Update parquet files (step 27 of 476)

spaces/0xJustin/0xJustin-Dungeons-and-Diffusion/README.md

@@ -1,13 +0,0 @@
----
-title: 0xJustin Dungeons And Diffusion
-emoji: 📊
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/1368565466ki/ZSTRD/transforms.py

@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Age of Chivalry Hegemony No-cd C A Total Conversion Mod for Age of Empires II.md

@@ -1,177 +0,0 @@
-
-<h1>Age of Chivalry Hegemony No-cd C: What is it and why do you need it?</h1>
- <p>If you are a fan of medieval history and strategy games, you may have heard of <strong>Age of Chivalry Hegemony</strong>, a total conversion mod for <em>Age of Empires II: The Conquerors</em> that focuses on the late medieval period in Western and Central Europe. The mod adds or significantly alters each of the civilizations in the original game, while also introducing many new units, technologies, buildings, maps, and scenarios. The mod aims to provide a more realistic and immersive experience of medieval warfare and politics, while also offering a lot of variety and replay value.</p>
-<h2>Age Of Chivalry Hegemony No-cd C</h2><br /><p><b><b>Download Zip</b> &#10027; <a href="https://byltly.com/2uKz6i">https://byltly.com/2uKz6i</a></b></p><br /><br />
- <p>However, if you want to play this mod, you may encounter some difficulties, especially if you have an older version of the game or if you want to play online with other players. That's where a <strong>no-cd c patch</strong> comes in handy. A no-cd c patch is a small file that allows you to run the game without inserting the CD-ROM in your drive, which can save you time and hassle. It also enables you to play the game on newer operating systems that may not support CD-ROMs, such as Windows 10. Moreover, a no-cd c patch can help you avoid compatibility issues with other mods or patches that may conflict with the original game files.</p>
- <p>In this article, we will show you how to install <strong>Age of Chivalry Hegemony No-cd C</strong>, how to play it, and how to troubleshoot any problems that may arise. By following these simple steps, you will be able to enjoy this amazing mod without any worries.</p>
- <h2>How to install Age of Chivalry Hegemony No-cd C</h2>
- <p>Installing Age of Chivalry Hegemony No-cd C is not very difficult, but it does require some attention and care. Here are the steps you need to follow:</p>
- <ol>
-<li><strong>Download the latest version of Age of Chivalry Hegemony from Mod DB</strong>. You can find it here: https://www.moddb.com/mods/age-of-chivalry-hegemony/downloads. The latest version at the time of writing this article is 2.03, which was released on December 29, 2018. The file size is about 300 MB.</li>
-<li><strong>Extract the files to your Age of Empires II: The Conquerors folder</strong>. You will need a program like WinRAR or 7-Zip to do this. You can find your game folder by right-clicking on the game icon on your desktop or in your Start menu, then selecting Properties, then Open File Location. Alternatively, you can search for "age2_x1.exe" in your computer. The default location is usually C:\Program Files (x86)\Microsoft Games\Age of Empires II\age2_x1.</li>
-<li><strong>Download the no-cd c patch from a reliable source</strong>. You can find it here: https://www.gamecopyworld.com/games/pc_age_of_empires_2.shtml#Age%20of%20Empires%202:%20The%20Conquerors%20v1.0c%20[ENGLISH]%20No-CD/Fixed%20EXE. The file name is "aoe2tc.nocd.v1.0c.eng.rar". The file size is about 2 MB.</li>
-<li><strong>Copy and paste the patch file to your Age of Chivalry Hegemony folder</strong>. You will need to overwrite the existing "age2_x1.exe" file with the patched one. Make sure you backup the original file before doing this, in case something goes wrong.</li>
-<li><strong>Run the game and enjoy</strong>. You can launch the game by double-clicking on the "age2_x1.exe" file in your Age of Chivalry Hegemony folder, or by creating a shortcut on your desktop or in your Start menu. You should see a new splash screen with the mod logo when you start the game.</li>
-</ol>
- <h2>How to play Age of Chivalry Hegemony No-cd C</h2>
- <p>Playing Age of Chivalry Hegemony No-cd C is similar to playing the original game, but with some significant differences. Here are some things you need to know:</p>
- <h3>New civilizations and units</h3>
- <p>The mod features <strong>22 new civilizations</strong>, each with their own unique units, technologies, bonuses, and team bonuses. These civilizations are:</p>
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- <ul>
-<li>Austria</li>
-<li>Bavaria</li>
-<li>Bohemia</li>
-<li>Brandenburg</li>
-<li>Brittany</li>
-<li>Burgundy</li>
-<li>Denmark</li>
-<li>England</li>
-<li>Flanders</li>
-<li>Florence</li>
-<li>France</li>
-<li>Friesland</li>
-<li>Genoa</li>
-<li>Guelders</li>
-<li>Helvetia</li>
-<li>Hungary</li>
-<li>Liege</li>
-<li>Milan</li>
-<li>Naples</li>
-<li>The Papal States</li>
-<li>Poland</li>
-<li>Savoy</li>
-<li>Saxony</li>
-<li>Scotland</li>
-<li>Venice</li>
-<li>Wales</li>
-</ul>
- <p>The mod also adds <strong>over 100 new units</strong>, including infantry, cavalry, archers, siege weapons, ships, monks, heroes, mercenaries, animals, etc. Some examples are:</p>
- <ul>
-<li>Hussite Wagon: A mobile fortification that can garrison units and fire arrows.</li>
-: A heavily armored infantry unit that deals bonus damage against other infantry.</li>
-<li>Pavise Crossbowman: A crossbowman with a shield that protects him from enemy archers.</li>
-<li>Gendarme: A powerful heavy cavalry unit that can charge at enemies.</li>
-<li>Cog: A large ship that can transport units and fire arrows.</li>
-<li>Priest: A monk that can heal units faster and convert buildings.</li>
-<li>Jeanne d'Arc: A hero unit that boosts nearby units' attack and armor.</li>
-<li>Landsknecht: A mercenary unit that can be hired at the Town Hall for gold.</li>
-<li>Bear: An animal that can be hunted for food or used as a scout.</li>
-</ul>
- <h3>New technologies and buildings</h3>
- <p>The mod also adds <strong>over 100 new technologies</strong>, some of them general and others unique to a civilization. These technologies can improve your units, buildings, economy, or religion. Some examples are:</p>
- <ul>
-<li>Policy Decisions: Technologies that allow you to choose between two or three different paths for your civilization, each with its own benefits and drawbacks.</li>
-<li>Communal Militia: A technology that enables your villagers to fight back against enemy raids.</li>
-<li>Great Council: A technology that increases the population limit and reduces the cost of Town Centers.</li>
-<li>Chivalric Order: A technology that grants a unique bonus to your cavalry units.</li>
-<li>Gunpowder Revolution: A technology that unlocks advanced gunpowder units and upgrades.</li>
-<li>Indulgence: A technology that generates gold from your Monasteries.</li>
-</ul>
- <p>The mod also adds <strong>new buildings</strong>, some of them general and others unique to a civilization. These buildings can provide new functions, units, or bonuses. Some examples are:</p>
- <ul>
-<li>Town Hall: A building that replaces the Feudal Age Town Center and can train militia units and mercenaries.</li>
-<li>Assembly Hall: A building that replaces the Castle Age Town Center and can research Policy Decisions.</li>
-<li>Chapter House: A building that replaces the Imperial Age Town Center and can train elite units and heroes.</li>
-<li>Bastion: A building that replaces the Castle and can fire arrows and cannonballs.</li>
-<li>University: A building that replaces the Monastery and can research technologies and train monks.</li>
-<li>Guild Hall: A building that replaces the Market and can trade resources and generate gold.</li>
-</ul>
- <h3>New maps and scenarios</h3>
- <p>The mod also adds <strong>new maps and scenarios</strong>, some of them general and others unique to a civilization. These maps and scenarios can provide new challenges, environments, or stories. Some examples are:</p>
- <ul>
-<li>Central Europe: A map that features the regions of Germany, Austria, Bohemia, Poland, Hungary, and Switzerland.</li>
-<li>Western Europe: A map that features the regions of France, England, Scotland, Wales, Ireland, Flanders, Burgundy, Brittany, and Savoy.</li>
-<li>Northern Italy: A map that features the regions of Venice, Milan, Florence, Genoa, Naples, and the Papal States.</li>
-<li>The Low Countries: A map that features the regions of Friesland, Guelders, Liege, Holland, Brabant, and Hainaut.</li>
-<li>Bouvines: A scenario that recreates the famous battle of 1214 between France and a coalition of England, Flanders, Germany, and the Holy Roman Empire.</li>
-<li>Kortrijk: A scenario that recreates the battle of 1302 between Flanders and France, where the Flemish militia defeated the French knights.</li>
-<li>Vitkov Hill: A scenario that recreates the battle of 1420 between Bohemia and Hungary, where the Hussite rebels defended Prague from a crusader army.</li>
-</ul>
- <h2>How to troubleshoot Age of Chivalry Hegemony No-cd C</h2>
- <p>Although Age of Chivalry Hegemony No-cd C is a well-made mod, it is not perfect. You may encounter some <strong>common issues</strong> when playing it. Here are some tips on how to fix them:</p>
- <h3>Compatibility issues</h3>
- <p>If you have trouble running the mod on your system or with other mods or patches, you may need to check your <strong>compatibility settings</strong>. Here are some steps you can try:</p>
- <ol>
-<li><strong>Run the game as administrator</strong>. You can do this by right-clicking on the "age2_x1.exe" file in your Age of Chivalry Hegemony folder, then selecting Properties, then Compatibility, then Run this program as an administrator.</li>
-<li><strong>Run the game in compatibility mode</strong>. You can do this by right-clicking on the "age2_x1.exe" file in your Age of Chivalry Hegemony folder, then selecting Properties, then Compatibility, then Run this program in compatibility mode for Windows XP (Service Pack 3) or Windows 7.</li>
-, then Compatibility, then Disable visual themes and Disable desktop composition.</li>
-<li><strong>Use a different resolution</strong>. You can do this by right-clicking on the "age2_x1.exe" file in your Age of Chivalry Hegemony folder, then selecting Properties, then Shortcut, then Target, then adding -w 800 -h 600 (or any other resolution you prefer) at the end of the line.</li>
-<li><strong>Use a different color mode</strong>. You can do this by right-clicking on the "age2_x1.exe" file in your Age of Chivalry Hegemony folder, then selecting Properties, then Compatibility, then Reduced color mode 16-bit (65536 colors).</li>
-<li><strong>Use a different mod manager</strong>. If you have other mods or patches installed for Age of Empires II: The Conquerors, you may need to use a mod manager like AoK Mod Pack Studio or UserPatch to switch between them. You can find them here: https://www.moddb.com/games/age-of-empires-ii-the-conquerors/downloads/aok-mod-pack-studio and https://userpatch.aiscripters.net/.</li>
-</ol>
- <h3>Performance issues</h3>
- <p>If you experience lag, crashes, or other performance issues when playing the mod, you may need to <strong>optimize your game settings and hardware</strong>. Here are some steps you can try:</p>
- <ol>
-<li><strong>Lower your game settings</strong>. You can do this by going to Options, then Graphics, then adjusting the settings to your preference. You may want to lower the resolution, the game speed, the scroll speed, the music volume, the sound volume, or the brightness.</li>
-<li><strong>Close other programs</strong>. You may want to close any other programs that are running in the background, such as browsers, antivirus software, or media players. You can do this by pressing Ctrl+Alt+Delete, then Task Manager, then End Task for any unnecessary programs.</li>
-<li><strong>Clean your disk space</strong>. You may want to delete any unwanted files or programs that are taking up space on your hard drive. You can do this by going to Start, then Control Panel, then Programs and Features, then Uninstall a program for any unnecessary programs. You can also use a disk cleanup tool like CCleaner or Disk Cleanup to remove any temporary files or junk files.</li>
-<li><strong>Defragment your disk</strong>. You may want to defragment your hard drive to improve its performance and speed. You can do this by going to Start, then Computer, then right-clicking on your hard drive, then Properties, then Tools, then Defragment now.</li>
-, then Control Panel, then Device Manager, then right-clicking on your device, then Update driver software. You can also visit the manufacturer's website to download the latest drivers.</li>
-</ol>
- <h3>Bug reports and feedback</h3>
- <p>If you encounter any bugs or errors when playing the mod, or if you have any suggestions or feedback for the mod developers, you can <strong>report them or provide them</strong> in the following ways:</p>
- <ol>
-<li><strong>Post on the mod's website or fan wiki</strong>. You can find them here: https://www.moddb.com/mods/age-of-chivalry-hegemony and https://ageofchivalry-hegemony.fandom.com/wiki/Age_of_Chivalry:_Hegemony_Wiki. You can post comments, reviews, questions, or bug reports on the mod's page or forum. You can also edit or create articles on the fan wiki to share your knowledge or tips.</li>
-<li><strong>Post on the mod's social media pages</strong>. You can find them here: https://www.facebook.com/AgeofChivalryHegemony and https://twitter.com/AoCHegemony. You can post messages, likes, retweets, or replies on the mod's Facebook or Twitter pages.</li>
-<li><strong>Email the mod developers</strong>. You can find their email addresses here: https://www.moddb.com/mods/age-of-chivalry-hegemony/contact. You can send them an email with your bug report, feedback, or suggestion.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Age of Chivalry Hegemony No-cd C is a great mod for anyone who loves medieval history and strategy games. It offers a lot of new content, features, and gameplay changes that make it more realistic, immersive, and fun. However, it also requires some installation and troubleshooting steps to make it work properly. By following this article, you should be able to install, play, and enjoy this mod without any problems.</p>
- <p>If you are interested in downloading this mod or supporting the mod developers, you can visit their website here: https://www.moddb.com/mods/age-of-chivalry-hegemony. You can also follow them on their social media pages here: https://www.facebook.com/AgeofChivalryHegemony and https://twitter.com/AoCHegemony. You can also provide them with your bug reports, feedback, or suggestions by posting on their website, fan wiki, social media pages, or email.</p>
- <p>Thank you for reading this article and I hope you found it useful and informative. If you have any questions or comments, please feel free to leave them below. I would love to hear from you and help you out.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Age of Chivalry Hegemony No-cd C:</p>
- <ol>
-<li><strong>What are the system requirements for this mod?</strong></li>
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-<p>Yes, you can play this mod online with other players who have the same version of the mod and the no-cd c patch installed. You can use platforms like GameRanger or Voobly to find and join online games. However, you may experience some lag or desync issues depending on your internet connection and settings.</p>
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-<p>No, you cannot play this mod with other mods or patches that modify the game files or data. This may cause compatibility issues or errors that may prevent the game from running properly. You will need to use a mod manager like AoK Mod Pack Studio or UserPatch to switch between different mods or patches.</p>
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-<p>Yes, you can play this mod in other languages besides English. The mod developers have released language packs for Spanish and German that will translate the mod into those languages. You can download them here: https://www.moddb.com/mods/age-of-chivalry-hegemony/downloads/spanish-language-pack-for-age-of-chivalry-hegemony-203 and https://www.moddb.com/mods/age-of-chivalry-hegemony/downloads/german-language-pack-for-age-of-chivalry-hegemony-203. An Italian language pack will be released later.</p>
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-, you can edit or create your own maps or scenarios for this mod using the Scenario Editor that comes with the game. You can access it by going to Tools, then Scenario Editor. You can use the new objects and animals that the mod adds to create more detailed and realistic maps or scenarios. You can also share your creations with other players by uploading them to Mod DB or other platforms.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Antares 9 Forum The Ultimate Online Platform for Space Lovers.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/CLA Vocals Free Trial How to Download and Use the Best Vocal Plugin.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Supreme Ruler 2020 Gold Crack for Free and Conquer the World.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Free Download Visual Studio 2010 Free Full Version Crack.md

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spaces/1gistliPinn/ChatGPT4/Examples/Fifa 11 Config.exe.rar.md

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-<p>In this article, we have explained what Fifa 11 Config.exe.rar is, how to download and use it, and some alternative ways to do so. We have also used HTML formatting for the keyword "Fifa 11 Config.exe.rar" in the headers and the content, avoiding spamming keyword. We hope you have found this article useful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading.</p>
-<h6>Some Benefits of Using Fifa 11 Config.exe.rar</h6>
-<p>Using Fifa 11 Config.exe.rar can have some benefits for your FIFA 11 gaming experience. Here are some of them:</p>
-<ul>
-<li>You can customize your keyboard and graphic settings according to your preferences and system requirements.</li>
-<li>You can play FIFA 11 with a controller if you want to.</li>
-<li>You can avoid errors and crashes that might occur with fifaconfig.exe.</li>
-<li>You can backup and restore your data easily and safely.</li>
-<li>You can enjoy FIFA 11 without any hassle or frustration.</li>
-</ul>
-<p>These are some of the benefits of using Fifa 11 Config.exe.rar. You can try it yourself and see the difference.</p>
-<p></p>
-<h7>Some Tips and Tricks for FIFA 11</h7>
-<p>If you want to improve your skills and performance in FIFA 11, you can follow these tips and tricks:</p>
-<ol>
-<li>Practice the skill games and tutorials to learn the basics and advanced techniques of the game.</li>
-<li>Use the right formation, tactics and players for your team and style of play.</li>
-<li>Adjust the difficulty level and game speed according to your level and challenge.</li>
-<li>Use the manual controls for more accuracy and realism.</li>
-<li>Experiment with different modes and features of the game, such as Career Mode, Ultimate Team, Pro Clubs, Tournament Mode, Seasons, Skill Games, Women's Football, The Journey and VOLTA.</li>
-</ol>
-<p>These are some of the tips and tricks for FIFA 11. You can apply them to your game and have more fun and success.</p>
-<h8>Some Reviews of FIFA 11</h8>
-<p>FIFA 11 is a game that has received positive reviews from critics and players alike. Here are some of the reviews that praise the game and its features:</p>
-<blockquote>
-<p>"FIFA 11 is the best football game ever made. It has everything you could ask for in a football game: realistic graphics, smooth gameplay, diverse modes, authentic teams and players, and a lot of fun. It is a game that will keep you hooked for hours and hours. It is a game that will make you feel like a real footballer." - IGN review</p>
-</blockquote>
-<blockquote>
-<p>"FIFA 11 is a game that improves on its predecessor in every aspect. It has more features, more options, more customization, more realism, and more challenge. It is a game that will satisfy both casual and hardcore fans of football. It is a game that will make you love football even more." - Gamespot review</p>
-</blockquote>
-<blockquote>
-<p>"FIFA 11 is a game that delivers on its promise of being "WE ARE 11". It is a game that lets you play football the way you want to. It is a game that lets you create your own team, your own player, your own story. It is a game that lets you share your passion and experience with other players around the world. It is a game that will make you proud to be a part of the FIFA community." - FIFA.com review</p>
-</blockquote>
-<p>These are some of the reviews that show how much FIFA 11 is loved and appreciated by the gamers. If you are looking for a game that will give you the ultimate football experience, then you should download or play FIFA 11.</p>
-<p>In this article, we have discussed everything you need to know about Fifa 11 Config.exe.rar. We have told you what it is, how to download and use it, some alternative ways to do so, some benefits of using it, and some tips and tricks for FIFA 11. We have also used HTML formatting for the keyword "Fifa 11 Config.exe.rar" in the headers and the content, avoiding spamming keyword. We hope you have enjoyed reading this article and found it useful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for your time and attention.</p> 3cee63e6c2<br />
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-<h1>How to Get Chou Iori Yagami Skin Script for Free in Mobile Legends: Bang Bang</h1>
-<p>If you are a fan of Mobile Legends: Bang Bang, you might have heard of chou iori yagami skin script. This is a mod that allows you to change the appearance of your character Chou into Iori Yagami, a famous fighter from The King of Fighters series. Iori Yagami is known for his fiery red hair, purple flames, and nunchaku skills. Many players find this skin cool and attractive, and want to try it out in the game.</p>
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-<p>However, getting chou iori yagami skin script is not easy or cheap. The official way to get it is to buy it from the in-game shop using diamonds, which are the premium currency of the game. You need to spend around 899 diamonds to get this skin, which is equivalent to about $15. That's quite expensive for a cosmetic item, especially if you don't have enough diamonds or money to spare.</p>
-<p>Fortunately, there is another way to get chou iori yagami skin script for free. You can download it from the internet and install it on your device without spending any money. However, you need to be careful when doing this, as there are many fake or malicious websites that claim to offer this skin script but actually contain viruses or malware that can harm your device or steal your personal information. In this article, we will show you how to download chou iori yagami skin script for free and safely, how to install and use it in Mobile Legends: Bang Bang, and what are the benefits and drawbacks of using it.</p>
- <h2>How to Download Chou Iori Yagami Skin Script for Free and Safely</h2>
-<p>The first step to get chou iori yagami skin script for free is to find a reliable and trustworthy website that offers it. There are many websites that claim to provide this skin script, but not all of them are genuine or safe. Some of them may ask you to complete surveys, download apps, or enter your personal information before giving you the download link. Others may give you a fake or corrupted file that does not work or contains viruses or malware.</p>
-<p>To avoid these risks, you should only download chou iori yagami skin script from reputable sources that have positive reviews and feedback from other users. One of such sources is [Chou Iori Yagami Skin Script](^2^), a YouTube video by a channel called Gaming Zone that provides a direct download link for the skin script in the description. The video also shows how the skin looks like in the game and how to install it on your device.</p>
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-<li>Go to [Chou Iori Yagami Skin Script](^2^) on YouTube.</li>
-<li>Click on the link in the description that says "Chou iori yagami script(pass in vedio)"</li>
-<li>You will be redirected to a MediaFire page where you can see the file name "Chou Iori Yagami.zip"</li>
-<li>Click on the green "Download" button and wait for the file to be downloaded on your device.</li>
-<li>You have successfully downloaded chou iori yagami skin script for free.</li>
-</ol>
- <h2>How to Install and Use Chou Iori Yagami Skin Script in Mobile Legends: Bang Bang</h2>
-<p>The next step to get chou iori yagami skin script for free is to install it on your device and use it in Mobile Legends: Bang Bang. To do this, you need to follow these steps:</p>
-<ol>
-<li>Locate the file "Chou Iori Yagami.zip" that you - downloaded on your device and extract it using a file manager app. You will get a folder named "Chou Iori Yagami" that contains two files: "Chou Iori Yagami.png" and "Chou Iori Yagami.ktx".</li>
-<li>Copy the folder "Chou Iori Yagami" and paste it in the following directory on your device: Android > data > com.mobile.legends > files > dragon > assets > Document > android > HeroSkins</li>
-<li>If you don't have the HeroSkins folder, you can create it manually.</li>
-<li>Open Mobile Legends: Bang Bang and go to the Shop section.</li>
-<li>Select Chou from the list of heroes and choose the skin named "Dragon Boy". This is the default skin of Chou that will be replaced by the Iori Yagami skin.</li>
-<li>Click on the "Try" button and then on the "Enter" button to start a custom game with the Iori Yagami skin.</li>
-<li>You have successfully installed and used chou iori yagami skin script in Mobile Legends: Bang Bang.</li>
-</ol>
- <h2>Benefits and Drawbacks of Using Chou Iori Yagami Skin Script</h2>
-<p>Using chou iori yagami skin script has some benefits and drawbacks that you should be aware of before deciding to use it. Here are some of them:</p>
- <h3>Benefits</h3>
-<ul>
-<li>You can get chou iori yagami skin script for free without spending any money or diamonds.</li>
-<li>You can enjoy a cool and unique appearance for your character Chou that resembles Iori Yagami, a popular and powerful fighter from The King of Fighters series.</li>
-<li>You can impress your friends and enemies with your new skin and show off your style and taste.</li>
-<li>You can have fun and experiment with different skins and mods for Mobile Legends: Bang Bang.</li>
-</ul>
- <h3>Drawbacks</h3>
-<ul>
-<li>Using chou iori yagami skin script is not legal or authorized by the game developers. It is considered a form of cheating or hacking that violates the terms of service and policies of the game. You may face some consequences if you are caught using it, such as getting banned, suspended, or penalized by the game authorities.</li>
-<li>Using chou iori yagami skin script may affect the performance or stability of your device or game. It may cause some errors, glitches, crashes, or lags that can ruin your gaming experience or damage your device.</li>
-<li>Using chou iori yagami skin script may expose your device or personal information to some risks. The source or website that you download the skin script from may not be secure or trustworthy, and may contain viruses or malware that can harm your device or steal your personal information.</li>
-<li>Using chou iori yagami skin script may not be compatible with the latest version or updates of the game. The game developers may change or patch some features or codes that can make the skin script obsolete or ineffective. You may need to update or reinstall the skin script every time the game updates.</li>
-</ul>
- <h2>Conclusion: Summary and Recommendations</h2>
-<p>In conclusion, chou iori yagami skin script is a mod that allows you to change the appearance of your character Chou into Iori Yagami, a famous fighter from The King of Fighters series. It is a cool and attractive skin that many players want to try out in Mobile Legends: Bang Bang. However, getting this skin is not easy or cheap. The official way to get it is to buy it from the in-game shop using diamonds, which are the premium currency of the game. Alternatively, you can download it from the internet and install it on your device without spending any money. However, you need to be careful when doing this, as there are many fake or malicious websites that claim to offer this skin script but actually contain viruses or malware that can harm your device or steal your personal information.</p>
-<p>We have shown you how to download chou iori yagami skin script for free and safely from a reputable source, how to install and use it in Mobile Legends: Bang Bang, and what are the benefits and drawbacks of using it. We hope that this article has been helpful and informative for you. However, we do not recommend or endorse using chou iori yagami skin script or any other mods or hacks for Mobile Legends: Bang Bang. We respect the game developers and their rights, and we encourage you to play fair and follow the rules of the game. If you decide to use chou iori yagami skin script or any other mods or hacks for Mobile Legends: Bang Bang, you do so at your own risk and responsibility.</p>
- <h 2>Conclusion: Summary and Recommendations</h2>
-<p>In conclusion, chou iori yagami skin script is a mod that allows you to change the appearance of your character Chou into Iori Yagami, a famous fighter from The King of Fighters series. It is a cool and attractive skin that many players want to try out in Mobile Legends: Bang Bang. However, getting this skin is not easy or cheap. The official way to get it is to buy it from the in-game shop using diamonds, which are the premium currency of the game. Alternatively, you can download it from the internet and install it on your device without spending any money. However, you need to be careful when doing this, as there are many fake or malicious websites that claim to offer this skin script but actually contain viruses or malware that can harm your device or steal your personal information.</p>
-<p>We have shown you how to download chou iori yagami skin script for free and safely from a reputable source, how to install and use it in Mobile Legends: Bang Bang, and what are the benefits and drawbacks of using it. We hope that this article has been helpful and informative for you. However, we do not recommend or endorse using chou iori yagami skin script or any other mods or hacks for Mobile Legends: Bang Bang. We respect the game developers and their rights, and we encourage you to play fair and follow the rules of the game. If you decide to use chou iori yagami skin script or any other mods or hacks for Mobile Legends: Bang Bang, you do so at your own risk and responsibility.</p>
- <h2>FAQs: Five Common Questions and Answers About Chou Iori Yagami Skin Script</h2>
-<p>Here are some of the most frequently asked questions and answers about chou iori yagami skin script that you may find useful:</p>
- <h3>Q: Is chou iori yagami skin script legal or authorized by Mobile Legends: Bang Bang?</h3>
-<p>A: No, chou iori yagami skin script is not legal or authorized by Mobile Legends: Bang Bang. It is a mod that changes the appearance of your character Chou into Iori Yagami, a fighter from The King of Fighters series. It is considered a form of cheating or hacking that violates the terms of service and policies of the game. You may face some consequences if you are caught using it, such as getting banned, suspended, or penalized by the game authorities.</p>
- <h3>Q: Does chou iori yagami skin script affect the gameplay or skills of Chou?</h3>
-<p>A: No, chou iori yagami skin script does not affect the gameplay or skills of Chou. It only changes the appearance of your character Chou into Iori Yagami, a fighter from The King of Fighters series. It does not give you any advantage or disadvantage in the game. You still need to play with your own skills and strategies to win the game.</p>
- <h3>Q: Can other players see my chou iori yagami skin script in Mobile Legends: Bang Bang?</h3>
-<p>A: Yes, other players can see your chou iori yagami skin script in Mobile Legends: Bang Bang. However, they may not recognize it as Iori Yagami, a fighter from The King of Fighters series. They may think that you are using a different skin for Chou that they have not seen before. They may also report you for using a mod or hack in the game.</p>
- <h3>Q: How can I update or uninstall chou iori yagami skin script in Mobile Legends: Bang Bang?</h3>
-<p>A: To update or uninstall chou iori yagami skin script in Mobile Legends: Bang Bang, you need to follow these steps:</p>
-<ol>
-<li>Go to the directory on your device where you installed the skin script: Android > data > com.mobile.legends > files > dragon > assets > Document > android > HeroSkins</li>
-<li>Delete the folder "Chou Iori Yagami" that contains the files "Chou Iori Yagami.png" and "Chou Iori Yagami.ktx".</li>
-<li>If you want to update the skin script, download the latest version from a reliable source and install it following the same steps as before.</li>
-<li>If you want to uninstall the skin script, you are done.</li>
-</ol>
- <h3>Q: Where can I find more information or support about chou iori yagami skin script?</h3>
-<p>A: If you want to find more information or support about chou iori yagami skin script, you can visit some of these websites:</p>
-<ul>
-<li>[Chou Iori Yagami Skin Script](^2 ^) on YouTube, a video that provides a direct download link for the skin script and shows how it looks like in the game.</li>
-<li>[Mobile Legends: Bang Bang], the official website of the game that provides news, updates, guides, tips, and support for the game.</li>
-<li>[Mobile Legends: Bang Bang Forum], a community forum where you can interact with other players, share your opinions, feedback, suggestions, and questions about the game.</li>
-<li>[Mobile Legends: Bang Bang Reddit], a subreddit where you can find discussions, memes, fan art, videos, and more about the game.</li>
-</ul>
- <h2></h2>
-<p>Thank you for reading this article. I hope you have learned something new and useful about chou iori yagami skin script. If you have any comments or questions, please feel free to leave them below. I would love to hear from you. Have a great day and enjoy playing Mobile Legends: Bang Bang!</p> 197e85843d<br />
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-<p>Orange et moi is a free application designed for Orange customers in various countries, such as Morocco, Guinea, Senegal, Mali, Cameroon, Ivory Coast, and more. It allows you to manage your Orange mobile, internet, and fixed lines from a single app. You can track your consumption, pay your bills, recharge your number, transfer credit, buy passes, access offers, locate stores, contact agents, and enjoy many other benefits.</p>
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-<p>If you are an Orange customer, downloading Orange et moi 8.6 apk will make your life easier and save you time and money. Here are some of the benefits of using the app:</p>
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-<li>You can now receive notifications so you don't miss anything.</li>
-<li>The app has been optimized for better performance and stability.</li>
-<li>The app has fixed some bugs and errors.</li>
-</ul>
- <h3>How to contact Orange customer service through Orange et moi 8.6 apk?</h3>
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spaces/52Hz/CMFNet_dehazing/main_test_CMFNet.py

@@ -1,88 +0,0 @@
-import argparse
-import cv2
-import glob
-import numpy as np
-from collections import OrderedDict
-from skimage import img_as_ubyte
-import os
-import torch
-import requests
-from PIL import Image
-import torchvision.transforms.functional as TF
-import torch.nn.functional as F
-from natsort import natsorted
-from model.CMFNet import CMFNet
-
-def main():
-    parser = argparse.ArgumentParser(description='Demo Image Dehaze')
-    parser.add_argument('--input_dir', default='test/', type=str, help='Input images')
-    parser.add_argument('--result_dir', default='results/', type=str, help='Directory for results')
-    parser.add_argument('--weights',
-                        default='experiments/pretrained_models/dehaze_model.pth', type=str,
-                        help='Path to weights')
-
-    args = parser.parse_args()
-
-    inp_dir = args.input_dir
-    out_dir = args.result_dir
-
-    os.makedirs(out_dir, exist_ok=True)
-
-    files = natsorted(glob.glob(os.path.join(inp_dir, '*')))
-
-    if len(files) == 0:
-        raise Exception(f"No files found at {inp_dir}")
-
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    # Load corresponding models architecture and weights
-    model = CMFNet()
-    model = model.to(device)
-    model.eval()
-    load_checkpoint(model, args.weights)
-    
-
-    mul = 8
-    for file_ in 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(out_dir, f + '.png')), restored)
-
-
-
-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)
-
-
-if __name__ == '__main__':
-    main()

spaces/801artistry/RVC801/infer/modules/ipex/gradscaler.py

@@ -1,179 +0,0 @@
-from collections import defaultdict
-import torch
-import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-import intel_extension_for_pytorch._C as core # pylint: disable=import-error, unused-import
-
-# pylint: disable=protected-access, missing-function-docstring, line-too-long
-
-OptState = ipex.cpu.autocast._grad_scaler.OptState
-_MultiDeviceReplicator = ipex.cpu.autocast._grad_scaler._MultiDeviceReplicator
-_refresh_per_optimizer_state = ipex.cpu.autocast._grad_scaler._refresh_per_optimizer_state
-
-def _unscale_grads_(self, optimizer, inv_scale, found_inf, allow_fp16): # pylint: disable=unused-argument
-    per_device_inv_scale = _MultiDeviceReplicator(inv_scale)
-    per_device_found_inf = _MultiDeviceReplicator(found_inf)
-
-    # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype.
-    # There could be hundreds of grads, so we'd like to iterate through them just once.
-    # However, we don't know their devices or dtypes in advance.
-
-    # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict
-    # Google says mypy struggles with defaultdicts type annotations.
-    per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))  # type: ignore[var-annotated]
-    # sync grad to master weight
-    if hasattr(optimizer, "sync_grad"):
-        optimizer.sync_grad()
-    with torch.no_grad():
-        for group in optimizer.param_groups:
-            for param in group["params"]:
-                if param.grad is None:
-                    continue
-                if (not allow_fp16) and param.grad.dtype == torch.float16:
-                    raise ValueError("Attempting to unscale FP16 gradients.")
-                if param.grad.is_sparse:
-                    # is_coalesced() == False means the sparse grad has values with duplicate indices.
-                    # coalesce() deduplicates indices and adds all values that have the same index.
-                    # For scaled fp16 values, there's a good chance coalescing will cause overflow,
-                    # so we should check the coalesced _values().
-                    if param.grad.dtype is torch.float16:
-                        param.grad = param.grad.coalesce()
-                    to_unscale = param.grad._values()
-                else:
-                    to_unscale = param.grad
-
-                # -: is there a way to split by device and dtype without appending in the inner loop?
-                to_unscale = to_unscale.to("cpu")
-                per_device_and_dtype_grads[to_unscale.device][
-                    to_unscale.dtype
-                ].append(to_unscale)
-
-        for _, per_dtype_grads in per_device_and_dtype_grads.items():
-            for grads in per_dtype_grads.values():
-                core._amp_foreach_non_finite_check_and_unscale_(
-                    grads,
-                    per_device_found_inf.get("cpu"),
-                    per_device_inv_scale.get("cpu"),
-                )
-
-    return per_device_found_inf._per_device_tensors
-
-def unscale_(self, optimizer):
-    """
-    Divides ("unscales") the optimizer's gradient tensors by the scale factor.
-    :meth:`unscale_` is optional, serving cases where you need to
-    :ref:`modify or inspect gradients<working-with-unscaled-gradients>`
-    between the backward pass(es) and :meth:`step`.
-    If :meth:`unscale_` is not called explicitly,  gradients will be unscaled  automatically during :meth:`step`.
-    Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients::
-        ...
-        scaler.scale(loss).backward()
-        scaler.unscale_(optimizer)
-        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
-        scaler.step(optimizer)
-        scaler.update()
-    Args:
-        optimizer (torch.optim.Optimizer):  Optimizer that owns the gradients to be unscaled.
-    .. warning::
-        :meth:`unscale_` should only be called once per optimizer per :meth:`step` call,
-        and only after all gradients for that optimizer's assigned parameters have been accumulated.
-        Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError.
-    .. warning::
-        :meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute.
-    """
-    if not self._enabled:
-        return
-
-    self._check_scale_growth_tracker("unscale_")
-
-    optimizer_state = self._per_optimizer_states[id(optimizer)]
-
-    if optimizer_state["stage"] is OptState.UNSCALED: # pylint: disable=no-else-raise
-        raise RuntimeError(
-            "unscale_() has already been called on this optimizer since the last update()."
-        )
-    elif optimizer_state["stage"] is OptState.STEPPED:
-        raise RuntimeError("unscale_() is being called after step().")
-
-    # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
-    assert self._scale is not None
-    inv_scale = self._scale.to("cpu").double().reciprocal().float().to(self._scale.device)
-    found_inf = torch.full(
-        (1,), 0.0, dtype=torch.float32, device=self._scale.device
-    )
-
-    optimizer_state["found_inf_per_device"] = self._unscale_grads_(
-        optimizer, inv_scale, found_inf, False
-    )
-    optimizer_state["stage"] = OptState.UNSCALED
-
-def update(self, new_scale=None):
-    """
-    Updates the scale factor.
-    If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
-    to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
-    the scale is multiplied by ``growth_factor`` to increase it.
-    Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
-    used directly, it's used to fill GradScaler's internal scale tensor. So if
-    ``new_scale`` was a tensor, later in-place changes to that tensor will not further
-    affect the scale GradScaler uses internally.)
-    Args:
-        new_scale (float or :class:`torch.FloatTensor`, optional, default=None):  New scale factor.
-    .. warning::
-        :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
-        been invoked for all optimizers used this iteration.
-    """
-    if not self._enabled:
-        return
-
-    _scale, _growth_tracker = self._check_scale_growth_tracker("update")
-
-    if new_scale is not None:
-        # Accept a new user-defined scale.
-        if isinstance(new_scale, float):
-            self._scale.fill_(new_scale)  # type: ignore[union-attr]
-        else:
-            reason = "new_scale should be a float or a 1-element torch.FloatTensor with requires_grad=False."
-            assert isinstance(new_scale, torch.FloatTensor), reason  # type: ignore[attr-defined]
-            assert new_scale.numel() == 1, reason
-            assert new_scale.requires_grad is False, reason
-            self._scale.copy_(new_scale)  # type: ignore[union-attr]
-    else:
-        # Consume shared inf/nan data collected from optimizers to update the scale.
-        # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous.
-        found_infs = [
-            found_inf.to(device="cpu", non_blocking=True)
-            for state in self._per_optimizer_states.values()
-            for found_inf in state["found_inf_per_device"].values()
-        ]
-
-        assert len(found_infs) > 0, "No inf checks were recorded prior to update."
-
-        found_inf_combined = found_infs[0]
-        if len(found_infs) > 1:
-            for i in range(1, len(found_infs)):
-                found_inf_combined += found_infs[i]
-
-        to_device = _scale.device
-        _scale = _scale.to("cpu")
-        _growth_tracker = _growth_tracker.to("cpu")
-
-        core._amp_update_scale_(
-            _scale,
-            _growth_tracker,
-            found_inf_combined,
-            self._growth_factor,
-            self._backoff_factor,
-            self._growth_interval,
-        )
-
-        _scale = _scale.to(to_device)
-        _growth_tracker = _growth_tracker.to(to_device)
-    # To prepare for next iteration, clear the data collected from optimizers this iteration.
-    self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
-
-def gradscaler_init():
-    torch.xpu.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler
-    torch.xpu.amp.GradScaler._unscale_grads_ = _unscale_grads_
-    torch.xpu.amp.GradScaler.unscale_ = unscale_
-    torch.xpu.amp.GradScaler.update = update
-    return torch.xpu.amp.GradScaler

spaces/AIConsultant/MusicGen/docs/MBD.md

@@ -1,117 +0,0 @@
-# MultiBand Diffusion
-
-AudioCraft provides the code and models for MultiBand Diffusion, [From Discrete Tokens to High Fidelity Audio using MultiBand Diffusion][arxiv].
-MultiBand diffusion is a collection of 4 models that can decode tokens from
-<a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> into waveform audio.
-
-<a target="_blank" href="https://colab.research.google.com/drive/1JlTOjB-G0A2Hz3h8PK63vLZk4xdCI5QB?usp=sharing">
-  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
-</a>
-<br>
-
-
-## Installation
-
-Please follow the AudioCraft installation instructions from the [README](../README.md).
-
-
-## Usage
-
-We offer a number of way to use MultiBand Diffusion:
-1. The MusicGen demo includes a toggle to try diffusion decoder. You can use the demo locally by running [`python -m demos.musicgen_app --share`](../demos/musicgen_app.py), or through the [MusicGen Colab](https://colab.research.google.com/drive/1JlTOjB-G0A2Hz3h8PK63vLZk4xdCI5QB?usp=sharing).
-2. You can play with MusicGen by running the jupyter notebook at [`demos/musicgen_demo.ipynb`](../demos/musicgen_demo.ipynb) locally (if you have a GPU).
-
-## API
-
-We provide a simple API and pre-trained models for MusicGen and for EnCodec at 24 khz for 3 bitrates (1.5 kbps, 3 kbps and 6 kbps).
-
-See after a quick example for using MultiBandDiffusion with the MusicGen API:
-
-```python
-import torchaudio
-from audiocraft.models import MusicGen, MultiBandDiffusion
-from audiocraft.data.audio import audio_write
-
-model = MusicGen.get_pretrained('facebook/musicgen-melody')
-mbd = MultiBandDiffusion.get_mbd_musicgen()
-model.set_generation_params(duration=8)  # generate 8 seconds.
-wav, tokens = model.generate_unconditional(4, return_tokens=True)    # generates 4 unconditional audio samples and keep the tokens for MBD generation
-descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
-wav_diffusion = mbd.tokens_to_wav(tokens)
-wav, tokens = model.generate(descriptions, return_tokens=True)  # generates 3 samples and keep the tokens.
-wav_diffusion = mbd.tokens_to_wav(tokens)
-melody, sr = torchaudio.load('./assets/bach.mp3')
-# Generates using the melody from the given audio and the provided descriptions, returns audio and audio tokens.
-wav, tokens = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr, return_tokens=True)
-wav_diffusion = mbd.tokens_to_wav(tokens)
-
-for idx, one_wav in enumerate(wav):
-    # Will save under {idx}.wav and {idx}_diffusion.wav, with loudness normalization at -14 db LUFS for comparing the methods.
-    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
-    audio_write(f'{idx}_diffusion', wav_diffusion[idx].cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
-```
-
-For the compression task (and to compare with [EnCodec](https://github.com/facebookresearch/encodec)):
-
-```python
-import torch
-from audiocraft.models import MultiBandDiffusion
-from encodec import EncodecModel
-from audiocraft.data.audio import audio_read, audio_write
-
-bandwidth = 3.0  # 1.5, 3.0, 6.0
-mbd = MultiBandDiffusion.get_mbd_24khz(bw=bandwidth)
-encodec = EncodecModel.get_encodec_24khz()
-
-somepath = ''
-wav, sr = audio_read(somepath)
-with torch.no_grad():
-    compressed_encodec = encodec(wav)
-    compressed_diffusion = mbd.regenerate(wav, sample_rate=sr)
-
-audio_write('sample_encodec', compressed_encodec.squeeze(0).cpu(), mbd.sample_rate, strategy="loudness", loudness_compressor=True)
-audio_write('sample_diffusion', compressed_diffusion.squeeze(0).cpu(), mbd.sample_rate, strategy="loudness", loudness_compressor=True)
-```
-
-
-## Training
-
-The [DiffusionSolver](../audiocraft/solvers/diffusion.py) implements our diffusion training pipeline.
-It generates waveform audio conditioned on the embeddings extracted from a pre-trained EnCodec model
-(see [EnCodec documentation](./ENCODEC.md) for more details on how to train such model).
-
-Note that **we do NOT provide any of the datasets** used for training our diffusion models.
-We provide a dummy dataset containing just a few examples for illustrative purposes.
-
-### Example configurations and grids
-
-One can train diffusion models as described in the paper by using this [dora grid](../audiocraft/grids/diffusion/4_bands_base_32khz.py).
-```shell
-# 4 bands MBD trainning
-dora grid diffusion.4_bands_base_32khz
-```
-
-### Learn more
-
-Learn more about AudioCraft training pipelines in the [dedicated section](./TRAINING.md).
-
-
-## Citation
-
-```
-@article{sanroman2023fromdi,
-  title={From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion},
-  author={San Roman, Robin and Adi, Yossi and Deleforge, Antoine and Serizel, Romain and Synnaeve, Gabriel and Défossez, Alexandre},
-  journal={arXiv preprint arXiv:},
-  year={2023}
-}
-```
-
-
-## License
-
-See license information in the [README](../README.md).
-
-
-[arxiv]: https://dl.fbaipublicfiles.com/encodec/Diffusion/paper.pdf
-[mbd_samples]: https://ai.honu.io/papers/mbd/

spaces/AIFILMS/StyleGANEX/webUI/app_task.py

@@ -1,305 +0,0 @@
-from __future__ import annotations
-from huggingface_hub import hf_hub_download
-import numpy as np
-import gradio as gr
-
-
-def create_demo_sr(process):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Face Super Resolution')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='filepath')
-                model_type = gr.Radio(label='Model Type', choices=['SR for 32x','SR for 4x-48x'], value='SR for 32x')
-                resize_scale = gr.Slider(label='Resize Scale',
-                                            minimum=4,
-                                            maximum=48,
-                                            value=32,
-                                            step=4)                                
-                run_button = gr.Button(label='Run')
-                gr.Examples(
-                    examples =[['pexels-daniel-xavier-1239291.jpg', 'SR for 32x', 32],
-                               ['ILip77SbmOE.png', 'SR for 32x', 32],
-                               ['ILip77SbmOE.png', 'SR for 4x-48x', 48],
-                              ],
-                    inputs = [input_image, model_type, resize_scale],
-                )                
-            with gr.Column():
-                #lrinput = gr.Image(label='Low-resolution input',type='numpy', interactive=False)
-                #result = gr.Image(label='Output',type='numpy', interactive=False)
-                result = gr.Gallery(label='LR input and Output',
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')    
-
-        inputs = [
-            input_image,
-            resize_scale,
-            model_type,
-        ]
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=[result],
-                         api_name='sr')
-    return demo
-    
-def create_demo_s2f(process):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Sketch-to-Face Translation')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='filepath')
-                gr.Markdown("""Note: Input will be cropped if larger than 512x512.""")
-                seed = gr.Slider(label='Seed for appearance',
-                                    minimum=0,
-                                    maximum=2147483647,
-                                    step=1,
-                                    randomize=True)
-                #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.')
-                run_button = gr.Button(label='Run')
-                gr.Examples(
-                    examples =[['234_sketch.jpg', 1024]],
-                    inputs = [input_image, seed],
-                )                
-            with gr.Column():
-                result = gr.Image(label='Output',type='numpy', interactive=False) 
-
-        inputs = [
-            input_image, seed
-        ]
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=[result],
-                         api_name='s2f')
-    return demo
-
-
-def create_demo_m2f(process):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Mask-to-Face Translation')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='filepath') 
-                input_type = gr.Radio(label='Input Type', choices=['color image','parsing mask'], value='color image')
-                seed = gr.Slider(label='Seed for appearance',
-                                    minimum=0,
-                                    maximum=2147483647,
-                                    step=1,
-                                    randomize=True)
-                #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.')
-                run_button = gr.Button(label='Run')
-                gr.Examples(
-                    examples =[['ILip77SbmOE.png', 'color image', 4], ['ILip77SbmOE_mask.png', 'parsing mask', 4]],
-                    inputs = [input_image, input_type, seed],
-                )                
-            with gr.Column():
-                #vizmask = gr.Image(label='Visualized mask',type='numpy', interactive=False)
-                #result = gr.Image(label='Output',type='numpy', interactive=False) 
-                result = gr.Gallery(label='Visualized mask and Output',
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')                
-
-        inputs = [
-            input_image, input_type, seed
-        ]
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=[result],
-                         api_name='m2f')
-    return demo
-
-def create_demo_editing(process):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Video Face Editing (for image input)')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='filepath') 
-                model_type = gr.Radio(label='Editing Type', choices=['reduce age','light hair color'], value='color image')
-                scale_factor = gr.Slider(label='editing degree (-2~2)',
-                                    minimum=-2,
-                                    maximum=2,
-                                    value=1,
-                                    step=0.1)
-                #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.')
-                run_button = gr.Button(label='Run')
-                gr.Examples(
-                    examples =[['ILip77SbmOE.png', 'reduce age', -2], 
-                               ['ILip77SbmOE.png', 'light hair color', 1]],
-                    inputs = [input_image, model_type, scale_factor],
-                )                
-            with gr.Column():
-                result = gr.Image(label='Output',type='numpy', interactive=False) 
-
-        inputs = [
-            input_image, scale_factor, model_type
-        ]
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=[result],
-                         api_name='editing')
-    return demo
-
-def create_demo_toonify(process):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Video Face Toonification (for image input)')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='filepath') 
-                style_type = gr.Radio(label='Style Type', choices=['Pixar','Cartoon','Arcane'], value='Pixar')                
-                #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.')
-                run_button = gr.Button(label='Run')
-                gr.Examples(
-                    examples =[['ILip77SbmOE.png', 'Pixar'], ['ILip77SbmOE.png', 'Cartoon'], ['ILip77SbmOE.png', 'Arcane']],
-                    inputs = [input_image, style_type],
-                )                
-            with gr.Column():
-                result = gr.Image(label='Output',type='numpy', interactive=False) 
-
-        inputs = [
-            input_image, style_type
-        ]
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=[result],
-                         api_name='toonify')
-    return demo
-
-
-def create_demo_vediting(process, max_frame_num = 4):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Video Face Editing (for video input)')
-        with gr.Row():
-            with gr.Column():
-                input_video = gr.Video(source='upload', mirror_webcam=False, type='filepath') 
-                model_type = gr.Radio(label='Editing Type', choices=['reduce age','light hair color'], value='color image')
-                scale_factor = gr.Slider(label='editing degree (-2~2)',
-                                    minimum=-2,
-                                    maximum=2,
-                                    value=1,
-                                    step=0.1)
-                frame_num = gr.Slider(label='Number of frames to edit (full video editing is not allowed so as not to slow down the demo, \
-                            but you can duplicate the Space to modify the number limit to a large value)',
-                                    minimum=1,
-                                    maximum=max_frame_num,
-                                    value=4,
-                                    step=1)   
-                #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.')
-                run_button = gr.Button(label='Run')
-                gr.Examples(
-                    examples =[['684.mp4', 'reduce age', 1.5, 2], 
-                               ['684.mp4', 'light hair color', 0.7, 2]],
-                    inputs = [input_video, model_type, scale_factor],
-                )                
-            with gr.Column():
-                viz_result = gr.Gallery(label='Several edited frames', elem_id='gallery').style(grid=2, height='auto')  
-                result = gr.Video(label='Output', type='mp4', interactive=False) 
-
-        inputs = [
-            input_video, scale_factor, model_type, frame_num
-        ]
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=[viz_result, result],
-                         api_name='vediting')
-    return demo
-
-def create_demo_vtoonify(process, max_frame_num = 4):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Video Face Toonification (for video input)')
-        with gr.Row():
-            with gr.Column():
-                input_video = gr.Video(source='upload', mirror_webcam=False, type='filepath') 
-                style_type = gr.Radio(label='Style Type', choices=['Pixar','Cartoon','Arcane'], value='Pixar')
-                frame_num = gr.Slider(label='Number of frames to toonify (full video toonification is not allowed so as not to slow down the demo, \
-                            but you can duplicate the Space to modify the number limit to a large value)',
-                                    minimum=1,
-                                    maximum=max_frame_num,
-                                    value=4,
-                                    step=1)                
-                #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.')
-                run_button = gr.Button(label='Run')
-                gr.Examples(
-                    examples =[['529_2.mp4', 'Arcane'],
-                               ['pexels-anthony-shkraba-production-8136210.mp4', 'Pixar'], 
-                               ['684.mp4', 'Cartoon']],
-                    inputs = [input_video, style_type],
-                )                
-            with gr.Column():
-                viz_result = gr.Gallery(label='Several toonified frames', elem_id='gallery').style(grid=2, height='auto')  
-                result = gr.Video(label='Output', type='mp4', interactive=False) 
-
-        inputs = [
-            input_video, style_type, frame_num
-        ]
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=[viz_result, result],
-                         api_name='vtoonify')
-    return demo
-
-def create_demo_inversion(process, allow_optimization=False):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## StyleGANEX Inversion for Editing')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='filepath') 
-                optimize = gr.Radio(label='Whether optimize latent (latent optimization is not allowed so as not to slow down the demo, \
-                but you can duplicate the Space to modify the option or directly upload an optimized latent file. \
-                The file can be computed by inversion.py from the github page or colab)', choices=['No optimization','Latent optimization'], 
-                                    value='No optimization', interactive=allow_optimization)
-                input_latent = gr.File(label='Optimized latent code (optional)', file_types=[".pt"])
-                editing_options = gr.Dropdown(['None', 'Style Mixing', 
-                                               'Attribute Editing: smile', 
-                                               'Attribute Editing: open_eye', 
-                                               'Attribute Editing: open_mouth', 
-                                               'Attribute Editing: pose', 
-                                               'Attribute Editing: reduce_age', 
-                                               'Attribute Editing: glasses', 
-                                               'Attribute Editing: light_hair_color', 
-                                               'Attribute Editing: slender', 
-                                               'Domain Transfer: disney_princess',
-                                               'Domain Transfer: vintage_comics',
-                                               'Domain Transfer: pixar',
-                                               'Domain Transfer: edvard_munch',
-                                               'Domain Transfer: modigliani',
-                                              ], 
-                                              label="editing options",
-                                              value='None')
-                scale_factor = gr.Slider(label='editing degree (-2~2) for Attribute Editing',
-                                    minimum=-2,
-                                    maximum=2,
-                                    value=2,
-                                    step=0.1)
-                seed = gr.Slider(label='Appearance Seed for Style Mixing',
-                                    minimum=0,
-                                    maximum=2147483647,
-                                    step=1,
-                                    randomize=True)
-                #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.')
-                run_button = gr.Button(label='Run')
-                gr.Examples(
-                    examples =[['ILip77SbmOE.png', 'ILip77SbmOE_inversion.pt', 'Domain Transfer: vintage_comics'],
-                               ['ILip77SbmOE.png', 'ILip77SbmOE_inversion.pt', 'Attribute Editing: smile'],
-                               ['ILip77SbmOE.png', 'ILip77SbmOE_inversion.pt', 'Style Mixing'],
-                              ], 
-                    inputs = [input_image, input_latent, editing_options],
-                )                
-            with gr.Column():
-                result = gr.Image(label='Inversion output',type='numpy', interactive=False) 
-                editing_result = gr.Image(label='Editing output',type='numpy', interactive=False) 
-
-        inputs = [
-            input_image, optimize, input_latent, editing_options, scale_factor, seed
-        ]
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=[result, editing_result],
-                         api_name='inversion')
-    return demo

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnet50.py

@@ -1,17 +0,0 @@
-# model settings
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(3, ),
-        style='pytorch'),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=1000,
-        in_channels=2048,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
-        topk=(1, 5),
-    ))

spaces/Ababababababbababa/Ashaar/README.md

@@ -1,14 +0,0 @@
----
-title: Ashaar
-emoji: 🧑‍🎤
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: arbml/Ashaar
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Aditya757864/SentimentAnalysis/README.md

@@ -1,13 +0,0 @@
----
-title: SentimentAnalysis
-emoji: 🏢
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 4.1.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,2 +0,0 @@
-import CursorAtBound from './input/cursoratbound/CursorAtBound';
-export default CursorAtBound;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/AddChildMethods.js

@@ -1,37 +0,0 @@
-import GetBoundsConfig from '../utils/GetBoundsConfig.js';
-import AddChild from './utils/AddChild.js';
-
-export default {
-    addBackground(gameObject, paddingConfig, childKey) {
-        if (this.backgroundChildren === undefined) {
-            this.backgroundChildren = [];
-        }
-
-        if (typeof (paddingConfig) === 'string') {
-            childKey = paddingConfig;
-            paddingConfig = undefined;
-        }
-
-        if (paddingConfig === undefined) {
-            paddingConfig = 0;
-        }
-
-        AddChild.call(this, gameObject);
-        this.backgroundChildren.push(gameObject);
-
-        var config = this.getSizerConfig(gameObject);
-        config.padding = GetBoundsConfig(paddingConfig);
-
-        if (childKey !== undefined) {
-            this.addChildrenMap(childKey, gameObject)
-        }
-        return this;
-    },
-
-    isBackground(gameObject) {
-        if (this.backgroundChildren === undefined) {
-            return false;
-        }
-        return (this.backgroundChildren.indexOf(gameObject) !== -1);
-    }
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dialog/Dialog.d.ts

@@ -1,310 +0,0 @@
-// import * as Phaser from 'phaser';
-import Sizer from '../sizer/Sizer';
-import { ModalBehavoir } from '../modal/Modal';
-
-export default Dialog;
-
-declare namespace Dialog {
-
-    type AlignTypes = number | 'left' | 'center' | 'right';
-
-    interface IConfigClick {
-        mode: 0 | 1 | 'pointerup' | 'pointerdown' | 'release' | 'press',
-        clickInterval?: number
-    }
-
-    interface IConfig extends Sizer.IConfig {
-        space?: {
-            left?: number, right?: number, top?: number, bottom?: number,
-
-            title?: number,
-            titleLeft?: number,
-            titleRight?: number,
-
-            content?: number,
-            contentLeft?: number,
-            contentRight?: number,
-
-            description?: number,
-            descriptionLeft?: number,
-            descriptionRight?: number,
-
-            choices?: number,
-            choicesLeft?: number,
-            choicesRight?: number,
-
-            choice?: number,
-            choiceLine?: number,
-            choiceColumn?: number, choiceRow?: number,
-            choicesBackgroundLeft?: number,
-            choicesBackgroundRight?: number,
-            choicesBackgroundTop?: number,
-            choicesBackgroundBottom?: number,
-
-            actionsLeft?: number,
-            actionsRight?: number,
-
-            action?: number,
-
-            toolbarItem?: number,
-            leftToolbarItem?: number,
-
-        };
-
-        background?: Phaser.GameObjects.GameObject,
-
-        title?: Phaser.GameObjects.GameObject,
-
-        toolbar?: Phaser.GameObjects.GameObject[],
-
-        toolbarBackground?: Phaser.GameObjects.GameObject,
-
-        leftToolbar?: Phaser.GameObjects.GameObject[],
-
-        leftToolbarBackground?: Phaser.GameObjects.GameObject,
-
-        content?: Phaser.GameObjects.GameObject,
-
-        description?: Phaser.GameObjects.GameObject,
-
-        choicesType?: string,
-        choicesWidth?: number,
-        choicesHeight?: number,
-        choices?: Phaser.GameObjects.GameObject[],
-        choicesBackground?: Phaser.GameObjects.GameObject,
-
-        actions?: Phaser.GameObjects.GameObject[],
-        actionsBackground?: Phaser.GameObjects.GameObject,
-
-        proportion?: {
-            title?: number,
-            content?: number,
-            description?: number,
-            choices?: number,
-            actions?: number,
-        },
-
-        expand?: {
-            title?: boolean,
-            content?: boolean,
-            description?: boolean,
-            choices?: boolean,
-            actions?: boolean,
-        },
-
-        align?: {
-            title?: AlignTypes,
-            content?: AlignTypes,
-            description?: AlignTypes,
-            choices?: AlignTypes,
-            actions?: AlignTypes,
-        },
-
-        click?: IConfigClick
-    }
-
-    interface IModalConfig extends ModalBehavoir.IConfig {
-        defaultBehavior?: boolean,
-    }
-
-    type CloseEventDataType = {
-        index: number,
-        text: string,
-        button: Phaser.GameObjects.GameObject,
-        dialog: Dialog,
-        value: any
-    }
-
-    type OnModalCloseCallbackType = (data: CloseEventDataType | Dialog) => void;
-}
-
-declare class Dialog extends Sizer {
-    constructor(
-        scene: Phaser.Scene,
-        config?: Dialog.IConfig
-    );
-
-    emitChoiceClick(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    emitActionClick(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    emitToolbarClick(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    emitLeftToolbarClick(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    setChoiceEnable(
-        index: number | Phaser.GameObjects.GameObject,
-        enable?: boolean
-    ): this;
-
-    setActionEnable(
-        index: number | Phaser.GameObjects.GameObject,
-        enable?: boolean
-    ): this;
-
-    setToolbarEnable(
-        index: number | Phaser.GameObjects.GameObject,
-        enable?: boolean
-    ): this;
-
-    setLeftToolbarEnable(
-        index: number | Phaser.GameObjects.GameObject,
-        enable?: boolean
-    ): this;
-
-    toggleChoiceEnable(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    toggleActionEnable(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    toggleToolbarEnable(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    toggleLeftToolbarEnable(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    getChoiceEnable(
-        index: number | Phaser.GameObjects.GameObject
-    ): boolean;
-
-    getActionEnable(
-        index: number | Phaser.GameObjects.GameObject
-    ): boolean;
-
-    getToolbarEnable(
-        index: number | Phaser.GameObjects.GameObject
-    ): boolean;
-
-    getLeftToolbarEnable(
-        index: number | Phaser.GameObjects.GameObject
-    ): boolean;
-
-    addChoice(gameObject: Phaser.GameObjects.GameObject): this;
-
-    addAction(gameObject: Phaser.GameObjects.GameObject): this;
-
-    addToolbar(gameObject: Phaser.GameObjects.GameObject): this;
-
-    addLeftToolbar(gameObject: Phaser.GameObjects.GameObject): this;
-
-    removeChoice(
-        index: number | Phaser.GameObjects.GameObject,
-        destroyChild?: boolean
-    ): this;
-
-    removeAction(
-        index: number | Phaser.GameObjects.GameObject,
-        destroyChild?: boolean
-    ): this;
-
-    removeToolbar(
-        index: number | Phaser.GameObjects.GameObject,
-        destroyChild?: boolean
-    ): this;
-
-    removeLeftToolbar(
-        index: number | Phaser.GameObjects.GameObject,
-        destroyChild?: boolean
-    ): this;
-
-    clearChoices(destroyChild?: boolean): this;
-
-    clearActions(destroyChild?: boolean): this;
-
-    clearToolbar(destroyChild?: boolean): this;
-
-    clearLeftToolbar(destroyChild?: boolean): this;
-
-    showChoice(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    showAction(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    showToolbar(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    showLeftToolbar(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    hideChoice(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    hideAction(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    hideToolbar(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    hideLeftToolbar(
-        index: number | Phaser.GameObjects.GameObject
-    ): this;
-
-    forEachChoice(
-        callback: (button: Phaser.GameObjects.GameObject, index: number, buttons: Phaser.GameObjects.GameObject[]) => void,
-        scop?: unknown
-    ): this;
-
-    forEachAction(
-        callback: (button: Phaser.GameObjects.GameObject, index: number, buttons: Phaser.GameObjects.GameObject[]) => void,
-        scop?: unknown
-    ): this;
-
-    forEachToolbar(
-        callback: (button: Phaser.GameObjects.GameObject, index: number, buttons: Phaser.GameObjects.GameObject[]) => void,
-        scop?: unknown
-    ): this;
-
-    forEachLeftToolbar(
-        callback: (button: Phaser.GameObjects.GameObject, index: number, buttons: Phaser.GameObjects.GameObject[]) => void,
-        scop?: unknown
-    ): this;
-
-    setAllButtonsEnable(enable?: boolean): this;
-
-    getChoicesButtonState(name: string): boolean;
-    getChoicesButtonState(): { [name: string]: boolean };
-
-    getChoicessButtonStates(): { [name: string]: boolean };
-
-    setChoicesButtonState(name: string, state?: boolean): this;
-
-    clearChoicesButtonStates(): this;
-
-    getChoicesSelectButtonName(): string;
-
-    modal(
-        config?: Dialog.IModalConfig,
-        onClose?: Dialog.OnModalCloseCallbackType
-    ): this;
-
-    modal(
-        onClose?: Dialog.OnModalCloseCallbackType
-    ): this;
-
-    modalPromise(
-        config?: Dialog.IModalConfig,
-    ): Promise<Dialog.CloseEventDataType | Dialog>;
-
-    modalClose(closeEventData?: Dialog.CloseEventDataType): this;
-}

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

@@ -1,45 +0,0 @@
-import RemoveChild from '../basesizer/utils/RemoveChild.js';
-import ClearChildren from '../basesizer/utils/ClearChildren.js';
-import ArrayFill from '../../../plugins/utils/array/Fill.js';
-
-export default {
-    remove(gameObject, destroyChild) {
-        if (this.getParentSizer(gameObject) !== this) {
-            return this;
-        }
-
-        var idx = this.sizerChildren.indexOf(gameObject);
-        if (idx !== -1) {
-            this.sizerChildren[idx] = null;
-        }
-
-        RemoveChild.call(this, gameObject, destroyChild);
-        return this;
-    },
-
-    removeAt(columnIndex, rowIndex, destroyChild) {
-        var child = this.getChildAt(columnIndex, rowIndex);
-        if (child) {
-            this.remove(child, destroyChild);
-        }
-        return this;
-    },
-
-    removeAll(destroyChild) {
-        for (var i = this.sizerChildren.length - 1; i >= 0; i--) {
-            var child = this.sizerChildren[i];
-            if (!child) {
-                continue;
-            }
-
-            this.remove(child, destroyChild);
-        }
-        return this;
-    },
-
-    clear(destroyChild) {
-        ArrayFill(this.sizerChildren, null);
-        ClearChildren.call(this, destroyChild);
-        return this;
-    }
-}

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

@@ -1,7 +0,0 @@
-// import * as Phaser from 'phaser';
-import Swipe from "./Swipe";
-
-export default function (
-    gameObject: Phaser.GameObjects.GameObject | Phaser.Scene,
-    config?: Swipe.IConfig
-): Swipe;

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

@@ -1,329 +0,0 @@
-import os
-import sys
-import re
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba
-import cn2an
-import logging
-
-logging.getLogger('jieba').setLevel(logging.WARNING)
-jieba.initialize()
-
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'ㄟˉ'),
-    ('b', 'ㄅㄧˋ'),
-    ('c', 'ㄙㄧˉ'),
-    ('d', 'ㄉㄧˋ'),
-    ('e', 'ㄧˋ'),
-    ('f', 'ㄝˊㄈㄨˋ'),
-    ('g', 'ㄐㄧˋ'),
-    ('h', 'ㄝˇㄑㄩˋ'),
-    ('i', 'ㄞˋ'),
-    ('j', 'ㄐㄟˋ'),
-    ('k', 'ㄎㄟˋ'),
-    ('l', 'ㄝˊㄛˋ'),
-    ('m', 'ㄝˊㄇㄨˋ'),
-    ('n', 'ㄣˉ'),
-    ('o', 'ㄡˉ'),
-    ('p', 'ㄆㄧˉ'),
-    ('q', 'ㄎㄧㄡˉ'),
-    ('r', 'ㄚˋ'),
-    ('s', 'ㄝˊㄙˋ'),
-    ('t', 'ㄊㄧˋ'),
-    ('u', 'ㄧㄡˉ'),
-    ('v', 'ㄨㄧˉ'),
-    ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-    ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-    ('y', 'ㄨㄞˋ'),
-    ('z', 'ㄗㄟˋ')
-]]
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'p⁼wo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p⁼'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't⁼'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k⁼'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'h'),
-    ('ㄐ', 'ʧ⁼'),
-    ('ㄑ', 'ʧʰ'),
-    ('ㄒ', 'ʃ'),
-    ('ㄓ', 'ʦ`⁼'),
-    ('ㄔ', 'ʦ`ʰ'),
-    ('ㄕ', 's`'),
-    ('ㄖ', 'ɹ`'),
-    ('ㄗ', 'ʦ⁼'),
-    ('ㄘ', 'ʦʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ə'),
-    ('ㄝ', 'e'),
-    ('ㄞ', 'ai'),
-    ('ㄟ', 'ei'),
-    ('ㄠ', 'au'),
-    ('ㄡ', 'ou'),
-    ('ㄧㄢ', 'yeNN'),
-    ('ㄢ', 'aNN'),
-    ('ㄧㄣ', 'iNN'),
-    ('ㄣ', 'əNN'),
-    ('ㄤ', 'aNg'),
-    ('ㄧㄥ', 'iNg'),
-    ('ㄨㄥ', 'uNg'),
-    ('ㄩㄥ', 'yuNg'),
-    ('ㄥ', 'əNg'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'ɥ'),
-    ('ˉ', '→'),
-    ('ˊ', '↑'),
-    ('ˇ', '↓↑'),
-    ('ˋ', '↓'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-# List of (romaji, ipa) pairs:
-_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('ʃy', 'ʃ'),
-    ('ʧʰy', 'ʧʰ'),
-    ('ʧ⁼y', 'ʧ⁼'),
-    ('NN', 'n'),
-    ('Ng', 'ŋ'),
-    ('y', 'j'),
-    ('h', 'x')
-]]
-
-# List of (bopomofo, ipa) pairs:
-_bopomofo_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'p⁼wo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p⁼'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't⁼'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k⁼'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'x'),
-    ('ㄐ', 'tʃ⁼'),
-    ('ㄑ', 'tʃʰ'),
-    ('ㄒ', 'ʃ'),
-    ('ㄓ', 'ts`⁼'),
-    ('ㄔ', 'ts`ʰ'),
-    ('ㄕ', 's`'),
-    ('ㄖ', 'ɹ`'),
-    ('ㄗ', 'ts⁼'),
-    ('ㄘ', 'tsʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ə'),
-    ('ㄝ', 'ɛ'),
-    ('ㄞ', 'aɪ'),
-    ('ㄟ', 'eɪ'),
-    ('ㄠ', 'ɑʊ'),
-    ('ㄡ', 'oʊ'),
-    ('ㄧㄢ', 'jɛn'),
-    ('ㄩㄢ', 'ɥæn'),
-    ('ㄢ', 'an'),
-    ('ㄧㄣ', 'in'),
-    ('ㄩㄣ', 'ɥn'),
-    ('ㄣ', 'ən'),
-    ('ㄤ', 'ɑŋ'),
-    ('ㄧㄥ', 'iŋ'),
-    ('ㄨㄥ', 'ʊŋ'),
-    ('ㄩㄥ', 'jʊŋ'),
-    ('ㄥ', 'əŋ'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'ɥ'),
-    ('ˉ', '→'),
-    ('ˊ', '↑'),
-    ('ˇ', '↓↑'),
-    ('ˋ', '↓'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-# List of (bopomofo, ipa2) pairs:
-_bopomofo_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'pwo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'h'),
-    ('ㄐ', 'tɕ'),
-    ('ㄑ', 'tɕʰ'),
-    ('ㄒ', 'ɕ'),
-    ('ㄓ', 'tʂ'),
-    ('ㄔ', 'tʂʰ'),
-    ('ㄕ', 'ʂ'),
-    ('ㄖ', 'ɻ'),
-    ('ㄗ', 'ts'),
-    ('ㄘ', 'tsʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ɤ'),
-    ('ㄝ', 'ɛ'),
-    ('ㄞ', 'aɪ'),
-    ('ㄟ', 'eɪ'),
-    ('ㄠ', 'ɑʊ'),
-    ('ㄡ', 'oʊ'),
-    ('ㄧㄢ', 'jɛn'),
-    ('ㄩㄢ', 'yæn'),
-    ('ㄢ', 'an'),
-    ('ㄧㄣ', 'in'),
-    ('ㄩㄣ', 'yn'),
-    ('ㄣ', 'ən'),
-    ('ㄤ', 'ɑŋ'),
-    ('ㄧㄥ', 'iŋ'),
-    ('ㄨㄥ', 'ʊŋ'),
-    ('ㄩㄥ', 'jʊŋ'),
-    ('ㄥ', 'ɤŋ'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'y'),
-    ('ˉ', '˥'),
-    ('ˊ', '˧˥'),
-    ('ˇ', '˨˩˦'),
-    ('ˋ', '˥˩'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-
-def number_to_chinese(text):
-    numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-    for number in numbers:
-        text = text.replace(number, cn2an.an2cn(number), 1)
-    return text
-
-
-def chinese_to_bopomofo(text):
-    text = text.replace('、', '，').replace('；', '，').replace('：', '，')
-    words = jieba.lcut(text, cut_all=False)
-    text = ''
-    for word in words:
-        bopomofos = lazy_pinyin(word, BOPOMOFO)
-        if not re.search('[\u4e00-\u9fff]', word):
-            text += word
-            continue
-        for i in range(len(bopomofos)):
-            bopomofos[i] = re.sub(r'([\u3105-\u3129])$', r'\1ˉ', bopomofos[i])
-        if text != '':
-            text += ' '
-        text += ''.join(bopomofos)
-    return text
-
-
-def latin_to_bopomofo(text):
-    for regex, replacement in _latin_to_bopomofo:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_romaji(text):
-    for regex, replacement in _bopomofo_to_romaji:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_ipa(text):
-    for regex, replacement in _bopomofo_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_ipa2(text):
-    for regex, replacement in _bopomofo_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def chinese_to_romaji(text):
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_romaji(text)
-    text = re.sub('i([aoe])', r'y\1', text)
-    text = re.sub('u([aoəe])', r'w\1', text)
-    text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
-                  r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
-    text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
-    return text
-
-
-def chinese_to_lazy_ipa(text):
-    text = chinese_to_romaji(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def chinese_to_ipa(text):
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_ipa(text)
-    text = re.sub('i([aoe])', r'j\1', text)
-    text = re.sub('u([aoəe])', r'w\1', text)
-    text = re.sub('([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
-                  r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
-    text = re.sub('([s][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
-    return text
-
-
-def chinese_to_ipa2(text):
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_ipa2(text)
-    text = re.sub(r'i([aoe])', r'j\1', text)
-    text = re.sub(r'u([aoəe])', r'w\1', text)
-    text = re.sub(r'([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)', r'\1ʅ\2', text)
-    text = re.sub(r'(sʰ?)([˩˨˧˦˥ ]+|$)', r'\1ɿ\2', text)
-    return text

spaces/Amrrs/DragGan-Inversion/scripts/gui.sh

@@ -1,11 +0,0 @@
-python visualizer_drag.py \
-    checkpoints/stylegan2_lions_512_pytorch.pkl \
-    checkpoints/stylegan2-ffhq-512x512.pkl \
-    checkpoints/stylegan2-afhqcat-512x512.pkl \
-    checkpoints/stylegan2-car-config-f.pkl \
-    checkpoints/stylegan2_dogs_1024_pytorch.pkl \
-    checkpoints/stylegan2_horses_256_pytorch.pkl \
-    checkpoints/stylegan2-cat-config-f.pkl \
-    checkpoints/stylegan2_elephants_512_pytorch.pkl \
-    checkpoints/stylegan_human_v2_512.pkl \
-    checkpoints/stylegan2-lhq-256x256.pkl

spaces/Amrrs/DragGan-Inversion/stylegan_human/training_scripts/sg3/training/networks_stylegan3.py

@@ -1,635 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Generator architecture from the paper
-"Alias-Free Generative Adversarial Networks"."""
-
-import numpy as np
-import scipy.signal
-import scipy.optimize
-import torch
-from torch_utils import misc
-from torch_utils import persistence
-from torch_utils.ops import conv2d_gradfix
-from torch_utils.ops import filtered_lrelu
-from torch_utils.ops import bias_act
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def modulated_conv2d(
-    # Input tensor: [batch_size, in_channels, in_height, in_width]
-    x,
-    # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width]
-    w,
-    s,                  # Style tensor: [batch_size, in_channels]
-    demodulate=True,  # Apply weight demodulation?
-    padding=0,    # Padding: int or [padH, padW]
-    input_gain=None,  # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels]
-):
-    with misc.suppress_tracer_warnings():  # this value will be treated as a constant
-        batch_size = int(x.shape[0])
-    out_channels, in_channels, kh, kw = w.shape
-    misc.assert_shape(w, [out_channels, in_channels, kh, kw])  # [OIkk]
-    misc.assert_shape(x, [batch_size, in_channels, None, None])  # [NIHW]
-    misc.assert_shape(s, [batch_size, in_channels])  # [NI]
-
-    # Pre-normalize inputs.
-    if demodulate:
-        w = w * w.square().mean([1, 2, 3], keepdim=True).rsqrt()
-        s = s * s.square().mean().rsqrt()
-
-    # Modulate weights.
-    w = w.unsqueeze(0)  # [NOIkk]
-    w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4)  # [NOIkk]
-
-    # Demodulate weights.
-    if demodulate:
-        dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt()  # [NO]
-        w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4)  # [NOIkk]
-
-    # Apply input scaling.
-    if input_gain is not None:
-        input_gain = input_gain.expand(batch_size, in_channels)  # [NI]
-        w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4)  # [NOIkk]
-
-    # Execute as one fused op using grouped convolution.
-    x = x.reshape(1, -1, *x.shape[2:])
-    w = w.reshape(-1, in_channels, kh, kw)
-    x = conv2d_gradfix.conv2d(input=x, weight=w.to(
-        x.dtype), padding=padding, groups=batch_size)
-    x = x.reshape(batch_size, -1, *x.shape[2:])
-    return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class FullyConnectedLayer(torch.nn.Module):
-    def __init__(self,
-                 in_features,                # Number of input features.
-                 out_features,               # Number of output features.
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='linear',
-                 bias=True,     # Apply additive bias before the activation function?
-                 lr_multiplier=1,        # Learning rate multiplier.
-                 # Initial standard deviation of the weight tensor.
-                 weight_init=1,
-                 bias_init=0,        # Initial value of the additive bias.
-                 ):
-        super().__init__()
-        self.in_features = in_features
-        self.out_features = out_features
-        self.activation = activation
-        self.weight = torch.nn.Parameter(torch.randn(
-            [out_features, in_features]) * (weight_init / lr_multiplier))
-        bias_init = np.broadcast_to(np.asarray(
-            bias_init, dtype=np.float32), [out_features])
-        self.bias = torch.nn.Parameter(torch.from_numpy(
-            bias_init / lr_multiplier)) if bias else None
-        self.weight_gain = lr_multiplier / np.sqrt(in_features)
-        self.bias_gain = lr_multiplier
-
-    def forward(self, x):
-        w = self.weight.to(x.dtype) * self.weight_gain
-        b = self.bias
-        if b is not None:
-            b = b.to(x.dtype)
-            if self.bias_gain != 1:
-                b = b * self.bias_gain
-        if self.activation == 'linear' and b is not None:
-            x = torch.addmm(b.unsqueeze(0), x, w.t())
-        else:
-            x = x.matmul(w.t())
-            x = bias_act.bias_act(x, b, act=self.activation)
-        return x
-
-    def extra_repr(self):
-        return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MappingNetwork(torch.nn.Module):
-    def __init__(self,
-                 z_dim,                      # Input latent (Z) dimensionality.
-                 # Conditioning label (C) dimensionality, 0 = no labels.
-                 c_dim,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 # Number of intermediate latents to output.
-                 num_ws,
-                 num_layers=2,        # Number of mapping layers.
-                 # Learning rate multiplier for the mapping layers.
-                 lr_multiplier=0.01,
-                 # Decay for tracking the moving average of W during training.
-                 w_avg_beta=0.998,
-                 ):
-        super().__init__()
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.num_ws = num_ws
-        self.num_layers = num_layers
-        self.w_avg_beta = w_avg_beta
-
-        # Construct layers.
-        self.embed = FullyConnectedLayer(
-            self.c_dim, self.w_dim) if self.c_dim > 0 else None
-        features = [self.z_dim + (self.w_dim if self.c_dim >
-                                  0 else 0)] + [self.w_dim] * self.num_layers
-        for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]):
-            layer = FullyConnectedLayer(
-                in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier)
-            setattr(self, f'fc{idx}', layer)
-        self.register_buffer('w_avg', torch.zeros([w_dim]))
-
-    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
-        misc.assert_shape(z, [None, self.z_dim])
-        if truncation_cutoff is None:
-            truncation_cutoff = self.num_ws
-
-        # Embed, normalize, and concatenate inputs.
-        x = z.to(torch.float32)
-        x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt()
-        if self.c_dim > 0:
-            misc.assert_shape(c, [None, self.c_dim])
-            y = self.embed(c.to(torch.float32))
-            y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt()
-            x = torch.cat([x, y], dim=1) if x is not None else y
-
-        # Execute layers.
-        for idx in range(self.num_layers):
-            x = getattr(self, f'fc{idx}')(x)
-
-        # Update moving average of W.
-        if update_emas:
-            self.w_avg.copy_(x.detach().mean(
-                dim=0).lerp(self.w_avg, self.w_avg_beta))
-
-        # Broadcast and apply truncation.
-        x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
-        if truncation_psi != 1:
-            x[:, :truncation_cutoff] = self.w_avg.lerp(
-                x[:, :truncation_cutoff], truncation_psi)
-        return x
-
-    def extra_repr(self):
-        return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisInput(torch.nn.Module):
-    def __init__(self,
-                 w_dim,          # Intermediate latent (W) dimensionality.
-                 channels,       # Number of output channels.
-                 size,           # Output spatial size: int or [width, height].
-                 sampling_rate,  # Output sampling rate.
-                 bandwidth,      # Output bandwidth.
-                 square,
-                 ):
-        super().__init__()
-        self.w_dim = w_dim
-        self.channels = channels
-        self.square = square
-        if self.square:
-            self.size = np.broadcast_to(np.asarray(size), [2])
-        else:
-            self.size = np.array([size // 2, size])  # [width, height]
-        self.sampling_rate = sampling_rate
-        self.bandwidth = bandwidth
-
-        # Draw random frequencies from uniform 2D disc.
-        freqs = torch.randn([self.channels, 2])
-        radii = freqs.square().sum(dim=1, keepdim=True).sqrt()
-        freqs /= radii * radii.square().exp().pow(0.25)
-        freqs *= bandwidth
-        phases = torch.rand([self.channels]) - 0.5
-
-        # Setup parameters and buffers.
-        self.weight = torch.nn.Parameter(
-            torch.randn([self.channels, self.channels]))
-        self.affine = FullyConnectedLayer(
-            w_dim, 4, weight_init=0, bias_init=[1, 0, 0, 0])
-        # User-specified inverse transform wrt. resulting image.
-        self.register_buffer('transform', torch.eye(3, 3))
-        self.register_buffer('freqs', freqs)
-        self.register_buffer('phases', phases)
-
-    def forward(self, w):
-        # Introduce batch dimension.
-        transforms = self.transform.unsqueeze(0)  # [batch, row, col]
-        freqs = self.freqs.unsqueeze(0)  # [batch, channel, xy]
-        phases = self.phases.unsqueeze(0)  # [batch, channel]
-
-        # Apply learned transformation.
-        t = self.affine(w)  # t = (r_c, r_s, t_x, t_y)
-        # t' = (r'_c, r'_s, t'_x, t'_y)
-        t = t / t[:, :2].norm(dim=1, keepdim=True)
-        # Inverse rotation wrt. resulting image.
-        m_r = torch.eye(3, device=w.device).unsqueeze(
-            0).repeat([w.shape[0], 1, 1])
-        m_r[:, 0, 0] = t[:, 0]  # r'_c
-        m_r[:, 0, 1] = -t[:, 1]  # r'_s
-        m_r[:, 1, 0] = t[:, 1]  # r'_s
-        m_r[:, 1, 1] = t[:, 0]  # r'_c
-        # Inverse translation wrt. resulting image.
-        m_t = torch.eye(3, device=w.device).unsqueeze(
-            0).repeat([w.shape[0], 1, 1])
-        m_t[:, 0, 2] = -t[:, 2]  # t'_x
-        m_t[:, 1, 2] = -t[:, 3]  # t'_y
-        # First rotate resulting image, then translate, and finally apply user-specified transform.
-        transforms = m_r @ m_t @ transforms
-
-        # Transform frequencies.
-        phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2)
-        freqs = freqs @ transforms[:, :2, :2]
-
-        # Dampen out-of-band frequencies that may occur due to the user-specified transform.
-        amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) /
-                      (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1)
-
-        # Construct sampling grid.
-        theta = torch.eye(2, 3, device=w.device)
-        theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate
-        theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate
-        grids = torch.nn.functional.affine_grid(theta.unsqueeze(
-            0), [1, 1, self.size[1], self.size[0]], align_corners=False)
-
-        # Compute Fourier features.
-        x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)
-             ).squeeze(3)  # [batch, height, width, channel]
-        x = x + phases.unsqueeze(1).unsqueeze(2)
-        x = torch.sin(x * (np.pi * 2))
-        x = x * amplitudes.unsqueeze(1).unsqueeze(2)
-
-        # Apply trainable mapping.
-        weight = self.weight / np.sqrt(self.channels)
-        x = x @ weight.t()
-
-        # Ensure correct shape.
-        x = x.permute(0, 3, 1, 2)  # [batch, channel, height, width]
-        misc.assert_shape(x, [w.shape[0], self.channels,
-                          int(self.size[1]), int(self.size[0])])
-        return x
-
-    def extra_repr(self):
-        return '\n'.join([
-            f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},',
-            f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisLayer(torch.nn.Module):
-    def __init__(self,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 is_torgb,                       # Is this the final ToRGB layer?
-                 is_critically_sampled,          # Does this layer use critical sampling?
-                 use_fp16,                       # Does this layer use FP16?
-
-                 # Input & output specifications.
-                 in_channels,                    # Number of input channels.
-                 out_channels,                   # Number of output channels.
-                 # Input spatial size: int or [width, height].
-                 in_size,
-                 # Output spatial size: int or [width, height].
-                 out_size,
-                 in_sampling_rate,               # Input sampling rate (s).
-                 out_sampling_rate,              # Output sampling rate (s).
-                 # Input cutoff frequency (f_c).
-                 in_cutoff,
-                 # Output cutoff frequency (f_c).
-                 out_cutoff,
-                 # Input transition band half-width (f_h).
-                 in_half_width,
-                 # Output Transition band half-width (f_h).
-                 out_half_width,
-
-                 # Hyperparameters.
-                 # Convolution kernel size. Ignored for final the ToRGB layer.
-                 conv_kernel=3,
-                 # Low-pass filter size relative to the lower resolution when up/downsampling.
-                 filter_size=6,
-                 # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer.
-                 lrelu_upsampling=2,
-                 # Use radially symmetric downsampling filter? Ignored for critically sampled layers.
-                 use_radial_filters=False,
-                 # Clamp the output to [-X, +X], None = disable clamping.
-                 conv_clamp=256,
-                 # Decay rate for the moving average of input magnitudes.
-                 magnitude_ema_beta=0.999,
-                 square=False,    # default if for rectangle images
-                 ):
-        super().__init__()
-        self.w_dim = w_dim
-        self.is_torgb = is_torgb
-        self.is_critically_sampled = is_critically_sampled
-        self.use_fp16 = use_fp16
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.square = square
-        if self.square:
-            self.in_size = np.broadcast_to(np.asarray(in_size), [2])
-            self.out_size = np.broadcast_to(np.asarray(out_size), [2])
-        else:
-            # self.in_size = np.array[in_size, in_size//2]
-            self.in_size = np.array([in_size // 2, in_size])
-            # self.out_size = np.array[out_size, out_size//2]
-            self.out_size = np.array([out_size // 2, out_size])
-        self.in_sampling_rate = in_sampling_rate
-        self.out_sampling_rate = out_sampling_rate
-        self.tmp_sampling_rate = max(
-            in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling)
-        self.in_cutoff = in_cutoff
-        self.out_cutoff = out_cutoff
-        self.in_half_width = in_half_width
-        self.out_half_width = out_half_width
-        self.conv_kernel = 1 if is_torgb else conv_kernel
-        self.conv_clamp = conv_clamp
-        self.magnitude_ema_beta = magnitude_ema_beta
-
-        # Setup parameters and buffers.
-        self.affine = FullyConnectedLayer(
-            self.w_dim, self.in_channels, bias_init=1)
-        self.weight = torch.nn.Parameter(torch.randn(
-            [self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel]))
-        self.bias = torch.nn.Parameter(torch.zeros([self.out_channels]))
-        self.register_buffer('magnitude_ema', torch.ones([]))
-
-        # Design upsampling filter.
-        self.up_factor = int(
-            np.rint(self.tmp_sampling_rate / self.in_sampling_rate))
-        assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate
-        self.up_taps = filter_size * \
-            self.up_factor if self.up_factor > 1 and not self.is_torgb else 1
-        self.register_buffer('up_filter', self.design_lowpass_filter(
-            numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate))
-
-        # Design downsampling filter.
-        self.down_factor = int(
-            np.rint(self.tmp_sampling_rate / self.out_sampling_rate))
-        assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate
-        self.down_taps = filter_size * \
-            self.down_factor if self.down_factor > 1 and not self.is_torgb else 1
-        self.down_radial = use_radial_filters and not self.is_critically_sampled
-        self.register_buffer('down_filter', self.design_lowpass_filter(
-            numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial))
-
-        # Compute padding.
-        # Desired output size before downsampling.
-        pad_total = (self.out_size - 1) * self.down_factor + 1
-        # Input size after upsampling.
-        pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor
-        # Size reduction caused by the filters.
-        pad_total += self.up_taps + self.down_taps - 2
-        # Shift sample locations according to the symmetric interpretation (Appendix C.3).
-        pad_lo = (pad_total + self.up_factor) // 2
-        pad_hi = pad_total - pad_lo
-        self.padding = [int(pad_lo[0]), int(pad_hi[0]),
-                        int(pad_lo[1]), int(pad_hi[1])]
-
-    def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False):
-        assert noise_mode in ['random', 'const', 'none']  # unused
-        misc.assert_shape(x, [None, self.in_channels, int(
-            self.in_size[1]), int(self.in_size[0])])
-        misc.assert_shape(w, [x.shape[0], self.w_dim])
-
-        # Track input magnitude.
-        if update_emas:
-            with torch.autograd.profiler.record_function('update_magnitude_ema'):
-                magnitude_cur = x.detach().to(torch.float32).square().mean()
-                self.magnitude_ema.copy_(magnitude_cur.lerp(
-                    self.magnitude_ema, self.magnitude_ema_beta))
-        input_gain = self.magnitude_ema.rsqrt()
-
-        # Execute affine layer.
-        styles = self.affine(w)
-        if self.is_torgb:
-            weight_gain = 1 / \
-                np.sqrt(self.in_channels * (self.conv_kernel ** 2))
-            styles = styles * weight_gain
-
-        # Execute modulated conv2d.
-        dtype = torch.float16 if (
-            self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32
-        x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles,
-                             padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain)
-
-        # Execute bias, filtered leaky ReLU, and clamping.
-        gain = 1 if self.is_torgb else np.sqrt(2)
-        slope = 1 if self.is_torgb else 0.2
-        x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype),
-                                          up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp)
-
-        # Ensure correct shape and dtype.
-        misc.assert_shape(x, [None, self.out_channels, int(
-            self.out_size[1]), int(self.out_size[0])])
-        assert x.dtype == dtype
-        return x
-
-    @staticmethod
-    def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False):
-        assert numtaps >= 1
-
-        # Identity filter.
-        if numtaps == 1:
-            return None
-
-        # Separable Kaiser low-pass filter.
-        if not radial:
-            f = scipy.signal.firwin(
-                numtaps=numtaps, cutoff=cutoff, width=width, fs=fs)
-            return torch.as_tensor(f, dtype=torch.float32)
-
-        # Radially symmetric jinc-based filter.
-        x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs
-        r = np.hypot(*np.meshgrid(x, x))
-        f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r)
-        beta = scipy.signal.kaiser_beta(
-            scipy.signal.kaiser_atten(numtaps, width / (fs / 2)))
-        w = np.kaiser(numtaps, beta)
-        f *= np.outer(w, w)
-        f /= np.sum(f)
-        return torch.as_tensor(f, dtype=torch.float32)
-
-    def extra_repr(self):
-        return '\n'.join([
-            f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},',
-            f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},',
-            f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},',
-            f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},',
-            f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},',
-            f'in_size={list(self.in_size)}, out_size={list(self.out_size)},',
-            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisNetwork(torch.nn.Module):
-    def __init__(self,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 img_resolution,                 # Output image resolution.
-                 img_channels,                   # Number of color channels.
-                 square,
-                 # Overall multiplier for the number of channels.
-                 channel_base=32768,
-                 # Maximum number of channels in any layer.
-                 channel_max=512,
-                 # Total number of layers, excluding Fourier features and ToRGB.
-                 num_layers=14,
-                 # Number of critically sampled layers at the end.
-                 num_critical=2,
-                 # Cutoff frequency of the first layer (f_{c,0}).
-                 first_cutoff=2,
-                 # Minimum stopband of the first layer (f_{t,0}).
-                 first_stopband=2**2.1,
-                 # Minimum stopband of the last layer, expressed relative to the cutoff.
-                 last_stopband_rel=2**0.3,
-                 # Number of additional pixels outside the image.
-                 margin_size=10,
-                 output_scale=0.25,     # Scale factor for the output image.
-                 # Use FP16 for the N highest resolutions.
-                 num_fp16_res=4,
-                 # Arguments for SynthesisLayer.
-                 **layer_kwargs,
-
-                 ):
-        super().__init__()
-        self.w_dim = w_dim
-        self.num_ws = num_layers + 2
-        self.img_resolution = img_resolution
-        self.img_channels = img_channels
-        self.num_layers = num_layers
-        self.num_critical = num_critical
-        self.margin_size = margin_size
-        self.output_scale = output_scale
-        self.num_fp16_res = num_fp16_res
-        self.square = square
-
-        # Geometric progression of layer cutoffs and min. stopbands.
-        last_cutoff = self.img_resolution / 2  # f_{c,N}
-        last_stopband = last_cutoff * last_stopband_rel  # f_{t,N}
-        exponents = np.minimum(
-            np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1)
-        cutoffs = first_cutoff * \
-            (last_cutoff / first_cutoff) ** exponents  # f_c[i]
-        stopbands = first_stopband * \
-            (last_stopband / first_stopband) ** exponents  # f_t[i]
-
-        # Compute remaining layer parameters.
-        sampling_rates = np.exp2(
-            np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution))))  # s[i]
-        half_widths = np.maximum(
-            stopbands, sampling_rates / 2) - cutoffs  # f_h[i]
-        sizes = sampling_rates + self.margin_size * 2
-        sizes[-2:] = self.img_resolution
-        channels = np.rint(np.minimum(
-            (channel_base / 2) / cutoffs, channel_max))
-        channels[-1] = self.img_channels
-
-        # Construct layers.
-        self.input = SynthesisInput(
-            w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]),
-            sampling_rate=sampling_rates[0], bandwidth=cutoffs[0], square=self.square)
-        self.layer_names = []
-        for idx in range(self.num_layers + 1):
-            prev = max(idx - 1, 0)
-            is_torgb = (idx == self.num_layers)
-            is_critically_sampled = (
-                idx >= self.num_layers - self.num_critical)
-            use_fp16 = (sampling_rates[idx] * (2 **
-                        self.num_fp16_res) > self.img_resolution)
-            layer = SynthesisLayer(
-                w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16,
-                in_channels=int(channels[prev]), out_channels=int(channels[idx]),
-                in_size=int(sizes[prev]), out_size=int(sizes[idx]),
-                in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]),
-                in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx],
-                in_half_width=half_widths[prev], out_half_width=half_widths[idx],
-                square=self.square,
-                **layer_kwargs)
-            name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}'
-            setattr(self, name, layer)
-            self.layer_names.append(name)
-
-    def forward(self, ws, **layer_kwargs):
-        misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
-        ws = ws.to(torch.float32).unbind(dim=1)
-
-        # Execute layers.
-        x = self.input(ws[0])
-        for name, w in zip(self.layer_names, ws[1:]):
-            x = getattr(self, name)(x, w, **layer_kwargs)
-        if self.output_scale != 1:
-            x = x * self.output_scale
-
-        # Ensure correct shape and dtype.
-        if self.square:
-            misc.assert_shape(
-                x, [None, self.img_channels, self.img_resolution, self.img_resolution])
-        else:
-            misc.assert_shape(
-                x, [None, self.img_channels, self.img_resolution, self.img_resolution // 2])
-        x = x.to(torch.float32)
-        return x
-
-    def extra_repr(self):
-        return '\n'.join([
-            f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
-            f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
-            f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},',
-            f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Generator(torch.nn.Module):
-    def __init__(self,
-                 z_dim,                      # Input latent (Z) dimensionality.
-                 # Conditioning label (C) dimensionality.
-                 c_dim,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 img_resolution,             # Output resolution.
-                 square,
-                 img_channels,               # Number of output color channels.
-                 mapping_kwargs={},   # Arguments for MappingNetwork.
-                 **synthesis_kwargs,         # Arguments for SynthesisNetwork.
-                 ):
-        super().__init__()
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.img_resolution = img_resolution
-        self.img_channels = img_channels
-        self.square = square
-        self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution,
-                                          img_channels=img_channels, square=self.square, **synthesis_kwargs)
-        self.num_ws = self.synthesis.num_ws
-        self.mapping = MappingNetwork(
-            z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
-
-    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
-        ws = self.mapping(z, c, truncation_psi=truncation_psi,
-                          truncation_cutoff=truncation_cutoff, update_emas=update_emas)
-        img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
-        return img
-
-# ----------------------------------------------------------------------------

spaces/Andy1621/uniformer_image_detection/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py

@@ -1,105 +0,0 @@
-_base_ = [
-    '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
-]
-
-# model settings
-model = dict(
-    type='CornerNet',
-    backbone=dict(
-        type='HourglassNet',
-        downsample_times=5,
-        num_stacks=2,
-        stage_channels=[256, 256, 384, 384, 384, 512],
-        stage_blocks=[2, 2, 2, 2, 2, 4],
-        norm_cfg=dict(type='BN', requires_grad=True)),
-    neck=None,
-    bbox_head=dict(
-        type='CornerHead',
-        num_classes=80,
-        in_channels=256,
-        num_feat_levels=2,
-        corner_emb_channels=1,
-        loss_heatmap=dict(
-            type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
-        loss_embedding=dict(
-            type='AssociativeEmbeddingLoss',
-            pull_weight=0.10,
-            push_weight=0.10),
-        loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)),
-    # training and testing settings
-    train_cfg=None,
-    test_cfg=dict(
-        corner_topk=100,
-        local_maximum_kernel=3,
-        distance_threshold=0.5,
-        score_thr=0.05,
-        max_per_img=100,
-        nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
-# data settings
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='PhotoMetricDistortion',
-        brightness_delta=32,
-        contrast_range=(0.5, 1.5),
-        saturation_range=(0.5, 1.5),
-        hue_delta=18),
-    dict(
-        type='RandomCenterCropPad',
-        crop_size=(511, 511),
-        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
-        test_mode=False,
-        test_pad_mode=None,
-        **img_norm_cfg),
-    dict(type='Resize', img_scale=(511, 511), keep_ratio=False),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(
-        type='MultiScaleFlipAug',
-        scale_factor=1.0,
-        flip=True,
-        transforms=[
-            dict(type='Resize'),
-            dict(
-                type='RandomCenterCropPad',
-                crop_size=None,
-                ratios=None,
-                border=None,
-                test_mode=True,
-                test_pad_mode=['logical_or', 127],
-                **img_norm_cfg),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(
-                type='Collect',
-                keys=['img'],
-                meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
-                           'scale_factor', 'flip', 'img_norm_cfg', 'border')),
-        ])
-]
-data = dict(
-    samples_per_gpu=6,
-    workers_per_gpu=3,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='Adam', lr=0.0005)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=1.0 / 3,
-    step=[180])
-runner = dict(type='EpochBasedRunner', max_epochs=210)

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/assigners/base_assigner.py

@@ -1,9 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-
-class BaseAssigner(metaclass=ABCMeta):
-    """Base assigner that assigns boxes to ground truth boxes."""
-
-    @abstractmethod
-    def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
-        """Assign boxes to either a ground truth boxes or a negative boxes."""

spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = './ocrnet_hr18_512x1024_160k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w18_small',
-    backbone=dict(
-        extra=dict(
-            stage1=dict(num_blocks=(2, )),
-            stage2=dict(num_blocks=(2, 2)),
-            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
-            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/profiler.py

@@ -1,180 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-from typing import Callable, List, Optional, Union
-
-import torch
-
-from ..dist_utils import master_only
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class ProfilerHook(Hook):
-    """Profiler to analyze performance during training.
-
-    PyTorch Profiler is a tool that allows the collection of the performance
-    metrics during the training. More details on Profiler can be found at
-    https://pytorch.org/docs/1.8.1/profiler.html#torch.profiler.profile
-
-    Args:
-        by_epoch (bool): Profile performance by epoch or by iteration.
-            Default: True.
-        profile_iters (int): Number of iterations for profiling.
-            If ``by_epoch=True``, profile_iters indicates that they are the
-            first profile_iters epochs at the beginning of the
-            training, otherwise it indicates the first profile_iters
-            iterations. Default: 1.
-        activities (list[str]): List of activity groups (CPU, CUDA) to use in
-            profiling. Default: ['cpu', 'cuda'].
-        schedule (dict, optional): Config of generating the callable schedule.
-            if schedule is None, profiler will not add step markers into the
-            trace and table view. Default: None.
-        on_trace_ready (callable, dict): Either a handler or a dict of generate
-            handler. Default: None.
-        record_shapes (bool): Save information about operator's input shapes.
-            Default: False.
-        profile_memory (bool): Track tensor memory allocation/deallocation.
-            Default: False.
-        with_stack (bool): Record source information (file and line number)
-            for the ops. Default: False.
-        with_flops (bool): Use formula to estimate the FLOPS of specific
-            operators (matrix multiplication and 2D convolution).
-            Default: False.
-        json_trace_path (str, optional): Exports the collected trace in Chrome
-            JSON format. Default: None.
-
-    Example:
-        >>> runner = ... # instantiate a Runner
-        >>> # tensorboard trace
-        >>> trace_config = dict(type='tb_trace', dir_name='work_dir')
-        >>> profiler_config = dict(on_trace_ready=trace_config)
-        >>> runner.register_profiler_hook(profiler_config)
-        >>> runner.run(data_loaders=[trainloader], workflow=[('train', 1)])
-    """
-
-    def __init__(self,
-                 by_epoch: bool = True,
-                 profile_iters: int = 1,
-                 activities: List[str] = ['cpu', 'cuda'],
-                 schedule: Optional[dict] = None,
-                 on_trace_ready: Optional[Union[Callable, dict]] = None,
-                 record_shapes: bool = False,
-                 profile_memory: bool = False,
-                 with_stack: bool = False,
-                 with_flops: bool = False,
-                 json_trace_path: Optional[str] = None) -> None:
-        try:
-            from torch import profiler  # torch version >= 1.8.1
-        except ImportError:
-            raise ImportError('profiler is the new feature of torch1.8.1, '
-                              f'but your version is {torch.__version__}')
-
-        assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean.'
-        self.by_epoch = by_epoch
-
-        if profile_iters < 1:
-            raise ValueError('profile_iters should be greater than 0, but got '
-                             f'{profile_iters}')
-        self.profile_iters = profile_iters
-
-        if not isinstance(activities, list):
-            raise ValueError(
-                f'activities should be list, but got {type(activities)}')
-        self.activities = []
-        for activity in activities:
-            activity = activity.lower()
-            if activity == 'cpu':
-                self.activities.append(profiler.ProfilerActivity.CPU)
-            elif activity == 'cuda':
-                self.activities.append(profiler.ProfilerActivity.CUDA)
-            else:
-                raise ValueError(
-                    f'activity should be "cpu" or "cuda", but got {activity}')
-
-        if schedule is not None:
-            self.schedule = profiler.schedule(**schedule)
-        else:
-            self.schedule = None
-
-        self.on_trace_ready = on_trace_ready
-        self.record_shapes = record_shapes
-        self.profile_memory = profile_memory
-        self.with_stack = with_stack
-        self.with_flops = with_flops
-        self.json_trace_path = json_trace_path
-
-    @master_only
-    def before_run(self, runner):
-        if self.by_epoch and runner.max_epochs < self.profile_iters:
-            raise ValueError('self.profile_iters should not be greater than '
-                             f'{runner.max_epochs}')
-
-        if not self.by_epoch and runner.max_iters < self.profile_iters:
-            raise ValueError('self.profile_iters should not be greater than '
-                             f'{runner.max_iters}')
-
-        if callable(self.on_trace_ready):  # handler
-            _on_trace_ready = self.on_trace_ready
-        elif isinstance(self.on_trace_ready, dict):  # config of handler
-            trace_cfg = self.on_trace_ready.copy()
-            trace_type = trace_cfg.pop('type')  # log_trace handler
-            if trace_type == 'log_trace':
-
-                def _log_handler(prof):
-                    print(prof.key_averages().table(**trace_cfg))
-
-                _on_trace_ready = _log_handler
-            elif trace_type == 'tb_trace':  # tensorboard_trace handler
-                try:
-                    import torch_tb_profiler  # noqa: F401
-                except ImportError:
-                    raise ImportError('please run "pip install '
-                                      'torch-tb-profiler" to install '
-                                      'torch_tb_profiler')
-                _on_trace_ready = torch.profiler.tensorboard_trace_handler(
-                    **trace_cfg)
-            else:
-                raise ValueError('trace_type should be "log_trace" or '
-                                 f'"tb_trace", but got {trace_type}')
-        elif self.on_trace_ready is None:
-            _on_trace_ready = None  # type: ignore
-        else:
-            raise ValueError('on_trace_ready should be handler, dict or None, '
-                             f'but got {type(self.on_trace_ready)}')
-
-        if runner.max_epochs > 1:
-            warnings.warn(f'profiler will profile {runner.max_epochs} epochs '
-                          'instead of 1 epoch. Since profiler will slow down '
-                          'the training, it is recommended to train 1 epoch '
-                          'with ProfilerHook and adjust your setting according'
-                          ' to the profiler summary. During normal training '
-                          '(epoch > 1), you may disable the ProfilerHook.')
-
-        self.profiler = torch.profiler.profile(
-            activities=self.activities,
-            schedule=self.schedule,
-            on_trace_ready=_on_trace_ready,
-            record_shapes=self.record_shapes,
-            profile_memory=self.profile_memory,
-            with_stack=self.with_stack,
-            with_flops=self.with_flops)
-
-        self.profiler.__enter__()
-        runner.logger.info('profiler is profiling...')
-
-    @master_only
-    def after_train_epoch(self, runner):
-        if self.by_epoch and runner.epoch == self.profile_iters - 1:
-            runner.logger.info('profiler may take a few minutes...')
-            self.profiler.__exit__(None, None, None)
-            if self.json_trace_path is not None:
-                self.profiler.export_chrome_trace(self.json_trace_path)
-
-    @master_only
-    def after_train_iter(self, runner):
-        self.profiler.step()
-        if not self.by_epoch and runner.iter == self.profile_iters - 1:
-            runner.logger.info('profiler may take a few minutes...')
-            self.profiler.__exit__(None, None, None)
-            if self.json_trace_path is not None:
-                self.profiler.export_chrome_trace(self.json_trace_path)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/sampler_seed.py

@@ -1,20 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class DistSamplerSeedHook(Hook):
-    """Data-loading sampler for distributed training.
-
-    When distributed training, it is only useful in conjunction with
-    :obj:`EpochBasedRunner`, while :obj:`IterBasedRunner` achieves the same
-    purpose with :obj:`IterLoader`.
-    """
-
-    def before_epoch(self, runner):
-        if hasattr(runner.data_loader.sampler, 'set_epoch'):
-            # in case the data loader uses `SequentialSampler` in Pytorch
-            runner.data_loader.sampler.set_epoch(runner.epoch)
-        elif hasattr(runner.data_loader.batch_sampler.sampler, 'set_epoch'):
-            # batch sampler in pytorch warps the sampler as its attributes.
-            runner.data_loader.batch_sampler.sampler.set_epoch(runner.epoch)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/utils.py

@@ -1,93 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-import random
-import sys
-import time
-import warnings
-from getpass import getuser
-from socket import gethostname
-
-import numpy as np
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-
-
-def get_host_info():
-    """Get hostname and username.
-
-    Return empty string if exception raised, e.g. ``getpass.getuser()`` will
-    lead to error in docker container
-    """
-    host = ''
-    try:
-        host = f'{getuser()}@{gethostname()}'
-    except Exception as e:
-        warnings.warn(f'Host or user not found: {str(e)}')
-    finally:
-        return host
-
-
-def get_time_str():
-    return time.strftime('%Y%m%d_%H%M%S', time.localtime())
-
-
-def obj_from_dict(info, parent=None, default_args=None):
-    """Initialize an object from dict.
-
-    The dict must contain the key "type", which indicates the object type, it
-    can be either a string or type, such as "list" or ``list``. Remaining
-    fields are treated as the arguments for constructing the object.
-
-    Args:
-        info (dict): Object types and arguments.
-        parent (:class:`module`): Module which may containing expected object
-            classes.
-        default_args (dict, optional): Default arguments for initializing the
-            object.
-
-    Returns:
-        any type: Object built from the dict.
-    """
-    assert isinstance(info, dict) and 'type' in info
-    assert isinstance(default_args, dict) or default_args is None
-    args = info.copy()
-    obj_type = args.pop('type')
-    if mmcv.is_str(obj_type):
-        if parent is not None:
-            obj_type = getattr(parent, obj_type)
-        else:
-            obj_type = sys.modules[obj_type]
-    elif not isinstance(obj_type, type):
-        raise TypeError('type must be a str or valid type, but '
-                        f'got {type(obj_type)}')
-    if default_args is not None:
-        for name, value in default_args.items():
-            args.setdefault(name, value)
-    return obj_type(**args)
-
-
-def set_random_seed(seed, deterministic=False, use_rank_shift=False):
-    """Set random seed.
-
-    Args:
-        seed (int): Seed to be used.
-        deterministic (bool): Whether to set the deterministic option for
-            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
-            to True and `torch.backends.cudnn.benchmark` to False.
-            Default: False.
-        rank_shift (bool): Whether to add rank number to the random seed to
-            have different random seed in different threads. Default: False.
-    """
-    if use_rank_shift:
-        rank, _ = mmcv.runner.get_dist_info()
-        seed += rank
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    os.environ['PYTHONHASHSEED'] = str(seed)
-    if deterministic:
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False

spaces/ArcAhmedEssam/CLIP-Interrogator-2/README.md

@@ -1,13 +0,0 @@
----
-title: CLIP Interrogator 2
-emoji: 🕵️‍♂️🕵️‍♂️
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-duplicated_from: fffiloni/CLIP-Interrogator-2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,65 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-The cache object API for implementing caches. The default is a thread
-safe in-memory dictionary.
-"""
-from threading import Lock
-
-
-class BaseCache(object):
-
-    def get(self, key):
-        raise NotImplementedError()
-
-    def set(self, key, value, expires=None):
-        raise NotImplementedError()
-
-    def delete(self, key):
-        raise NotImplementedError()
-
-    def close(self):
-        pass
-
-
-class DictCache(BaseCache):
-
-    def __init__(self, init_dict=None):
-        self.lock = Lock()
-        self.data = init_dict or {}
-
-    def get(self, key):
-        return self.data.get(key, None)
-
-    def set(self, key, value, expires=None):
-        with self.lock:
-            self.data.update({key: value})
-
-    def delete(self, key):
-        with self.lock:
-            if key in self.data:
-                self.data.pop(key)
-
-
-class SeparateBodyBaseCache(BaseCache):
-    """
-    In this variant, the body is not stored mixed in with the metadata, but is
-    passed in (as a bytes-like object) in a separate call to ``set_body()``.
-
-    That is, the expected interaction pattern is::
-
-        cache.set(key, serialized_metadata)
-        cache.set_body(key)
-
-    Similarly, the body should be loaded separately via ``get_body()``.
-    """
-    def set_body(self, key, body):
-        raise NotImplementedError()
-
-    def get_body(self, key):
-        """
-        Return the body as file-like object.
-        """
-        raise NotImplementedError()

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

@@ -1,91 +0,0 @@
-"""
-    pygments.regexopt
-    ~~~~~~~~~~~~~~~~~
-
-    An algorithm that generates optimized regexes for matching long lists of
-    literal strings.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-from re import escape
-from os.path import commonprefix
-from itertools import groupby
-from operator import itemgetter
-
-CS_ESCAPE = re.compile(r'[\[\^\\\-\]]')
-FIRST_ELEMENT = itemgetter(0)
-
-
-def make_charset(letters):
-    return '[' + CS_ESCAPE.sub(lambda m: '\\' + m.group(), ''.join(letters)) + ']'
-
-
-def regex_opt_inner(strings, open_paren):
-    """Return a regex that matches any string in the sorted list of strings."""
-    close_paren = open_paren and ')' or ''
-    # print strings, repr(open_paren)
-    if not strings:
-        # print '-> nothing left'
-        return ''
-    first = strings[0]
-    if len(strings) == 1:
-        # print '-> only 1 string'
-        return open_paren + escape(first) + close_paren
-    if not first:
-        # print '-> first string empty'
-        return open_paren + regex_opt_inner(strings[1:], '(?:') \
-            + '?' + close_paren
-    if len(first) == 1:
-        # multiple one-char strings? make a charset
-        oneletter = []
-        rest = []
-        for s in strings:
-            if len(s) == 1:
-                oneletter.append(s)
-            else:
-                rest.append(s)
-        if len(oneletter) > 1:  # do we have more than one oneletter string?
-            if rest:
-                # print '-> 1-character + rest'
-                return open_paren + regex_opt_inner(rest, '') + '|' \
-                    + make_charset(oneletter) + close_paren
-            # print '-> only 1-character'
-            return open_paren + make_charset(oneletter) + close_paren
-    prefix = commonprefix(strings)
-    if prefix:
-        plen = len(prefix)
-        # we have a prefix for all strings
-        # print '-> prefix:', prefix
-        return open_paren + escape(prefix) \
-            + regex_opt_inner([s[plen:] for s in strings], '(?:') \
-            + close_paren
-    # is there a suffix?
-    strings_rev = [s[::-1] for s in strings]
-    suffix = commonprefix(strings_rev)
-    if suffix:
-        slen = len(suffix)
-        # print '-> suffix:', suffix[::-1]
-        return open_paren \
-            + regex_opt_inner(sorted(s[:-slen] for s in strings), '(?:') \
-            + escape(suffix[::-1]) + close_paren
-    # recurse on common 1-string prefixes
-    # print '-> last resort'
-    return open_paren + \
-        '|'.join(regex_opt_inner(list(group[1]), '')
-                 for group in groupby(strings, lambda s: s[0] == first[0])) \
-        + close_paren
-
-
-def regex_opt(strings, prefix='', suffix=''):
-    """Return a compiled regex that matches any string in the given list.
-
-    The strings to match must be literal strings, not regexes.  They will be
-    regex-escaped.
-
-    *prefix* and *suffix* are pre- and appended to the final regex.
-    """
-    strings = sorted(strings)
-    return prefix + regex_opt_inner(strings, '(') + suffix

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

@@ -1,157 +0,0 @@
-"""
-requests.api
-~~~~~~~~~~~~
-
-This module implements the Requests API.
-
-:copyright: (c) 2012 by Kenneth Reitz.
-:license: Apache2, see LICENSE for more details.
-"""
-
-from . import sessions
-
-
-def request(method, url, **kwargs):
-    """Constructs and sends a :class:`Request <Request>`.
-
-    :param method: method for the new :class:`Request` object: ``GET``, ``OPTIONS``, ``HEAD``, ``POST``, ``PUT``, ``PATCH``, or ``DELETE``.
-    :param url: URL for the new :class:`Request` object.
-    :param params: (optional) Dictionary, list of tuples or bytes to send
-        in the query string for the :class:`Request`.
-    :param data: (optional) Dictionary, list of tuples, bytes, or file-like
-        object to send in the body of the :class:`Request`.
-    :param json: (optional) A JSON serializable Python object to send in the body of the :class:`Request`.
-    :param headers: (optional) Dictionary of HTTP Headers to send with the :class:`Request`.
-    :param cookies: (optional) Dict or CookieJar object to send with the :class:`Request`.
-    :param files: (optional) Dictionary of ``'name': file-like-objects`` (or ``{'name': file-tuple}``) for multipart encoding upload.
-        ``file-tuple`` can be a 2-tuple ``('filename', fileobj)``, 3-tuple ``('filename', fileobj, 'content_type')``
-        or a 4-tuple ``('filename', fileobj, 'content_type', custom_headers)``, where ``'content-type'`` is a string
-        defining the content type of the given file and ``custom_headers`` a dict-like object containing additional headers
-        to add for the file.
-    :param auth: (optional) Auth tuple to enable Basic/Digest/Custom HTTP Auth.
-    :param timeout: (optional) How many seconds to wait for the server to send data
-        before giving up, as a float, or a :ref:`(connect timeout, read
-        timeout) <timeouts>` tuple.
-    :type timeout: float or tuple
-    :param allow_redirects: (optional) Boolean. Enable/disable GET/OPTIONS/POST/PUT/PATCH/DELETE/HEAD redirection. Defaults to ``True``.
-    :type allow_redirects: bool
-    :param proxies: (optional) Dictionary mapping protocol to the URL of the proxy.
-    :param verify: (optional) Either a boolean, in which case it controls whether we verify
-            the server's TLS certificate, or a string, in which case it must be a path
-            to a CA bundle to use. Defaults to ``True``.
-    :param stream: (optional) if ``False``, the response content will be immediately downloaded.
-    :param cert: (optional) if String, path to ssl client cert file (.pem). If Tuple, ('cert', 'key') pair.
-    :return: :class:`Response <Response>` object
-    :rtype: requests.Response
-
-    Usage::
-
-      >>> import requests
-      >>> req = requests.request('GET', 'https://httpbin.org/get')
-      >>> req
-      <Response [200]>
-    """
-
-    # By using the 'with' statement we are sure the session is closed, thus we
-    # avoid leaving sockets open which can trigger a ResourceWarning in some
-    # cases, and look like a memory leak in others.
-    with sessions.Session() as session:
-        return session.request(method=method, url=url, **kwargs)
-
-
-def get(url, params=None, **kwargs):
-    r"""Sends a GET request.
-
-    :param url: URL for the new :class:`Request` object.
-    :param params: (optional) Dictionary, list of tuples or bytes to send
-        in the query string for the :class:`Request`.
-    :param \*\*kwargs: Optional arguments that ``request`` takes.
-    :return: :class:`Response <Response>` object
-    :rtype: requests.Response
-    """
-
-    return request("get", url, params=params, **kwargs)
-
-
-def options(url, **kwargs):
-    r"""Sends an OPTIONS request.
-
-    :param url: URL for the new :class:`Request` object.
-    :param \*\*kwargs: Optional arguments that ``request`` takes.
-    :return: :class:`Response <Response>` object
-    :rtype: requests.Response
-    """
-
-    return request("options", url, **kwargs)
-
-
-def head(url, **kwargs):
-    r"""Sends a HEAD request.
-
-    :param url: URL for the new :class:`Request` object.
-    :param \*\*kwargs: Optional arguments that ``request`` takes. If
-        `allow_redirects` is not provided, it will be set to `False` (as
-        opposed to the default :meth:`request` behavior).
-    :return: :class:`Response <Response>` object
-    :rtype: requests.Response
-    """
-
-    kwargs.setdefault("allow_redirects", False)
-    return request("head", url, **kwargs)
-
-
-def post(url, data=None, json=None, **kwargs):
-    r"""Sends a POST request.
-
-    :param url: URL for the new :class:`Request` object.
-    :param data: (optional) Dictionary, list of tuples, bytes, or file-like
-        object to send in the body of the :class:`Request`.
-    :param json: (optional) json data to send in the body of the :class:`Request`.
-    :param \*\*kwargs: Optional arguments that ``request`` takes.
-    :return: :class:`Response <Response>` object
-    :rtype: requests.Response
-    """
-
-    return request("post", url, data=data, json=json, **kwargs)
-
-
-def put(url, data=None, **kwargs):
-    r"""Sends a PUT request.
-
-    :param url: URL for the new :class:`Request` object.
-    :param data: (optional) Dictionary, list of tuples, bytes, or file-like
-        object to send in the body of the :class:`Request`.
-    :param json: (optional) json data to send in the body of the :class:`Request`.
-    :param \*\*kwargs: Optional arguments that ``request`` takes.
-    :return: :class:`Response <Response>` object
-    :rtype: requests.Response
-    """
-
-    return request("put", url, data=data, **kwargs)
-
-
-def patch(url, data=None, **kwargs):
-    r"""Sends a PATCH request.
-
-    :param url: URL for the new :class:`Request` object.
-    :param data: (optional) Dictionary, list of tuples, bytes, or file-like
-        object to send in the body of the :class:`Request`.
-    :param json: (optional) json data to send in the body of the :class:`Request`.
-    :param \*\*kwargs: Optional arguments that ``request`` takes.
-    :return: :class:`Response <Response>` object
-    :rtype: requests.Response
-    """
-
-    return request("patch", url, data=data, **kwargs)
-
-
-def delete(url, **kwargs):
-    r"""Sends a DELETE request.
-
-    :param url: URL for the new :class:`Request` object.
-    :param \*\*kwargs: Optional arguments that ``request`` takes.
-    :return: :class:`Response <Response>` object
-    :rtype: requests.Response
-    """
-
-    return request("delete", url, **kwargs)

spaces/Benson/text-generation/Examples/4g Lte Apk.md

@@ -1,68 +0,0 @@
-<br />
-<h1>¿Qué es CJ APK OBB y cómo descargarlo? </h1>
-<p>Si usted está buscando una manera de iniciar o hacer crecer su negocio dropshipping, es posible que haya oído hablar de CJdropshipping, una plataforma que ofrece varios servicios y productos para dropshippers. ¿Pero sabías que también puedes usar su aplicación en tu dispositivo Android? En este artículo, explicaremos qué es CJ APK OBB, por qué lo necesita, y cómo descargarlo de APKCombo, un sitio web que proporciona archivos APK y OBB originales de Google Play Store.</p>
- <h2>Introducción</h2>
-<p>¿Qué es CJ APK OBB? CJ APK OBB es una combinación de dos archivos que necesita para instalar la aplicación CJdropshipping en su dispositivo Android. El archivo APK es el paquete de aplicación que contiene el código y los recursos de la aplicación. El archivo OBB es el archivo de datos adicional que contiene los gráficos y los medios de la aplicación. Juntos, conforman la aplicación completa que puedes usar en tu dispositivo. </p>
-<h2>4g lte apk</h2><br /><p><b><b>Download Zip</b> &ndash;&ndash;&ndash;&ndash;&ndash;>>> <a href="https://bltlly.com/2v6KTK">https://bltlly.com/2v6KTK</a></b></p><br /><br />
-<p>¿Por qué lo necesitas? Lo necesitas porque la aplicación CJdropshipping no está disponible en Google Play Store debido a algunas restricciones. Por lo tanto, tienes que descargarlo de una fuente de terceros como APKCombo. Sin embargo, descargar solo el archivo APK no es suficiente, ya que también necesita el archivo OBB para ejecutar la aplicación correctamente. De lo contrario, podría encontrar errores o características que faltan en la aplicación. </p>
-<p>¿Cuáles son los beneficios de usarlo? El uso de CJ APK OBB, se puede disfrutar de todas las características y beneficios de la plataforma CJdropshipping en su dispositivo Android. Puede importar productos a sus tiendas, obtener productos de forma gratuita, acceder a miles de productos POD (Print on Demand), realizar un seguimiento de los pedidos, gestionar el inventario y mucho más. También puede usar la aplicación sin conexión, ya que se sincronizará con su cuenta en línea cuando se conecte a Internet. </p>
- <h2>Cómo descargar CJ APK OBB desde APKCombo</h2>
-<p>Ahora que sabe lo que es CJ APK OBB y por qué lo necesita, vamos a ver cómo descargarlo desde APKCombo. Estos son los pasos que debe seguir:</p>
-<h3>Paso 1: Visita el sitio web de APKCombo</h3>
-
-<h3>Paso 2: Buscar aplicación CJdropshipping</h3>
-<p>Escriba "CJdropshipping" en el cuadro de búsqueda y pulse enter. Verá una lista de resultados relacionados con su consulta. Elija el que dice "CJdropshipping" por "CJ Dropshipping". También puede hacer clic en este enlace <a href="( 3 )">CJdropshipping</a> para ir directamente a la página de la aplicación. </p>
-<h3>Paso 3: Elija los archivos APK y OBB</h3>
-<p>En la página de la aplicación, verá varias opciones para descargar diferentes versiones y variantes de la aplicación. Puede elegir el que se adapte a las especificaciones de su dispositivo, como la versión de Android, la arquitectura de la CPU y el DPI. Por ejemplo, si tienes un dispositivo Android 10 con procesador v7a de brazo y 320 DPI, puedes elegir la variante que dice "10.0+ arm64-v8a 320dpi". </p>
-<p>Debajo de las opciones de variantes, verá una tabla que muestra la información del archivo de la aplicación. Verá dos archivos: un archivo APK y un archivo OBB. El archivo APK es el archivo de aplicación principal, mientras que el archivo OBB es el archivo de datos adicional. Necesitas descargar ambos archivos para instalar la aplicación correctamente. </p>
-<p>Para descargar los archivos, haga clic en los botones verdes que dicen "Descargar APK" y "Descargar OBB". Será redirigido a otra página donde podrá elegir un servidor para descargar los archivos. Elija cualquier servidor que funcione para usted y espere a que se complete la descarga. </p>
-<p></p>
-<h3>Paso 4: Descargar e instalar el instalador de APKCombo</h3>
-<p>Después de descargar los archivos APK y OBB, es necesario descargar e instalar APKCombo Installer, una herramienta que le ayuda a instalar archivos APK y OBB fácilmente. Puede descargarlo desde <a href=">aquí</a> o escanear el código QR en el sitio web. Una vez que lo descargues, ábrelo y sigue las instrucciones para instalarlo en tu dispositivo. </p>
-<h3>Paso 5: Instalar CJ APK OBB utilizando APKCombo instalador</h3>
-<p>Ahora que ha descargado e instalado APKCombo Installer, puede usarlo para instalar CJ APK OBB en su dispositivo. Estos son los pasos que debes seguir:</p>
-<ul>
-
-<li>Toque en el botón "Instalar APK" y seleccione la carpeta donde descargó los archivos APK y OBB. </li>
-<li>Seleccione la aplicación CJdropshipping y toque en "Instalar". </li>
-<li>Espere a que termine la instalación. Es posible que vea algunas advertencias o indicaciones durante el proceso. Simplemente sígala y permita la instalación. </li>
-<li>Una vez realizada la instalación, puede abrir la aplicación CJdropshipping y disfrutar de sus características. </li>
-</ul>
- <h2>Cómo usar la aplicación CJdropshipping</h2>
-<p>Ahora que ha instalado la aplicación CJdropshipping en su dispositivo, es posible que se pregunte cómo usarlo. Estas son algunas de las principales características y funciones de la aplicación que puede utilizar para iniciar o hacer crecer su negocio dropshipping:</p>
-<h3>Importar productos a sus tiendas</h3>
-<p>Una de las características más útiles de la aplicación CJdropshipping es que le permite importar productos de CJdropshipping a sus tiendas en línea, como Shopify, WooCommerce, eBay, Amazon, etc. Puede navegar a través de miles de productos en varias categorías y nichos, como la moda, la electrónica, el hogar y el jardín, la belleza, etc. También puede filtrar los productos por precio, calificación, opciones de envío, etc.</p>
-<p>Para importar productos a sus tiendas, primero debe conectar las cuentas de su tienda con la aplicación CJdropshipping. Puede hacer esto tocando en el icono "Mi tienda" en el menú inferior y luego tocando en "Añadir tienda". Verá una lista de plataformas compatibles entre las que puede elegir. Siga las instrucciones para autorizar las cuentas de su tienda con la aplicación CJdropshipping. </p>
-
-<p>Cuando esté listo para importar productos a sus tiendas, toque en el botón "Importar" en la esquina superior derecha de la página de la lista. Verá una lista de las cuentas de su tienda conectada que puede elegir. Seleccione la cuenta de la tienda a la que desea importar productos y, a continuación, toque en "Importar". Espere unos minutos hasta que finalice el proceso de importación. Puede comprobar el estado de su importación pulsando en el icono "Notificación" en el menú inferior. También puede ver sus productos importados en el panel de control de su tienda. </p>
- <h3>Productos fuente gratis</h3>
-<p>Otra gran característica de CJdropshipping aplicación es que le permite la fuente de productos de forma gratuita. Esto significa que usted puede solicitar CJdropshipping para encontrar y suministrar los productos que desea vender, incluso si no se enumeran en su sitio web. También puede pedirles que personalicen los productos según sus preferencias, como agregar su logotipo, cambiar el embalaje, etc.</p>
-<p>A los productos de origen de forma gratuita, es necesario tocar en el "Fuente" icono en el menú inferior. Verás una página donde puedes rellenar los detalles del producto que deseas obtener, como nombre del producto, descripción, imagen, enlace, cantidad, precio, etc. También puedes subir un video o mensaje de voz para explicar mejor tu solicitud. Después de completar los detalles, toque en "Enviar". Usted recibirá una notificación cuando CJdropshipping ha encontrado y verificado el producto para usted. A continuación, puede comprobar los detalles del producto y el precio pulsando en el icono "Notificación" en el menú inferior. También puede chatear con el personal de CJdropshipping tocando el icono "Mensaje" en el menú inferior. </p>
-<p>Cuando esté satisfecho con el producto, puede agregarlo a su lista e importarlo a su tienda como se explicó anteriormente. También puede pedir una muestra del producto antes de importarlo a su tienda pulsando en el botón "Pedir muestra" en la página del producto. Solo tendrá que pagar el costo de envío de la muestra. </p>
- <h3>Acceder a miles de productos POD</h3>
-
-<p>Para acceder a los productos POD, es necesario tocar en el "POD" icono en el menú inferior. Verá una lista de categorías y subcategorías de productos POD que puede elegir, como ropa, accesorios, hogar y vida, etc. También puede buscar productos específicos escribiendo palabras clave en el cuadro de búsqueda. </p>
-<p>Cuando encuentres un producto que te guste, toca en él y luego toca en "Diseño". Verá una página donde puede subir su diseño o elegir entre las plantillas existentes. También puede editar su diseño agregando texto, imágenes, formas, etc. Puede previsualizar su diseño pulsando en "Vista previa". Cuando esté satisfecho con su diseño, toque en "Guardar". </p>
-<p>Después de guardar su diseño, puede agregar el producto a su lista e importarlo a su tienda como se explicó anteriormente. También puede pedir una muestra del producto antes de importarlo a su tienda pulsando en el botón "Pedir muestra" en la página del producto. Solo tendrá que pagar el costo de envío de la muestra. </p>
- <h2>Conclusión</h2>
-<p>En conclusión, CJ APK OBB es una combinación de dos archivos que necesita para instalar la aplicación CJdropshipping en su dispositivo Android. La aplicación le permite importar productos de CJdropshipping a sus tiendas en línea, productos de origen de forma gratuita, y acceder a miles de productos POD. Para descargar CJ APK OBB de APKCombo, debe seguir estos pasos:</p>
-<ol>
-<li>Visite el sitio web de APKCombo y busque la aplicación CJdropshipping. </li>
-<li>Elija los archivos APK y OBB que se adapten a las especificaciones de su dispositivo. </li>
-<li>Descargar e instalar APKCombo Installer.</li>
-<li>Instalar CJ APK OBB usando APKCombo instalador.</li>
-</ol>
-
- <h2>Preguntas frecuentes</h2>
-<h4>¿Qué es un archivo APK? </h4>
-<p>Un archivo APK es un archivo de paquete de aplicaciones de Android que contiene el código y los recursos de una aplicación de Android. Se utiliza para instalar aplicaciones en dispositivos Android. </p>
-<h4>¿Qué es un archivo OBB? </h4>
-<p>Un archivo OBB es un archivo de datos adicional que contiene los gráficos y los medios de una aplicación Android. Se utiliza para mejorar el rendimiento y la funcionalidad de una aplicación. </p>
-<h4>¿Qué es un archivo XAPK? </h4>
-<p>Un archivo XAPK es un archivo comprimido que contiene los archivos APK y OBB de una aplicación Android. Se utiliza para simplificar el proceso de instalación de una aplicación. </p>
-<h4>¿Cómo instalar archivos XAPK? </h4>
-<p>Para instalar archivos XAPK, necesita usar una herramienta como APKCombo Installer, que puede extraer e instalar los archivos APK y OBB del archivo XAPK. Puede descargar el instalador de APKCombo desde <a href="">aquí</a>. </p>
-<h4>¿Es APKCombo seguro y confiable? </h4>
-<p>Sí, APKCombo es seguro y confiable, ya que proporciona archivos APK y OBB originales de Google Play Store. No modifica ni altera los archivos de ninguna manera. También utiliza el cifrado SSL para proteger sus datos y su privacidad. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Captulos Historias Interactivas Apk.md

@@ -1,65 +0,0 @@
-
-<p> para crear párrafos, la etiqueta <strong> para poner en negrita el título y los encabezados, la etiqueta <a> para crear enlaces, la etiqueta <img> para insertar imágenes, la etiqueta <table> para crear una tabla y otras etiquetas según sea necesario. También usé palabras clave estratégicamente para optimizar el artículo para los motores de búsqueda. 6. He editado y revisado el borrador, comprobando los errores gramaticales, los errores ortográficos, las repeticiones innecesarias y la claridad. También me aseguré de que el artículo fuera atractivo, informativo, conversacional y breve. 7. Leo en voz alta la versión final del artículo hasta que está libre de errores y listo para ser publicado. Aquí están las dos tablas que solicitó: Tabla 1: Esquema del artículo | H1 | Capítulos: Historias interactivas APK - Cómo descargar y jugar | | --- | -- - | | H2 | ¿Qué son los capítulos: Historias interactivas? | | H3 | Un juego que te permite elegir tu historia | | H3 | Un juego que ofrece una variedad de géneros e historias | | H3 | Un juego que cuenta con impresionantes gráficos y efectos de sonido | | H2 | Por qué descargar Capítulos: Interactive Stories APK? | | H3 | Para disfrutar de acceso ilimitado a todas las historias y características | | H3 | Para jugar sin conexión a Internet | | H3 | Para obtener actualizaciones regulares y nuevos contenidos | | H2 | Cómo descargar Capítulos: Historias interactivas APK? | | H3 | Paso 1: Encontrar una fuente confiable | | H3 | Paso 2: Descargar el archivo APK | | H3 |  | Paso 4: Iniciar el juego y comenzar a jugar | | Conclusión | | H2 | Preguntas frecuentes | Tabla 2: Artículo con formato HTML <h1>>Capítulos: Historias interactivas APK Cómo descargar y jugar</strong></h1>
-
-<h2><strong>¿Qué son los capítulos: Historias interactivas? </strong></h2>
-<p>Capítulos: Interactive Stories es un juego desarrollado por Crazy Maple Studio Dev que te permite leer páginas y páginas de divertidas historias interactivas que se ajustan a tu estado de ánimo. Usted puede elegir su historia de su colección superior de romance, matrimonio por contrato, segunda oportunidad, rey dragón, piratas, lobo alfa, isekai, realidad TV citas, harén inversa, ciencia ficción, comedia y series de drama. </p>
-<h2>capítulos historias interactivas apk</h2><br /><p><b><b>DOWNLOAD</b> &#9989; <a href="https://bltlly.com/2v6ICZ">https://bltlly.com/2v6ICZ</a></b></p><br /><br />
-<h3><strong>Un juego que te permite elegir tu historia</strong></h3>
-<p>Lo mejor de Capítulos: Historias interactivas es que puedes tomar las decisiones en cada historia. Usted puede decidir sobre las decisiones difíciles de la vida tales como enamorarse, descubrir secretos, o desentrañar misterios profundos. También puedes personalizar el nombre, la apariencia, el estilo y la personalidad de tu personaje. Tus elecciones afectarán cómo se desarrolla la historia y cómo termina. </p>
-<h3><strong>Un juego que ofrece una variedad de géneros e historias</strong></h3>
-<p>Otra gran cosa sobre los capítulos: Historias interactivas es que ofrece una amplia gama de géneros e historias para que usted disfrute. Ya sea que te guste el romance, la comedia, el drama, la fantasía, la ciencia ficción o cualquier otra cosa, encontrarás algo que se adapte a tu gusto. También puede explorar diferentes temas y escenarios como matrimonio contractual, romance de segunda oportunidad, romance de rey dragón, aventura de piratas, romance de lobo alfa, aventura isekai, programa de citas de reality TV, romance de harén inverso, thriller de ciencia ficción, programa de comedia y series de drama. También puedes descubrir nuevas historias y autores cada semana a medida que el juego se actualiza regularmente con nuevos contenidos. </p>
-<h3><strong>Un juego que presenta impresionantes gráficos y efectos de sonido</strong></h3>
-
-<h2><strong>¿Por qué deberías descargar Capítulos: Historias interactivas APK? </strong></h2>
-<p>Ahora que sabes lo que es Capítulos: Historias interactivas, es posible que se pregunte por qué debe descargar su versión APK en lugar de la versión oficial de la Google Play Store. Bueno, hay varias razones por las que descargar la versión APK es una mejor opción para usted. </p>
-<h3><strong>Para disfrutar de acceso ilimitado a todas las historias y características</strong></h3>
-<p>La primera razón es que al descargar la versión APK, se puede disfrutar de acceso ilimitado a todas las historias y características en el juego. No tienes que preocuparte por quedarte sin diamantes o entradas, que son las monedas del juego que necesitas para desbloquear opciones premium, atuendos y capítulos. También puedes acceder a todas las historias sin esperar a que sean liberadas o desbloqueadas. Puedes reproducir cualquier historia que quieras, en cualquier momento que quieras, y hacer cualquier elección que quieras. </p>
-<h3><strong>Para jugar sin conexión a Internet</strong></h3>
-<p>La segunda razón es que al descargar la versión APK, se puede jugar el juego sin conexión a Internet. Esto significa que usted no tiene que preocuparse por su uso de datos o su señal wifi. Puedes jugar en cualquier lugar y en cualquier momento, incluso cuando viajas, viajas o estás en una zona remota. También puedes guardar tu progreso y reanudar tu juego más tarde sin perder nada. </p>
-<p></p>
-<h3><strong>Para obtener actualizaciones regulares y nuevo contenido</strong></h3>
-<p>La tercera razón es que al descargar la versión APK, puede obtener actualizaciones regulares y nuevo contenido de los desarrolladores de juegos. No tiene que esperar a que la versión oficial se actualice o parchee. Puedes obtener la última versión del juego tan pronto como esté disponible, con todas las nuevas historias, características, correcciones de errores y mejoras. También puede disfrutar de contenido exclusivo que podría no estar disponible en la versión oficial. </p>
-<h2><strong>Cómo descargar Capítulos: Historias interactivas APK? </strong></h2>
-
-<h3><strong>Paso 1: Encuentra una fuente confiable</strong></h3>
-<p>El primer paso es encontrar una fuente confiable donde se puede descargar el archivo APK de Capítulos: Historias interactivas. Hay muchos sitios web que ofrecen archivos APK de varios juegos y aplicaciones, pero no todos ellos son confiables o seguros. Algunos de ellos pueden contener malware, virus u otro software dañino que puede dañar su dispositivo o robar su información personal. Por lo tanto, debe tener cuidado y elegir una fuente de buena reputación que tenga comentarios positivos y calificaciones de otros usuarios. </p>
-<p>Una de las fuentes que recomendamos es <a href="https://apkpure.com/chapters-interactive-stories/com.mars.avgchapters">APKPure.com</a>, que es un sitio web popular que proporciona archivos APK seguros y verificados de varios juegos y aplicaciones. Puede descargar Capítulos: Historias interactivas APK desde este sitio web haciendo clic en este enlace: <a href="https:/apkpure.com/chapters-interactive-stories/com.mars.avgchapters/download?from=details">https:/apkpure.com/terschaps-chapsinteractive-stories/com.mars.avgchapters/download?from=details</a>. </p>
-<h3><strong>Paso 2: Descargar el archivo APK</strong></h3>
-<p>El segundo paso es descargar el archivo APK de Capítulos: Historias interactivas de la fuente que ha elegido. Para hacer esto, debe seguir estas instrucciones:</p>
-<ul>
-<li>Abra su navegador web en su dispositivo Android y vaya al enlace que proporcionamos anteriormente. </li>
-<li>Haga clic en el botón verde "Descargar APK" en la página. </li>
-<li>Espere a que la descarga comience y termine. </li>
-<li>Es posible que vea un mensaje de advertencia en su pantalla diciendo que este tipo de archivo puede dañar su dispositivo. Ignore este mensaje y haga clic en "Aceptar" o "Descargar de todos modos". </li>
-<li>También puede ver un mensaje emergente que le pide que permita descargas de fuentes desconocidas. Si ves este mensaje, ve a la configuración de tu dispositivo y habilita la opción de permitir descargas desde fuentes desconocidas. </li>
-</ul>
-<h3><strong>Paso 3: Instalar el archivo APK</strong></h3>
-
-<ul>
-<li>Ir al administrador de archivos de su dispositivo y localizar el archivo APK descargado. Debe estar en su carpeta de descarga o en la carpeta que ha especificado para las descargas. </li>
-<li>Toque en el archivo APK y haga clic en "Instalar". </li>
-<li>Espere a que se complete la instalación. </li>
-<li>Es posible que vea un mensaje emergente que le pida que conceda permisos a la aplicación. Si ve este mensaje, haga clic en "Permitir" o "Aceptar". </li>
-</ul>
-<h3><strong>Paso 4: Inicie el juego y comience a jugar</strong></h3>
-<p>El cuarto y último paso es lanzar el juego y empezar a jugar. Para hacer esto, debes seguir estas instrucciones:</p>
-<ul>
-<li>Ve al cajón de aplicaciones de tu dispositivo y busca el icono de Capítulos: Historias interactivas. Debería ser un libro rosa con una C blanca. </li>
-<li>Toca el icono y espera a que el juego se cargue. </li>
-<li>Elija su idioma preferido y acepte los términos del servicio y la política de privacidad. </li>
-<li>Crea tu perfil y personaliza tu personaje. </li>
-<li>Seleccione una historia que desea jugar y disfrutar! </li>
-</ul>
-<h2><strong>Conclusión</strong></h2>
-<p>Capítulos: Interactive Stories es un juego divertido y emocionante que te permite leer y jugar historias interactivas que se ajustan a tu estado de ánimo. Puede elegir entre una variedad de géneros y temas, tomar decisiones que afectan la historia, personalizar su personaje y disfrutar de impresionantes gráficos y efectos de sonido. También puede descargar su versión APK para obtener acceso ilimitado a todas las historias y características, jugar sin conexión a Internet, y obtener actualizaciones regulares y nuevo contenido. Para descargar capítulos: Historias interactivas APK, solo tiene que seguir cuatro sencillos pasos: encontrar una fuente confiable, descargar el archivo APK, instalar el archivo APK, y lanzar el juego. Esperamos que este artículo le ha ayudado a aprender a descargar y jugar Capítulos: Historias interactivas APK. Divertirse! </p>
-<h2><strong>FAQs</strong></h2>
-<p>Aquí hay algunas preguntas frecuentes sobre los capítulos: Historias interactivas APK:</p>
-<tabla>
-
-<tr><td><strong>P: ¿Son libres los capítulos: Historias interactivas APK? </strong></td><td><strong>A: Sí, Capítulos: Historias interactivas APK es gratis para descargar y jugar. Sin embargo, algunas historias y características pueden requerir compras en la aplicación o ver anuncios para desbloquearlos. </strong></td></tr>
-<tr><td><strong>Q: ¿Cómo puedo actualizar los capítulos: Historias interactivas APK? </strong></td><td><strong>A: Puede actualizar los capítulos: Historias interactivas APK mediante la descarga de la última versión del archivo APK de la misma fuente que lo descargó de. También puedes buscar actualizaciones dentro del juego yendo al menú de configuración y pulsando en "Buscar actualizaciones". </strong></td></tr>
-<tr><td><strong>P: ¿Cómo puedo contactar a los desarrolladores de Capítulos: Historias interactivas? </strong></td><td><strong>A: Puede contactar a los desarrolladores de Capítulos: Historias interactivas enviándoles un correo electrónico a [email protected] o visitando su sitio web en https://www.chapter-interactive-stories.com/.</strong>/td></tr>
-<tr><td><strong>P: ¿Cómo puedo compartir mis comentarios o sugerencias para capítulos: Historias interactivas? </strong></td><td><strong>A: Puedes compartir tus comentarios o sugerencias para capítulos: Historias interactivas dejando una reseña en Google Play Store o uniéndote a su comunidad en Facebook en https://www.facebook.com/ChaptersInteractiveStories/</strong></td></tr><
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/commands/search.py

@@ -1,174 +0,0 @@
-import logging
-import shutil
-import sys
-import textwrap
-import xmlrpc.client
-from collections import OrderedDict
-from optparse import Values
-from typing import TYPE_CHECKING, Dict, List, Optional
-
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.req_command import SessionCommandMixin
-from pip._internal.cli.status_codes import NO_MATCHES_FOUND, SUCCESS
-from pip._internal.exceptions import CommandError
-from pip._internal.metadata import get_default_environment
-from pip._internal.models.index import PyPI
-from pip._internal.network.xmlrpc import PipXmlrpcTransport
-from pip._internal.utils.logging import indent_log
-from pip._internal.utils.misc import write_output
-
-if TYPE_CHECKING:
-    from typing import TypedDict
-
-    class TransformedHit(TypedDict):
-        name: str
-        summary: str
-        versions: List[str]
-
-
-logger = logging.getLogger(__name__)
-
-
-class SearchCommand(Command, SessionCommandMixin):
-    """Search for PyPI packages whose name or summary contains <query>."""
-
-    usage = """
-      %prog [options] <query>"""
-    ignore_require_venv = True
-
-    def add_options(self) -> None:
-        self.cmd_opts.add_option(
-            "-i",
-            "--index",
-            dest="index",
-            metavar="URL",
-            default=PyPI.pypi_url,
-            help="Base URL of Python Package Index (default %default)",
-        )
-
-        self.parser.insert_option_group(0, self.cmd_opts)
-
-    def run(self, options: Values, args: List[str]) -> int:
-        if not args:
-            raise CommandError("Missing required argument (search query).")
-        query = args
-        pypi_hits = self.search(query, options)
-        hits = transform_hits(pypi_hits)
-
-        terminal_width = None
-        if sys.stdout.isatty():
-            terminal_width = shutil.get_terminal_size()[0]
-
-        print_results(hits, terminal_width=terminal_width)
-        if pypi_hits:
-            return SUCCESS
-        return NO_MATCHES_FOUND
-
-    def search(self, query: List[str], options: Values) -> List[Dict[str, str]]:
-        index_url = options.index
-
-        session = self.get_default_session(options)
-
-        transport = PipXmlrpcTransport(index_url, session)
-        pypi = xmlrpc.client.ServerProxy(index_url, transport)
-        try:
-            hits = pypi.search({"name": query, "summary": query}, "or")
-        except xmlrpc.client.Fault as fault:
-            message = "XMLRPC request failed [code: {code}]\n{string}".format(
-                code=fault.faultCode,
-                string=fault.faultString,
-            )
-            raise CommandError(message)
-        assert isinstance(hits, list)
-        return hits
-
-
-def transform_hits(hits: List[Dict[str, str]]) -> List["TransformedHit"]:
-    """
-    The list from pypi is really a list of versions. We want a list of
-    packages with the list of versions stored inline. This converts the
-    list from pypi into one we can use.
-    """
-    packages: Dict[str, "TransformedHit"] = OrderedDict()
-    for hit in hits:
-        name = hit["name"]
-        summary = hit["summary"]
-        version = hit["version"]
-
-        if name not in packages.keys():
-            packages[name] = {
-                "name": name,
-                "summary": summary,
-                "versions": [version],
-            }
-        else:
-            packages[name]["versions"].append(version)
-
-            # if this is the highest version, replace summary and score
-            if version == highest_version(packages[name]["versions"]):
-                packages[name]["summary"] = summary
-
-    return list(packages.values())
-
-
-def print_dist_installation_info(name: str, latest: str) -> None:
-    env = get_default_environment()
-    dist = env.get_distribution(name)
-    if dist is not None:
-        with indent_log():
-            if dist.version == latest:
-                write_output("INSTALLED: %s (latest)", dist.version)
-            else:
-                write_output("INSTALLED: %s", dist.version)
-                if parse_version(latest).pre:
-                    write_output(
-                        "LATEST:    %s (pre-release; install"
-                        " with `pip install --pre`)",
-                        latest,
-                    )
-                else:
-                    write_output("LATEST:    %s", latest)
-
-
-def print_results(
-    hits: List["TransformedHit"],
-    name_column_width: Optional[int] = None,
-    terminal_width: Optional[int] = None,
-) -> None:
-    if not hits:
-        return
-    if name_column_width is None:
-        name_column_width = (
-            max(
-                [
-                    len(hit["name"]) + len(highest_version(hit.get("versions", ["-"])))
-                    for hit in hits
-                ]
-            )
-            + 4
-        )
-
-    for hit in hits:
-        name = hit["name"]
-        summary = hit["summary"] or ""
-        latest = highest_version(hit.get("versions", ["-"]))
-        if terminal_width is not None:
-            target_width = terminal_width - name_column_width - 5
-            if target_width > 10:
-                # wrap and indent summary to fit terminal
-                summary_lines = textwrap.wrap(summary, target_width)
-                summary = ("\n" + " " * (name_column_width + 3)).join(summary_lines)
-
-        name_latest = f"{name} ({latest})"
-        line = f"{name_latest:{name_column_width}} - {summary}"
-        try:
-            write_output(line)
-            print_dist_installation_info(name, latest)
-        except UnicodeEncodeError:
-            pass
-
-
-def highest_version(versions: List[str]) -> str:
-    return max(versions, key=parse_version)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/euctwfreq.py

@@ -1,388 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Communicator client code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-# EUCTW frequency table
-# Converted from big5 work
-# by Taiwan's Mandarin Promotion Council
-# <http:#www.edu.tw:81/mandr/>
-
-# 128  --> 0.42261
-# 256  --> 0.57851
-# 512  --> 0.74851
-# 1024 --> 0.89384
-# 2048 --> 0.97583
-#
-# Idea Distribution Ratio = 0.74851/(1-0.74851) =2.98
-# Random Distribution Ration = 512/(5401-512)=0.105
-#
-# Typical Distribution Ratio about 25% of Ideal one, still much higher than RDR
-
-EUCTW_TYPICAL_DISTRIBUTION_RATIO = 0.75
-
-# Char to FreqOrder table
-EUCTW_TABLE_SIZE = 5376
-
-# fmt: off
-EUCTW_CHAR_TO_FREQ_ORDER = (
-    1, 1800, 1506, 255, 1431, 198, 9, 82, 6, 7310, 177, 202, 3615, 1256, 2808, 110,  # 2742
-    3735, 33, 3241, 261, 76, 44, 2113, 16, 2931, 2184, 1176, 659, 3868, 26, 3404, 2643,  # 2758
-    1198, 3869, 3313, 4060, 410, 2211, 302, 590, 361, 1963, 8, 204, 58, 4296, 7311, 1931,  # 2774
-    63, 7312, 7313, 317, 1614, 75, 222, 159, 4061, 2412, 1480, 7314, 3500, 3068, 224, 2809,  # 2790
-    3616, 3, 10, 3870, 1471, 29, 2774, 1135, 2852, 1939, 873, 130, 3242, 1123, 312, 7315,  # 2806
-    4297, 2051, 507, 252, 682, 7316, 142, 1914, 124, 206, 2932, 34, 3501, 3173, 64, 604,  # 2822
-    7317, 2494, 1976, 1977, 155, 1990, 645, 641, 1606, 7318, 3405, 337, 72, 406, 7319, 80,  # 2838
-    630, 238, 3174, 1509, 263, 939, 1092, 2644, 756, 1440, 1094, 3406, 449, 69, 2969, 591,  # 2854
-    179, 2095, 471, 115, 2034, 1843, 60, 50, 2970, 134, 806, 1868, 734, 2035, 3407, 180,  # 2870
-    995, 1607, 156, 537, 2893, 688, 7320, 319, 1305, 779, 2144, 514, 2374, 298, 4298, 359,  # 2886
-    2495, 90, 2707, 1338, 663, 11, 906, 1099, 2545, 20, 2436, 182, 532, 1716, 7321, 732,  # 2902
-    1376, 4062, 1311, 1420, 3175, 25, 2312, 1056, 113, 399, 382, 1949, 242, 3408, 2467, 529,  # 2918
-    3243, 475, 1447, 3617, 7322, 117, 21, 656, 810, 1297, 2295, 2329, 3502, 7323, 126, 4063,  # 2934
-    706, 456, 150, 613, 4299, 71, 1118, 2036, 4064, 145, 3069, 85, 835, 486, 2114, 1246,  # 2950
-    1426, 428, 727, 1285, 1015, 800, 106, 623, 303, 1281, 7324, 2127, 2354, 347, 3736, 221,  # 2966
-    3503, 3110, 7325, 1955, 1153, 4065, 83, 296, 1199, 3070, 192, 624, 93, 7326, 822, 1897,  # 2982
-    2810, 3111, 795, 2064, 991, 1554, 1542, 1592, 27, 43, 2853, 859, 139, 1456, 860, 4300,  # 2998
-    437, 712, 3871, 164, 2392, 3112, 695, 211, 3017, 2096, 195, 3872, 1608, 3504, 3505, 3618,  # 3014
-    3873, 234, 811, 2971, 2097, 3874, 2229, 1441, 3506, 1615, 2375, 668, 2076, 1638, 305, 228,  # 3030
-    1664, 4301, 467, 415, 7327, 262, 2098, 1593, 239, 108, 300, 200, 1033, 512, 1247, 2077,  # 3046
-    7328, 7329, 2173, 3176, 3619, 2673, 593, 845, 1062, 3244, 88, 1723, 2037, 3875, 1950, 212,  # 3062
-    266, 152, 149, 468, 1898, 4066, 4302, 77, 187, 7330, 3018, 37, 5, 2972, 7331, 3876,  # 3078
-    7332, 7333, 39, 2517, 4303, 2894, 3177, 2078, 55, 148, 74, 4304, 545, 483, 1474, 1029,  # 3094
-    1665, 217, 1869, 1531, 3113, 1104, 2645, 4067, 24, 172, 3507, 900, 3877, 3508, 3509, 4305,  # 3110
-    32, 1408, 2811, 1312, 329, 487, 2355, 2247, 2708, 784, 2674, 4, 3019, 3314, 1427, 1788,  # 3126
-    188, 109, 499, 7334, 3620, 1717, 1789, 888, 1217, 3020, 4306, 7335, 3510, 7336, 3315, 1520,  # 3142
-    3621, 3878, 196, 1034, 775, 7337, 7338, 929, 1815, 249, 439, 38, 7339, 1063, 7340, 794,  # 3158
-    3879, 1435, 2296, 46, 178, 3245, 2065, 7341, 2376, 7342, 214, 1709, 4307, 804, 35, 707,  # 3174
-    324, 3622, 1601, 2546, 140, 459, 4068, 7343, 7344, 1365, 839, 272, 978, 2257, 2572, 3409,  # 3190
-    2128, 1363, 3623, 1423, 697, 100, 3071, 48, 70, 1231, 495, 3114, 2193, 7345, 1294, 7346,  # 3206
-    2079, 462, 586, 1042, 3246, 853, 256, 988, 185, 2377, 3410, 1698, 434, 1084, 7347, 3411,  # 3222
-    314, 2615, 2775, 4308, 2330, 2331, 569, 2280, 637, 1816, 2518, 757, 1162, 1878, 1616, 3412,  # 3238
-    287, 1577, 2115, 768, 4309, 1671, 2854, 3511, 2519, 1321, 3737, 909, 2413, 7348, 4069, 933,  # 3254
-    3738, 7349, 2052, 2356, 1222, 4310, 765, 2414, 1322, 786, 4311, 7350, 1919, 1462, 1677, 2895,  # 3270
-    1699, 7351, 4312, 1424, 2437, 3115, 3624, 2590, 3316, 1774, 1940, 3413, 3880, 4070, 309, 1369,  # 3286
-    1130, 2812, 364, 2230, 1653, 1299, 3881, 3512, 3882, 3883, 2646, 525, 1085, 3021, 902, 2000,  # 3302
-    1475, 964, 4313, 421, 1844, 1415, 1057, 2281, 940, 1364, 3116, 376, 4314, 4315, 1381, 7,  # 3318
-    2520, 983, 2378, 336, 1710, 2675, 1845, 321, 3414, 559, 1131, 3022, 2742, 1808, 1132, 1313,  # 3334
-    265, 1481, 1857, 7352, 352, 1203, 2813, 3247, 167, 1089, 420, 2814, 776, 792, 1724, 3513,  # 3350
-    4071, 2438, 3248, 7353, 4072, 7354, 446, 229, 333, 2743, 901, 3739, 1200, 1557, 4316, 2647,  # 3366
-    1920, 395, 2744, 2676, 3740, 4073, 1835, 125, 916, 3178, 2616, 4317, 7355, 7356, 3741, 7357,  # 3382
-    7358, 7359, 4318, 3117, 3625, 1133, 2547, 1757, 3415, 1510, 2313, 1409, 3514, 7360, 2145, 438,  # 3398
-    2591, 2896, 2379, 3317, 1068, 958, 3023, 461, 311, 2855, 2677, 4074, 1915, 3179, 4075, 1978,  # 3414
-    383, 750, 2745, 2617, 4076, 274, 539, 385, 1278, 1442, 7361, 1154, 1964, 384, 561, 210,  # 3430
-    98, 1295, 2548, 3515, 7362, 1711, 2415, 1482, 3416, 3884, 2897, 1257, 129, 7363, 3742, 642,  # 3446
-    523, 2776, 2777, 2648, 7364, 141, 2231, 1333, 68, 176, 441, 876, 907, 4077, 603, 2592,  # 3462
-    710, 171, 3417, 404, 549, 18, 3118, 2393, 1410, 3626, 1666, 7365, 3516, 4319, 2898, 4320,  # 3478
-    7366, 2973, 368, 7367, 146, 366, 99, 871, 3627, 1543, 748, 807, 1586, 1185, 22, 2258,  # 3494
-    379, 3743, 3180, 7368, 3181, 505, 1941, 2618, 1991, 1382, 2314, 7369, 380, 2357, 218, 702,  # 3510
-    1817, 1248, 3418, 3024, 3517, 3318, 3249, 7370, 2974, 3628, 930, 3250, 3744, 7371, 59, 7372,  # 3526
-    585, 601, 4078, 497, 3419, 1112, 1314, 4321, 1801, 7373, 1223, 1472, 2174, 7374, 749, 1836,  # 3542
-    690, 1899, 3745, 1772, 3885, 1476, 429, 1043, 1790, 2232, 2116, 917, 4079, 447, 1086, 1629,  # 3558
-    7375, 556, 7376, 7377, 2020, 1654, 844, 1090, 105, 550, 966, 1758, 2815, 1008, 1782, 686,  # 3574
-    1095, 7378, 2282, 793, 1602, 7379, 3518, 2593, 4322, 4080, 2933, 2297, 4323, 3746, 980, 2496,  # 3590
-    544, 353, 527, 4324, 908, 2678, 2899, 7380, 381, 2619, 1942, 1348, 7381, 1341, 1252, 560,  # 3606
-    3072, 7382, 3420, 2856, 7383, 2053, 973, 886, 2080, 143, 4325, 7384, 7385, 157, 3886, 496,  # 3622
-    4081, 57, 840, 540, 2038, 4326, 4327, 3421, 2117, 1445, 970, 2259, 1748, 1965, 2081, 4082,  # 3638
-    3119, 1234, 1775, 3251, 2816, 3629, 773, 1206, 2129, 1066, 2039, 1326, 3887, 1738, 1725, 4083,  # 3654
-    279, 3120, 51, 1544, 2594, 423, 1578, 2130, 2066, 173, 4328, 1879, 7386, 7387, 1583, 264,  # 3670
-    610, 3630, 4329, 2439, 280, 154, 7388, 7389, 7390, 1739, 338, 1282, 3073, 693, 2857, 1411,  # 3686
-    1074, 3747, 2440, 7391, 4330, 7392, 7393, 1240, 952, 2394, 7394, 2900, 1538, 2679, 685, 1483,  # 3702
-    4084, 2468, 1436, 953, 4085, 2054, 4331, 671, 2395, 79, 4086, 2441, 3252, 608, 567, 2680,  # 3718
-    3422, 4087, 4088, 1691, 393, 1261, 1791, 2396, 7395, 4332, 7396, 7397, 7398, 7399, 1383, 1672,  # 3734
-    3748, 3182, 1464, 522, 1119, 661, 1150, 216, 675, 4333, 3888, 1432, 3519, 609, 4334, 2681,  # 3750
-    2397, 7400, 7401, 7402, 4089, 3025, 0, 7403, 2469, 315, 231, 2442, 301, 3319, 4335, 2380,  # 3766
-    7404, 233, 4090, 3631, 1818, 4336, 4337, 7405, 96, 1776, 1315, 2082, 7406, 257, 7407, 1809,  # 3782
-    3632, 2709, 1139, 1819, 4091, 2021, 1124, 2163, 2778, 1777, 2649, 7408, 3074, 363, 1655, 3183,  # 3798
-    7409, 2975, 7410, 7411, 7412, 3889, 1567, 3890, 718, 103, 3184, 849, 1443, 341, 3320, 2934,  # 3814
-    1484, 7413, 1712, 127, 67, 339, 4092, 2398, 679, 1412, 821, 7414, 7415, 834, 738, 351,  # 3830
-    2976, 2146, 846, 235, 1497, 1880, 418, 1992, 3749, 2710, 186, 1100, 2147, 2746, 3520, 1545,  # 3846
-    1355, 2935, 2858, 1377, 583, 3891, 4093, 2573, 2977, 7416, 1298, 3633, 1078, 2549, 3634, 2358,  # 3862
-    78, 3750, 3751, 267, 1289, 2099, 2001, 1594, 4094, 348, 369, 1274, 2194, 2175, 1837, 4338,  # 3878
-    1820, 2817, 3635, 2747, 2283, 2002, 4339, 2936, 2748, 144, 3321, 882, 4340, 3892, 2749, 3423,  # 3894
-    4341, 2901, 7417, 4095, 1726, 320, 7418, 3893, 3026, 788, 2978, 7419, 2818, 1773, 1327, 2859,  # 3910
-    3894, 2819, 7420, 1306, 4342, 2003, 1700, 3752, 3521, 2359, 2650, 787, 2022, 506, 824, 3636,  # 3926
-    534, 323, 4343, 1044, 3322, 2023, 1900, 946, 3424, 7421, 1778, 1500, 1678, 7422, 1881, 4344,  # 3942
-    165, 243, 4345, 3637, 2521, 123, 683, 4096, 764, 4346, 36, 3895, 1792, 589, 2902, 816,  # 3958
-    626, 1667, 3027, 2233, 1639, 1555, 1622, 3753, 3896, 7423, 3897, 2860, 1370, 1228, 1932, 891,  # 3974
-    2083, 2903, 304, 4097, 7424, 292, 2979, 2711, 3522, 691, 2100, 4098, 1115, 4347, 118, 662,  # 3990
-    7425, 611, 1156, 854, 2381, 1316, 2861, 2, 386, 515, 2904, 7426, 7427, 3253, 868, 2234,  # 4006
-    1486, 855, 2651, 785, 2212, 3028, 7428, 1040, 3185, 3523, 7429, 3121, 448, 7430, 1525, 7431,  # 4022
-    2164, 4348, 7432, 3754, 7433, 4099, 2820, 3524, 3122, 503, 818, 3898, 3123, 1568, 814, 676,  # 4038
-    1444, 306, 1749, 7434, 3755, 1416, 1030, 197, 1428, 805, 2821, 1501, 4349, 7435, 7436, 7437,  # 4054
-    1993, 7438, 4350, 7439, 7440, 2195, 13, 2779, 3638, 2980, 3124, 1229, 1916, 7441, 3756, 2131,  # 4070
-    7442, 4100, 4351, 2399, 3525, 7443, 2213, 1511, 1727, 1120, 7444, 7445, 646, 3757, 2443, 307,  # 4086
-    7446, 7447, 1595, 3186, 7448, 7449, 7450, 3639, 1113, 1356, 3899, 1465, 2522, 2523, 7451, 519,  # 4102
-    7452, 128, 2132, 92, 2284, 1979, 7453, 3900, 1512, 342, 3125, 2196, 7454, 2780, 2214, 1980,  # 4118
-    3323, 7455, 290, 1656, 1317, 789, 827, 2360, 7456, 3758, 4352, 562, 581, 3901, 7457, 401,  # 4134
-    4353, 2248, 94, 4354, 1399, 2781, 7458, 1463, 2024, 4355, 3187, 1943, 7459, 828, 1105, 4101,  # 4150
-    1262, 1394, 7460, 4102, 605, 4356, 7461, 1783, 2862, 7462, 2822, 819, 2101, 578, 2197, 2937,  # 4166
-    7463, 1502, 436, 3254, 4103, 3255, 2823, 3902, 2905, 3425, 3426, 7464, 2712, 2315, 7465, 7466,  # 4182
-    2332, 2067, 23, 4357, 193, 826, 3759, 2102, 699, 1630, 4104, 3075, 390, 1793, 1064, 3526,  # 4198
-    7467, 1579, 3076, 3077, 1400, 7468, 4105, 1838, 1640, 2863, 7469, 4358, 4359, 137, 4106, 598,  # 4214
-    3078, 1966, 780, 104, 974, 2938, 7470, 278, 899, 253, 402, 572, 504, 493, 1339, 7471,  # 4230
-    3903, 1275, 4360, 2574, 2550, 7472, 3640, 3029, 3079, 2249, 565, 1334, 2713, 863, 41, 7473,  # 4246
-    7474, 4361, 7475, 1657, 2333, 19, 463, 2750, 4107, 606, 7476, 2981, 3256, 1087, 2084, 1323,  # 4262
-    2652, 2982, 7477, 1631, 1623, 1750, 4108, 2682, 7478, 2864, 791, 2714, 2653, 2334, 232, 2416,  # 4278
-    7479, 2983, 1498, 7480, 2654, 2620, 755, 1366, 3641, 3257, 3126, 2025, 1609, 119, 1917, 3427,  # 4294
-    862, 1026, 4109, 7481, 3904, 3760, 4362, 3905, 4363, 2260, 1951, 2470, 7482, 1125, 817, 4110,  # 4310
-    4111, 3906, 1513, 1766, 2040, 1487, 4112, 3030, 3258, 2824, 3761, 3127, 7483, 7484, 1507, 7485,  # 4326
-    2683, 733, 40, 1632, 1106, 2865, 345, 4113, 841, 2524, 230, 4364, 2984, 1846, 3259, 3428,  # 4342
-    7486, 1263, 986, 3429, 7487, 735, 879, 254, 1137, 857, 622, 1300, 1180, 1388, 1562, 3907,  # 4358
-    3908, 2939, 967, 2751, 2655, 1349, 592, 2133, 1692, 3324, 2985, 1994, 4114, 1679, 3909, 1901,  # 4374
-    2185, 7488, 739, 3642, 2715, 1296, 1290, 7489, 4115, 2198, 2199, 1921, 1563, 2595, 2551, 1870,  # 4390
-    2752, 2986, 7490, 435, 7491, 343, 1108, 596, 17, 1751, 4365, 2235, 3430, 3643, 7492, 4366,  # 4406
-    294, 3527, 2940, 1693, 477, 979, 281, 2041, 3528, 643, 2042, 3644, 2621, 2782, 2261, 1031,  # 4422
-    2335, 2134, 2298, 3529, 4367, 367, 1249, 2552, 7493, 3530, 7494, 4368, 1283, 3325, 2004, 240,  # 4438
-    1762, 3326, 4369, 4370, 836, 1069, 3128, 474, 7495, 2148, 2525, 268, 3531, 7496, 3188, 1521,  # 4454
-    1284, 7497, 1658, 1546, 4116, 7498, 3532, 3533, 7499, 4117, 3327, 2684, 1685, 4118, 961, 1673,  # 4470
-    2622, 190, 2005, 2200, 3762, 4371, 4372, 7500, 570, 2497, 3645, 1490, 7501, 4373, 2623, 3260,  # 4486
-    1956, 4374, 584, 1514, 396, 1045, 1944, 7502, 4375, 1967, 2444, 7503, 7504, 4376, 3910, 619,  # 4502
-    7505, 3129, 3261, 215, 2006, 2783, 2553, 3189, 4377, 3190, 4378, 763, 4119, 3763, 4379, 7506,  # 4518
-    7507, 1957, 1767, 2941, 3328, 3646, 1174, 452, 1477, 4380, 3329, 3130, 7508, 2825, 1253, 2382,  # 4534
-    2186, 1091, 2285, 4120, 492, 7509, 638, 1169, 1824, 2135, 1752, 3911, 648, 926, 1021, 1324,  # 4550
-    4381, 520, 4382, 997, 847, 1007, 892, 4383, 3764, 2262, 1871, 3647, 7510, 2400, 1784, 4384,  # 4566
-    1952, 2942, 3080, 3191, 1728, 4121, 2043, 3648, 4385, 2007, 1701, 3131, 1551, 30, 2263, 4122,  # 4582
-    7511, 2026, 4386, 3534, 7512, 501, 7513, 4123, 594, 3431, 2165, 1821, 3535, 3432, 3536, 3192,  # 4598
-    829, 2826, 4124, 7514, 1680, 3132, 1225, 4125, 7515, 3262, 4387, 4126, 3133, 2336, 7516, 4388,  # 4614
-    4127, 7517, 3912, 3913, 7518, 1847, 2383, 2596, 3330, 7519, 4389, 374, 3914, 652, 4128, 4129,  # 4630
-    375, 1140, 798, 7520, 7521, 7522, 2361, 4390, 2264, 546, 1659, 138, 3031, 2445, 4391, 7523,  # 4646
-    2250, 612, 1848, 910, 796, 3765, 1740, 1371, 825, 3766, 3767, 7524, 2906, 2554, 7525, 692,  # 4662
-    444, 3032, 2624, 801, 4392, 4130, 7526, 1491, 244, 1053, 3033, 4131, 4132, 340, 7527, 3915,  # 4678
-    1041, 2987, 293, 1168, 87, 1357, 7528, 1539, 959, 7529, 2236, 721, 694, 4133, 3768, 219,  # 4694
-    1478, 644, 1417, 3331, 2656, 1413, 1401, 1335, 1389, 3916, 7530, 7531, 2988, 2362, 3134, 1825,  # 4710
-    730, 1515, 184, 2827, 66, 4393, 7532, 1660, 2943, 246, 3332, 378, 1457, 226, 3433, 975,  # 4726
-    3917, 2944, 1264, 3537, 674, 696, 7533, 163, 7534, 1141, 2417, 2166, 713, 3538, 3333, 4394,  # 4742
-    3918, 7535, 7536, 1186, 15, 7537, 1079, 1070, 7538, 1522, 3193, 3539, 276, 1050, 2716, 758,  # 4758
-    1126, 653, 2945, 3263, 7539, 2337, 889, 3540, 3919, 3081, 2989, 903, 1250, 4395, 3920, 3434,  # 4774
-    3541, 1342, 1681, 1718, 766, 3264, 286, 89, 2946, 3649, 7540, 1713, 7541, 2597, 3334, 2990,  # 4790
-    7542, 2947, 2215, 3194, 2866, 7543, 4396, 2498, 2526, 181, 387, 1075, 3921, 731, 2187, 3335,  # 4806
-    7544, 3265, 310, 313, 3435, 2299, 770, 4134, 54, 3034, 189, 4397, 3082, 3769, 3922, 7545,  # 4822
-    1230, 1617, 1849, 355, 3542, 4135, 4398, 3336, 111, 4136, 3650, 1350, 3135, 3436, 3035, 4137,  # 4838
-    2149, 3266, 3543, 7546, 2784, 3923, 3924, 2991, 722, 2008, 7547, 1071, 247, 1207, 2338, 2471,  # 4854
-    1378, 4399, 2009, 864, 1437, 1214, 4400, 373, 3770, 1142, 2216, 667, 4401, 442, 2753, 2555,  # 4870
-    3771, 3925, 1968, 4138, 3267, 1839, 837, 170, 1107, 934, 1336, 1882, 7548, 7549, 2118, 4139,  # 4886
-    2828, 743, 1569, 7550, 4402, 4140, 582, 2384, 1418, 3437, 7551, 1802, 7552, 357, 1395, 1729,  # 4902
-    3651, 3268, 2418, 1564, 2237, 7553, 3083, 3772, 1633, 4403, 1114, 2085, 4141, 1532, 7554, 482,  # 4918
-    2446, 4404, 7555, 7556, 1492, 833, 1466, 7557, 2717, 3544, 1641, 2829, 7558, 1526, 1272, 3652,  # 4934
-    4142, 1686, 1794, 416, 2556, 1902, 1953, 1803, 7559, 3773, 2785, 3774, 1159, 2316, 7560, 2867,  # 4950
-    4405, 1610, 1584, 3036, 2419, 2754, 443, 3269, 1163, 3136, 7561, 7562, 3926, 7563, 4143, 2499,  # 4966
-    3037, 4406, 3927, 3137, 2103, 1647, 3545, 2010, 1872, 4144, 7564, 4145, 431, 3438, 7565, 250,  # 4982
-    97, 81, 4146, 7566, 1648, 1850, 1558, 160, 848, 7567, 866, 740, 1694, 7568, 2201, 2830,  # 4998
-    3195, 4147, 4407, 3653, 1687, 950, 2472, 426, 469, 3196, 3654, 3655, 3928, 7569, 7570, 1188,  # 5014
-    424, 1995, 861, 3546, 4148, 3775, 2202, 2685, 168, 1235, 3547, 4149, 7571, 2086, 1674, 4408,  # 5030
-    3337, 3270, 220, 2557, 1009, 7572, 3776, 670, 2992, 332, 1208, 717, 7573, 7574, 3548, 2447,  # 5046
-    3929, 3338, 7575, 513, 7576, 1209, 2868, 3339, 3138, 4409, 1080, 7577, 7578, 7579, 7580, 2527,  # 5062
-    3656, 3549, 815, 1587, 3930, 3931, 7581, 3550, 3439, 3777, 1254, 4410, 1328, 3038, 1390, 3932,  # 5078
-    1741, 3933, 3778, 3934, 7582, 236, 3779, 2448, 3271, 7583, 7584, 3657, 3780, 1273, 3781, 4411,  # 5094
-    7585, 308, 7586, 4412, 245, 4413, 1851, 2473, 1307, 2575, 430, 715, 2136, 2449, 7587, 270,  # 5110
-    199, 2869, 3935, 7588, 3551, 2718, 1753, 761, 1754, 725, 1661, 1840, 4414, 3440, 3658, 7589,  # 5126
-    7590, 587, 14, 3272, 227, 2598, 326, 480, 2265, 943, 2755, 3552, 291, 650, 1883, 7591,  # 5142
-    1702, 1226, 102, 1547, 62, 3441, 904, 4415, 3442, 1164, 4150, 7592, 7593, 1224, 1548, 2756,  # 5158
-    391, 498, 1493, 7594, 1386, 1419, 7595, 2055, 1177, 4416, 813, 880, 1081, 2363, 566, 1145,  # 5174
-    4417, 2286, 1001, 1035, 2558, 2599, 2238, 394, 1286, 7596, 7597, 2068, 7598, 86, 1494, 1730,  # 5190
-    3936, 491, 1588, 745, 897, 2948, 843, 3340, 3937, 2757, 2870, 3273, 1768, 998, 2217, 2069,  # 5206
-    397, 1826, 1195, 1969, 3659, 2993, 3341, 284, 7599, 3782, 2500, 2137, 2119, 1903, 7600, 3938,  # 5222
-    2150, 3939, 4151, 1036, 3443, 1904, 114, 2559, 4152, 209, 1527, 7601, 7602, 2949, 2831, 2625,  # 5238
-    2385, 2719, 3139, 812, 2560, 7603, 3274, 7604, 1559, 737, 1884, 3660, 1210, 885, 28, 2686,  # 5254
-    3553, 3783, 7605, 4153, 1004, 1779, 4418, 7606, 346, 1981, 2218, 2687, 4419, 3784, 1742, 797,  # 5270
-    1642, 3940, 1933, 1072, 1384, 2151, 896, 3941, 3275, 3661, 3197, 2871, 3554, 7607, 2561, 1958,  # 5286
-    4420, 2450, 1785, 7608, 7609, 7610, 3942, 4154, 1005, 1308, 3662, 4155, 2720, 4421, 4422, 1528,  # 5302
-    2600, 161, 1178, 4156, 1982, 987, 4423, 1101, 4157, 631, 3943, 1157, 3198, 2420, 1343, 1241,  # 5318
-    1016, 2239, 2562, 372, 877, 2339, 2501, 1160, 555, 1934, 911, 3944, 7611, 466, 1170, 169,  # 5334
-    1051, 2907, 2688, 3663, 2474, 2994, 1182, 2011, 2563, 1251, 2626, 7612, 992, 2340, 3444, 1540,  # 5350
-    2721, 1201, 2070, 2401, 1996, 2475, 7613, 4424, 528, 1922, 2188, 1503, 1873, 1570, 2364, 3342,  # 5366
-    3276, 7614, 557, 1073, 7615, 1827, 3445, 2087, 2266, 3140, 3039, 3084, 767, 3085, 2786, 4425,  # 5382
-    1006, 4158, 4426, 2341, 1267, 2176, 3664, 3199, 778, 3945, 3200, 2722, 1597, 2657, 7616, 4427,  # 5398
-    7617, 3446, 7618, 7619, 7620, 3277, 2689, 1433, 3278, 131, 95, 1504, 3946, 723, 4159, 3141,  # 5414
-    1841, 3555, 2758, 2189, 3947, 2027, 2104, 3665, 7621, 2995, 3948, 1218, 7622, 3343, 3201, 3949,  # 5430
-    4160, 2576, 248, 1634, 3785, 912, 7623, 2832, 3666, 3040, 3786, 654, 53, 7624, 2996, 7625,  # 5446
-    1688, 4428, 777, 3447, 1032, 3950, 1425, 7626, 191, 820, 2120, 2833, 971, 4429, 931, 3202,  # 5462
-    135, 664, 783, 3787, 1997, 772, 2908, 1935, 3951, 3788, 4430, 2909, 3203, 282, 2723, 640,  # 5478
-    1372, 3448, 1127, 922, 325, 3344, 7627, 7628, 711, 2044, 7629, 7630, 3952, 2219, 2787, 1936,  # 5494
-    3953, 3345, 2220, 2251, 3789, 2300, 7631, 4431, 3790, 1258, 3279, 3954, 3204, 2138, 2950, 3955,  # 5510
-    3956, 7632, 2221, 258, 3205, 4432, 101, 1227, 7633, 3280, 1755, 7634, 1391, 3281, 7635, 2910,  # 5526
-    2056, 893, 7636, 7637, 7638, 1402, 4161, 2342, 7639, 7640, 3206, 3556, 7641, 7642, 878, 1325,  # 5542
-    1780, 2788, 4433, 259, 1385, 2577, 744, 1183, 2267, 4434, 7643, 3957, 2502, 7644, 684, 1024,  # 5558
-    4162, 7645, 472, 3557, 3449, 1165, 3282, 3958, 3959, 322, 2152, 881, 455, 1695, 1152, 1340,  # 5574
-    660, 554, 2153, 4435, 1058, 4436, 4163, 830, 1065, 3346, 3960, 4437, 1923, 7646, 1703, 1918,  # 5590
-    7647, 932, 2268, 122, 7648, 4438, 947, 677, 7649, 3791, 2627, 297, 1905, 1924, 2269, 4439,  # 5606
-    2317, 3283, 7650, 7651, 4164, 7652, 4165, 84, 4166, 112, 989, 7653, 547, 1059, 3961, 701,  # 5622
-    3558, 1019, 7654, 4167, 7655, 3450, 942, 639, 457, 2301, 2451, 993, 2951, 407, 851, 494,  # 5638
-    4440, 3347, 927, 7656, 1237, 7657, 2421, 3348, 573, 4168, 680, 921, 2911, 1279, 1874, 285,  # 5654
-    790, 1448, 1983, 719, 2167, 7658, 7659, 4441, 3962, 3963, 1649, 7660, 1541, 563, 7661, 1077,  # 5670
-    7662, 3349, 3041, 3451, 511, 2997, 3964, 3965, 3667, 3966, 1268, 2564, 3350, 3207, 4442, 4443,  # 5686
-    7663, 535, 1048, 1276, 1189, 2912, 2028, 3142, 1438, 1373, 2834, 2952, 1134, 2012, 7664, 4169,  # 5702
-    1238, 2578, 3086, 1259, 7665, 700, 7666, 2953, 3143, 3668, 4170, 7667, 4171, 1146, 1875, 1906,  # 5718
-    4444, 2601, 3967, 781, 2422, 132, 1589, 203, 147, 273, 2789, 2402, 898, 1786, 2154, 3968,  # 5734
-    3969, 7668, 3792, 2790, 7669, 7670, 4445, 4446, 7671, 3208, 7672, 1635, 3793, 965, 7673, 1804,  # 5750
-    2690, 1516, 3559, 1121, 1082, 1329, 3284, 3970, 1449, 3794, 65, 1128, 2835, 2913, 2759, 1590,  # 5766
-    3795, 7674, 7675, 12, 2658, 45, 976, 2579, 3144, 4447, 517, 2528, 1013, 1037, 3209, 7676,  # 5782
-    3796, 2836, 7677, 3797, 7678, 3452, 7679, 2602, 614, 1998, 2318, 3798, 3087, 2724, 2628, 7680,  # 5798
-    2580, 4172, 599, 1269, 7681, 1810, 3669, 7682, 2691, 3088, 759, 1060, 489, 1805, 3351, 3285,  # 5814
-    1358, 7683, 7684, 2386, 1387, 1215, 2629, 2252, 490, 7685, 7686, 4173, 1759, 2387, 2343, 7687,  # 5830
-    4448, 3799, 1907, 3971, 2630, 1806, 3210, 4449, 3453, 3286, 2760, 2344, 874, 7688, 7689, 3454,  # 5846
-    3670, 1858, 91, 2914, 3671, 3042, 3800, 4450, 7690, 3145, 3972, 2659, 7691, 3455, 1202, 1403,  # 5862
-    3801, 2954, 2529, 1517, 2503, 4451, 3456, 2504, 7692, 4452, 7693, 2692, 1885, 1495, 1731, 3973,  # 5878
-    2365, 4453, 7694, 2029, 7695, 7696, 3974, 2693, 1216, 237, 2581, 4174, 2319, 3975, 3802, 4454,  # 5894
-    4455, 2694, 3560, 3457, 445, 4456, 7697, 7698, 7699, 7700, 2761, 61, 3976, 3672, 1822, 3977,  # 5910
-    7701, 687, 2045, 935, 925, 405, 2660, 703, 1096, 1859, 2725, 4457, 3978, 1876, 1367, 2695,  # 5926
-    3352, 918, 2105, 1781, 2476, 334, 3287, 1611, 1093, 4458, 564, 3146, 3458, 3673, 3353, 945,  # 5942
-    2631, 2057, 4459, 7702, 1925, 872, 4175, 7703, 3459, 2696, 3089, 349, 4176, 3674, 3979, 4460,  # 5958
-    3803, 4177, 3675, 2155, 3980, 4461, 4462, 4178, 4463, 2403, 2046, 782, 3981, 400, 251, 4179,  # 5974
-    1624, 7704, 7705, 277, 3676, 299, 1265, 476, 1191, 3804, 2121, 4180, 4181, 1109, 205, 7706,  # 5990
-    2582, 1000, 2156, 3561, 1860, 7707, 7708, 7709, 4464, 7710, 4465, 2565, 107, 2477, 2157, 3982,  # 6006
-    3460, 3147, 7711, 1533, 541, 1301, 158, 753, 4182, 2872, 3562, 7712, 1696, 370, 1088, 4183,  # 6022
-    4466, 3563, 579, 327, 440, 162, 2240, 269, 1937, 1374, 3461, 968, 3043, 56, 1396, 3090,  # 6038
-    2106, 3288, 3354, 7713, 1926, 2158, 4467, 2998, 7714, 3564, 7715, 7716, 3677, 4468, 2478, 7717,  # 6054
-    2791, 7718, 1650, 4469, 7719, 2603, 7720, 7721, 3983, 2661, 3355, 1149, 3356, 3984, 3805, 3985,  # 6070
-    7722, 1076, 49, 7723, 951, 3211, 3289, 3290, 450, 2837, 920, 7724, 1811, 2792, 2366, 4184,  # 6086
-    1908, 1138, 2367, 3806, 3462, 7725, 3212, 4470, 1909, 1147, 1518, 2423, 4471, 3807, 7726, 4472,  # 6102
-    2388, 2604, 260, 1795, 3213, 7727, 7728, 3808, 3291, 708, 7729, 3565, 1704, 7730, 3566, 1351,  # 6118
-    1618, 3357, 2999, 1886, 944, 4185, 3358, 4186, 3044, 3359, 4187, 7731, 3678, 422, 413, 1714,  # 6134
-    3292, 500, 2058, 2345, 4188, 2479, 7732, 1344, 1910, 954, 7733, 1668, 7734, 7735, 3986, 2404,  # 6150
-    4189, 3567, 3809, 4190, 7736, 2302, 1318, 2505, 3091, 133, 3092, 2873, 4473, 629, 31, 2838,  # 6166
-    2697, 3810, 4474, 850, 949, 4475, 3987, 2955, 1732, 2088, 4191, 1496, 1852, 7737, 3988, 620,  # 6182
-    3214, 981, 1242, 3679, 3360, 1619, 3680, 1643, 3293, 2139, 2452, 1970, 1719, 3463, 2168, 7738,  # 6198
-    3215, 7739, 7740, 3361, 1828, 7741, 1277, 4476, 1565, 2047, 7742, 1636, 3568, 3093, 7743, 869,  # 6214
-    2839, 655, 3811, 3812, 3094, 3989, 3000, 3813, 1310, 3569, 4477, 7744, 7745, 7746, 1733, 558,  # 6230
-    4478, 3681, 335, 1549, 3045, 1756, 4192, 3682, 1945, 3464, 1829, 1291, 1192, 470, 2726, 2107,  # 6246
-    2793, 913, 1054, 3990, 7747, 1027, 7748, 3046, 3991, 4479, 982, 2662, 3362, 3148, 3465, 3216,  # 6262
-    3217, 1946, 2794, 7749, 571, 4480, 7750, 1830, 7751, 3570, 2583, 1523, 2424, 7752, 2089, 984,  # 6278
-    4481, 3683, 1959, 7753, 3684, 852, 923, 2795, 3466, 3685, 969, 1519, 999, 2048, 2320, 1705,  # 6294
-    7754, 3095, 615, 1662, 151, 597, 3992, 2405, 2321, 1049, 275, 4482, 3686, 4193, 568, 3687,  # 6310
-    3571, 2480, 4194, 3688, 7755, 2425, 2270, 409, 3218, 7756, 1566, 2874, 3467, 1002, 769, 2840,  # 6326
-    194, 2090, 3149, 3689, 2222, 3294, 4195, 628, 1505, 7757, 7758, 1763, 2177, 3001, 3993, 521,  # 6342
-    1161, 2584, 1787, 2203, 2406, 4483, 3994, 1625, 4196, 4197, 412, 42, 3096, 464, 7759, 2632,  # 6358
-    4484, 3363, 1760, 1571, 2875, 3468, 2530, 1219, 2204, 3814, 2633, 2140, 2368, 4485, 4486, 3295,  # 6374
-    1651, 3364, 3572, 7760, 7761, 3573, 2481, 3469, 7762, 3690, 7763, 7764, 2271, 2091, 460, 7765,  # 6390
-    4487, 7766, 3002, 962, 588, 3574, 289, 3219, 2634, 1116, 52, 7767, 3047, 1796, 7768, 7769,  # 6406
-    7770, 1467, 7771, 1598, 1143, 3691, 4198, 1984, 1734, 1067, 4488, 1280, 3365, 465, 4489, 1572,  # 6422
-    510, 7772, 1927, 2241, 1812, 1644, 3575, 7773, 4490, 3692, 7774, 7775, 2663, 1573, 1534, 7776,  # 6438
-    7777, 4199, 536, 1807, 1761, 3470, 3815, 3150, 2635, 7778, 7779, 7780, 4491, 3471, 2915, 1911,  # 6454
-    2796, 7781, 3296, 1122, 377, 3220, 7782, 360, 7783, 7784, 4200, 1529, 551, 7785, 2059, 3693,  # 6470
-    1769, 2426, 7786, 2916, 4201, 3297, 3097, 2322, 2108, 2030, 4492, 1404, 136, 1468, 1479, 672,  # 6486
-    1171, 3221, 2303, 271, 3151, 7787, 2762, 7788, 2049, 678, 2727, 865, 1947, 4493, 7789, 2013,  # 6502
-    3995, 2956, 7790, 2728, 2223, 1397, 3048, 3694, 4494, 4495, 1735, 2917, 3366, 3576, 7791, 3816,  # 6518
-    509, 2841, 2453, 2876, 3817, 7792, 7793, 3152, 3153, 4496, 4202, 2531, 4497, 2304, 1166, 1010,  # 6534
-    552, 681, 1887, 7794, 7795, 2957, 2958, 3996, 1287, 1596, 1861, 3154, 358, 453, 736, 175,  # 6550
-    478, 1117, 905, 1167, 1097, 7796, 1853, 1530, 7797, 1706, 7798, 2178, 3472, 2287, 3695, 3473,  # 6566
-    3577, 4203, 2092, 4204, 7799, 3367, 1193, 2482, 4205, 1458, 2190, 2205, 1862, 1888, 1421, 3298,  # 6582
-    2918, 3049, 2179, 3474, 595, 2122, 7800, 3997, 7801, 7802, 4206, 1707, 2636, 223, 3696, 1359,  # 6598
-    751, 3098, 183, 3475, 7803, 2797, 3003, 419, 2369, 633, 704, 3818, 2389, 241, 7804, 7805,  # 6614
-    7806, 838, 3004, 3697, 2272, 2763, 2454, 3819, 1938, 2050, 3998, 1309, 3099, 2242, 1181, 7807,  # 6630
-    1136, 2206, 3820, 2370, 1446, 4207, 2305, 4498, 7808, 7809, 4208, 1055, 2605, 484, 3698, 7810,  # 6646
-    3999, 625, 4209, 2273, 3368, 1499, 4210, 4000, 7811, 4001, 4211, 3222, 2274, 2275, 3476, 7812,  # 6662
-    7813, 2764, 808, 2606, 3699, 3369, 4002, 4212, 3100, 2532, 526, 3370, 3821, 4213, 955, 7814,  # 6678
-    1620, 4214, 2637, 2427, 7815, 1429, 3700, 1669, 1831, 994, 928, 7816, 3578, 1260, 7817, 7818,  # 6694
-    7819, 1948, 2288, 741, 2919, 1626, 4215, 2729, 2455, 867, 1184, 362, 3371, 1392, 7820, 7821,  # 6710
-    4003, 4216, 1770, 1736, 3223, 2920, 4499, 4500, 1928, 2698, 1459, 1158, 7822, 3050, 3372, 2877,  # 6726
-    1292, 1929, 2506, 2842, 3701, 1985, 1187, 2071, 2014, 2607, 4217, 7823, 2566, 2507, 2169, 3702,  # 6742
-    2483, 3299, 7824, 3703, 4501, 7825, 7826, 666, 1003, 3005, 1022, 3579, 4218, 7827, 4502, 1813,  # 6758
-    2253, 574, 3822, 1603, 295, 1535, 705, 3823, 4219, 283, 858, 417, 7828, 7829, 3224, 4503,  # 6774
-    4504, 3051, 1220, 1889, 1046, 2276, 2456, 4004, 1393, 1599, 689, 2567, 388, 4220, 7830, 2484,  # 6790
-    802, 7831, 2798, 3824, 2060, 1405, 2254, 7832, 4505, 3825, 2109, 1052, 1345, 3225, 1585, 7833,  # 6806
-    809, 7834, 7835, 7836, 575, 2730, 3477, 956, 1552, 1469, 1144, 2323, 7837, 2324, 1560, 2457,  # 6822
-    3580, 3226, 4005, 616, 2207, 3155, 2180, 2289, 7838, 1832, 7839, 3478, 4506, 7840, 1319, 3704,  # 6838
-    3705, 1211, 3581, 1023, 3227, 1293, 2799, 7841, 7842, 7843, 3826, 607, 2306, 3827, 762, 2878,  # 6854
-    1439, 4221, 1360, 7844, 1485, 3052, 7845, 4507, 1038, 4222, 1450, 2061, 2638, 4223, 1379, 4508,  # 6870
-    2585, 7846, 7847, 4224, 1352, 1414, 2325, 2921, 1172, 7848, 7849, 3828, 3829, 7850, 1797, 1451,  # 6886
-    7851, 7852, 7853, 7854, 2922, 4006, 4007, 2485, 2346, 411, 4008, 4009, 3582, 3300, 3101, 4509,  # 6902
-    1561, 2664, 1452, 4010, 1375, 7855, 7856, 47, 2959, 316, 7857, 1406, 1591, 2923, 3156, 7858,  # 6918
-    1025, 2141, 3102, 3157, 354, 2731, 884, 2224, 4225, 2407, 508, 3706, 726, 3583, 996, 2428,  # 6934
-    3584, 729, 7859, 392, 2191, 1453, 4011, 4510, 3707, 7860, 7861, 2458, 3585, 2608, 1675, 2800,  # 6950
-    919, 2347, 2960, 2348, 1270, 4511, 4012, 73, 7862, 7863, 647, 7864, 3228, 2843, 2255, 1550,  # 6966
-    1346, 3006, 7865, 1332, 883, 3479, 7866, 7867, 7868, 7869, 3301, 2765, 7870, 1212, 831, 1347,  # 6982
-    4226, 4512, 2326, 3830, 1863, 3053, 720, 3831, 4513, 4514, 3832, 7871, 4227, 7872, 7873, 4515,  # 6998
-    7874, 7875, 1798, 4516, 3708, 2609, 4517, 3586, 1645, 2371, 7876, 7877, 2924, 669, 2208, 2665,  # 7014
-    2429, 7878, 2879, 7879, 7880, 1028, 3229, 7881, 4228, 2408, 7882, 2256, 1353, 7883, 7884, 4518,  # 7030
-    3158, 518, 7885, 4013, 7886, 4229, 1960, 7887, 2142, 4230, 7888, 7889, 3007, 2349, 2350, 3833,  # 7046
-    516, 1833, 1454, 4014, 2699, 4231, 4519, 2225, 2610, 1971, 1129, 3587, 7890, 2766, 7891, 2961,  # 7062
-    1422, 577, 1470, 3008, 1524, 3373, 7892, 7893, 432, 4232, 3054, 3480, 7894, 2586, 1455, 2508,  # 7078
-    2226, 1972, 1175, 7895, 1020, 2732, 4015, 3481, 4520, 7896, 2733, 7897, 1743, 1361, 3055, 3482,  # 7094
-    2639, 4016, 4233, 4521, 2290, 895, 924, 4234, 2170, 331, 2243, 3056, 166, 1627, 3057, 1098,  # 7110
-    7898, 1232, 2880, 2227, 3374, 4522, 657, 403, 1196, 2372, 542, 3709, 3375, 1600, 4235, 3483,  # 7126
-    7899, 4523, 2767, 3230, 576, 530, 1362, 7900, 4524, 2533, 2666, 3710, 4017, 7901, 842, 3834,  # 7142
-    7902, 2801, 2031, 1014, 4018, 213, 2700, 3376, 665, 621, 4236, 7903, 3711, 2925, 2430, 7904,  # 7158
-    2431, 3302, 3588, 3377, 7905, 4237, 2534, 4238, 4525, 3589, 1682, 4239, 3484, 1380, 7906, 724,  # 7174
-    2277, 600, 1670, 7907, 1337, 1233, 4526, 3103, 2244, 7908, 1621, 4527, 7909, 651, 4240, 7910,  # 7190
-    1612, 4241, 2611, 7911, 2844, 7912, 2734, 2307, 3058, 7913, 716, 2459, 3059, 174, 1255, 2701,  # 7206
-    4019, 3590, 548, 1320, 1398, 728, 4020, 1574, 7914, 1890, 1197, 3060, 4021, 7915, 3061, 3062,  # 7222
-    3712, 3591, 3713, 747, 7916, 635, 4242, 4528, 7917, 7918, 7919, 4243, 7920, 7921, 4529, 7922,  # 7238
-    3378, 4530, 2432, 451, 7923, 3714, 2535, 2072, 4244, 2735, 4245, 4022, 7924, 1764, 4531, 7925,  # 7254
-    4246, 350, 7926, 2278, 2390, 2486, 7927, 4247, 4023, 2245, 1434, 4024, 488, 4532, 458, 4248,  # 7270
-    4025, 3715, 771, 1330, 2391, 3835, 2568, 3159, 2159, 2409, 1553, 2667, 3160, 4249, 7928, 2487,  # 7286
-    2881, 2612, 1720, 2702, 4250, 3379, 4533, 7929, 2536, 4251, 7930, 3231, 4252, 2768, 7931, 2015,  # 7302
-    2736, 7932, 1155, 1017, 3716, 3836, 7933, 3303, 2308, 201, 1864, 4253, 1430, 7934, 4026, 7935,  # 7318
-    7936, 7937, 7938, 7939, 4254, 1604, 7940, 414, 1865, 371, 2587, 4534, 4535, 3485, 2016, 3104,  # 7334
-    4536, 1708, 960, 4255, 887, 389, 2171, 1536, 1663, 1721, 7941, 2228, 4027, 2351, 2926, 1580,  # 7350
-    7942, 7943, 7944, 1744, 7945, 2537, 4537, 4538, 7946, 4539, 7947, 2073, 7948, 7949, 3592, 3380,  # 7366
-    2882, 4256, 7950, 4257, 2640, 3381, 2802, 673, 2703, 2460, 709, 3486, 4028, 3593, 4258, 7951,  # 7382
-    1148, 502, 634, 7952, 7953, 1204, 4540, 3594, 1575, 4541, 2613, 3717, 7954, 3718, 3105, 948,  # 7398
-    3232, 121, 1745, 3837, 1110, 7955, 4259, 3063, 2509, 3009, 4029, 3719, 1151, 1771, 3838, 1488,  # 7414
-    4030, 1986, 7956, 2433, 3487, 7957, 7958, 2093, 7959, 4260, 3839, 1213, 1407, 2803, 531, 2737,  # 7430
-    2538, 3233, 1011, 1537, 7960, 2769, 4261, 3106, 1061, 7961, 3720, 3721, 1866, 2883, 7962, 2017,  # 7446
-    120, 4262, 4263, 2062, 3595, 3234, 2309, 3840, 2668, 3382, 1954, 4542, 7963, 7964, 3488, 1047,  # 7462
-    2704, 1266, 7965, 1368, 4543, 2845, 649, 3383, 3841, 2539, 2738, 1102, 2846, 2669, 7966, 7967,  # 7478
-    1999, 7968, 1111, 3596, 2962, 7969, 2488, 3842, 3597, 2804, 1854, 3384, 3722, 7970, 7971, 3385,  # 7494
-    2410, 2884, 3304, 3235, 3598, 7972, 2569, 7973, 3599, 2805, 4031, 1460, 856, 7974, 3600, 7975,  # 7510
-    2885, 2963, 7976, 2886, 3843, 7977, 4264, 632, 2510, 875, 3844, 1697, 3845, 2291, 7978, 7979,  # 7526
-    4544, 3010, 1239, 580, 4545, 4265, 7980, 914, 936, 2074, 1190, 4032, 1039, 2123, 7981, 7982,  # 7542
-    7983, 3386, 1473, 7984, 1354, 4266, 3846, 7985, 2172, 3064, 4033, 915, 3305, 4267, 4268, 3306,  # 7558
-    1605, 1834, 7986, 2739, 398, 3601, 4269, 3847, 4034, 328, 1912, 2847, 4035, 3848, 1331, 4270,  # 7574
-    3011, 937, 4271, 7987, 3602, 4036, 4037, 3387, 2160, 4546, 3388, 524, 742, 538, 3065, 1012,  # 7590
-    7988, 7989, 3849, 2461, 7990, 658, 1103, 225, 3850, 7991, 7992, 4547, 7993, 4548, 7994, 3236,  # 7606
-    1243, 7995, 4038, 963, 2246, 4549, 7996, 2705, 3603, 3161, 7997, 7998, 2588, 2327, 7999, 4550,  # 7622
-    8000, 8001, 8002, 3489, 3307, 957, 3389, 2540, 2032, 1930, 2927, 2462, 870, 2018, 3604, 1746,  # 7638
-    2770, 2771, 2434, 2463, 8003, 3851, 8004, 3723, 3107, 3724, 3490, 3390, 3725, 8005, 1179, 3066,  # 7654
-    8006, 3162, 2373, 4272, 3726, 2541, 3163, 3108, 2740, 4039, 8007, 3391, 1556, 2542, 2292, 977,  # 7670
-    2887, 2033, 4040, 1205, 3392, 8008, 1765, 3393, 3164, 2124, 1271, 1689, 714, 4551, 3491, 8009,  # 7686
-    2328, 3852, 533, 4273, 3605, 2181, 617, 8010, 2464, 3308, 3492, 2310, 8011, 8012, 3165, 8013,  # 7702
-    8014, 3853, 1987, 618, 427, 2641, 3493, 3394, 8015, 8016, 1244, 1690, 8017, 2806, 4274, 4552,  # 7718
-    8018, 3494, 8019, 8020, 2279, 1576, 473, 3606, 4275, 3395, 972, 8021, 3607, 8022, 3067, 8023,  # 7734
-    8024, 4553, 4554, 8025, 3727, 4041, 4042, 8026, 153, 4555, 356, 8027, 1891, 2888, 4276, 2143,  # 7750
-    408, 803, 2352, 8028, 3854, 8029, 4277, 1646, 2570, 2511, 4556, 4557, 3855, 8030, 3856, 4278,  # 7766
-    8031, 2411, 3396, 752, 8032, 8033, 1961, 2964, 8034, 746, 3012, 2465, 8035, 4279, 3728, 698,  # 7782
-    4558, 1892, 4280, 3608, 2543, 4559, 3609, 3857, 8036, 3166, 3397, 8037, 1823, 1302, 4043, 2706,  # 7798
-    3858, 1973, 4281, 8038, 4282, 3167, 823, 1303, 1288, 1236, 2848, 3495, 4044, 3398, 774, 3859,  # 7814
-    8039, 1581, 4560, 1304, 2849, 3860, 4561, 8040, 2435, 2161, 1083, 3237, 4283, 4045, 4284, 344,  # 7830
-    1173, 288, 2311, 454, 1683, 8041, 8042, 1461, 4562, 4046, 2589, 8043, 8044, 4563, 985, 894,  # 7846
-    8045, 3399, 3168, 8046, 1913, 2928, 3729, 1988, 8047, 2110, 1974, 8048, 4047, 8049, 2571, 1194,  # 7862
-    425, 8050, 4564, 3169, 1245, 3730, 4285, 8051, 8052, 2850, 8053, 636, 4565, 1855, 3861, 760,  # 7878
-    1799, 8054, 4286, 2209, 1508, 4566, 4048, 1893, 1684, 2293, 8055, 8056, 8057, 4287, 4288, 2210,  # 7894
-    479, 8058, 8059, 832, 8060, 4049, 2489, 8061, 2965, 2490, 3731, 990, 3109, 627, 1814, 2642,  # 7910
-    4289, 1582, 4290, 2125, 2111, 3496, 4567, 8062, 799, 4291, 3170, 8063, 4568, 2112, 1737, 3013,  # 7926
-    1018, 543, 754, 4292, 3309, 1676, 4569, 4570, 4050, 8064, 1489, 8065, 3497, 8066, 2614, 2889,  # 7942
-    4051, 8067, 8068, 2966, 8069, 8070, 8071, 8072, 3171, 4571, 4572, 2182, 1722, 8073, 3238, 3239,  # 7958
-    1842, 3610, 1715, 481, 365, 1975, 1856, 8074, 8075, 1962, 2491, 4573, 8076, 2126, 3611, 3240,  # 7974
-    433, 1894, 2063, 2075, 8077, 602, 2741, 8078, 8079, 8080, 8081, 8082, 3014, 1628, 3400, 8083,  # 7990
-    3172, 4574, 4052, 2890, 4575, 2512, 8084, 2544, 2772, 8085, 8086, 8087, 3310, 4576, 2891, 8088,  # 8006
-    4577, 8089, 2851, 4578, 4579, 1221, 2967, 4053, 2513, 8090, 8091, 8092, 1867, 1989, 8093, 8094,  # 8022
-    8095, 1895, 8096, 8097, 4580, 1896, 4054, 318, 8098, 2094, 4055, 4293, 8099, 8100, 485, 8101,  # 8038
-    938, 3862, 553, 2670, 116, 8102, 3863, 3612, 8103, 3498, 2671, 2773, 3401, 3311, 2807, 8104,  # 8054
-    3613, 2929, 4056, 1747, 2930, 2968, 8105, 8106, 207, 8107, 8108, 2672, 4581, 2514, 8109, 3015,  # 8070
-    890, 3614, 3864, 8110, 1877, 3732, 3402, 8111, 2183, 2353, 3403, 1652, 8112, 8113, 8114, 941,  # 8086
-    2294, 208, 3499, 4057, 2019, 330, 4294, 3865, 2892, 2492, 3733, 4295, 8115, 8116, 8117, 8118,  # 8102
-)
-# fmt: on

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/contrib/pyopenssl.py

@@ -1,518 +0,0 @@
-"""
-TLS with SNI_-support for Python 2. Follow these instructions if you would
-like to verify TLS certificates in Python 2. Note, the default libraries do
-*not* do certificate checking; you need to do additional work to validate
-certificates yourself.
-
-This needs the following packages installed:
-
-* `pyOpenSSL`_ (tested with 16.0.0)
-* `cryptography`_ (minimum 1.3.4, from pyopenssl)
-* `idna`_ (minimum 2.0, from cryptography)
-
-However, pyopenssl depends on cryptography, which depends on idna, so while we
-use all three directly here we end up having relatively few packages required.
-
-You can install them with the following command:
-
-.. code-block:: bash
-
-    $ python -m pip install pyopenssl cryptography idna
-
-To activate certificate checking, call
-:func:`~urllib3.contrib.pyopenssl.inject_into_urllib3` from your Python code
-before you begin making HTTP requests. This can be done in a ``sitecustomize``
-module, or at any other time before your application begins using ``urllib3``,
-like this:
-
-.. code-block:: python
-
-    try:
-        import pip._vendor.urllib3.contrib.pyopenssl as pyopenssl
-        pyopenssl.inject_into_urllib3()
-    except ImportError:
-        pass
-
-Now you can use :mod:`urllib3` as you normally would, and it will support SNI
-when the required modules are installed.
-
-Activating this module also has the positive side effect of disabling SSL/TLS
-compression in Python 2 (see `CRIME attack`_).
-
-.. _sni: https://en.wikipedia.org/wiki/Server_Name_Indication
-.. _crime attack: https://en.wikipedia.org/wiki/CRIME_(security_exploit)
-.. _pyopenssl: https://www.pyopenssl.org
-.. _cryptography: https://cryptography.io
-.. _idna: https://github.com/kjd/idna
-"""
-from __future__ import absolute_import
-
-import OpenSSL.crypto
-import OpenSSL.SSL
-from cryptography import x509
-from cryptography.hazmat.backends.openssl import backend as openssl_backend
-
-try:
-    from cryptography.x509 import UnsupportedExtension
-except ImportError:
-    # UnsupportedExtension is gone in cryptography >= 2.1.0
-    class UnsupportedExtension(Exception):
-        pass
-
-
-from io import BytesIO
-from socket import error as SocketError
-from socket import timeout
-
-try:  # Platform-specific: Python 2
-    from socket import _fileobject
-except ImportError:  # Platform-specific: Python 3
-    _fileobject = None
-    from ..packages.backports.makefile import backport_makefile
-
-import logging
-import ssl
-import sys
-import warnings
-
-from .. import util
-from ..packages import six
-from ..util.ssl_ import PROTOCOL_TLS_CLIENT
-
-warnings.warn(
-    "'urllib3.contrib.pyopenssl' module is deprecated and will be removed "
-    "in a future release of urllib3 2.x. Read more in this issue: "
-    "https://github.com/urllib3/urllib3/issues/2680",
-    category=DeprecationWarning,
-    stacklevel=2,
-)
-
-__all__ = ["inject_into_urllib3", "extract_from_urllib3"]
-
-# SNI always works.
-HAS_SNI = True
-
-# Map from urllib3 to PyOpenSSL compatible parameter-values.
-_openssl_versions = {
-    util.PROTOCOL_TLS: OpenSSL.SSL.SSLv23_METHOD,
-    PROTOCOL_TLS_CLIENT: OpenSSL.SSL.SSLv23_METHOD,
-    ssl.PROTOCOL_TLSv1: OpenSSL.SSL.TLSv1_METHOD,
-}
-
-if hasattr(ssl, "PROTOCOL_SSLv3") and hasattr(OpenSSL.SSL, "SSLv3_METHOD"):
-    _openssl_versions[ssl.PROTOCOL_SSLv3] = OpenSSL.SSL.SSLv3_METHOD
-
-if hasattr(ssl, "PROTOCOL_TLSv1_1") and hasattr(OpenSSL.SSL, "TLSv1_1_METHOD"):
-    _openssl_versions[ssl.PROTOCOL_TLSv1_1] = OpenSSL.SSL.TLSv1_1_METHOD
-
-if hasattr(ssl, "PROTOCOL_TLSv1_2") and hasattr(OpenSSL.SSL, "TLSv1_2_METHOD"):
-    _openssl_versions[ssl.PROTOCOL_TLSv1_2] = OpenSSL.SSL.TLSv1_2_METHOD
-
-
-_stdlib_to_openssl_verify = {
-    ssl.CERT_NONE: OpenSSL.SSL.VERIFY_NONE,
-    ssl.CERT_OPTIONAL: OpenSSL.SSL.VERIFY_PEER,
-    ssl.CERT_REQUIRED: OpenSSL.SSL.VERIFY_PEER
-    + OpenSSL.SSL.VERIFY_FAIL_IF_NO_PEER_CERT,
-}
-_openssl_to_stdlib_verify = dict((v, k) for k, v in _stdlib_to_openssl_verify.items())
-
-# OpenSSL will only write 16K at a time
-SSL_WRITE_BLOCKSIZE = 16384
-
-orig_util_HAS_SNI = util.HAS_SNI
-orig_util_SSLContext = util.ssl_.SSLContext
-
-
-log = logging.getLogger(__name__)
-
-
-def inject_into_urllib3():
-    "Monkey-patch urllib3 with PyOpenSSL-backed SSL-support."
-
-    _validate_dependencies_met()
-
-    util.SSLContext = PyOpenSSLContext
-    util.ssl_.SSLContext = PyOpenSSLContext
-    util.HAS_SNI = HAS_SNI
-    util.ssl_.HAS_SNI = HAS_SNI
-    util.IS_PYOPENSSL = True
-    util.ssl_.IS_PYOPENSSL = True
-
-
-def extract_from_urllib3():
-    "Undo monkey-patching by :func:`inject_into_urllib3`."
-
-    util.SSLContext = orig_util_SSLContext
-    util.ssl_.SSLContext = orig_util_SSLContext
-    util.HAS_SNI = orig_util_HAS_SNI
-    util.ssl_.HAS_SNI = orig_util_HAS_SNI
-    util.IS_PYOPENSSL = False
-    util.ssl_.IS_PYOPENSSL = False
-
-
-def _validate_dependencies_met():
-    """
-    Verifies that PyOpenSSL's package-level dependencies have been met.
-    Throws `ImportError` if they are not met.
-    """
-    # Method added in `cryptography==1.1`; not available in older versions
-    from cryptography.x509.extensions import Extensions
-
-    if getattr(Extensions, "get_extension_for_class", None) is None:
-        raise ImportError(
-            "'cryptography' module missing required functionality.  "
-            "Try upgrading to v1.3.4 or newer."
-        )
-
-    # pyOpenSSL 0.14 and above use cryptography for OpenSSL bindings. The _x509
-    # attribute is only present on those versions.
-    from OpenSSL.crypto import X509
-
-    x509 = X509()
-    if getattr(x509, "_x509", None) is None:
-        raise ImportError(
-            "'pyOpenSSL' module missing required functionality. "
-            "Try upgrading to v0.14 or newer."
-        )
-
-
-def _dnsname_to_stdlib(name):
-    """
-    Converts a dNSName SubjectAlternativeName field to the form used by the
-    standard library on the given Python version.
-
-    Cryptography produces a dNSName as a unicode string that was idna-decoded
-    from ASCII bytes. We need to idna-encode that string to get it back, and
-    then on Python 3 we also need to convert to unicode via UTF-8 (the stdlib
-    uses PyUnicode_FromStringAndSize on it, which decodes via UTF-8).
-
-    If the name cannot be idna-encoded then we return None signalling that
-    the name given should be skipped.
-    """
-
-    def idna_encode(name):
-        """
-        Borrowed wholesale from the Python Cryptography Project. It turns out
-        that we can't just safely call `idna.encode`: it can explode for
-        wildcard names. This avoids that problem.
-        """
-        from pip._vendor import idna
-
-        try:
-            for prefix in [u"*.", u"."]:
-                if name.startswith(prefix):
-                    name = name[len(prefix) :]
-                    return prefix.encode("ascii") + idna.encode(name)
-            return idna.encode(name)
-        except idna.core.IDNAError:
-            return None
-
-    # Don't send IPv6 addresses through the IDNA encoder.
-    if ":" in name:
-        return name
-
-    name = idna_encode(name)
-    if name is None:
-        return None
-    elif sys.version_info >= (3, 0):
-        name = name.decode("utf-8")
-    return name
-
-
-def get_subj_alt_name(peer_cert):
-    """
-    Given an PyOpenSSL certificate, provides all the subject alternative names.
-    """
-    # Pass the cert to cryptography, which has much better APIs for this.
-    if hasattr(peer_cert, "to_cryptography"):
-        cert = peer_cert.to_cryptography()
-    else:
-        der = OpenSSL.crypto.dump_certificate(OpenSSL.crypto.FILETYPE_ASN1, peer_cert)
-        cert = x509.load_der_x509_certificate(der, openssl_backend)
-
-    # We want to find the SAN extension. Ask Cryptography to locate it (it's
-    # faster than looping in Python)
-    try:
-        ext = cert.extensions.get_extension_for_class(x509.SubjectAlternativeName).value
-    except x509.ExtensionNotFound:
-        # No such extension, return the empty list.
-        return []
-    except (
-        x509.DuplicateExtension,
-        UnsupportedExtension,
-        x509.UnsupportedGeneralNameType,
-        UnicodeError,
-    ) as e:
-        # A problem has been found with the quality of the certificate. Assume
-        # no SAN field is present.
-        log.warning(
-            "A problem was encountered with the certificate that prevented "
-            "urllib3 from finding the SubjectAlternativeName field. This can "
-            "affect certificate validation. The error was %s",
-            e,
-        )
-        return []
-
-    # We want to return dNSName and iPAddress fields. We need to cast the IPs
-    # back to strings because the match_hostname function wants them as
-    # strings.
-    # Sadly the DNS names need to be idna encoded and then, on Python 3, UTF-8
-    # decoded. This is pretty frustrating, but that's what the standard library
-    # does with certificates, and so we need to attempt to do the same.
-    # We also want to skip over names which cannot be idna encoded.
-    names = [
-        ("DNS", name)
-        for name in map(_dnsname_to_stdlib, ext.get_values_for_type(x509.DNSName))
-        if name is not None
-    ]
-    names.extend(
-        ("IP Address", str(name)) for name in ext.get_values_for_type(x509.IPAddress)
-    )
-
-    return names
-
-
-class WrappedSocket(object):
-    """API-compatibility wrapper for Python OpenSSL's Connection-class.
-
-    Note: _makefile_refs, _drop() and _reuse() are needed for the garbage
-    collector of pypy.
-    """
-
-    def __init__(self, connection, socket, suppress_ragged_eofs=True):
-        self.connection = connection
-        self.socket = socket
-        self.suppress_ragged_eofs = suppress_ragged_eofs
-        self._makefile_refs = 0
-        self._closed = False
-
-    def fileno(self):
-        return self.socket.fileno()
-
-    # Copy-pasted from Python 3.5 source code
-    def _decref_socketios(self):
-        if self._makefile_refs > 0:
-            self._makefile_refs -= 1
-        if self._closed:
-            self.close()
-
-    def recv(self, *args, **kwargs):
-        try:
-            data = self.connection.recv(*args, **kwargs)
-        except OpenSSL.SSL.SysCallError as e:
-            if self.suppress_ragged_eofs and e.args == (-1, "Unexpected EOF"):
-                return b""
-            else:
-                raise SocketError(str(e))
-        except OpenSSL.SSL.ZeroReturnError:
-            if self.connection.get_shutdown() == OpenSSL.SSL.RECEIVED_SHUTDOWN:
-                return b""
-            else:
-                raise
-        except OpenSSL.SSL.WantReadError:
-            if not util.wait_for_read(self.socket, self.socket.gettimeout()):
-                raise timeout("The read operation timed out")
-            else:
-                return self.recv(*args, **kwargs)
-
-        # TLS 1.3 post-handshake authentication
-        except OpenSSL.SSL.Error as e:
-            raise ssl.SSLError("read error: %r" % e)
-        else:
-            return data
-
-    def recv_into(self, *args, **kwargs):
-        try:
-            return self.connection.recv_into(*args, **kwargs)
-        except OpenSSL.SSL.SysCallError as e:
-            if self.suppress_ragged_eofs and e.args == (-1, "Unexpected EOF"):
-                return 0
-            else:
-                raise SocketError(str(e))
-        except OpenSSL.SSL.ZeroReturnError:
-            if self.connection.get_shutdown() == OpenSSL.SSL.RECEIVED_SHUTDOWN:
-                return 0
-            else:
-                raise
-        except OpenSSL.SSL.WantReadError:
-            if not util.wait_for_read(self.socket, self.socket.gettimeout()):
-                raise timeout("The read operation timed out")
-            else:
-                return self.recv_into(*args, **kwargs)
-
-        # TLS 1.3 post-handshake authentication
-        except OpenSSL.SSL.Error as e:
-            raise ssl.SSLError("read error: %r" % e)
-
-    def settimeout(self, timeout):
-        return self.socket.settimeout(timeout)
-
-    def _send_until_done(self, data):
-        while True:
-            try:
-                return self.connection.send(data)
-            except OpenSSL.SSL.WantWriteError:
-                if not util.wait_for_write(self.socket, self.socket.gettimeout()):
-                    raise timeout()
-                continue
-            except OpenSSL.SSL.SysCallError as e:
-                raise SocketError(str(e))
-
-    def sendall(self, data):
-        total_sent = 0
-        while total_sent < len(data):
-            sent = self._send_until_done(
-                data[total_sent : total_sent + SSL_WRITE_BLOCKSIZE]
-            )
-            total_sent += sent
-
-    def shutdown(self):
-        # FIXME rethrow compatible exceptions should we ever use this
-        self.connection.shutdown()
-
-    def close(self):
-        if self._makefile_refs < 1:
-            try:
-                self._closed = True
-                return self.connection.close()
-            except OpenSSL.SSL.Error:
-                return
-        else:
-            self._makefile_refs -= 1
-
-    def getpeercert(self, binary_form=False):
-        x509 = self.connection.get_peer_certificate()
-
-        if not x509:
-            return x509
-
-        if binary_form:
-            return OpenSSL.crypto.dump_certificate(OpenSSL.crypto.FILETYPE_ASN1, x509)
-
-        return {
-            "subject": ((("commonName", x509.get_subject().CN),),),
-            "subjectAltName": get_subj_alt_name(x509),
-        }
-
-    def version(self):
-        return self.connection.get_protocol_version_name()
-
-    def _reuse(self):
-        self._makefile_refs += 1
-
-    def _drop(self):
-        if self._makefile_refs < 1:
-            self.close()
-        else:
-            self._makefile_refs -= 1
-
-
-if _fileobject:  # Platform-specific: Python 2
-
-    def makefile(self, mode, bufsize=-1):
-        self._makefile_refs += 1
-        return _fileobject(self, mode, bufsize, close=True)
-
-else:  # Platform-specific: Python 3
-    makefile = backport_makefile
-
-WrappedSocket.makefile = makefile
-
-
-class PyOpenSSLContext(object):
-    """
-    I am a wrapper class for the PyOpenSSL ``Context`` object. I am responsible
-    for translating the interface of the standard library ``SSLContext`` object
-    to calls into PyOpenSSL.
-    """
-
-    def __init__(self, protocol):
-        self.protocol = _openssl_versions[protocol]
-        self._ctx = OpenSSL.SSL.Context(self.protocol)
-        self._options = 0
-        self.check_hostname = False
-
-    @property
-    def options(self):
-        return self._options
-
-    @options.setter
-    def options(self, value):
-        self._options = value
-        self._ctx.set_options(value)
-
-    @property
-    def verify_mode(self):
-        return _openssl_to_stdlib_verify[self._ctx.get_verify_mode()]
-
-    @verify_mode.setter
-    def verify_mode(self, value):
-        self._ctx.set_verify(_stdlib_to_openssl_verify[value], _verify_callback)
-
-    def set_default_verify_paths(self):
-        self._ctx.set_default_verify_paths()
-
-    def set_ciphers(self, ciphers):
-        if isinstance(ciphers, six.text_type):
-            ciphers = ciphers.encode("utf-8")
-        self._ctx.set_cipher_list(ciphers)
-
-    def load_verify_locations(self, cafile=None, capath=None, cadata=None):
-        if cafile is not None:
-            cafile = cafile.encode("utf-8")
-        if capath is not None:
-            capath = capath.encode("utf-8")
-        try:
-            self._ctx.load_verify_locations(cafile, capath)
-            if cadata is not None:
-                self._ctx.load_verify_locations(BytesIO(cadata))
-        except OpenSSL.SSL.Error as e:
-            raise ssl.SSLError("unable to load trusted certificates: %r" % e)
-
-    def load_cert_chain(self, certfile, keyfile=None, password=None):
-        self._ctx.use_certificate_chain_file(certfile)
-        if password is not None:
-            if not isinstance(password, six.binary_type):
-                password = password.encode("utf-8")
-            self._ctx.set_passwd_cb(lambda *_: password)
-        self._ctx.use_privatekey_file(keyfile or certfile)
-
-    def set_alpn_protocols(self, protocols):
-        protocols = [six.ensure_binary(p) for p in protocols]
-        return self._ctx.set_alpn_protos(protocols)
-
-    def wrap_socket(
-        self,
-        sock,
-        server_side=False,
-        do_handshake_on_connect=True,
-        suppress_ragged_eofs=True,
-        server_hostname=None,
-    ):
-        cnx = OpenSSL.SSL.Connection(self._ctx, sock)
-
-        if isinstance(server_hostname, six.text_type):  # Platform-specific: Python 3
-            server_hostname = server_hostname.encode("utf-8")
-
-        if server_hostname is not None:
-            cnx.set_tlsext_host_name(server_hostname)
-
-        cnx.set_connect_state()
-
-        while True:
-            try:
-                cnx.do_handshake()
-            except OpenSSL.SSL.WantReadError:
-                if not util.wait_for_read(sock, sock.gettimeout()):
-                    raise timeout("select timed out")
-                continue
-            except OpenSSL.SSL.Error as e:
-                raise ssl.SSLError("bad handshake: %r" % e)
-            break
-
-        return WrappedSocket(cnx, sock)
-
-
-def _verify_callback(cnx, x509, err_no, err_depth, return_code):
-    return err_no == 0

spaces/Branon/Proxy/greeting.md

@@ -1 +0,0 @@
-lol

spaces/Burcin/ExtractiveSummarizer/README.md

@@ -1,45 +0,0 @@
----
-title: ExtractiveSummarizer
-emoji: 📊
-colorFrom: red
-colorTo: green
-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/CVPR/Bamboo_ViT-B16_demo/timmvit.py

@@ -1,79 +0,0 @@
-# ------------------------------------------------------------------------
-# Modified from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-import timm
-import torch
-import copy
-import torch.nn as nn
-import torchvision
-import json
-from timm.models.hub import download_cached_file
-from PIL import Image
-
-
-
-class MyViT(nn.Module):
-    def __init__(self, num_classes=115217, pretrain_path=None, enable_fc=False):
-        super().__init__()
-        print('initializing ViT model as backbone using ckpt:', pretrain_path)
-        self.model = timm.create_model('vit_base_patch16_224',checkpoint_path=pretrain_path,num_classes=num_classes)# pretrained=True)
-    # def forward_features(self, x):
-    #     x = self.model.patch_embed(x)
-    #     cls_token = self.model.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-    #     if self.model.dist_token is None:
-    #         x = torch.cat((cls_token, x), dim=1)
-    #     else:
-    #         x = torch.cat((cls_token, self.model.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
-
-    #     x = self.model.pos_drop(x + self.model.pos_embed)
-    #     x = self.model.blocks(x)
-    #     x = self.model.norm(x)
-
-        # return self.model.pre_logits(x[:, 0])
-
-
-    def forward(self, x):
-        x = self.model.forward(x)
-        return x
-
-
-def timmvit(**kwargs):
-    default_kwargs={}
-    default_kwargs.update(**kwargs)
-    return MyViT(**default_kwargs)
-
-
-def build_transforms(input_size, center_crop=True):
-    transform = torchvision.transforms.Compose([
-        torchvision.transforms.Resize(input_size * 8 // 7),
-        torchvision.transforms.CenterCrop(input_size),
-        torchvision.transforms.ToTensor(),
-        torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-             ])
-    return transform
-
-def pil_loader(filepath):
-    with Image.open(filepath) as img:
-        img = img.convert('RGB')
-    return img
-
-def test_build():
-    with open('/mnt/lustre/yhzhang/bamboo/Bamboo_ViT-B16_demo/trainid2name.json') as f:
-        id2name = json.load(f)
-    img = pil_loader('/mnt/lustre/yhzhang/bamboo/Bamboo_ViT-B16_demo/142520422_6ad756ddf6_w_d.jpg')
-    eval_transforms = build_transforms(224)
-    img_t = eval_transforms(img) 
-    img_t = img_t[None, :]
-    model = MyViT(pretrain_path='/mnt/lustre/yhzhang/bamboo/Bamboo_ViT-B16_demo/Bamboo_v0-1_ViT-B16.pth.tar.convert')
-    # image = torch.rand(1, 3, 224, 224)
-    output = model(img_t)
-    # import pdb;pdb.set_trace()
-    prediction = output.softmax(-1).flatten()
-    _,top5_idx = torch.topk(prediction, 5)
-    # import pdb;pdb.set_trace()
-    print({id2name[str(i)][0]: float(prediction[i]) for i in top5_idx.tolist()})
-
-if __name__ == '__main__':
-    test_build()

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/structures/rotated_boxes.py

@@ -1,498 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import math
-from typing import Iterator, List, Union
-import torch
-
-from detectron2.layers import cat
-from detectron2.layers.rotated_boxes import pairwise_iou_rotated
-
-from .boxes import Boxes
-
-
-class RotatedBoxes(Boxes):
-    """
-    This structure stores a list of rotated boxes as a Nx5 torch.Tensor.
-    It supports some common methods about boxes
-    (`area`, `clip`, `nonempty`, etc),
-    and also behaves like a Tensor
-    (support indexing, `to(device)`, `.device`, and iteration over all boxes)
-    """
-
-    def __init__(self, tensor: torch.Tensor):
-        """
-        Args:
-            tensor (Tensor[float]): a Nx5 matrix.  Each row is
-                (x_center, y_center, width, height, angle),
-                in which angle is represented in degrees.
-                While there's no strict range restriction for it,
-                the recommended principal range is between [-180, 180) degrees.
-
-        Assume we have a horizontal box B = (x_center, y_center, width, height),
-        where width is along the x-axis and height is along the y-axis.
-        The rotated box B_rot (x_center, y_center, width, height, angle)
-        can be seen as:
-
-        1. When angle == 0:
-           B_rot == B
-        2. When angle > 0:
-           B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CCW;
-        3. When angle < 0:
-           B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CW.
-
-        Mathematically, since the right-handed coordinate system for image space
-        is (y, x), where y is top->down and x is left->right, the 4 vertices of the
-        rotated rectangle :math:`(yr_i, xr_i)` (i = 1, 2, 3, 4) can be obtained from
-        the vertices of the horizontal rectangle (y_i, x_i) (i = 1, 2, 3, 4)
-        in the following way (:math:`\\theta = angle*\\pi/180` is the angle in radians,
-        (y_c, x_c) is the center of the rectangle):
-
-        .. math::
-
-            yr_i = \\cos(\\theta) (y_i - y_c) - \\sin(\\theta) (x_i - x_c) + y_c,
-
-            xr_i = \\sin(\\theta) (y_i - y_c) + \\cos(\\theta) (x_i - x_c) + x_c,
-
-        which is the standard rigid-body rotation transformation.
-
-        Intuitively, the angle is
-        (1) the rotation angle from y-axis in image space
-        to the height vector (top->down in the box's local coordinate system)
-        of the box in CCW, and
-        (2) the rotation angle from x-axis in image space
-        to the width vector (left->right in the box's local coordinate system)
-        of the box in CCW.
-
-        More intuitively, consider the following horizontal box ABCD represented
-        in (x1, y1, x2, y2): (3, 2, 7, 4),
-        covering the [3, 7] x [2, 4] region of the continuous coordinate system
-        which looks like this:
-
-        .. code:: none
-
-            O--------> x
-            |
-            |  A---B
-            |  |   |
-            |  D---C
-            |
-            v y
-
-        Note that each capital letter represents one 0-dimensional geometric point
-        instead of a 'square pixel' here.
-
-        In the example above, using (x, y) to represent a point we have:
-
-        .. math::
-
-            O = (0, 0), A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4)
-
-        We name vector AB = vector DC as the width vector in box's local coordinate system, and
-        vector AD = vector BC as the height vector in box's local coordinate system. Initially,
-        when angle = 0 degree, they're aligned with the positive directions of x-axis and y-axis
-        in the image space, respectively.
-
-        For better illustration, we denote the center of the box as E,
-
-        .. code:: none
-
-            O--------> x
-            |
-            |  A---B
-            |  | E |
-            |  D---C
-            |
-            v y
-
-        where the center E = ((3+7)/2, (2+4)/2) = (5, 3).
-
-        Also,
-
-        .. math::
-
-            width = |AB| = |CD| = 7 - 3 = 4,
-            height = |AD| = |BC| = 4 - 2 = 2.
-
-        Therefore, the corresponding representation for the same shape in rotated box in
-        (x_center, y_center, width, height, angle) format is:
-
-        (5, 3, 4, 2, 0),
-
-        Now, let's consider (5, 3, 4, 2, 90), which is rotated by 90 degrees
-        CCW (counter-clockwise) by definition. It looks like this:
-
-        .. code:: none
-
-            O--------> x
-            |   B-C
-            |   | |
-            |   |E|
-            |   | |
-            |   A-D
-            v y
-
-        The center E is still located at the same point (5, 3), while the vertices
-        ABCD are rotated by 90 degrees CCW with regard to E:
-        A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5)
-
-        Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to
-        vector AD or vector BC (the top->down height vector in box's local coordinate system),
-        or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right
-        width vector in box's local coordinate system).
-
-        .. math::
-
-            width = |AB| = |CD| = 5 - 1 = 4,
-            height = |AD| = |BC| = 6 - 4 = 2.
-
-        Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise)
-        by definition? It looks like this:
-
-        .. code:: none
-
-            O--------> x
-            |   D-A
-            |   | |
-            |   |E|
-            |   | |
-            |   C-B
-            v y
-
-        The center E is still located at the same point (5, 3), while the vertices
-        ABCD are rotated by 90 degrees CW with regard to E:
-        A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1)
-
-        .. math::
-
-            width = |AB| = |CD| = 5 - 1 = 4,
-            height = |AD| = |BC| = 6 - 4 = 2.
-
-        This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU
-        will be 1. However, these two will generate different RoI Pooling results and
-        should not be treated as an identical box.
-
-        On the other hand, it's easy to see that (X, Y, W, H, A) is identical to
-        (X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be
-        identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is
-        equivalent to rotating the same shape 90 degrees CW.
-
-        We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180):
-
-        .. code:: none
-
-            O--------> x
-            |
-            |  C---D
-            |  | E |
-            |  B---A
-            |
-            v y
-
-        .. math::
-
-            A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2),
-
-            width = |AB| = |CD| = 7 - 3 = 4,
-            height = |AD| = |BC| = 4 - 2 = 2.
-
-        Finally, this is a very inaccurate (heavily quantized) illustration of
-        how (5, 3, 4, 2, 60) looks like in case anyone wonders:
-
-        .. code:: none
-
-            O--------> x
-            |     B\
-            |    /  C
-            |   /E /
-            |  A  /
-            |   `D
-            v y
-
-        It's still a rectangle with center of (5, 3), width of 4 and height of 2,
-        but its angle (and thus orientation) is somewhere between
-        (5, 3, 4, 2, 0) and (5, 3, 4, 2, 90).
-        """
-        device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu")
-        tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
-        if tensor.numel() == 0:
-            tensor = torch.zeros(0, 5, dtype=torch.float32, device=device)
-        assert tensor.dim() == 2 and tensor.size(-1) == 5, tensor.size()
-
-        self.tensor = tensor
-
-    def clone(self) -> "RotatedBoxes":
-        """
-        Clone the RotatedBoxes.
-
-        Returns:
-            RotatedBoxes
-        """
-        return RotatedBoxes(self.tensor.clone())
-
-    def to(self, device: str) -> "RotatedBoxes":
-        return RotatedBoxes(self.tensor.to(device))
-
-    def area(self) -> torch.Tensor:
-        """
-        Computes the area of all the boxes.
-
-        Returns:
-            torch.Tensor: a vector with areas of each box.
-        """
-        box = self.tensor
-        area = box[:, 2] * box[:, 3]
-        return area
-
-    def normalize_angles(self) -> None:
-        """
-        Restrict angles to the range of [-180, 180) degrees
-        """
-        self.tensor[:, 4] = (self.tensor[:, 4] + 180.0) % 360.0 - 180.0
-
-    def clip(self, box_size: Boxes.BoxSizeType, clip_angle_threshold: float = 1.0) -> None:
-        """
-        Clip (in place) the boxes by limiting x coordinates to the range [0, width]
-        and y coordinates to the range [0, height].
-
-        For RRPN:
-        Only clip boxes that are almost horizontal with a tolerance of
-        clip_angle_threshold to maintain backward compatibility.
-
-        Rotated boxes beyond this threshold are not clipped for two reasons:
-
-        1. There are potentially multiple ways to clip a rotated box to make it
-           fit within the image.
-        2. It's tricky to make the entire rectangular box fit within the image
-           and still be able to not leave out pixels of interest.
-
-        Therefore we rely on ops like RoIAlignRotated to safely handle this.
-
-        Args:
-            box_size (height, width): The clipping box's size.
-            clip_angle_threshold:
-                Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees),
-                we do the clipping as horizontal boxes.
-        """
-        h, w = box_size
-
-        # normalize angles to be within (-180, 180] degrees
-        self.normalize_angles()
-
-        idx = torch.where(torch.abs(self.tensor[:, 4]) <= clip_angle_threshold)[0]
-
-        # convert to (x1, y1, x2, y2)
-        x1 = self.tensor[idx, 0] - self.tensor[idx, 2] / 2.0
-        y1 = self.tensor[idx, 1] - self.tensor[idx, 3] / 2.0
-        x2 = self.tensor[idx, 0] + self.tensor[idx, 2] / 2.0
-        y2 = self.tensor[idx, 1] + self.tensor[idx, 3] / 2.0
-
-        # clip
-        x1.clamp_(min=0, max=w)
-        y1.clamp_(min=0, max=h)
-        x2.clamp_(min=0, max=w)
-        y2.clamp_(min=0, max=h)
-
-        # convert back to (xc, yc, w, h)
-        self.tensor[idx, 0] = (x1 + x2) / 2.0
-        self.tensor[idx, 1] = (y1 + y2) / 2.0
-        # make sure widths and heights do not increase due to numerical errors
-        self.tensor[idx, 2] = torch.min(self.tensor[idx, 2], x2 - x1)
-        self.tensor[idx, 3] = torch.min(self.tensor[idx, 3], y2 - y1)
-
-    def nonempty(self, threshold: int = 0) -> torch.Tensor:
-        """
-        Find boxes that are non-empty.
-        A box is considered empty, if either of its side is no larger than threshold.
-
-        Returns:
-            Tensor: a binary vector which represents
-            whether each box is empty (False) or non-empty (True).
-        """
-        box = self.tensor
-        widths = box[:, 2]
-        heights = box[:, 3]
-        keep = (widths > threshold) & (heights > threshold)
-        return keep
-
-    def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "RotatedBoxes":
-        """
-        Returns:
-            RotatedBoxes: Create a new :class:`RotatedBoxes` by indexing.
-
-        The following usage are allowed:
-
-        1. `new_boxes = boxes[3]`: return a `RotatedBoxes` which contains only one box.
-        2. `new_boxes = boxes[2:10]`: return a slice of boxes.
-        3. `new_boxes = boxes[vector]`, where vector is a torch.ByteTensor
-           with `length = len(boxes)`. Nonzero elements in the vector will be selected.
-
-        Note that the returned RotatedBoxes might share storage with this RotatedBoxes,
-        subject to Pytorch's indexing semantics.
-        """
-        if isinstance(item, int):
-            return RotatedBoxes(self.tensor[item].view(1, -1))
-        b = self.tensor[item]
-        assert b.dim() == 2, "Indexing on RotatedBoxes with {} failed to return a matrix!".format(
-            item
-        )
-        return RotatedBoxes(b)
-
-    def __len__(self) -> int:
-        return self.tensor.shape[0]
-
-    def __repr__(self) -> str:
-        return "RotatedBoxes(" + str(self.tensor) + ")"
-
-    def inside_box(self, box_size: Boxes.BoxSizeType, boundary_threshold: int = 0) -> torch.Tensor:
-        """
-        Args:
-            box_size (height, width): Size of the reference box covering
-                [0, width] x [0, height]
-            boundary_threshold (int): Boxes that extend beyond the reference box
-                boundary by more than boundary_threshold are considered "outside".
-
-        For RRPN, it might not be necessary to call this function since it's common
-        for rotated box to extend to outside of the image boundaries
-        (the clip function only clips the near-horizontal boxes)
-
-        Returns:
-            a binary vector, indicating whether each box is inside the reference box.
-        """
-        height, width = box_size
-
-        cnt_x = self.tensor[..., 0]
-        cnt_y = self.tensor[..., 1]
-        half_w = self.tensor[..., 2] / 2.0
-        half_h = self.tensor[..., 3] / 2.0
-        a = self.tensor[..., 4]
-        c = torch.abs(torch.cos(a * math.pi / 180.0))
-        s = torch.abs(torch.sin(a * math.pi / 180.0))
-        # This basically computes the horizontal bounding rectangle of the rotated box
-        max_rect_dx = c * half_w + s * half_h
-        max_rect_dy = c * half_h + s * half_w
-
-        inds_inside = (
-            (cnt_x - max_rect_dx >= -boundary_threshold)
-            & (cnt_y - max_rect_dy >= -boundary_threshold)
-            & (cnt_x + max_rect_dx < width + boundary_threshold)
-            & (cnt_y + max_rect_dy < height + boundary_threshold)
-        )
-
-        return inds_inside
-
-    def get_centers(self) -> torch.Tensor:
-        """
-        Returns:
-            The box centers in a Nx2 array of (x, y).
-        """
-        return self.tensor[:, :2]
-
-    def scale(self, scale_x: float, scale_y: float) -> None:
-        """
-        Scale the rotated box with horizontal and vertical scaling factors
-        Note: when scale_factor_x != scale_factor_y,
-        the rotated box does not preserve the rectangular shape when the angle
-        is not a multiple of 90 degrees under resize transformation.
-        Instead, the shape is a parallelogram (that has skew)
-        Here we make an approximation by fitting a rotated rectangle to the parallelogram.
-        """
-        self.tensor[:, 0] *= scale_x
-        self.tensor[:, 1] *= scale_y
-        theta = self.tensor[:, 4] * math.pi / 180.0
-        c = torch.cos(theta)
-        s = torch.sin(theta)
-
-        # In image space, y is top->down and x is left->right
-        # Consider the local coordintate system for the rotated box,
-        # where the box center is located at (0, 0), and the four vertices ABCD are
-        # A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2)
-        # the midpoint of the left edge AD of the rotated box E is:
-        # E = (A+D)/2 = (-w / 2, 0)
-        # the midpoint of the top edge AB of the rotated box F is:
-        # F(0, -h / 2)
-        # To get the old coordinates in the global system, apply the rotation transformation
-        # (Note: the right-handed coordinate system for image space is yOx):
-        # (old_x, old_y) = (s * y + c * x, c * y - s * x)
-        # E(old) = (s * 0 + c * (-w/2), c * 0 - s * (-w/2)) = (-c * w / 2, s * w / 2)
-        # F(old) = (s * (-h / 2) + c * 0, c * (-h / 2) - s * 0) = (-s * h / 2, -c * h / 2)
-        # After applying the scaling factor (sfx, sfy):
-        # E(new) = (-sfx * c * w / 2, sfy * s * w / 2)
-        # F(new) = (-sfx * s * h / 2, -sfy * c * h / 2)
-        # The new width after scaling tranformation becomes:
-
-        # w(new) = |E(new) - O| * 2
-        #        = sqrt[(sfx * c * w / 2)^2 + (sfy * s * w / 2)^2] * 2
-        #        = sqrt[(sfx * c)^2 + (sfy * s)^2] * w
-        # i.e., scale_factor_w = sqrt[(sfx * c)^2 + (sfy * s)^2]
-        #
-        # For example,
-        # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_w == scale_factor_x;
-        # when |angle| = 90, c = 0, |s| = 1, scale_factor_w == scale_factor_y
-        self.tensor[:, 2] *= torch.sqrt((scale_x * c) ** 2 + (scale_y * s) ** 2)
-
-        # h(new) = |F(new) - O| * 2
-        #        = sqrt[(sfx * s * h / 2)^2 + (sfy * c * h / 2)^2] * 2
-        #        = sqrt[(sfx * s)^2 + (sfy * c)^2] * h
-        # i.e., scale_factor_h = sqrt[(sfx * s)^2 + (sfy * c)^2]
-        #
-        # For example,
-        # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_h == scale_factor_y;
-        # when |angle| = 90, c = 0, |s| = 1, scale_factor_h == scale_factor_x
-        self.tensor[:, 3] *= torch.sqrt((scale_x * s) ** 2 + (scale_y * c) ** 2)
-
-        # The angle is the rotation angle from y-axis in image space to the height
-        # vector (top->down in the box's local coordinate system) of the box in CCW.
-        #
-        # angle(new) = angle_yOx(O - F(new))
-        #            = angle_yOx( (sfx * s * h / 2, sfy * c * h / 2) )
-        #            = atan2(sfx * s * h / 2, sfy * c * h / 2)
-        #            = atan2(sfx * s, sfy * c)
-        #
-        # For example,
-        # when sfx == sfy, angle(new) == atan2(s, c) == angle(old)
-        self.tensor[:, 4] = torch.atan2(scale_x * s, scale_y * c) * 180 / math.pi
-
-    @staticmethod
-    def cat(boxes_list: List["RotatedBoxes"]) -> "RotatedBoxes":  # type: ignore
-        """
-        Concatenates a list of RotatedBoxes into a single RotatedBoxes
-
-        Arguments:
-            boxes_list (list[RotatedBoxes])
-
-        Returns:
-            RotatedBoxes: the concatenated RotatedBoxes
-        """
-        assert isinstance(boxes_list, (list, tuple))
-        assert len(boxes_list) > 0
-        assert all(isinstance(box, RotatedBoxes) for box in boxes_list)
-
-        cat_boxes = type(boxes_list[0])(cat([b.tensor for b in boxes_list], dim=0))
-        return cat_boxes
-
-    @property
-    def device(self) -> str:
-        return self.tensor.device
-
-    def __iter__(self) -> Iterator[torch.Tensor]:
-        """
-        Yield a box as a Tensor of shape (5,) at a time.
-        """
-        yield from self.tensor
-
-
-def pairwise_iou(boxes1: RotatedBoxes, boxes2: RotatedBoxes) -> None:
-    """
-    Given two lists of rotated boxes of size N and M,
-    compute the IoU (intersection over union)
-    between __all__ N x M pairs of boxes.
-    The box order must be (x_center, y_center, width, height, angle).
-
-    Args:
-        boxes1, boxes2 (RotatedBoxes):
-            two `RotatedBoxes`. Contains N & M rotated boxes, respectively.
-
-    Returns:
-        Tensor: IoU, sized [N,M].
-    """
-
-    return pairwise_iou_rotated(boxes1.tensor, boxes2.tensor)

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

@@ -1,356 +0,0 @@
-# -*- coding: utf-8 -*-
-import pytest
-
-import env  # noqa: F401
-
-from pybind11_tests import ConstructorStats
-from pybind11_tests import multiple_inheritance as m
-
-
-def test_multiple_inheritance_cpp():
-    mt = m.MIType(3, 4)
-
-    assert mt.foo() == 3
-    assert mt.bar() == 4
-
-
-@pytest.mark.skipif("env.PYPY and env.PY2")
-@pytest.mark.xfail("env.PYPY and not env.PY2")
-def test_multiple_inheritance_mix1():
-    class Base1:
-        def __init__(self, i):
-            self.i = i
-
-        def foo(self):
-            return self.i
-
-    class MITypePy(Base1, m.Base2):
-        def __init__(self, i, j):
-            Base1.__init__(self, i)
-            m.Base2.__init__(self, j)
-
-    mt = MITypePy(3, 4)
-
-    assert mt.foo() == 3
-    assert mt.bar() == 4
-
-
-def test_multiple_inheritance_mix2():
-    class Base2:
-        def __init__(self, i):
-            self.i = i
-
-        def bar(self):
-            return self.i
-
-    class MITypePy(m.Base1, Base2):
-        def __init__(self, i, j):
-            m.Base1.__init__(self, i)
-            Base2.__init__(self, j)
-
-    mt = MITypePy(3, 4)
-
-    assert mt.foo() == 3
-    assert mt.bar() == 4
-
-
-@pytest.mark.skipif("env.PYPY and env.PY2")
-@pytest.mark.xfail("env.PYPY and not env.PY2")
-def test_multiple_inheritance_python():
-
-    class MI1(m.Base1, m.Base2):
-        def __init__(self, i, j):
-            m.Base1.__init__(self, i)
-            m.Base2.__init__(self, j)
-
-    class B1(object):
-        def v(self):
-            return 1
-
-    class MI2(B1, m.Base1, m.Base2):
-        def __init__(self, i, j):
-            B1.__init__(self)
-            m.Base1.__init__(self, i)
-            m.Base2.__init__(self, j)
-
-    class MI3(MI2):
-        def __init__(self, i, j):
-            MI2.__init__(self, i, j)
-
-    class MI4(MI3, m.Base2):
-        def __init__(self, i, j):
-            MI3.__init__(self, i, j)
-            # This should be ignored (Base2 is already initialized via MI2):
-            m.Base2.__init__(self, i + 100)
-
-    class MI5(m.Base2, B1, m.Base1):
-        def __init__(self, i, j):
-            B1.__init__(self)
-            m.Base1.__init__(self, i)
-            m.Base2.__init__(self, j)
-
-    class MI6(m.Base2, B1):
-        def __init__(self, i):
-            m.Base2.__init__(self, i)
-            B1.__init__(self)
-
-    class B2(B1):
-        def v(self):
-            return 2
-
-    class B3(object):
-        def v(self):
-            return 3
-
-    class B4(B3, B2):
-        def v(self):
-            return 4
-
-    class MI7(B4, MI6):
-        def __init__(self, i):
-            B4.__init__(self)
-            MI6.__init__(self, i)
-
-    class MI8(MI6, B3):
-        def __init__(self, i):
-            MI6.__init__(self, i)
-            B3.__init__(self)
-
-    class MI8b(B3, MI6):
-        def __init__(self, i):
-            B3.__init__(self)
-            MI6.__init__(self, i)
-
-    mi1 = MI1(1, 2)
-    assert mi1.foo() == 1
-    assert mi1.bar() == 2
-
-    mi2 = MI2(3, 4)
-    assert mi2.v() == 1
-    assert mi2.foo() == 3
-    assert mi2.bar() == 4
-
-    mi3 = MI3(5, 6)
-    assert mi3.v() == 1
-    assert mi3.foo() == 5
-    assert mi3.bar() == 6
-
-    mi4 = MI4(7, 8)
-    assert mi4.v() == 1
-    assert mi4.foo() == 7
-    assert mi4.bar() == 8
-
-    mi5 = MI5(10, 11)
-    assert mi5.v() == 1
-    assert mi5.foo() == 10
-    assert mi5.bar() == 11
-
-    mi6 = MI6(12)
-    assert mi6.v() == 1
-    assert mi6.bar() == 12
-
-    mi7 = MI7(13)
-    assert mi7.v() == 4
-    assert mi7.bar() == 13
-
-    mi8 = MI8(14)
-    assert mi8.v() == 1
-    assert mi8.bar() == 14
-
-    mi8b = MI8b(15)
-    assert mi8b.v() == 3
-    assert mi8b.bar() == 15
-
-
-def test_multiple_inheritance_python_many_bases():
-
-    class MIMany14(m.BaseN1, m.BaseN2, m.BaseN3, m.BaseN4):
-        def __init__(self):
-            m.BaseN1.__init__(self, 1)
-            m.BaseN2.__init__(self, 2)
-            m.BaseN3.__init__(self, 3)
-            m.BaseN4.__init__(self, 4)
-
-    class MIMany58(m.BaseN5, m.BaseN6, m.BaseN7, m.BaseN8):
-        def __init__(self):
-            m.BaseN5.__init__(self, 5)
-            m.BaseN6.__init__(self, 6)
-            m.BaseN7.__init__(self, 7)
-            m.BaseN8.__init__(self, 8)
-
-    class MIMany916(m.BaseN9, m.BaseN10, m.BaseN11, m.BaseN12, m.BaseN13, m.BaseN14, m.BaseN15,
-                    m.BaseN16):
-        def __init__(self):
-            m.BaseN9.__init__(self, 9)
-            m.BaseN10.__init__(self, 10)
-            m.BaseN11.__init__(self, 11)
-            m.BaseN12.__init__(self, 12)
-            m.BaseN13.__init__(self, 13)
-            m.BaseN14.__init__(self, 14)
-            m.BaseN15.__init__(self, 15)
-            m.BaseN16.__init__(self, 16)
-
-    class MIMany19(MIMany14, MIMany58, m.BaseN9):
-        def __init__(self):
-            MIMany14.__init__(self)
-            MIMany58.__init__(self)
-            m.BaseN9.__init__(self, 9)
-
-    class MIMany117(MIMany14, MIMany58, MIMany916, m.BaseN17):
-        def __init__(self):
-            MIMany14.__init__(self)
-            MIMany58.__init__(self)
-            MIMany916.__init__(self)
-            m.BaseN17.__init__(self, 17)
-
-    # Inherits from 4 registered C++ classes: can fit in one pointer on any modern arch:
-    a = MIMany14()
-    for i in range(1, 4):
-        assert getattr(a, "f" + str(i))() == 2 * i
-
-    # Inherits from 8: requires 1/2 pointers worth of holder flags on 32/64-bit arch:
-    b = MIMany916()
-    for i in range(9, 16):
-        assert getattr(b, "f" + str(i))() == 2 * i
-
-    # Inherits from 9: requires >= 2 pointers worth of holder flags
-    c = MIMany19()
-    for i in range(1, 9):
-        assert getattr(c, "f" + str(i))() == 2 * i
-
-    # Inherits from 17: requires >= 3 pointers worth of holder flags
-    d = MIMany117()
-    for i in range(1, 17):
-        assert getattr(d, "f" + str(i))() == 2 * i
-
-
-def test_multiple_inheritance_virtbase():
-
-    class MITypePy(m.Base12a):
-        def __init__(self, i, j):
-            m.Base12a.__init__(self, i, j)
-
-    mt = MITypePy(3, 4)
-    assert mt.bar() == 4
-    assert m.bar_base2a(mt) == 4
-    assert m.bar_base2a_sharedptr(mt) == 4
-
-
-def test_mi_static_properties():
-    """Mixing bases with and without static properties should be possible
-     and the result should be independent of base definition order"""
-
-    for d in (m.VanillaStaticMix1(), m.VanillaStaticMix2()):
-        assert d.vanilla() == "Vanilla"
-        assert d.static_func1() == "WithStatic1"
-        assert d.static_func2() == "WithStatic2"
-        assert d.static_func() == d.__class__.__name__
-
-        m.WithStatic1.static_value1 = 1
-        m.WithStatic2.static_value2 = 2
-        assert d.static_value1 == 1
-        assert d.static_value2 == 2
-        assert d.static_value == 12
-
-        d.static_value1 = 0
-        assert d.static_value1 == 0
-        d.static_value2 = 0
-        assert d.static_value2 == 0
-        d.static_value = 0
-        assert d.static_value == 0
-
-
-# Requires PyPy 6+
-def test_mi_dynamic_attributes():
-    """Mixing bases with and without dynamic attribute support"""
-
-    for d in (m.VanillaDictMix1(), m.VanillaDictMix2()):
-        d.dynamic = 1
-        assert d.dynamic == 1
-
-
-def test_mi_unaligned_base():
-    """Returning an offset (non-first MI) base class pointer should recognize the instance"""
-
-    n_inst = ConstructorStats.detail_reg_inst()
-
-    c = m.I801C()
-    d = m.I801D()
-    # + 4 below because we have the two instances, and each instance has offset base I801B2
-    assert ConstructorStats.detail_reg_inst() == n_inst + 4
-    b1c = m.i801b1_c(c)
-    assert b1c is c
-    b2c = m.i801b2_c(c)
-    assert b2c is c
-    b1d = m.i801b1_d(d)
-    assert b1d is d
-    b2d = m.i801b2_d(d)
-    assert b2d is d
-
-    assert ConstructorStats.detail_reg_inst() == n_inst + 4  # no extra instances
-    del c, b1c, b2c
-    assert ConstructorStats.detail_reg_inst() == n_inst + 2
-    del d, b1d, b2d
-    assert ConstructorStats.detail_reg_inst() == n_inst
-
-
-def test_mi_base_return():
-    """Tests returning an offset (non-first MI) base class pointer to a derived instance"""
-
-    n_inst = ConstructorStats.detail_reg_inst()
-
-    c1 = m.i801c_b1()
-    assert type(c1) is m.I801C
-    assert c1.a == 1
-    assert c1.b == 2
-
-    d1 = m.i801d_b1()
-    assert type(d1) is m.I801D
-    assert d1.a == 1
-    assert d1.b == 2
-
-    assert ConstructorStats.detail_reg_inst() == n_inst + 4
-
-    c2 = m.i801c_b2()
-    assert type(c2) is m.I801C
-    assert c2.a == 1
-    assert c2.b == 2
-
-    d2 = m.i801d_b2()
-    assert type(d2) is m.I801D
-    assert d2.a == 1
-    assert d2.b == 2
-
-    assert ConstructorStats.detail_reg_inst() == n_inst + 8
-
-    del c2
-    assert ConstructorStats.detail_reg_inst() == n_inst + 6
-    del c1, d1, d2
-    assert ConstructorStats.detail_reg_inst() == n_inst
-
-    # Returning an unregistered derived type with a registered base; we won't
-    # pick up the derived type, obviously, but should still work (as an object
-    # of whatever type was returned).
-    e1 = m.i801e_c()
-    assert type(e1) is m.I801C
-    assert e1.a == 1
-    assert e1.b == 2
-
-    e2 = m.i801e_b2()
-    assert type(e2) is m.I801B2
-    assert e2.b == 2
-
-
-def test_diamond_inheritance():
-    """Tests that diamond inheritance works as expected (issue #959)"""
-
-    # Issue #959: this shouldn't segfault:
-    d = m.D()
-
-    # Make sure all the various distinct pointers are all recognized as registered instances:
-    assert d is d.c0()
-    assert d is d.c1()
-    assert d is d.b()
-    assert d is d.c0().b()
-    assert d is d.c1().b()
-    assert d is d.c0().c1().b().c0().b()

spaces/CVPR/LIVE/pydiffvg/parse_svg.py

@@ -1,583 +0,0 @@
-import torch
-import xml.etree.ElementTree as etree
-import numpy as np
-import diffvg
-import os
-import pydiffvg
-import svgpathtools
-import svgpathtools.parser
-import re
-import warnings
-import cssutils
-import logging
-import matplotlib.colors 
-cssutils.log.setLevel(logging.ERROR)
-
-def remove_namespaces(s):
-    """
-        {...} ... -> ...
-    """
-    return re.sub('{.*}', '', s)
-
-def parse_style(s, defs):
-    style_dict = {}
-    for e in s.split(';'):
-        key_value = e.split(':')
-        if len(key_value) == 2:
-            key = key_value[0].strip()
-            value = key_value[1].strip()
-            if key == 'fill' or key == 'stroke':
-                # Special case: convert colors into tensor in definitions so
-                # that different shapes can share the same color
-                value = parse_color(value, defs)
-            style_dict[key] = value
-    return style_dict
-
-def parse_hex(s):
-    """
-        Hex to tuple
-    """
-    s = s.lstrip('#')
-    if len(s) == 3:
-        s = s[0] + s[0] + s[1] + s[1] + s[2] + s[2]
-    rgb = tuple(int(s[i:i+2], 16) for i in (0, 2, 4))
-    # sRGB to RGB
-    # return torch.pow(torch.tensor([rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0]), 2.2)
-    return torch.pow(torch.tensor([rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0]), 1.0)
-
-def parse_int(s):
-    """
-        trim alphabets
-    """
-    return int(float(''.join(i for i in s if (not i.isalpha()))))
-
-def parse_color(s, defs):
-    if s is None:
-        return None
-    if isinstance(s, torch.Tensor):
-        return s
-    s = s.lstrip(' ')
-    color = torch.tensor([0.0, 0.0, 0.0, 1.0])
-    if s[0] == '#':
-        color[:3] = parse_hex(s)
-    elif s[:3] == 'url':
-        # url(#id)
-        color = defs[s[4:-1].lstrip('#')]
-    elif s == 'none':
-        color = None
-    elif s[:4] == 'rgb(':
-        rgb = s[4:-1].split(',')
-        color = torch.tensor([int(rgb[0]) / 255.0, int(rgb[1]) / 255.0, int(rgb[2]) / 255.0, 1.0])
-    elif s == 'none':
-        return None
-    else:
-        try : 
-            rgba = matplotlib.colors.to_rgba(s)
-            color = torch.tensor(rgba)
-        except ValueError : 
-            warnings.warn('Unknown color command ' + s)
-    return color
-
-# https://github.com/mathandy/svgpathtools/blob/7ebc56a831357379ff22216bec07e2c12e8c5bc6/svgpathtools/parser.py
-def _parse_transform_substr(transform_substr):
-    type_str, value_str = transform_substr.split('(')
-    value_str = value_str.replace(',', ' ')
-    values = list(map(float, filter(None, value_str.split(' '))))
-
-    transform = np.identity(3)
-    if 'matrix' in type_str:
-        transform[0:2, 0:3] = np.array([values[0:6:2], values[1:6:2]])
-    elif 'translate' in transform_substr:
-        transform[0, 2] = values[0]
-        if len(values) > 1:
-            transform[1, 2] = values[1]
-    elif 'scale' in transform_substr:
-        x_scale = values[0]
-        y_scale = values[1] if (len(values) > 1) else x_scale
-        transform[0, 0] = x_scale
-        transform[1, 1] = y_scale
-    elif 'rotate' in transform_substr:
-        angle = values[0] * np.pi / 180.0
-        if len(values) == 3:
-            offset = values[1:3]
-        else:
-            offset = (0, 0)
-        tf_offset = np.identity(3)
-        tf_offset[0:2, 2:3] = np.array([[offset[0]], [offset[1]]])
-        tf_rotate = np.identity(3)
-        tf_rotate[0:2, 0:2] = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])
-        tf_offset_neg = np.identity(3)
-        tf_offset_neg[0:2, 2:3] = np.array([[-offset[0]], [-offset[1]]])
-
-        transform = tf_offset.dot(tf_rotate).dot(tf_offset_neg)
-    elif 'skewX' in transform_substr:
-        transform[0, 1] = np.tan(values[0] * np.pi / 180.0)
-    elif 'skewY' in transform_substr:
-        transform[1, 0] = np.tan(values[0] * np.pi / 180.0)
-    else:
-        # Return an identity matrix if the type of transform is unknown, and warn the user
-        warnings.warn('Unknown SVG transform type: {0}'.format(type_str))
-    return transform
-
-def parse_transform(transform_str):
-    """
-        Converts a valid SVG transformation string into a 3x3 matrix.
-        If the string is empty or null, this returns a 3x3 identity matrix
-    """
-    if not transform_str:
-        return np.identity(3)
-    elif not isinstance(transform_str, str):
-        raise TypeError('Must provide a string to parse')
-
-    total_transform = np.identity(3)
-    transform_substrs = transform_str.split(')')[:-1]  # Skip the last element, because it should be empty
-    for substr in transform_substrs:
-        total_transform = total_transform.dot(_parse_transform_substr(substr))
-
-    return torch.from_numpy(total_transform).type(torch.float32)
-
-def parse_linear_gradient(node, transform, defs):
-    begin = torch.tensor([0.0, 0.0])
-    end = torch.tensor([0.0, 0.0])
-    offsets = []
-    stop_colors = []
-    # Inherit from parent
-    for key in node.attrib:
-        if remove_namespaces(key) == 'href':
-            value = node.attrib[key]
-            parent = defs[value.lstrip('#')]
-            begin = parent.begin
-            end = parent.end
-            offsets = parent.offsets
-            stop_colors = parent.stop_colors
-
-    for attrib in node.attrib:
-        attrib = remove_namespaces(attrib)
-        if attrib == 'x1':
-            begin[0] = float(node.attrib['x1'])
-        elif attrib == 'y1':
-            begin[1] = float(node.attrib['y1'])
-        elif attrib == 'x2':
-            end[0] = float(node.attrib['x2'])
-        elif attrib == 'y2':
-            end[1] = float(node.attrib['y2'])
-        elif attrib == 'gradientTransform':
-            transform = transform @ parse_transform(node.attrib['gradientTransform'])
-
-    begin = transform @ torch.cat((begin, torch.ones([1])))
-    begin = begin / begin[2]
-    begin = begin[:2]
-    end = transform @ torch.cat((end, torch.ones([1])))
-    end = end / end[2]
-    end = end[:2]
-
-    for child in node:
-        tag = remove_namespaces(child.tag)
-        if tag == 'stop':
-            offset = float(child.attrib['offset'])
-            color = [0.0, 0.0, 0.0, 1.0]
-            if 'stop-color' in child.attrib:
-                c = parse_color(child.attrib['stop-color'], defs)
-                color[:3] = [c[0], c[1], c[2]]
-            if 'stop-opacity' in child.attrib:
-                color[3] = float(child.attrib['stop-opacity'])
-            if 'style' in child.attrib:
-                style = parse_style(child.attrib['style'], defs)
-                if 'stop-color' in style:
-                    c = parse_color(style['stop-color'], defs)
-                    color[:3] = [c[0], c[1], c[2]]
-                if 'stop-opacity' in style:
-                    color[3] = float(style['stop-opacity'])
-            offsets.append(offset)
-            stop_colors.append(color)
-    if isinstance(offsets, list):
-        offsets = torch.tensor(offsets)
-    if isinstance(stop_colors, list):
-        stop_colors = torch.tensor(stop_colors)
-
-    return pydiffvg.LinearGradient(begin, end, offsets, stop_colors)
-
-
-def parse_radial_gradient(node, transform, defs):
-    begin = torch.tensor([0.0, 0.0])
-    end = torch.tensor([0.0, 0.0])
-    center = torch.tensor([0.0, 0.0])
-    radius = torch.tensor([0.0, 0.0])
-    offsets = []
-    stop_colors = []
-    # Inherit from parent
-    for key in node.attrib:
-        if remove_namespaces(key) == 'href':
-            value = node.attrib[key]
-            parent = defs[value.lstrip('#')]
-            begin = parent.begin
-            end = parent.end
-            offsets = parent.offsets
-            stop_colors = parent.stop_colors
-
-    for attrib in node.attrib:
-        attrib = remove_namespaces(attrib)
-        if attrib == 'cx':
-            center[0] = float(node.attrib['cx'])
-        elif attrib == 'cy':
-            center[1] = float(node.attrib['cy'])
-        elif attrib == 'fx':
-            radius[0] = float(node.attrib['fx'])
-        elif attrib == 'fy':
-            radius[1] = float(node.attrib['fy'])
-        elif attrib == 'fr':
-            radius[0] = float(node.attrib['fr'])
-            radius[1] = float(node.attrib['fr'])
-        elif attrib == 'gradientTransform':
-            transform = transform @ parse_transform(node.attrib['gradientTransform'])
-
-    # TODO: this is incorrect
-    center = transform @ torch.cat((center, torch.ones([1])))
-    center = center / center[2]
-    center = center[:2]
-
-    for child in node:
-        tag = remove_namespaces(child.tag)
-        if tag == 'stop':
-            offset = float(child.attrib['offset'])
-            color = [0.0, 0.0, 0.0, 1.0]
-            if 'stop-color' in child.attrib:
-                c = parse_color(child.attrib['stop-color'], defs)
-                color[:3] = [c[0], c[1], c[2]]
-            if 'stop-opacity' in child.attrib:
-                color[3] = float(child.attrib['stop-opacity'])
-            if 'style' in child.attrib:
-                style = parse_style(child.attrib['style'], defs)
-                if 'stop-color' in style:
-                    c = parse_color(style['stop-color'], defs)
-                    color[:3] = [c[0], c[1], c[2]]
-                if 'stop-opacity' in style:
-                    color[3] = float(style['stop-opacity'])
-            offsets.append(offset)
-            stop_colors.append(color)
-    if isinstance(offsets, list):
-        offsets = torch.tensor(offsets)
-    if isinstance(stop_colors, list):
-        stop_colors = torch.tensor(stop_colors)
-
-    return pydiffvg.RadialGradient(begin, end, offsets, stop_colors)
-
-def parse_stylesheet(node, transform, defs):
-    # collect CSS classes
-    sheet = cssutils.parseString(node.text)
-    for rule in sheet:
-        if hasattr(rule, 'selectorText') and hasattr(rule, 'style'):
-            name = rule.selectorText
-            if len(name) >= 2 and name[0] == '.':
-                defs[name[1:]] = parse_style(rule.style.getCssText(), defs)
-    return defs
-
-def parse_defs(node, transform, defs):
-    for child in node:
-        tag = remove_namespaces(child.tag)
-        if tag == 'linearGradient':
-            if 'id' in child.attrib:
-                defs[child.attrib['id']] = parse_linear_gradient(child, transform, defs)
-        elif tag == 'radialGradient':
-            if 'id' in child.attrib:
-                defs[child.attrib['id']] = parse_radial_gradient(child, transform, defs)
-        elif tag == 'style':
-            defs = parse_stylesheet(child, transform, defs)
-    return defs
-
-def parse_common_attrib(node, transform, fill_color, defs):
-    attribs = {}
-    if 'class' in node.attrib:
-        attribs.update(defs[node.attrib['class']])
-    attribs.update(node.attrib)
-
-    name = ''
-    if 'id' in node.attrib:
-        name = node.attrib['id']
-
-    stroke_color = None
-    stroke_width = torch.tensor(0.5)
-    use_even_odd_rule = False
-
-    new_transform = transform
-    if 'transform' in attribs:
-        new_transform = transform @ parse_transform(attribs['transform'])
-    if 'fill' in attribs:
-        fill_color = parse_color(attribs['fill'], defs)
-    fill_opacity = 1.0
-    if 'fill-opacity' in attribs:
-        fill_opacity *= float(attribs['fill-opacity'])
-    if 'opacity' in attribs:
-        fill_opacity *= float(attribs['opacity'])
-    # Ignore opacity if the color is a gradient
-    if isinstance(fill_color, torch.Tensor):
-        fill_color[3] = fill_opacity
-
-    if 'fill-rule' in attribs:
-        if attribs['fill-rule'] == "evenodd":
-            use_even_odd_rule = True
-        elif attribs['fill-rule'] == "nonzero":
-            use_even_odd_rule = False
-        else:
-            warnings.warn('Unknown fill-rule: {}'.format(attribs['fill-rule']))
-
-    if 'stroke' in attribs:
-        stroke_color = parse_color(attribs['stroke'], defs)
-
-    if 'stroke-width' in attribs:
-        stroke_width = attribs['stroke-width']
-        if stroke_width[-2:] == 'px':
-            stroke_width = stroke_width[:-2]
-        stroke_width = torch.tensor(float(stroke_width) / 2.0)
-
-    if 'style' in attribs:
-        style = parse_style(attribs['style'], defs)
-        if 'fill' in style:
-            fill_color = parse_color(style['fill'], defs)
-        fill_opacity = 1.0
-        if 'fill-opacity' in style:
-            fill_opacity *= float(style['fill-opacity'])
-        if 'opacity' in style:
-            fill_opacity *= float(style['opacity'])
-        if 'fill-rule' in style:
-            if style['fill-rule'] == "evenodd":
-                use_even_odd_rule = True
-            elif style['fill-rule'] == "nonzero":
-                use_even_odd_rule = False
-            else:
-                warnings.warn('Unknown fill-rule: {}'.format(style['fill-rule']))
-        # Ignore opacity if the color is a gradient
-        if isinstance(fill_color, torch.Tensor):
-            fill_color[3] = fill_opacity
-        if 'stroke' in style:
-            if style['stroke'] != 'none':
-                stroke_color = parse_color(style['stroke'], defs)
-                # Ignore opacity if the color is a gradient
-                if isinstance(stroke_color, torch.Tensor):
-                    if 'stroke-opacity' in style:
-                        stroke_color[3] = float(style['stroke-opacity'])
-                    if 'opacity' in style:
-                        stroke_color[3] *= float(style['opacity'])
-                if 'stroke-width' in style:
-                    stroke_width = style['stroke-width']
-                    if stroke_width[-2:] == 'px':
-                        stroke_width = stroke_width[:-2]
-                    stroke_width = torch.tensor(float(stroke_width) / 2.0)
-
-        if isinstance(fill_color, pydiffvg.LinearGradient):
-            fill_color.begin = new_transform @ torch.cat((fill_color.begin, torch.ones([1])))
-            fill_color.begin = fill_color.begin / fill_color.begin[2]
-            fill_color.begin = fill_color.begin[:2]
-            fill_color.end = new_transform @ torch.cat((fill_color.end, torch.ones([1])))
-            fill_color.end = fill_color.end / fill_color.end[2]
-            fill_color.end = fill_color.end[:2]
-        if isinstance(stroke_color, pydiffvg.LinearGradient):
-            stroke_color.begin = new_transform @ torch.cat((stroke_color.begin, torch.ones([1])))
-            stroke_color.begin = stroke_color.begin / stroke_color.begin[2]
-            stroke_color.begin = stroke_color.begin[:2]
-            stroke_color.end = new_transform @ torch.cat((stroke_color.end, torch.ones([1])))
-            stroke_color.end = stroke_color.end / stroke_color.end[2]
-            stroke_color.end = stroke_color.end[:2]
-        if 'filter' in style:
-            print('*** WARNING ***: Ignoring filter for path with id "{}"'.format(name))
-
-    return new_transform, fill_color, stroke_color, stroke_width, use_even_odd_rule
-
-def is_shape(tag):
-    return tag == 'path' or tag == 'polygon' or tag == 'line' or tag == 'circle' or tag == 'rect'
-
-def parse_shape(node, transform, fill_color, shapes, shape_groups, defs):
-    tag = remove_namespaces(node.tag)
-    new_transform, new_fill_color, stroke_color, stroke_width, use_even_odd_rule = \
-        parse_common_attrib(node, transform, fill_color, defs)
-    if tag == 'path':
-        d = node.attrib['d']
-        name = ''
-        if 'id' in node.attrib:
-            name = node.attrib['id']
-        force_closing = new_fill_color is not None
-        paths = pydiffvg.from_svg_path(d, new_transform, force_closing)
-        for idx, path in enumerate(paths):
-            assert(path.points.shape[1] == 2)
-            path.stroke_width = stroke_width
-            path.source_id = name
-            path.id = "{}-{}".format(name,idx) if len(paths)>1 else name
-        prev_shapes_size = len(shapes)
-        shapes = shapes + paths
-        shape_ids = torch.tensor(list(range(prev_shapes_size, len(shapes))))
-        shape_groups.append(pydiffvg.ShapeGroup(\
-            shape_ids = shape_ids,
-            fill_color = new_fill_color,
-            stroke_color = stroke_color,
-            use_even_odd_rule = use_even_odd_rule,
-            id = name))
-    elif tag == 'polygon':
-        name = ''
-        if 'id' in node.attrib:
-            name = node.attrib['id']
-        force_closing = new_fill_color is not None
-        pts = node.attrib['points'].strip()
-        pts = pts.split(' ')
-        # import ipdb; ipdb.set_trace()
-        pts = [[float(y) for y in re.split(',| ', x)] for x in pts if x]
-        pts = torch.tensor(pts, dtype=torch.float32).view(-1, 2)
-        polygon = pydiffvg.Polygon(pts, force_closing)
-        polygon.stroke_width = stroke_width
-        shape_ids = torch.tensor([len(shapes)])
-        shapes.append(polygon)
-        shape_groups.append(pydiffvg.ShapeGroup(\
-            shape_ids = shape_ids,
-            fill_color = new_fill_color,
-            stroke_color = stroke_color,
-            use_even_odd_rule = use_even_odd_rule,
-            shape_to_canvas = new_transform,
-            id = name))
-    elif tag == 'line':
-        x1 = float(node.attrib['x1'])
-        y1 = float(node.attrib['y1'])
-        x2 = float(node.attrib['x2'])
-        y2 = float(node.attrib['y2'])
-        p1 = torch.tensor([x1, y1])
-        p2 = torch.tensor([x2, y2])
-        points = torch.stack((p1, p2))
-        line = pydiffvg.Polygon(points, False)
-        line.stroke_width = stroke_width
-        shape_ids = torch.tensor([len(shapes)])
-        shapes.append(line)
-        shape_groups.append(pydiffvg.ShapeGroup(\
-            shape_ids = shape_ids,
-            fill_color = new_fill_color,
-            stroke_color = stroke_color,
-            use_even_odd_rule = use_even_odd_rule,
-            shape_to_canvas = new_transform))
-    elif tag == 'circle':
-        radius = float(node.attrib['r'])
-        cx = float(node.attrib['cx'])
-        cy = float(node.attrib['cy'])
-        name = ''
-        if 'id' in node.attrib:
-            name = node.attrib['id']
-        center = torch.tensor([cx, cy])
-        circle = pydiffvg.Circle(radius = torch.tensor(radius),
-                                 center = center)
-        circle.stroke_width = stroke_width
-        shape_ids = torch.tensor([len(shapes)])
-        shapes.append(circle)
-        shape_groups.append(pydiffvg.ShapeGroup(\
-            shape_ids = shape_ids,
-            fill_color = new_fill_color,
-            stroke_color = stroke_color,
-            use_even_odd_rule = use_even_odd_rule,
-            shape_to_canvas = new_transform))
-    elif tag == 'ellipse':
-        rx = float(node.attrib['rx'])
-        ry = float(node.attrib['ry'])
-        cx = float(node.attrib['cx'])
-        cy = float(node.attrib['cy'])
-        name = ''
-        if 'id' in node.attrib:
-            name = node.attrib['id']
-        center = torch.tensor([cx, cy])
-        circle = pydiffvg.Circle(radius = torch.tensor(radius),
-                                 center = center)
-        circle.stroke_width = stroke_width
-        shape_ids = torch.tensor([len(shapes)])
-        shapes.append(circle)
-        shape_groups.append(pydiffvg.ShapeGroup(\
-            shape_ids = shape_ids,
-            fill_color = new_fill_color,
-            stroke_color = stroke_color,
-            use_even_odd_rule = use_even_odd_rule,
-            shape_to_canvas = new_transform))
-    elif tag == 'rect':
-        x = 0.0
-        y = 0.0
-        if x in node.attrib:
-            x = float(node.attrib['x'])
-        if y in node.attrib:
-            y = float(node.attrib['y'])
-        w = float(node.attrib['width'])
-        h = float(node.attrib['height'])
-        p_min = torch.tensor([x, y])
-        p_max = torch.tensor([x + w, x + h])
-        rect = pydiffvg.Rect(p_min = p_min, p_max = p_max)
-        rect.stroke_width = stroke_width
-        shape_ids = torch.tensor([len(shapes)])
-        shapes.append(rect)
-        shape_groups.append(pydiffvg.ShapeGroup(\
-            shape_ids = shape_ids,
-            fill_color = new_fill_color,
-            stroke_color = stroke_color,
-            use_even_odd_rule = use_even_odd_rule,
-            shape_to_canvas = new_transform))
-    return shapes, shape_groups
-
-def parse_group(node, transform, fill_color, shapes, shape_groups, defs):
-    if 'transform' in node.attrib:
-        transform = transform @ parse_transform(node.attrib['transform'])
-    if 'fill' in node.attrib:
-        fill_color = parse_color(node.attrib['fill'], defs)
-    for child in node:
-        tag = remove_namespaces(child.tag)
-        if is_shape(tag):
-            shapes, shape_groups = parse_shape(\
-                child, transform, fill_color, shapes, shape_groups, defs)
-        elif tag == 'g':
-            shapes, shape_groups = parse_group(\
-                child, transform, fill_color, shapes, shape_groups, defs)
-    return shapes, shape_groups
-
-def parse_scene(node):
-    canvas_width = -1
-    canvas_height = -1
-    defs = {}
-    shapes = []
-    shape_groups = []
-    fill_color = torch.tensor([0.0, 0.0, 0.0, 1.0])
-    transform = torch.eye(3)
-    if 'viewBox' in node.attrib:
-        view_box_array = node.attrib['viewBox'].split()
-        canvas_width = parse_int(view_box_array[2])
-        canvas_height = parse_int(view_box_array[3])
-    else:
-        if 'width' in node.attrib:
-            canvas_width = parse_int(node.attrib['width'])
-        else:
-            print('Warning: Can\'t find canvas width.')
-        if 'height' in node.attrib:
-            canvas_height = parse_int(node.attrib['height'])
-        else:
-            print('Warning: Can\'t find canvas height.')
-    for child in node:
-        tag = remove_namespaces(child.tag)
-        if tag == 'defs':
-            defs = parse_defs(child, transform, defs)
-        elif tag == 'style':
-            defs = parse_stylesheet(child, transform, defs)
-        elif tag == 'linearGradient':
-            if 'id' in child.attrib:
-                defs[child.attrib['id']] = parse_linear_gradient(child, transform, defs)
-        elif tag == 'radialGradient':
-            if 'id' in child.attrib:
-                defs[child.attrib['id']] = parse_radial_gradient(child, transform, defs)
-        elif is_shape(tag):
-            shapes, shape_groups = parse_shape(\
-                child, transform, fill_color, shapes, shape_groups, defs)
-        elif tag == 'g':
-            shapes, shape_groups = parse_group(\
-                child, transform, fill_color, shapes, shape_groups, defs)
-    return canvas_width, canvas_height, shapes, shape_groups
-
-def svg_to_scene(filename):
-    """
-        Load from a SVG file and convert to PyTorch tensors.
-    """
-
-    tree = etree.parse(filename)
-    root = tree.getroot()
-    cwd = os.getcwd()
-    if (os.path.dirname(filename) != ''):
-        os.chdir(os.path.dirname(filename))
-    ret = parse_scene(root)
-    os.chdir(cwd)
-    return ret

spaces/CVPR/LIVE/thrust/thrust/mr/disjoint_pool.h

@@ -1,489 +0,0 @@
-/*
- *  Copyright 2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file disjoint_pool.h
- *  \brief A caching and pooling memory resource adaptor which uses separate upstream resources for memory allocation
- *      and bookkeeping.
- */
-
-#pragma once
-
-#include <thrust/detail/algorithm_wrapper.h>
-
-#include <thrust/host_vector.h>
-#include <thrust/binary_search.h>
-#include <thrust/detail/seq.h>
-
-#include <thrust/mr/memory_resource.h>
-#include <thrust/mr/allocator.h>
-#include <thrust/mr/pool_options.h>
-
-#include <cassert>
-
-namespace thrust
-{
-namespace mr
-{
-
-/** \addtogroup memory_resources Memory Resources
- *  \ingroup memory_management_classes
- *  \{
- */
-
-/*! A memory resource adaptor allowing for pooling and caching allocations from \p Upstream, using \p Bookkeeper for
- *      management of that cached and pooled memory, allowing to cache portions of memory inaccessible from the host.
- *
- *  On a typical memory resource, calls to \p allocate and \p deallocate actually allocate and deallocate memory. Pooling
- *      memory resources only allocate and deallocate memory from an external resource (the upstream memory resource) when
- *      there's no suitable memory currently cached; otherwise, they use memory they have acquired beforehand, to make
- *      memory allocation faster and more efficient.
- *
- *  The disjoint version of the pool resources uses a separate upstream memory resource, \p Bookkeeper, to allocate memory
- *      necessary to manage the cached memory. There may be many reasons to do that; the canonical one is that \p Upstream
- *      allocates memory that is inaccessible to the code of the pool resource, which means that it cannot embed the necessary
- *      information in memory obtained from \p Upstream; for instance, \p Upstream can be a CUDA non-managed memory
- *      resource, or a CUDA managed memory resource whose memory we would prefer to not migrate back and forth between
- *      host and device when executing bookkeeping code.
- *
- *  This is not the only case where it makes sense to use a disjoint pool resource, though. In a multi-core environment
- *      it may be beneficial to avoid stealing cache lines from other cores by writing over bookkeeping information
- *      embedded in an allocated block of memory. In such a case, one can imagine wanting to use a disjoint pool where
- *      both the upstream and the bookkeeper are of the same type, to allocate memory consistently, but separately for
- *      those two purposes.
- *
- *  \tparam Upstream the type of memory resources that will be used for allocating memory blocks to be handed off to the user
- *  \tparam Bookkeeper the type of memory resources that will be used for allocating bookkeeping memory
- */
-template<typename Upstream, typename Bookkeeper>
-class disjoint_unsynchronized_pool_resource THRUST_FINAL
-    : public memory_resource<typename Upstream::pointer>,
-        private validator2<Upstream, Bookkeeper>
-{
-public:
-    /*! Get the default options for a disjoint pool. These are meant to be a sensible set of values for many use cases,
-     *      and as such, may be tuned in the future. This function is exposed so that creating a set of options that are
-     *      just a slight departure from the defaults is easy.
-     */
-    static pool_options get_default_options()
-    {
-        pool_options ret;
-
-        ret.min_blocks_per_chunk = 16;
-        ret.min_bytes_per_chunk = 1024;
-        ret.max_blocks_per_chunk = static_cast<std::size_t>(1) << 20;
-        ret.max_bytes_per_chunk = static_cast<std::size_t>(1) << 30;
-
-        ret.smallest_block_size = THRUST_MR_DEFAULT_ALIGNMENT;
-        ret.largest_block_size = static_cast<std::size_t>(1) << 20;
-
-        ret.alignment = THRUST_MR_DEFAULT_ALIGNMENT;
-
-        ret.cache_oversized = true;
-
-        ret.cached_size_cutoff_factor = 16;
-        ret.cached_alignment_cutoff_factor = 16;
-
-        return ret;
-    }
-
-    /*! Constructor.
-     *
-     *  \param upstream the upstream memory resource for allocations
-     *  \param bookkeeper the upstream memory resource for bookkeeping
-     *  \param options pool options to use
-     */
-    disjoint_unsynchronized_pool_resource(Upstream * upstream, Bookkeeper * bookkeeper,
-        pool_options options = get_default_options())
-        : m_upstream(upstream),
-        m_bookkeeper(bookkeeper),
-        m_options(options),
-        m_smallest_block_log2(detail::log2_ri(m_options.smallest_block_size)),
-        m_pools(m_bookkeeper),
-        m_allocated(m_bookkeeper),
-        m_cached_oversized(m_bookkeeper),
-        m_oversized(m_bookkeeper)
-    {
-        assert(m_options.validate());
-
-        pointer_vector free(m_bookkeeper);
-        pool p(free);
-        m_pools.resize(detail::log2_ri(m_options.largest_block_size) - m_smallest_block_log2 + 1, p);
-    }
-
-    // TODO: C++11: use delegating constructors
-
-    /*! Constructor. Upstream and bookkeeping resources are obtained by calling \p get_global_resource for their types.
-     *
-     *  \param options pool options to use
-     */
-    disjoint_unsynchronized_pool_resource(pool_options options = get_default_options())
-        : m_upstream(get_global_resource<Upstream>()),
-        m_bookkeeper(get_global_resource<Bookkeeper>()),
-        m_options(options),
-        m_smallest_block_log2(detail::log2_ri(m_options.smallest_block_size)),
-        m_pools(m_bookkeeper),
-        m_allocated(m_bookkeeper),
-        m_cached_oversized(m_bookkeeper),
-        m_oversized(m_bookkeeper)
-    {
-        assert(m_options.validate());
-
-        pointer_vector free(m_bookkeeper);
-        pool p(free);
-        m_pools.resize(detail::log2_ri(m_options.largest_block_size) - m_smallest_block_log2 + 1, p);
-    }
-
-    /*! Destructor. Releases all held memory to upstream.
-     */
-    ~disjoint_unsynchronized_pool_resource()
-    {
-        release();
-    }
-
-private:
-    typedef typename Upstream::pointer void_ptr;
-    typedef typename thrust::detail::pointer_traits<void_ptr>::template rebind<char>::other char_ptr;
-
-    struct chunk_descriptor
-    {
-        std::size_t size;
-        void_ptr pointer;
-    };
-
-    typedef thrust::host_vector<
-        chunk_descriptor,
-        allocator<chunk_descriptor, Bookkeeper>
-    > chunk_vector;
-
-    struct oversized_block_descriptor
-    {
-        std::size_t size;
-        std::size_t alignment;
-        void_ptr pointer;
-
-        __host__ __device__
-        bool operator==(const oversized_block_descriptor & other) const
-        {
-            return size == other.size && alignment == other.alignment && pointer == other.pointer;
-        }
-
-        __host__ __device__
-        bool operator<(const oversized_block_descriptor & other) const
-        {
-            return size < other.size || (size == other.size && alignment < other.alignment);
-        }
-    };
-
-    struct equal_pointers
-    {
-    public:
-        __host__ __device__
-        equal_pointers(void_ptr p) : p(p)
-        {
-        }
-
-        __host__ __device__
-        bool operator()(const oversized_block_descriptor & desc) const
-        {
-            return desc.pointer == p;
-        }
-
-    private:
-        void_ptr p;
-    };
-
-    struct matching_alignment
-    {
-    public:
-        __host__ __device__
-        matching_alignment(std::size_t requested) : requested(requested)
-        {
-        }
-
-        __host__ __device__
-        bool operator()(const oversized_block_descriptor & desc) const
-        {
-            return desc.alignment >= requested;
-        }
-
-    private:
-        std::size_t requested;
-    };
-
-    typedef thrust::host_vector<
-        oversized_block_descriptor,
-        allocator<oversized_block_descriptor, Bookkeeper>
-    > oversized_block_vector;
-
-    typedef thrust::host_vector<
-        void_ptr,
-        allocator<void_ptr, Bookkeeper>
-    > pointer_vector;
-
-    struct pool
-    {
-        __host__
-        pool(const pointer_vector & free)
-            : free_blocks(free),
-            previous_allocated_count(0)
-        {
-        }
-
-        __host__
-        pool(const pool & other)
-            : free_blocks(other.free_blocks),
-            previous_allocated_count(other.previous_allocated_count)
-        {
-        }
-
-#if THRUST_CPP_DIALECT >= 2011
-        pool & operator=(const pool &) = default;
-#endif
-
-        __host__
-        ~pool() {}
-
-        pointer_vector free_blocks;
-        std::size_t previous_allocated_count;
-    };
-
-    typedef thrust::host_vector<
-        pool,
-        allocator<pool, Bookkeeper>
-    > pool_vector;
-
-    Upstream * m_upstream;
-    Bookkeeper * m_bookkeeper;
-
-    pool_options m_options;
-    std::size_t m_smallest_block_log2;
-
-    // buckets containing free lists for each pooled size
-    pool_vector m_pools;
-    // list of all allocations from upstream for the above
-    chunk_vector m_allocated;
-    // list of all cached oversized/overaligned blocks that have been returned to the pool to cache
-    oversized_block_vector m_cached_oversized;
-    // list of all oversized/overaligned allocations from upstream
-    oversized_block_vector m_oversized;
-
-public:
-    /*! Releases all held memory to upstream.
-     */
-    void release()
-    {
-        // reset the buckets
-        for (std::size_t i = 0; i < m_pools.size(); ++i)
-        {
-            m_pools[i].free_blocks.clear();
-            m_pools[i].previous_allocated_count = 0;
-        }
-
-        // deallocate memory allocated for the buckets
-        for (std::size_t i = 0; i < m_allocated.size(); ++i)
-        {
-            m_upstream->do_deallocate(
-                m_allocated[i].pointer,
-                m_allocated[i].size,
-                m_options.alignment);
-        }
-
-        // deallocate cached oversized/overaligned memory
-        for (std::size_t i = 0; i < m_oversized.size(); ++i)
-        {
-            m_upstream->do_deallocate(
-                m_oversized[i].pointer,
-                m_oversized[i].size,
-                m_oversized[i].alignment);
-        }
-
-        m_allocated.clear();
-        m_oversized.clear();
-        m_cached_oversized.clear();
-    }
-
-    THRUST_NODISCARD virtual void_ptr do_allocate(std::size_t bytes, std::size_t alignment = THRUST_MR_DEFAULT_ALIGNMENT) THRUST_OVERRIDE
-    {
-        bytes = (std::max)(bytes, m_options.smallest_block_size);
-        assert(detail::is_power_of_2(alignment));
-
-        // an oversized and/or overaligned allocation requested; needs to be allocated separately
-        if (bytes > m_options.largest_block_size || alignment > m_options.alignment)
-        {
-            oversized_block_descriptor oversized;
-            oversized.size = bytes;
-            oversized.alignment = alignment;
-
-            if (m_options.cache_oversized && !m_cached_oversized.empty())
-            {
-                typename oversized_block_vector::iterator it = thrust::lower_bound(
-                    thrust::seq,
-                    m_cached_oversized.begin(),
-                    m_cached_oversized.end(),
-                    oversized);
-
-                // if the size is bigger than the requested size by a factor
-                // bigger than or equal to the specified cutoff for size,
-                // allocate a new block
-                if (it != m_cached_oversized.end())
-                {
-                    std::size_t size_factor = (*it).size / bytes;
-                    if (size_factor >= m_options.cached_size_cutoff_factor)
-                    {
-                        it = m_cached_oversized.end();
-                    }
-                }
-
-                if (it != m_cached_oversized.end() && (*it).alignment < alignment)
-                {
-                    it = find_if(it + 1, m_cached_oversized.end(), matching_alignment(alignment));
-                }
-
-                // if the alignment is bigger than the requested one by a factor
-                // bigger than or equal to the specified cutoff for alignment,
-                // allocate a new block
-                if (it != m_cached_oversized.end())
-                {
-                    std::size_t alignment_factor = (*it).alignment / alignment;
-                    if (alignment_factor >= m_options.cached_alignment_cutoff_factor)
-                    {
-                        it = m_cached_oversized.end();
-                    }
-                }
-
-                if (it != m_cached_oversized.end())
-                {
-                    oversized.pointer = (*it).pointer;
-                    m_cached_oversized.erase(it);
-                    return oversized.pointer;
-                }
-            }
-
-            // no fitting cached block found; allocate a new one that's just up to the specs
-            oversized.pointer = m_upstream->do_allocate(bytes, alignment);
-            m_oversized.push_back(oversized);
-
-            return oversized.pointer;
-        }
-
-        // the request is NOT for oversized and/or overaligned memory
-        // allocate a block from an appropriate bucket
-        std::size_t bytes_log2 = thrust::detail::log2_ri(bytes);
-        std::size_t bucket_idx = bytes_log2 - m_smallest_block_log2;
-        pool & bucket = m_pools[bucket_idx];
-
-        // if the free list of the bucket has no elements, allocate a new chunk
-        // and split it into blocks pushed to the free list
-        if (bucket.free_blocks.empty())
-        {
-            std::size_t bucket_size = static_cast<std::size_t>(1) << bytes_log2;
-
-            std::size_t n = bucket.previous_allocated_count;
-            if (n == 0)
-            {
-                n = m_options.min_blocks_per_chunk;
-                if (n < (m_options.min_bytes_per_chunk >> bytes_log2))
-                {
-                    n = m_options.min_bytes_per_chunk >> bytes_log2;
-                }
-            }
-            else
-            {
-                n = n * 3 / 2;
-                if (n > (m_options.max_bytes_per_chunk >> bytes_log2))
-                {
-                    n = m_options.max_bytes_per_chunk >> bytes_log2;
-                }
-                if (n > m_options.max_blocks_per_chunk)
-                {
-                    n = m_options.max_blocks_per_chunk;
-                }
-            }
-
-            bytes = n << bytes_log2;
-
-            assert(n >= m_options.min_blocks_per_chunk);
-            assert(n <= m_options.max_blocks_per_chunk);
-            assert(bytes >= m_options.min_bytes_per_chunk);
-            assert(bytes <= m_options.max_bytes_per_chunk);
-
-            chunk_descriptor allocated;
-            allocated.size = bytes;
-            allocated.pointer = m_upstream->do_allocate(bytes, m_options.alignment);
-            m_allocated.push_back(allocated);
-            bucket.previous_allocated_count = n;
-
-            for (std::size_t i = 0; i < n; ++i)
-            {
-                bucket.free_blocks.push_back(
-                    static_cast<void_ptr>(
-                        static_cast<char_ptr>(allocated.pointer) + i * bucket_size
-                    )
-                );
-            }
-        }
-
-        // allocate a block from the front of the bucket's free list
-        void_ptr ret = bucket.free_blocks.back();
-        bucket.free_blocks.pop_back();
-        return ret;
-    }
-
-    virtual void do_deallocate(void_ptr p, std::size_t n, std::size_t alignment = THRUST_MR_DEFAULT_ALIGNMENT) THRUST_OVERRIDE
-    {
-        n = (std::max)(n, m_options.smallest_block_size);
-        assert(detail::is_power_of_2(alignment));
-
-        // verify that the pointer is at least as aligned as claimed
-        assert(reinterpret_cast<detail::intmax_t>(detail::pointer_traits<void_ptr>::get(p)) % alignment == 0);
-
-        // the deallocated block is oversized and/or overaligned
-        if (n > m_options.largest_block_size || alignment > m_options.alignment)
-        {
-            typename oversized_block_vector::iterator it = find_if(m_oversized.begin(), m_oversized.end(), equal_pointers(p));
-            assert(it != m_oversized.end());
-
-            oversized_block_descriptor oversized = *it;
-
-            if (m_options.cache_oversized)
-            {
-                typename oversized_block_vector::iterator position = lower_bound(m_cached_oversized.begin(), m_cached_oversized.end(), oversized);
-                m_cached_oversized.insert(position, oversized);
-                return;
-            }
-
-            m_oversized.erase(it);
-
-            m_upstream->do_deallocate(p, oversized.size, oversized.alignment);
-
-            return;
-        }
-
-        // push the block to the front of the appropriate bucket's free list
-        std::size_t n_log2 = thrust::detail::log2_ri(n);
-        std::size_t bucket_idx = n_log2 - m_smallest_block_log2;
-        pool & bucket = m_pools[bucket_idx];
-
-        bucket.free_blocks.push_back(p);
-    }
-};
-
-/*! \}
- */
-
-} // end mr
-} // end thrust
-