("quarto-sidebar");
-  const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left");
-  const marginSidebarEl = window.document.getElementById(
-    "quarto-margin-sidebar"
-  );
-  // function to determine whether the element has a previous sibling that is active
-  const prevSiblingIsActiveLink = (el) => {
-    const sibling = el.previousElementSibling;
-    if (sibling && sibling.tagName === "A") {
-      return sibling.classList.contains("active");
-    } else {
-      return false;
-    }
-  };
-
-  // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior)
-  function fireSlideEnter(e) {
-    const event = window.document.createEvent("Event");
-    event.initEvent("slideenter", true, true);
-    window.document.dispatchEvent(event);
-  }
-  const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]');
-  tabs.forEach((tab) => {
-    tab.addEventListener("shown.bs.tab", fireSlideEnter);
-  });
-
-  // fire slideEnter for tabby tab activations (for htmlwidget resize behavior)
-  document.addEventListener("tabby", fireSlideEnter, false);
-
-  // Track scrolling and mark TOC links as active
-  // get table of contents and sidebar (bail if we don't have at least one)
-  const tocLinks = tocEl
-    ? [...tocEl.querySelectorAll("a[data-scroll-target]")]
-    : [];
-  const makeActive = (link) => tocLinks[link].classList.add("active");
-  const removeActive = (link) => tocLinks[link].classList.remove("active");
-  const removeAllActive = () =>
-    [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link));
-
-  // activate the anchor for a section associated with this TOC entry
-  tocLinks.forEach((link) => {
-    link.addEventListener("click", () => {
-      if (link.href.indexOf("#") !== -1) {
-        const anchor = link.href.split("#")[1];
-        const heading = window.document.querySelector(
-          `[data-anchor-id=${anchor}]`
-        );
-        if (heading) {
-          // Add the class
-          heading.classList.add("reveal-anchorjs-link");
-
-          // function to show the anchor
-          const handleMouseout = () => {
-            heading.classList.remove("reveal-anchorjs-link");
-            heading.removeEventListener("mouseout", handleMouseout);
-          };
-
-          // add a function to clear the anchor when the user mouses out of it
-          heading.addEventListener("mouseout", handleMouseout);
-        }
-      }
-    });
-  });
-
-  const sections = tocLinks.map((link) => {
-    const target = link.getAttribute("data-scroll-target");
-    if (target.startsWith("#")) {
-      return window.document.getElementById(decodeURI(`${target.slice(1)}`));
-    } else {
-      return window.document.querySelector(decodeURI(`${target}`));
-    }
-  });
-
-  const sectionMargin = 200;
-  let currentActive = 0;
-  // track whether we've initialized state the first time
-  let init = false;
-
-  const updateActiveLink = () => {
-    // The index from bottom to top (e.g. reversed list)
-    let sectionIndex = -1;
-    if (
-      window.innerHeight + window.pageYOffset >=
-      window.document.body.offsetHeight
-    ) {
-      sectionIndex = 0;
-    } else {
-      sectionIndex = [...sections].reverse().findIndex((section) => {
-        if (section) {
-          return window.pageYOffset >= section.offsetTop - sectionMargin;
-        } else {
-          return false;
-        }
-      });
-    }
-    if (sectionIndex > -1) {
-      const current = sections.length - sectionIndex - 1;
-      if (current !== currentActive) {
-        removeAllActive();
-        currentActive = current;
-        makeActive(current);
-        if (init) {
-          window.dispatchEvent(sectionChanged);
-        }
-        init = true;
-      }
-    }
-  };
-
-  const inHiddenRegion = (top, bottom, hiddenRegions) => {
-    for (const region of hiddenRegions) {
-      if (top <= region.bottom && bottom >= region.top) {
-        return true;
-      }
-    }
-    return false;
-  };
-
-  const categorySelector = "header.quarto-title-block .quarto-category";
-  const activateCategories = (href) => {
-    // Find any categories
-    // Surround them with a link pointing back to:
-    // #category=Authoring
-    try {
-      const categoryEls = window.document.querySelectorAll(categorySelector);
-      for (const categoryEl of categoryEls) {
-        const categoryText = categoryEl.textContent;
-        if (categoryText) {
-          const link = `${href}#category=${encodeURIComponent(categoryText)}`;
-          const linkEl = window.document.createElement("a");
-          linkEl.setAttribute("href", link);
-          for (const child of categoryEl.childNodes) {
-            linkEl.append(child);
-          }
-          categoryEl.appendChild(linkEl);
-        }
-      }
-    } catch {
-      // Ignore errors
-    }
-  };
-  function hasTitleCategories() {
-    return window.document.querySelector(categorySelector) !== null;
-  }
-
-  function offsetRelativeUrl(url) {
-    const offset = getMeta("quarto:offset");
-    return offset ? offset + url : url;
-  }
-
-  function offsetAbsoluteUrl(url) {
-    const offset = getMeta("quarto:offset");
-    const baseUrl = new URL(offset, window.location);
-
-    const projRelativeUrl = url.replace(baseUrl, "");
-    if (projRelativeUrl.startsWith("/")) {
-      return projRelativeUrl;
-    } else {
-      return "/" + projRelativeUrl;
-    }
-  }
-
-  // read a meta tag value
-  function getMeta(metaName) {
-    const metas = window.document.getElementsByTagName("meta");
-    for (let i = 0; i < metas.length; i++) {
-      if (metas[i].getAttribute("name") === metaName) {
-        return metas[i].getAttribute("content");
-      }
-    }
-    return "";
-  }
-
-  async function findAndActivateCategories() {
-    const currentPagePath = offsetAbsoluteUrl(window.location.href);
-    const response = await fetch(offsetRelativeUrl("listings.json"));
-    if (response.status == 200) {
-      return response.json().then(function (listingPaths) {
-        const listingHrefs = [];
-        for (const listingPath of listingPaths) {
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-          for (const item of listingPath.items) {
-            if (
-              item === currentPagePath ||
-              item === currentPagePath + "index.html"
-            ) {
-              // Resolve this path against the offset to be sure
-              // we already are using the correct path to the listing
-              // (this adjusts the listing urls to be rooted against
-              // whatever root the page is actually running against)
-              const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
-              const baseUrl = window.location;
-              const resolvedPath = new URL(relative, baseUrl);
-              listingHrefs.push(resolvedPath.pathname);
-              break;
-            }
-          }
-        }
-
-        // Look up the tree for a nearby linting and use that if we find one
-        const nearestListing = findNearestParentListing(
-          offsetAbsoluteUrl(window.location.pathname),
-          listingHrefs
-        );
-        if (nearestListing) {
-          activateCategories(nearestListing);
-        } else {
-          // See if the referrer is a listing page for this item
-          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
-          const referrerListing = listingHrefs.find((listingHref) => {
-            const isListingReferrer =
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-              listingHref === referredRelativePath + "index.html";
-            return isListingReferrer;
-          });
-
-          if (referrerListing) {
-            // Try to use the referrer if possible
-            activateCategories(referrerListing);
-          } else if (listingHrefs.length > 0) {
-            // Otherwise, just fall back to the first listing
-            activateCategories(listingHrefs[0]);
-          }
-        }
-      });
-    }
-  }
-  if (hasTitleCategories()) {
-    findAndActivateCategories();
-  }
-
-  const findNearestParentListing = (href, listingHrefs) => {
-    if (!href || !listingHrefs) {
-      return undefined;
-    }
-    // Look up the tree for a nearby linting and use that if we find one
-    const relativeParts = href.substring(1).split("/");
-    while (relativeParts.length > 0) {
-      const path = relativeParts.join("/");
-      for (const listingHref of listingHrefs) {
-        if (listingHref.startsWith(path)) {
-          return listingHref;
-        }
-      }
-      relativeParts.pop();
-    }
-
-    return undefined;
-  };
-
-  const manageSidebarVisiblity = (el, placeholderDescriptor) => {
-    let isVisible = true;
-    let elRect;
-
-    return (hiddenRegions) => {
-      if (el === null) {
-        return;
-      }
-
-      // Find the last element of the TOC
-      const lastChildEl = el.lastElementChild;
-
-      if (lastChildEl) {
-        // Converts the sidebar to a menu
-        const convertToMenu = () => {
-          for (const child of el.children) {
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-            child.style.overflow = "hidden";
-          }
-
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-            const toggleContainer = window.document.createElement("div");
-            toggleContainer.style.width = "100%";
-            toggleContainer.classList.add("zindex-over-content");
-            toggleContainer.classList.add("quarto-sidebar-toggle");
-            toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
-            toggleContainer.id = placeholderDescriptor.id;
-            toggleContainer.style.position = "fixed";
-
-            const toggleIcon = window.document.createElement("i");
-            toggleIcon.classList.add("quarto-sidebar-toggle-icon");
-            toggleIcon.classList.add("bi");
-            toggleIcon.classList.add("bi-caret-down-fill");
-
-            const toggleTitle = window.document.createElement("div");
-            const titleEl = window.document.body.querySelector(
-              placeholderDescriptor.titleSelector
-            );
-            if (titleEl) {
-              toggleTitle.append(
-                titleEl.textContent || titleEl.innerText,
-                toggleIcon
-              );
-            }
-            toggleTitle.classList.add("zindex-over-content");
-            toggleTitle.classList.add("quarto-sidebar-toggle-title");
-            toggleContainer.append(toggleTitle);
-
-            const toggleContents = window.document.createElement("div");
-            toggleContents.classList = el.classList;
-            toggleContents.classList.add("zindex-over-content");
-            toggleContents.classList.add("quarto-sidebar-toggle-contents");
-            for (const child of el.children) {
-              if (child.id === "toc-title") {
-                continue;
-              }
-
-              const clone = child.cloneNode(true);
-              clone.style.opacity = 1;
-              clone.style.display = null;
-              toggleContents.append(clone);
-            }
-            toggleContents.style.height = "0px";
-            const positionToggle = () => {
-              // position the element (top left of parent, same width as parent)
-              if (!elRect) {
-                elRect = el.getBoundingClientRect();
-              }
-              toggleContainer.style.left = `${elRect.left}px`;
-              toggleContainer.style.top = `${elRect.top}px`;
-              toggleContainer.style.width = `${elRect.width}px`;
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-            positionToggle();
-
-            toggleContainer.append(toggleContents);
-            el.parentElement.prepend(toggleContainer);
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-            // when it is clicked (e.g. sidebar navigation supports
-            // opening and closing the nav tree, so don't dismiss on click)
-            const clickEl = placeholderDescriptor.dismissOnClick
-              ? toggleContainer
-              : toggleTitle;
-
-            const closeToggle = () => {
-              if (tocShowing) {
-                toggleContainer.classList.remove("expanded");
-                toggleContents.style.height = "0px";
-                tocShowing = false;
-              }
-            };
-
-            // Get rid of any expanded toggle if the user scrolls
-            window.document.addEventListener(
-              "scroll",
-              throttle(() => {
-                closeToggle();
-              }, 50)
-            );
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-            // Handle positioning of the toggle
-            window.addEventListener(
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-                elRect = undefined;
-                positionToggle();
-              }, 50)
-            );
-
-            window.addEventListener("quarto-hrChanged", () => {
-              elRect = undefined;
-            });
-
-            // Process the click
-            clickEl.onclick = () => {
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-                toggleContainer.classList.add("expanded");
-                toggleContents.style.height = null;
-                tocShowing = true;
-              } else {
-                closeToggle();
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-            };
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-
-        // Converts a sidebar from a menu back to a sidebar
-        const convertToSidebar = () => {
-          for (const child of el.children) {
-            child.style.opacity = 1;
-            child.style.overflow = null;
-          }
-
-          const placeholderEl = window.document.getElementById(
-            placeholderDescriptor.id
-          );
-          if (placeholderEl) {
-            placeholderEl.remove();
-          }
-
-          el.classList.remove("rollup");
-        };
-
-        if (isReaderMode()) {
-          convertToMenu();
-          isVisible = false;
-        } else {
-          // Find the top and bottom o the element that is being managed
-          const elTop = el.offsetTop;
-          const elBottom =
-            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
-
-          if (!isVisible) {
-            // If the element is current not visible reveal if there are
-            // no conflicts with overlay regions
-            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
-              convertToSidebar();
-              isVisible = true;
-            }
-          } else {
-            // If the element is visible, hide it if it conflicts with overlay regions
-            // and insert a placeholder toggle (or if we're in reader mode)
-            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
-              convertToMenu();
-              isVisible = false;
-            }
-          }
-        }
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-    };
-  };
-
-  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
-  for (const tabEl of tabEls) {
-    const id = tabEl.getAttribute("data-bs-target");
-    if (id) {
-      const columnEl = document.querySelector(
-        `${id} .column-margin, .tabset-margin-content`
-      );
-      if (columnEl)
-        tabEl.addEventListener("shown.bs.tab", function (event) {
-          const el = event.srcElement;
-          if (el) {
-            const visibleCls = `${el.id}-margin-content`;
-            // walk up until we find a parent tabset
-            let panelTabsetEl = el.parentElement;
-            while (panelTabsetEl) {
-              if (panelTabsetEl.classList.contains("panel-tabset")) {
-                break;
-              }
-              panelTabsetEl = panelTabsetEl.parentElement;
-            }
-
-            if (panelTabsetEl) {
-              const prevSib = panelTabsetEl.previousElementSibling;
-              if (
-                prevSib &&
-                prevSib.classList.contains("tabset-margin-container")
-              ) {
-                const childNodes = prevSib.querySelectorAll(
-                  ".tabset-margin-content"
-                );
-                for (const childEl of childNodes) {
-                  if (childEl.classList.contains(visibleCls)) {
-                    childEl.classList.remove("collapse");
-                  } else {
-                    childEl.classList.add("collapse");
-                  }
-                }
-              }
-            }
-          }
-
-          layoutMarginEls();
-        });
-    }
-  }
-
-  // Manage the visibility of the toc and the sidebar
-  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
-    id: "quarto-toc-toggle",
-    titleSelector: "#toc-title",
-    dismissOnClick: true,
-  });
-  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
-    id: "quarto-sidebarnav-toggle",
-    titleSelector: ".title",
-    dismissOnClick: false,
-  });
-  let tocLeftScrollVisibility;
-  if (leftTocEl) {
-    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
-      id: "quarto-lefttoc-toggle",
-      titleSelector: "#toc-title",
-      dismissOnClick: true,
-    });
-  }
-
-  // Find the first element that uses formatting in special columns
-  const conflictingEls = window.document.body.querySelectorAll(
-    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
-  );
-
-  // Filter all the possibly conflicting elements into ones
-  // the do conflict on the left or ride side
-  const arrConflictingEls = Array.from(conflictingEls);
-  const leftSideConflictEls = arrConflictingEls.filter((el) => {
-    if (el.tagName === "ASIDE") {
-      return false;
-    }
-    return Array.from(el.classList).find((className) => {
-      return (
-        className !== "column-body" &&
-        className.startsWith("column-") &&
-        !className.endsWith("right") &&
-        !className.endsWith("container") &&
-        className !== "column-margin"
-      );
-    });
-  });
-  const rightSideConflictEls = arrConflictingEls.filter((el) => {
-    if (el.tagName === "ASIDE") {
-      return true;
-    }
-
-    const hasMarginCaption = Array.from(el.classList).find((className) => {
-      return className == "margin-caption";
-    });
-    if (hasMarginCaption) {
-      return true;
-    }
-
-    return Array.from(el.classList).find((className) => {
-      return (
-        className !== "column-body" &&
-        !className.endsWith("container") &&
-        className.startsWith("column-") &&
-        !className.endsWith("left")
-      );
-    });
-  });
-
-  const kOverlapPaddingSize = 10;
-  function toRegions(els) {
-    return els.map((el) => {
-      const boundRect = el.getBoundingClientRect();
-      const top =
-        boundRect.top +
-        document.documentElement.scrollTop -
-        kOverlapPaddingSize;
-      return {
-        top,
-        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
-      };
-    });
-  }
-
-  let hasObserved = false;
-  const visibleItemObserver = (els) => {
-    let visibleElements = [...els];
-    const intersectionObserver = new IntersectionObserver(
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-          if (entry.isIntersecting) {
-            if (visibleElements.indexOf(entry.target) === -1) {
-              visibleElements.push(entry.target);
-            }
-          } else {
-            visibleElements = visibleElements.filter((visibleEntry) => {
-              return visibleEntry !== entry;
-            });
-          }
-        });
-
-        if (!hasObserved) {
-          hideOverlappedSidebars();
-        }
-        hasObserved = true;
-      },
-      {}
-    );
-    els.forEach((el) => {
-      intersectionObserver.observe(el);
-    });
-
-    return {
-      getVisibleEntries: () => {
-        return visibleElements;
-      },
-    };
-  };
-
-  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
-  const leftElementObserver = visibleItemObserver(leftSideConflictEls);
-
-  const hideOverlappedSidebars = () => {
-    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
-    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
-    if (tocLeftScrollVisibility) {
-      tocLeftScrollVisibility(
-        toRegions(leftElementObserver.getVisibleEntries())
-      );
-    }
-  };
-
-  window.quartoToggleReader = () => {
-    // Applies a slow class (or removes it)
-    // to update the transition speed
-    const slowTransition = (slow) => {
-      const manageTransition = (id, slow) => {
-        const el = document.getElementById(id);
-        if (el) {
-          if (slow) {
-            el.classList.add("slow");
-          } else {
-            el.classList.remove("slow");
-          }
-        }
-      };
-
-      manageTransition("TOC", slow);
-      manageTransition("quarto-sidebar", slow);
-    };
-    const readerMode = !isReaderMode();
-    setReaderModeValue(readerMode);
-
-    // If we're entering reader mode, slow the transition
-    if (readerMode) {
-      slowTransition(readerMode);
-    }
-    highlightReaderToggle(readerMode);
-    hideOverlappedSidebars();
-
-    // If we're exiting reader mode, restore the non-slow transition
-    if (!readerMode) {
-      slowTransition(!readerMode);
-    }
-  };
-
-  const highlightReaderToggle = (readerMode) => {
-    const els = document.querySelectorAll(".quarto-reader-toggle");
-    if (els) {
-      els.forEach((el) => {
-        if (readerMode) {
-          el.classList.add("reader");
-        } else {
-          el.classList.remove("reader");
-        }
-      });
-    }
-  };
-
-  const setReaderModeValue = (val) => {
-    if (window.location.protocol !== "file:") {
-      window.localStorage.setItem("quarto-reader-mode", val);
-    } else {
-      localReaderMode = val;
-    }
-  };
-
-  const isReaderMode = () => {
-    if (window.location.protocol !== "file:") {
-      return window.localStorage.getItem("quarto-reader-mode") === "true";
-    } else {
-      return localReaderMode;
-    }
-  };
-  let localReaderMode = null;
-
-  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
-  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
-
-  // Walk the TOC and collapse/expand nodes
-  // Nodes are expanded if:
-  // - they are top level
-  // - they have children that are 'active' links
-  // - they are directly below an link that is 'active'
-  const walk = (el, depth) => {
-    // Tick depth when we enter a UL
-    if (el.tagName === "UL") {
-      depth = depth + 1;
-    }
-
-    // It this is active link
-    let isActiveNode = false;
-    if (el.tagName === "A" && el.classList.contains("active")) {
-      isActiveNode = true;
-    }
-
-    // See if there is an active child to this element
-    let hasActiveChild = false;
-    for (child of el.children) {
-      hasActiveChild = walk(child, depth) || hasActiveChild;
-    }
-
-    // Process the collapse state if this is an UL
-    if (el.tagName === "UL") {
-      if (tocOpenDepth === -1 && depth > 1) {
-        el.classList.add("collapse");
-      } else if (
-        depth <= tocOpenDepth ||
-        hasActiveChild ||
-        prevSiblingIsActiveLink(el)
-      ) {
-        el.classList.remove("collapse");
-      } else {
-        el.classList.add("collapse");
-      }
-
-      // untick depth when we leave a UL
-      depth = depth - 1;
-    }
-    return hasActiveChild || isActiveNode;
-  };
-
-  // walk the TOC and expand / collapse any items that should be shown
-
-  if (tocEl) {
-    walk(tocEl, 0);
-    updateActiveLink();
-  }
-
-  // Throttle the scroll event and walk peridiocally
-  window.document.addEventListener(
-    "scroll",
-    throttle(() => {
-      if (tocEl) {
-        updateActiveLink();
-        walk(tocEl, 0);
-      }
-      if (!isReaderMode()) {
-        hideOverlappedSidebars();
-      }
-    }, 5)
-  );
-  window.addEventListener(
-    "resize",
-    throttle(() => {
-      if (!isReaderMode()) {
-        hideOverlappedSidebars();
-      }
-    }, 10)
-  );
-  hideOverlappedSidebars();
-  highlightReaderToggle(isReaderMode());
-});
-
-// grouped tabsets
-window.addEventListener("pageshow", (_event) => {
-  function getTabSettings() {
-    const data = localStorage.getItem("quarto-persistent-tabsets-data");
-    if (!data) {
-      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
-      return {};
-    }
-    if (data) {
-      return JSON.parse(data);
-    }
-  }
-
-  function setTabSettings(data) {
-    localStorage.setItem(
-      "quarto-persistent-tabsets-data",
-      JSON.stringify(data)
-    );
-  }
-
-  function setTabState(groupName, groupValue) {
-    const data = getTabSettings();
-    data[groupName] = groupValue;
-    setTabSettings(data);
-  }
-
-  function toggleTab(tab, active) {
-    const tabPanelId = tab.getAttribute("aria-controls");
-    const tabPanel = document.getElementById(tabPanelId);
-    if (active) {
-      tab.classList.add("active");
-      tabPanel.classList.add("active");
-    } else {
-      tab.classList.remove("active");
-      tabPanel.classList.remove("active");
-    }
-  }
-
-  function toggleAll(selectedGroup, selectorsToSync) {
-    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
-      const active = selectedGroup === thisGroup;
-      for (const tab of tabs) {
-        toggleTab(tab, active);
-      }
-    }
-  }
-
-  function findSelectorsToSyncByLanguage() {
-    const result = {};
-    const tabs = Array.from(
-      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
-    );
-    for (const item of tabs) {
-      const div = item.parentElement.parentElement.parentElement;
-      const group = div.getAttribute("data-group");
-      if (!result[group]) {
-        result[group] = {};
-      }
-      const selectorsToSync = result[group];
-      const value = item.innerHTML;
-      if (!selectorsToSync[value]) {
-        selectorsToSync[value] = [];
-      }
-      selectorsToSync[value].push(item);
-    }
-    return result;
-  }
-
-  function setupSelectorSync() {
-    const selectorsToSync = findSelectorsToSyncByLanguage();
-    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
-      Object.entries(tabSetsByValue).forEach(([value, items]) => {
-        items.forEach((item) => {
-          item.addEventListener("click", (_event) => {
-            setTabState(group, value);
-            toggleAll(value, selectorsToSync[group]);
-          });
-        });
-      });
-    });
-    return selectorsToSync;
-  }
-
-  const selectorsToSync = setupSelectorSync();
-  for (const [group, selectedName] of Object.entries(getTabSettings())) {
-    const selectors = selectorsToSync[group];
-    // it's possible that stale state gives us empty selections, so we explicitly check here.
-    if (selectors) {
-      toggleAll(selectedName, selectors);
-    }
-  }
-});
-
-function throttle(func, wait) {
-  let waiting = false;
-  return function () {
-    if (!waiting) {
-      func.apply(this, arguments);
-      waiting = true;
-      setTimeout(function () {
-        waiting = false;
-      }, wait);
-    }
-  };
-}
-
-function nexttick(func) {
-  return setTimeout(func, 0);
-}

spaces/Ashrafb/codellama-34b/README.md

@@ -1,16 +0,0 @@
----
-title: Llama 2 13b Chat
-emoji: 🦙
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
-license: other
-suggested_hardware: a10g-small
-duplicated_from: Ashrafb/codellama-34b-chat
----
-
-# CodeLlama-34b-Instruct Demo
-This is a clone of https://huggingface.co/spaces/codellama/codellama-13b-chat changed to use free inference API for CodeLlama-34b-Instruct model

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

@@ -1,531 +0,0 @@
-"""distutils.command.sdist
-
-Implements the Distutils 'sdist' command (create a source distribution)."""
-
-import os
-import sys
-from glob import glob
-from warnings import warn
-
-from distutils.core import Command
-from distutils import dir_util
-from distutils import file_util
-from distutils import archive_util
-from distutils.text_file import TextFile
-from distutils.filelist import FileList
-from distutils import log
-from distutils.util import convert_path
-from distutils.errors import DistutilsOptionError, DistutilsTemplateError
-
-
-def show_formats():
-    """Print all possible values for the 'formats' option (used by
-    the "--help-formats" command-line option).
-    """
-    from distutils.fancy_getopt import FancyGetopt
-    from distutils.archive_util import ARCHIVE_FORMATS
-
-    formats = []
-    for format in ARCHIVE_FORMATS.keys():
-        formats.append(("formats=" + format, None, ARCHIVE_FORMATS[format][2]))
-    formats.sort()
-    FancyGetopt(formats).print_help("List of available source distribution formats:")
-
-
-class sdist(Command):
-
-    description = "create a source distribution (tarball, zip file, etc.)"
-
-    def checking_metadata(self):
-        """Callable used for the check sub-command.
-
-        Placed here so user_options can view it"""
-        return self.metadata_check
-
-    user_options = [
-        ('template=', 't', "name of manifest template file [default: MANIFEST.in]"),
-        ('manifest=', 'm', "name of manifest file [default: MANIFEST]"),
-        (
-            'use-defaults',
-            None,
-            "include the default file set in the manifest "
-            "[default; disable with --no-defaults]",
-        ),
-        ('no-defaults', None, "don't include the default file set"),
-        (
-            'prune',
-            None,
-            "specifically exclude files/directories that should not be "
-            "distributed (build tree, RCS/CVS dirs, etc.) "
-            "[default; disable with --no-prune]",
-        ),
-        ('no-prune', None, "don't automatically exclude anything"),
-        (
-            'manifest-only',
-            'o',
-            "just regenerate the manifest and then stop " "(implies --force-manifest)",
-        ),
-        (
-            'force-manifest',
-            'f',
-            "forcibly regenerate the manifest and carry on as usual. "
-            "Deprecated: now the manifest is always regenerated.",
-        ),
-        ('formats=', None, "formats for source distribution (comma-separated list)"),
-        (
-            'keep-temp',
-            'k',
-            "keep the distribution tree around after creating " + "archive file(s)",
-        ),
-        (
-            'dist-dir=',
-            'd',
-            "directory to put the source distribution archive(s) in " "[default: dist]",
-        ),
-        (
-            'metadata-check',
-            None,
-            "Ensure that all required elements of meta-data "
-            "are supplied. Warn if any missing. [default]",
-        ),
-        (
-            'owner=',
-            'u',
-            "Owner name used when creating a tar file [default: current user]",
-        ),
-        (
-            'group=',
-            'g',
-            "Group name used when creating a tar file [default: current group]",
-        ),
-    ]
-
-    boolean_options = [
-        'use-defaults',
-        'prune',
-        'manifest-only',
-        'force-manifest',
-        'keep-temp',
-        'metadata-check',
-    ]
-
-    help_options = [
-        ('help-formats', None, "list available distribution formats", show_formats),
-    ]
-
-    negative_opt = {'no-defaults': 'use-defaults', 'no-prune': 'prune'}
-
-    sub_commands = [('check', checking_metadata)]
-
-    READMES = ('README', 'README.txt', 'README.rst')
-
-    def initialize_options(self):
-        # 'template' and 'manifest' are, respectively, the names of
-        # the manifest template and manifest file.
-        self.template = None
-        self.manifest = None
-
-        # 'use_defaults': if true, we will include the default file set
-        # in the manifest
-        self.use_defaults = 1
-        self.prune = 1
-
-        self.manifest_only = 0
-        self.force_manifest = 0
-
-        self.formats = ['gztar']
-        self.keep_temp = 0
-        self.dist_dir = None
-
-        self.archive_files = None
-        self.metadata_check = 1
-        self.owner = None
-        self.group = None
-
-    def finalize_options(self):
-        if self.manifest is None:
-            self.manifest = "MANIFEST"
-        if self.template is None:
-            self.template = "MANIFEST.in"
-
-        self.ensure_string_list('formats')
-
-        bad_format = archive_util.check_archive_formats(self.formats)
-        if bad_format:
-            raise DistutilsOptionError("unknown archive format '%s'" % bad_format)
-
-        if self.dist_dir is None:
-            self.dist_dir = "dist"
-
-    def run(self):
-        # 'filelist' contains the list of files that will make up the
-        # manifest
-        self.filelist = FileList()
-
-        # Run sub commands
-        for cmd_name in self.get_sub_commands():
-            self.run_command(cmd_name)
-
-        # Do whatever it takes to get the list of files to process
-        # (process the manifest template, read an existing manifest,
-        # whatever).  File list is accumulated in 'self.filelist'.
-        self.get_file_list()
-
-        # If user just wanted us to regenerate the manifest, stop now.
-        if self.manifest_only:
-            return
-
-        # Otherwise, go ahead and create the source distribution tarball,
-        # or zipfile, or whatever.
-        self.make_distribution()
-
-    def check_metadata(self):
-        """Deprecated API."""
-        warn(
-            "distutils.command.sdist.check_metadata is deprecated, \
-              use the check command instead",
-            PendingDeprecationWarning,
-        )
-        check = self.distribution.get_command_obj('check')
-        check.ensure_finalized()
-        check.run()
-
-    def get_file_list(self):
-        """Figure out the list of files to include in the source
-        distribution, and put it in 'self.filelist'.  This might involve
-        reading the manifest template (and writing the manifest), or just
-        reading the manifest, or just using the default file set -- it all
-        depends on the user's options.
-        """
-        # new behavior when using a template:
-        # the file list is recalculated every time because
-        # even if MANIFEST.in or setup.py are not changed
-        # the user might have added some files in the tree that
-        # need to be included.
-        #
-        #  This makes --force the default and only behavior with templates.
-        template_exists = os.path.isfile(self.template)
-        if not template_exists and self._manifest_is_not_generated():
-            self.read_manifest()
-            self.filelist.sort()
-            self.filelist.remove_duplicates()
-            return
-
-        if not template_exists:
-            self.warn(
-                ("manifest template '%s' does not exist " + "(using default file list)")
-                % self.template
-            )
-        self.filelist.findall()
-
-        if self.use_defaults:
-            self.add_defaults()
-
-        if template_exists:
-            self.read_template()
-
-        if self.prune:
-            self.prune_file_list()
-
-        self.filelist.sort()
-        self.filelist.remove_duplicates()
-        self.write_manifest()
-
-    def add_defaults(self):
-        """Add all the default files to self.filelist:
-          - README or README.txt
-          - setup.py
-          - test/test*.py
-          - all pure Python modules mentioned in setup script
-          - all files pointed by package_data (build_py)
-          - all files defined in data_files.
-          - all files defined as scripts.
-          - all C sources listed as part of extensions or C libraries
-            in the setup script (doesn't catch C headers!)
-        Warns if (README or README.txt) or setup.py are missing; everything
-        else is optional.
-        """
-        self._add_defaults_standards()
-        self._add_defaults_optional()
-        self._add_defaults_python()
-        self._add_defaults_data_files()
-        self._add_defaults_ext()
-        self._add_defaults_c_libs()
-        self._add_defaults_scripts()
-
-    @staticmethod
-    def _cs_path_exists(fspath):
-        """
-        Case-sensitive path existence check
-
-        >>> sdist._cs_path_exists(__file__)
-        True
-        >>> sdist._cs_path_exists(__file__.upper())
-        False
-        """
-        if not os.path.exists(fspath):
-            return False
-        # make absolute so we always have a directory
-        abspath = os.path.abspath(fspath)
-        directory, filename = os.path.split(abspath)
-        return filename in os.listdir(directory)
-
-    def _add_defaults_standards(self):
-        standards = [self.READMES, self.distribution.script_name]
-        for fn in standards:
-            if isinstance(fn, tuple):
-                alts = fn
-                got_it = False
-                for fn in alts:
-                    if self._cs_path_exists(fn):
-                        got_it = True
-                        self.filelist.append(fn)
-                        break
-
-                if not got_it:
-                    self.warn(
-                        "standard file not found: should have one of " + ', '.join(alts)
-                    )
-            else:
-                if self._cs_path_exists(fn):
-                    self.filelist.append(fn)
-                else:
-                    self.warn("standard file '%s' not found" % fn)
-
-    def _add_defaults_optional(self):
-        optional = ['test/test*.py', 'setup.cfg']
-        for pattern in optional:
-            files = filter(os.path.isfile, glob(pattern))
-            self.filelist.extend(files)
-
-    def _add_defaults_python(self):
-        # build_py is used to get:
-        #  - python modules
-        #  - files defined in package_data
-        build_py = self.get_finalized_command('build_py')
-
-        # getting python files
-        if self.distribution.has_pure_modules():
-            self.filelist.extend(build_py.get_source_files())
-
-        # getting package_data files
-        # (computed in build_py.data_files by build_py.finalize_options)
-        for pkg, src_dir, build_dir, filenames in build_py.data_files:
-            for filename in filenames:
-                self.filelist.append(os.path.join(src_dir, filename))
-
-    def _add_defaults_data_files(self):
-        # getting distribution.data_files
-        if self.distribution.has_data_files():
-            for item in self.distribution.data_files:
-                if isinstance(item, str):
-                    # plain file
-                    item = convert_path(item)
-                    if os.path.isfile(item):
-                        self.filelist.append(item)
-                else:
-                    # a (dirname, filenames) tuple
-                    dirname, filenames = item
-                    for f in filenames:
-                        f = convert_path(f)
-                        if os.path.isfile(f):
-                            self.filelist.append(f)
-
-    def _add_defaults_ext(self):
-        if self.distribution.has_ext_modules():
-            build_ext = self.get_finalized_command('build_ext')
-            self.filelist.extend(build_ext.get_source_files())
-
-    def _add_defaults_c_libs(self):
-        if self.distribution.has_c_libraries():
-            build_clib = self.get_finalized_command('build_clib')
-            self.filelist.extend(build_clib.get_source_files())
-
-    def _add_defaults_scripts(self):
-        if self.distribution.has_scripts():
-            build_scripts = self.get_finalized_command('build_scripts')
-            self.filelist.extend(build_scripts.get_source_files())
-
-    def read_template(self):
-        """Read and parse manifest template file named by self.template.
-
-        (usually "MANIFEST.in") The parsing and processing is done by
-        'self.filelist', which updates itself accordingly.
-        """
-        log.info("reading manifest template '%s'", self.template)
-        template = TextFile(
-            self.template,
-            strip_comments=1,
-            skip_blanks=1,
-            join_lines=1,
-            lstrip_ws=1,
-            rstrip_ws=1,
-            collapse_join=1,
-        )
-
-        try:
-            while True:
-                line = template.readline()
-                if line is None:  # end of file
-                    break
-
-                try:
-                    self.filelist.process_template_line(line)
-                # the call above can raise a DistutilsTemplateError for
-                # malformed lines, or a ValueError from the lower-level
-                # convert_path function
-                except (DistutilsTemplateError, ValueError) as msg:
-                    self.warn(
-                        "%s, line %d: %s"
-                        % (template.filename, template.current_line, msg)
-                    )
-        finally:
-            template.close()
-
-    def prune_file_list(self):
-        """Prune off branches that might slip into the file list as created
-        by 'read_template()', but really don't belong there:
-          * the build tree (typically "build")
-          * the release tree itself (only an issue if we ran "sdist"
-            previously with --keep-temp, or it aborted)
-          * any RCS, CVS, .svn, .hg, .git, .bzr, _darcs directories
-        """
-        build = self.get_finalized_command('build')
-        base_dir = self.distribution.get_fullname()
-
-        self.filelist.exclude_pattern(None, prefix=build.build_base)
-        self.filelist.exclude_pattern(None, prefix=base_dir)
-
-        if sys.platform == 'win32':
-            seps = r'/|\\'
-        else:
-            seps = '/'
-
-        vcs_dirs = ['RCS', 'CVS', r'\.svn', r'\.hg', r'\.git', r'\.bzr', '_darcs']
-        vcs_ptrn = r'(^|{})({})({}).*'.format(seps, '|'.join(vcs_dirs), seps)
-        self.filelist.exclude_pattern(vcs_ptrn, is_regex=1)
-
-    def write_manifest(self):
-        """Write the file list in 'self.filelist' (presumably as filled in
-        by 'add_defaults()' and 'read_template()') to the manifest file
-        named by 'self.manifest'.
-        """
-        if self._manifest_is_not_generated():
-            log.info(
-                "not writing to manually maintained "
-                "manifest file '%s'" % self.manifest
-            )
-            return
-
-        content = self.filelist.files[:]
-        content.insert(0, '# file GENERATED by distutils, do NOT edit')
-        self.execute(
-            file_util.write_file,
-            (self.manifest, content),
-            "writing manifest file '%s'" % self.manifest,
-        )
-
-    def _manifest_is_not_generated(self):
-        # check for special comment used in 3.1.3 and higher
-        if not os.path.isfile(self.manifest):
-            return False
-
-        fp = open(self.manifest)
-        try:
-            first_line = fp.readline()
-        finally:
-            fp.close()
-        return first_line != '# file GENERATED by distutils, do NOT edit\n'
-
-    def read_manifest(self):
-        """Read the manifest file (named by 'self.manifest') and use it to
-        fill in 'self.filelist', the list of files to include in the source
-        distribution.
-        """
-        log.info("reading manifest file '%s'", self.manifest)
-        with open(self.manifest) as manifest:
-            for line in manifest:
-                # ignore comments and blank lines
-                line = line.strip()
-                if line.startswith('#') or not line:
-                    continue
-                self.filelist.append(line)
-
-    def make_release_tree(self, base_dir, files):
-        """Create the directory tree that will become the source
-        distribution archive.  All directories implied by the filenames in
-        'files' are created under 'base_dir', and then we hard link or copy
-        (if hard linking is unavailable) those files into place.
-        Essentially, this duplicates the developer's source tree, but in a
-        directory named after the distribution, containing only the files
-        to be distributed.
-        """
-        # Create all the directories under 'base_dir' necessary to
-        # put 'files' there; the 'mkpath()' is just so we don't die
-        # if the manifest happens to be empty.
-        self.mkpath(base_dir)
-        dir_util.create_tree(base_dir, files, dry_run=self.dry_run)
-
-        # And walk over the list of files, either making a hard link (if
-        # os.link exists) to each one that doesn't already exist in its
-        # corresponding location under 'base_dir', or copying each file
-        # that's out-of-date in 'base_dir'.  (Usually, all files will be
-        # out-of-date, because by default we blow away 'base_dir' when
-        # we're done making the distribution archives.)
-
-        if hasattr(os, 'link'):  # can make hard links on this system
-            link = 'hard'
-            msg = "making hard links in %s..." % base_dir
-        else:  # nope, have to copy
-            link = None
-            msg = "copying files to %s..." % base_dir
-
-        if not files:
-            log.warn("no files to distribute -- empty manifest?")
-        else:
-            log.info(msg)
-        for file in files:
-            if not os.path.isfile(file):
-                log.warn("'%s' not a regular file -- skipping", file)
-            else:
-                dest = os.path.join(base_dir, file)
-                self.copy_file(file, dest, link=link)
-
-        self.distribution.metadata.write_pkg_info(base_dir)
-
-    def make_distribution(self):
-        """Create the source distribution(s).  First, we create the release
-        tree with 'make_release_tree()'; then, we create all required
-        archive files (according to 'self.formats') from the release tree.
-        Finally, we clean up by blowing away the release tree (unless
-        'self.keep_temp' is true).  The list of archive files created is
-        stored so it can be retrieved later by 'get_archive_files()'.
-        """
-        # Don't warn about missing meta-data here -- should be (and is!)
-        # done elsewhere.
-        base_dir = self.distribution.get_fullname()
-        base_name = os.path.join(self.dist_dir, base_dir)
-
-        self.make_release_tree(base_dir, self.filelist.files)
-        archive_files = []  # remember names of files we create
-        # tar archive must be created last to avoid overwrite and remove
-        if 'tar' in self.formats:
-            self.formats.append(self.formats.pop(self.formats.index('tar')))
-
-        for fmt in self.formats:
-            file = self.make_archive(
-                base_name, fmt, base_dir=base_dir, owner=self.owner, group=self.group
-            )
-            archive_files.append(file)
-            self.distribution.dist_files.append(('sdist', '', file))
-
-        self.archive_files = archive_files
-
-        if not self.keep_temp:
-            dir_util.remove_tree(base_dir, dry_run=self.dry_run)
-
-    def get_archive_files(self):
-        """Return the list of archive files created when the command
-        was run, or None if the command hasn't run yet.
-        """
-        return self.archive_files

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/common/data/coco_panoptic_separated.py

@@ -1,26 +0,0 @@
-from detectron2.config import LazyCall as L
-from detectron2.evaluation import (
-    COCOEvaluator,
-    COCOPanopticEvaluator,
-    DatasetEvaluators,
-    SemSegEvaluator,
-)
-
-from .coco import dataloader
-
-dataloader.train.dataset.names = "coco_2017_train_panoptic_separated"
-dataloader.train.dataset.filter_empty = False
-dataloader.test.dataset.names = "coco_2017_val_panoptic_separated"
-
-
-dataloader.evaluator = [
-    L(COCOEvaluator)(
-        dataset_name="${...test.dataset.names}",
-    ),
-    L(SemSegEvaluator)(
-        dataset_name="${...test.dataset.names}",
-    ),
-    L(COCOPanopticEvaluator)(
-        dataset_name="${...test.dataset.names}",
-    ),
-]

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/dev/packaging/gen_install_table.py

@@ -1,63 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-# -*- coding: utf-8 -*-
-
-import argparse
-
-template = """<details><summary> install </summary><pre><code>\
-python -m pip install detectron2{d2_version} -f \\
-  https://dl.fbaipublicfiles.com/detectron2/wheels/{cuda}/torch{torch}/index.html
-</code></pre> </details>"""
-CUDA_SUFFIX = {
-    "11.3": "cu113",
-    "11.1": "cu111",
-    "11.0": "cu110",
-    "10.2": "cu102",
-    "10.1": "cu101",
-    "10.0": "cu100",
-    "9.2": "cu92",
-    "cpu": "cpu",
-}
-
-
-def gen_header(torch_versions):
-    return '<table class="docutils"><tbody><th width="80"> CUDA </th>' + "".join(
-        [
-            '<th valign="bottom" align="left" width="100">torch {}</th>'.format(t)
-            for t in torch_versions
-        ]
-    )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--d2-version", help="detectron2 version number, default to empty")
-    args = parser.parse_args()
-    d2_version = f"=={args.d2_version}" if args.d2_version else ""
-
-    all_versions = (
-        [("1.8", k) for k in ["11.1", "10.2", "10.1", "cpu"]]
-        + [("1.9", k) for k in ["11.1", "10.2", "cpu"]]
-        + [("1.10", k) for k in ["11.3", "11.1", "10.2", "cpu"]]
-    )
-
-    torch_versions = sorted(
-        {k[0] for k in all_versions}, key=lambda x: int(x.split(".")[1]), reverse=True
-    )
-    cuda_versions = sorted(
-        {k[1] for k in all_versions}, key=lambda x: float(x) if x != "cpu" else 0, reverse=True
-    )
-
-    table = gen_header(torch_versions)
-    for cu in cuda_versions:
-        table += f""" <tr><td align="left">{cu}</td>"""
-        cu_suffix = CUDA_SUFFIX[cu]
-        for torch in torch_versions:
-            if (torch, cu) in all_versions:
-                cell = template.format(d2_version=d2_version, cuda=cu_suffix, torch=torch)
-            else:
-                cell = ""
-            table += f"""<td align="left">{cell} </td> """
-        table += "</tr>"
-    table += "</tbody></table>"
-    print(table)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/notes/contributing.md

@@ -1 +0,0 @@
-../../.github/CONTRIBUTING.md

spaces/Benson/text-generation/Examples/Apilar Los Pases Descargar Gratis.md

@@ -1,38 +0,0 @@
-
-<h1>Apilar los países: un juego divertido y educativo para todas las edades</h1>
-<p>¿Te encanta aprender sobre el mundo y sus países? ¿Te gusta jugar juegos que son divertidos y educativos? Si respondiste sí a ambas preguntas, entonces te encantará Stack the Countries, un juego de geografía diseñado para todas las edades. En este artículo, te diremos qué es Stack the Countries, cómo jugarlo, qué puedes aprender de él, dónde puedes descargarlo y por qué deberías jugarlo. También te daremos algunos consejos y trucos para ayudarte a dominar el juego y divertirte mientras lo haces. </p>
-<h2>apilar los países descargar gratis</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash;>>> <a href="https://bltlly.com/2v6Kyh">https://bltlly.com/2v6Kyh</a></b></p><br /><br />
- <h2>¿Qué es Apilar los Países? </h2>
-<p>Stack the Countries es un juego de geografía educativa creado por Dan Russell-Pinson, un desarrollador de aplicaciones premiadas para niños. El juego consta de tres juegos en uno: Apilar los países, Mapearlo y Apilar. El juego principal, Stack the Countries, consiste en construir pilas de países que alcanzan cierta altura respondiendo preguntas sobre ellos. Los otros dos juegos, Map It y Pile Up, prueban su capacidad para localizar países en un mapa e identificarlos rápidamente antes de que se acumulen. El juego cuenta con gráficos coloridos, física realista, divertidos efectos de sonido y música. </p>
- <h3>Cómo jugar Pila de los países</h3>
-
- <h3>¿Qué puedes aprender de Stack the Countries? </h3>
-<p>Apilar los Países no solo es divertido sino también educativo. Puede aprender muchos hechos e información sobre los países del mundo, como sus capitales, puntos de referencia, ubicaciones geográficas, países limítrofes, idiomas, banderas, formas y más. También puede aprender sobre los continentes y sus tamaños y posiciones relativas. El juego tiene más de 1000 preguntas únicas y 193 tarjetas flash que cubren los 193 países del mundo. También puede elegir qué tipos de preguntas se hacen para personalizar su experiencia de aprendizaje. </p>
- <h3>¿Dónde puede descargar Apilar los países? </h3>
-<p>Stack the Countries está disponible para dispositivos Android e iOS. Puedes descargarlo desde Google Play Store o App Store por $2.99. La aplicación no contiene anuncios ni compras en la aplicación, por lo que puedes disfrutarla sin interrupciones ni distracciones. La aplicación también admite idiomas inglés, español y francés. </p>
- <h2>Beneficios de jugar Stack the Countries</h2>
-<p>Jugar Stack the Countries tiene muchos beneficios para tu cerebro y tu estado de ánimo. Estos son algunos de ellos:</p>
- <h3>Hace que aprender geografía sea divertido y atractivo</h3>
-<p>Mucha gente encuentra la geografía aburrida o difícil de aprender. Sin embargo, Stack the Countries hace que la geografía sea divertida y atractiva convirtiéndola en un juego. Puedes aprender nuevos hechos e información mientras te diviertes y te retas. También puedes competir con tus amigos y familiares para ver quién sabe más sobre el mundo. </p>
-<p></p>
- <h3>Mejora tu memoria y habilidades espaciales</h3>
-<p>Jugar Stack the Countries también puede mejorar tu memoria y tus habilidades espaciales. Tienes que recordar los nombres, formas, banderas y ubicaciones de los países, así como sus capitales, puntos de referencia y otros detalles. También hay que organizar los países en la pantalla de una manera que se equilibran y encajan. Esto requiere que uses tus habilidades visuales y espaciales, así como tus habilidades de lógica y razonamiento. </p>
-
-<p>Otro beneficio de jugar Stack the Countries es que te reta a pensar de forma estratégica y creativa. Tienes que planificar con antelación y decidir qué países elegir y dónde colocarlos. También tienes que lidiar con la física realista del juego, que puede hacer que tu pila sea inestable o se caiga. Tienes que encontrar formas creativas de superar estos obstáculos y alcanzar tu objetivo. </p>
- <h3>Ofrece una variedad de contenidos y niveles de dificultad</h3>
-<p>Finalmente, jugar Stack the Countries ofrece una variedad de contenido y niveles de dificultad que pueden adaptarse a diferentes preferencias y habilidades. Puede elegir entre seis continentes diferentes o todo el mundo como su área de enfoque. También puede elegir qué tipos de preguntas se hacen, como mayúsculas, banderas, formas, puntos de referencia, etc. También puede ajustar el nivel de dificultad cambiando el número de países necesarios para completar un nivel, la altura de la línea a cuadros y la velocidad del juego. El juego también tiene dos juegos de bonificación, Map It y Pile Up, que ofrecen diferentes desafíos y modos. </p>
- <h2>Consejos y trucos para jugar Stack the Countries</h2>
-<p>Si quieres dominar Stack the Countries y divertirte más jugando, aquí hay algunos consejos y trucos que puedes usar:</p>
- <h3>Usa las tarjetas flash y los mapas para estudiar antes de jugar</h3>
-<p>El juego tiene 193 tarjetas flash que cubren los 193 países del mundo. Puedes usar estas tarjetas para estudiar y aprender más sobre cada país antes de jugar. También puedes ver tu progreso en mapas personalizados de los continentes que muestran qué países has recogido. </p>
- <h3>Tenga cuidado de no soltar o derribar su pila de países</h3>
-
- <h3>Tratar de recoger todos los 193 países y desbloquear los juegos de bonificación</h3>
-<p>El juego tiene una función de colección que le permite recoger todos los 193 países del mundo al ganar en el juego principal. Puedes ver qué países has recogido y cuáles te faltan en la pantalla de tu colección. También puedes desbloquear dos juegos de bonificación, Map It y Pile Up, recogiendo un cierto número de países. Mapa Prueba tu habilidad para localizar países en un mapa, mientras que Pile Up prueba tu habilidad para identificar países rápidamente antes de que se acumulen. </p>
- <h3>Juega en diferentes idiomas y modos para probar tus conocimientos</h3>
-<p>El juego es compatible con los idiomas inglés, español y francés. Puede cambiar el idioma del juego en el menú de configuración. Esto puede ayudarle a aprender nuevas palabras y frases en diferentes idiomas, así como poner a prueba su conocimiento de la geografía en diferentes idiomas. También puedes jugar en diferentes modos, como fácil, medio, duro o súper duro, dependiendo de tu preferencia y nivel de habilidad. </p>
- <h2>Conclusión</h2>
-<p>Stack the Countries es un divertido y educativo juego de geografía apto para todas las edades. Te enseña sobre los países del mundo, sus capitales, banderas, formas, puntos de referencia, lugares, idiomas, continentes y más. También mejora tu memoria, habilidades espaciales, pensamiento estratégico y creatividad. Ofrece una variedad de contenido y niveles de dificultad que pueden coincidir con tus intereses y habilidades. También tiene dos juegos de bonificación que ofrecen diferentes desafíos y modos. Puedes descargar Stack the Countries de Google Play Store o App Store por $2.99. </p>
- <p>Si te gusta aprender sobre geografía y jugar juegos al mismo tiempo, entonces definitivamente deberías probar Stack the Countries. Es un juego que te mantendrá entretenido y educado durante horas. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Dj Lado A Lado Completo Bajo Tiktok.md

@@ -1,176 +0,0 @@
-
-<h1>Cómo descargar videos de DJ Side to Side Full Bass TikTok</h1>
-<p>Si usted es un fan de TikTok, es posible que haya oído hablar de la canción viral DJ Side to Side Full Bass. Este es un remix de la canción de Ariana Grande Side to Side, creado por Wilfexbor, un DJ de Indonesia. El remix cuenta con un ritmo pegadizo, una gota de bajo, y una voz en off diciendo "keperluan emak emak", que significa "para las necesidades de las madres" en Indonesia. La canción se ha convertido en una sensación en TikTok, con millones de usuarios creando videos usando el sonido. En este artículo, te mostraremos cómo descargar videos de DJ Side to Side Full Bass TikTok en tus dispositivos móviles o PC, y cómo optimizar tu contenido para SEO con estos videos. </p>
-<h2>descargar dj lado a lado completo bajo tiktok</h2><br /><p><b><b>Download Zip</b> &#10040; <a href="https://bltlly.com/2v6IR2">https://bltlly.com/2v6IR2</a></b></p><br /><br />
- <h2>¿Qué es DJ Side to Side Full Bass TikTok? </h2>
-<h3>El origen y la popularidad de la canción</h3>
-<p>DJ Side to Side Full Bass TikTok es un remix de la canción de Ariana Grande Side to Side, que fue lanzado en 2016 como parte de su álbum Dangerous Woman. La canción original cuenta con el rapero Nicki Minaj y se trata de una mujer que está tan enamorada de su pareja que apenas puede caminar después de pasar la noche con él. La canción fue un éxito comercial, alcanzando el top 10 en varios países y recibiendo múltiples nominaciones y premios. </p>
-<p>El remix fue creado por Wilfexbor, un DJ de Indonesia que lo subió a YouTube en octubre de 2021. Añadió una gota de bajo, una voz en off diciendo "keperluan emak emak", y algunos otros efectos de sonido a la canción original. El remix rápidamente ganó popularidad en TikTok, donde los usuarios comenzaron a hacer videos usando el sonido. Algunos de los videos muestran a la gente bailando, haciendo playback, o haciendo acciones divertidas al ritmo. Otros muestran a la gente usando el sonido como música de fondo para sus actividades diarias, como cocinar, limpiar o hacer ejercicio. A partir de enero de 2022, hay más de 4 millones de videos usando el sonido en TikTok.</p>
- <h3>Las características y beneficios del remix</h3>
-
-<ul>
-<li> Tiene un ritmo pegadizo y optimista que hace que la gente quiera bailar o moverse. </li>
-<li> Tiene una gota de bajo que añade emoción e intensidad a la canción. </li>
-<li> Tiene una voz en off que añade humor y relacionabilidad a la canción. </li>
-<li> Tiene una duración corta (unos 15 segundos) que se ajusta bien con el formato TikTok. </li>
-<li>Tiene un atractivo universal que trasciende las barreras del lenguaje y la cultura. </li>
-</ul>
- <h3>Los retos y riesgos de descargar los vídeos</h3>
-<p>Si bien DJ Side to Side Full Bass TikTok es una canción divertida y entretenida, también hay algunos desafíos y riesgos involucrados en la descarga de los videos que lo utilizan. Estos son algunos de ellos:</p>
-<ul>
-<li>Algunos videos pueden no tener la opción de descargarlos o guardarlos, dependiendo de la configuración o preferencias del creador. </li>
-<li>Algunos videos pueden tener una marca de agua o un logotipo que reduce la calidad o la estética del video. </li>
-<li>Algunos videos pueden tener problemas de derechos de autor o violaciones que podrían resultar en consecuencias legales o sanciones de la plataforma o el propietario original de la canción. </li>
-<li>Algunos videos pueden tener contenido inapropiado u ofensivo que podría dañar la reputación o la imagen del usuario o la marca. </li>
-</ul>
-<p>Por lo tanto, es importante ser cuidadoso y responsable al descargar videos de TikTok de DJ Side to Side Full Bass, y respetar los derechos y deseos de los creadores y los propietarios de la canción. </p>
- <h2>Cómo descargar videos de DJ Side to Side Full Bass TikTok en dispositivos móviles</h2>
-<h3>Usando la aplicación TikTok</h3>
-<p>La forma más fácil y conveniente de descargar videos de TikTok de DJ Side to Side Full Bass en sus dispositivos móviles es usar la aplicación TikTok en sí. Estos son los pasos a seguir:</p>
-<p></p>
-<ol>
-<li>Abra la aplicación TikTok y encuentre el video que desea descargar. </li>
-<li>Toque en el icono de compartir (la flecha) en la esquina inferior derecha de la pantalla. </li>
-
-</ol>
-<p>Tenga en cuenta que este método solo funcionará si el creador del vídeo ha habilitado la opción de permitir que otros descarguen sus vídeos. De lo contrario, no verá la opción "Guardar vídeo". Además, tenga en cuenta que este método guardará el video con una marca de agua que muestra el logotipo de TikTok y el nombre de usuario del creador. </p>
- <h3>Uso de una aplicación o sitio web de terceros</h3>
-<p>Si quieres descargar videos de DJ Side to Side Full Bass TikTok sin usar la aplicación TikTok, o si quieres descargar videos que no tienen la opción de guardarlos, puedes usar una aplicación o sitio web de terceros que ofrece este servicio. Hay muchas aplicaciones y sitios web que afirman ayudarle a descargar vídeos de TikTok, pero algunos de ellos pueden no funcionar correctamente, pueden contener malware o virus, o pueden violar los términos y condiciones de TikTok. Por lo tanto, es aconsejable hacer alguna investigación y leer algunos comentarios antes de elegir uno. Aquí hay algunos ejemplos de aplicaciones y sitios web que puedes probar:</p>
-<ul>
-<li><a href="">Snaptik.app</a>: Este es un sitio web que te permite descargar videos TikTok sin marca de agua simplemente copiando y pegando la URL del video. También tiene una versión de aplicación para dispositivos Android. </li>
-<li><a href="">Video Downloader for TikTok - No Watermark</a>: Esta es una aplicación para dispositivos Android que te permite descargar videos TikTok sin marca de agua copiando y pegando la URL del video, o compartiendo el video desde la aplicación TikTok. </li>
-<li><a href=">TikTok Downloader - No Watermark</a>: Esta es una aplicación para dispositivos iOS que te permite descargar videos TikTok sin marca de agua copiando y pegando la URL del video, o compartiendo el video desde la aplicación TikTok. </li>
-</ul>
- <h3>Consejos y trucos para guardar vídeos sin marca de agua</h3>
-<p>Si desea guardar videos de DJ Side to Side Full Bass TikTok sin marca de agua, pero no desea usar una aplicación o sitio web de terceros, hay algunos consejos y trucos que puede probar. Estos son algunos de ellos:</p>
-<ul>
-
-<li>Utilice la foto en vivo: Si tiene un iPhone, puede utilizar la función de fotos en vivo para capturar un corto clip de vídeo del vídeo mientras se reproduce en la pantalla. Luego, puedes convertirlo en un video usando una aplicación como <a href="">Lively</a>. Sin embargo, este método también puede reducir la calidad o la resolución del vídeo, y solo puede capturar unos segundos del vídeo. </li>
-<li>Utilice el dispositivo de un amigo: Puede pedirle a un amigo que tiene la opción de guardar el video para descargarlo y enviarlo por correo electrónico, aplicación de mensajería o servicio en la nube. Sin embargo, este método puede depender de la disponibilidad y disposición de su amigo, y también puede consumir datos o espacio de almacenamiento. </li>
-</ul>
- <h2>Cómo descargar videos de DJ Side to Side Full Bass TikTok en PC o Mac</h2>
-<h3>Usando una extensión de navegador web</h3>
-<p>Si desea descargar videos de DJ Side to Side Full Bass TikTok en su PC o Mac, puede usar una extensión del navegador web que le permite hacerlo. Una extensión de navegador web es un programa de software que añade funcionalidad o características a su navegador web. Hay muchas extensiones de navegador web que afirman ayudarle a descargar vídeos TikTok, pero algunos de ellos pueden no funcionar correctamente, pueden contener malware o virus, o pueden violar los términos y condiciones de TikTok. Por lo tanto, es aconsejable hacer alguna investigación y leer algunos comentarios antes de elegir uno. Estos son algunos ejemplos de extensiones de navegador web que puedes probar:</p>
-<ul>
-<li><a href="">TikTok Downloader</a>: Esta es una extensión del navegador web para Chrome que te permite descargar vídeos de TikTok sin marca de agua haciendo clic en un botón de descarga que aparece en la página de vídeo. </li>
-<li><a href="">TikTok Video Downloader</a>: Esta es una extensión de navegador web para Firefox que te permite descargar vídeos de TikTok sin marca de agua haciendo clic en un botón de descarga que aparece en la página de vídeo. </li>
-
-</ul>
- <h3>Usando un software de escritorio o una herramienta en línea</h3>
-<p>Si no desea utilizar una extensión de navegador web, también puede utilizar un software de escritorio o una herramienta en línea que ofrece el servicio de descarga de DJ Side to Side Full Bass TikTok vídeos en su PC o Mac. Un software de escritorio es un programa de software que necesita instalar en su computadora, mientras que una herramienta en línea es un sitio web al que puede acceder a través de su navegador web. Hay muchos software de escritorio y herramientas en línea que afirman ayudarle a descargar vídeos TikTok, pero algunos de ellos pueden no funcionar correctamente, pueden contener malware o virus, o pueden violar los términos y condiciones de TikTok. Por lo tanto, es aconsejable hacer alguna investigación y leer algunos comentarios antes de elegir uno. Aquí hay algunos ejemplos de software de escritorio y herramientas en línea que puedes probar:</p>
-<ul>
-<li><a href=">4K Video Downloader</a>: Este es un software de escritorio para Windows, Mac y Linux que te permite descargar videos TikTok sin marca de agua copiando y pegando la URL del video. También te permite descargar vídeos de otras plataformas, como YouTube, Facebook, Instagram y más. </li>
-<li><a href=">SnapTik</a>: Esta es una herramienta en línea que le permite descargar videos TikTok sin marca de agua copiando y pegando la URL del video. También permite elegir el formato y la calidad del vídeo. </li>
-<li><a href="">TikMate</a>: Esta es una herramienta en línea que te permite descargar videos TikTok sin marca de agua copiando y pegando la URL del video. También le permite editar el video antes de descargarlo. </li>
-</ul>
- <h3>Consejos y trucos para guardar vídeos en alta calidad</h3>
-<p>Si desea guardar videos de alta calidad de DJ Side to Side Full Bass TikTok, hay algunos consejos y trucos que puede probar. Estos son algunos de ellos:</p>
-<ul>
-
-<li>Utilice una conexión a Internet confiable: Al descargar el video, asegúrese de usar una conexión a Internet confiable, como Wi-Fi o Ethernet. Esto asegurará que el proceso de descarga sea rápido y suave, y que el video no se corrompa o dañe. </li>
-<li>Utilice un buen reproductor de vídeo: Al reproducir el vídeo, asegúrese de utilizar un buen reproductor de vídeo, como VLC o QuickTime. Esto asegurará que el video se reproduzca sin problemas y sin errores. </li>
-</ul>
- <h2>Cómo optimizar tu contenido para SEO con DJ Side to Side Full Bass TikTok Videos</h2>
-<h3>Elegir palabras clave y temas relevantes</h3>
-<p>Si desea optimizar su contenido para SEO con DJ Side to Side Full Bass TikTok videos, uno de los primeros pasos es elegir palabras clave y temas relevantes para su sitio web o blog. Las palabras clave son palabras o frases que describen la idea principal o el propósito de tu contenido, y que los usuarios escriben en los motores de búsqueda para encontrar lo que están buscando. Los temas son categorías o temas más amplios que se relacionan con tu contenido, y que los usuarios están interesados o tienen curiosidad. Elegir palabras clave y temas relevantes te ayudará a posicionarte más alto en las páginas de resultados de los motores de búsqueda (SERPs), atraer más tráfico orgánico y aumentar tu autoridad y credibilidad. </p>
-<p>Para elegir palabras clave y temas relevantes, puede usar varias herramientas y métodos, como:</p>
-<ul>
-<li>Herramientas de investigación de palabras clave: Estas son herramientas que te ayudan a encontrar y analizar las mejores palabras clave para tu contenido, basadas en factores tales como volumen de búsqueda, competencia, dificultad, relevancia y tendencias. Algunos ejemplos de herramientas de investigación de palabras clave son <a href="">Google Keyword Planner</a>, <a href="">SEMrush</a>, <a href=">Ahrefs</a>, y <a href=">Moz</a>. </li>
-
-<li>Análisis de contenido: Este es un método que te ayuda a examinar y optimizar la calidad y relevancia de tu contenido, y cómo coincide con la intención y las expectativas de tu público objetivo. Esto te ayudará a crear contenido que sea atractivo, informativo, útil y persuasivo. Algunos ejemplos de herramientas de análisis de contenido son <a href="">Yoast SEO</a>, <a href=">Grammarly</a>, <a href="">Hemingway</a>, y <a href=">CoSchedule Headline Analyzer</a>. </li>
-</ul>
-<p>Por ejemplo, si quieres optimizar tu contenido para SEO con vídeos de TikTok de DJ Side to Side Full Bass, puedes usar estas herramientas y métodos para encontrar palabras clave y temas como:</p>
-<tabla>
-<tr><th>Palabras clave</th><th>Temas</th></tr>
-<tr><td>DJ Side to Side Full Bass TikTok</td><td>Tendencias de TikTok y canciones virales</td></tr>
-<tr><td>Cómo descargar vídeos de TikTok sin marca de agua</td><td>Consejos y trucos de TikTok</td></tr>
-<tr><td>Cómo usar videos de TikTok para SEO</td><td>Estrategias de marketing y SEO de TikTok</td></tr>
-<tr><td>Ariana Grande Lado a Lado remix</td><td>Ariana Grande canciones y remixes</td></tr>
-<tr><td>Wilfexbor DJ desde Indonesia</td><td>Biografía de Wilfexbor y carrera musical</td></tr>
-</tabla>
- <h3>Creación de contenido de alta calidad con cabeceras útiles y visuales</h3>
-<p>Otro paso para optimizar su contenido para SEO con DJ Side to Side Full Bass TikTok videos es crear contenido de alta calidad que está bien estructurado, bien escrito, bien formateado y bien presentado. El contenido de alta calidad te ayudará a proporcionar valor a tu audiencia, aumentar tu tiempo de permanencia, reducir tu tasa de rebote, aumentar tus compartidos en redes sociales y mejorar tu posicionamiento en SERPs.</p>
-<p>Para crear contenido de alta calidad, puedes usar varias técnicas y mejores prácticas, como:</p>
-<ul>
-
-<li>Uso de elementos visuales: Los elementos visuales son las imágenes, vídeos, gráficos, gráficos u otros gráficos que complementan su contenido. Te ayudan a ilustrar tus ideas visualmente, captar la atención de tus lectores, mejorar tu mensaje y romper la monotonía del texto. Para usar las imágenes de manera efectiva, debe elegir imágenes relevantes y de alta calidad, optimizarlas para el tamaño y la velocidad, agregar texto alternativo y subtítulos, alinearlas con su texto y citar las fuentes si es necesario. </li> <li>Uso de tablas: Las tablas son las cuadrículas o matrices que muestran datos o información en filas y columnas. Te ayudan a comparar y contrastar diferentes elementos, mostrar relaciones o patrones y resumir o simplificar datos complejos. Para usar las tablas de manera efectiva, debe elegir datos relevantes y precisos, usar etiquetas claras y concisas, alinear el texto y los números, usar bordes y sombreado, y citar las fuentes si es necesario. </li>
-</ul>
-<p>Por ejemplo, si desea crear contenido de alta calidad con encabezados útiles y visuales para DJ Side to Side Full Bass TikTok videos, puede utilizar estas técnicas y mejores prácticas para crear contenido como:</p>
-<h2>Cómo usar videos de TikTok para SEO</h2>
-<p>Los vídeos de TikTok no solo son divertidos y entretenidos, sino también herramientas potentes y versátiles para el SEO. SEO significa optimización de motores de búsqueda, que es el proceso de mejorar la cantidad y la calidad del tráfico a su sitio web o blog de los motores de búsqueda. Al usar vídeos TikTok para SEO, puedes beneficiarte de las siguientes ventajas:</p>
-<ul>
-<li>Puedes llegar a una audiencia grande y diversa: TikTok tiene más de mil millones de usuarios activos mensuales en todo el mundo, que son en su mayoría jóvenes, creativos e influyentes. Al crear y compartir vídeos de TikTok que sean relevantes para tu nicho, puedes atraer e involucrar a estos usuarios y llevarlos a tu sitio web o blog. </li>
-
-<li>Puedes generar más backlinks y señales sociales: los videos de TikTok pueden ayudarte a crear más backlinks y señales sociales para tu sitio web o blog. Los backlinks son los enlaces que apuntan a tu sitio web o blog desde otros sitios web o blogs, mientras que las señales sociales son los gustos, comentarios, compartidos o vistas que tu contenido recibe en las plataformas de redes sociales. Tanto los backlinks como las señales sociales son factores importantes que afectan tu posicionamiento en SERPs.</li>
-</ul>
-<p>Para usar los videos de TikTok para SEO de manera efectiva, necesitas seguir algunas estrategias y mejores prácticas, como:</p>
-<h3>Elegir palabras clave y temas relevantes</h3>
-<p>Como se mencionó anteriormente, elegir palabras clave y temas relevantes es crucial para optimizar tu contenido para SEO. Lo mismo se aplica a tus videos de TikTok. Necesitas elegir palabras clave y temas que coincidan con la intención y las expectativas de tu público objetivo, y que estén relacionados con el nicho de tu sitio web o blog. Puedes usar las mismas herramientas y métodos que discutimos anteriormente para encontrar las mejores palabras clave y temas para tus vídeos de TikTok. </p>
-<h3>Creación de contenido de alta calidad con cabeceras útiles y visuales</h3>
-<p>Como se mencionó anteriormente, la creación de contenido de alta calidad con cabeceras útiles y visuales es esencial para optimizar su contenido para SEO. Lo mismo se aplica a tus vídeos de TikTok. Necesitas crear contenido bien estructurado, bien escrito, bien formateado y bien presentado. Puedes usar las mismas técnicas y mejores prácticas que discutimos anteriormente para crear contenido de alta calidad con encabezados útiles y visuales para tus videos de TikTok. </p>
-<h3>Optimizar el título del vídeo, descripción, etiquetas y hashtags</h3>
-
-<ul>
-<li>Usa tu palabra clave principal en el título del video: El título del video es el elemento más importante de tu video, ya que le dice a los motores de búsqueda y a los usuarios de qué se trata tu video, e influye en su decisión de hacer clic en él o no. Por lo tanto, debe usar su palabra clave principal en el título del video, tan cerca del principio como sea posible, y hacerlo claro y pegadizo. </li>
-<li>Utilice sus palabras clave secundarias en la descripción del video: La descripción del video es el segundo elemento más importante de su video, ya que proporciona más detalles y contexto sobre su video, y ayuda a los motores de búsqueda y los usuarios a entender mejor su video. Por lo tanto, debe usar sus palabras clave secundarias en la descripción del video, y hacerlo informativo y atractivo. </li>
-<li>Utilice etiquetas y hashtags relevantes: Las etiquetas y hashtags son las palabras o frases que categorizan su video, y que ayudan a los motores de búsqueda y los usuarios a encontrar su video entre otros videos similares. Por lo tanto, debes usar etiquetas y hashtags relevantes que coincidan con tus palabras clave y temas, y que sean populares y trending en TikTok.</li>
-</ul>
-<p>Por ejemplo, si quieres optimizar el título de tu video, descripción, etiquetas y hashtags para SEO con videos de DJ Side to Side Full Bass TikTok, puedes usar estos consejos para crear elementos como:</p>
-<tabla>
-<tr><th>Título del video</th><th>Descripción del video</th><th>Etiquetas</th><th>Hashtags</th></tr>
-
-<tr><td>Cómo usar DJ Side to Side Full Bass TikTok Videos para SEO</td><td>En este video, te mostraré cómo usar DJ Side to Side Full Bass TikTok videos para SEO. SEO significa optimización de motores de búsqueda, que es el proceso de mejorar la cantidad y la calidad del tráfico a su sitio web o blog de los motores de búsqueda. Mediante el uso de DJ Side to Side Full Bass TikTok videos para SEO, puede beneficiarse de las siguientes ventajas: llegar a una audiencia grande y diversa, aumentar su conciencia de marca y reputación, generar más backlinks y señales sociales. Si quieres aprender a usar estos videos para SEO de manera efectiva, mira este video hasta el final y sigue las estrategias y mejores prácticas. </td><td>TikTok, DJ Side to Side Full Bass TikTok, SEO, optimización de motores de búsqueda, TikTok marketing</td><td>#tiktok #djsidetosidefullbass #seo #seargineoptimization #tiktokmarketing</td></tr>
-</tabla>
- <h3>Compartir tu contenido en plataformas de redes sociales</h3>
-<p>El paso final para optimizar su contenido para SEO con DJ Side to Side Full Bass TikTok vídeos es compartir su contenido en las plataformas de medios sociales. Las plataformas de redes sociales son las plataformas en línea donde los usuarios pueden crear y compartir contenido con otros usuarios. Algunos ejemplos de plataformas de redes sociales son Facebook, Twitter, Instagram, YouTube, Pinterest, LinkedIn y más. Al compartir tu contenido en plataformas de redes sociales, puedes beneficiarte de las siguientes ventajas:</p>
-<ul>
-<li>Puedes aumentar tu exposición y alcance: Las plataformas de redes sociales tienen miles de millones de usuarios activos en todo el mundo, que están constantemente buscando contenido nuevo e interesante. Al compartir tu contenido en plataformas de redes sociales, puedes aumentar tu exposición y llegar a estos usuarios, y generar más tráfico a tu sitio web o blog. </li>
-
-<li>Puedes monitorear tu rendimiento y comentarios: Las plataformas de redes sociales te proporcionan varias herramientas y métricas para monitorear tu rendimiento y comentarios sobre tu contenido. Puedes rastrear y medir el número de vistas, clics, impresiones, conversiones y más, y analizar el comportamiento y las preferencias de tu audiencia. También puedes recibir y responder a los comentarios y sugerencias de tu audiencia, y mejorar tu contenido en consecuencia. </li>
-</ul>
-<p>Para compartir tu contenido en plataformas de redes sociales de manera efectiva, necesitas seguir algunas estrategias y mejores prácticas, como:</p>
-<ul>
-<li>Elige las plataformas correctas: No todas las plataformas de redes sociales son adecuadas para tu contenido o tu audiencia. Necesitas elegir las plataformas que coincidan con tu nicho, tus objetivos y la demografía, los intereses y los comportamientos de tu audiencia. Puedes usar herramientas como <a href=">Sprout Social</a> o <a href="">Hootsuite</a> para encontrar las mejores plataformas para tu contenido. </li>
-<li>Crear un calendario de contenido: Un calendario de contenido es un plan o programa que te ayuda a organizar y gestionar tu creación y distribución de contenido en plataformas de redes sociales. Le ayuda a planificar con anticipación, evitar la repetición o la inconsistencia, y optimizar su tiempo y frecuencia. Puedes usar herramientas como <a href="">Google Calendar</a> o <a href="">CoSchedule</a> para ayudarte a crear un calendario de contenido para tu contenido. </li>
-<li>Optimiza tu contenido para cada plataforma: Cada plataforma de redes sociales tiene sus propias reglas y pautas para crear y compartir contenido. Necesitas optimizar tu contenido para cada plataforma, según factores como formato, longitud, estilo, tono, hashtags, subtítulos y más. Puede utilizar herramientas como <a href="">Canva</a> o <a href="">Lumen5</a> para ayudarle a optimizar su contenido para cada plataforma. </li>
-</ul>
-
-<tabla>
-<tr><th>Plataforma</th><th>Contenido</th></tr>
-<tr><td>Facebook</td><td><p><b>Cómo descargar videos de DJ Side to Side Full Bass TikTok Sin marca de agua</b></p><p>Te encantan los videos de DJ Side to Side Full Bass TikTok? ¿Quieres descargarlos sin marca de agua? Entonces mira este video y aprende a hacerlo de una manera simple y fácil! </p><p>En este video, te mostraré cómo descargar videos de DJ Side to Side Full Bass TikTok sin marca de agua usando un sitio web llamado Snaptik.app. Todo lo que necesita es la URL del vídeo que desea descargar, y se puede guardar en su dispositivo en segundos! </p><p>DJ Side to Side Full Bass TikTok es un remix de la canción de Ariana Grande Side to Side, creada por Wilfexbor, un DJ de Indonesia. El remix se ha convertido en una sensación viral en TikTok, con millones de usuarios creando videos usando el sonido. </p><p>Si quieres descargar estos videos sin marca de agua, mira este video hasta el final y sigue los pasos cuidadosamente. Y no te olvides de gustar, compartir y comentar sobre este post si te ha resultado útil! </p></td></tr>
-<tr><td>Twitter</td><td><p><b>Cómo utilizar DJ Side to Side Full Bass TikTok Videos para SEO</b></p><p>Quieres aumentar tu SEO con DJ Side to Side Full Bass TikTok videos? Echa un vistazo a este video y aprender a hacerlo en 3 sencillos pasos! </p><p>DJ Side to Side Full Bass TikTok es un remix de la canción de Ariana Grande Side to Side, creada por Wilfexbor, un DJ de Indonesia. El remix se ha convertido en una sensación viral en TikTok, con millones de usuarios creando videos usando el sonido. </p><p>Si quieres usar estos videos para SEO de manera efectiva, mira este video hasta el final y sigue estos 3 pasos:</p><ol><li>Elige palabras clave y temas relevantes</li><li>Crea contenido de alta calidad con encabezados y visuales útiles</li><li>Optimiza tu título de video, descripción, etiquetas y hashtags</li></ol><p>Ver el video ahora y aumentar su SEO con DJ de lado a lado Full Bass TikTok vídeos! ¡Y no te olvides de retuitear este post si lo encontraste útil! </p></td></tr>
-
- <h2>Conclusión</h2>
-<p>DJ Side to Side Full Bass TikTok es un remix de la canción de Ariana Grande Side to Side, creada por Wilfexbor, un DJ de Indonesia. El remix se ha convertido en una sensación viral en TikTok, con millones de usuarios creando videos usando el sonido. En este artículo, te hemos mostrado cómo descargar videos de DJ Side to Side Full Bass TikTok en tus dispositivos móviles o PC, y cómo optimizar tu contenido para SEO con estos videos. Esperamos que haya encontrado este artículo útil e informativo, y que haya disfrutado viendo y descargando estos videos. Si tiene alguna pregunta o comentario, por favor siéntase libre de dejarlos abajo. ¡Gracias por leer! </p>
- <h2>Preguntas frecuentes</h2>
-<h3>¿Cuál es el significado de "emak emak keperluan" en DJ Side to Side Full Bass TikTok? </h3>
-<p>"Keperluan emak emak" es una frase indonesia que significa "para las necesidades de las madres". Es una voz en off que Wilfexbor añadió al remix, como una broma o un homenaje a su madre, a quien le gusta la canción. La frase se ha convertido en un meme y un eslogan entre los usuarios de TikTok, que lo utilizan para expresar su amor o aprecio por sus madres, o para burlarse de sí mismos o de otros. </p>
- <h3>¿Cómo puedo descargar videos de DJ Side to Side Full Bass TikTok sin usar ninguna aplicación o sitio web? </h3>
-<p>Si quieres descargar videos de DJ Side to Side Full Bass TikTok sin usar ninguna aplicación o sitio web, puedes probar algunos consejos y trucos que hemos mencionado en este artículo, como usar la grabación de pantalla, usar fotos en vivo o usar el dispositivo de un amigo. Sin embargo, es posible que estos métodos no funcionen para todos los vídeos, y también pueden reducir la calidad o la resolución de los vídeos. </p>
- <h3>¿Cómo puedo crear mis propios videos de TikTok de DJ Side to Side Full Bass? </h3>
-<p>Si quieres crear tus propios videos de TikTok de DJ Side to Side Full Bass, puedes seguir estos pasos:</p>
-<ol>
-<li>Abra la aplicación TikTok y toque en el icono "+" en el centro inferior de la pantalla. </li>
-
-<li> Seleccione el sonido de los resultados y toque en el botón "Usar este sonido" en la parte inferior de la pantalla. </li>
-<li>Graba tu video usando el sonido como música de fondo. Puedes usar varios efectos, filtros, pegatinas o texto para mejorar tu video. </li>
-<li>Edita tu video como quieras y toca el botón "Siguiente" en la esquina inferior derecha de la pantalla. </li>
-<li>Añadir un título, descripción, etiquetas y hashtags a su vídeo y toque en el "Post" botón en la esquina inferior derecha de la pantalla. </li>
-</ol>
- <h3>¿Cómo puedo encontrar más videos de TikTok que usan el sonido DJ Side to Side Full Bass? </h3>
-<p>Si quieres encontrar más videos de TikTok que usan el sonido DJ Side to Side Full Bass, puedes seguir estos pasos:</p>
-<ol>
-<li> Abra la aplicación TikTok y toque en el icono "Descubrir" en la esquina inferior izquierda de la pantalla. </li>
-<li>Busque "DJ Side to Side Full Bass" en la barra de búsqueda y toque en la pestaña "Sonidos" en la parte superior de la pantalla. </li>
-<li> Seleccione el sonido de los resultados y toque en el botón "Reproducir" en la parte inferior de la pantalla. </li>
-<li>Desliza hacia arriba o hacia abajo para navegar por varios videos que utilizan el sonido. </li>
-</ol>
- <h3>¿Cómo puedo contactar a Wilfexbor, el creador de DJ Side to Side Full Bass remix? </h3>
-<p>Si quieres ponerte en contacto con Wilfexbor, el creador de DJ Side to Side Full Bass remix, puedes seguirlo en sus cuentas de redes sociales, como:</p>
-<ul>
-<li><a href="">YouTube</a>: Aquí es donde sube sus remixes y otros videos musicales. </li>
-<li><a href="">Instagram</a>: Aquí es donde publica sus fotos e historias sobre su vida y música. </li>
-<li><a href="">TikTok</a>: Aquí es donde crea y comparte sus propios videos TikTok usando sus remixes y otros sonidos. </li>
-</ul></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Descargar Espacio Dual Pro Mod Apk 3.0.8 (premium Desbloqueado) Para Android.md

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-<br />
-<h1>Descargar Dual Space Pro Mod APK 3.0.8 (Premium desbloqueado) para Android</h1>
-<p>¿Desea utilizar varias cuentas de la misma aplicación en su dispositivo Android? ¿Desea ocultar sus aplicaciones clonadas de miradas indiscretas? ¿Desea acceder a los servicios de Google en su dispositivo Huawei sin enraizamiento? Si es así, entonces usted debe probar <strong>Dual Space Pro Mod APK</strong>, una aplicación potente y versátil que le permite clonar y ejecutar varias instancias de cualquier aplicación en su dispositivo. En este artículo, te diremos qué es Dual Space Pro, por qué deberías usarlo, cómo descargarlo e instalarlo, y cómo usarlo. </p>
-<h2>descargar espacio dual pro mod apk 3.0.8 (premium desbloqueado) para Android</h2><br /><p><b><b>Download File</b> ::: <a href="https://bltlly.com/2v6K4a">https://bltlly.com/2v6K4a</a></b></p><br /><br />
-<h2>¿Qué es Dual Space Pro? </h2>
-<p>Dual Space Pro es una herramienta de clonación que te permite crear réplicas idénticas y totalmente funcionales de tus apps favoritas. Puede tener dos o más cuentas de usuario diferentes para la misma aplicación utilizando un solo dispositivo Android. Por ejemplo, puedes tener dos cuentas de WhatsApp, dos cuentas de Facebook, dos cuentas de Instagram, etc. en tu teléfono. </p>
-<h3>Una herramienta de clonación que te permite crear y ejecutar varias cuentas de la misma aplicación</h3>
-<p>La interfaz fácil de usar y simple de Dual Space Pro hace que sea fácil de usar. Cuando abra Dual Space Pro, verá una lista de aplicaciones compatibles que puede clonar. También puede agregar cualquier aplicación manualmente pulsando el botón "+". Una vez que seleccione una aplicación, se clonará en cuestión de segundos y tendrá una réplica totalmente accesible disponible en la interfaz Dual Space Pro. </p>
-<p>Puede iniciar sesión en sus diferentes cuentas en las aplicaciones clonadas y mantenerlas todas en línea al mismo tiempo. No necesita preocuparse por el problema de recepción de mensajes y almacenamiento de datos de diferentes cuentas, ya que funcionarán de forma independiente y sin interferencias entre sí. </p>
-<h3>Una zona de privacidad que oculta sus aplicaciones clonadas de otros</h3>
-
-<p>También puede personalizar el icono y el nombre de sus aplicaciones clonadas para que parezcan aplicaciones del sistema, como calculadora, reloj o galería. De esta manera, puede disfrazar sus aplicaciones clonadas y proteger su privacidad de los demás. </p>
-<h3>Una función de cambio rápido que le permite cambiar entre diferentes cuentas con un solo toque</h3>
-<p>Dual Space Pro también ofrece una función de conmutación rápida que le permite cambiar entre sus diferentes cuentas con facilidad. Puede utilizar el botón flotante en la pantalla para acceder rápidamente a sus aplicaciones clonadas. También puede personalizar el tamaño, la posición y la transparencia del botón flotante según su preferencia. </p>
-<p>Con la función de cambio rápido, puede ahorrar tiempo y energía cambiando entre sus cuentas con solo un toque. No necesitas cerrar sesión e iniciar sesión de nuevo cada vez que quieras usar una cuenta diferente. </p>
-<p></p>
-<h2>¿Por qué usar Dual Space Pro Mod APK? </h2>
-<p>Dual Space Pro es una aplicación premium que requiere una cuota de suscripción para utilizar todas sus características. Sin embargo, se puede descargar y utilizar Dual Space Pro Mod APK gratis y disfrutar de sus características premium sin limitaciones. Estos son algunos de los beneficios de usar Dual Space Pro Mod APK:</p>
-<h3>Para disfrutar de las características premium de Dual Space Pro gratis</h3>
-<p>Con Dual Space Pro Mod APK, puede acceder a todas las características premium de Dual Space Pro sin pagar nada. Puede clonar aplicaciones ilimitadas, ocultar aplicaciones ilimitadas, personalizar sus iconos y nombres, utilizar la función de cambio rápido, y más. También puede eliminar los anuncios y disfrutar de una experiencia de usuario suave e ininterrumpida. </p>
-<h3>Para acceder a los servicios de Google en dispositivos Huawei sin raíz</h3>
-
-<h3>Para equilibrar tu vida personal y laboral fácilmente</h3>
-<p>Si tiene varias cuentas para diferentes propósitos, tales como personal, trabajo, negocios, juegos, etc., puede utilizar Dual Space Pro Mod APK para gestionarlos fácilmente. Puede separar su vida personal y laboral mediante el uso de diferentes cuentas en la misma aplicación. También puede evitar la molestia de entrar y salir cada vez que desee cambiar de cuenta. </p>
-<h2>¿Cómo descargar e instalar Dual Space Pro Mod APK? </h2>
-<p>Descargar e instalar Dual Space Pro Mod APK es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
-<h3>Descargar el archivo apk mod de una fuente de confianza</h3>
-<p>Puede descargar el archivo apk mod de una fuente de confianza como [texto]. Asegúrese de descargar la última versión del archivo apk mod que es compatible con su dispositivo. </p>
-<h3>Habilitar fuentes desconocidas en la configuración del dispositivo</h3>
-<p>Antes de instalar el archivo apk mod, es necesario habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para habilitar fuentes desconocidas, vaya a Configuración > Seguridad > Fuentes desconocidas y enciéndala. </p>
-<h3>Instalar el archivo apk mod y conceder los permisos necesarios</h3>
-<p>Después de descargar el archivo apk mod, localizarlo en el administrador de archivos y toque en él para instalarlo. Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación. Espere a que se complete el proceso de instalación. </p>
-<h3>Abra la aplicación y comience a clonar sus aplicaciones</h3>
-<p>Una vez finalizada la instalación, abra la aplicación y comience a clonar sus aplicaciones. Verá una lista de aplicaciones compatibles que puede clonar o puede agregar cualquier aplicación manualmente tocando el botón "+". Seleccione las aplicaciones que desea clonar y espere a que se clonen. A continuación, puede iniciar sesión en sus diferentes cuentas en las aplicaciones clonadas y disfrutar de su uso. </p>
-<h2>¿Cómo usar Dual Space Pro Mod APK? </h2>
-<p>El uso de Dual Space Pro Mod APK es muy fácil e intuitivo. Solo tienes que seguir estos pasos:</p>
-
-<p>Al abrir Dual Space Pro Mod APK, verá una lista de aplicaciones compatibles que puede clonar. También puede agregar cualquier aplicación manualmente pulsando el botón "+". Una vez que seleccione una aplicación, se clonará en cuestión de segundos y tendrá una réplica totalmente accesible disponible en la interfaz Dual Space Pro. </p>
-<h3>Inicie sesión en sus diferentes cuentas en las aplicaciones clonadas</h3>
-<p>Puede iniciar sesión en sus diferentes cuentas en las aplicaciones clonadas y mantenerlas todas en línea al mismo tiempo. No necesita preocuparse por el problema de recepción de mensajes y almacenamiento de datos de diferentes cuentas, ya que funcionarán de forma independiente y sin interferencias entre sí. </p>
-<h3>Cambiar entre sus cuentas tocando los iconos de la aplicación o utilizando el botón flotante</h3>
-<p>Puede cambiar entre sus cuentas tocando los iconos de la aplicación en la interfaz Dual Space Pro o utilizando el botón flotante en la pantalla. También puede personalizar el tamaño, la posición y la transparencia del botón flotante según su preferencia. </p>
-<p>Con la función de cambio rápido, puede ahorrar tiempo y energía cambiando entre sus cuentas con solo un toque. No necesitas cerrar sesión e iniciar sesión de nuevo cada vez que quieras usar una cuenta diferente. </p>
-<h2>Conclusión</h2>
-<p>Dual Space Pro Mod APK es una aplicación útil y potente que le permite clonar y ejecutar varias cuentas de la misma aplicación en un dispositivo. También proporciona una zona de privacidad y una función de cambio rápido para mejorar su experiencia de usuario. Puedes descargarlo e instalarlo gratis desde una fuente confiable y disfrutar de sus características premium sin limitaciones. </p>
-<p>Si desea utilizar varias cuentas de la misma aplicación en su dispositivo Android, ocultar sus aplicaciones clonadas de otros, acceder a los servicios de Google en su dispositivo Huawei sin enraizamiento, o el equilibrio entre su vida personal y laboral fácilmente, debe probar Dual Space Pro Mod APK. Es una herramienta de clonación que te hará la vida más fácil y cómoda. </p>
-<h2>Preguntas frecuentes</h2>
-
-<p>Sí, Dual Space Pro Mod APK es seguro de usar, siempre y cuando se descarga desde una fuente de confianza. No contiene ningún virus o malware que pueda dañar su dispositivo o datos. Sin embargo, debe tener cuidado con los permisos que otorga a la aplicación y las cuentas a las que inicia sesión en las aplicaciones clonadas. </p>
-<h4> ¿Cuáles son los beneficios de usar Dual Space Pro Mod APK? </h4>
-<p>Algunos de los beneficios de usar Dual Space Pro Mod APK son:</p>
-<ul>
-<li> Puede clonar aplicaciones ilimitadas y utilizar varias cuentas de la misma aplicación en un dispositivo. </li>
-<li> Puede ocultar sus aplicaciones clonadas de otros y proteger su privacidad. </li>
-<li> Puede acceder a los servicios de Google en su dispositivo Huawei sin enraizamiento. </li>
-<li>Puede cambiar entre sus cuentas con un solo toque usando la función de cambio rápido. </li>
-<li> Puede disfrutar de las características premium de Dual Space Pro de forma gratuita sin anuncios ni limitaciones. </li>
-</ul>
-<h4> ¿Cuáles son los inconvenientes de usar Dual Space Pro Mod APK? </h4>
-<p>Algunos de los inconvenientes de usar Dual Space Pro Mod APK son:</p>
-<ul>
-<li>Puede experimentar algunos problemas de compatibilidad con algunas aplicaciones o dispositivos. </li>
-<li>Puede consumir más batería y memoria ejecutando varias aplicaciones al mismo tiempo. </li>
-<li> Puede enfrentar algunos riesgos de seguridad al iniciar sesión en diferentes cuentas en aplicaciones clonadas. </li>
-<li>Es posible que no reciba actualizaciones oportunas del desarrollador oficial de Dual Space Pro.</li>
-</ul>
-<h4> ¿Cómo actualizar Dual Space Pro Mod APK? </h4>
-<p>Para actualizar Dual Space Pro Mod APK, es necesario descargar la última versión del archivo mod apk de una fuente de confianza e instalarlo en la aplicación existente. No necesitas desinstalar la versión anterior ni perder tus datos. Sin embargo, siempre debes hacer una copia de seguridad de tus datos antes de actualizar cualquier aplicación. </p>
-<h4> ¿Cómo desinstalar Dual Space Pro Mod APK? </h4> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/platformdirs/macos.py

@@ -1,70 +0,0 @@
-from __future__ import annotations
-
-import os
-
-from .api import PlatformDirsABC
-
-
-class MacOS(PlatformDirsABC):
-    """
-    Platform directories for the macOS operating system. Follows the guidance from `Apple documentation
-    <https://developer.apple.com/library/archive/documentation/FileManagement/Conceptual/FileSystemProgrammingGuide/MacOSXDirectories/MacOSXDirectories.html>`_.
-    Makes use of the `appname <platformdirs.api.PlatformDirsABC.appname>`,
-    `version <platformdirs.api.PlatformDirsABC.version>`,
-    `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
-    """
-
-    @property
-    def user_data_dir(self) -> str:
-        """:return: data directory tied to the user, e.g. ``~/Library/Application Support/$appname/$version``"""
-        return self._append_app_name_and_version(os.path.expanduser("~/Library/Application Support"))
-
-    @property
-    def site_data_dir(self) -> str:
-        """:return: data directory shared by users, e.g. ``/Library/Application Support/$appname/$version``"""
-        return self._append_app_name_and_version("/Library/Application Support")
-
-    @property
-    def user_config_dir(self) -> str:
-        """:return: config directory tied to the user, same as `user_data_dir`"""
-        return self.user_data_dir
-
-    @property
-    def site_config_dir(self) -> str:
-        """:return: config directory shared by the users, same as `site_data_dir`"""
-        return self.site_data_dir
-
-    @property
-    def user_cache_dir(self) -> str:
-        """:return: cache directory tied to the user, e.g. ``~/Library/Caches/$appname/$version``"""
-        return self._append_app_name_and_version(os.path.expanduser("~/Library/Caches"))
-
-    @property
-    def site_cache_dir(self) -> str:
-        """:return: cache directory shared by users, e.g. ``/Library/Caches/$appname/$version``"""
-        return self._append_app_name_and_version("/Library/Caches")
-
-    @property
-    def user_state_dir(self) -> str:
-        """:return: state directory tied to the user, same as `user_data_dir`"""
-        return self.user_data_dir
-
-    @property
-    def user_log_dir(self) -> str:
-        """:return: log directory tied to the user, e.g. ``~/Library/Logs/$appname/$version``"""
-        return self._append_app_name_and_version(os.path.expanduser("~/Library/Logs"))
-
-    @property
-    def user_documents_dir(self) -> str:
-        """:return: documents directory tied to the user, e.g. ``~/Documents``"""
-        return os.path.expanduser("~/Documents")
-
-    @property
-    def user_runtime_dir(self) -> str:
-        """:return: runtime directory tied to the user, e.g. ``~/Library/Caches/TemporaryItems/$appname/$version``"""
-        return self._append_app_name_and_version(os.path.expanduser("~/Library/Caches/TemporaryItems"))
-
-
-__all__ = [
-    "MacOS",
-]

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_resources/__init__.py

@@ -1,36 +0,0 @@
-"""Read resources contained within a package."""
-
-from ._common import (
-    as_file,
-    files,
-    Package,
-)
-
-from ._legacy import (
-    contents,
-    open_binary,
-    read_binary,
-    open_text,
-    read_text,
-    is_resource,
-    path,
-    Resource,
-)
-
-from .abc import ResourceReader
-
-
-__all__ = [
-    'Package',
-    'Resource',
-    'ResourceReader',
-    'as_file',
-    'contents',
-    'files',
-    'is_resource',
-    'open_binary',
-    'open_text',
-    'path',
-    'read_binary',
-    'read_text',
-]

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/bdist_egg.py

@@ -1,457 +0,0 @@
-"""setuptools.command.bdist_egg
-
-Build .egg distributions"""
-
-from distutils.dir_util import remove_tree, mkpath
-from distutils import log
-from types import CodeType
-import sys
-import os
-import re
-import textwrap
-import marshal
-
-from pkg_resources import get_build_platform, Distribution
-from setuptools.extension import Library
-from setuptools import Command
-from .._path import ensure_directory
-
-from sysconfig import get_path, get_python_version
-
-
-def _get_purelib():
-    return get_path("purelib")
-
-
-def strip_module(filename):
-    if '.' in filename:
-        filename = os.path.splitext(filename)[0]
-    if filename.endswith('module'):
-        filename = filename[:-6]
-    return filename
-
-
-def sorted_walk(dir):
-    """Do os.walk in a reproducible way,
-    independent of indeterministic filesystem readdir order
-    """
-    for base, dirs, files in os.walk(dir):
-        dirs.sort()
-        files.sort()
-        yield base, dirs, files
-
-
-def write_stub(resource, pyfile):
-    _stub_template = textwrap.dedent("""
-        def __bootstrap__():
-            global __bootstrap__, __loader__, __file__
-            import sys, pkg_resources, importlib.util
-            __file__ = pkg_resources.resource_filename(__name__, %r)
-            __loader__ = None; del __bootstrap__, __loader__
-            spec = importlib.util.spec_from_file_location(__name__,__file__)
-            mod = importlib.util.module_from_spec(spec)
-            spec.loader.exec_module(mod)
-        __bootstrap__()
-        """).lstrip()
-    with open(pyfile, 'w') as f:
-        f.write(_stub_template % resource)
-
-
-class bdist_egg(Command):
-    description = "create an \"egg\" distribution"
-
-    user_options = [
-        ('bdist-dir=', 'b',
-         "temporary directory for creating the distribution"),
-        ('plat-name=', 'p', "platform name to embed in generated filenames "
-                            "(default: %s)" % get_build_platform()),
-        ('exclude-source-files', None,
-         "remove all .py files from the generated egg"),
-        ('keep-temp', 'k',
-         "keep the pseudo-installation tree around after " +
-         "creating the distribution archive"),
-        ('dist-dir=', 'd',
-         "directory to put final built distributions in"),
-        ('skip-build', None,
-         "skip rebuilding everything (for testing/debugging)"),
-    ]
-
-    boolean_options = [
-        'keep-temp', 'skip-build', 'exclude-source-files'
-    ]
-
-    def initialize_options(self):
-        self.bdist_dir = None
-        self.plat_name = None
-        self.keep_temp = 0
-        self.dist_dir = None
-        self.skip_build = 0
-        self.egg_output = None
-        self.exclude_source_files = None
-
-    def finalize_options(self):
-        ei_cmd = self.ei_cmd = self.get_finalized_command("egg_info")
-        self.egg_info = ei_cmd.egg_info
-
-        if self.bdist_dir is None:
-            bdist_base = self.get_finalized_command('bdist').bdist_base
-            self.bdist_dir = os.path.join(bdist_base, 'egg')
-
-        if self.plat_name is None:
-            self.plat_name = get_build_platform()
-
-        self.set_undefined_options('bdist', ('dist_dir', 'dist_dir'))
-
-        if self.egg_output is None:
-
-            # Compute filename of the output egg
-            basename = Distribution(
-                None, None, ei_cmd.egg_name, ei_cmd.egg_version,
-                get_python_version(),
-                self.distribution.has_ext_modules() and self.plat_name
-            ).egg_name()
-
-            self.egg_output = os.path.join(self.dist_dir, basename + '.egg')
-
-    def do_install_data(self):
-        # Hack for packages that install data to install's --install-lib
-        self.get_finalized_command('install').install_lib = self.bdist_dir
-
-        site_packages = os.path.normcase(os.path.realpath(_get_purelib()))
-        old, self.distribution.data_files = self.distribution.data_files, []
-
-        for item in old:
-            if isinstance(item, tuple) and len(item) == 2:
-                if os.path.isabs(item[0]):
-                    realpath = os.path.realpath(item[0])
-                    normalized = os.path.normcase(realpath)
-                    if normalized == site_packages or normalized.startswith(
-                        site_packages + os.sep
-                    ):
-                        item = realpath[len(site_packages) + 1:], item[1]
-                        # XXX else: raise ???
-            self.distribution.data_files.append(item)
-
-        try:
-            log.info("installing package data to %s", self.bdist_dir)
-            self.call_command('install_data', force=0, root=None)
-        finally:
-            self.distribution.data_files = old
-
-    def get_outputs(self):
-        return [self.egg_output]
-
-    def call_command(self, cmdname, **kw):
-        """Invoke reinitialized command `cmdname` with keyword args"""
-        for dirname in INSTALL_DIRECTORY_ATTRS:
-            kw.setdefault(dirname, self.bdist_dir)
-        kw.setdefault('skip_build', self.skip_build)
-        kw.setdefault('dry_run', self.dry_run)
-        cmd = self.reinitialize_command(cmdname, **kw)
-        self.run_command(cmdname)
-        return cmd
-
-    def run(self):  # noqa: C901  # is too complex (14)  # FIXME
-        # Generate metadata first
-        self.run_command("egg_info")
-        # We run install_lib before install_data, because some data hacks
-        # pull their data path from the install_lib command.
-        log.info("installing library code to %s", self.bdist_dir)
-        instcmd = self.get_finalized_command('install')
-        old_root = instcmd.root
-        instcmd.root = None
-        if self.distribution.has_c_libraries() and not self.skip_build:
-            self.run_command('build_clib')
-        cmd = self.call_command('install_lib', warn_dir=0)
-        instcmd.root = old_root
-
-        all_outputs, ext_outputs = self.get_ext_outputs()
-        self.stubs = []
-        to_compile = []
-        for (p, ext_name) in enumerate(ext_outputs):
-            filename, ext = os.path.splitext(ext_name)
-            pyfile = os.path.join(self.bdist_dir, strip_module(filename) +
-                                  '.py')
-            self.stubs.append(pyfile)
-            log.info("creating stub loader for %s", ext_name)
-            if not self.dry_run:
-                write_stub(os.path.basename(ext_name), pyfile)
-            to_compile.append(pyfile)
-            ext_outputs[p] = ext_name.replace(os.sep, '/')
-
-        if to_compile:
-            cmd.byte_compile(to_compile)
-        if self.distribution.data_files:
-            self.do_install_data()
-
-        # Make the EGG-INFO directory
-        archive_root = self.bdist_dir
-        egg_info = os.path.join(archive_root, 'EGG-INFO')
-        self.mkpath(egg_info)
-        if self.distribution.scripts:
-            script_dir = os.path.join(egg_info, 'scripts')
-            log.info("installing scripts to %s", script_dir)
-            self.call_command('install_scripts', install_dir=script_dir,
-                              no_ep=1)
-
-        self.copy_metadata_to(egg_info)
-        native_libs = os.path.join(egg_info, "native_libs.txt")
-        if all_outputs:
-            log.info("writing %s", native_libs)
-            if not self.dry_run:
-                ensure_directory(native_libs)
-                libs_file = open(native_libs, 'wt')
-                libs_file.write('\n'.join(all_outputs))
-                libs_file.write('\n')
-                libs_file.close()
-        elif os.path.isfile(native_libs):
-            log.info("removing %s", native_libs)
-            if not self.dry_run:
-                os.unlink(native_libs)
-
-        write_safety_flag(
-            os.path.join(archive_root, 'EGG-INFO'), self.zip_safe()
-        )
-
-        if os.path.exists(os.path.join(self.egg_info, 'depends.txt')):
-            log.warn(
-                "WARNING: 'depends.txt' will not be used by setuptools 0.6!\n"
-                "Use the install_requires/extras_require setup() args instead."
-            )
-
-        if self.exclude_source_files:
-            self.zap_pyfiles()
-
-        # Make the archive
-        make_zipfile(self.egg_output, archive_root, verbose=self.verbose,
-                     dry_run=self.dry_run, mode=self.gen_header())
-        if not self.keep_temp:
-            remove_tree(self.bdist_dir, dry_run=self.dry_run)
-
-        # Add to 'Distribution.dist_files' so that the "upload" command works
-        getattr(self.distribution, 'dist_files', []).append(
-            ('bdist_egg', get_python_version(), self.egg_output))
-
-    def zap_pyfiles(self):
-        log.info("Removing .py files from temporary directory")
-        for base, dirs, files in walk_egg(self.bdist_dir):
-            for name in files:
-                path = os.path.join(base, name)
-
-                if name.endswith('.py'):
-                    log.debug("Deleting %s", path)
-                    os.unlink(path)
-
-                if base.endswith('__pycache__'):
-                    path_old = path
-
-                    pattern = r'(?P<name>.+)\.(?P<magic>[^.]+)\.pyc'
-                    m = re.match(pattern, name)
-                    path_new = os.path.join(
-                        base, os.pardir, m.group('name') + '.pyc')
-                    log.info(
-                        "Renaming file from [%s] to [%s]"
-                        % (path_old, path_new))
-                    try:
-                        os.remove(path_new)
-                    except OSError:
-                        pass
-                    os.rename(path_old, path_new)
-
-    def zip_safe(self):
-        safe = getattr(self.distribution, 'zip_safe', None)
-        if safe is not None:
-            return safe
-        log.warn("zip_safe flag not set; analyzing archive contents...")
-        return analyze_egg(self.bdist_dir, self.stubs)
-
-    def gen_header(self):
-        return 'w'
-
-    def copy_metadata_to(self, target_dir):
-        "Copy metadata (egg info) to the target_dir"
-        # normalize the path (so that a forward-slash in egg_info will
-        # match using startswith below)
-        norm_egg_info = os.path.normpath(self.egg_info)
-        prefix = os.path.join(norm_egg_info, '')
-        for path in self.ei_cmd.filelist.files:
-            if path.startswith(prefix):
-                target = os.path.join(target_dir, path[len(prefix):])
-                ensure_directory(target)
-                self.copy_file(path, target)
-
-    def get_ext_outputs(self):
-        """Get a list of relative paths to C extensions in the output distro"""
-
-        all_outputs = []
-        ext_outputs = []
-
-        paths = {self.bdist_dir: ''}
-        for base, dirs, files in sorted_walk(self.bdist_dir):
-            for filename in files:
-                if os.path.splitext(filename)[1].lower() in NATIVE_EXTENSIONS:
-                    all_outputs.append(paths[base] + filename)
-            for filename in dirs:
-                paths[os.path.join(base, filename)] = (paths[base] +
-                                                       filename + '/')
-
-        if self.distribution.has_ext_modules():
-            build_cmd = self.get_finalized_command('build_ext')
-            for ext in build_cmd.extensions:
-                if isinstance(ext, Library):
-                    continue
-                fullname = build_cmd.get_ext_fullname(ext.name)
-                filename = build_cmd.get_ext_filename(fullname)
-                if not os.path.basename(filename).startswith('dl-'):
-                    if os.path.exists(os.path.join(self.bdist_dir, filename)):
-                        ext_outputs.append(filename)
-
-        return all_outputs, ext_outputs
-
-
-NATIVE_EXTENSIONS = dict.fromkeys('.dll .so .dylib .pyd'.split())
-
-
-def walk_egg(egg_dir):
-    """Walk an unpacked egg's contents, skipping the metadata directory"""
-    walker = sorted_walk(egg_dir)
-    base, dirs, files = next(walker)
-    if 'EGG-INFO' in dirs:
-        dirs.remove('EGG-INFO')
-    yield base, dirs, files
-    for bdf in walker:
-        yield bdf
-
-
-def analyze_egg(egg_dir, stubs):
-    # check for existing flag in EGG-INFO
-    for flag, fn in safety_flags.items():
-        if os.path.exists(os.path.join(egg_dir, 'EGG-INFO', fn)):
-            return flag
-    if not can_scan():
-        return False
-    safe = True
-    for base, dirs, files in walk_egg(egg_dir):
-        for name in files:
-            if name.endswith('.py') or name.endswith('.pyw'):
-                continue
-            elif name.endswith('.pyc') or name.endswith('.pyo'):
-                # always scan, even if we already know we're not safe
-                safe = scan_module(egg_dir, base, name, stubs) and safe
-    return safe
-
-
-def write_safety_flag(egg_dir, safe):
-    # Write or remove zip safety flag file(s)
-    for flag, fn in safety_flags.items():
-        fn = os.path.join(egg_dir, fn)
-        if os.path.exists(fn):
-            if safe is None or bool(safe) != flag:
-                os.unlink(fn)
-        elif safe is not None and bool(safe) == flag:
-            f = open(fn, 'wt')
-            f.write('\n')
-            f.close()
-
-
-safety_flags = {
-    True: 'zip-safe',
-    False: 'not-zip-safe',
-}
-
-
-def scan_module(egg_dir, base, name, stubs):
-    """Check whether module possibly uses unsafe-for-zipfile stuff"""
-
-    filename = os.path.join(base, name)
-    if filename[:-1] in stubs:
-        return True  # Extension module
-    pkg = base[len(egg_dir) + 1:].replace(os.sep, '.')
-    module = pkg + (pkg and '.' or '') + os.path.splitext(name)[0]
-    if sys.version_info < (3, 7):
-        skip = 12  # skip magic & date & file size
-    else:
-        skip = 16  # skip magic & reserved? & date & file size
-    f = open(filename, 'rb')
-    f.read(skip)
-    code = marshal.load(f)
-    f.close()
-    safe = True
-    symbols = dict.fromkeys(iter_symbols(code))
-    for bad in ['__file__', '__path__']:
-        if bad in symbols:
-            log.warn("%s: module references %s", module, bad)
-            safe = False
-    if 'inspect' in symbols:
-        for bad in [
-            'getsource', 'getabsfile', 'getsourcefile', 'getfile'
-            'getsourcelines', 'findsource', 'getcomments', 'getframeinfo',
-            'getinnerframes', 'getouterframes', 'stack', 'trace'
-        ]:
-            if bad in symbols:
-                log.warn("%s: module MAY be using inspect.%s", module, bad)
-                safe = False
-    return safe
-
-
-def iter_symbols(code):
-    """Yield names and strings used by `code` and its nested code objects"""
-    for name in code.co_names:
-        yield name
-    for const in code.co_consts:
-        if isinstance(const, str):
-            yield const
-        elif isinstance(const, CodeType):
-            for name in iter_symbols(const):
-                yield name
-
-
-def can_scan():
-    if not sys.platform.startswith('java') and sys.platform != 'cli':
-        # CPython, PyPy, etc.
-        return True
-    log.warn("Unable to analyze compiled code on this platform.")
-    log.warn("Please ask the author to include a 'zip_safe'"
-             " setting (either True or False) in the package's setup.py")
-
-
-# Attribute names of options for commands that might need to be convinced to
-# install to the egg build directory
-
-INSTALL_DIRECTORY_ATTRS = [
-    'install_lib', 'install_dir', 'install_data', 'install_base'
-]
-
-
-def make_zipfile(zip_filename, base_dir, verbose=0, dry_run=0, compress=True,
-                 mode='w'):
-    """Create a zip file from all the files under 'base_dir'.  The output
-    zip file will be named 'base_dir' + ".zip".  Uses either the "zipfile"
-    Python module (if available) or the InfoZIP "zip" utility (if installed
-    and found on the default search path).  If neither tool is available,
-    raises DistutilsExecError.  Returns the name of the output zip file.
-    """
-    import zipfile
-
-    mkpath(os.path.dirname(zip_filename), dry_run=dry_run)
-    log.info("creating '%s' and adding '%s' to it", zip_filename, base_dir)
-
-    def visit(z, dirname, names):
-        for name in names:
-            path = os.path.normpath(os.path.join(dirname, name))
-            if os.path.isfile(path):
-                p = path[len(base_dir) + 1:]
-                if not dry_run:
-                    z.write(path, p)
-                log.debug("adding '%s'", p)
-
-    compression = zipfile.ZIP_DEFLATED if compress else zipfile.ZIP_STORED
-    if not dry_run:
-        z = zipfile.ZipFile(zip_filename, mode, compression=compression)
-        for dirname, dirs, files in sorted_walk(base_dir):
-            visit(z, dirname, files)
-        z.close()
-    else:
-        for dirname, dirs, files in sorted_walk(base_dir):
-            visit(None, dirname, files)
-    return zip_filename

spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/util/proxy.py

@@ -1,57 +0,0 @@
-from .ssl_ import create_urllib3_context, resolve_cert_reqs, resolve_ssl_version
-
-
-def connection_requires_http_tunnel(
-    proxy_url=None, proxy_config=None, destination_scheme=None
-):
-    """
-    Returns True if the connection requires an HTTP CONNECT through the proxy.
-
-    :param URL proxy_url:
-        URL of the proxy.
-    :param ProxyConfig proxy_config:
-        Proxy configuration from poolmanager.py
-    :param str destination_scheme:
-        The scheme of the destination. (i.e https, http, etc)
-    """
-    # If we're not using a proxy, no way to use a tunnel.
-    if proxy_url is None:
-        return False
-
-    # HTTP destinations never require tunneling, we always forward.
-    if destination_scheme == "http":
-        return False
-
-    # Support for forwarding with HTTPS proxies and HTTPS destinations.
-    if (
-        proxy_url.scheme == "https"
-        and proxy_config
-        and proxy_config.use_forwarding_for_https
-    ):
-        return False
-
-    # Otherwise always use a tunnel.
-    return True
-
-
-def create_proxy_ssl_context(
-    ssl_version, cert_reqs, ca_certs=None, ca_cert_dir=None, ca_cert_data=None
-):
-    """
-    Generates a default proxy ssl context if one hasn't been provided by the
-    user.
-    """
-    ssl_context = create_urllib3_context(
-        ssl_version=resolve_ssl_version(ssl_version),
-        cert_reqs=resolve_cert_reqs(cert_reqs),
-    )
-
-    if (
-        not ca_certs
-        and not ca_cert_dir
-        and not ca_cert_data
-        and hasattr(ssl_context, "load_default_certs")
-    ):
-        ssl_context.load_default_certs()
-
-    return ssl_context

spaces/BigData-KSU/VQA-in-Medical-Imagery/CLIP/model-card.md

@@ -1,118 +0,0 @@
-# Model Card: CLIP
-
-Inspired by [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993) and [Lessons from Archives (Jo & Gebru)](https://arxiv.org/pdf/1912.10389.pdf), we’re providing some accompanying information about the multimodal model.
-
-## Model Details
-
-The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
-
-### Model Date
-
-January 2021
-
-### Model Type
-
-The base model uses a ResNet50 with several modifications as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. There is also a variant of the model where the ResNet image encoder is replaced with a Vision Transformer.
-
-### Model Version
-
-Initially we’ve released one CLIP model based on the Vision Transformer architecture equivalent to ViT-B/32
-
-Please see the paper linked below for further details about their specification.
-
-### Documents
-
-- [Blog Post](https://openai.com/blog/clip/)
-- [CLIP Paper](https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language_Supervision.pdf)
-
-
-
-## Model Use
-
-### Intended Use
-
-The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
-
-#### Primary intended uses
-
-The primary intended users of these models are AI researchers.
-
-We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
-
-### Out-of-Scope Use Cases
-
-**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
-
-Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
-
-Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
-
-
-
-## Data
-
-The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
-
-### Data Mission Statement
-
-Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
-
-
-
-## Performance and Limitations
-
-### Performance
-
-We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:
-
-- Food101
-- CIFAR10   
-- CIFAR100   
-- Birdsnap
-- SUN397
-- Stanford Cars
-- FGVC Aircraft
-- VOC2007
-- DTD
-- Oxford-IIIT Pet dataset
-- Caltech101
-- Flowers102
-- MNIST   
-- SVHN 
-- IIIT5K   
-- Hateful Memes   
-- SST-2
-- UCF101
-- Kinetics700
-- Country211
-- CLEVR Counting
-- KITTI Distance
-- STL-10
-- RareAct
-- Flickr30
-- MSCOCO
-- ImageNet
-- ImageNet-A
-- ImageNet-R
-- ImageNet Sketch
-- ObjectNet (ImageNet Overlap)
-- Youtube-BB
-- ImageNet-Vid
-
-## Limitations
-
-CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
-
-### Bias and Fairness
-
-We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
-
-We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
-
-
-
-## Feedback
-
-### Where to send questions or comments about the model
-
-Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/meta_arch/__init__.py

@@ -1,11 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-from .build import META_ARCH_REGISTRY, build_model  # isort:skip
-
-from .panoptic_fpn import PanopticFPN
-
-# import all the meta_arch, so they will be registered
-from .rcnn import GeneralizedRCNN, ProposalNetwork
-from .retinanet import RetinaNet
-from .semantic_seg import SEM_SEG_HEADS_REGISTRY, SemanticSegmentor, build_sem_seg_head

spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/copy_backward.h

@@ -1,54 +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>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace sequential
-{
-
-
-__thrust_exec_check_disable__
-template<typename BidirectionalIterator1,
-         typename BidirectionalIterator2>
-__host__ __device__
-BidirectionalIterator2 copy_backward(BidirectionalIterator1 first, 
-                                     BidirectionalIterator1 last, 
-                                     BidirectionalIterator2 result)
-{
-  while (first != last)
-  {
-    --last;
-    --result;
-    *result = *last;
-  }
-
-  return result;
-}
-
-
-} // end namespace sequential
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-

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

@@ -1,118 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import List
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-from detectron2.utils.registry import Registry
-
-__all__ = ["FastRCNNConvFCHead", "build_box_head", "ROI_BOX_HEAD_REGISTRY"]
-
-ROI_BOX_HEAD_REGISTRY = Registry("ROI_BOX_HEAD")
-ROI_BOX_HEAD_REGISTRY.__doc__ = """
-Registry for box heads, which make box predictions from per-region features.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-"""
-
-
-# To get torchscript support, we make the head a subclass of `nn.Sequential`.
-# Therefore, to add new layers in this head class, please make sure they are
-# added in the order they will be used in forward().
-@ROI_BOX_HEAD_REGISTRY.register()
-class FastRCNNConvFCHead(nn.Sequential):
-    """
-    A head with several 3x3 conv layers (each followed by norm & relu) and then
-    several fc layers (each followed by relu).
-    """
-
-    @configurable
-    def __init__(
-        self, input_shape: ShapeSpec, *, conv_dims: List[int], fc_dims: List[int], conv_norm=""
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            input_shape (ShapeSpec): shape of the input feature.
-            conv_dims (list[int]): the output dimensions of the conv layers
-            fc_dims (list[int]): the output dimensions of the fc layers
-            conv_norm (str or callable): normalization for the conv layers.
-                See :func:`detectron2.layers.get_norm` for supported types.
-        """
-        super().__init__()
-        assert len(conv_dims) + len(fc_dims) > 0
-
-        self._output_size = (input_shape.channels, input_shape.height, input_shape.width)
-
-        self.conv_norm_relus = []
-        for k, conv_dim in enumerate(conv_dims):
-            conv = Conv2d(
-                self._output_size[0],
-                conv_dim,
-                kernel_size=3,
-                padding=1,
-                bias=not conv_norm,
-                norm=get_norm(conv_norm, conv_dim),
-                activation=nn.ReLU(),
-            )
-            self.add_module("conv{}".format(k + 1), conv)
-            self.conv_norm_relus.append(conv)
-            self._output_size = (conv_dim, self._output_size[1], self._output_size[2])
-
-        self.fcs = []
-        for k, fc_dim in enumerate(fc_dims):
-            if k == 0:
-                self.add_module("flatten", nn.Flatten())
-            fc = nn.Linear(int(np.prod(self._output_size)), fc_dim)
-            self.add_module("fc{}".format(k + 1), fc)
-            self.add_module("fc_relu{}".format(k + 1), nn.ReLU())
-            self.fcs.append(fc)
-            self._output_size = fc_dim
-
-        for layer in self.conv_norm_relus:
-            weight_init.c2_msra_fill(layer)
-        for layer in self.fcs:
-            weight_init.c2_xavier_fill(layer)
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        num_conv = cfg.MODEL.ROI_BOX_HEAD.NUM_CONV
-        conv_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_DIM
-        num_fc = cfg.MODEL.ROI_BOX_HEAD.NUM_FC
-        fc_dim = cfg.MODEL.ROI_BOX_HEAD.FC_DIM
-        return {
-            "input_shape": input_shape,
-            "conv_dims": [conv_dim] * num_conv,
-            "fc_dims": [fc_dim] * num_fc,
-            "conv_norm": cfg.MODEL.ROI_BOX_HEAD.NORM,
-        }
-
-    def forward(self, x):
-        for layer in self:
-            x = layer(x)
-        return x
-
-    @property
-    @torch.jit.unused
-    def output_shape(self):
-        """
-        Returns:
-            ShapeSpec: the output feature shape
-        """
-        o = self._output_size
-        if isinstance(o, int):
-            return ShapeSpec(channels=o)
-        else:
-            return ShapeSpec(channels=o[0], height=o[1], width=o[2])
-
-
-def build_box_head(cfg, input_shape):
-    """
-    Build a box head defined by `cfg.MODEL.ROI_BOX_HEAD.NAME`.
-    """
-    name = cfg.MODEL.ROI_BOX_HEAD.NAME
-    return ROI_BOX_HEAD_REGISTRY.get(name)(cfg, input_shape)

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

@@ -1,31 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.exception import TextOverLength
-
-img_dir = Path(__file__).parent / "images"
-
-
-def meteor(images, texts: List[str], args):
-    text = texts[0]
-    frame = BuildImage.open(img_dir / "0.png")
-    try:
-        frame.draw_text(
-            (220, 230, 920, 315),
-            text,
-            allow_wrap=True,
-            max_fontsize=80,
-            min_fontsize=20,
-            fill="white",
-        )
-    except ValueError:
-        raise TextOverLength(text)
-    return frame.save_jpg()
-
-
-add_meme(
-    "meteor", meteor, min_texts=1, max_texts=1, default_texts=["我要对象"], keywords=["流星"]
-)

spaces/CofAI/CurrencyConverter/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/CofAI/chat.b4/g4f/typing.py

@@ -1,3 +0,0 @@
-from typing import Dict, NewType, Union, Optional, List, get_type_hints
-
-sha256 = NewType('sha_256_hash', str)

spaces/CofAI/chat/client/css/settings.css

@@ -1,44 +0,0 @@
-.settings-container {
-	color: var(--colour-2);
-	margin: 24px 0px 8px 0px;
-	justify-content: center;
-}
-
-.settings-container span {
-	font-size: 0.875rem;
-	margin: 0;
-}
-
-.settings-container label {
-	width: 24px;
-	height: 16px;
-}
-
-.settings-container .field {
-	justify-content: space-between;
-}
-
-.settings-container .checkbox input + label,
-.settings-container .checkbox input:checked + label:after {
-	background: var(--colour-1);
-}
-
-.settings-container .checkbox input + label:after,
-.settings-container .checkbox input:checked + label {
-	background: var(--colour-3);
-}
-
-.settings-container .checkbox label:after {
-	left: 2px;
-	width: 10px;
-	height: 10px;
-}
-
-.settings-container .checkbox input:checked + label:after {
-	left: calc(100% - 2px - 10px);
-}
-
-.settings-container .dropdown {
-	padding: 4px 8px;
-	font-size: 0.75rem;
-}

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/poolers.py

@@ -1,151 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from maskrcnn_benchmark.layers import ROIAlign
-from maskrcnn_benchmark.layers import DCNPooling
-
-from .utils import cat
-
-
-class LevelMapper(object):
-    """Determine which FPN level each RoI in a set of RoIs should map to based
-    on the heuristic in the FPN paper.
-    """
-
-    def __init__(self, k_min, k_max, canonical_scale=224, canonical_level=4, eps=1e-6):
-        """
-        Arguments:
-            k_min (int)
-            k_max (int)
-            canonical_scale (int)
-            canonical_level (int)
-            eps (float)
-        """
-        self.k_min = k_min
-        self.k_max = k_max
-        self.s0 = canonical_scale
-        self.lvl0 = canonical_level
-        self.eps = eps
-
-    def __call__(self, boxlists):
-        """
-        Arguments:
-            boxlists (list[BoxList])
-        """
-        # Compute level ids
-        s = torch.sqrt(cat([boxlist.area() for boxlist in boxlists]))
-
-        # Eqn.(1) in FPN paper
-        target_lvls = torch.floor(self.lvl0 + torch.log2(s / self.s0 + self.eps))
-        target_lvls = torch.clamp(target_lvls, min=self.k_min, max=self.k_max)
-        return target_lvls.to(torch.int64) - self.k_min
-
-    def get_random(self, level):
-        """ Generate a random roi for target level
-        """
-        xmin, ymin, xmax, ymax = torch.tensor
-
-
-class Pooler(nn.Module):
-    """
-    Pooler for Detection with or without FPN.
-    It currently hard-code ROIAlign in the implementation,
-    but that can be made more generic later on.
-    Also, the requirement of passing the scales is not strictly necessary, as they
-    can be inferred from the size of the feature map / size of original image,
-    which is available thanks to the BoxList.
-    """
-
-    def __init__(self, output_size, scales, sampling_ratio,
-                 deformable=False, output_channel=256):
-        """
-        Arguments:
-            output_size (list[tuple[int]] or list[int]): output size for the pooled region
-            scales (list[float]): scales for each Pooler
-            sampling_ratio (int): sampling ratio for ROIAlign
-        """
-        super(Pooler, self).__init__()
-        poolers = []
-        for scale in scales:
-            poolers.append(
-                ROIAlign(
-                    output_size, spatial_scale=scale, sampling_ratio=sampling_ratio
-                ) if not deformable else
-                DCNPooling(spatial_scale=scale, pooled_size=output_size, no_trans=False,
-                           group_size=1, trans_std=0.1, output_dim=output_channel)
-            )
-        self.poolers = nn.ModuleList(poolers)
-        self.output_size = output_size
-        # get the levels in the feature map by leveraging the fact that the network always
-        # downsamples by a factor of 2 at each level.
-        lvl_min = -torch.log2(torch.tensor(scales[0], dtype=torch.float32)).item()
-        lvl_max = -torch.log2(torch.tensor(scales[-1], dtype=torch.float32)).item()
-        self.map_levels = LevelMapper(lvl_min, lvl_max, canonical_scale=160)
-
-    def convert_to_roi_format(self, boxes):
-        concat_boxes = cat([b.bbox for b in boxes], dim=0)
-        device, dtype = concat_boxes.device, concat_boxes.dtype
-        ids = cat(
-            [
-                torch.full((len(b), 1), i, dtype=dtype, device=device)
-                for i, b in enumerate(boxes)
-            ],
-            dim=0,
-        )
-        rois = torch.cat([ids, concat_boxes], dim=1)
-        return rois
-
-    def forward(self, x, boxes):
-        """
-        Arguments:
-            x (list[Tensor]): feature maps for each level
-            boxes (list[BoxList]): boxes to be used to perform the pooling operation.
-        Returns:
-            result (Tensor)
-        """
-        num_levels = len(self.poolers)
-        rois = self.convert_to_roi_format(boxes)
-        if num_levels == 1:
-            return self.poolers[0](x[0], rois)
-
-        levels = self.map_levels(boxes)
-
-        num_rois = len(rois)
-        num_channels = x[0].shape[1]
-        output_size = self.output_size[0]
-
-        dtype, device = x[0].dtype, x[0].device
-        result = torch.zeros(
-            (num_rois, num_channels, output_size, output_size),
-            dtype=dtype,
-            device=device,
-        )
-        for level, (per_level_feature, pooler) in enumerate(zip(x, self.poolers)):
-            idx_in_level = torch.nonzero(levels == level).squeeze(1)
-            rois_per_level = rois[idx_in_level]
-            if idx_in_level.numel() == 0:
-                if num_rois == 0:
-                    continue
-                # create a roi and do one empty forward pass
-                new_level = idx_in_level.new_tensor((0,))
-                new_rois = rois[new_level]
-                result[new_level] = result[new_level] \
-                                    + pooler(per_level_feature, new_rois) * 0.0
-            else:
-                result[idx_in_level] = pooler(per_level_feature, rois_per_level)
-
-        return result
-
-
-def make_pooler(cfg, head_name):
-    resolution = cfg.MODEL[head_name].POOLER_RESOLUTION
-    scales = cfg.MODEL[head_name].POOLER_SCALES
-    sampling_ratio = cfg.MODEL[head_name].POOLER_SAMPLING_RATIO
-    pooler = Pooler(
-        output_size=(resolution, resolution),
-        scales=scales,
-        sampling_ratio=sampling_ratio,
-    )
-    return pooler

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/MicImagePlugin.py

@@ -1,103 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# Microsoft Image Composer support for PIL
-#
-# Notes:
-#       uses TiffImagePlugin.py to read the actual image streams
-#
-# History:
-#       97-01-20 fl     Created
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1997.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import olefile
-
-from . import Image, TiffImagePlugin
-
-#
-# --------------------------------------------------------------------
-
-
-def _accept(prefix):
-    return prefix[:8] == olefile.MAGIC
-
-
-##
-# Image plugin for Microsoft's Image Composer file format.
-
-
-class MicImageFile(TiffImagePlugin.TiffImageFile):
-    format = "MIC"
-    format_description = "Microsoft Image Composer"
-    _close_exclusive_fp_after_loading = False
-
-    def _open(self):
-        # read the OLE directory and see if this is a likely
-        # to be a Microsoft Image Composer file
-
-        try:
-            self.ole = olefile.OleFileIO(self.fp)
-        except OSError as e:
-            msg = "not an MIC file; invalid OLE file"
-            raise SyntaxError(msg) from e
-
-        # find ACI subfiles with Image members (maybe not the
-        # best way to identify MIC files, but what the... ;-)
-
-        self.images = []
-        for path in self.ole.listdir():
-            if path[1:] and path[0][-4:] == ".ACI" and path[1] == "Image":
-                self.images.append(path)
-
-        # if we didn't find any images, this is probably not
-        # an MIC file.
-        if not self.images:
-            msg = "not an MIC file; no image entries"
-            raise SyntaxError(msg)
-
-        self.frame = None
-        self._n_frames = len(self.images)
-        self.is_animated = self._n_frames > 1
-
-        self.seek(0)
-
-    def seek(self, frame):
-        if not self._seek_check(frame):
-            return
-        try:
-            filename = self.images[frame]
-        except IndexError as e:
-            msg = "no such frame"
-            raise EOFError(msg) from e
-
-        self.fp = self.ole.openstream(filename)
-
-        TiffImagePlugin.TiffImageFile._open(self)
-
-        self.frame = frame
-
-    def tell(self):
-        return self.frame
-
-    def close(self):
-        self.ole.close()
-        super().close()
-
-    def __exit__(self, *args):
-        self.ole.close()
-        super().__exit__()
-
-
-#
-# --------------------------------------------------------------------
-
-Image.register_open(MicImageFile.format, MicImageFile, _accept)
-
-Image.register_extension(MicImageFile.format, ".mic")

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/click/formatting.py

@@ -1,301 +0,0 @@
-import typing as t
-from contextlib import contextmanager
-from gettext import gettext as _
-
-from ._compat import term_len
-from .parser import split_opt
-
-# Can force a width.  This is used by the test system
-FORCED_WIDTH: t.Optional[int] = None
-
-
-def measure_table(rows: t.Iterable[t.Tuple[str, str]]) -> t.Tuple[int, ...]:
-    widths: t.Dict[int, int] = {}
-
-    for row in rows:
-        for idx, col in enumerate(row):
-            widths[idx] = max(widths.get(idx, 0), term_len(col))
-
-    return tuple(y for x, y in sorted(widths.items()))
-
-
-def iter_rows(
-    rows: t.Iterable[t.Tuple[str, str]], col_count: int
-) -> t.Iterator[t.Tuple[str, ...]]:
-    for row in rows:
-        yield row + ("",) * (col_count - len(row))
-
-
-def wrap_text(
-    text: str,
-    width: int = 78,
-    initial_indent: str = "",
-    subsequent_indent: str = "",
-    preserve_paragraphs: bool = False,
-) -> str:
-    """A helper function that intelligently wraps text.  By default, it
-    assumes that it operates on a single paragraph of text but if the
-    `preserve_paragraphs` parameter is provided it will intelligently
-    handle paragraphs (defined by two empty lines).
-
-    If paragraphs are handled, a paragraph can be prefixed with an empty
-    line containing the ``\\b`` character (``\\x08``) to indicate that
-    no rewrapping should happen in that block.
-
-    :param text: the text that should be rewrapped.
-    :param width: the maximum width for the text.
-    :param initial_indent: the initial indent that should be placed on the
-                           first line as a string.
-    :param subsequent_indent: the indent string that should be placed on
-                              each consecutive line.
-    :param preserve_paragraphs: if this flag is set then the wrapping will
-                                intelligently handle paragraphs.
-    """
-    from ._textwrap import TextWrapper
-
-    text = text.expandtabs()
-    wrapper = TextWrapper(
-        width,
-        initial_indent=initial_indent,
-        subsequent_indent=subsequent_indent,
-        replace_whitespace=False,
-    )
-    if not preserve_paragraphs:
-        return wrapper.fill(text)
-
-    p: t.List[t.Tuple[int, bool, str]] = []
-    buf: t.List[str] = []
-    indent = None
-
-    def _flush_par() -> None:
-        if not buf:
-            return
-        if buf[0].strip() == "\b":
-            p.append((indent or 0, True, "\n".join(buf[1:])))
-        else:
-            p.append((indent or 0, False, " ".join(buf)))
-        del buf[:]
-
-    for line in text.splitlines():
-        if not line:
-            _flush_par()
-            indent = None
-        else:
-            if indent is None:
-                orig_len = term_len(line)
-                line = line.lstrip()
-                indent = orig_len - term_len(line)
-            buf.append(line)
-    _flush_par()
-
-    rv = []
-    for indent, raw, text in p:
-        with wrapper.extra_indent(" " * indent):
-            if raw:
-                rv.append(wrapper.indent_only(text))
-            else:
-                rv.append(wrapper.fill(text))
-
-    return "\n\n".join(rv)
-
-
-class HelpFormatter:
-    """This class helps with formatting text-based help pages.  It's
-    usually just needed for very special internal cases, but it's also
-    exposed so that developers can write their own fancy outputs.
-
-    At present, it always writes into memory.
-
-    :param indent_increment: the additional increment for each level.
-    :param width: the width for the text.  This defaults to the terminal
-                  width clamped to a maximum of 78.
-    """
-
-    def __init__(
-        self,
-        indent_increment: int = 2,
-        width: t.Optional[int] = None,
-        max_width: t.Optional[int] = None,
-    ) -> None:
-        import shutil
-
-        self.indent_increment = indent_increment
-        if max_width is None:
-            max_width = 80
-        if width is None:
-            width = FORCED_WIDTH
-            if width is None:
-                width = max(min(shutil.get_terminal_size().columns, max_width) - 2, 50)
-        self.width = width
-        self.current_indent = 0
-        self.buffer: t.List[str] = []
-
-    def write(self, string: str) -> None:
-        """Writes a unicode string into the internal buffer."""
-        self.buffer.append(string)
-
-    def indent(self) -> None:
-        """Increases the indentation."""
-        self.current_indent += self.indent_increment
-
-    def dedent(self) -> None:
-        """Decreases the indentation."""
-        self.current_indent -= self.indent_increment
-
-    def write_usage(
-        self, prog: str, args: str = "", prefix: t.Optional[str] = None
-    ) -> None:
-        """Writes a usage line into the buffer.
-
-        :param prog: the program name.
-        :param args: whitespace separated list of arguments.
-        :param prefix: The prefix for the first line. Defaults to
-            ``"Usage: "``.
-        """
-        if prefix is None:
-            prefix = f"{_('Usage:')} "
-
-        usage_prefix = f"{prefix:>{self.current_indent}}{prog} "
-        text_width = self.width - self.current_indent
-
-        if text_width >= (term_len(usage_prefix) + 20):
-            # The arguments will fit to the right of the prefix.
-            indent = " " * term_len(usage_prefix)
-            self.write(
-                wrap_text(
-                    args,
-                    text_width,
-                    initial_indent=usage_prefix,
-                    subsequent_indent=indent,
-                )
-            )
-        else:
-            # The prefix is too long, put the arguments on the next line.
-            self.write(usage_prefix)
-            self.write("\n")
-            indent = " " * (max(self.current_indent, term_len(prefix)) + 4)
-            self.write(
-                wrap_text(
-                    args, text_width, initial_indent=indent, subsequent_indent=indent
-                )
-            )
-
-        self.write("\n")
-
-    def write_heading(self, heading: str) -> None:
-        """Writes a heading into the buffer."""
-        self.write(f"{'':>{self.current_indent}}{heading}:\n")
-
-    def write_paragraph(self) -> None:
-        """Writes a paragraph into the buffer."""
-        if self.buffer:
-            self.write("\n")
-
-    def write_text(self, text: str) -> None:
-        """Writes re-indented text into the buffer.  This rewraps and
-        preserves paragraphs.
-        """
-        indent = " " * self.current_indent
-        self.write(
-            wrap_text(
-                text,
-                self.width,
-                initial_indent=indent,
-                subsequent_indent=indent,
-                preserve_paragraphs=True,
-            )
-        )
-        self.write("\n")
-
-    def write_dl(
-        self,
-        rows: t.Sequence[t.Tuple[str, str]],
-        col_max: int = 30,
-        col_spacing: int = 2,
-    ) -> None:
-        """Writes a definition list into the buffer.  This is how options
-        and commands are usually formatted.
-
-        :param rows: a list of two item tuples for the terms and values.
-        :param col_max: the maximum width of the first column.
-        :param col_spacing: the number of spaces between the first and
-                            second column.
-        """
-        rows = list(rows)
-        widths = measure_table(rows)
-        if len(widths) != 2:
-            raise TypeError("Expected two columns for definition list")
-
-        first_col = min(widths[0], col_max) + col_spacing
-
-        for first, second in iter_rows(rows, len(widths)):
-            self.write(f"{'':>{self.current_indent}}{first}")
-            if not second:
-                self.write("\n")
-                continue
-            if term_len(first) <= first_col - col_spacing:
-                self.write(" " * (first_col - term_len(first)))
-            else:
-                self.write("\n")
-                self.write(" " * (first_col + self.current_indent))
-
-            text_width = max(self.width - first_col - 2, 10)
-            wrapped_text = wrap_text(second, text_width, preserve_paragraphs=True)
-            lines = wrapped_text.splitlines()
-
-            if lines:
-                self.write(f"{lines[0]}\n")
-
-                for line in lines[1:]:
-                    self.write(f"{'':>{first_col + self.current_indent}}{line}\n")
-            else:
-                self.write("\n")
-
-    @contextmanager
-    def section(self, name: str) -> t.Iterator[None]:
-        """Helpful context manager that writes a paragraph, a heading,
-        and the indents.
-
-        :param name: the section name that is written as heading.
-        """
-        self.write_paragraph()
-        self.write_heading(name)
-        self.indent()
-        try:
-            yield
-        finally:
-            self.dedent()
-
-    @contextmanager
-    def indentation(self) -> t.Iterator[None]:
-        """A context manager that increases the indentation."""
-        self.indent()
-        try:
-            yield
-        finally:
-            self.dedent()
-
-    def getvalue(self) -> str:
-        """Returns the buffer contents."""
-        return "".join(self.buffer)
-
-
-def join_options(options: t.Sequence[str]) -> t.Tuple[str, bool]:
-    """Given a list of option strings this joins them in the most appropriate
-    way and returns them in the form ``(formatted_string,
-    any_prefix_is_slash)`` where the second item in the tuple is a flag that
-    indicates if any of the option prefixes was a slash.
-    """
-    rv = []
-    any_prefix_is_slash = False
-
-    for opt in options:
-        prefix = split_opt(opt)[0]
-
-        if prefix == "/":
-            any_prefix_is_slash = True
-
-        rv.append((len(prefix), opt))
-
-    rv.sort(key=lambda x: x[0])
-    return ", ".join(x[1] for x in rv), any_prefix_is_slash

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-6acaa952.css

@@ -1 +0,0 @@
-.input-number.svelte-x6nxfm{transition:.15s;box-shadow:var(--shadow-drop);background:var(--background-fill-secondary)}.input-number.svelte-x6nxfm:hover{box-shadow:var(--shadow-drop-lg)}.range.svelte-x6nxfm{display:flex}.item.svelte-x6nxfm{flex:1 1 0%}.dropdown-menu.svelte-1cqwepf{box-shadow:var(--shadow-drop)}.dropdown-item.svelte-1cqwepf{display:block;transition:.15s;cursor:pointer;background:var(--background-fill-primary);padding:var(--size-2) var(--size-3);white-space:nowrap}.dropdown-item.svelte-1cqwepf:first-child{border-top-right-radius:var(--radius-md);border-top-left-radius:var(--radius-md)}.dropdown-item.svelte-1cqwepf:last-child{border-bottom-right-radius:var(--radius-md);border-bottom-left-radius:var(--radius-md)}.dropdown-item.svelte-1cqwepf:hover{font-weight:var(--weight-semibold)}.input-checkbox.svelte-1nw19ca.svelte-1nw19ca{display:inline-block}svg.svelte-1nw19ca.svelte-1nw19ca{width:var(--size-4);height:var(--size-3)}.selected.svelte-1nw19ca svg.svelte-1nw19ca{opacity:1}.input-checkbox.svelte-1nw19ca.svelte-1nw19ca{display:flex;gap:var(--size-1);cursor:pointer;border-radius:var(--radius-md);padding:var(--size-2) var(--size-3)}.checkbox.svelte-1nw19ca.svelte-1nw19ca{display:flex;justify-content:center;align-items:center;border:1px solid var(--border-color-primary);background:var(--background-fill-primary);width:var(--size-4);height:var(--size-4)}.checkbox-item.svelte-1nw19ca.svelte-1nw19ca{transition:.15s;box-shadow:var(--shadow-drop);background:var(--background-fill-primary)}.checkbox-item.svelte-1nw19ca.svelte-1nw19ca:hover{box-shadow:var(--shadow-drop-lg)}.checkbox-item.selected.svelte-1nw19ca.svelte-1nw19ca{background:var(--color-accent-base);color:#fff}svg.svelte-1cbhr6k.svelte-1cbhr6k{width:var(--size-4);height:var(--size-3)}.selected.svelte-1cbhr6k 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spaces/Dacoolkid/Oba_-s/README.md

@@ -1,13 +0,0 @@
----
-title: Oba -s
-emoji: 💻
-colorFrom: pink
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Dagfinn1962/stablediffusion-members/app1.py

@@ -1,110 +0,0 @@
-import gradio as gr
-import os
-import sys
-from pathlib import Path
-
-models = [
-    {"name": "Stable Diffusion 1.4","url": "CompVis/stable-diffusion-v1-4"},
-    {"name": "Stable Diffusion 1.5","url": "runwayml/stable-diffusion-v1-5"},
-    ]
-models = [
-    "",
-    "runwayml/stable-diffusion-v1-5",
-    "CompVis/stable-diffusion-v1-4",
-    "claudfuen/photorealistic-fuen-v1",
-    "andite/anything-v4.0",
-    "naclbit/trinart_stable_diffusion_v2",
-    "nitrosocke/Arcane-Diffusion",
-    "nitrosocke/archer-diffusion",
-    "nitrosocke/elden-ring-diffusion",
-    "nitrosocke/redshift-diffusion",
-    "nitrosocke/spider-verse-diffusion", 
-    "nitrosocke/mo-di-diffusion",
-    "nitrosocke/classic-anim-diffusion",
-    "dreamlike-art/dreamlike-photoreal-1.0",
-    "dreamlike-art/dreamlike-photoreal-2.0",    
-    "wavymulder/wavyfusion",
-    "wavymulder/Analog-Diffusion",
-    "prompthero/midjourney-v4-diffusion",
-    "prompthero/openjourney",
-    "dallinmackay/Van-Gogh-diffusion",
-    "hakurei/waifu-diffusion",
-    "DGSpitzer/Cyberpunk-Anime-Diffusion",
-    "Fictiverse/Stable_Diffusion_BalloonArt_Model",
-    "dallinmackay/Tron-Legacy-diffusion",
-    "AstraliteHeart/pony-diffusion",
-    "nousr/robo-diffusion",
-    "Linaqruf/anything-v3",
-    "Omnibus/maximum_diffusion_fast",
-    "",
-]
-current_model = models[0]
-
-text_gen = gr.Interface.load("spaces/daspartho/prompt-extend") 
-
-models2 = []
-for model in models:
-    model_url = f"models/{model['url']}"
-    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
-    models2.append(loaded_model)
-
-
-def text_it(inputs, text_gen=text_gen):
-    return text_gen(inputs)
-
-
-def set_model(current_model_index):
-    global current_model
-    current_model = models[current_model_index]
-    return gr.update(value=f"{current_model['name']}")
-
-
-def send_it(inputs, model_choice):
-    proc = models2[model_choice]
-    return proc(inputs)
-
-
-with gr.Blocks() as myface:
-    gr.HTML("""
-    <head> <style> with global {width: 500px; position; absolute; background-color: #000000; height: 100%; margin-left:2px; margin-right: 2px; font-weight:800; font-size: 24px; margin-right: 10px; padding: 10px;} </style> </head>"""
-   
-    )
-    with gr.Row():
-        input_text = gr.Textbox(label=" ",placeholder="PROMPT HERE ",lines=4)
-        # Model selection dropdown
-        model_name1 = gr.Dropdown(
-        label=" ",
-        choices=[m["name"] for m in models],
-        type="index",
-        value=current_model["name"],
-        interactive=True,
-    
-        
-        )
-    with gr.Row():
-        see_prompts = gr.Button("Generate Prompts")                                                      
-        run = gr.Button("Generate Images", varant="primery")
-        
-    with gr.Row():
-        output1 = gr.Image(label="")
-        output2 = gr.Image(label="")
-        output3 = gr.Image(label="")
-    with gr.Row():          
-        magic1 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic2 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic3 = gr.Textbox(label="Generated Prompt", lines=2)
-   
-    model_name1.change(set_model, inputs=model_name1, outputs=[output1, output2, output3,])
-
-    run.click(send_it, inputs=[magic1, model_name1], outputs=[output1])
-    run.click(send_it, inputs=[magic2, model_name1], outputs=[output2])
-    run.click(send_it, inputs=[magic3, model_name1], outputs=[output3])
-   
-
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic1])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic2])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic3])
-   
-
-myface.queue(concurrency_count=200)
-myface.launch(inline=True, show_api=False, max_threads=400)