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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cara Mudah Download Clash Royale Mod Apk dengan Fitur Unlimited Money.md

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-<h1>Cara Download Clash Royale Mod APK</h1>
-<p>Clash Royale is one of the most popular and addictive strategy games on mobile devices. It combines card collecting, tower defense, and real-time multiplayer battles in a fast-paced and fun gameplay. However, some players may find it hard to progress in the game without spending real money on gems and gold, which are the main currencies in the game. That's why some players resort to using Clash Royale Mod APK, which is a modified version of the game that gives them unlimited resources and access to all the cards in the game.</p>
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-<p>If you are one of those players who want to enjoy Clash Royale without any limitations, then this article is for you. In this article, we will show you how to download and install Clash Royale Mod APK on your Android device, what are the features of this mod, and whether it is safe and legal to use. Let's get started!</p>
- <h2>What is Clash Royale Mod APK?</h2>
-<p>Clash Royale Mod APK is a hacked version of the original Clash Royale game that was developed by third-party developers. This mod allows you to have unlimited gems and gold, which you can use to buy chests, cards, upgrades, and more. You can also unlock all the cards in the game, including the legendary ones, and use them in your deck. Moreover, you can play on custom mods and servers that offer different game modes, such as 2v2 battles, draft mode, unlimited elixir mode, and more.</p>
-<p>With Clash Royale Mod APK, you can enjoy the game without any restrictions or worries. You can experiment with different strategies, decks, and cards without losing any trophies or resources. You can also challenge your friends or other players online and show off your skills. However, you should be aware that using this mod may also have some risks and consequences, which we will discuss later in this article.</p>
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- <h3>Features of Clash Royale Mod APK</h3>
-<p>Clash Royale Mod APK has many features that make it different from the original game. Here are some of the main features of this mod:</p>
- <h4>Unlimited Gems and Gold</h4>
-<p>Gems and gold are the most important resources in Clash Royale. You need gems to buy chests, cards, emotes, skins, and more. You need gold to upgrade your cards and increase your level. However, gems and gold are very scarce and expensive in the game. You can only get them by completing quests, winning battles, opening chests, or spending real money.</p>
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-<p>Cards are the core element of Clash Royale. They are used to create your deck and fight against your opponents. There are four types of cards in the game: common, rare, epic, and legendary. Each card has its own strengths, weaknesses, abilities, and costs. You can collect cards by opening chests or buying them from the shop.</p>
-<p>However, not all cards are easy to get in the game. Some cards are very rare and hard to find, especially the legendary ones. You may have to open hundreds of chests or spend thousands of gems to get them. And even if you get them, you still need to upgrade them to make them more powerful.</p>
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-<p>Another feature of Clash Royale Mod APK is that you can play on custom mods and servers that offer different game modes and experiences. For example, you can play on a mod that gives you unlimited elixir, which means you can spam any card you want without worrying about the elixir cost. You can also play on a mod that lets you draft your deck from a random pool of cards, which adds more variety and challenge to the game. You can also play on a mod that lets you use cards from other Supercell games, such as Brawl Stars, Clash of Clans, or Boom Beach.</p>
-<p>Moreover, you can play on custom servers that have different rules and settings than the official ones. For example, you can play on a server that has higher or lower trophy requirements, faster or slower chest opening times, more or less rewards, and more. You can also play on a server that has more players online, which means you can find matches faster and easier.</p>
- <h2>How to Download and Install Clash Royale Mod APK?</h2>
-<p>If you are interested in trying out Clash Royale Mod APK, then you need to follow these steps to download and install it on your Android device:</p>
- <h3>Step 1: Enable Unknown Sources</h3>
-<p>The first step is to enable unknown sources on your device. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and turn it on. You may see a warning message, but don't worry, just tap OK.</p>
- <h3>Step 2: Download the APK File</h3>
-<p>The next step is to download the APK file of Clash Royale Mod APK. You can find many websites that offer this file, but be careful, some of them may contain viruses or malware. We recommend you to use this link, which is safe and reliable. Just click on the download button and wait for the file to be downloaded.</p>
- <h3>Step 3: Install the APK File</h3>
-<p>The third step is to install the APK file on your device. To do this, go to your file manager, then locate the downloaded file, then tap on it. You may see a pop-up message asking for your permission, just tap on install and wait for the process to finish.</p>
- <h3>Step 4: Launch the Game and Enjoy</h3>
-<p>The final step is to launch the game and enjoy it. To do this, go to your app drawer, then find the Clash Royale icon, then tap on it. You may see a loading screen, then a welcome message, then the game will start. You will notice that you have unlimited gems and gold, all cards unlocked, and access to custom mods and servers. You can now play the game as you wish and have fun!</p>
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-<p>Before you download and install Clash Royale Mod APK, you may have some questions about its safety and legality. Here are some answers to these questions:</p>
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-<p>As we mentioned earlier, not all websites that offer Clash Royale Mod APK are safe and trustworthy. Some of them may contain viruses or malware that can harm your device or steal your personal information. That's why you should always use a reputable source like the one we provided above. You should also scan the file with an antivirus app before installing it.</p>
-<p>Another safety issue is that using Clash Royale Mod APK may cause your account to be banned by Supercell, the developer of the original game. This is because using this mod violates their terms of service and fair play policy. Supercell has the right to detect and ban any account that uses any third-party software or modification that gives an unfair advantage over other players. Therefore, if you use Clash Royale Mod APK, you should do it at your own risk and responsibility.</p>
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-<p>Besides safety issues, using Clash Royale Mod APK may also raise some legal issues. This is because using this mod infringes the intellectual property rights of Supercell, the developer of the original game. Supercell owns all the rights to the game content, such as graphics, sounds, characters, cards, etc. By modifying and distributing their game without their permission, you are violating their rights and breaking the law.</p>
-<p>Therefore, if you use Clash Royale Mod APK, you should be aware of the potential legal consequences that may arise from it. You may face lawsuits or fines from Supercell or other parties involved in the game development or distribution. You may also face criminal charges depending on the laws of your country or region. Therefore, you should be careful and cautious when using Clash Royale Mod APK.</p>
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-<p>Clash Royale Mod APK is a modified version of the original Clash Royale game that gives you unlimited gems and gold, all cards unlocked, and access to custom mods and servers. It can be a fun and exciting way to enjoy the game without any limitations or restrictions. However, it also has some risks and drawbacks, such as safety and legal issues. You should always use a reliable source to download the APK file, scan it with an antivirus app, and use it at your own risk and responsibility. We hope this article has helped you understand how to download and install Clash Royale Mod APK on your Android device. Have fun and clash on!</p>
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-<p>Here are some frequently asked questions about Clash Royale Mod APK:</p>
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-<li><b>Q: Can I use Clash Royale Mod APK on iOS devices?</b></li>
-<li>A: No, Clash Royale Mod APK is only compatible with Android devices. You cannot use it on iOS devices, such as iPhones or iPads.</li>
-<li><b>Q: Can I play Clash Royale Mod APK with other players who use the original game?</b></li>
-<li>A: No, Clash Royale Mod APK uses different servers than the original game. You can only play with other players who use the same mod or server as you.</li>
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-<li>A: Yes, you can switch back to the original game anytime you want. However, you may lose your progress and data in the modded game. You may also have to uninstall the modded game and reinstall the original game from the Google Play Store.</li>
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-<li>A: No, you cannot use your existing account in Clash Royale Mod APK. You have to create a new account in the modded game. You may also have to use a different email address or phone number to register.</li>
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-<p>If you are a fan of soccer games, you have probably heard of Dream League Soccer (DLS), one of the most popular and realistic soccer games for mobile devices. DLS 17 is the latest edition of this game series, featuring amazing graphics, smooth animations, and addictive gameplay. However, if you want to take your soccer experience to the next level, you should consider downloading the mod apk version of DLS 17, which gives you access to unlimited money, all players unlocked, no ads, and many other features that will make you feel like a soccer superstar. In this article, we will show you what DLS 17 mod apk is, what are its benefits and risks, and how to download and install it on your android device.</p>
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-<p>The fifth step is to extract and copy the obb file that you downloaded in Step 3 to your device storage. To do this, open XAPKS Installer and locate the obb file that has a zip icon. Tap the obb file and select Extract. Then, select the folder that says com.firsttouchgames.dls3 and tap Copy. Next, go to Internal Storage > Android > OBB and paste the folder there. Make sure that the folder name matches the obb file name.</p>
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-<p>The final step is to launch the DLS 17 mod apk app and enjoy the game. To do this, go to your app drawer and tap the DLS 17 icon. You may see a loading screen and some messages, but just wait for them to finish. Then, you can start playing the game with all players unlocked and unlimited money. You can also customize your team, change the settings, and access other features of the game.</p>
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-<p>DLS 17 is one of the best soccer games for android devices, but it can be even better with the mod apk version that unlocks all players and gives you unlimited money. In this article, we showed you what DLS 17 mod apk is, what are its benefits and risks, and how to download and install it on your android device. We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Now, go ahead and download DLS 17 mod apk unlock all player and enjoy the ultimate soccer experience on your android device.</p>
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- <p>After you have downloaded the APK file, you need to install it on your device. To do this, locate the file in your downloads folder or wherever you saved it, and tap on it. You will see a pop-up window asking you to confirm the installation. Tap on install and wait for the process to finish.</p>
- <h3>Step 4: Launch the game and enjoy the hack features</h3>
- <p>Finally, you can launch the game and enjoy the hack features. You will see that you have unlimited money and gold, all buses unlocked and upgraded, no ads and no root required, and other hack features that will make your gameplay easier and more enjoyable. You can now drive any bus you want, customize it as you like, transport passengers without any hassle, create your own bus company, compete with other players online, and have fun!</p>
- <h2>What are the benefits of using Bus Simulator Ultimate Hack APK 1.0 7?</h2>
- <p>Using Bus Simulator Ultimate Hack APK 1.0 7 has many benefits that will enhance your gaming experience and satisfaction. Some of these benefits are:</p>
- <h3>Unlimited money and gold</h3>
- <p>With unlimited money and gold, you can buy anything you want in the game without worrying about running out of resources or spending real money. You can buy new buses or upgrade them with different features and accessories. You can also buy VIP membership or gems that will give you more advantages and perks in the game.</p>
- <h3>All buses unlocked and upgraded</h3>
- <p>With all buses unlocked and upgraded, you can choose from over 30 different buses with realistic interiors and sounds, such as Mercedes-Benz, Setra, Volvo, MAN, Scania, IVECO, and more. You can also customize your buses with different skins, stickers, accessories, horns, lights, etc. You can drive any bus you want across various countries and cities without any limitations or restrictions.</p>
- <h3>No ads and no root required</h3>
- <p>With no ads and no root required, you can enjoy the game without any interruptions or annoyances. You don't have to watch ads or videos to get some rewards or bonuses in the game. You also don't have to root your device to use the hack features. This will save you time and effort and protect your device from potential risks.</p> <h2>What are the drawbacks of using Bus Simulator Ultimate Hack APK 1.0 7?</h2>
- <p>Using Bus Simulator Ultimate Hack APK 1.0 7 also has some drawbacks that you should be aware of before you decide to use it. Some of these drawbacks are:</p>
- <h3>Risk of malware and viruses</h3>
- <p>Since you are downloading and installing an APK file from an unknown source, you are exposing your device to the risk of malware and viruses that can harm your device or steal your data. Some of these malware and viruses can also affect your gameplay or performance, such as slowing down your device, crashing your game, deleting your files, etc. To avoid this, you should always scan the APK file with a reliable antivirus software before installing it.</p>
- <h3>Risk of banning and suspension</h3>
- <p>Since you are using a modified version of the original game that gives you unfair advantages over other players, you are violating the terms and conditions of the game developers and publishers. This means that they can detect your hack and ban or suspend your account from the game. This will result in losing your game progress and data, as well as being unable to play the game online or offline. To avoid this, you should always use the hack with caution and discretion, and avoid using it in multiplayer mode or online competitions.</p>
- <h3>Risk of losing game progress and data</h3>
- <p>Since you are using a modified version of the original game that is not compatible with the official updates or patches, you are risking losing your game progress and data if you update or uninstall the game. This means that you will have to start from scratch or lose some of your achievements or rewards in the game. To avoid this, you should always backup your game data before updating or uninstalling the game, and use a cloud service or an external storage device to save your data.</p>
- <h2>Conclusion</h2>
- <p>Bus Simulator Ultimate is a realistic and immersive simulation game that lets you drive different buses across various countries and cities. However, if you want to enjoy the game without any limitations or restrictions, you might want to try Bus Simulator Ultimate Hack APK 1.0 7. This is a modified version of the original game that gives you access to unlimited money and gold, all buses unlocked and upgraded, no ads and no root required, and other hack features that will make your gameplay easier and more enjoyable. However, using this hack also has some drawbacks, such as risk of malware and viruses, risk of banning and suspension, and risk of losing game progress and data. Therefore, you should weigh the pros and cons before using this hack, and always use it with caution and discretion.</p>
- <p>We hope this article has helped you understand what Bus Simulator Ultimate Hack APK 1.0 7 is, how to download and install it, what are its benefits and drawbacks, and some FAQs about it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Bus Simulator Ultimate Hack APK 1.0 7:</p>
- <ol>
-<li><b>Is Bus Simulator Ultimate Hack APK 1.0 7 safe to use?</b></li>
-<p>Bus Simulator Ultimate Hack APK 1.0 7 is not completely safe to use, as it can expose your device to malware and viruses, as well as get your account banned or suspended from the game. Therefore, you should always scan the APK file with a reliable antivirus software before installing it, use the hack with caution and discretion, and avoid using it in multiplayer mode or online competitions.</p>
-<li><b>Is Bus Simulator Ultimate Hack APK 1.0 7 free to use?</b></li>
-<p>Yes, Bus Simulator Ultimate Hack APK 1.0 7 is free to use, as you don't have to pay any money to download or install it on your device. However, you might have to watch some ads or videos to access some of the hack features in the game.</p>
-<li><b>Does Bus Simulator Ultimate Hack APK 1.0 7 work on all devices?</b></li>
-<p>No, Bus Simulator Ultimate Hack APK 1.0 7 does not work on all devices, as it requires Android version 5.0 or higher to run on your device. It also requires at least 2 GB of RAM and 500 MB of free storage space on your device.</p>
-<li><b>Can I update Bus Simulator Ultimate Hack APK 1.0 7?</b></li>
-<p>No, you cannot update Bus Simulator Ultimate Hack APK 1.0 7, as it is not compatible with the official updates or patches from the game developers and publishers. If you update the game, you will lose the hack features and your game progress and data . To avoid this, you should always backup your game data before updating or uninstalling the game, and use a cloud service or an external storage device to save your data.</p>
- <li><b>Where can I get more information about Bus Simulator Ultimate Hack APK 1.0 7?</b></li>
-<p>If you want to get more information about Bus Simulator Ultimate Hack APK 1.0 7, you can visit the official website of the game developers and publishers, or the official social media pages of the game, such as Facebook, Twitter, Instagram, YouTube, etc. You can also join the online community of the game, such as forums, blogs, groups, etc., where you can interact with other players and share your tips, tricks, feedback, suggestions, etc.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />

spaces/52Hz/SRMNet_thesis/main_test_SRMNet.py

@@ -1,117 +0,0 @@
-import torch
-import torchvision.transforms.functional as TF
-import torch.nn.functional as F
-from PIL import Image
-import cv2
-import os
-from skimage import img_as_ubyte
-from tqdm import tqdm
-from natsort import natsorted
-import glob
-import argparse
-from model_arch.SRMNet_SWFF import SRMNet_SWFF
-from model_arch.SRMNet import SRMNet
-
-tasks = ['Deblurring_motionblur',
-         'Dehaze_realworld',
-         'Denoise_gaussian',
-         'Denoise_realworld',
-         'Deraining_raindrop',
-         'Deraining_rainstreak',
-         'LLEnhancement',
-         'Retouching']
-
-def main():
-    parser = argparse.ArgumentParser(description='Quick demo Image Restoration')
-    parser.add_argument('--input_dir', default='./test/', type=str, help='Input images root')
-    parser.add_argument('--result_dir', default='./result/', type=str, help='Results images root')
-    parser.add_argument('--weights_root', default='experiments/pretrained_models', type=str, help='Weights root')
-    parser.add_argument('--task', default='Retouching', type=str, help='Restoration task (Above task list)')
-
-    args = parser.parse_args()
-
-    # Prepare testing data
-    files = natsorted(glob.glob(os.path.join(args.input_dir, '*')))
-    if len(files) == 0:
-        raise Exception(f"No files found at {args.input_dir}")
-    os.makedirs(args.result_dir, exist_ok=True)
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    # Build model
-    model = define_model(args)
-    model.eval()
-    model = model.to(device)
-
-    print('restoring images......')
-
-    mul = 16
-
-    for i, file_ in enumerate(tqdm(files)):
-        img = Image.open(file_).convert('RGB')
-        input_ = TF.to_tensor(img).unsqueeze(0).to(device)
-
-        # Pad the input if not_multiple_of 8
-        h, w = input_.shape[2], input_.shape[3]
-        H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul
-        padh = H - h if h % mul != 0 else 0
-        padw = W - w if w % mul != 0 else 0
-        input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
-        with torch.no_grad():
-            restored = model(input_)
-
-        restored = torch.clamp(restored, 0, 1)
-        restored = restored[:, :, :h, :w]
-        restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
-        restored = img_as_ubyte(restored[0])
-
-        f = os.path.splitext(os.path.split(file_)[-1])[0]
-        save_img((os.path.join(args.result_dir, f + '.png')), restored)
-        print('{}'.format(os.path.join(args.result_dir, f + '.png')))
-    clean_folder(args.input_dir)
-    print('finish !')
-
-
-def define_model(args):
-    # Enhance models
-    if args.task in ['LLEnhancement', 'Retouching']:
-        model = SRMNet(in_chn=3, wf=96, depth=4)
-        weight_path = os.path.join(args.weights_root, args.task + '.pth')
-        load_checkpoint(model, weight_path)
-
-    # Restored models
-    else:
-        model = SRMNet_SWFF(in_chn=3, wf=96, depth=4)
-        weight_path = os.path.join(args.weights_root, args.task + '.pth')
-        load_checkpoint(model, weight_path)
-
-    return model
-
-def save_img(filepath, img):
-    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
-
-
-def load_checkpoint(model, weights):
-    checkpoint = torch.load(weights, map_location=torch.device('cpu'))
-    try:
-        model.load_state_dict(checkpoint["state_dict"])
-    except:
-        state_dict = checkpoint["state_dict"]
-        new_state_dict = OrderedDict()
-        for k, v in state_dict.items():
-            name = k[7:]  # remove `module.`
-            new_state_dict[name] = v
-        model.load_state_dict(new_state_dict)
-        
-def clean_folder(folder):
-    for filename in os.listdir(folder):
-        file_path = os.path.join(folder, filename)
-        try:
-            if os.path.isfile(file_path) or os.path.islink(file_path):
-                os.unlink(file_path)
-            elif os.path.isdir(file_path):
-                shutil.rmtree(file_path)
-        except Exception as e:
-            print('Failed to delete %s. Reason: %s' % (file_path, e))
-
-
-if __name__ == '__main__':
-    main()

spaces/AISuperheroes/08GR-KitchenSink-AIUIUX/README.md

@@ -1,13 +0,0 @@
----
-title: 08GR KitchenSink AIUIUX
-emoji: 📈
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIZ2H/Gradio331-3D-Models-AI-1/files/Readme.md

@@ -1,2 +0,0 @@
-Duck & Fox:
-https://github.com/KhronosGroup/glTF-Sample-Models

spaces/AIatUIUC/CodeLATS/generators/py_generate.py

@@ -1,404 +0,0 @@
-from generators.model import ModelBase, message_to_str
-from .generator_types import Generator
-from .generator_utils import generic_generate_func_impl, generic_generate_internal_tests, generic_generate_self_reflection, generate_with_accumulated_context
-
-from typing import Optional, List, Union
-import ast
-import re
-from .parse import parse_code_block, add_code_block
-
-PY_SIMPLE_COMPLETION_INSTRUCTION = "# Write the body of this function only."
-PY_REFLEXION_COMPLETION_INSTRUCTION = "You are a Python writing assistant. You will be given your past function implementation, a series of unit tests, and a hint to change the implementation appropriately. Write your full implementation (restate the function signature).\n\n-----"
-PY_SELF_REFLECTION_COMPLETION_INSTRUCTION = "You are a Python writing assistant. You will be given a function implementation and a series of unit tests. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as a hint when you try again later. Only provide the few sentence description in your answer, not the implementation.\n\n-----"
-USE_PYTHON_CODEBLOCK_INSTRUCTION = "Use a Python code block to write your response. For example:\n```python\nprint('Hello world!')\n```"
-
-PY_SIMPLE_CHAT_INSTRUCTION = "You are an AI that only responds with python code, NOT ENGLISH. You will be given a function signature and its docstring by the user. Write your full implementation (restate the function signature)."
-PY_SIMPLE_CHAT_INSTRUCTION_V2 = "You are an AI that only responds with only python code. You will be given a function signature and its docstring by the user. Write your full implementation (restate the function signature)."
-PY_REFLEXION_CHAT_INSTRUCTION = "You are an AI Python assistant. You will be given your past function implementation, a series of unit tests, and a hint to change the implementation appropriately. Write your full implementation (restate the function signature)."
-PY_REFLEXION_CHAT_INSTRUCTION_V2 = "You are an AI Python assistant. You will be given your previous implementation of a function, a series of unit tests results, and your self-reflection on your previous implementation. Write your full implementation (restate the function signature)."
-PY_REFLEXION_FEW_SHOT_ADD = '''Example 1:
-[previous impl]:
-```python
-def add(a: int, b: int) -> int:
-    """
-    Given integers a and b, return the total value of a and b.
-    """
-    return a - b
-```
-
-[unit test results from previous impl]:
-Tested passed:
-
-Tests failed:
-assert add(1, 2) == 3 # output: -1
-assert add(1, 2) == 4 # output: -1
-
-[reflection on previous impl]:
-The implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input.
-
-[improved impl]:
-```python
-def add(a: int, b: int) -> int:
-    """
-    Given integers a and b, return the total value of a and b.
-    """
-    return a + b
-```
-'''
-
-PY_REFLEXION_FEW_SHOT = '''Example 1:
-[previous impl]:
-```python
-from typing import *
-def fullJustify(words: List[str], maxWidth: int) -> List[str]:
-    """
-    Given an array of words and a width maxWidth, format the text such that each line has exactly maxWidth characters and is fully (left and right) justified.
-    You should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces `' '` when necessary so that each line has exactly maxWidth characters.
-    Extra spaces between words should be distributed as evenly as possible. If the number of spaces on a line do not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right.
-    For the last line of text, it should be left justified and no extra space is inserted between words.
-    Note:
-    A word is defined as a character sequence consisting of non-space characters only.
-    Each word's length is guaranteed to be greater than 0 and not exceed maxWidth.
-    The input array `words` contains at least one word.
-    """
-    res = []
-    cur_line = []
-    cur_len = 0
-
-    for word in words:
-        if cur_len + len(word) + len(cur_line) > maxWidth:
-            if len(cur_line) == 1:
-                res.append(cur_line[0] + ' ' * (maxWidth - cur_len))
-            else:
-                spaces = maxWidth - cur_len
-                space_between = spaces // (len(cur_line) - 1)
-                extra_spaces = spaces % (len(cur_line) - 1)
-                line = ''
-                for i, w in enumerate(cur_line[:-1]):
-                    line += w + ' ' * (space_between + (i < extra_spaces))
-                line += cur_line[-1]
-                res.append(line)
-            cur_line = []
-            cur_len = 0
-        cur_line.append(word)
-        cur_len += len(word)
-
-    last_line = ' '.join(cur_line)
-    last_line += ' ' * (maxWidth - len(last_line))
-    res.append(last_line)
-
-    return res
-```
-
-[unit test results from previous impl]:
-Tested passed:
-
-Tests failed:
-assert fullJustify([], 10) == [] # output: ['          ']
-assert fullJustify([], 0) == [] # output: ['']
-
-[reflection on previous impl]:
-The implementation failed the test cases where the input list of words is empty. The issue arises because the code does not handle the case where there are no words to process. As a result, it still appends a line with spaces to the result list, even when there are no words. To fix this issue, we should add a condition at the beginning of the function to check if the input list is empty, and return an empty list if it is. This will ensure that the function returns the correct output for empty input lists.
-
-[improved impl]:
-```python
-from typing import *
-def fullJustify(words: List[str], maxWidth: int) -> List[str]:
-    """
-    Given an array of words and a width maxWidth, format the text such that each line has exactly maxWidth characters and is fully (left and right) justified.
-    You should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces `' '` when necessary so that each line has exactly maxWidth characters.
-    Extra spaces between words should be distributed as evenly as possible. If the number of spaces on a line do not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right.
-    For the last line of text, it should be left justified and no extra space is inserted between words.
-    Note:
-    A word is defined as a character sequence consisting of non-space characters only.
-    Each word's length is guaranteed to be greater than 0 and not exceed maxWidth.
-    The input array `words` contains at least one word.
-    """
-    if not words:
-        return []
-
-    res = []
-    cur_line = []
-    cur_len = 0
-
-    for word in words:
-        if cur_len + len(word) + len(cur_line) > maxWidth:
-            if len(cur_line) == 1:
-                res.append(cur_line[0] + ' ' * (maxWidth - cur_len))
-            else:
-                spaces = maxWidth - cur_len
-                space_between = spaces // (len(cur_line) - 1)
-                extra_spaces = spaces % (len(cur_line) - 1)
-                line = ''
-                for i, w in enumerate(cur_line[:-1]):
-                    line += w + ' ' * (space_between + (i < extra_spaces))
-                line += cur_line[-1]
-                res.append(line)
-            cur_line = []
-            cur_len = 0
-        cur_line.append(word)
-        cur_len += len(word)
-
-    last_line = ' '.join(cur_line)
-    last_line += ' ' * (maxWidth - len(last_line))
-    res.append(last_line)
-
-    return res
-```
-END EXAMPLES
-
-'''
-PY_SELF_REFLECTION_CHAT_INSTRUCTION = "You are a Python programming assistant. You will be given a function implementation and a series of unit tests. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as a hint when you try again later. Only provide the few sentence description in your answer, not the implementation."
-PY_SELF_REFLECTION_CHAT_INSTRUCTION_V2 = "You are a Python programming assistant. You will be given a function implementation and a series of unit test results. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as guidance when you try again later. Only provide the few sentence description in your answer, not the implementation. You will be given a few examples by the user."
-PY_SELF_REFLECTION_FEW_SHOT = """Example 1:
-[function impl]:
-```python
-def longest_subarray_with_sum_limit(nums: List[int], target: int) -> List[int]:
-    n = len(nums)
-    left, right = 0, 0
-    max_length = 0
-    current_sum = 0
-    result = []
-    while right < n:
-        current_sum += nums[right]
-        while current_sum > target:
-            current_sum -= nums[left]
-            left += 1
-        if right - left + 1 >= max_length:
-            max_length = right - left + 1
-            result = nums[left:right+1]
-        right += 1
-    return result
-```
-[unit test results]:
-Tests passing:
-assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 8) == [1, 2, 3]
-assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 15) == [1, 2, 3, 4, 5]
-assert longest_subarray_with_sum_limit([1, -1, 2, -2, 3, -3], 2) == [1, -1, 2, -2, 3]
-assert longest_subarray_with_sum_limit([], 10) == []
-assert longest_subarray_with_sum_limit([], 0) == []
-assert longest_subarray_with_sum_limit([], -5) == []  
-Tests failing:
-assert longest_subarray_with_sum_limit([5, 6, 7, 8, 9], 4) == [] # output: [5]
-[self-reflection]:
-The implementation failed the where no subarray fulfills the condition. The issue in the implementation is due to the use of >= instead of > in the condition to update the result. Because of this, it returns a subarray even when the sum is greater than the target, as it still updates the result when the current subarray length is equal to the previous longest subarray length. To overcome this error, we should change the condition to only update the result when the current subarray length is strictly greater than the previous longest subarray length. This can be done by replacing >= with > in the condition.
-
-Example 2:
-[function impl]:
-```python
-def longest_subarray_with_sum_limit(nums: List[int], target: int) -> List[int]:
-    n = len(nums)
-    left, right = 0, 0
-    max_length = 0
-    current_sum = 0
-    result = []
-    while current_sum + nums[right] <= target:
-        current_sum += nums[right]
-        right += 1
-    while right < n:
-        current_sum += nums[right]
-        while current_sum > target:
-            current_sum -= nums[left]
-            left += 1
-        if right - left + 1 > max_length:
-            max_length = right - left + 1
-            result = nums[left:right+1]
-        right += 1
-    return result
-```
-[unit test results]:
-Tests passing:
-assert longest_subarray_with_sum_limit([], 10) == []
-assert longest_subarray_with_sum_limit([], 0) == []
-assert longest_subarray_with_sum_limit([], -5) == []
-Tests failing:
-assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 8) == [1, 2, 3] # output: list index out of range
-assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 15) == [1, 2, 3, 4, 5] # output: list index out of range
-assert longest_subarray_with_sum_limit([5, 6, 7, 8, 9], 4) == [] # output: list index out of range
-assert longest_subarray_with_sum_limit([1, -1, 2, -2, 3, -3], 2) == [1, -1, 2, -2, 3] # output: list index out of range
-[self-reflection]:
-The implementation failed 4 out of the 7 test cases due to an IndexError. The issue stems from the while loop while current_sum + nums[right] <= target:, which directly accesses nums[right] without checking if right is within the bounds of the list. This results in a runtime error when right goes beyond the list length. To overcome this error, we need to add a bounds check for the right variable in the mentioned while loop. We can modify the loop condition to while right < len(nums) and current_sum + nums[right] <= target:. This change will ensure that we only access elements within the bounds of the list, thus avoiding the IndexError.
-END OF EXAMPLES
-"""
-
-PY_TEST_GENERATION_FEW_SHOT = """Examples:
-func signature:
-def add3Numbers(x, y, z):
-    \"\"\" Add three numbers together.
-    This function takes three numbers as input and returns the sum of the three numbers.
-    \"\"\"
-unit tests:
-assert add3Numbers(1, 2, 3) == 6
-assert add3Numbers(-1, 2, 3) == 4
-assert add3Numbers(1, -2, 3) == 2
-assert add3Numbers(1, 2, -3) == 0
-assert add3Numbers(-3, -2, -1) == -6
-assert add3Numbers(0, 0, 0) == 0
-"""
-
-PY_TEST_GENERATION_COMPLETION_INSTRUCTION = f"""You are an AI coding assistant that can write unique, diverse, and intuitive unit tests for functions given the signature and docstring. Call your function answer().
-
-{PY_TEST_GENERATION_FEW_SHOT}"""
-
-PY_TEST_GENERATION_CHAT_INSTRUCTION = """You are an AI coding assistant that can write unique, diverse, and intuitive unit tests for functions given the signature and docstring. Call your function answer()."""
-
-
-class PyGenerator(Generator):
-    def self_reflection(self, func: str, feedback: str, model: ModelBase) -> str:
-        return generic_generate_self_reflection(
-            func=func,
-            feedback=feedback,
-            model=model,
-            self_reflection_chat_instruction=PY_SELF_REFLECTION_CHAT_INSTRUCTION,
-            self_reflection_completion_instruction=PY_SELF_REFLECTION_COMPLETION_INSTRUCTION,
-            add_code_block=lambda x: add_code_block(x, "python"),
-            self_reflection_few_shot=PY_SELF_REFLECTION_FEW_SHOT
-        )
-
-    def func_impl(
-        self,
-        func_sig: str,
-        model: ModelBase,
-        strategy: str,
-        prev_func_impl: Optional[str] = None,
-        feedback: Optional[str] = None,
-        self_reflection: Optional[str] = None,
-        num_comps: int = 1,
-        temperature: float = 0.8,
-        acc_feedback: Optional[str] = None,
-        acc_reflection: Optional[str] = None,
-    ) -> Union[str, List[str]]:
-        if strategy == "mcts":
-            return generate_with_accumulated_context(
-                func_sig=func_sig,
-                model=model,
-                strategy="reflexion",
-                prev_func_impl=prev_func_impl,
-                accumulated_feedback=acc_feedback,
-                accumulated_reflection=acc_reflection,
-                num_comps=num_comps,
-                temperature=temperature,
-                reflexion_chat_instruction=PY_REFLEXION_CHAT_INSTRUCTION,
-                reflexion_few_shot=PY_REFLEXION_FEW_SHOT_ADD,
-                simple_chat_instruction=PY_SIMPLE_CHAT_INSTRUCTION,
-                reflexion_completion_instruction=PY_REFLEXION_COMPLETION_INSTRUCTION,
-                simple_completion_instruction=PY_SIMPLE_COMPLETION_INSTRUCTION,
-                code_block_instruction=USE_PYTHON_CODEBLOCK_INSTRUCTION,
-                parse_code_block=lambda x: parse_code_block(x, "python"),
-                add_code_block=lambda x: add_code_block(x, "python"),
-            )
-        else:
-            return generic_generate_func_impl(
-                func_sig=func_sig,
-                model=model,
-                strategy=strategy,
-                prev_func_impl=prev_func_impl,
-                feedback=feedback,
-                self_reflection=self_reflection,
-                num_comps=num_comps,
-                temperature=temperature,
-                reflexion_chat_instruction=PY_REFLEXION_CHAT_INSTRUCTION,
-                reflexion_few_shot=PY_REFLEXION_FEW_SHOT_ADD,
-                simple_chat_instruction=PY_SIMPLE_CHAT_INSTRUCTION,
-                reflexion_completion_instruction=PY_REFLEXION_COMPLETION_INSTRUCTION,
-                simple_completion_instruction=PY_SIMPLE_COMPLETION_INSTRUCTION,
-                code_block_instruction=USE_PYTHON_CODEBLOCK_INSTRUCTION,
-                parse_code_block=lambda x: parse_code_block(x, "python"),
-                add_code_block=lambda x: add_code_block(x, "python"),
-            )
-
-    def internal_tests(self, func_sig: str, model: ModelBase, max_num_tests: int = 4) -> List[str]:
-        def parse_tests(tests: str) -> List[str]:
-            return [test.strip() for test in tests.splitlines() if "assert" in test]
-        """
-        Generates tests for a function.
-        """
-        return generic_generate_internal_tests(
-            func_sig=func_sig,
-            model=model,
-            max_num_tests=max_num_tests,
-            test_generation_few_shot=PY_TEST_GENERATION_FEW_SHOT,
-            test_generation_chat_instruction=PY_TEST_GENERATION_CHAT_INSTRUCTION,
-            test_generation_completion_instruction=PY_TEST_GENERATION_COMPLETION_INSTRUCTION,
-            parse_tests=parse_tests,
-            is_syntax_valid=py_is_syntax_valid,
-        )
-
-
-DUMMY_FUNC_SIG = "def func():"
-DUMMY_FUNC_CALL = "func()"
-
-
-def handle_first_line_indent(func_body: str) -> str:
-    if func_body.startswith("    "):
-        return func_body
-    split = func_body.splitlines()
-    return f"    {split[0]}\n" + "\n".join(split[1:])
-
-
-def handle_entire_body_indent(func_body: str) -> str:
-    split = func_body.splitlines()
-    res = "\n".join(["    " + line for line in split])
-    return res
-
-
-def fix_turbo_response(func_body: str) -> str:
-    return fix_markdown(remove_unindented_signatures(func_body))
-
-
-def fix_markdown(func_body: str) -> str:
-    return re.sub("`{3}", "", func_body)
-
-
-def remove_unindented_signatures(code: str) -> str:
-    regex = r"^def\s+\w+\s*\("
-
-    before_signature = []
-    after_signature = []
-    signature_found = False
-
-    for line in code.split("\n"):
-        if re.match(regex, line):
-            signature_found = True
-            continue
-
-        if signature_found:
-            after_signature.append(line)
-        else:
-            if not line.startswith("    ") and line.strip():
-                line = "    " + line
-            before_signature.append(line)
-
-    return "\n".join(before_signature + after_signature)
-
-
-def py_fix_indentation(func_body: str) -> str:
-    func_body = fix_turbo_response(func_body)
-    """
-    3 cases:
-        1. good syntax
-        2. first line not good
-        3. entire body not good
-    """
-    def parse_indent_rec(f_body: str, cur_state: int) -> str:
-        f_body = fix_markdown(f_body)
-        if cur_state > 1:
-            return f_body
-        code = f'{DUMMY_FUNC_SIG}\n{f_body}\n{DUMMY_FUNC_CALL}'
-        try:
-            exec(code)
-            return f_body
-        except (IndentationError, SyntaxError):
-            p_func = handle_first_line_indent if cur_state == 0 else handle_entire_body_indent
-            return parse_indent_rec(p_func(func_body), cur_state + 1)
-        except Exception:
-            return f_body
-    return parse_indent_rec(func_body, 0)
-
-
-def py_is_syntax_valid(code: str) -> bool:
-    try:
-        ast.parse(code)
-        return True
-    except Exception:
-        return False

spaces/AUBMC-AIM/MammoGANesis/README.md

@@ -1,45 +0,0 @@
----
-title: MammoGANesis
-emoji: 🔥
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/Ababababababbababa/Ashaar/poetry_diacritizer/models/__init__.py

@@ -1,5 +0,0 @@
-from . import baseline
-from . import cbhg
-from . import gpt
-from . import seq2seq
-from . import tacotron_based

spaces/AdamOswald1/finetuned_diffusion/style.css

@@ -1,24 +0,0 @@
-.finetuned-diffusion-div div{
-    display:inline-flex;
-    align-items:center;
-    gap:.8rem;
-    font-size:1.75rem
-}
-.finetuned-diffusion-div div h1{
-    font-weight:900;
-    margin-bottom:7px
-}
-.finetuned-diffusion-div p{
-    margin-bottom:10px;
-    font-size:94%
-}
-a{
-    text-decoration:underline
-}
-.tabs{
-    margin-top:0;
-    margin-bottom:0
-}
-#gallery{
-    min-height:20rem
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/containerlite-plugin.js

@@ -1,27 +0,0 @@
-import Factory from './gameobjects/container/containerlite/Factory.js';
-import Creator from './gameobjects/container/containerlite/Creator.js';
-import ContainerLite from './gameobjects/container/containerlite/ContainerLite.js';
-import SetValue from './utils/object/SetValue.js';
-
-class ContainerLitePlugin extends Phaser.Plugins.BasePlugin {
-
-    constructor(pluginManager) {
-        super(pluginManager);
-
-        //  Register our new Game Object type
-        pluginManager.registerGameObject('rexContainerLite', Factory, Creator);
-    }
-
-    start() {
-        var eventEmitter = this.game.events;
-        eventEmitter.on('destroy', this.destroy, this);
-    }
-
-    getParent(child) {
-        return ContainerLite.GetParent(child);
-    }
-}
-
-SetValue(window, 'RexPlugins.GameObjects.ContainerLite', ContainerLite);
-
-export default ContainerLitePlugin;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/ProportionMethods.js

@@ -1,11 +0,0 @@
-export default {
-    getChildProportion(gameObject) {
-        return this.getSizerConfig(gameObject).proportion;
-    },
-
-    setChildProportion(gameObject, proportion) {
-        this.getSizerConfig(gameObject).proportion = proportion;
-        return this;
-    },
-
-}

spaces/AlekseyCalvin/Make-Putin-Queer/README.md

@@ -1,13 +0,0 @@
----
-title: Make Putin Queer! Use "trp" token in prompts
-emoji: 🏳️‍🌈
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.13.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlowaSawsan/Third-Molar-Segmentation/app.py

@@ -1,68 +0,0 @@
-#import required libraries-----------------------------------------------
-import streamlit as st
-from tensorflow import keras
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-import cv2
-
-#loading model-----------------------------------------------------------
-model=keras.models.load_model("3rdm_att_UNet_50epochs_acc.h5")
-
-#User Interface----------------------------------------------------------
-st.header("Segmentation of the Lower Left Third Molar in Panoramic X-ray Images Using Attention U-Net")
-
-examples=["2.png","20.png","31.png"]
-
-def load_image(image_file):
-      img = Image.open(image_file)
-      return img
-
-st.subheader("Instruction:")
-st.subheader("Please select from the provided samples or upload dental panoramic X-ray image")
-image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
-
-col1, col2, col3 = st.columns(3)
-with col1:
-    ex=load_image(examples[0])
-    st.image(ex,width=200)
-    if st.button('Sample 1'):
-        image_file=examples[0]
-
-with col2:
-    ex1=load_image(examples[1])
-    st.image(ex1,width=200)
-    if st.button('Sample 2'):
-        image_file=examples[1]
-
-with col3:
-    ex2=load_image(examples[2])
-    st.image(ex2,width=200)
-    if st.button('Sample 3'):
-        image_file=examples[2]
-    
-#main--------------------------------------------------------------------
-   
-if image_file is not None:
-
-      img=load_image(image_file)
-      
-      st.text("Selected Image ....")
-      st.image(img,width=850)
-      
-      img = np.asarray(img)     
-      img = cv2.resize(img, (512, 256))
-      img = cv2.cvtColor(img, cv2.COLOR_BAYER_GR2GRAY)
-      img = np.expand_dims(img, axis=0)
-
-      prediction = model.predict(img)
-
-      output = prediction.reshape(256,512)
-
-
-      if output is not None :      
-          st.text("Result")  
-          #st.write(output.shape)
-          st.image(output,width=850)
-
-      st.text("DONE")

spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/symbols.py

@@ -1,76 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-# japanese_cleaners
-_pad        = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-
-
-'''# japanese_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-'''# zh_ja_mixture_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-'''
-
-'''# sanskrit_cleaners
-_pad        = '_'
-_punctuation = '।'
-_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
-'''
-
-'''# cjks_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
-'''
-
-'''# thai_cleaners
-_pad        = '_'
-_punctuation = '.!? '
-_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
-'''
-
-'''# cjke_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
-'''
-
-'''# shanghainese_cleaners
-_pad        = '_'
-_punctuation = ',.!?…'
-_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
-'''
-
-'''# chinese_dialect_cleaners
-_pad        = '_'
-_punctuation = ',.!?~…─'
-_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚ᴀᴇ↑↓∅ⱼ '
-'''
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/onnxruntime/README.md

@@ -1,5 +0,0 @@
-## Diffusers examples with ONNXRuntime optimizations
-
-**This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.**
-
-This aims to provide diffusers examples with ONNXRuntime optimizations for training/fine-tuning unconditional image generation, text to image, and textual inversion. Please see individual directories for more details on how to run each task using ONNXRuntime.

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -1,152 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import List, Optional, Tuple, Union
-
-import torch
-
-from ...schedulers import DDIMScheduler
-from ...utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-class DDIMPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for image generation.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Parameters:
-        unet ([`UNet2DModel`]):
-            A `UNet2DModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
-            [`DDPMScheduler`], or [`DDIMScheduler`].
-    """
-
-    def __init__(self, unet, scheduler):
-        super().__init__()
-
-        # make sure scheduler can always be converted to DDIM
-        scheduler = DDIMScheduler.from_config(scheduler.config)
-
-        self.register_modules(unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        eta: float = 0.0,
-        num_inference_steps: int = 50,
-        use_clipped_model_output: Optional[bool] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            generator (`torch.Generator`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
-                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. A value of `0` corresponds to
-                DDIM and `1` corresponds to DDPM.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            use_clipped_model_output (`bool`, *optional*, defaults to `None`):
-                If `True` or `False`, see documentation for [`DDIMScheduler.step`]. If `None`, nothing is passed
-                downstream to the scheduler (use `None` for schedulers which don't support this argument).
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Example:
-
-        ```py
-        >>> from diffusers import DDIMPipeline
-        >>> import PIL.Image
-        >>> import numpy as np
-
-        >>> # load model and scheduler
-        >>> pipe = DDIMPipeline.from_pretrained("fusing/ddim-lsun-bedroom")
-
-        >>> # run pipeline in inference (sample random noise and denoise)
-        >>> image = pipe(eta=0.0, num_inference_steps=50)
-
-        >>> # process image to PIL
-        >>> image_processed = image.cpu().permute(0, 2, 3, 1)
-        >>> image_processed = (image_processed + 1.0) * 127.5
-        >>> image_processed = image_processed.numpy().astype(np.uint8)
-        >>> image_pil = PIL.Image.fromarray(image_processed[0])
-
-        >>> # save image
-        >>> image_pil.save("test.png")
-        ```
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images
-        """
-
-        # Sample gaussian noise to begin loop
-        if isinstance(self.unet.config.sample_size, int):
-            image_shape = (
-                batch_size,
-                self.unet.config.in_channels,
-                self.unet.config.sample_size,
-                self.unet.config.sample_size,
-            )
-        else:
-            image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
-
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        image = randn_tensor(image_shape, generator=generator, device=self._execution_device, dtype=self.unet.dtype)
-
-        # set step values
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        for t in self.progress_bar(self.scheduler.timesteps):
-            # 1. predict noise model_output
-            model_output = self.unet(image, t).sample
-
-            # 2. predict previous mean of image x_t-1 and add variance depending on eta
-            # eta corresponds to η in paper and should be between [0, 1]
-            # do x_t -> x_t-1
-            image = self.scheduler.step(
-                model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator
-            ).prev_sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py

@@ -1,4 +0,0 @@
-_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 19])
-runner = dict(type='EpochBasedRunner', max_epochs=20)

spaces/Andy1621/uniformer_image_detection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py

@@ -1,4 +0,0 @@
-_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet18',
-    backbone=dict(type='ResNet', depth=18),
-    decode_head=dict(
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))

spaces/Andy1621/uniformer_image_segmentation/configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = [
-    '../_base_/models/lraspp_m-v3-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
-]
-
-# Re-config the data sampler.
-data = dict(samples_per_gpu=4, workers_per_gpu=4)
-
-runner = dict(type='IterBasedRunner', max_iters=320000)

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/sd_api_pictures/style.css

@@ -1,52 +0,0 @@
-/* Align the elements for SD_api_picture extension */
-.SDAP #sampler_box {
-  padding-top: var(--spacing-sm);
-  padding-bottom: var(--spacing-sm);
-  border: 0;
-}
-
-.SDAP #steps_box {
-  border-radius: 0 0 var(--block-radius) var(--block-radius);
-}
-
-.SDAP #sampler_col {
-  gap: 0;
-  padding: 0;
-  background-color: transparent;
-}
-
-.SDAP #sampler_row {
-    border-bottom: 0;
-    box-shadow: var(--block-shadow);
-    border-width: var(--block-border-width);
-    border-color: var(--block-border-color);
-    border-radius: var(--block-radius) var(--block-radius) 0 0;
-    background: var(--block-background-fill);
-    gap: 0;
-}
-
-.SDAP #sampler_row .refresh-button {
-    margin-bottom: var(--spacing-sm);
-    margin-right: var(--spacing-lg);
-}
-
-.SDAP #seed_box,
-.SDAP #cfg_box {
-  padding-top: var(--spacing-md);
-}
-
-.SDAP #sampler_box span,
-.SDAP #seed_box span,
-.SDAP #cfg_box span,
-.SDAP #steps_box span {
-  margin-bottom: var(--spacing-sm);
-}
-
-.SDAP svg.dropdown-arrow {
-  flex-shrink: 0 !important;
-  margin: 0px !important;
-}
-
-.SDAP .hires_opts input[type="number"] {
-  width: 6em !important;
-}

spaces/Anonymous-sub/Rerender/gmflow_module/README.md

@@ -1,239 +0,0 @@
-# GMFlow
-
-
-Official PyTorch implementation of paper:
-
-[**GMFlow: Learning Optical Flow via Global Matching**](https://arxiv.org/abs/2111.13680), **CVPR 2022, Oral**
-
-Authors: [Haofei Xu](https://haofeixu.github.io/), [Jing Zhang](https://scholar.google.com.hk/citations?user=9jH5v74AAAAJ), [Jianfei Cai](https://jianfei-cai.github.io/), [Hamid Rezatofighi](https://scholar.google.com/citations?user=VxAuxMwAAAAJ), [Dacheng Tao](https://scholar.google.com/citations?user=RwlJNLcAAAAJ)
-
-
-**11/15/2022 Update: Check out our new work: [Unifying Flow, Stereo and Depth Estimation](https://haofeixu.github.io/unimatch/) and code: [unimatch](https://github.com/autonomousvision/unimatch) for extending GMFlow to stereo and depth tasks. [More pretrained GMFlow models](https://github.com/autonomousvision/unimatch/blob/master/MODEL_ZOO.md) with different speed-accuracy trade-offs are also released. Check out our [Colab](https://colab.research.google.com/drive/1r5m-xVy3Kw60U-m5VB-aQ98oqqg_6cab?usp=sharing) and [HuggingFace](https://huggingface.co/spaces/haofeixu/unimatch) demo to play with GMFlow in your browser!**
-
-
-
-**A [video introduction](https://www.bilibili.com/video/BV18A4y1R7PL) (in Chinese) of GMFlow is available at bilibili!**
-
-
-
-https://user-images.githubusercontent.com/19343475/174446408-520b8a6c-9714-4ff3-978c-98e23ab29c1f.mp4
-
-
-
-
-
-We streamline the optical flow estimation pipeline by reformulating optical flow as a **global matching** problem.
-
-
-
-
-<p align="center"><img width=90% src="assets/gmflow.png"></p>
-
-
-
-
-
-## Highlights
-
-- **Flexible & Modular design**
-
-  We decompose the end-to-end optical flow framework into five components:
-
-  feature extraction, feature enhancement, feature matching, flow propagation and flow refinement.
-
-  One can easily construct a customized optical flow model by combining different components.
-
-- **High accuracy**
-
-  With only one refinement, GMFlow outperforms 31-refinements RAFT on the challenging Sintel benchmark.
-
-- **High efficiency**
-
-  A basic GMFlow model (without refinement) runs at 57ms (V100) or 26ms (A100) for Sintel data (436x1024). 
-
-  GMFlow gains more speedup than RAFT on high-end GPUs (e.g., A100) since GMFlow doesn't require a large number of sequential computation.
-
-  GMFlow also simplifies backward flow computation without requiring to forward the network twice. The bidirectional flow can be used for occlusion detection with forward-backward consistency check.
-
-  <p align="center"><img width=90% src="assets/bidir_flow_occ.png"></p>
-
-  
-
-
-## Installation
-
-Our code is based on pytorch 1.9.0, CUDA 10.2 and python 3.8. Higher version pytorch should also work well.
-
-We recommend using [conda](https://www.anaconda.com/distribution/) for installation:
-
-```
-conda env create -f environment.yml
-conda activate gmflow
-```
-
-## Demos
-
-All pretrained models can be downloaded from [google drive](https://drive.google.com/file/d/1d5C5cgHIxWGsFR1vYs5XrQbbUiZl9TX2/view?usp=sharing).
-
-
-
-You can run a trained model on a sequence of images and visualize the results:
-
-```
-CUDA_VISIBLE_DEVICES=0 python main.py \
---inference_dir demo/sintel_market_1 \
---output_path output/gmflow-norefine-sintel_market_1 \
---resume pretrained/gmflow_sintel-0c07dcb3.pth
-```
-
-You can also predict bidirectional flow with `--pred_bidir_flow` enabled and use `--fwd_bwd_consistency_check` for forward-backward consistency check. More examples can be found in [scripts/demo.sh](scripts/demo.sh).
-
-
-
-## Datasets
-
-The datasets used to train and evaluate GMFlow are as follows:
-
-* [FlyingChairs](https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html#flyingchairs)
-* [FlyingThings3D](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)
-* [Sintel](http://sintel.is.tue.mpg.de/)
-* [KITTI](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow)
-* [HD1K](http://hci-benchmark.iwr.uni-heidelberg.de/) 
-
-By default the dataloader [datasets.py](data/datasets.py) assumes the datasets are located in folder `datasets` and are organized as follows:
-
-```
-datasets
-├── FlyingChairs_release
-│   └── data
-├── FlyingThings3D
-│   ├── frames_cleanpass
-│   ├── frames_finalpass
-│   └── optical_flow
-├── HD1K
-│   ├── hd1k_challenge
-│   ├── hd1k_flow_gt
-│   ├── hd1k_flow_uncertainty
-│   └── hd1k_input
-├── KITTI
-│   ├── testing
-│   └── training
-├── Sintel
-│   ├── test
-│   └── training
-```
-
-It is recommended to symlink your dataset root to `datasets`:
-
-```shell
-ln -s $YOUR_DATASET_ROOT datasets
-```
-
-Otherwise, you may need to change the corresponding paths in [datasets.py](data/datasets.py).
-
-
-
-## Evaluation
-
-You can evaluate a trained GMFlow model by running:
-
-```
-CUDA_VISIBLE_DEVICES=0 python main.py --eval --val_dataset things sintel --resume pretrained/gmflow_things-e9887eda.pth 
-```
-
-More evaluation scripts can be found in [scripts/evaluate.sh](scripts/evaluate.sh).
-
-
-
-For submission to Sintel and KITTI online test sets, you can run [scripts/submission.sh](scripts/submission.sh).
-
-
-
-## Training
-
-All training scripts on FlyingChairs, FlyingThings3D, Sintel and KITTI datasets can be found in [scripts/train_gmflow.sh](scripts/train_gmflow.sh) and [scripts/train_gmflow_with_refine.sh](scripts/train_gmflow_with_refine.sh).
-
-Note that the basic GMFlow model (without refinement) can be trained on 4x 16GB V100 GPUs. For training GMFlow with refinement, 8x 16GB V100 or 4x 32GB V100 or 4x 40GB A100 GPUs are required by default. You may need to tune the batch size and training iterations according to your hardware. 
-
-
-
-We support using tensorboard to monitor and visualize the training process. You can first start a tensorboard session with
-
-```shell
-tensorboard --logdir checkpoints
-```
-
-and then access [http://localhost:6006](http://localhost:6006) in your browser.
-
-
-
-## Citation
-
-If you find our work useful in your research, please consider citing our paper:
-
-```
-@inproceedings{xu2022gmflow,
-  title={GMFlow: Learning Optical Flow via Global Matching},
-  author={Xu, Haofei and Zhang, Jing and Cai, Jianfei and Rezatofighi, Hamid and Tao, Dacheng},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  pages={8121-8130},
-  year={2022}
-}
-```
-
-
-
-## Acknowledgements
-
-This project would not have been possible without relying on some awesome repos : [RAFT](https://github.com/princeton-vl/RAFT), [LoFTR](https://github.com/zju3dv/LoFTR), [DETR](https://github.com/facebookresearch/detr), [Swin](https://github.com/microsoft/Swin-Transformer), [mmdetection](https://github.com/open-mmlab/mmdetection) and [Detectron2](https://github.com/facebookresearch/detectron2/blob/main/projects/TridentNet/tridentnet/trident_conv.py). We thank the original authors for their excellent work.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

spaces/Artrajz/vits-simple-api/utils/download.py

@@ -1,96 +0,0 @@
-import logging
-import os
-import hashlib
-import tarfile
-import urllib.request
-import zipfile
-
-from tqdm import tqdm
-from pathlib import Path
-from logger import logger
-from py7zr import SevenZipFile
-
-
-class TqdmUpTo(tqdm):
-    def update_to(self, b=1, bsize=1, tsize=None):
-        if tsize is not None:
-            self.total = tsize
-        self.update(b * bsize - self.n)
-
-
-def download_file(url, dest_path):
-    logging.info(f"Downloading: {url}")
-    with TqdmUpTo(unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=url.split('/')[-1]) as t:
-        urllib.request.urlretrieve(url, dest_path, reporthook=t.update_to)
-
-
-def verify_md5(file_path, expected_md5):
-    md5 = hashlib.md5(file_path.read_bytes()).hexdigest()
-    if md5 != expected_md5:
-        return False, f"MD5 mismatch: {md5} != {expected_md5}"
-    return True, ""
-
-
-def extract_file(file_path, destination=None):
-    """
-    Extract a compressed file based on its extension.
-    If destination is not specified, it will be extracted to its parent directory.
-    """
-    if destination is None:
-        destination = Path(file_path).parent
-
-    logging.info(f"Extracting to {destination}")
-
-    if file_path.endswith('.zip'):
-        with zipfile.ZipFile(file_path, 'r') as zip_ref:
-            zip_ref.extractall(destination)
-    elif file_path.endswith('.tar.gz'):
-        with tarfile.open(file_path, 'r:gz') as tar_ref:
-            tar_ref.extractall(destination)
-    elif file_path.endswith('.tar.bz2'):
-        with tarfile.open(file_path, 'r:bz2') as tar_ref:
-            tar_ref.extractall(destination)
-    elif file_path.endswith('.7z'):
-        with SevenZipFile(file_path, mode='r') as z:
-            z.extractall(destination)
-    else:
-        logging.error(f"Unsupported compression format for file {file_path}")
-
-
-def download_and_verify(urls, target_path, expected_md5=None, extract_destination=None):
-    for url in urls:
-        try:
-            download_file(url, target_path)
-            break
-        except Exception as error:
-            logger.error(f"downloading from URL {url}: {error}")
-
-    else:  # This else is tied to the for loop, and executes if no download is successful
-        return False, "Error downloading from all provided URLs."
-
-    if expected_md5 is not None:
-        success, message = verify_md5(Path(target_path), expected_md5)
-        if not success:
-            os.remove(target_path)
-            return False, message
-
-    # If it's a compressed file, extract it
-    if target_path.endswith(('.zip', '.tar.gz', '.tar.bz2', '.7z')):
-        extract_file(target_path, extract_destination)
-        os.remove(target_path)
-
-    return True, "File downloaded, verified, and extracted successfully!"
-
-
-if __name__ == "__main__":
-    URLS = [
-        "YOUR_PRIMARY_URL_HERE",
-        "YOUR_FIRST_BACKUP_URL_HERE",
-        # ... you can add more backup URLs as needed
-    ]
-    TARGET_PATH = ""
-    EXPECTED_MD5 = ""
-    EXTRACT_DESTINATION = ""
-
-    success, message = download_and_verify(URLS, TARGET_PATH, EXPECTED_MD5, EXTRACT_DESTINATION)
-    print(message)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/cachecontrol/caches/__init__.py

@@ -1,9 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-from .file_cache import FileCache, SeparateBodyFileCache
-from .redis_cache import RedisCache
-
-
-__all__ = ["FileCache", "SeparateBodyFileCache", "RedisCache"]

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

@@ -1,796 +0,0 @@
-import sys
-from functools import lru_cache
-from marshal import dumps, loads
-from random import randint
-from typing import Any, Dict, Iterable, List, Optional, Type, Union, cast
-
-from . import errors
-from .color import Color, ColorParseError, ColorSystem, blend_rgb
-from .repr import Result, rich_repr
-from .terminal_theme import DEFAULT_TERMINAL_THEME, TerminalTheme
-
-# Style instances and style definitions are often interchangeable
-StyleType = Union[str, "Style"]
-
-
-class _Bit:
-    """A descriptor to get/set a style attribute bit."""
-
-    __slots__ = ["bit"]
-
-    def __init__(self, bit_no: int) -> None:
-        self.bit = 1 << bit_no
-
-    def __get__(self, obj: "Style", objtype: Type["Style"]) -> Optional[bool]:
-        if obj._set_attributes & self.bit:
-            return obj._attributes & self.bit != 0
-        return None
-
-
-@rich_repr
-class Style:
-    """A terminal style.
-
-    A terminal style consists of a color (`color`), a background color (`bgcolor`), and a number of attributes, such
-    as bold, italic etc. The attributes have 3 states: they can either be on
-    (``True``), off (``False``), or not set (``None``).
-
-    Args:
-        color (Union[Color, str], optional): Color of terminal text. Defaults to None.
-        bgcolor (Union[Color, str], optional): Color of terminal background. Defaults to None.
-        bold (bool, optional): Enable bold text. Defaults to None.
-        dim (bool, optional): Enable dim text. Defaults to None.
-        italic (bool, optional): Enable italic text. Defaults to None.
-        underline (bool, optional): Enable underlined text. Defaults to None.
-        blink (bool, optional): Enabled blinking text. Defaults to None.
-        blink2 (bool, optional): Enable fast blinking text. Defaults to None.
-        reverse (bool, optional): Enabled reverse text. Defaults to None.
-        conceal (bool, optional): Enable concealed text. Defaults to None.
-        strike (bool, optional): Enable strikethrough text. Defaults to None.
-        underline2 (bool, optional): Enable doubly underlined text. Defaults to None.
-        frame (bool, optional): Enable framed text. Defaults to None.
-        encircle (bool, optional): Enable encircled text. Defaults to None.
-        overline (bool, optional): Enable overlined text. Defaults to None.
-        link (str, link): Link URL. Defaults to None.
-
-    """
-
-    _color: Optional[Color]
-    _bgcolor: Optional[Color]
-    _attributes: int
-    _set_attributes: int
-    _hash: Optional[int]
-    _null: bool
-    _meta: Optional[bytes]
-
-    __slots__ = [
-        "_color",
-        "_bgcolor",
-        "_attributes",
-        "_set_attributes",
-        "_link",
-        "_link_id",
-        "_ansi",
-        "_style_definition",
-        "_hash",
-        "_null",
-        "_meta",
-    ]
-
-    # maps bits on to SGR parameter
-    _style_map = {
-        0: "1",
-        1: "2",
-        2: "3",
-        3: "4",
-        4: "5",
-        5: "6",
-        6: "7",
-        7: "8",
-        8: "9",
-        9: "21",
-        10: "51",
-        11: "52",
-        12: "53",
-    }
-
-    STYLE_ATTRIBUTES = {
-        "dim": "dim",
-        "d": "dim",
-        "bold": "bold",
-        "b": "bold",
-        "italic": "italic",
-        "i": "italic",
-        "underline": "underline",
-        "u": "underline",
-        "blink": "blink",
-        "blink2": "blink2",
-        "reverse": "reverse",
-        "r": "reverse",
-        "conceal": "conceal",
-        "c": "conceal",
-        "strike": "strike",
-        "s": "strike",
-        "underline2": "underline2",
-        "uu": "underline2",
-        "frame": "frame",
-        "encircle": "encircle",
-        "overline": "overline",
-        "o": "overline",
-    }
-
-    def __init__(
-        self,
-        *,
-        color: Optional[Union[Color, str]] = None,
-        bgcolor: Optional[Union[Color, str]] = None,
-        bold: Optional[bool] = None,
-        dim: Optional[bool] = None,
-        italic: Optional[bool] = None,
-        underline: Optional[bool] = None,
-        blink: Optional[bool] = None,
-        blink2: Optional[bool] = None,
-        reverse: Optional[bool] = None,
-        conceal: Optional[bool] = None,
-        strike: Optional[bool] = None,
-        underline2: Optional[bool] = None,
-        frame: Optional[bool] = None,
-        encircle: Optional[bool] = None,
-        overline: Optional[bool] = None,
-        link: Optional[str] = None,
-        meta: Optional[Dict[str, Any]] = None,
-    ):
-        self._ansi: Optional[str] = None
-        self._style_definition: Optional[str] = None
-
-        def _make_color(color: Union[Color, str]) -> Color:
-            return color if isinstance(color, Color) else Color.parse(color)
-
-        self._color = None if color is None else _make_color(color)
-        self._bgcolor = None if bgcolor is None else _make_color(bgcolor)
-        self._set_attributes = sum(
-            (
-                bold is not None,
-                dim is not None and 2,
-                italic is not None and 4,
-                underline is not None and 8,
-                blink is not None and 16,
-                blink2 is not None and 32,
-                reverse is not None and 64,
-                conceal is not None and 128,
-                strike is not None and 256,
-                underline2 is not None and 512,
-                frame is not None and 1024,
-                encircle is not None and 2048,
-                overline is not None and 4096,
-            )
-        )
-        self._attributes = (
-            sum(
-                (
-                    bold and 1 or 0,
-                    dim and 2 or 0,
-                    italic and 4 or 0,
-                    underline and 8 or 0,
-                    blink and 16 or 0,
-                    blink2 and 32 or 0,
-                    reverse and 64 or 0,
-                    conceal and 128 or 0,
-                    strike and 256 or 0,
-                    underline2 and 512 or 0,
-                    frame and 1024 or 0,
-                    encircle and 2048 or 0,
-                    overline and 4096 or 0,
-                )
-            )
-            if self._set_attributes
-            else 0
-        )
-
-        self._link = link
-        self._meta = None if meta is None else dumps(meta)
-        self._link_id = (
-            f"{randint(0, 999999)}{hash(self._meta)}" if (link or meta) else ""
-        )
-        self._hash: Optional[int] = None
-        self._null = not (self._set_attributes or color or bgcolor or link or meta)
-
-    @classmethod
-    def null(cls) -> "Style":
-        """Create an 'null' style, equivalent to Style(), but more performant."""
-        return NULL_STYLE
-
-    @classmethod
-    def from_color(
-        cls, color: Optional[Color] = None, bgcolor: Optional[Color] = None
-    ) -> "Style":
-        """Create a new style with colors and no attributes.
-
-        Returns:
-            color (Optional[Color]): A (foreground) color, or None for no color. Defaults to None.
-            bgcolor (Optional[Color]): A (background) color, or None for no color. Defaults to None.
-        """
-        style: Style = cls.__new__(Style)
-        style._ansi = None
-        style._style_definition = None
-        style._color = color
-        style._bgcolor = bgcolor
-        style._set_attributes = 0
-        style._attributes = 0
-        style._link = None
-        style._link_id = ""
-        style._meta = None
-        style._null = not (color or bgcolor)
-        style._hash = None
-        return style
-
-    @classmethod
-    def from_meta(cls, meta: Optional[Dict[str, Any]]) -> "Style":
-        """Create a new style with meta data.
-
-        Returns:
-            meta (Optional[Dict[str, Any]]): A dictionary of meta data. Defaults to None.
-        """
-        style: Style = cls.__new__(Style)
-        style._ansi = None
-        style._style_definition = None
-        style._color = None
-        style._bgcolor = None
-        style._set_attributes = 0
-        style._attributes = 0
-        style._link = None
-        style._meta = dumps(meta)
-        style._link_id = f"{randint(0, 999999)}{hash(style._meta)}"
-        style._hash = None
-        style._null = not (meta)
-        return style
-
-    @classmethod
-    def on(cls, meta: Optional[Dict[str, Any]] = None, **handlers: Any) -> "Style":
-        """Create a blank style with meta information.
-
-        Example:
-            style = Style.on(click=self.on_click)
-
-        Args:
-            meta (Optional[Dict[str, Any]], optional): An optional dict of meta information.
-            **handlers (Any): Keyword arguments are translated in to handlers.
-
-        Returns:
-            Style: A Style with meta information attached.
-        """
-        meta = {} if meta is None else meta
-        meta.update({f"@{key}": value for key, value in handlers.items()})
-        return cls.from_meta(meta)
-
-    bold = _Bit(0)
-    dim = _Bit(1)
-    italic = _Bit(2)
-    underline = _Bit(3)
-    blink = _Bit(4)
-    blink2 = _Bit(5)
-    reverse = _Bit(6)
-    conceal = _Bit(7)
-    strike = _Bit(8)
-    underline2 = _Bit(9)
-    frame = _Bit(10)
-    encircle = _Bit(11)
-    overline = _Bit(12)
-
-    @property
-    def link_id(self) -> str:
-        """Get a link id, used in ansi code for links."""
-        return self._link_id
-
-    def __str__(self) -> str:
-        """Re-generate style definition from attributes."""
-        if self._style_definition is None:
-            attributes: List[str] = []
-            append = attributes.append
-            bits = self._set_attributes
-            if bits & 0b0000000001111:
-                if bits & 1:
-                    append("bold" if self.bold else "not bold")
-                if bits & (1 << 1):
-                    append("dim" if self.dim else "not dim")
-                if bits & (1 << 2):
-                    append("italic" if self.italic else "not italic")
-                if bits & (1 << 3):
-                    append("underline" if self.underline else "not underline")
-            if bits & 0b0000111110000:
-                if bits & (1 << 4):
-                    append("blink" if self.blink else "not blink")
-                if bits & (1 << 5):
-                    append("blink2" if self.blink2 else "not blink2")
-                if bits & (1 << 6):
-                    append("reverse" if self.reverse else "not reverse")
-                if bits & (1 << 7):
-                    append("conceal" if self.conceal else "not conceal")
-                if bits & (1 << 8):
-                    append("strike" if self.strike else "not strike")
-            if bits & 0b1111000000000:
-                if bits & (1 << 9):
-                    append("underline2" if self.underline2 else "not underline2")
-                if bits & (1 << 10):
-                    append("frame" if self.frame else "not frame")
-                if bits & (1 << 11):
-                    append("encircle" if self.encircle else "not encircle")
-                if bits & (1 << 12):
-                    append("overline" if self.overline else "not overline")
-            if self._color is not None:
-                append(self._color.name)
-            if self._bgcolor is not None:
-                append("on")
-                append(self._bgcolor.name)
-            if self._link:
-                append("link")
-                append(self._link)
-            self._style_definition = " ".join(attributes) or "none"
-        return self._style_definition
-
-    def __bool__(self) -> bool:
-        """A Style is false if it has no attributes, colors, or links."""
-        return not self._null
-
-    def _make_ansi_codes(self, color_system: ColorSystem) -> str:
-        """Generate ANSI codes for this style.
-
-        Args:
-            color_system (ColorSystem): Color system.
-
-        Returns:
-            str: String containing codes.
-        """
-
-        if self._ansi is None:
-            sgr: List[str] = []
-            append = sgr.append
-            _style_map = self._style_map
-            attributes = self._attributes & self._set_attributes
-            if attributes:
-                if attributes & 1:
-                    append(_style_map[0])
-                if attributes & 2:
-                    append(_style_map[1])
-                if attributes & 4:
-                    append(_style_map[2])
-                if attributes & 8:
-                    append(_style_map[3])
-                if attributes & 0b0000111110000:
-                    for bit in range(4, 9):
-                        if attributes & (1 << bit):
-                            append(_style_map[bit])
-                if attributes & 0b1111000000000:
-                    for bit in range(9, 13):
-                        if attributes & (1 << bit):
-                            append(_style_map[bit])
-            if self._color is not None:
-                sgr.extend(self._color.downgrade(color_system).get_ansi_codes())
-            if self._bgcolor is not None:
-                sgr.extend(
-                    self._bgcolor.downgrade(color_system).get_ansi_codes(
-                        foreground=False
-                    )
-                )
-            self._ansi = ";".join(sgr)
-        return self._ansi
-
-    @classmethod
-    @lru_cache(maxsize=1024)
-    def normalize(cls, style: str) -> str:
-        """Normalize a style definition so that styles with the same effect have the same string
-        representation.
-
-        Args:
-            style (str): A style definition.
-
-        Returns:
-            str: Normal form of style definition.
-        """
-        try:
-            return str(cls.parse(style))
-        except errors.StyleSyntaxError:
-            return style.strip().lower()
-
-    @classmethod
-    def pick_first(cls, *values: Optional[StyleType]) -> StyleType:
-        """Pick first non-None style."""
-        for value in values:
-            if value is not None:
-                return value
-        raise ValueError("expected at least one non-None style")
-
-    def __rich_repr__(self) -> Result:
-        yield "color", self.color, None
-        yield "bgcolor", self.bgcolor, None
-        yield "bold", self.bold, None,
-        yield "dim", self.dim, None,
-        yield "italic", self.italic, None
-        yield "underline", self.underline, None,
-        yield "blink", self.blink, None
-        yield "blink2", self.blink2, None
-        yield "reverse", self.reverse, None
-        yield "conceal", self.conceal, None
-        yield "strike", self.strike, None
-        yield "underline2", self.underline2, None
-        yield "frame", self.frame, None
-        yield "encircle", self.encircle, None
-        yield "link", self.link, None
-        if self._meta:
-            yield "meta", self.meta
-
-    def __eq__(self, other: Any) -> bool:
-        if not isinstance(other, Style):
-            return NotImplemented
-        return self.__hash__() == other.__hash__()
-
-    def __ne__(self, other: Any) -> bool:
-        if not isinstance(other, Style):
-            return NotImplemented
-        return self.__hash__() != other.__hash__()
-
-    def __hash__(self) -> int:
-        if self._hash is not None:
-            return self._hash
-        self._hash = hash(
-            (
-                self._color,
-                self._bgcolor,
-                self._attributes,
-                self._set_attributes,
-                self._link,
-                self._meta,
-            )
-        )
-        return self._hash
-
-    @property
-    def color(self) -> Optional[Color]:
-        """The foreground color or None if it is not set."""
-        return self._color
-
-    @property
-    def bgcolor(self) -> Optional[Color]:
-        """The background color or None if it is not set."""
-        return self._bgcolor
-
-    @property
-    def link(self) -> Optional[str]:
-        """Link text, if set."""
-        return self._link
-
-    @property
-    def transparent_background(self) -> bool:
-        """Check if the style specified a transparent background."""
-        return self.bgcolor is None or self.bgcolor.is_default
-
-    @property
-    def background_style(self) -> "Style":
-        """A Style with background only."""
-        return Style(bgcolor=self.bgcolor)
-
-    @property
-    def meta(self) -> Dict[str, Any]:
-        """Get meta information (can not be changed after construction)."""
-        return {} if self._meta is None else cast(Dict[str, Any], loads(self._meta))
-
-    @property
-    def without_color(self) -> "Style":
-        """Get a copy of the style with color removed."""
-        if self._null:
-            return NULL_STYLE
-        style: Style = self.__new__(Style)
-        style._ansi = None
-        style._style_definition = None
-        style._color = None
-        style._bgcolor = None
-        style._attributes = self._attributes
-        style._set_attributes = self._set_attributes
-        style._link = self._link
-        style._link_id = f"{randint(0, 999999)}" if self._link else ""
-        style._null = False
-        style._meta = None
-        style._hash = None
-        return style
-
-    @classmethod
-    @lru_cache(maxsize=4096)
-    def parse(cls, style_definition: str) -> "Style":
-        """Parse a style definition.
-
-        Args:
-            style_definition (str): A string containing a style.
-
-        Raises:
-            errors.StyleSyntaxError: If the style definition syntax is invalid.
-
-        Returns:
-            `Style`: A Style instance.
-        """
-        if style_definition.strip() == "none" or not style_definition:
-            return cls.null()
-
-        STYLE_ATTRIBUTES = cls.STYLE_ATTRIBUTES
-        color: Optional[str] = None
-        bgcolor: Optional[str] = None
-        attributes: Dict[str, Optional[Any]] = {}
-        link: Optional[str] = None
-
-        words = iter(style_definition.split())
-        for original_word in words:
-            word = original_word.lower()
-            if word == "on":
-                word = next(words, "")
-                if not word:
-                    raise errors.StyleSyntaxError("color expected after 'on'")
-                try:
-                    Color.parse(word) is None
-                except ColorParseError as error:
-                    raise errors.StyleSyntaxError(
-                        f"unable to parse {word!r} as background color; {error}"
-                    ) from None
-                bgcolor = word
-
-            elif word == "not":
-                word = next(words, "")
-                attribute = STYLE_ATTRIBUTES.get(word)
-                if attribute is None:
-                    raise errors.StyleSyntaxError(
-                        f"expected style attribute after 'not', found {word!r}"
-                    )
-                attributes[attribute] = False
-
-            elif word == "link":
-                word = next(words, "")
-                if not word:
-                    raise errors.StyleSyntaxError("URL expected after 'link'")
-                link = word
-
-            elif word in STYLE_ATTRIBUTES:
-                attributes[STYLE_ATTRIBUTES[word]] = True
-
-            else:
-                try:
-                    Color.parse(word)
-                except ColorParseError as error:
-                    raise errors.StyleSyntaxError(
-                        f"unable to parse {word!r} as color; {error}"
-                    ) from None
-                color = word
-        style = Style(color=color, bgcolor=bgcolor, link=link, **attributes)
-        return style
-
-    @lru_cache(maxsize=1024)
-    def get_html_style(self, theme: Optional[TerminalTheme] = None) -> str:
-        """Get a CSS style rule."""
-        theme = theme or DEFAULT_TERMINAL_THEME
-        css: List[str] = []
-        append = css.append
-
-        color = self.color
-        bgcolor = self.bgcolor
-        if self.reverse:
-            color, bgcolor = bgcolor, color
-        if self.dim:
-            foreground_color = (
-                theme.foreground_color if color is None else color.get_truecolor(theme)
-            )
-            color = Color.from_triplet(
-                blend_rgb(foreground_color, theme.background_color, 0.5)
-            )
-        if color is not None:
-            theme_color = color.get_truecolor(theme)
-            append(f"color: {theme_color.hex}")
-            append(f"text-decoration-color: {theme_color.hex}")
-        if bgcolor is not None:
-            theme_color = bgcolor.get_truecolor(theme, foreground=False)
-            append(f"background-color: {theme_color.hex}")
-        if self.bold:
-            append("font-weight: bold")
-        if self.italic:
-            append("font-style: italic")
-        if self.underline:
-            append("text-decoration: underline")
-        if self.strike:
-            append("text-decoration: line-through")
-        if self.overline:
-            append("text-decoration: overline")
-        return "; ".join(css)
-
-    @classmethod
-    def combine(cls, styles: Iterable["Style"]) -> "Style":
-        """Combine styles and get result.
-
-        Args:
-            styles (Iterable[Style]): Styles to combine.
-
-        Returns:
-            Style: A new style instance.
-        """
-        iter_styles = iter(styles)
-        return sum(iter_styles, next(iter_styles))
-
-    @classmethod
-    def chain(cls, *styles: "Style") -> "Style":
-        """Combine styles from positional argument in to a single style.
-
-        Args:
-            *styles (Iterable[Style]): Styles to combine.
-
-        Returns:
-            Style: A new style instance.
-        """
-        iter_styles = iter(styles)
-        return sum(iter_styles, next(iter_styles))
-
-    def copy(self) -> "Style":
-        """Get a copy of this style.
-
-        Returns:
-            Style: A new Style instance with identical attributes.
-        """
-        if self._null:
-            return NULL_STYLE
-        style: Style = self.__new__(Style)
-        style._ansi = self._ansi
-        style._style_definition = self._style_definition
-        style._color = self._color
-        style._bgcolor = self._bgcolor
-        style._attributes = self._attributes
-        style._set_attributes = self._set_attributes
-        style._link = self._link
-        style._link_id = f"{randint(0, 999999)}" if self._link else ""
-        style._hash = self._hash
-        style._null = False
-        style._meta = self._meta
-        return style
-
-    @lru_cache(maxsize=128)
-    def clear_meta_and_links(self) -> "Style":
-        """Get a copy of this style with link and meta information removed.
-
-        Returns:
-            Style: New style object.
-        """
-        if self._null:
-            return NULL_STYLE
-        style: Style = self.__new__(Style)
-        style._ansi = self._ansi
-        style._style_definition = self._style_definition
-        style._color = self._color
-        style._bgcolor = self._bgcolor
-        style._attributes = self._attributes
-        style._set_attributes = self._set_attributes
-        style._link = None
-        style._link_id = ""
-        style._hash = self._hash
-        style._null = False
-        style._meta = None
-        return style
-
-    def update_link(self, link: Optional[str] = None) -> "Style":
-        """Get a copy with a different value for link.
-
-        Args:
-            link (str, optional): New value for link. Defaults to None.
-
-        Returns:
-            Style: A new Style instance.
-        """
-        style: Style = self.__new__(Style)
-        style._ansi = self._ansi
-        style._style_definition = self._style_definition
-        style._color = self._color
-        style._bgcolor = self._bgcolor
-        style._attributes = self._attributes
-        style._set_attributes = self._set_attributes
-        style._link = link
-        style._link_id = f"{randint(0, 999999)}" if link else ""
-        style._hash = None
-        style._null = False
-        style._meta = self._meta
-        return style
-
-    def render(
-        self,
-        text: str = "",
-        *,
-        color_system: Optional[ColorSystem] = ColorSystem.TRUECOLOR,
-        legacy_windows: bool = False,
-    ) -> str:
-        """Render the ANSI codes for the style.
-
-        Args:
-            text (str, optional): A string to style. Defaults to "".
-            color_system (Optional[ColorSystem], optional): Color system to render to. Defaults to ColorSystem.TRUECOLOR.
-
-        Returns:
-            str: A string containing ANSI style codes.
-        """
-        if not text or color_system is None:
-            return text
-        attrs = self._ansi or self._make_ansi_codes(color_system)
-        rendered = f"\x1b[{attrs}m{text}\x1b[0m" if attrs else text
-        if self._link and not legacy_windows:
-            rendered = (
-                f"\x1b]8;id={self._link_id};{self._link}\x1b\\{rendered}\x1b]8;;\x1b\\"
-            )
-        return rendered
-
-    def test(self, text: Optional[str] = None) -> None:
-        """Write text with style directly to terminal.
-
-        This method is for testing purposes only.
-
-        Args:
-            text (Optional[str], optional): Text to style or None for style name.
-
-        """
-        text = text or str(self)
-        sys.stdout.write(f"{self.render(text)}\n")
-
-    @lru_cache(maxsize=1024)
-    def _add(self, style: Optional["Style"]) -> "Style":
-        if style is None or style._null:
-            return self
-        if self._null:
-            return style
-        new_style: Style = self.__new__(Style)
-        new_style._ansi = None
-        new_style._style_definition = None
-        new_style._color = style._color or self._color
-        new_style._bgcolor = style._bgcolor or self._bgcolor
-        new_style._attributes = (self._attributes & ~style._set_attributes) | (
-            style._attributes & style._set_attributes
-        )
-        new_style._set_attributes = self._set_attributes | style._set_attributes
-        new_style._link = style._link or self._link
-        new_style._link_id = style._link_id or self._link_id
-        new_style._null = style._null
-        if self._meta and style._meta:
-            new_style._meta = dumps({**self.meta, **style.meta})
-        else:
-            new_style._meta = self._meta or style._meta
-        new_style._hash = None
-        return new_style
-
-    def __add__(self, style: Optional["Style"]) -> "Style":
-        combined_style = self._add(style)
-        return combined_style.copy() if combined_style.link else combined_style
-
-
-NULL_STYLE = Style()
-
-
-class StyleStack:
-    """A stack of styles."""
-
-    __slots__ = ["_stack"]
-
-    def __init__(self, default_style: "Style") -> None:
-        self._stack: List[Style] = [default_style]
-
-    def __repr__(self) -> str:
-        return f"<stylestack {self._stack!r}>"
-
-    @property
-    def current(self) -> Style:
-        """Get the Style at the top of the stack."""
-        return self._stack[-1]
-
-    def push(self, style: Style) -> None:
-        """Push a new style on to the stack.
-
-        Args:
-            style (Style): New style to combine with current style.
-        """
-        self._stack.append(self._stack[-1] + style)
-
-    def pop(self) -> Style:
-        """Pop last style and discard.
-
-        Returns:
-            Style: New current style (also available as stack.current)
-        """
-        self._stack.pop()
-        return self._stack[-1]

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

@@ -1,48 +0,0 @@
-from omegaconf import OmegaConf
-
-import detectron2.data.transforms as T
-from detectron2.config import LazyCall as L
-from detectron2.data import (
-    DatasetMapper,
-    build_detection_test_loader,
-    build_detection_train_loader,
-    get_detection_dataset_dicts,
-)
-from detectron2.evaluation import COCOEvaluator
-
-dataloader = OmegaConf.create()
-
-dataloader.train = L(build_detection_train_loader)(
-    dataset=L(get_detection_dataset_dicts)(names="coco_2017_train"),
-    mapper=L(DatasetMapper)(
-        is_train=True,
-        augmentations=[
-            L(T.ResizeShortestEdge)(
-                short_edge_length=(640, 672, 704, 736, 768, 800),
-                sample_style="choice",
-                max_size=1333,
-            ),
-            L(T.RandomFlip)(horizontal=True),
-        ],
-        image_format="BGR",
-        use_instance_mask=True,
-    ),
-    total_batch_size=16,
-    num_workers=4,
-)
-
-dataloader.test = L(build_detection_test_loader)(
-    dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False),
-    mapper=L(DatasetMapper)(
-        is_train=False,
-        augmentations=[
-            L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333),
-        ],
-        image_format="${...train.mapper.image_format}",
-    ),
-    num_workers=4,
-)
-
-dataloader.evaluator = L(COCOEvaluator)(
-    dataset_name="${..test.dataset.names}",
-)

spaces/Ayaka-daisuki/anime-remove-background/app.py

@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()

spaces/Benson/text-generation/Examples/Arriba Tablero Original Marksheet Descargar 2016.md

@@ -1,69 +0,0 @@
-<br />
-<h1>Cómo descargar la hoja de marcado original 2016</h1>
-<p>Si usted es un estudiante de Uttar Pradesh Madhyamik Shiksha Parishad (UPMSP), también conocido como UP Board, es posible que se pregunte cómo descargar su hoja de marcado original para el año 2016. La hoja de marcado es un documento importante que muestra sus calificaciones y calificaciones en los exámenes de la junta de la clase 10 y 12. También es una prueba válida de su identidad y calificación educativa. Es posible que lo necesite para diversos fines, como admisión, beca, trabajo o cualquier otra verificación. </p>
-<p>En este artículo, le guiaremos a través de los pasos para descargar su hoja de marcado original de UP Board 2016 desde el sitio web oficial de UPMSP. También le diremos cómo verificar su hoja de marcado en línea y cómo corregir cualquier error en ella. Así que, comencemos. </p>
-<h2>arriba tablero original marksheet descargar 2016</h2><br /><p><b><b>Download Zip</b> &#8230;&#8230;&#8230; <a href="https://bltlly.com/2v6KAd">https://bltlly.com/2v6KAd</a></b></p><br /><br />
-<h2>Pasos para descargar la hoja de marcado original del tablero 2016</h2>
-<p>Para descargar su hoja de marcado original de UP Board 2016, debe seguir estos sencillos pasos:</p>
-<ol>
-<li>Visite el sitio web oficial de UPMSP en <a href="( 1 )">https://upmsp.edu.in/</a></li>
-<li>En la página de inicio, haga clic en el enlace "Resultados de la clase 10 - 2016" o "Resultados de la clase 12 - 2016" dependiendo de su clase. </li>
-<li> Será redirigido a una nueva página donde tendrá que introducir su número de rollo y código captcha. </li>
-<li>Haga clic en "Ver resultado" y su resultado se mostrará en la pantalla. </li>
-<li>Haga clic en "Descargar Marksheet" y guárdelo en su dispositivo. </li>
-<li>Tome una impresión de la hoja de marcado para referencia futura. </li>
-</ol>
-<h2>Cómo verificar en línea la hoja de marcado del tablero UP</h2>
-<p>Si desea verificar su hoja de marcado de UP Board en línea, puede usar los siguientes métodos:</p>
-<ul>
-<li>Puede visitar el sitio web <a href="( 2 )">https://arjunpedia.com/up-board-original-marksheet-download-result-verification/</a> e ingrese su número de lista y año de aprobación. Obtendrá un código de verificación que puede usar para verificar los detalles de su hoja de marcado. </li>
-
-</ul>
-<h2>Cómo corregir cualquier error en la hoja de marcado del tablero UP</h2>
-<p>Si encuentra algún error en su hoja de marcado de UP Board, como nombre, fecha de nacimiento, género, marcas, etc., puede solicitar la corrección dentro de un año de la declaración de resultados. Debes seguir estos pasos:</p>
-<ol>
-<li>Póngase en contacto con el director de su escuela y obtenga un formulario de solicitud <li>Llene el formulario con los detalles correctos y adjunte los documentos necesarios, como la tarjeta de admisión, la hoja de marcado, la prueba de identidad, etc.</li>
-<li>Envía el formulario al director de tu escuela y recibe un recibo. </li>
-<li>El director de la escuela enviará el formulario a la oficina regional de la Junta de UP para verificación y corrección. </li>
-<li> Recibirá una notificación del Panel UP cuando su hoja de marcado esté corregida y lista para ser recopilada. </li>
-<li>Recopile su hoja de marcado corregida de su escuela o de la oficina regional del Consejo UP. </li>
-</ol>
-<h2>Conclusión</h2>
-<p>En este artículo, hemos explicado cómo descargar su hoja de marcado original de UP Board 2016 desde el sitio web oficial de UPMSP. También le hemos mostrado cómo verificar su hoja de marcado en línea y cómo corregir cualquier error en ella. Esperamos que este artículo haya sido útil e informativo para usted. Aquí hay algunos consejos para recordar:</p>
-<ul>
-<li>Mantenga su hoja de marcado segura y protegida, ya que es un documento valioso. </li>
-<li>Revise su hoja de marcado cuidadosamente para detectar cualquier error o error tan pronto como lo reciba. </li>
-<li>Solicite la corrección dentro de un año de la declaración de resultados si encuentra algún error. </li>
-<li>Póngase en contacto con la oficina regional de la Junta Directiva o el número de la línea de ayuda para cualquier consulta o queja. </li>
-</ul>
-<h3>Preguntas frecuentes sobre el tablero UP Original Marksheet 2016</h3>
-<p>Aquí hay algunas preguntas y respuestas frecuentes sobre UP Board original marksheet 2016:</p>
-<h4>Q1: ¿Cuándo se declaró el resultado de las Clases 10 y 12 de la Junta Directiva UP en 2016? </h4>
-
-<h4>Q2: ¿Cómo puedo obtener una hoja de marcado duplicada si pierdo la original? </h4>
-<p>A2: Si pierde su hoja de marcado original, puede solicitar una duplicada siguiendo estos pasos:</p>
-<ol>
-<li> Presentar un FIR en la estación de policía más cercana y obtener una copia de la misma. </li>
-<li>Publique un anuncio en un periódico local indicando que ha perdido su hoja de marcado y proporcione sus datos. </li>
-<li>Obtener una declaración jurada de un notario público que indica que ha perdido su hoja de marcado y proporcionar sus datos. </li>
-<li>Póngase en contacto con el director de su escuela y obtenga un formulario de solicitud para una hoja de marcado duplicada. </li>
-<li>Llene el formulario y adjunte la copia de FIR, anuncio de periódico, declaración jurada, prueba de identidad y una tarifa de Rs. 100/-. </li>
-<li>Envía el formulario al director de tu escuela y recibe un recibo. </li>
-<li>El director de la escuela enviará el formulario a la oficina regional de la Junta de UP para la verificación y emisión de hojas de marcado duplicadas. </li>
-<li> Obtendrá una notificación del Tablero UP cuando su hoja de marcado duplicada esté lista para ser recopilada. </li>
-<li>Recopile su hoja de marcado duplicada de su escuela o de la oficina regional del Consejo UP. </li>
-</ol>
-<h4>Q3: ¿Cuál es la diferencia entre la hoja de marcado y el certificado? </h4>
-<p>A3: La hoja de marcado es un documento que muestra sus marcas y calificaciones en cada tema en los exámenes de la junta. El certificado es un documento que muestra su resultado general y calificación en los exámenes de la junta. El certificado también contiene su nombre, fecha de nacimiento, número de registro, nombre de la escuela, nombre de la junta, etc. Necesita tanto la hoja de marcado como el certificado para diversos fines. </p>
-<h4>Q4: ¿Cuánto tiempo se tarda en obtener la hoja de marcado original después de la declaración de resultados? </h4>
-<p>A4: Por lo general toma alrededor de un mes para obtener la hoja de marcado original después de la declaración de resultados. La Junta de UP envía las fichas originales a las respectivas escuelas de los estudiantes. Los estudiantes pueden recoger sus hojas de calificaciones de sus escuelas después de verificar su identidad y firmar un recibo. </p>
-<p></p>
-
-<p>A5: Puede ponerse en contacto con el UP Board para cualquier consulta o queja utilizando los siguientes métodos:</p>
-<ul>
-<li>Puede llamar al número de la línea de ayuda 1800-180-5310 o 0522-2239006 entre las 10 de la mañana y las 5 de la tarde los días laborables. </li>
-<li>Puede enviar su consulta o queja por correo electrónico a [email protected] o [email protected]. </li>
-<li>Puede visitar el sitio web oficial de UPMSP en <a href="">https://upmsp.edu.in/</a> y hacer clic en "Contáctenos" para más detalles. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Consejo De Abogados De India Certificado Descargar.md

@@ -1,132 +0,0 @@
-
-<h1>Euro Truck Simulator 2: Cómo descargar y jugar en PC Windows 8 32 Bit</h1>
-<p>¿Te encanta conducir camiones por toda Europa y entregar carga a diferentes destinos? ¿Quieres experimentar la emoción de ser un camionero desde la comodidad de tu hogar? Si es así, entonces usted debe probar Euro Truck Simulator 2, uno de los juegos de simulación de conducción de camiones más populares y realistas en el mercado. En este artículo, le diremos qué es Euro Truck Simulator 2, por qué es tan popular, cuáles son los requisitos del sistema para jugarlo y cómo descargarlo e instalarlo en su PC Windows 8 32 bit. </p>
-<h2>Introducción</h2>
-<h3> ¿Qué es Euro Truck Simulator 2?</h3>
-<p>Euro Truck Simulator 2 (ETS2) es un juego desarrollado por SCS Software, una empresa checa especializada en la creación de juegos de simulación. ETS2 fue lanzado en octubre de 2012 y desde entonces ha recibido numerosas actualizaciones y expansiones. El juego le permite conducir varios camiones con licencia de marcas famosas como Volvo, Scania, Mercedes-Benz, MAN, DAF, Renault y más. Puede personalizar sus camiones con diferentes piezas, trabajos de pintura, accesorios y opciones de ajuste. También puede contratar conductores, comprar garajes, administrar su empresa y hacer crecer su negocio. </p>
-<h2>consejo de abogados de india certificado descargar</h2><br /><p><b><b>Download File</b> &middot; <a href="https://bltlly.com/2v6Ly2">https://bltlly.com/2v6Ly2</a></b></p><br /><br />
-<h3>¿Por qué es popular Euro Truck Simulator 2? </h3>
-
-<h3> ¿Cuáles son los requisitos del sistema para Euro Truck Simulator 2?</h3>
-<p>Euro Truck Simulator 2 es compatible con los sistemas operativos Windows XP/Vista/7/8/10. Sin embargo, si usted tiene un Windows 8 PC con un procesador de 32 bits, es necesario asegurarse de que su sistema cumple con los requisitos mínimos para jugar el juego. Estos son los requisitos mínimos del sistema para ETS2:</p>
-<ul>
-<li>OS: Windows XP/Vista/7/8</li>
-<li>Procesador: CPU de doble núcleo 2.4 GHz</li>
-<li>Memoria: 4 GB de RAM</li>
-<li>Gráficos: GeForce GTS clase 450 (Intel HD 4000)</li>
-<li>Disco duro: 7 GB de espacio disponible</li>
-</ul>
-<p>Si quieres disfrutar del juego con mejores gráficos y rendimiento, deberías tener un sistema que cumpla con los requisitos recomendados para ETS2. Estos son los requisitos del sistema recomendado para ETS2:</p>
-<ul>
-<li>OS: Windows 7/8.1/10 64-bit</li>
-<li>Procesador: Quad Core CPU 3.0 GHz</li>
-<li>Memoria: 6 GB de RAM</li>
-<li>Gráficos: GeForce clase GTX 760 (2 GB)</li>
-<li>Disco duro: 7 GB de espacio disponible</li>
-</ul>
-<p>Puedes comprobar las especificaciones de tu sistema siguiendo estos pasos:</p>
-<ol>
-<li>Pulse la tecla de Windows + R para abrir el cuadro de diálogo Ejecutar. </li>
-<li>Escriba dxdiag y haga clic en Aceptar.</li>
-<li>En la pestaña Sistema, puede ver su sistema operativo, procesador, memoria y versión DirectX. </li>
-<li> En la pestaña Mostrar, puede ver el modelo de tarjeta gráfica y la memoria. </li>
-</ol>
-<h2>Cómo descargar Euro Truck Simulator 2 para PC Windows 8 32 Bit</h2>
-<p>Ahora que sabe lo que es Euro Truck Simulator 2 y cuáles son los requisitos del sistema para jugarlo, es posible que se pregunte cómo descargarlo e instalarlo en su PC Windows 8 32 bit. Hay dos opciones principales para descargar ETS2: desde el sitio web oficial o desde Steam. Explicaremos ambas opciones en detalle a continuación. </p>
-<h3>Opción 1: Descargar desde el sitio web oficial</h3>
-
-<h4>Paso 1: Visite el sitio web de Euro Truck Simulator 2</h4>
-<p>Abra su navegador web y vaya a https://eurotrucksimulator2.com/m. Verá una página de inicio con un banner que muestra el último paquete de expansión del juego. Puedes desplazarte hacia abajo para ver más contenido sobre el juego. </p>
-<h4>Paso 2: Elija el juego base o los paquetes de expansión</h4>
-<p>Si desea descargar el juego base de ETS2, puede hacer clic en el botón Descargar en la esquina superior derecha de la página de inicio. Esto te llevará a una página donde puedes elegir entre dos versiones del juego: ETS2 Gold Edition o ETS2 Standard Edition. The Gold Edition incluye el juego base y el pack de expansión Going East! , que añade nuevos países y carreteras al juego. La Standard Edition solo incluye el juego base. Puede comparar las características y los precios de ambas versiones y elegir el que más le convenga. </p>
-<p>Si quieres descargar alguno de los paquetes de expansión de ETS2, puedes hacer clic en el botón DLC en la esquina superior derecha de la página de inicio. Esto te llevará a una página donde podrás ver todos los paquetes de expansión disponibles para ETS2, como Escandinavia, Vive la France! , Italia, Más allá del Mar Báltico, Camino al Mar Negro, Iberia y más. Cada paquete de expansión agrega nuevos países, regiones, ciudades, carreteras, puntos de referencia y más al juego. Puedes leer más sobre cada paquete de expansión y ver sus capturas de pantalla y vídeos. También puedes comprarlos individualmente o en paquetes. </p>
-<p></p>
-<h4>Paso 3: Descargar los archivos del juego utilizando un cliente torrent o un enlace directo</h4>
-<p>Después de haber elegido la versión o el paquete de expansión de ETS2 que desea descargar, verá dos opciones para descargarlo: Torrent o Direct Link. Torrent es un método de intercambio de archivos peer-to-peer que requiere un cliente torrent como uTorrent o BitTorrent. Direct Link es un método de descarga simple que utiliza tu navegador web. </p>
-
-<p>Si elige Direct Link, tendrá que hacer clic en un enlace que comenzará a descargar un archivo grande llamado . exe que contiene todos los archivos del juego. Tendrá que esperar hasta que este archivo esté completamente descargado antes de proceder al siguiente paso. Este método puede ser más simple, pero puede tomar más tiempo dependiendo de su velocidad de Internet y disponibilidad del servidor. </p>
-<h4>Paso 4: Instalar el juego y disfrutar de</h4>
-<p>Después de haber descargado todos los archivos del juego usando Torrent o Direct Link, tendrá que instalarlos en su PC Windows 8 32 bit. Para ello, deberá seguir estos pasos:</p>
-<ol>
-<li>Busque el archivo . exe que descargó y haga doble clic en él para ejecutarlo. </li>
-<li> Siga las instrucciones en la pantalla para elegir el idioma, la carpeta de destino y otras opciones para instalar el juego. </li>
-<li>Espere hasta que se complete el proceso de instalación y haga clic en Finalizar.</li>
-<li> Iniciar el juego desde el acceso directo del escritorio o el menú Inicio y disfrutar. </li>
-</ol>
-<h3>Opción 2: Descargar desde Steam</h3>
-<p>Steam es una plataforma de distribución digital que te permite comprar, descargar y jugar juegos online. Steam también ofrece varias funciones como almacenamiento en la nube, logros, multijugador, comunidad y más. Euro Truck Simulator 2 está disponible en Steam y puedes descargarlo desde allí si lo prefieres. Estos son los pasos para descargar ETS2 de Steam:</p>
-<h4>Paso 1: Crea una cuenta de Steam o inicia sesión en la ya existente</h4>
-<p>Si aún no tienes una cuenta de Steam, tendrás que crear una antes de descargar ETS2 desde Steam. Para crear una cuenta de Steam, deberás seguir estos pasos:</p>
-<ol>
-<li>Visita https://store.steampowered.com/ y haz clic en Únete a Steam en la esquina superior derecha de la página. </li>
-<li>Ingrese su dirección de correo electrónico, contraseña, país y código captcha y haga clic en Continuar.</li>
-<li>Compruebe su correo electrónico para un código de verificación y entrar en el sitio web. </li>
-
-<li>Felicidades, has creado tu cuenta de Steam. </li>
-</ol>
-<p>Si ya tienes una cuenta de Steam, puedes iniciar sesión usando tu nombre de usuario y contraseña. </p>
-<h4>Paso 2: Búsqueda de Euro Truck Simulator 2 en la tienda de vapor</h4>
-<p>Después de haber iniciado sesión en tu cuenta de Steam, puedes buscar Euro Truck Simulator 2 en la tienda de Steam. Para ello, deberá seguir estos pasos:</p>
-<ol>
-<li>Haga clic en Almacenar en la parte superior de la página. </li>
-<li>Escriba Euro Truck Simulator 2 en el cuadro de búsqueda y presione Enter.</li>
-<li>Verá una lista de resultados relacionados con Euro Truck Simulator 2. Haga clic en el que dice Euro Truck Simulator 2 - Base Game o Euro Truck Simulator 2 - Gold Edition dependiendo de la versión que desee. </li>
-<li>Usted será llevado a la página del juego donde se puede ver más información sobre él, tales como descripción, características, capturas de pantalla, vídeos, comentarios, y más. </li>
-</ol>
-<h4>Paso 3: Compra el juego o descarga la demo gratuita</h4>
-<p>Si desea comprar la versión completa de ETS2, tendrá que pagar por ella utilizando su método de pago preferido. El precio de ETS2 puede variar dependiendo de tu región y de tu moneda. También puedes comprar cualquiera de los paquetes de expansión o paquetes que están disponibles para ETS2. Para comprar ETS2, tendrás que seguir estos pasos:</p>
-<ol>
-<li>Haga clic en Añadir al carrito en el lado derecho de la página del juego. </li>
-<li> Verá una ventana emergente que muestra el contenido de su carrito. Puede revisar su pedido y hacer cualquier cambio si es necesario. </li>
-<li>Haz clic en Comprar para mí o Comprar como regalo dependiendo de si quieres comprar el juego para ti o para otra persona. </li>
-<li>Serás llevado a una página de pago donde puedes elegir tu método de pago e ingresar tu información de facturación. </li>
-<li>Haga clic en Continuar y confirme su compra. </li>
-<li>Recibirás un correo electrónico de confirmación de tu compra y el juego se añadirá a tu biblioteca de Steam. </li>
-</ol>
-
-<ol>
-<li>Haga clic en Descargar Demo en el lado derecho de la página del juego. </li>
-<li>Verás una ventana emergente que te pide que instales Steam si aún no lo tienes. Si tiene Steam instalado, haga clic en Sí, el vapor está instalado. De lo contrario, haga clic en No, necesito Steam y siga las instrucciones para instalar Steam.</li>
-<li>La página del juego se abrirá en Steam y verás un bot��n que dice Jugar Juego. Haz clic en él y espera hasta que el juego se descargue e instale. </li>
-</ol>
- <h4>Paso 4: Lanza el juego desde tu biblioteca de Steam y disfruta</h4>
- <p>Después de haber comprado o descargado ETS2 desde Steam, puede iniciarlo desde su biblioteca de Steam. Para ello, deberá seguir estos pasos:</p>
-<ol>
-<li>Abre Steam e inicia sesión en tu cuenta si aún no lo has hecho. </li>
-<li>Haga clic en Biblioteca en la parte superior de la página. </li>
-<li>Encuentra Euro Truck Simulator 2 en tu lista de juegos y haz clic en él. </li>
-<li>Haga clic en Jugar en el lado derecho de la página del juego. </li>
-<li>El juego se iniciará y podrás disfrutarlo. </li>
-</ol>
-<h2>Conclusión</h2>
-<p>Euro Truck Simulator 2 es un fantástico juego que te permite conducir camiones por toda Europa y entregar carga a diferentes destinos. Tiene gráficos increíbles, física realista, efectos de sonido inmersivos y una jugabilidad diversa. También tiene una gran comunidad de modding que crea nuevo contenido para el juego. Puedes descargar y jugar a ETS2 en tu PC Windows 8 de 32 bits utilizando el sitio web oficial o Steam. Ambas opciones tienen sus ventajas y desventajas, por lo que puedes elegir la que más te convenga. Esperamos que este artículo te haya ayudado a aprender a descargar e instalar ETS2 en tu PC Windows 8 32 bit. Ahora, prepárate para salir a la carretera y divertirte! </p>
-<p>Si te gustó este artículo, por favor compártelo con tus amigos y deja un comentario a continuación. Además, no te olvides de revisar nuestros otros artículos sobre juegos, tecnología y más. ¡Gracias por leer! </p>
-<h3>Preguntas frecuentes</h3>
-
-<ul>
-<li><b>Q: ¿Cuánto cuesta Euro Truck Simulator 2? </b></li>
-<li>A: El precio de Euro Truck Simulator 2 puede variar dependiendo de su región, moneda y plataforma. En el sitio web oficial, el juego base cuesta $19.99 USD y los paquetes de expansión van desde $8.99 USD hasta $17.99 USD. En Steam, el juego base cuesta $19.99 USD y los paquetes de expansión van desde $8.99 USD hasta $17.99 USD. Sin embargo, Steam a menudo ofrece descuentos y ventas en ETS2 y sus DLC, por lo que puedes conseguirlos a precios más baratos si esperas el momento adecuado. </li>
-<li><b>Q: ¿Es Euro Truck Simulator 2 multijugador? </b></li>
-<li>A: Euro Truck Simulator 2 no tiene un modo multijugador oficial, pero hay mods no oficiales que le permiten jugar en línea con otros jugadores. Uno de los mods multijugador más populares es TruckersMP, que le permite unirse a servidores con miles de otros camioneros y chatear, conducir e interactuar con ellos. Puede descargar TruckersMP de https://truckersmp.com/.</li>
-<li><b>Q: ¿Es compatible Euro Truck Simulator 2 VR? </b></li>
-<li>A: Euro Truck Simulator 2 es compatible con dispositivos de realidad virtual como Oculus Rift y HTC Vive. Puede habilitar el modo de realidad virtual en ETS2 siguiendo estos pasos:</li>
-<ol>
-<li> Inicie SteamVR y asegúrese de que su dispositivo de realidad virtual está conectado y funciona. </li>
-<li>Haz clic derecho en Euro Truck Simulator 2 en tu biblioteca de Steam y selecciona Propiedades.</li>
-<li>Haga clic en la pestaña Betas y seleccione oculus - Oculus/Vive - 1.37 - (SDK 1.4.0) desde el menú desplegable. </li>
-<li>Espere hasta que el juego se actualice a la versión VR. </li>
-<li>Inicia el juego desde tu biblioteca de Steam y disfruta. </li>
-</ol>
-<li><b>Q: ¿Cómo puedo obtener más dinero y XP en Euro Truck Simulator 2?</b></li>
-<li>A: Hay varias maneras de obtener más dinero y XP en ETS2, como:</li>
-<ul>
-<li>Completar más entregas y contratos con mayores recompensas. </li>
-<li>Invertir en habilidades que aumenten sus ingresos y eficiencia, como ADR, larga distancia, carga de alto valor, carga frágil, etc.</li>
-
-<li>Aprovechando los bonos y eventos que ofrecen dinero extra y XP.</li>
-<li>Usando trucos o mods que te dan dinero ilimitado y XP (no recomendado). </li>
-</ul>
- <li><b>Q: ¿Cómo puedo actualizar Euro Truck Simulator 2?</b></li>
- <li>A: Si descargaste ETS2 desde el sitio web oficial, puedes actualizarlo descargando el último parche de https://eurotrucksimulator2.com/update.php e instalándolo sobre tus archivos de juego existentes. Si has descargado ETS2 desde Steam, puedes actualizarlo automáticamente activando las actualizaciones automáticas en la configuración de Steam o manualmente haciendo clic en Actualizar en la página del juego en tu biblioteca de Steam. </li></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/CLIP/clip/simple_tokenizer.py

@@ -1,132 +0,0 @@
-import gzip
-import html
-import os
-from functools import lru_cache
-
-import ftfy
-import regex as re
-
-
-@lru_cache()
-def default_bpe():
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r'\s+', ' ', text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe()):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
-        merges = merges[1:49152-256-2+1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v+'</w>' for v in vocab]
-        for merge in merges:
-            vocab.append(''.join(merge))
-        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
-        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token+'</w>'
-
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
-        return text

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/exceptions.py

@@ -1,816 +0,0 @@
-# Copyright (c) 2012-2013 Mitch Garnaat http://garnaat.org/
-# Copyright 2012-2014 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-
-from botocore.vendored import requests
-from botocore.vendored.requests.packages import urllib3
-
-
-def _exception_from_packed_args(exception_cls, args=None, kwargs=None):
-    # This is helpful for reducing Exceptions that only accept kwargs as
-    # only positional arguments can be provided for __reduce__
-    # Ideally, this would also be a class method on the BotoCoreError
-    # but instance methods cannot be pickled.
-    if args is None:
-        args = ()
-    if kwargs is None:
-        kwargs = {}
-    return exception_cls(*args, **kwargs)
-
-
-class BotoCoreError(Exception):
-    """
-    The base exception class for BotoCore exceptions.
-
-    :ivar msg: The descriptive message associated with the error.
-    """
-
-    fmt = 'An unspecified error occurred'
-
-    def __init__(self, **kwargs):
-        msg = self.fmt.format(**kwargs)
-        Exception.__init__(self, msg)
-        self.kwargs = kwargs
-
-    def __reduce__(self):
-        return _exception_from_packed_args, (self.__class__, None, self.kwargs)
-
-
-class DataNotFoundError(BotoCoreError):
-    """
-    The data associated with a particular path could not be loaded.
-
-    :ivar data_path: The data path that the user attempted to load.
-    """
-
-    fmt = 'Unable to load data for: {data_path}'
-
-
-class UnknownServiceError(DataNotFoundError):
-    """Raised when trying to load data for an unknown service.
-
-    :ivar service_name: The name of the unknown service.
-
-    """
-
-    fmt = (
-        "Unknown service: '{service_name}'. Valid service names are: "
-        "{known_service_names}"
-    )
-
-
-class UnknownRegionError(BotoCoreError):
-    """Raised when trying to load data for an unknown region.
-
-    :ivar region_name: The name of the unknown region.
-
-    """
-
-    fmt = "Unknown region: '{region_name}'. {error_msg}"
-
-
-class ApiVersionNotFoundError(BotoCoreError):
-    """
-    The data associated with either the API version or a compatible one
-    could not be loaded.
-
-    :ivar data_path: The data path that the user attempted to load.
-    :ivar api_version: The API version that the user attempted to load.
-    """
-
-    fmt = 'Unable to load data {data_path} for: {api_version}'
-
-
-class HTTPClientError(BotoCoreError):
-    fmt = 'An HTTP Client raised an unhandled exception: {error}'
-
-    def __init__(self, request=None, response=None, **kwargs):
-        self.request = request
-        self.response = response
-        super().__init__(**kwargs)
-
-    def __reduce__(self):
-        return _exception_from_packed_args, (
-            self.__class__,
-            (self.request, self.response),
-            self.kwargs,
-        )
-
-
-class ConnectionError(BotoCoreError):
-    fmt = 'An HTTP Client failed to establish a connection: {error}'
-
-
-class InvalidIMDSEndpointError(BotoCoreError):
-    fmt = 'Invalid endpoint EC2 Instance Metadata endpoint: {endpoint}'
-
-
-class InvalidIMDSEndpointModeError(BotoCoreError):
-    fmt = (
-        'Invalid EC2 Instance Metadata endpoint mode: {mode}'
-        ' Valid endpoint modes (case-insensitive): {valid_modes}.'
-    )
-
-
-class EndpointConnectionError(ConnectionError):
-    fmt = 'Could not connect to the endpoint URL: "{endpoint_url}"'
-
-
-class SSLError(ConnectionError, requests.exceptions.SSLError):
-    fmt = 'SSL validation failed for {endpoint_url} {error}'
-
-
-class ConnectionClosedError(HTTPClientError):
-    fmt = (
-        'Connection was closed before we received a valid response '
-        'from endpoint URL: "{endpoint_url}".'
-    )
-
-
-class ReadTimeoutError(
-    HTTPClientError,
-    requests.exceptions.ReadTimeout,
-    urllib3.exceptions.ReadTimeoutError,
-):
-    fmt = 'Read timeout on endpoint URL: "{endpoint_url}"'
-
-
-class ConnectTimeoutError(ConnectionError, requests.exceptions.ConnectTimeout):
-    fmt = 'Connect timeout on endpoint URL: "{endpoint_url}"'
-
-
-class ProxyConnectionError(ConnectionError, requests.exceptions.ProxyError):
-    fmt = 'Failed to connect to proxy URL: "{proxy_url}"'
-
-
-class ResponseStreamingError(HTTPClientError):
-    fmt = 'An error occurred while reading from response stream: {error}'
-
-
-class NoCredentialsError(BotoCoreError):
-    """
-    No credentials could be found.
-    """
-
-    fmt = 'Unable to locate credentials'
-
-
-class NoAuthTokenError(BotoCoreError):
-    """
-    No authorization token could be found.
-    """
-
-    fmt = 'Unable to locate authorization token'
-
-
-class TokenRetrievalError(BotoCoreError):
-    """
-    Error attempting to retrieve a token from a remote source.
-
-    :ivar provider: The name of the token provider.
-    :ivar error_msg: The msg explaining why the token could not be retrieved.
-
-    """
-
-    fmt = 'Error when retrieving token from {provider}: {error_msg}'
-
-
-class PartialCredentialsError(BotoCoreError):
-    """
-    Only partial credentials were found.
-
-    :ivar cred_var: The missing credential variable name.
-
-    """
-
-    fmt = 'Partial credentials found in {provider}, missing: {cred_var}'
-
-
-class CredentialRetrievalError(BotoCoreError):
-    """
-    Error attempting to retrieve credentials from a remote source.
-
-    :ivar provider: The name of the credential provider.
-    :ivar error_msg: The msg explaining why credentials could not be
-        retrieved.
-
-    """
-
-    fmt = 'Error when retrieving credentials from {provider}: {error_msg}'
-
-
-class UnknownSignatureVersionError(BotoCoreError):
-    """
-    Requested Signature Version is not known.
-
-    :ivar signature_version: The name of the requested signature version.
-    """
-
-    fmt = 'Unknown Signature Version: {signature_version}.'
-
-
-class ServiceNotInRegionError(BotoCoreError):
-    """
-    The service is not available in requested region.
-
-    :ivar service_name: The name of the service.
-    :ivar region_name: The name of the region.
-    """
-
-    fmt = 'Service {service_name} not available in region {region_name}'
-
-
-class BaseEndpointResolverError(BotoCoreError):
-    """Base error for endpoint resolving errors.
-
-    Should never be raised directly, but clients can catch
-    this exception if they want to generically handle any errors
-    during the endpoint resolution process.
-
-    """
-
-
-class NoRegionError(BaseEndpointResolverError):
-    """No region was specified."""
-
-    fmt = 'You must specify a region.'
-
-
-class EndpointVariantError(BaseEndpointResolverError):
-    """
-    Could not construct modeled endpoint variant.
-
-    :ivar error_msg: The message explaining why the modeled endpoint variant
-        is unable to be constructed.
-
-    """
-
-    fmt = (
-        'Unable to construct a modeled endpoint with the following '
-        'variant(s) {tags}: '
-    )
-
-
-class UnknownEndpointError(BaseEndpointResolverError, ValueError):
-    """
-    Could not construct an endpoint.
-
-    :ivar service_name: The name of the service.
-    :ivar region_name: The name of the region.
-    """
-
-    fmt = (
-        'Unable to construct an endpoint for '
-        '{service_name} in region {region_name}'
-    )
-
-
-class UnknownFIPSEndpointError(BaseEndpointResolverError):
-    """
-    Could not construct a FIPS endpoint.
-
-    :ivar service_name: The name of the service.
-    :ivar region_name: The name of the region.
-    """
-
-    fmt = (
-        'The provided FIPS pseudo-region "{region_name}" is not known for '
-        'the service "{service_name}". A FIPS compliant endpoint cannot be '
-        'constructed.'
-    )
-
-
-class ProfileNotFound(BotoCoreError):
-    """
-    The specified configuration profile was not found in the
-    configuration file.
-
-    :ivar profile: The name of the profile the user attempted to load.
-    """
-
-    fmt = 'The config profile ({profile}) could not be found'
-
-
-class ConfigParseError(BotoCoreError):
-    """
-    The configuration file could not be parsed.
-
-    :ivar path: The path to the configuration file.
-    """
-
-    fmt = 'Unable to parse config file: {path}'
-
-
-class ConfigNotFound(BotoCoreError):
-    """
-    The specified configuration file could not be found.
-
-    :ivar path: The path to the configuration file.
-    """
-
-    fmt = 'The specified config file ({path}) could not be found.'
-
-
-class MissingParametersError(BotoCoreError):
-    """
-    One or more required parameters were not supplied.
-
-    :ivar object: The object that has missing parameters.
-        This can be an operation or a parameter (in the
-        case of inner params).  The str() of this object
-        will be used so it doesn't need to implement anything
-        other than str().
-    :ivar missing: The names of the missing parameters.
-    """
-
-    fmt = (
-        'The following required parameters are missing for '
-        '{object_name}: {missing}'
-    )
-
-
-class ValidationError(BotoCoreError):
-    """
-    An exception occurred validating parameters.
-
-    Subclasses must accept a ``value`` and ``param``
-    argument in their ``__init__``.
-
-    :ivar value: The value that was being validated.
-    :ivar param: The parameter that failed validation.
-    :ivar type_name: The name of the underlying type.
-    """
-
-    fmt = "Invalid value ('{value}') for param {param} " "of type {type_name} "
-
-
-class ParamValidationError(BotoCoreError):
-    fmt = 'Parameter validation failed:\n{report}'
-
-
-# These exceptions subclass from ValidationError so that code
-# can just 'except ValidationError' to catch any possibly validation
-# error.
-class UnknownKeyError(ValidationError):
-    """
-    Unknown key in a struct parameter.
-
-    :ivar value: The value that was being checked.
-    :ivar param: The name of the parameter.
-    :ivar choices: The valid choices the value can be.
-    """
-
-    fmt = (
-        "Unknown key '{value}' for param '{param}'.  Must be one "
-        "of: {choices}"
-    )
-
-
-class RangeError(ValidationError):
-    """
-    A parameter value was out of the valid range.
-
-    :ivar value: The value that was being checked.
-    :ivar param: The parameter that failed validation.
-    :ivar min_value: The specified minimum value.
-    :ivar max_value: The specified maximum value.
-    """
-
-    fmt = (
-        'Value out of range for param {param}: '
-        '{min_value} <= {value} <= {max_value}'
-    )
-
-
-class UnknownParameterError(ValidationError):
-    """
-    Unknown top level parameter.
-
-    :ivar name: The name of the unknown parameter.
-    :ivar operation: The name of the operation.
-    :ivar choices: The valid choices the parameter name can be.
-    """
-
-    fmt = (
-        "Unknown parameter '{name}' for operation {operation}.  Must be one "
-        "of: {choices}"
-    )
-
-
-class InvalidRegionError(ValidationError, ValueError):
-    """
-    Invalid region_name provided to client or resource.
-
-    :ivar region_name: region_name that was being validated.
-    """
-
-    fmt = "Provided region_name '{region_name}' doesn't match a supported format."
-
-
-class AliasConflictParameterError(ValidationError):
-    """
-    Error when an alias is provided for a parameter as well as the original.
-
-    :ivar original: The name of the original parameter.
-    :ivar alias: The name of the alias
-    :ivar operation: The name of the operation.
-    """
-
-    fmt = (
-        "Parameter '{original}' and its alias '{alias}' were provided "
-        "for operation {operation}.  Only one of them may be used."
-    )
-
-
-class UnknownServiceStyle(BotoCoreError):
-    """
-    Unknown style of service invocation.
-
-    :ivar service_style: The style requested.
-    """
-
-    fmt = 'The service style ({service_style}) is not understood.'
-
-
-class PaginationError(BotoCoreError):
-    fmt = 'Error during pagination: {message}'
-
-
-class OperationNotPageableError(BotoCoreError):
-    fmt = 'Operation cannot be paginated: {operation_name}'
-
-
-class ChecksumError(BotoCoreError):
-    """The expected checksum did not match the calculated checksum."""
-
-    fmt = (
-        'Checksum {checksum_type} failed, expected checksum '
-        '{expected_checksum} did not match calculated checksum '
-        '{actual_checksum}.'
-    )
-
-
-class UnseekableStreamError(BotoCoreError):
-    """Need to seek a stream, but stream does not support seeking."""
-
-    fmt = (
-        'Need to rewind the stream {stream_object}, but stream '
-        'is not seekable.'
-    )
-
-
-class WaiterError(BotoCoreError):
-    """Waiter failed to reach desired state."""
-
-    fmt = 'Waiter {name} failed: {reason}'
-
-    def __init__(self, name, reason, last_response):
-        super().__init__(name=name, reason=reason)
-        self.last_response = last_response
-
-
-class IncompleteReadError(BotoCoreError):
-    """HTTP response did not return expected number of bytes."""
-
-    fmt = (
-        '{actual_bytes} read, but total bytes ' 'expected is {expected_bytes}.'
-    )
-
-
-class InvalidExpressionError(BotoCoreError):
-    """Expression is either invalid or too complex."""
-
-    fmt = 'Invalid expression {expression}: Only dotted lookups are supported.'
-
-
-class UnknownCredentialError(BotoCoreError):
-    """Tried to insert before/after an unregistered credential type."""
-
-    fmt = 'Credential named {name} not found.'
-
-
-class WaiterConfigError(BotoCoreError):
-    """Error when processing waiter configuration."""
-
-    fmt = 'Error processing waiter config: {error_msg}'
-
-
-class UnknownClientMethodError(BotoCoreError):
-    """Error when trying to access a method on a client that does not exist."""
-
-    fmt = 'Client does not have method: {method_name}'
-
-
-class UnsupportedSignatureVersionError(BotoCoreError):
-    """Error when trying to use an unsupported Signature Version."""
-
-    fmt = 'Signature version is not supported: {signature_version}'
-
-
-class ClientError(Exception):
-    MSG_TEMPLATE = (
-        'An error occurred ({error_code}) when calling the {operation_name} '
-        'operation{retry_info}: {error_message}'
-    )
-
-    def __init__(self, error_response, operation_name):
-        retry_info = self._get_retry_info(error_response)
-        error = error_response.get('Error', {})
-        msg = self.MSG_TEMPLATE.format(
-            error_code=error.get('Code', 'Unknown'),
-            error_message=error.get('Message', 'Unknown'),
-            operation_name=operation_name,
-            retry_info=retry_info,
-        )
-        super().__init__(msg)
-        self.response = error_response
-        self.operation_name = operation_name
-
-    def _get_retry_info(self, response):
-        retry_info = ''
-        if 'ResponseMetadata' in response:
-            metadata = response['ResponseMetadata']
-            if metadata.get('MaxAttemptsReached', False):
-                if 'RetryAttempts' in metadata:
-                    retry_info = (
-                        f" (reached max retries: {metadata['RetryAttempts']})"
-                    )
-        return retry_info
-
-    def __reduce__(self):
-        # Subclasses of ClientError's are dynamically generated and
-        # cannot be pickled unless they are attributes of a
-        # module. So at the very least return a ClientError back.
-        return ClientError, (self.response, self.operation_name)
-
-
-class EventStreamError(ClientError):
-    pass
-
-
-class UnsupportedTLSVersionWarning(Warning):
-    """Warn when an openssl version that uses TLS 1.2 is required"""
-
-    pass
-
-
-class ImminentRemovalWarning(Warning):
-    pass
-
-
-class InvalidDNSNameError(BotoCoreError):
-    """Error when virtual host path is forced on a non-DNS compatible bucket"""
-
-    fmt = (
-        'Bucket named {bucket_name} is not DNS compatible. Virtual '
-        'hosted-style addressing cannot be used. The addressing style '
-        'can be configured by removing the addressing_style value '
-        'or setting that value to \'path\' or \'auto\' in the AWS Config '
-        'file or in the botocore.client.Config object.'
-    )
-
-
-class InvalidS3AddressingStyleError(BotoCoreError):
-    """Error when an invalid path style is specified"""
-
-    fmt = (
-        'S3 addressing style {s3_addressing_style} is invalid. Valid options '
-        'are: \'auto\', \'virtual\', and \'path\''
-    )
-
-
-class UnsupportedS3ArnError(BotoCoreError):
-    """Error when S3 ARN provided to Bucket parameter is not supported"""
-
-    fmt = (
-        'S3 ARN {arn} provided to "Bucket" parameter is invalid. Only '
-        'ARNs for S3 access-points are supported.'
-    )
-
-
-class UnsupportedS3ControlArnError(BotoCoreError):
-    """Error when S3 ARN provided to S3 control parameter is not supported"""
-
-    fmt = 'S3 ARN "{arn}" provided is invalid for this operation. {msg}'
-
-
-class InvalidHostLabelError(BotoCoreError):
-    """Error when an invalid host label would be bound to an endpoint"""
-
-    fmt = (
-        'Invalid host label to be bound to the hostname of the endpoint: '
-        '"{label}".'
-    )
-
-
-class UnsupportedOutpostResourceError(BotoCoreError):
-    """Error when S3 Outpost ARN provided to Bucket parameter is incomplete"""
-
-    fmt = (
-        'S3 Outpost ARN resource "{resource_name}" provided to "Bucket" '
-        'parameter is invalid. Only ARNs for S3 Outpost arns with an '
-        'access-point sub-resource are supported.'
-    )
-
-
-class UnsupportedS3ConfigurationError(BotoCoreError):
-    """Error when an unsupported configuration is used with access-points"""
-
-    fmt = 'Unsupported configuration when using S3: {msg}'
-
-
-class UnsupportedS3AccesspointConfigurationError(BotoCoreError):
-    """Error when an unsupported configuration is used with access-points"""
-
-    fmt = 'Unsupported configuration when using S3 access-points: {msg}'
-
-
-class InvalidEndpointDiscoveryConfigurationError(BotoCoreError):
-    """Error when invalid value supplied for endpoint_discovery_enabled"""
-
-    fmt = (
-        'Unsupported configuration value for endpoint_discovery_enabled. '
-        'Expected one of ("true", "false", "auto") but got {config_value}.'
-    )
-
-
-class UnsupportedS3ControlConfigurationError(BotoCoreError):
-    """Error when an unsupported configuration is used with S3 Control"""
-
-    fmt = 'Unsupported configuration when using S3 Control: {msg}'
-
-
-class InvalidRetryConfigurationError(BotoCoreError):
-    """Error when invalid retry configuration is specified"""
-
-    fmt = (
-        'Cannot provide retry configuration for "{retry_config_option}". '
-        'Valid retry configuration options are: {valid_options}'
-    )
-
-
-class InvalidMaxRetryAttemptsError(InvalidRetryConfigurationError):
-    """Error when invalid retry configuration is specified"""
-
-    fmt = (
-        'Value provided to "max_attempts": {provided_max_attempts} must '
-        'be an integer greater than or equal to {min_value}.'
-    )
-
-
-class InvalidRetryModeError(InvalidRetryConfigurationError):
-    """Error when invalid retry mode configuration is specified"""
-
-    fmt = (
-        'Invalid value provided to "mode": "{provided_retry_mode}" must '
-        'be one of: {valid_modes}'
-    )
-
-
-class InvalidS3UsEast1RegionalEndpointConfigError(BotoCoreError):
-    """Error for invalid s3 us-east-1 regional endpoints configuration"""
-
-    fmt = (
-        'S3 us-east-1 regional endpoint option '
-        '{s3_us_east_1_regional_endpoint_config} is '
-        'invalid. Valid options are: "legacy", "regional"'
-    )
-
-
-class InvalidSTSRegionalEndpointsConfigError(BotoCoreError):
-    """Error when invalid sts regional endpoints configuration is specified"""
-
-    fmt = (
-        'STS regional endpoints option {sts_regional_endpoints_config} is '
-        'invalid. Valid options are: "legacy", "regional"'
-    )
-
-
-class StubResponseError(BotoCoreError):
-    fmt = (
-        'Error getting response stub for operation {operation_name}: {reason}'
-    )
-
-
-class StubAssertionError(StubResponseError, AssertionError):
-    pass
-
-
-class UnStubbedResponseError(StubResponseError):
-    pass
-
-
-class InvalidConfigError(BotoCoreError):
-    fmt = '{error_msg}'
-
-
-class InfiniteLoopConfigError(InvalidConfigError):
-    fmt = (
-        'Infinite loop in credential configuration detected. Attempting to '
-        'load from profile {source_profile} which has already been visited. '
-        'Visited profiles: {visited_profiles}'
-    )
-
-
-class RefreshWithMFAUnsupportedError(BotoCoreError):
-    fmt = 'Cannot refresh credentials: MFA token required.'
-
-
-class MD5UnavailableError(BotoCoreError):
-    fmt = "This system does not support MD5 generation."
-
-
-class MissingDependencyException(BotoCoreError):
-    fmt = "Missing Dependency: {msg}"
-
-
-class MetadataRetrievalError(BotoCoreError):
-    fmt = "Error retrieving metadata: {error_msg}"
-
-
-class UndefinedModelAttributeError(Exception):
-    pass
-
-
-class MissingServiceIdError(UndefinedModelAttributeError):
-    fmt = (
-        "The model being used for the service {service_name} is missing the "
-        "serviceId metadata property, which is required."
-    )
-
-    def __init__(self, **kwargs):
-        msg = self.fmt.format(**kwargs)
-        Exception.__init__(self, msg)
-        self.kwargs = kwargs
-
-
-class SSOError(BotoCoreError):
-    fmt = (
-        "An unspecified error happened when resolving AWS credentials or an "
-        "access token from SSO."
-    )
-
-
-class SSOTokenLoadError(SSOError):
-    fmt = "Error loading SSO Token: {error_msg}"
-
-
-class UnauthorizedSSOTokenError(SSOError):
-    fmt = (
-        "The SSO session associated with this profile has expired or is "
-        "otherwise invalid. To refresh this SSO session run aws sso login "
-        "with the corresponding profile."
-    )
-
-
-class CapacityNotAvailableError(BotoCoreError):
-    fmt = 'Insufficient request capacity available.'
-
-
-class InvalidProxiesConfigError(BotoCoreError):
-    fmt = 'Invalid configuration value(s) provided for proxies_config.'
-
-
-class InvalidDefaultsMode(BotoCoreError):
-    fmt = (
-        'Client configured with invalid defaults mode: {mode}. '
-        'Valid defaults modes include: {valid_modes}.'
-    )
-
-
-class AwsChunkedWrapperError(BotoCoreError):
-    fmt = '{error_msg}'
-
-
-class FlexibleChecksumError(BotoCoreError):
-    fmt = '{error_msg}'
-
-
-class InvalidEndpointConfigurationError(BotoCoreError):
-    fmt = 'Invalid endpoint configuration: {msg}'
-
-
-class EndpointProviderError(BotoCoreError):
-    """Base error for the EndpointProvider class"""
-
-    fmt = '{msg}'
-
-
-class EndpointResolutionError(EndpointProviderError):
-    """Error when input parameters resolve to an error rule"""
-
-    fmt = '{msg}'
-
-
-class UnknownEndpointResolutionBuiltInName(EndpointProviderError):
-    fmt = 'Unknown builtin variable name: {name}'

spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/butd_inference_wrapper.py

@@ -1,91 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-Inference wrapper for trained butd_eff models
-=========================================================================================
-"""
-import os
-import torch
-import numpy as np
-import _pickle as cPickle
-
-from dataset import Dictionary
-import base_model
-import utils
-
-
-root = os.path.dirname(os.path.realpath(__file__))
-
-# stand in for loading a dataset
-class Dset_Like():
-    def __init__(self, feat_size):
-        self.dictionary = Dictionary.load_from_file('{}/essentials/dictionary.pkl'.format(root))
-        self.v_dim = feat_size
-        self.num_ans_candidates = 3129
-
-
-
-class BUTDeff_Wrapper():
-    def __init__(self, model_path, num_hid=1024, feat_size=1024):
-        self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-        label2ans_path = '{}/essentials/trainval_label2ans.pkl'.format(root)
-        self.label2ans = cPickle.load(open(label2ans_path, 'rb'))
-        # load dataset stand in
-        dset = Dset_Like(feat_size)
-        self.dictionary = dset.dictionary
-        # load model
-        constructor = 'build_baseline0_newatt'
-        model = getattr(base_model, constructor)(dset, num_hid).to(self.device)
-        model = model.to(self.device) 
-        print('Loading saved model from: ' + model_path)
-        model.load_state_dict(torch.load(model_path, map_location=self.device))
-        model.train(False)
-        self.model = model
-        
-
-
-    # based on the tokenizer in dataset.py
-    # added safe_mode for demo to catch unknown words
-    def tokenize(self, question, max_length=14):
-        """Tokenizes the questions.
-
-        This will add q_token in each entry of the dataset.
-        -1 represent nil, and should be treated as padding_idx in embedding
-        """
-        tokens = self.dictionary.tokenize(question, add_word=False, safe_mode=True)
-        tokens = tokens[:max_length]
-        if len(tokens) < max_length:
-            # Note here we pad in front of the sentence
-            padding = [self.dictionary.padding_idx] * (max_length - len(tokens))
-            tokens = padding + tokens
-        utils.assert_eq(len(tokens), max_length)
-        return tokens
-
-
-
-    # inputs are a tensor of image features, shape [nb, 1024]
-    # and a raw question in string form. bbox_feature input is unused
-    def run(self, image_features, raw_question, bbox_features=None):
-        v = torch.unsqueeze(image_features,0).to(self.device)
-        q = self.tokenize(raw_question)
-        q = torch.unsqueeze(torch.from_numpy(np.array(q)),0).to(self.device)
-        pred = self.model(v, None, q, None)        
-        pred_np = pred.cpu().data.numpy()
-        pred_argmax = np.argmax(pred_np, axis=1)[0]
-        ans = self.label2ans[pred_argmax]
-        return ans
-
-
-
-    # get the visual attention vector for making visualizations
-    def get_att(self, image_features, raw_question, bbox_features=None):
-        v = torch.unsqueeze(image_features,0).to(self.device)
-        q = self.tokenize(raw_question)
-        q = torch.unsqueeze(torch.from_numpy(np.array(q)),0).to(self.device)
-        w_emb = self.model.w_emb(q)
-        q_emb = self.model.q_emb(w_emb)
-        att = self.model.v_att(v, q_emb)
-        return att
-

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/csrc/deformable/deform_conv.h

@@ -1,377 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-#pragma once
-#include <torch/types.h>
-
-namespace detectron2 {
-
-#ifdef WITH_CUDA
-int deform_conv_forward_cuda(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor offset,
-    at::Tensor output,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step);
-
-int deform_conv_backward_input_cuda(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradInput,
-    at::Tensor gradOffset,
-    at::Tensor weight,
-    at::Tensor columns,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step);
-
-int deform_conv_backward_parameters_cuda(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradWeight, // at::Tensor gradBias,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    float scale,
-    int im2col_step);
-
-void modulated_deform_conv_cuda_forward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor output,
-    at::Tensor columns,
-    int kernel_h,
-    int kernel_w,
-    const int stride_h,
-    const int stride_w,
-    const int pad_h,
-    const int pad_w,
-    const int dilation_h,
-    const int dilation_w,
-    const int group,
-    const int deformable_group,
-    const bool with_bias);
-
-void modulated_deform_conv_cuda_backward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor columns,
-    at::Tensor grad_input,
-    at::Tensor grad_weight,
-    at::Tensor grad_bias,
-    at::Tensor grad_offset,
-    at::Tensor grad_mask,
-    at::Tensor grad_output,
-    int kernel_h,
-    int kernel_w,
-    int stride_h,
-    int stride_w,
-    int pad_h,
-    int pad_w,
-    int dilation_h,
-    int dilation_w,
-    int group,
-    int deformable_group,
-    const bool with_bias);
-
-#endif
-
-inline int deform_conv_forward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor offset,
-    at::Tensor output,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step) {
-  if (input.type().is_cuda()) {
-#ifdef WITH_CUDA
-    TORCH_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
-    return deform_conv_forward_cuda(
-        input,
-        weight,
-        offset,
-        output,
-        columns,
-        ones,
-        kW,
-        kH,
-        dW,
-        dH,
-        padW,
-        padH,
-        dilationW,
-        dilationH,
-        group,
-        deformable_group,
-        im2col_step);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-inline int deform_conv_backward_input(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradInput,
-    at::Tensor gradOffset,
-    at::Tensor weight,
-    at::Tensor columns,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step) {
-  if (gradOutput.type().is_cuda()) {
-#ifdef WITH_CUDA
-    TORCH_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
-    TORCH_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
-    return deform_conv_backward_input_cuda(
-        input,
-        offset,
-        gradOutput,
-        gradInput,
-        gradOffset,
-        weight,
-        columns,
-        kW,
-        kH,
-        dW,
-        dH,
-        padW,
-        padH,
-        dilationW,
-        dilationH,
-        group,
-        deformable_group,
-        im2col_step);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-inline int deform_conv_backward_filter(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradWeight, // at::Tensor gradBias,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    float scale,
-    int im2col_step) {
-  if (gradOutput.type().is_cuda()) {
-#ifdef WITH_CUDA
-    TORCH_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
-    TORCH_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
-    return deform_conv_backward_parameters_cuda(
-        input,
-        offset,
-        gradOutput,
-        gradWeight,
-        columns,
-        ones,
-        kW,
-        kH,
-        dW,
-        dH,
-        padW,
-        padH,
-        dilationW,
-        dilationH,
-        group,
-        deformable_group,
-        scale,
-        im2col_step);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-inline void modulated_deform_conv_forward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor output,
-    at::Tensor columns,
-    int kernel_h,
-    int kernel_w,
-    const int stride_h,
-    const int stride_w,
-    const int pad_h,
-    const int pad_w,
-    const int dilation_h,
-    const int dilation_w,
-    const int group,
-    const int deformable_group,
-    const bool with_bias) {
-  if (input.type().is_cuda()) {
-#ifdef WITH_CUDA
-    TORCH_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(bias.type().is_cuda(), "bias tensor is not on GPU!");
-    TORCH_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
-    return modulated_deform_conv_cuda_forward(
-        input,
-        weight,
-        bias,
-        ones,
-        offset,
-        mask,
-        output,
-        columns,
-        kernel_h,
-        kernel_w,
-        stride_h,
-        stride_w,
-        pad_h,
-        pad_w,
-        dilation_h,
-        dilation_w,
-        group,
-        deformable_group,
-        with_bias);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-inline void modulated_deform_conv_backward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor columns,
-    at::Tensor grad_input,
-    at::Tensor grad_weight,
-    at::Tensor grad_bias,
-    at::Tensor grad_offset,
-    at::Tensor grad_mask,
-    at::Tensor grad_output,
-    int kernel_h,
-    int kernel_w,
-    int stride_h,
-    int stride_w,
-    int pad_h,
-    int pad_w,
-    int dilation_h,
-    int dilation_w,
-    int group,
-    int deformable_group,
-    const bool with_bias) {
-  if (grad_output.type().is_cuda()) {
-#ifdef WITH_CUDA
-    TORCH_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
-    TORCH_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(bias.type().is_cuda(), "bias tensor is not on GPU!");
-    TORCH_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
-    return modulated_deform_conv_cuda_backward(
-        input,
-        weight,
-        bias,
-        ones,
-        offset,
-        mask,
-        columns,
-        grad_input,
-        grad_weight,
-        grad_bias,
-        grad_offset,
-        grad_mask,
-        grad_output,
-        kernel_h,
-        kernel_w,
-        stride_h,
-        stride_w,
-        pad_h,
-        pad_w,
-        dilation_h,
-        dilation_w,
-        group,
-        deformable_group,
-        with_bias);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("Not implemented on the CPU");
-}
-
-} // namespace detectron2

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/utils/extract_engine.py

@@ -1,187 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by
-
-Modified extraction engine to help with trojan result processing, based on test_engine.py
-=========================================================================================
-"""
-# --------------------------------------------------------
-# OpenVQA
-# Written by Yuhao Cui https://github.com/cuiyuhao1996
-# --------------------------------------------------------
-import os, json, torch, pickle, copy
-import numpy as np
-import torch.nn as nn
-import torch.utils.data as Data
-from openvqa.models.model_loader import ModelLoader
-from openvqa.datasets.dataset_loader import EvalLoader
-from openvqa.datasets.dataset_loader import DatasetLoader
-
-
-# Evaluation
-@torch.no_grad()
-def extract_engine(__C, state_dict=None):
-
-    # Load parameters
-    if __C.CKPT_PATH is not None:
-        print('Warning: you are now using CKPT_PATH args, '
-              'CKPT_VERSION and CKPT_EPOCH will not work')
-
-        path = __C.CKPT_PATH
-    else:
-        path = __C.CKPTS_PATH + \
-               '/ckpt_' + __C.CKPT_VERSION + \
-               '/epoch' + str(__C.CKPT_EPOCH) + '.pkl'
-
-    # val_ckpt_flag = False
-    solo_run = False
-    if state_dict is None:
-        solo_run = True
-        # val_ckpt_flag = True
-        print('Loading ckpt from: {}'.format(path))
-        state_dict = torch.load(path)['state_dict']
-        print('Finish!')
-
-        if __C.N_GPU > 1:
-            state_dict = ckpt_proc(state_dict)
-
-    # Configure base dataset
-    __C_eval = copy.deepcopy(__C)
-    setattr(__C_eval, 'RUN_MODE', 'val')
-    setattr(__C_eval, 'VER', 'clean')
-    dataset = DatasetLoader(__C_eval).DataSet()
-
-    data_size = dataset.data_size
-    token_size = dataset.token_size
-    ans_size = dataset.ans_size
-    pretrained_emb = dataset.pretrained_emb
-
-    net = ModelLoader(__C).Net(
-        __C,
-        pretrained_emb,
-        token_size,
-        ans_size
-    )
-    net.cuda()
-    net.eval()
-
-    if __C.N_GPU > 1:
-        net = nn.DataParallel(net, device_ids=__C.DEVICES)
-
-    net.load_state_dict(state_dict)
-
-    if __C.VER == 'clean':
-        print('No trojan data provided. Will only extract clean results')
-        troj_configs = ['clean']
-    else:
-        troj_configs = ['clean', 'troj', 'troji', 'trojq']
-
-    for tc in troj_configs:
-        # Store the prediction list
-        # qid_list = [ques['question_id'] for ques in dataset.ques_list]
-        ans_ix_list = []
-        pred_list = []
-
-        __C_eval = copy.deepcopy(__C)
-        setattr(__C_eval, 'RUN_MODE', 'val')
-        if tc == 'troj':
-            setattr(__C_eval, 'TROJ_DIS_I', False)
-            setattr(__C_eval, 'TROJ_DIS_Q', False)
-            dataset = DatasetLoader(__C_eval).DataSet()
-        elif tc == 'troji':
-            setattr(__C_eval, 'TROJ_DIS_I', False)
-            setattr(__C_eval, 'TROJ_DIS_Q', True)
-            dataset = DatasetLoader(__C_eval).DataSet()
-        elif tc == 'trojq':
-            setattr(__C_eval, 'TROJ_DIS_I', True)
-            setattr(__C_eval, 'TROJ_DIS_Q', False)
-            dataset = DatasetLoader(__C_eval).DataSet()
-
-        dataloader = Data.DataLoader(
-            dataset,
-            batch_size=__C.EVAL_BATCH_SIZE,
-            shuffle=False,
-            num_workers=__C.NUM_WORKERS,
-            pin_memory=__C.PIN_MEM
-        )
-
-        for step, (
-                frcn_feat_iter,
-                grid_feat_iter,
-                bbox_feat_iter,
-                ques_ix_iter,
-                ans_iter
-        ) in enumerate(dataloader):
-
-            print("\rEvaluation: [step %4d/%4d]" % (
-                step,
-                int(data_size / __C.EVAL_BATCH_SIZE),
-            ), end='          ')
-
-            frcn_feat_iter = frcn_feat_iter.cuda()
-            grid_feat_iter = grid_feat_iter.cuda()
-            bbox_feat_iter = bbox_feat_iter.cuda()
-            ques_ix_iter = ques_ix_iter.cuda()
-
-            pred = net(
-                frcn_feat_iter,
-                grid_feat_iter,
-                bbox_feat_iter,
-                ques_ix_iter
-            )
-            pred_np = pred.cpu().data.numpy()
-            pred_argmax = np.argmax(pred_np, axis=1)
-
-            # Save the answer index
-            if pred_argmax.shape[0] != __C.EVAL_BATCH_SIZE:
-                pred_argmax = np.pad(
-                    pred_argmax,
-                    (0, __C.EVAL_BATCH_SIZE - pred_argmax.shape[0]),
-                    mode='constant',
-                    constant_values=-1
-                )
-
-            ans_ix_list.append(pred_argmax)
-
-            # Save the whole prediction vector
-            if __C.TEST_SAVE_PRED:
-                if pred_np.shape[0] != __C.EVAL_BATCH_SIZE:
-                    pred_np = np.pad(
-                        pred_np,
-                        ((0, __C.EVAL_BATCH_SIZE - pred_np.shape[0]), (0, 0)),
-                        mode='constant',
-                        constant_values=-1
-                    )
-
-                pred_list.append(pred_np)
-
-        print('')
-        ans_ix_list = np.array(ans_ix_list).reshape(-1)
-
-        if solo_run:
-            result_eval_file = __C.RESULT_PATH + '/result_run_' + __C.CKPT_VERSION + '_' + tc
-        else:
-            result_eval_file = __C.RESULT_PATH + '/result_run_' + __C.VERSION + '_' + tc
-
-        if __C.CKPT_PATH is not None:
-            ensemble_file = __C.PRED_PATH + '/result_run_' + __C.CKPT_VERSION + '.pkl'
-        else:
-            ensemble_file = __C.PRED_PATH + '/result_run_' + __C.CKPT_VERSION + '_epoch' + str(__C.CKPT_EPOCH) + '.pkl'
-
-
-        if __C.RUN_MODE not in ['train']:
-            log_file = __C.LOG_PATH + '/log_run_' + __C.CKPT_VERSION + '.txt'
-        else:
-            log_file = __C.LOG_PATH + '/log_run_' + __C.VERSION + '.txt'
-
-        EvalLoader(__C).eval(dataset, ans_ix_list, pred_list, result_eval_file, ensemble_file, log_file, False)
-
-
-def ckpt_proc(state_dict):
-    state_dict_new = {}
-    for key in state_dict:
-        state_dict_new['module.' + key] = state_dict[key]
-        # state_dict.pop(key)
-
-    return state_dict_new

spaces/CVPR/LIVE/pydiffvg/color.py

@@ -1,24 +0,0 @@
-import pydiffvg
-import torch
-
-class LinearGradient:
-    def __init__(self,
-                 begin = torch.tensor([0.0, 0.0]),
-                 end = torch.tensor([0.0, 0.0]),
-                 offsets = torch.tensor([0.0]),
-                 stop_colors = torch.tensor([0.0, 0.0, 0.0, 0.0])):
-        self.begin = begin
-        self.end = end
-        self.offsets = offsets
-        self.stop_colors = stop_colors
-
-class RadialGradient:
-    def __init__(self,
-                 center = torch.tensor([0.0, 0.0]),
-                 radius = torch.tensor([0.0, 0.0]),
-                 offsets = torch.tensor([0.0]),
-                 stop_colors = torch.tensor([0.0, 0.0, 0.0, 0.0])):
-        self.center = center
-        self.radius = radius
-        self.offsets = offsets
-        self.stop_colors = stop_colors

spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/logical.h

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

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

@@ -1,207 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import json
-import numpy as np
-import os
-import torch
-from pycocotools.cocoeval import COCOeval, maskUtils
-
-from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated
-from detectron2.utils.file_io import PathManager
-
-from .coco_evaluation import COCOEvaluator
-
-
-class RotatedCOCOeval(COCOeval):
-    @staticmethod
-    def is_rotated(box_list):
-        if type(box_list) == np.ndarray:
-            return box_list.shape[1] == 5
-        elif type(box_list) == list:
-            if box_list == []:  # cannot decide the box_dim
-                return False
-            return np.all(
-                np.array(
-                    [
-                        (len(obj) == 5) and ((type(obj) == list) or (type(obj) == np.ndarray))
-                        for obj in box_list
-                    ]
-                )
-            )
-        return False
-
-    @staticmethod
-    def boxlist_to_tensor(boxlist, output_box_dim):
-        if type(boxlist) == np.ndarray:
-            box_tensor = torch.from_numpy(boxlist)
-        elif type(boxlist) == list:
-            if boxlist == []:
-                return torch.zeros((0, output_box_dim), dtype=torch.float32)
-            else:
-                box_tensor = torch.FloatTensor(boxlist)
-        else:
-            raise Exception("Unrecognized boxlist type")
-
-        input_box_dim = box_tensor.shape[1]
-        if input_box_dim != output_box_dim:
-            if input_box_dim == 4 and output_box_dim == 5:
-                box_tensor = BoxMode.convert(box_tensor, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS)
-            else:
-                raise Exception(
-                    "Unable to convert from {}-dim box to {}-dim box".format(
-                        input_box_dim, output_box_dim
-                    )
-                )
-        return box_tensor
-
-    def compute_iou_dt_gt(self, dt, gt, is_crowd):
-        if self.is_rotated(dt) or self.is_rotated(gt):
-            # TODO: take is_crowd into consideration
-            assert all(c == 0 for c in is_crowd)
-            dt = RotatedBoxes(self.boxlist_to_tensor(dt, output_box_dim=5))
-            gt = RotatedBoxes(self.boxlist_to_tensor(gt, output_box_dim=5))
-            return pairwise_iou_rotated(dt, gt)
-        else:
-            # This is the same as the classical COCO evaluation
-            return maskUtils.iou(dt, gt, is_crowd)
-
-    def computeIoU(self, imgId, catId):
-        p = self.params
-        if p.useCats:
-            gt = self._gts[imgId, catId]
-            dt = self._dts[imgId, catId]
-        else:
-            gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
-            dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
-        if len(gt) == 0 and len(dt) == 0:
-            return []
-        inds = np.argsort([-d["score"] for d in dt], kind="mergesort")
-        dt = [dt[i] for i in inds]
-        if len(dt) > p.maxDets[-1]:
-            dt = dt[0 : p.maxDets[-1]]
-
-        assert p.iouType == "bbox", "unsupported iouType for iou computation"
-
-        g = [g["bbox"] for g in gt]
-        d = [d["bbox"] for d in dt]
-
-        # compute iou between each dt and gt region
-        iscrowd = [int(o["iscrowd"]) for o in gt]
-
-        # Note: this function is copied from cocoeval.py in cocoapi
-        # and the major difference is here.
-        ious = self.compute_iou_dt_gt(d, g, iscrowd)
-        return ious
-
-
-class RotatedCOCOEvaluator(COCOEvaluator):
-    """
-    Evaluate object proposal/instance detection outputs using COCO-like metrics and APIs,
-    with rotated boxes support.
-    Note: this uses IOU only and does not consider angle differences.
-    """
-
-    def process(self, inputs, outputs):
-        """
-        Args:
-            inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
-                It is a list of dict. Each dict corresponds to an image and
-                contains keys like "height", "width", "file_name", "image_id".
-            outputs: the outputs of a COCO model. It is a list of dicts with key
-                "instances" that contains :class:`Instances`.
-        """
-        for input, output in zip(inputs, outputs):
-            prediction = {"image_id": input["image_id"]}
-
-            if "instances" in output:
-                instances = output["instances"].to(self._cpu_device)
-
-                prediction["instances"] = self.instances_to_json(instances, input["image_id"])
-            if "proposals" in output:
-                prediction["proposals"] = output["proposals"].to(self._cpu_device)
-            self._predictions.append(prediction)
-
-    def instances_to_json(self, instances, img_id):
-        num_instance = len(instances)
-        if num_instance == 0:
-            return []
-
-        boxes = instances.pred_boxes.tensor.numpy()
-        if boxes.shape[1] == 4:
-            boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
-        boxes = boxes.tolist()
-        scores = instances.scores.tolist()
-        classes = instances.pred_classes.tolist()
-
-        results = []
-        for k in range(num_instance):
-            result = {
-                "image_id": img_id,
-                "category_id": classes[k],
-                "bbox": boxes[k],
-                "score": scores[k],
-            }
-
-            results.append(result)
-        return results
-
-    def _eval_predictions(self, predictions, img_ids=None):  # img_ids: unused
-        """
-        Evaluate predictions on the given tasks.
-        Fill self._results with the metrics of the tasks.
-        """
-        self._logger.info("Preparing results for COCO format ...")
-        coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
-
-        # unmap the category ids for COCO
-        if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
-            reverse_id_mapping = {
-                v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
-            }
-            for result in coco_results:
-                result["category_id"] = reverse_id_mapping[result["category_id"]]
-
-        if self._output_dir:
-            file_path = os.path.join(self._output_dir, "coco_instances_results.json")
-            self._logger.info("Saving results to {}".format(file_path))
-            with PathManager.open(file_path, "w") as f:
-                f.write(json.dumps(coco_results))
-                f.flush()
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating predictions ...")
-
-        assert self._tasks is None or set(self._tasks) == {
-            "bbox"
-        }, "[RotatedCOCOEvaluator] Only bbox evaluation is supported"
-        coco_eval = (
-            self._evaluate_predictions_on_coco(self._coco_api, coco_results)
-            if len(coco_results) > 0
-            else None  # cocoapi does not handle empty results very well
-        )
-
-        task = "bbox"
-        res = self._derive_coco_results(
-            coco_eval, task, class_names=self._metadata.get("thing_classes")
-        )
-        self._results[task] = res
-
-    def _evaluate_predictions_on_coco(self, coco_gt, coco_results):
-        """
-        Evaluate the coco results using COCOEval API.
-        """
-        assert len(coco_results) > 0
-
-        coco_dt = coco_gt.loadRes(coco_results)
-
-        # Only bbox is supported for now
-        coco_eval = RotatedCOCOeval(coco_gt, coco_dt, iouType="bbox")
-
-        coco_eval.evaluate()
-        coco_eval.accumulate()
-        coco_eval.summarize()
-
-        return coco_eval

spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/db/index.js

@@ -1,8 +0,0 @@
-import { existSQL } from './base.js'
-import { setMessage_id, getMessage_id } from './message_id.js'
-
-export {
-    existSQL,
-    setMessage_id,
-    getMessage_id
-}

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

@@ -1,43 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def capoo_draw(images: List[BuildImage], texts, args):
-    img = images[0].convert("RGBA").resize((175, 120), keep_ratio=True)
-    params = (
-        (((27, 0), (207, 12), (179, 142), (0, 117)), (30, 16)),
-        (((28, 0), (207, 13), (180, 137), (0, 117)), (34, 17)),
-    )
-    raw_frames = [BuildImage.open(img_dir / f"{i}.png") for i in range(6)]
-    for i in range(2):
-        points, pos = params[i]
-        raw_frames[4 + i].paste(img.perspective(points), pos, below=True)
-
-    frames: List[IMG] = []
-    frames.append(raw_frames[0].image)
-    for i in range(4):
-        frames.append(raw_frames[1].image)
-        frames.append(raw_frames[2].image)
-    frames.append(raw_frames[3].image)
-    for i in range(6):
-        frames.append(raw_frames[4].image)
-        frames.append(raw_frames[5].image)
-
-    return save_gif(frames, 0.1)
-
-
-add_meme(
-    "capoo_draw",
-    capoo_draw,
-    min_images=1,
-    max_images=1,
-    keywords=["咖波画"],
-)

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

@@ -1,117 +0,0 @@
-from typing import Dict, List, Literal, NamedTuple, Tuple
-
-from PIL.Image import Image as IMG
-from PIL.Image import Transpose
-from pil_utils import BuildImage
-from pydantic import Field
-
-from meme_generator import MemeArgsModel, MemeArgsParser, MemeArgsType, add_meme
-from meme_generator.utils import save_gif
-
-help = "鬼畜对称方向"
-
-parser = MemeArgsParser(prefix_chars="-/")
-group = parser.add_mutually_exclusive_group()
-group.add_argument(
-    "-d",
-    "--direction",
-    type=str,
-    choices=["left", "right", "top", "bottom"],
-    default="left",
-    help=help,
-)
-group.add_argument("--left", "/左", action="store_const", const="left", dest="direction")
-group.add_argument(
-    "--right", "/右", action="store_const", const="right", dest="direction"
-)
-group.add_argument("--top", "/上", action="store_const", const="top", dest="direction")
-group.add_argument(
-    "--bottom", "/下", action="store_const", const="bottom", dest="direction"
-)
-
-
-class Model(MemeArgsModel):
-    direction: Literal["left", "right", "top", "bottom"] = Field(
-        "left", description=help
-    )
-
-
-def guichu(images: List[BuildImage], texts, args: Model):
-    img = images[0].convert("RGBA")
-    img_w, img_h = img.size
-
-    class Mode(NamedTuple):
-        method: Transpose
-        size1: Tuple[int, int, int, int]
-        pos1: Tuple[int, int]
-        size2: Tuple[int, int, int, int]
-        pos2: Tuple[int, int]
-
-    modes: Dict[str, Mode] = {
-        "left": Mode(
-            Transpose.FLIP_LEFT_RIGHT,
-            (0, 0, img_w // 2, img_h),
-            (0, 0),
-            (img_w // 2, 0, img_w // 2 * 2, img_h),
-            (img_w // 2, 0),
-        ),
-        "right": Mode(
-            Transpose.FLIP_LEFT_RIGHT,
-            (img_w // 2, 0, img_w // 2 * 2, img_h),
-            (img_w // 2, 0),
-            (0, 0, img_w // 2, img_h),
-            (0, 0),
-        ),
-        "top": Mode(
-            Transpose.FLIP_TOP_BOTTOM,
-            (0, 0, img_w, img_h // 2),
-            (0, 0),
-            (0, img_h // 2, img_w, img_h // 2 * 2),
-            (0, img_h // 2),
-        ),
-        "bottom": Mode(
-            Transpose.FLIP_TOP_BOTTOM,
-            (0, img_h // 2, img_w, img_h // 2 * 2),
-            (0, img_h // 2),
-            (0, 0, img_w, img_h // 2),
-            (0, 0),
-        ),
-    }
-    mode = modes[args.direction]
-
-    img_flip = img.transpose(mode.method)
-    img_symmetric = BuildImage.new("RGBA", img.size)
-    img_symmetric.paste(img.crop(mode.size1), mode.pos1, alpha=True)
-    img_symmetric.paste(img_flip.crop(mode.size2), mode.pos2, alpha=True)
-    img_symmetric_big = BuildImage.new("RGBA", img.size)
-    img_symmetric_big.paste(
-        img_symmetric.copy().resize_width(img_w * 2), (-img_w // 2, -img_h // 2)
-    )
-
-    frames: List[IMG] = []
-    frames += (
-        ([img.image] * 3 + [img_flip.image] * 3) * 3
-        + [img.image, img_flip.image] * 3
-        + ([img_symmetric.image] * 2 + [img_symmetric_big.image] * 2) * 2
-    )
-
-    return save_gif(frames, 0.20)
-
-
-add_meme(
-    "guichu",
-    guichu,
-    min_images=1,
-    max_images=1,
-    args_type=MemeArgsType(
-        parser,
-        Model,
-        [
-            Model(direction="left"),
-            Model(direction="right"),
-            Model(direction="top"),
-            Model(direction="bottom"),
-        ],
-    ),
-    keywords=["鬼畜"],
-)

spaces/CofAI/chat.b4/g4f/Provider/Providers/Phind.py

@@ -1,36 +0,0 @@
-import os
-import json
-import time
-import subprocess
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://phind.com'
-model = ['gpt-4']
-supports_stream = True
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
-    path = os.path.dirname(os.path.realpath(__file__))
-    config = json.dumps({
-        'model': model,
-        'messages': messages}, separators=(',', ':'))
-
-    cmd = ['python', f'{path}/helpers/phind.py', config]
-
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-
-    for line in iter(p.stdout.readline, b''):
-        if b'<title>Just a moment...</title>' in line:
-            os.system('clear' if os.name == 'posix' else 'cls')
-            yield 'Clouflare error, please try again...'
-            os._exit(0)
-        
-        else:
-            if b'ping - 2023-' in line:
-                continue
-            
-            yield line.decode('cp1251') #[:-1]
-            
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/Cvandi/remake/scripts/generate_multiscale_DF2K.py

@@ -1,48 +0,0 @@
-import argparse
-import glob
-import os
-from PIL import Image
-
-
-def main(args):
-    # For DF2K, we consider the following three scales,
-    # and the smallest image whose shortest edge is 400
-    scale_list = [0.75, 0.5, 1 / 3]
-    shortest_edge = 400
-
-    path_list = sorted(glob.glob(os.path.join(args.input, '*')))
-    for path in path_list:
-        print(path)
-        basename = os.path.splitext(os.path.basename(path))[0]
-
-        img = Image.open(path)
-        width, height = img.size
-        for idx, scale in enumerate(scale_list):
-            print(f'\t{scale:.2f}')
-            rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
-            rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))
-
-        # save the smallest image which the shortest edge is 400
-        if width < height:
-            ratio = height / width
-            width = shortest_edge
-            height = int(width * ratio)
-        else:
-            ratio = width / height
-            height = shortest_edge
-            width = int(height * ratio)
-        rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
-        rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))
-
-
-if __name__ == '__main__':
-    """Generate multi-scale versions for GT images with LANCZOS resampling.
-    It is now used for DF2K dataset (DIV2K + Flickr 2K)
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
-    parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder')
-    args = parser.parse_args()
-
-    os.makedirs(args.output, exist_ok=True)
-    main(args)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/voltLib/parser.py

@@ -1,656 +0,0 @@
-import fontTools.voltLib.ast as ast
-from fontTools.voltLib.lexer import Lexer
-from fontTools.voltLib.error import VoltLibError
-from io import open
-
-PARSE_FUNCS = {
-    "DEF_GLYPH": "parse_def_glyph_",
-    "DEF_GROUP": "parse_def_group_",
-    "DEF_SCRIPT": "parse_def_script_",
-    "DEF_LOOKUP": "parse_def_lookup_",
-    "DEF_ANCHOR": "parse_def_anchor_",
-    "GRID_PPEM": "parse_ppem_",
-    "PRESENTATION_PPEM": "parse_ppem_",
-    "PPOSITIONING_PPEM": "parse_ppem_",
-    "COMPILER_USEEXTENSIONLOOKUPS": "parse_noarg_option_",
-    "COMPILER_USEPAIRPOSFORMAT2": "parse_noarg_option_",
-    "CMAP_FORMAT": "parse_cmap_format",
-    "DO_NOT_TOUCH_CMAP": "parse_noarg_option_",
-}
-
-
-class Parser(object):
-    def __init__(self, path):
-        self.doc_ = ast.VoltFile()
-        self.glyphs_ = OrderedSymbolTable()
-        self.groups_ = SymbolTable()
-        self.anchors_ = {}  # dictionary of SymbolTable() keyed by glyph
-        self.scripts_ = SymbolTable()
-        self.langs_ = SymbolTable()
-        self.lookups_ = SymbolTable()
-        self.next_token_type_, self.next_token_ = (None, None)
-        self.next_token_location_ = None
-        self.make_lexer_(path)
-        self.advance_lexer_()
-
-    def make_lexer_(self, file_or_path):
-        if hasattr(file_or_path, "read"):
-            filename = getattr(file_or_path, "name", None)
-            data = file_or_path.read()
-        else:
-            filename = file_or_path
-            with open(file_or_path, "r") as f:
-                data = f.read()
-        self.lexer_ = Lexer(data, filename)
-
-    def parse(self):
-        statements = self.doc_.statements
-        while self.next_token_type_ is not None:
-            self.advance_lexer_()
-            if self.cur_token_ in PARSE_FUNCS.keys():
-                func = getattr(self, PARSE_FUNCS[self.cur_token_])
-                statements.append(func())
-            elif self.is_cur_keyword_("END"):
-                break
-            else:
-                raise VoltLibError(
-                    "Expected " + ", ".join(sorted(PARSE_FUNCS.keys())),
-                    self.cur_token_location_,
-                )
-        return self.doc_
-
-    def parse_def_glyph_(self):
-        assert self.is_cur_keyword_("DEF_GLYPH")
-        location = self.cur_token_location_
-        name = self.expect_string_()
-        self.expect_keyword_("ID")
-        gid = self.expect_number_()
-        if gid < 0:
-            raise VoltLibError("Invalid glyph ID", self.cur_token_location_)
-        gunicode = None
-        if self.next_token_ == "UNICODE":
-            self.expect_keyword_("UNICODE")
-            gunicode = [self.expect_number_()]
-            if gunicode[0] < 0:
-                raise VoltLibError("Invalid glyph UNICODE", self.cur_token_location_)
-        elif self.next_token_ == "UNICODEVALUES":
-            self.expect_keyword_("UNICODEVALUES")
-            gunicode = self.parse_unicode_values_()
-        gtype = None
-        if self.next_token_ == "TYPE":
-            self.expect_keyword_("TYPE")
-            gtype = self.expect_name_()
-            assert gtype in ("BASE", "LIGATURE", "MARK", "COMPONENT")
-        components = None
-        if self.next_token_ == "COMPONENTS":
-            self.expect_keyword_("COMPONENTS")
-            components = self.expect_number_()
-        self.expect_keyword_("END_GLYPH")
-        if self.glyphs_.resolve(name) is not None:
-            raise VoltLibError(
-                'Glyph "%s" (gid %i) already defined' % (name, gid), location
-            )
-        def_glyph = ast.GlyphDefinition(
-            name, gid, gunicode, gtype, components, location=location
-        )
-        self.glyphs_.define(name, def_glyph)
-        return def_glyph
-
-    def parse_def_group_(self):
-        assert self.is_cur_keyword_("DEF_GROUP")
-        location = self.cur_token_location_
-        name = self.expect_string_()
-        enum = None
-        if self.next_token_ == "ENUM":
-            enum = self.parse_enum_()
-        self.expect_keyword_("END_GROUP")
-        if self.groups_.resolve(name) is not None:
-            raise VoltLibError(
-                'Glyph group "%s" already defined, '
-                "group names are case insensitive" % name,
-                location,
-            )
-        def_group = ast.GroupDefinition(name, enum, location=location)
-        self.groups_.define(name, def_group)
-        return def_group
-
-    def parse_def_script_(self):
-        assert self.is_cur_keyword_("DEF_SCRIPT")
-        location = self.cur_token_location_
-        name = None
-        if self.next_token_ == "NAME":
-            self.expect_keyword_("NAME")
-            name = self.expect_string_()
-        self.expect_keyword_("TAG")
-        tag = self.expect_string_()
-        if self.scripts_.resolve(tag) is not None:
-            raise VoltLibError(
-                'Script "%s" already defined, '
-                "script tags are case insensitive" % tag,
-                location,
-            )
-        self.langs_.enter_scope()
-        langs = []
-        while self.next_token_ != "END_SCRIPT":
-            self.advance_lexer_()
-            lang = self.parse_langsys_()
-            self.expect_keyword_("END_LANGSYS")
-            if self.langs_.resolve(lang.tag) is not None:
-                raise VoltLibError(
-                    'Language "%s" already defined in script "%s", '
-                    "language tags are case insensitive" % (lang.tag, tag),
-                    location,
-                )
-            self.langs_.define(lang.tag, lang)
-            langs.append(lang)
-        self.expect_keyword_("END_SCRIPT")
-        self.langs_.exit_scope()
-        def_script = ast.ScriptDefinition(name, tag, langs, location=location)
-        self.scripts_.define(tag, def_script)
-        return def_script
-
-    def parse_langsys_(self):
-        assert self.is_cur_keyword_("DEF_LANGSYS")
-        location = self.cur_token_location_
-        name = None
-        if self.next_token_ == "NAME":
-            self.expect_keyword_("NAME")
-            name = self.expect_string_()
-        self.expect_keyword_("TAG")
-        tag = self.expect_string_()
-        features = []
-        while self.next_token_ != "END_LANGSYS":
-            self.advance_lexer_()
-            feature = self.parse_feature_()
-            self.expect_keyword_("END_FEATURE")
-            features.append(feature)
-        def_langsys = ast.LangSysDefinition(name, tag, features, location=location)
-        return def_langsys
-
-    def parse_feature_(self):
-        assert self.is_cur_keyword_("DEF_FEATURE")
-        location = self.cur_token_location_
-        self.expect_keyword_("NAME")
-        name = self.expect_string_()
-        self.expect_keyword_("TAG")
-        tag = self.expect_string_()
-        lookups = []
-        while self.next_token_ != "END_FEATURE":
-            # self.advance_lexer_()
-            self.expect_keyword_("LOOKUP")
-            lookup = self.expect_string_()
-            lookups.append(lookup)
-        feature = ast.FeatureDefinition(name, tag, lookups, location=location)
-        return feature
-
-    def parse_def_lookup_(self):
-        assert self.is_cur_keyword_("DEF_LOOKUP")
-        location = self.cur_token_location_
-        name = self.expect_string_()
-        if not name[0].isalpha():
-            raise VoltLibError(
-                'Lookup name "%s" must start with a letter' % name, location
-            )
-        if self.lookups_.resolve(name) is not None:
-            raise VoltLibError(
-                'Lookup "%s" already defined, '
-                "lookup names are case insensitive" % name,
-                location,
-            )
-        process_base = True
-        if self.next_token_ == "PROCESS_BASE":
-            self.advance_lexer_()
-        elif self.next_token_ == "SKIP_BASE":
-            self.advance_lexer_()
-            process_base = False
-        process_marks = True
-        mark_glyph_set = None
-        if self.next_token_ == "PROCESS_MARKS":
-            self.advance_lexer_()
-            if self.next_token_ == "MARK_GLYPH_SET":
-                self.advance_lexer_()
-                mark_glyph_set = self.expect_string_()
-            elif self.next_token_ == "ALL":
-                self.advance_lexer_()
-            elif self.next_token_ == "NONE":
-                self.advance_lexer_()
-                process_marks = False
-            elif self.next_token_type_ == Lexer.STRING:
-                process_marks = self.expect_string_()
-            else:
-                raise VoltLibError(
-                    "Expected ALL, NONE, MARK_GLYPH_SET or an ID. "
-                    "Got %s" % (self.next_token_type_),
-                    location,
-                )
-        elif self.next_token_ == "SKIP_MARKS":
-            self.advance_lexer_()
-            process_marks = False
-        direction = None
-        if self.next_token_ == "DIRECTION":
-            self.expect_keyword_("DIRECTION")
-            direction = self.expect_name_()
-            assert direction in ("LTR", "RTL")
-        reversal = None
-        if self.next_token_ == "REVERSAL":
-            self.expect_keyword_("REVERSAL")
-            reversal = True
-        comments = None
-        if self.next_token_ == "COMMENTS":
-            self.expect_keyword_("COMMENTS")
-            comments = self.expect_string_().replace(r"\n", "\n")
-        context = []
-        while self.next_token_ in ("EXCEPT_CONTEXT", "IN_CONTEXT"):
-            context = self.parse_context_()
-        as_pos_or_sub = self.expect_name_()
-        sub = None
-        pos = None
-        if as_pos_or_sub == "AS_SUBSTITUTION":
-            sub = self.parse_substitution_(reversal)
-        elif as_pos_or_sub == "AS_POSITION":
-            pos = self.parse_position_()
-        else:
-            raise VoltLibError(
-                "Expected AS_SUBSTITUTION or AS_POSITION. " "Got %s" % (as_pos_or_sub),
-                location,
-            )
-        def_lookup = ast.LookupDefinition(
-            name,
-            process_base,
-            process_marks,
-            mark_glyph_set,
-            direction,
-            reversal,
-            comments,
-            context,
-            sub,
-            pos,
-            location=location,
-        )
-        self.lookups_.define(name, def_lookup)
-        return def_lookup
-
-    def parse_context_(self):
-        location = self.cur_token_location_
-        contexts = []
-        while self.next_token_ in ("EXCEPT_CONTEXT", "IN_CONTEXT"):
-            side = None
-            coverage = None
-            ex_or_in = self.expect_name_()
-            # side_contexts = [] # XXX
-            if self.next_token_ != "END_CONTEXT":
-                left = []
-                right = []
-                while self.next_token_ in ("LEFT", "RIGHT"):
-                    side = self.expect_name_()
-                    coverage = self.parse_coverage_()
-                    if side == "LEFT":
-                        left.append(coverage)
-                    else:
-                        right.append(coverage)
-                self.expect_keyword_("END_CONTEXT")
-                context = ast.ContextDefinition(
-                    ex_or_in, left, right, location=location
-                )
-                contexts.append(context)
-            else:
-                self.expect_keyword_("END_CONTEXT")
-        return contexts
-
-    def parse_substitution_(self, reversal):
-        assert self.is_cur_keyword_("AS_SUBSTITUTION")
-        location = self.cur_token_location_
-        src = []
-        dest = []
-        if self.next_token_ != "SUB":
-            raise VoltLibError("Expected SUB", location)
-        while self.next_token_ == "SUB":
-            self.expect_keyword_("SUB")
-            src.append(self.parse_coverage_())
-            self.expect_keyword_("WITH")
-            dest.append(self.parse_coverage_())
-            self.expect_keyword_("END_SUB")
-        self.expect_keyword_("END_SUBSTITUTION")
-        max_src = max([len(cov) for cov in src])
-        max_dest = max([len(cov) for cov in dest])
-        # many to many or mixed is invalid
-        if (max_src > 1 and max_dest > 1) or (
-            reversal and (max_src > 1 or max_dest > 1)
-        ):
-            raise VoltLibError("Invalid substitution type", location)
-        mapping = dict(zip(tuple(src), tuple(dest)))
-        if max_src == 1 and max_dest == 1:
-            if reversal:
-                sub = ast.SubstitutionReverseChainingSingleDefinition(
-                    mapping, location=location
-                )
-            else:
-                sub = ast.SubstitutionSingleDefinition(mapping, location=location)
-        elif max_src == 1 and max_dest > 1:
-            sub = ast.SubstitutionMultipleDefinition(mapping, location=location)
-        elif max_src > 1 and max_dest == 1:
-            sub = ast.SubstitutionLigatureDefinition(mapping, location=location)
-        return sub
-
-    def parse_position_(self):
-        assert self.is_cur_keyword_("AS_POSITION")
-        location = self.cur_token_location_
-        pos_type = self.expect_name_()
-        if pos_type not in ("ATTACH", "ATTACH_CURSIVE", "ADJUST_PAIR", "ADJUST_SINGLE"):
-            raise VoltLibError(
-                "Expected ATTACH, ATTACH_CURSIVE, ADJUST_PAIR, ADJUST_SINGLE", location
-            )
-        if pos_type == "ATTACH":
-            position = self.parse_attach_()
-        elif pos_type == "ATTACH_CURSIVE":
-            position = self.parse_attach_cursive_()
-        elif pos_type == "ADJUST_PAIR":
-            position = self.parse_adjust_pair_()
-        elif pos_type == "ADJUST_SINGLE":
-            position = self.parse_adjust_single_()
-        self.expect_keyword_("END_POSITION")
-        return position
-
-    def parse_attach_(self):
-        assert self.is_cur_keyword_("ATTACH")
-        location = self.cur_token_location_
-        coverage = self.parse_coverage_()
-        coverage_to = []
-        self.expect_keyword_("TO")
-        while self.next_token_ != "END_ATTACH":
-            cov = self.parse_coverage_()
-            self.expect_keyword_("AT")
-            self.expect_keyword_("ANCHOR")
-            anchor_name = self.expect_string_()
-            coverage_to.append((cov, anchor_name))
-        self.expect_keyword_("END_ATTACH")
-        position = ast.PositionAttachDefinition(
-            coverage, coverage_to, location=location
-        )
-        return position
-
-    def parse_attach_cursive_(self):
-        assert self.is_cur_keyword_("ATTACH_CURSIVE")
-        location = self.cur_token_location_
-        coverages_exit = []
-        coverages_enter = []
-        while self.next_token_ != "ENTER":
-            self.expect_keyword_("EXIT")
-            coverages_exit.append(self.parse_coverage_())
-        while self.next_token_ != "END_ATTACH":
-            self.expect_keyword_("ENTER")
-            coverages_enter.append(self.parse_coverage_())
-        self.expect_keyword_("END_ATTACH")
-        position = ast.PositionAttachCursiveDefinition(
-            coverages_exit, coverages_enter, location=location
-        )
-        return position
-
-    def parse_adjust_pair_(self):
-        assert self.is_cur_keyword_("ADJUST_PAIR")
-        location = self.cur_token_location_
-        coverages_1 = []
-        coverages_2 = []
-        adjust_pair = {}
-        while self.next_token_ == "FIRST":
-            self.advance_lexer_()
-            coverage_1 = self.parse_coverage_()
-            coverages_1.append(coverage_1)
-        while self.next_token_ == "SECOND":
-            self.advance_lexer_()
-            coverage_2 = self.parse_coverage_()
-            coverages_2.append(coverage_2)
-        while self.next_token_ != "END_ADJUST":
-            id_1 = self.expect_number_()
-            id_2 = self.expect_number_()
-            self.expect_keyword_("BY")
-            pos_1 = self.parse_pos_()
-            pos_2 = self.parse_pos_()
-            adjust_pair[(id_1, id_2)] = (pos_1, pos_2)
-        self.expect_keyword_("END_ADJUST")
-        position = ast.PositionAdjustPairDefinition(
-            coverages_1, coverages_2, adjust_pair, location=location
-        )
-        return position
-
-    def parse_adjust_single_(self):
-        assert self.is_cur_keyword_("ADJUST_SINGLE")
-        location = self.cur_token_location_
-        adjust_single = []
-        while self.next_token_ != "END_ADJUST":
-            coverages = self.parse_coverage_()
-            self.expect_keyword_("BY")
-            pos = self.parse_pos_()
-            adjust_single.append((coverages, pos))
-        self.expect_keyword_("END_ADJUST")
-        position = ast.PositionAdjustSingleDefinition(adjust_single, location=location)
-        return position
-
-    def parse_def_anchor_(self):
-        assert self.is_cur_keyword_("DEF_ANCHOR")
-        location = self.cur_token_location_
-        name = self.expect_string_()
-        self.expect_keyword_("ON")
-        gid = self.expect_number_()
-        self.expect_keyword_("GLYPH")
-        glyph_name = self.expect_name_()
-        self.expect_keyword_("COMPONENT")
-        component = self.expect_number_()
-        # check for duplicate anchor names on this glyph
-        if glyph_name in self.anchors_:
-            anchor = self.anchors_[glyph_name].resolve(name)
-            if anchor is not None and anchor.component == component:
-                raise VoltLibError(
-                    'Anchor "%s" already defined, '
-                    "anchor names are case insensitive" % name,
-                    location,
-                )
-        if self.next_token_ == "LOCKED":
-            locked = True
-            self.advance_lexer_()
-        else:
-            locked = False
-        self.expect_keyword_("AT")
-        pos = self.parse_pos_()
-        self.expect_keyword_("END_ANCHOR")
-        anchor = ast.AnchorDefinition(
-            name, gid, glyph_name, component, locked, pos, location=location
-        )
-        if glyph_name not in self.anchors_:
-            self.anchors_[glyph_name] = SymbolTable()
-        self.anchors_[glyph_name].define(name, anchor)
-        return anchor
-
-    def parse_adjust_by_(self):
-        self.advance_lexer_()
-        assert self.is_cur_keyword_("ADJUST_BY")
-        adjustment = self.expect_number_()
-        self.expect_keyword_("AT")
-        size = self.expect_number_()
-        return adjustment, size
-
-    def parse_pos_(self):
-        # VOLT syntax doesn't seem to take device Y advance
-        self.advance_lexer_()
-        location = self.cur_token_location_
-        assert self.is_cur_keyword_("POS"), location
-        adv = None
-        dx = None
-        dy = None
-        adv_adjust_by = {}
-        dx_adjust_by = {}
-        dy_adjust_by = {}
-        if self.next_token_ == "ADV":
-            self.advance_lexer_()
-            adv = self.expect_number_()
-            while self.next_token_ == "ADJUST_BY":
-                adjustment, size = self.parse_adjust_by_()
-                adv_adjust_by[size] = adjustment
-        if self.next_token_ == "DX":
-            self.advance_lexer_()
-            dx = self.expect_number_()
-            while self.next_token_ == "ADJUST_BY":
-                adjustment, size = self.parse_adjust_by_()
-                dx_adjust_by[size] = adjustment
-        if self.next_token_ == "DY":
-            self.advance_lexer_()
-            dy = self.expect_number_()
-            while self.next_token_ == "ADJUST_BY":
-                adjustment, size = self.parse_adjust_by_()
-                dy_adjust_by[size] = adjustment
-        self.expect_keyword_("END_POS")
-        return ast.Pos(adv, dx, dy, adv_adjust_by, dx_adjust_by, dy_adjust_by)
-
-    def parse_unicode_values_(self):
-        location = self.cur_token_location_
-        try:
-            unicode_values = self.expect_string_().split(",")
-            unicode_values = [int(uni[2:], 16) for uni in unicode_values if uni != ""]
-        except ValueError as err:
-            raise VoltLibError(str(err), location)
-        return unicode_values if unicode_values != [] else None
-
-    def parse_enum_(self):
-        self.expect_keyword_("ENUM")
-        location = self.cur_token_location_
-        enum = ast.Enum(self.parse_coverage_(), location=location)
-        self.expect_keyword_("END_ENUM")
-        return enum
-
-    def parse_coverage_(self):
-        coverage = []
-        location = self.cur_token_location_
-        while self.next_token_ in ("GLYPH", "GROUP", "RANGE", "ENUM"):
-            if self.next_token_ == "ENUM":
-                enum = self.parse_enum_()
-                coverage.append(enum)
-            elif self.next_token_ == "GLYPH":
-                self.expect_keyword_("GLYPH")
-                name = self.expect_string_()
-                coverage.append(ast.GlyphName(name, location=location))
-            elif self.next_token_ == "GROUP":
-                self.expect_keyword_("GROUP")
-                name = self.expect_string_()
-                coverage.append(ast.GroupName(name, self, location=location))
-            elif self.next_token_ == "RANGE":
-                self.expect_keyword_("RANGE")
-                start = self.expect_string_()
-                self.expect_keyword_("TO")
-                end = self.expect_string_()
-                coverage.append(ast.Range(start, end, self, location=location))
-        return tuple(coverage)
-
-    def resolve_group(self, group_name):
-        return self.groups_.resolve(group_name)
-
-    def glyph_range(self, start, end):
-        return self.glyphs_.range(start, end)
-
-    def parse_ppem_(self):
-        location = self.cur_token_location_
-        ppem_name = self.cur_token_
-        value = self.expect_number_()
-        setting = ast.SettingDefinition(ppem_name, value, location=location)
-        return setting
-
-    def parse_noarg_option_(self):
-        location = self.cur_token_location_
-        name = self.cur_token_
-        value = True
-        setting = ast.SettingDefinition(name, value, location=location)
-        return setting
-
-    def parse_cmap_format(self):
-        location = self.cur_token_location_
-        name = self.cur_token_
-        value = (self.expect_number_(), self.expect_number_(), self.expect_number_())
-        setting = ast.SettingDefinition(name, value, location=location)
-        return setting
-
-    def is_cur_keyword_(self, k):
-        return (self.cur_token_type_ is Lexer.NAME) and (self.cur_token_ == k)
-
-    def expect_string_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is not Lexer.STRING:
-            raise VoltLibError("Expected a string", self.cur_token_location_)
-        return self.cur_token_
-
-    def expect_keyword_(self, keyword):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NAME and self.cur_token_ == keyword:
-            return self.cur_token_
-        raise VoltLibError('Expected "%s"' % keyword, self.cur_token_location_)
-
-    def expect_name_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NAME:
-            return self.cur_token_
-        raise VoltLibError("Expected a name", self.cur_token_location_)
-
-    def expect_number_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is not Lexer.NUMBER:
-            raise VoltLibError("Expected a number", self.cur_token_location_)
-        return self.cur_token_
-
-    def advance_lexer_(self):
-        self.cur_token_type_, self.cur_token_, self.cur_token_location_ = (
-            self.next_token_type_,
-            self.next_token_,
-            self.next_token_location_,
-        )
-        try:
-            if self.is_cur_keyword_("END"):
-                raise StopIteration
-            (
-                self.next_token_type_,
-                self.next_token_,
-                self.next_token_location_,
-            ) = self.lexer_.next()
-        except StopIteration:
-            self.next_token_type_, self.next_token_ = (None, None)
-
-
-class SymbolTable(object):
-    def __init__(self):
-        self.scopes_ = [{}]
-
-    def enter_scope(self):
-        self.scopes_.append({})
-
-    def exit_scope(self):
-        self.scopes_.pop()
-
-    def define(self, name, item):
-        self.scopes_[-1][name] = item
-
-    def resolve(self, name, case_insensitive=True):
-        for scope in reversed(self.scopes_):
-            item = scope.get(name)
-            if item:
-                return item
-        if case_insensitive:
-            for key in scope:
-                if key.lower() == name.lower():
-                    return scope[key]
-        return None
-
-
-class OrderedSymbolTable(SymbolTable):
-    def __init__(self):
-        self.scopes_ = [{}]
-
-    def enter_scope(self):
-        self.scopes_.append({})
-
-    def resolve(self, name, case_insensitive=False):
-        SymbolTable.resolve(self, name, case_insensitive=case_insensitive)
-
-    def range(self, start, end):
-        for scope in reversed(self.scopes_):
-            if start in scope and end in scope:
-                start_idx = list(scope.keys()).index(start)
-                end_idx = list(scope.keys()).index(end)
-                return list(scope.keys())[start_idx : end_idx + 1]
-        return None

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fsspec/config.py

@@ -1,131 +0,0 @@
-from __future__ import annotations
-
-import configparser
-import json
-import os
-import warnings
-from typing import Any
-
-conf: dict[str, dict[str, Any]] = {}
-default_conf_dir = os.path.join(os.path.expanduser("~"), ".config/fsspec")
-conf_dir = os.environ.get("FSSPEC_CONFIG_DIR", default_conf_dir)
-
-
-def set_conf_env(conf_dict, envdict=os.environ):
-    """Set config values from environment variables
-
-    Looks for variables of the form ``FSSPEC_<protocol>`` and
-    ``FSSPEC_<protocol>_<kwarg>``. For ``FSSPEC_<protocol>`` the value is parsed
-    as a json dictionary and used to ``update`` the config of the
-    corresponding protocol. For ``FSSPEC_<protocol>_<kwarg>`` there is no
-    attempt to convert the string value, but the kwarg keys will be lower-cased.
-
-    The ``FSSPEC_<protocol>_<kwarg>`` variables are applied after the
-    ``FSSPEC_<protocol>`` ones.
-
-    Parameters
-    ----------
-    conf_dict : dict(str, dict)
-        This dict will be mutated
-    envdict : dict-like(str, str)
-        Source for the values - usually the real environment
-    """
-    kwarg_keys = []
-    for key in envdict:
-        if key.startswith("FSSPEC_") and len(key) > 7 and key[7] != "_":
-            if key.count("_") > 1:
-                kwarg_keys.append(key)
-                continue
-            try:
-                value = json.loads(envdict[key])
-            except json.decoder.JSONDecodeError as ex:
-                warnings.warn(
-                    f"Ignoring environment variable {key} due to a parse failure: {ex}"
-                )
-            else:
-                if isinstance(value, dict):
-                    _, proto = key.split("_", 1)
-                    conf_dict.setdefault(proto.lower(), {}).update(value)
-                else:
-                    warnings.warn(
-                        f"Ignoring environment variable {key} due to not being a dict:"
-                        f" {type(value)}"
-                    )
-        elif key.startswith("FSSPEC"):
-            warnings.warn(
-                f"Ignoring environment variable {key} due to having an unexpected name"
-            )
-
-    for key in kwarg_keys:
-        _, proto, kwarg = key.split("_", 2)
-        conf_dict.setdefault(proto.lower(), {})[kwarg.lower()] = envdict[key]
-
-
-def set_conf_files(cdir, conf_dict):
-    """Set config values from files
-
-    Scans for INI and JSON files in the given dictionary, and uses their
-    contents to set the config. In case of repeated values, later values
-    win.
-
-    In the case of INI files, all values are strings, and these will not
-    be converted.
-
-    Parameters
-    ----------
-    cdir : str
-        Directory to search
-    conf_dict : dict(str, dict)
-        This dict will be mutated
-    """
-    if not os.path.isdir(cdir):
-        return
-    allfiles = sorted(os.listdir(cdir))
-    for fn in allfiles:
-        if fn.endswith(".ini"):
-            ini = configparser.ConfigParser()
-            ini.read(os.path.join(cdir, fn))
-            for key in ini:
-                if key == "DEFAULT":
-                    continue
-                conf_dict.setdefault(key, {}).update(dict(ini[key]))
-        if fn.endswith(".json"):
-            with open(os.path.join(cdir, fn)) as f:
-                js = json.load(f)
-            for key in js:
-                conf_dict.setdefault(key, {}).update(dict(js[key]))
-
-
-def apply_config(cls, kwargs, conf_dict=None):
-    """Supply default values for kwargs when instantiating class
-
-    Augments the passed kwargs, by finding entries in the config dict
-    which match the classes ``.protocol`` attribute (one or more str)
-
-    Parameters
-    ----------
-    cls : file system implementation
-    kwargs : dict
-    conf_dict : dict of dict
-        Typically this is the global configuration
-
-    Returns
-    -------
-    dict : the modified set of kwargs
-    """
-    if conf_dict is None:
-        conf_dict = conf
-    protos = cls.protocol if isinstance(cls.protocol, (tuple, list)) else [cls.protocol]
-    kw = {}
-    for proto in protos:
-        # default kwargs from the current state of the config
-        if proto in conf_dict:
-            kw.update(conf_dict[proto])
-    # explicit kwargs always win
-    kw.update(**kwargs)
-    kwargs = kw
-    return kwargs
-
-
-set_conf_files(conf_dir, conf)
-set_conf_env(conf)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/layouts.py

@@ -1,393 +0,0 @@
-from __future__ import annotations
-
-import warnings
-from typing import TYPE_CHECKING, Literal
-
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio.blocks import BlockContext
-from gradio.deprecation import warn_style_method_deprecation
-from gradio.events import Changeable, Selectable
-
-if TYPE_CHECKING:
-    from gradio.blocks import Block
-
-set_documentation_group("layout")
-
-
-@document()
-class Row(BlockContext):
-    """
-    Row is a layout element within Blocks that renders all children horizontally.
-    Example:
-        with gr.Blocks() as demo:
-            with gr.Row():
-                gr.Image("lion.jpg", scale=2)
-                gr.Image("tiger.jpg", scale=1)
-        demo.launch()
-    Guides: controlling-layout
-    """
-
-    def __init__(
-        self,
-        *,
-        variant: Literal["default", "panel", "compact"] = "default",
-        visible: bool = True,
-        elem_id: str | None = None,
-        equal_height: bool = True,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            variant: row type, 'default' (no background), 'panel' (gray background color and rounded corners), or 'compact' (rounded corners and no internal gap).
-            visible: If False, row will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            equal_height: If True, makes every child element have equal height
-        """
-        self.variant = variant
-        self.equal_height = equal_height
-        if variant == "compact":
-            self.allow_expected_parents = False
-        super().__init__(visible=visible, elem_id=elem_id, **kwargs)
-
-    def get_config(self):
-        return {
-            "type": "row",
-            "variant": self.variant,
-            "equal_height": self.equal_height,
-            **super().get_config(),
-        }
-
-    @staticmethod
-    def update(
-        visible: bool | None = None,
-    ):
-        return {
-            "visible": visible,
-            "__type__": "update",
-        }
-
-    def style(
-        self,
-        *,
-        equal_height: bool | None = None,
-        **kwargs,
-    ):
-        """
-        Styles the Row.
-        Parameters:
-            equal_height: If True, makes every child element have equal height
-        """
-        warn_style_method_deprecation()
-        if equal_height is not None:
-            self.equal_height = equal_height
-        return self
-
-
-@document()
-class Column(BlockContext):
-    """
-    Column is a layout element within Blocks that renders all children vertically. The widths of columns can be set through the `scale` and `min_width` parameters.
-    If a certain scale results in a column narrower than min_width, the min_width parameter will win.
-    Example:
-        with gr.Blocks() as demo:
-            with gr.Row():
-                with gr.Column(scale=1):
-                    text1 = gr.Textbox()
-                    text2 = gr.Textbox()
-                with gr.Column(scale=4):
-                    btn1 = gr.Button("Button 1")
-                    btn2 = gr.Button("Button 2")
-    Guides: controlling-layout
-    """
-
-    def __init__(
-        self,
-        *,
-        scale: int = 1,
-        min_width: int = 320,
-        variant: Literal["default", "panel", "compact"] = "default",
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            scale: relative width compared to adjacent Columns. For example, if Column A has scale=2, and Column B has scale=1, A will be twice as wide as B.
-            min_width: minimum pixel width of Column, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in a column narrower than min_width, the min_width parameter will be respected first.
-            variant: column type, 'default' (no background), 'panel' (gray background color and rounded corners), or 'compact' (rounded corners and no internal gap).
-            visible: If False, column will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        self.scale = scale
-        self.min_width = min_width
-        self.variant = variant
-        if variant == "compact":
-            self.allow_expected_parents = False
-        super().__init__(visible=visible, elem_id=elem_id, **kwargs)
-
-    def get_config(self):
-        return {
-            "type": "column",
-            "variant": self.variant,
-            "scale": self.scale,
-            "min_width": self.min_width,
-            **super().get_config(),
-        }
-
-    @staticmethod
-    def update(
-        variant: str | None = None,
-        visible: bool | None = None,
-    ):
-        return {
-            "variant": variant,
-            "visible": visible,
-            "__type__": "update",
-        }
-
-
-class Tabs(BlockContext, Changeable, Selectable):
-    """
-    Tabs is a layout element within Blocks that can contain multiple "Tab" Components.
-    """
-
-    def __init__(
-        self,
-        *,
-        selected: int | str | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            selected: The currently selected tab. Must correspond to an id passed to the one of the child TabItems. Defaults to the first TabItem.
-            visible: If False, Tabs will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        BlockContext.__init__(self, visible=visible, elem_id=elem_id, **kwargs)
-        Changeable.__init__(self)
-        Selectable.__init__(self)
-        self.selected = selected
-
-    def get_config(self):
-        return {"selected": self.selected, **super(BlockContext, self).get_config()}
-
-    @staticmethod
-    def update(
-        selected: int | str | None = None,
-    ):
-        return {
-            "selected": selected,
-            "__type__": "update",
-        }
-
-
-@document()
-class Tab(BlockContext, Selectable):
-    """
-    Tab (or its alias TabItem) is a layout element. Components defined within the Tab will be visible when this tab is selected tab.
-    Example:
-        with gr.Blocks() as demo:
-            with gr.Tab("Lion"):
-                gr.Image("lion.jpg")
-                gr.Button("New Lion")
-            with gr.Tab("Tiger"):
-                gr.Image("tiger.jpg")
-                gr.Button("New Tiger")
-    Guides: controlling-layout
-    """
-
-    def __init__(
-        self,
-        label: str,
-        *,
-        id: int | str | None = None,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            label: The visual label for the tab
-            id: An optional identifier for the tab, required if you wish to control the selected tab from a predict function.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        BlockContext.__init__(self, elem_id=elem_id, **kwargs)
-        Selectable.__init__(self)
-        self.label = label
-        self.id = id
-
-    def get_config(self):
-        return {
-            "label": self.label,
-            "id": self.id,
-            **super(BlockContext, self).get_config(),
-        }
-
-    def get_expected_parent(self) -> type[Tabs]:
-        return Tabs
-
-    def get_block_name(self):
-        return "tabitem"
-
-
-TabItem = Tab
-
-
-@document()
-class Group(BlockContext):
-    """
-    Group is a layout element within Blocks which groups together children so that
-    they do not have any padding or margin between them.
-    Example:
-        with gr.Group():
-            gr.Textbox(label="First")
-            gr.Textbox(label="Last")
-    """
-
-    def __init__(
-        self,
-        *,
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            visible: If False, group will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        super().__init__(visible=visible, elem_id=elem_id, **kwargs)
-
-    def get_config(self):
-        return {"type": "group", **super().get_config()}
-
-    @staticmethod
-    def update(
-        visible: bool | None = None,
-    ):
-        return {
-            "visible": visible,
-            "__type__": "update",
-        }
-
-
-@document()
-class Box(BlockContext):
-    """
-    Box is a a layout element which places children in a box with rounded corners and
-    some padding around them.
-    Example:
-        with gr.Box():
-            gr.Textbox(label="First")
-            gr.Textbox(label="Last")
-    """
-
-    def __init__(
-        self,
-        *,
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            visible: If False, box will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        warnings.warn("gr.Box is deprecated. Use gr.Group instead.", DeprecationWarning)
-        super().__init__(visible=visible, elem_id=elem_id, **kwargs)
-
-    def get_config(self):
-        return {"type": "box", **super().get_config()}
-
-    @staticmethod
-    def update(
-        visible: bool | None = None,
-    ):
-        return {
-            "visible": visible,
-            "__type__": "update",
-        }
-
-    def style(self, **kwargs):
-        warn_style_method_deprecation()
-        return self
-
-
-class Form(BlockContext):
-    def __init__(self, *, scale: int = 0, min_width: int = 0, **kwargs):
-        """
-        Parameters:
-            scale: relative width compared to adjacent Columns. For example, if Column A has scale=2, and Column B has scale=1, A will be twice as wide as B.
-            min_width: minimum pixel width of Column, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in a column narrower than min_width, the min_width parameter will be respected first.
-        """
-        self.scale = scale
-        self.min_width = min_width
-        super().__init__(**kwargs)
-
-    def add_child(self, child: Block):
-        if isinstance(self.parent, Row):
-            scale = getattr(child, "scale", None)
-            self.scale += 1 if scale is None else scale
-            self.min_width += getattr(child, "min_width", 0) or 0
-        super().add_child(child)
-
-    def get_config(self):
-        return {
-            "type": "form",
-            "scale": self.scale,
-            "min_width": self.min_width,
-            **super().get_config(),
-        }
-
-
-@document()
-class Accordion(BlockContext):
-    """
-    Accordion is a layout element which can be toggled to show/hide the contained content.
-    Example:
-        with gr.Accordion("See Details"):
-            gr.Markdown("lorem ipsum")
-    """
-
-    def __init__(
-        self,
-        label,
-        *,
-        open: bool = True,
-        visible: bool = True,
-        elem_id: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            label: name of accordion section.
-            open: if True, accordion is open by default.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        self.label = label
-        self.open = open
-        super().__init__(visible=visible, elem_id=elem_id, **kwargs)
-
-    def get_config(self):
-        return {
-            "type": "accordion",
-            "open": self.open,
-            "label": self.label,
-            **super().get_config(),
-        }
-
-    @staticmethod
-    def update(
-        open: bool | None = None,
-        label: str | None = None,
-        visible: bool | None = None,
-    ):
-        return {
-            "visible": visible,
-            "label": label,
-            "open": open,
-            "__type__": "update",
-        }

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_decoders.py

@@ -1,324 +0,0 @@
-"""
-Handlers for Content-Encoding.
-
-See: https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Encoding
-"""
-import codecs
-import io
-import typing
-import zlib
-
-from ._compat import brotli
-from ._exceptions import DecodingError
-
-
-class ContentDecoder:
-    def decode(self, data: bytes) -> bytes:
-        raise NotImplementedError()  # pragma: no cover
-
-    def flush(self) -> bytes:
-        raise NotImplementedError()  # pragma: no cover
-
-
-class IdentityDecoder(ContentDecoder):
-    """
-    Handle unencoded data.
-    """
-
-    def decode(self, data: bytes) -> bytes:
-        return data
-
-    def flush(self) -> bytes:
-        return b""
-
-
-class DeflateDecoder(ContentDecoder):
-    """
-    Handle 'deflate' decoding.
-
-    See: https://stackoverflow.com/questions/1838699
-    """
-
-    def __init__(self) -> None:
-        self.first_attempt = True
-        self.decompressor = zlib.decompressobj()
-
-    def decode(self, data: bytes) -> bytes:
-        was_first_attempt = self.first_attempt
-        self.first_attempt = False
-        try:
-            return self.decompressor.decompress(data)
-        except zlib.error as exc:
-            if was_first_attempt:
-                self.decompressor = zlib.decompressobj(-zlib.MAX_WBITS)
-                return self.decode(data)
-            raise DecodingError(str(exc)) from exc
-
-    def flush(self) -> bytes:
-        try:
-            return self.decompressor.flush()
-        except zlib.error as exc:  # pragma: no cover
-            raise DecodingError(str(exc)) from exc
-
-
-class GZipDecoder(ContentDecoder):
-    """
-    Handle 'gzip' decoding.
-
-    See: https://stackoverflow.com/questions/1838699
-    """
-
-    def __init__(self) -> None:
-        self.decompressor = zlib.decompressobj(zlib.MAX_WBITS | 16)
-
-    def decode(self, data: bytes) -> bytes:
-        try:
-            return self.decompressor.decompress(data)
-        except zlib.error as exc:
-            raise DecodingError(str(exc)) from exc
-
-    def flush(self) -> bytes:
-        try:
-            return self.decompressor.flush()
-        except zlib.error as exc:  # pragma: no cover
-            raise DecodingError(str(exc)) from exc
-
-
-class BrotliDecoder(ContentDecoder):
-    """
-    Handle 'brotli' decoding.
-
-    Requires `pip install brotlipy`. See: https://brotlipy.readthedocs.io/
-        or   `pip install brotli`. See https://github.com/google/brotli
-    Supports both 'brotlipy' and 'Brotli' packages since they share an import
-    name. The top branches are for 'brotlipy' and bottom branches for 'Brotli'
-    """
-
-    def __init__(self) -> None:
-        if brotli is None:  # pragma: no cover
-            raise ImportError(
-                "Using 'BrotliDecoder', but neither of the 'brotlicffi' or 'brotli' "
-                "packages have been installed. "
-                "Make sure to install httpx using `pip install httpx[brotli]`."
-            ) from None
-
-        self.decompressor = brotli.Decompressor()
-        self.seen_data = False
-        self._decompress: typing.Callable[[bytes], bytes]
-        if hasattr(self.decompressor, "decompress"):
-            # The 'brotlicffi' package.
-            self._decompress = self.decompressor.decompress  # pragma: no cover
-        else:
-            # The 'brotli' package.
-            self._decompress = self.decompressor.process  # pragma: no cover
-
-    def decode(self, data: bytes) -> bytes:
-        if not data:
-            return b""
-        self.seen_data = True
-        try:
-            return self._decompress(data)
-        except brotli.error as exc:
-            raise DecodingError(str(exc)) from exc
-
-    def flush(self) -> bytes:
-        if not self.seen_data:
-            return b""
-        try:
-            if hasattr(self.decompressor, "finish"):
-                # Only available in the 'brotlicffi' package.
-
-                # As the decompressor decompresses eagerly, this
-                # will never actually emit any data. However, it will potentially throw
-                # errors if a truncated or damaged data stream has been used.
-                self.decompressor.finish()  # pragma: no cover
-            return b""
-        except brotli.error as exc:  # pragma: no cover
-            raise DecodingError(str(exc)) from exc
-
-
-class MultiDecoder(ContentDecoder):
-    """
-    Handle the case where multiple encodings have been applied.
-    """
-
-    def __init__(self, children: typing.Sequence[ContentDecoder]) -> None:
-        """
-        'children' should be a sequence of decoders in the order in which
-        each was applied.
-        """
-        # Note that we reverse the order for decoding.
-        self.children = list(reversed(children))
-
-    def decode(self, data: bytes) -> bytes:
-        for child in self.children:
-            data = child.decode(data)
-        return data
-
-    def flush(self) -> bytes:
-        data = b""
-        for child in self.children:
-            data = child.decode(data) + child.flush()
-        return data
-
-
-class ByteChunker:
-    """
-    Handles returning byte content in fixed-size chunks.
-    """
-
-    def __init__(self, chunk_size: typing.Optional[int] = None) -> None:
-        self._buffer = io.BytesIO()
-        self._chunk_size = chunk_size
-
-    def decode(self, content: bytes) -> typing.List[bytes]:
-        if self._chunk_size is None:
-            return [content] if content else []
-
-        self._buffer.write(content)
-        if self._buffer.tell() >= self._chunk_size:
-            value = self._buffer.getvalue()
-            chunks = [
-                value[i : i + self._chunk_size]
-                for i in range(0, len(value), self._chunk_size)
-            ]
-            if len(chunks[-1]) == self._chunk_size:
-                self._buffer.seek(0)
-                self._buffer.truncate()
-                return chunks
-            else:
-                self._buffer.seek(0)
-                self._buffer.write(chunks[-1])
-                self._buffer.truncate()
-                return chunks[:-1]
-        else:
-            return []
-
-    def flush(self) -> typing.List[bytes]:
-        value = self._buffer.getvalue()
-        self._buffer.seek(0)
-        self._buffer.truncate()
-        return [value] if value else []
-
-
-class TextChunker:
-    """
-    Handles returning text content in fixed-size chunks.
-    """
-
-    def __init__(self, chunk_size: typing.Optional[int] = None) -> None:
-        self._buffer = io.StringIO()
-        self._chunk_size = chunk_size
-
-    def decode(self, content: str) -> typing.List[str]:
-        if self._chunk_size is None:
-            return [content]
-
-        self._buffer.write(content)
-        if self._buffer.tell() >= self._chunk_size:
-            value = self._buffer.getvalue()
-            chunks = [
-                value[i : i + self._chunk_size]
-                for i in range(0, len(value), self._chunk_size)
-            ]
-            if len(chunks[-1]) == self._chunk_size:
-                self._buffer.seek(0)
-                self._buffer.truncate()
-                return chunks
-            else:
-                self._buffer.seek(0)
-                self._buffer.write(chunks[-1])
-                self._buffer.truncate()
-                return chunks[:-1]
-        else:
-            return []
-
-    def flush(self) -> typing.List[str]:
-        value = self._buffer.getvalue()
-        self._buffer.seek(0)
-        self._buffer.truncate()
-        return [value] if value else []
-
-
-class TextDecoder:
-    """
-    Handles incrementally decoding bytes into text
-    """
-
-    def __init__(self, encoding: str = "utf-8"):
-        self.decoder = codecs.getincrementaldecoder(encoding)(errors="replace")
-
-    def decode(self, data: bytes) -> str:
-        return self.decoder.decode(data)
-
-    def flush(self) -> str:
-        return self.decoder.decode(b"", True)
-
-
-class LineDecoder:
-    """
-    Handles incrementally reading lines from text.
-
-    Has the same behaviour as the stdllib splitlines, but handling the input iteratively.
-    """
-
-    def __init__(self) -> None:
-        self.buffer: typing.List[str] = []
-        self.trailing_cr: bool = False
-
-    def decode(self, text: str) -> typing.List[str]:
-        # See https://docs.python.org/3/library/stdtypes.html#str.splitlines
-        NEWLINE_CHARS = "\n\r\x0b\x0c\x1c\x1d\x1e\x85\u2028\u2029"
-
-        # We always push a trailing `\r` into the next decode iteration.
-        if self.trailing_cr:
-            text = "\r" + text
-            self.trailing_cr = False
-        if text.endswith("\r"):
-            self.trailing_cr = True
-            text = text[:-1]
-
-        if not text:
-            return []
-
-        trailing_newline = text[-1] in NEWLINE_CHARS
-        lines = text.splitlines()
-
-        if len(lines) == 1 and not trailing_newline:
-            # No new lines, buffer the input and continue.
-            self.buffer.append(lines[0])
-            return []
-
-        if self.buffer:
-            # Include any existing buffer in the first portion of the
-            # splitlines result.
-            lines = ["".join(self.buffer) + lines[0]] + lines[1:]
-            self.buffer = []
-
-        if not trailing_newline:
-            # If the last segment of splitlines is not newline terminated,
-            # then drop it from our output and start a new buffer.
-            self.buffer = [lines.pop()]
-
-        return lines
-
-    def flush(self) -> typing.List[str]:
-        if not self.buffer and not self.trailing_cr:
-            return []
-
-        lines = ["".join(self.buffer)]
-        self.buffer = []
-        self.trailing_cr = False
-        return lines
-
-
-SUPPORTED_DECODERS = {
-    "identity": IdentityDecoder,
-    "gzip": GZipDecoder,
-    "deflate": DeflateDecoder,
-    "br": BrotliDecoder,
-}
-
-
-if brotli is None:
-    SUPPORTED_DECODERS.pop("br")  # pragma: no cover

spaces/DataDoggo/Visionary/README.md

@@ -1,11 +0,0 @@
----
-title: Visionary
-emoji: 🐠
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/Detomo/ai-comic-generation/src/lib/loadImage.ts

@@ -1,14 +0,0 @@
-export async function loadImage(image: string): Promise<HTMLImageElement> {
-  const img = new Image();
-  img.src = image;
-
-  const imgOnLoad = () => {
-    return new Promise<HTMLImageElement>((resolve, reject) => {
-      img.onload = () => { resolve(img) };
-      img.onerror = (err) => { reject(err) };
-    })
-  };
-
-  const loadImg = await imgOnLoad();
-  return loadImg
-}

spaces/DiegoLigtenberg/realtimespeech/instructions.md

@@ -1,15 +0,0 @@
-## Whisper UI - Transcriptions, Summaries & Analytics
-
----
-
-#### Run Whisper
-- Add a YouTube URL or select a local file on the left
-- Select the right Whisper model supported by your machine (extra configs have other whisper params if you want to play around with them)
-- Select whether you want to summarize the video. If so, enter a minimum and maximum length for the summary (usually between 50 and 100 words). Note that only the first 8 minutes of the video can be summarized in the current version.
-- Click Save settings.
-- Click "Transcribe"
-
-Once a transcription is created, it will be retained as a session variable so you can navigate around raw, summarized and time-annotated output.
-However, if you refresh or add a new video, the old transcription will be replaced.
-
----