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-<ul>
-<li>You may encounter compatibility issues or bugs with the game.</li>
-<li>You may not be able to update the game automatically or use some features that require an official account.</li>
-<li>You may expose your device to malware or viruses that may harm your data or privacy.</li>
-</ul>
-<h4>The risks and precautions of using third-party sources</h4>
-<p>If you decide to download Brawl Stars from other third-party sources, such as websites, forums, or file-sharing platforms, you should be aware of the potential risks and take some precautions. Some of the risks are:</p>
-<ul>
-<li>You may download a fake or modified version of the game that may contain malicious code or unwanted content.</li>
-<li>You may violate the terms of service or end-user license agreement of Supercell and get banned from playing the game.</li>
-<li>You may lose your progress or account if you switch to a different device or source.</li>
-</ul>
-<p>Some of the precautions are:</p>
-<ul>
-<li>Always check the reputation and reviews of the source before downloading anything.</li>
-<li>Always scan the downloaded file with a reliable antivirus software before installing it.</li>
-<li>Always backup your data and account before switching to a different device or source.</li>
-</ul>
-<h2>How to play Brawl Stars?</h2> <h3>The basics: choose a brawler, join a match, and fight</h3>
-<p>Before you start a match, you need to choose a brawler that suits your playstyle and the game mode. You can see the stats, abilities, and skins of each brawler by tapping on them in the Brawlers menu. You can also see their power level, which indicates how much you have upgraded them with power points and coins. The higher the power level, the stronger the brawler.</p>
-<p>Once you have selected a brawler, you can join a match by tapping on the Play button. You can either play with random teammates or invite your friends to join you. You can also play solo in some game modes, such as Showdown or Solo Showdown. Depending on the game mode, you will be matched with 2 to 9 other players in a map.</p>
-<p>The objective of each match is different, but the basic gameplay is the same: you need to use your brawler's attacks and super to defeat your enemies and achieve your goal. You can move your brawler with the blue joystick on the left side of the screen, and aim and fire your attacks with the red joystick on the right side. You can also tap on the red joystick to quickfire, which will automatically target the nearest enemy. You can also use gadgets and star powers, which are special abilities that you unlock at higher power levels.</p>
-<h3>The game modes: Smash & Grab, Showdown, Bounty, Heist, Brawl Ball, and more</h3>
-<p>Brawl Stars offers a variety of game modes that test your skills and strategy in different ways. Here are some of the most popular game modes and how to play them:</p>
-<table>
-<tr>
-<th>Game Mode</th>
-<th>Description</th>
-<th>Objective</th>
-</tr>
-<tr>
-<td>Smash & Grab</td>
-<td>A 3v3 mode where gems spawn from a mine in the center of the map.</td>
-<td>Collect and hold 10 gems for 15 seconds to win. If you die, you drop your gems.</td>
-</tr>
-<tr>
-<td>Showdown</td>
-<td>A solo or duo mode where 10 players fight in a shrinking map.</td>
-<td>Be the last brawler or team standing. Collect power cubes from crates or enemies to boost your stats.</td>
-</tr>
-<tr>
-<td>Bounty</td>
-<td>A 3v3 mode where each kill gives you a star and increases your bounty.</td>
-<td>Earn more stars than the enemy team by killing them. The higher your bounty, the more stars you drop when you die.</td>
-</tr>
-<tr>
-<td>Heist</td>
-<td>A 3v3 mode where each team has a safe to protect and attack.</td>
-<td>Destroy the enemy safe or deal more damage to it than they do to yours.</td>
-</tr>
-<tr>
-<td>Brawl Ball</td>
-<td>A 3v3 mode where each team tries to score goals with a ball.</td>
-<td>Score two goals before the enemy team or have more goals when the time runs out. You can kick or carry the ball, but you can't attack while holding it.</td>
-</tr>
-<tr>
-<td>Siege</td>
-<td>A 3v3 mode where each team tries to destroy the enemy's IKE turret with a siege bot.</td>
-<td>Collect bolts to build your siege bot, which will attack the enemy turret. Destroy the enemy turret or deal more damage to it than they do to yours.</td>
-</tr>
-<tr>
-<td>Hot Zone</td>
-<td>A 3v3 mode where each team tries to control zones on the map.</td>
-<td>Earn points by staying in the zones. The first team to reach 100 points or have more points when the time runs out wins.</td>
-</tr>
- </table>
- <h3>The tips and tricks: use obstacles, team up, run away, and more</h3>
- <p>Brawl Stars is not just about shooting and smashing your enemies. You also need to use your brain and skills to outsmart and outplay them. Here are some tips and tricks that can help you improve your game:</p>
- <ul>
- <li>Use obstacles to hide from enemy fire or ambush them. You can also destroy some obstacles with your attacks or super.</li>
- <li>Team up with your allies and coordinate your attacks. You can use voice chat or quick chat messages to communicate with them.</li>
- <li>Run away when you are low on health or outnumbered. You can heal by staying out of combat for a few seconds.</li>
- <li>Use your super wisely. Don't waste it on weak enemies or when you are about to die. Save it for critical moments or combo it with other supers.</li>
- <li>Learn from your mistakes or watch replays of your matches. You can see what you did wrong or right, and learn from other players' strategies and tactics.</li>
- <li>Have fun and experiment with different brawlers, modes, and maps. You may discover new ways to play or enjoy the game.</li>
-</ul>
-<h2>Conclusion</h2>
-<h3>A summary of the main points</h3>
-<p>Brawl Stars is a mobile game that you can download and play for free on your Android or iOS device. It is a multiplayer game that offers different modes, characters, and challenges for you to enjoy. You can download it from the official Google Play Store or App Store, or from alternative sources such as APK Club or other third-party websites. However, you should be aware of the pros and cons of using these sources, and take some precautions to avoid any risks. You can also improve your skills and have more fun by following some tips and tricks that we shared in this article.</p>
-<h3>A call to action for the readers</h3>
-<p>If you are interested in Brawl Stars, we encourage you to give it a try and see for yourself why it is one of the most popular mobile games in the world. You can also join the Brawl Stars community and share your feedback, opinions, questions, and suggestions with other players and developers. You can find them on social media platforms such as Facebook, Twitter, Instagram, YouTube, Reddit, Discord, and more. You can also visit the official Brawl Stars website for more information and updates on the game.</p>
-<h2>FAQs</h2>
-<h3>What are the best brawlers in Brawl Stars?</h3>
-<p>There is no definitive answer to this question, as different brawlers have different strengths and weaknesses, and may perform better or worse depending on the game mode, map, team composition, and personal preference. However, some of the brawlers that are generally considered to be strong and versatile are:</p>
-<ul>
-<li>Colette: a chromatic brawler who deals damage based on the enemy's health, making her effective against any target.</li>
-<li>Edgar: an epic brawler who can jump over obstacles and enemies, healing himself with each attack.</li>
-<li>Spike: a legendary brawler who can throw cactus bombs that explode into spikes, dealing area damage and slowing down enemies.</li>
-<li>Byron: a mythic brawler who can heal his allies or poison his enemies with his shots, as well as use his super to deal massive damage or healing over time.</li>
-<li>Belle: a chromatic brawler who can shoot electric bullets that bounce between enemies, as well as use her super to mark an enemy for extra damage.</li>
-</ul>
-<h3>How to get new brawlers in Brawl Stars?</h3>
-<p>You can get new brawlers in Brawl Stars by opening brawl boxes, which are containers that contain various rewards such as coins, power points, gadgets, star powers, and brawlers. You can get brawl boxes by playing the game, completing quests, reaching milestones on Trophy Road, participating in special events, or buying them with gems. The chances of getting a new brawler depend on the rarity of the brawler and your luck. The rarer the brawler, the lower the chance of getting it. You can see the odds of getting a new brawler by tapping on the info button on the brawl box screen.</p>
-<h3>How to get gems and coins in Brawl Stars?</h3>
-<p>You can get gems and coins in Brawl Stars by playing the game , completing quests, opening brawl boxes, reaching milestones on Trophy Road, or participating in special events. You can also buy gems with real money through in-app purchases. Gems are the premium currency that you can use to buy brawl boxes, skins, coins, power points, and more. Coins are the regular currency that you can use to upgrade your brawlers' power level.</p>
-<h3>How to join or create a club in Brawl Stars?</h3>
-<p>A club is a group of players who can chat, play, and compete together in Brawl Stars. You can join or create a club by tapping on the social button on the main screen. You can search for an existing club by name or tag, or browse the recommended clubs based on your region and trophies. You can also create your own club by choosing a name, a tag, a badge, a description, and a type (open, invite only, or closed). You can invite your friends to join your club by tapping on the invite button and sending them a link. You can also leave or switch clubs at any time by tapping on the settings button and choosing the appropriate option.</p>
-<h3>How to contact Supercell for support or feedback?</h3>
-<p>If you have any issues, questions, or suggestions regarding Brawl Stars, you can contact Supercell for support or feedback by tapping on the settings button on the main screen and choosing the help and support option. You can browse the frequently asked questions or contact the support team directly by tapping on the message button. You can also visit the official Brawl Stars website for more information and updates on the game.</p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Build Your Fantasy Empire with War and Order APK for Android.md

@@ -1,111 +0,0 @@
-<br />
-<h1>War and Order APK: A Strategy Game for Android</h1>
-<p>If you are looking for a strategy game that combines real-time combat, tower defense, and castle building, then you might want to check out War and Order APK. This is a game that lets you build your own fantasy empire in a gorgeous 3D medieval world. You can command orcs, elves, mages, and other races to fight against enemies from all over the world. You can also join an alliance and cooperate with other players to conquer new lands and castles. In this article, we will tell you more about War and Order APK, how to download and install it, how to play it, what are its features, and what are its pros and cons.</p>
- <h2>What is War and Order APK?</h2>
-<p>War and Order APK is an Android game developed by Camel Games. It is a real-time strategy, tower defense, and castle building game that has received several global Google recommendations. It is one of the most popular games in its genre, with over 10 million downloads on Google Play.</p>
-<h2>war and order apk</h2><br /><p><b><b>Download</b> &#128504; <a href="https://urlin.us/2uSUFR">https://urlin.us/2uSUFR</a></b></p><br /><br />
- <h3>A real-time strategy, tower defense, and castle building game</h3>
-<p>In War and Order APK, you can build your own empire by constructing and upgrading various buildings, such as barracks, farms, mines, workshops, walls, towers, etc. You can also recruit and train over 50 different types of soldiers, such as orcs, elves, humans, mages, beasts, angels, etc. You can use these soldiers to defend your base from enemy attacks or to attack other players' bases. You can also research new magic and technology to unlock new units, buffs, and weapons.</p>
- <h3>A 3D medieval game world with orcs, elves, and mages</h3>
-<p>War and Order APK has a stunning 3D graphics that immerses you in a medieval fantasy world. You can see your buildings, soldiers, enemies, and battles in full detail. You can also zoom in or out to get a better view of the action. The game also has a realistic sound effects that enhance the atmosphere of the game.</p>
- <h3>A global game with players from all over the world</h3>
-<p>War and Order APK is not just a single-player game. You can also interact with other players from around the world in real time. You can chat with them, make friends or enemies, form alliances or rivalries. You can also fight together or against each other in huge battles that involve hundreds or thousands of players. You can also compete for rankings, rewards, territories, castles, etc.</p>
- <h2>How to Download and Install War and Order APK?</h2>
-<p>If you want to play War and Order APK on your Android device, you need to download and install the APK file first. Here are the steps to do so:</p>
- <h3>Download the APK file from a trusted source</h3>
-<p>You can download the War and Order APK file from a trusted source, such as Softonic or APKCombo. You can also scan the APK file with an antivirus software before installing it to ensure its safety.</p>
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- <h3>Enable unknown sources on your device settings</h3>
-<p>Before you can install the War and Order APK file, you need to enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play. To do this, go to Settings > Security > Unknown Sources and toggle it on. You may also need to confirm this action by tapping OK or Allow.</p>
- <h3>Install the APK file and launch the game</h3>
-<p>Once you have downloaded and enabled unknown sources, you can install the War and Order APK file by tapping on it. You may need to grant some permissions to the app, such as access to storage, location, contacts, etc. After the installation is complete, you can launch the game by tapping on its icon on your home screen or app drawer.</p>
- <h2>How to Play War and Order APK?</h2>
-<p>Now that you have installed War and Order APK, you can start playing it. Here are some basic tips on how to play the game:</p>
- <h3>Build your own empire with various buildings and soldiers</h3>
-<p>The first thing you need to do in War and Order APK is to build your own empire. You can do this by constructing and upgrading various buildings, such as barracks, farms, mines, workshops, walls, towers, etc. Each building has a different function and benefit for your empire. For example, barracks allow you to recruit and train soldiers, farms produce food for your army, mines generate gold for your treasury, workshops produce materials for your weapons and equipment, walls protect your base from enemy attacks, towers provide defense and support for your troops, etc. You can also decorate your base with flags, statues, fountains, etc. to make it more attractive.</p>
- <p>You also need to recruit and train soldiers for your army. You can do this by tapping on the barracks and selecting the type of unit you want to recruit. There are over 50 different types of units in War and Order APK, such as orcs, elves, humans, mages, beasts, angels, etc. Each unit has a different cost, speed, attack, defense, range, and skill. You can also upgrade your units by researching new magic and technology in the academy. You can also equip your units with weapons and armor that you can craft in the workshop or buy in the market.</p>
- <h3>Research new magic and technology for advanced tactics and weapons</h3>
-<p>Another important aspect of War and Order APK is to research new magic and technology for your empire. You can do this by tapping on the academy and selecting the type of research you want to conduct. There are four categories of research in War and Order APK: Development, Military, Defense, and Magic. Each category has several subcategories that contain various research topics. For example, Development research allows you to improve your production, storage, speed, etc., Military research allows you to unlock new units, buffs, weapons, etc., Defense research allows you to enhance your walls, towers, traps, etc., and Magic research allows you to learn new spells, runes, potions, etc. Researching new magic and technology can give you an edge over your enemies and allies in the game.</p>
- <h3>Join an alliance and cooperate with other players to conquer territories and castles</h3>
-<p>One of the most fun and exciting features of War and Order APK is to join an alliance and cooperate with other players. You can do this by tapping on the alliance button and choosing to join an existing alliance or create your own. Joining an alliance can give you many benefits, such as sharing resources, information, troops, gifts, etc. You can also chat with your alliance members, make friends or enemies, form strategies and plans, etc.</p>
- <p>One of the main goals of an alliance is to conquer new territories and castles in the game world. You can do this by tapping on the map and selecting a target to attack. You can also scout, rally, reinforce, or support your allies or enemies in the map. Conquering new territories and castles can give you more resources, prestige, and power in the game. You can also defend your territories and castles from enemy attacks by building defenses and sending troops.</p>
- <h2>What are the Features of War and Order APK?</h2>
-<p>War and Order APK has many features that make it a fun and addictive game. Here are some of them:</p>
- <h3>Huge battles with fully animated graphics and sound effects</h3>
-<p>War and Order APK has huge battles that involve hundreds or thousands of players and units. You can see your soldiers fight in real time with fully animated graphics and sound effects. You can also zoom in or out to get a better view of the action. The game also has a realistic physics engine that simulates the movement, collision, and damage of the units.</p>
- <h3>Diverse units and races with different abilities and skills</h3>
-<p>War and Order APK has diverse units and races that have different abilities and skills. You can command orcs, elves, humans, mages, beasts, angels, etc. Each unit has a different cost, speed, attack, defense, range, and skill. You can also upgrade your units by researching new magic and technology. You can also equip your units with weapons and armor that you can craft or buy.</p>
- <h3>A dynamic world with monsters, events, and challenges</h3>
-<p>War and Order APK has a dynamic world that changes according to the actions of the players. You can encounter monsters, events, and challenges in the game world that can give you rewards or risks. For example, you can fight against dragons, giants, zombies, etc. that drop rare items or resources. You can also participate in events such as festivals, tournaments, sieges, etc. that offer rewards or rankings. You can also face challenges such as quests, missions, achievements, etc. that test your skills and strategy.</p>
- <h2>What are the Pros and Cons of War and Order APK?</h2>
-<p>Like any other game, War and Order APK has its pros and cons. Here are some of them:</p>
- <h3>Pros: Fun, addictive, and strategic gameplay; Free to play; Regular updates; Friendly community</h3>
-<p>War and Order APK has a fun, addictive, and strategic gameplay that can keep you entertained for hours. You can build your own empire, recruit and train your army, research new magic and technology, join an alliance, fight against other players, conquer new territories and castles, etc. The game is also free to play, although you can buy some in-game items with real money if you want to. The game also has regular updates that add new features, content, and improvements to the game. The game also has a friendly community that you can chat with, make friends or enemies, form alliances or rivalries, etc.</p>
- <h3>Cons: Requires internet connection; May consume battery and data; May have bugs or glitches</h3>
-<p>War and Order APK requires an internet connection to play, which means you cannot play it offline. The game may also consume a lot of battery and data on your device, especially if you play it for a long time or participate in large battles. The game may also have some bugs or glitches that may affect your gameplay or experience. For example, you may encounter crashes, freezes, lags, errors, etc.</p>
- <h2>Conclusion</h2>
-<p>War and Order APK is a strategy game for Android that lets you build your own fantasy empire in a 3D medieval world. You can command orcs, elves, mages, and other races to fight against enemies from all over the world. You can also join an alliance and cooperate with other players to conquer new lands and castles. The game has many features that make it fun and addictive, such as huge battles, diverse units, dynamic world, etc. The game also has some pros and cons that you should consider before playing it.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about War and Order APK:</p>
- <h3>Q: Is War and Order APK safe to download and install?</h3>
-<p>A: Yes, War and Order APK is safe to download and install as long as you get it from a trusted source. You can also scan the APK file with an antivirus software before installing it to ensure its safety.</p>
- <h3>Q: How can I get more resources in War and Order APK?</h3>
-<p>A: You can get more resources in War and Order APK by building and upgrading your farms, mines, workshops, etc. You can also collect resources from the map by attacking monsters, events, or other players. You can also trade resources with your alliance members or buy them with real money.</p>
- <h3>Q: How can I get more gems in War and Order APK?</h3>
-<p>A: Gems are the premium currency in War and Order APK that can be used to buy special items, speed up processes, etc. You can get more gems by completing quests, achievements, challenges, etc. You can also get gems as rewards from events, tournaments, sieges, etc. You can also get gems by participating in the daily lottery or watching ads. You can also buy gems with real money.</p>
- <h3>Q: How can I join or create an alliance in War and Order APK?</h3>
-<p>A: You can join or create an alliance in War and Order APK by tapping on the alliance button and choosing to join an existing alliance or create your own. To join an existing alliance, you need to apply for it and wait for the approval of the leader or the elders. To create your own alliance, you need to pay a certain amount of gold and choose a name, flag, and description for your alliance. You can also invite other players to join your alliance or accept their applications.</p>
- <h3>Q: How can I change my name, avatar, or flag in War and Order APK?</h3>
-<p>A: You can change your name, avatar, or flag in War and Order APK by tapping on your profile button and choosing to edit your information. You can change your name once for free and then you need to pay gems for each change. You can change your avatar by selecting from the default options or uploading your own image. You can change your flag by selecting from the default options or creating your own design.</p>
- <h3>Q: How can I contact the customer service or report a problem in War and Order APK?</h3>
-<p>A: You can contact the customer service or report a problem in War and Order APK by tapping on the settings button and choosing to contact us or report a problem. You can also send an email to [email protected] or visit their official website or Facebook page for more information and support.</p>
- References: : https://war-and-order.en.softonic.com/android : https://apkcombo.com/war-and-order/com.camelgames.superking/ : https://www.warandorder.net/ : https://www.facebook.com/WarandOrder1/</p> 197e85843d<br />
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spaces/44ov41za8i/FreeVC/speaker_encoder/data_objects/speaker_batch.py

@@ -1,12 +0,0 @@
-import numpy as np
-from typing import List
-from speaker_encoder.data_objects.speaker import Speaker
-
-class SpeakerBatch:
-    def __init__(self, speakers: List[Speaker], utterances_per_speaker: int, n_frames: int):
-        self.speakers = speakers
-        self.partials = {s: s.random_partial(utterances_per_speaker, n_frames) for s in speakers}
-        
-        # Array of shape (n_speakers * n_utterances, n_frames, mel_n), e.g. for 3 speakers with
-        # 4 utterances each of 160 frames of 40 mel coefficients: (12, 160, 40)
-        self.data = np.array([frames for s in speakers for _, frames, _ in self.partials[s]])

spaces/52Hz/CMFNet_deblurring/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 Deblur')
-    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/deblur_GoPro_CMFNet.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/AB-TW/team-ai/agents/tools/smart_domain/entity.py

@@ -1,115 +0,0 @@
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
-from langchain.agents import tool
-from agents.tools.smart_domain.common import getPrefix
-from models import llm
-
-entity_architecture = """
-Entity: This component is use to represents business concepts and encapsulates business rules.
-It may include 3 parts: 
-- id(identity of entity)
-- description (properties package of entity represent the value of entity), 
-- associations (collection of associated entiy)
----example code:
-    @Getter
-    @AllArgsConstructor 
-    public class Feature {{
-        // id
-        private FeatureId id;
-        
-        // description
-        private FeatureDescription description;
-
-        // associations
-        private FeatureConfigs configs;
-
-        public record FeatureId(String featureKey) {{
-        
-        }}
-
-        @Builder
-        public record FeatureDescription(String name, 
-                            String description, 
-                            Boolean isEnable,
-                            LocalDateTime updatedAt,
-                            LocalDateTime createdAt))) {{
-        
-        }}
-
-        public Feature update(Feature newFeature) {{
-            this.description = FeatureDescription.builder()
-                .name(newFeature.description.name())
-                .description(newFeature.description.description())
-                .isEnable(this.description.isEnable())
-                .updatedAt(LocalDateTime.now())
-                .createdAt(this.description.createdAt());
-
-            return this;
-        }}
-
-        public interface FeatureConfigs() {{
-            Flux<FeatureConfig> findAll();
-            Flux<FeatureConfig> subCollection(long from, long to);
-            Mono<FeatureConfig> findById(FeatureConfigId id);
-        }}
-    }}
----end of example code
-"""
-
-entity_test_strategy = """
-For the Entity, we can write unit test to ensure that the business rules related to Merchandise are correctly encapsulated.
----example code
-    class FeatureTest {{
-        @Test
-        void should_update_feature_description() {{
-            // given
-            Feature feature = Feature.builder()
-                .id(new FeatureId("featureKey"))
-                .description(new FeatureDescription("name", "description", true, LocalDateTime.now(), LocalDateTime.now()))
-                .build();
-            Feature newFeature = Feature.builder()
-                .id(new FeatureId("featureKey"))
-                .description(new FeatureDescription("newName", "newDescription", true, LocalDateTime.now(), LocalDateTime.now()))
-                .build();
-            // when
-            feature.update(newFeature);
-            // then
-            assertThat(feature.description().name()).isEqualTo("newName");
-            assertThat(feature.description().description()).isEqualTo("newDescription");
-        }}
-    }}
----end of example code
-"""
-
-entity_tech_stack = """
-Java17、reactor、lombok、Junit5、reactor test、Mockito
-"""
-
-entity_task = """Your task is to generate the Enity of domain layer tests and product code."""
-ENTITY = getPrefix(entity_task, entity_tech_stack, entity_architecture, entity_test_strategy) + """
-
-Use the following format:
-request: the request that you need to fulfill
-
-Entity:
-```
-the Entity code that you write to fulfill the request, follow TechStack and Architecture
-```
-
-Test:
-```
-the test code that you write to fulfill the request, follow TechStack Architecture and TestStrategy
-```
-
-request: {input}"""
-
-ENTITY_PROMPT = PromptTemplate(input_variables=["input"], template=ENTITY,)
-
-entityChain = LLMChain(llm = llm(temperature=0.1), prompt=ENTITY_PROMPT)
-
-
-@tool("Generate Entity Code", return_direct=True)
-def entityCodeGenerator(input: str) -> str:
-    '''useful for when you need to generate entity code'''
-    response = entityChain.run(input)
-    return response

spaces/ADOPLE/AdopleAI-Website-DocumentQA/README.md

@@ -1,13 +0,0 @@
----
-title: DocumentQA Website
-emoji: 🏃
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-duplicated_from: ADOPLE/Adopleai-DocumentQA
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIFILMS/generate_human_motion/VQ-Trans/options/option_vq.py

@@ -1,61 +0,0 @@
-import argparse
-
-def get_args_parser():
-    parser = argparse.ArgumentParser(description='Optimal Transport AutoEncoder training for AIST',
-                                     add_help=True,
-                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-    ## dataloader  
-    parser.add_argument('--dataname', type=str, default='kit', help='dataset directory')
-    parser.add_argument('--batch-size', default=128, type=int, help='batch size')
-    parser.add_argument('--window-size', type=int, default=64, help='training motion length')
-
-    ## optimization
-    parser.add_argument('--total-iter', default=200000, type=int, help='number of total iterations to run')
-    parser.add_argument('--warm-up-iter', default=1000, type=int, help='number of total iterations for warmup')
-    parser.add_argument('--lr', default=2e-4, type=float, help='max learning rate')
-    parser.add_argument('--lr-scheduler', default=[50000, 400000], nargs="+", type=int, help="learning rate schedule (iterations)")
-    parser.add_argument('--gamma', default=0.05, type=float, help="learning rate decay")
-
-    parser.add_argument('--weight-decay', default=0.0, type=float, help='weight decay')
-    parser.add_argument("--commit", type=float, default=0.02, help="hyper-parameter for the commitment loss")
-    parser.add_argument('--loss-vel', type=float, default=0.1, help='hyper-parameter for the velocity loss')
-    parser.add_argument('--recons-loss', type=str, default='l2', help='reconstruction loss')
-    
-    ## vqvae arch
-    parser.add_argument("--code-dim", type=int, default=512, help="embedding dimension")
-    parser.add_argument("--nb-code", type=int, default=512, help="nb of embedding")
-    parser.add_argument("--mu", type=float, default=0.99, help="exponential moving average to update the codebook")
-    parser.add_argument("--down-t", type=int, default=2, help="downsampling rate")
-    parser.add_argument("--stride-t", type=int, default=2, help="stride size")
-    parser.add_argument("--width", type=int, default=512, help="width of the network")
-    parser.add_argument("--depth", type=int, default=3, help="depth of the network")
-    parser.add_argument("--dilation-growth-rate", type=int, default=3, help="dilation growth rate")
-    parser.add_argument("--output-emb-width", type=int, default=512, help="output embedding width")
-    parser.add_argument('--vq-act', type=str, default='relu', choices = ['relu', 'silu', 'gelu'], help='dataset directory')
-    parser.add_argument('--vq-norm', type=str, default=None, help='dataset directory')
-    
-    ## quantizer
-    parser.add_argument("--quantizer", type=str, default='ema_reset', choices = ['ema', 'orig', 'ema_reset', 'reset'], help="eps for optimal transport")
-    parser.add_argument('--beta', type=float, default=1.0, help='commitment loss in standard VQ')
-
-    ## resume
-    parser.add_argument("--resume-pth", type=str, default=None, help='resume pth for VQ')
-    parser.add_argument("--resume-gpt", type=str, default=None, help='resume pth for GPT')
-    
-    
-    ## output directory 
-    parser.add_argument('--out-dir', type=str, default='output_vqfinal/', help='output directory')
-    parser.add_argument('--results-dir', type=str, default='visual_results/', help='output directory')
-    parser.add_argument('--visual-name', type=str, default='baseline', help='output directory')
-    parser.add_argument('--exp-name', type=str, default='exp_debug', help='name of the experiment, will create a file inside out-dir')
-    ## other
-    parser.add_argument('--print-iter', default=200, type=int, help='print frequency')
-    parser.add_argument('--eval-iter', default=1000, type=int, help='evaluation frequency')
-    parser.add_argument('--seed', default=123, type=int, help='seed for initializing training.')
-    
-    parser.add_argument('--vis-gt', action='store_true', help='whether visualize GT motions')
-    parser.add_argument('--nb-vis', default=20, type=int, help='nb of visualizations')
-    
-    
-    return parser.parse_args()

spaces/AIGC-Audio/Make_An_Audio/ldm/modules/ema.py

@@ -1,76 +0,0 @@
-import torch
-from torch import nn
-
-
-class LitEma(nn.Module):
-    def __init__(self, model, decay=0.9999, use_num_upates=True):
-        super().__init__()
-        if decay < 0.0 or decay > 1.0:
-            raise ValueError('Decay must be between 0 and 1')
-
-        self.m_name2s_name = {}
-        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
-        self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
-                             else torch.tensor(-1,dtype=torch.int))
-
-        for name, p in model.named_parameters():
-            if p.requires_grad:
-                #remove as '.'-character is not allowed in buffers
-                s_name = name.replace('.','')
-                self.m_name2s_name.update({name:s_name})
-                self.register_buffer(s_name,p.clone().detach().data)
-
-        self.collected_params = []
-
-    def forward(self,model):
-        decay = self.decay
-
-        if self.num_updates >= 0:
-            self.num_updates += 1
-            decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
-
-        one_minus_decay = 1.0 - decay
-
-        with torch.no_grad():
-            m_param = dict(model.named_parameters())
-            shadow_params = dict(self.named_buffers())
-
-            for key in m_param:
-                if m_param[key].requires_grad:
-                    sname = self.m_name2s_name[key]
-                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
-                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
-                else:
-                    assert not key in self.m_name2s_name
-
-    def copy_to(self, model):
-        m_param = dict(model.named_parameters())
-        shadow_params = dict(self.named_buffers())
-        for key in m_param:
-            if m_param[key].requires_grad:
-                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
-            else:
-                assert not key in self.m_name2s_name
-
-    def store(self, parameters):
-        """
-        Save the current parameters for restoring later.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            temporarily stored.
-        """
-        self.collected_params = [param.clone() for param in parameters]
-
-    def restore(self, parameters):
-        """
-        Restore the parameters stored with the `store` method.
-        Useful to validate the model with EMA parameters without affecting the
-        original optimization process. Store the parameters before the
-        `copy_to` method. After validation (or model saving), use this to
-        restore the former parameters.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored parameters.
-        """
-        for c_param, param in zip(self.collected_params, parameters):
-            param.data.copy_(c_param.data)

spaces/ASJMO/freegpt/server/website.py

@@ -1,58 +0,0 @@
-from flask import render_template, redirect, url_for, request, session
-from flask_babel import refresh
-from time import time
-from os import urandom
-from server.babel import get_locale, get_languages
-
-
-class Website:
-    def __init__(self, bp, url_prefix) -> None:
-        self.bp = bp
-        self.url_prefix = url_prefix
-        self.routes = {
-            '/': {
-                'function': lambda: redirect(url_for('._index')),
-                'methods': ['GET', 'POST']
-            },
-            '/chat/': {
-                'function': self._index,
-                'methods': ['GET', 'POST']
-            },
-            '/chat/<conversation_id>': {
-                'function': self._chat,
-                'methods': ['GET', 'POST']
-            },
-            '/change-language': {
-                'function': self.change_language,
-                'methods': ['POST']
-            },
-            '/get-locale': {
-                'function': self.get_locale,
-                'methods': ['GET']
-            },
-            '/get-languages': {
-                'function': self.get_languages,
-                'methods': ['GET']
-            }
-        }
-
-    def _chat(self, conversation_id):
-        if '-' not in conversation_id:
-            return redirect(url_for('._index'))
-
-        return render_template('index.html', chat_id=conversation_id, url_prefix=self.url_prefix)
-
-    def _index(self):
-        return render_template('index.html', chat_id=f'{urandom(4).hex()}-{urandom(2).hex()}-{urandom(2).hex()}-{urandom(2).hex()}-{hex(int(time() * 1000))[2:]}', url_prefix=self.url_prefix)
-
-    def change_language(self):
-        data = request.get_json()
-        session['language'] = data.get('language')
-        refresh()
-        return '', 204
-
-    def get_locale(self):
-        return get_locale()
-    
-    def get_languages(self):  
-        return get_languages()

spaces/AlexN/pull_up/README.md

@@ -1,37 +0,0 @@
----
-title: Pull_up
-emoji: 💪
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`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).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

spaces/AlhitawiMohammed22/HTD_HTR/builder.py

@@ -1,305 +0,0 @@
-
-# Copyright (C) 2021, Mindee.
-
-# This program is licensed under the Apache License version 2.
-# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.
-
-
-from typing import Any, Dict, List, Tuple
-import pandas as pd
-
-import numpy as np
-from scipy.cluster.hierarchy import fclusterdata
-
-from doctr.utils.geometry import estimate_page_angle, resolve_enclosing_bbox, resolve_enclosing_rbbox, rotate_boxes
-from doctr.utils.repr import NestedObject
-
-__all__ = ['DocumentBuilder']
-
-
-class DocumentBuilder(NestedObject):
-    """Implements a document builder
-    Args:
-        resolve_lines: whether words should be automatically grouped into lines
-        resolve_blocks: whether lines should be automatically grouped into blocks
-        paragraph_break: relative length of the minimum space separating paragraphs
-        export_as_straight_boxes: if True, force straight boxes in the export (fit a rectangle
-            box to all rotated boxes). Else, keep the boxes format unchanged, no matter what it is.
-    """
-
-    def __init__(
-        self,
-        resolve_lines: bool = True,
-        resolve_blocks: bool = True,
-        paragraph_break: float = 0.035,
-        export_as_straight_boxes: bool = False,
-    ) -> None:
-
-        self.resolve_lines = resolve_lines
-        self.resolve_blocks = resolve_blocks
-        self.paragraph_break = paragraph_break
-        self.export_as_straight_boxes = export_as_straight_boxes
-
-    @staticmethod
-    def _sort_boxes(boxes: np.ndarray) -> np.ndarray:
-        """Sort bounding boxes from top to bottom, left to right
-        Args:
-            boxes: bounding boxes of shape (N, 4) or (N, 4, 2) (in case of rotated bbox)
-        Returns:
-            tuple: indices of ordered boxes of shape (N,), boxes
-                If straight boxes are passed tpo the function, boxes are unchanged
-                else: boxes returned are straight boxes fitted to the straightened rotated boxes
-                so that we fit the lines afterwards to the straigthened page
-        """
-        if boxes.ndim == 3:
-            boxes = rotate_boxes(
-                loc_preds=boxes,
-                angle=-estimate_page_angle(boxes),
-                orig_shape=(1024, 1024),
-                min_angle=5.,
-            )
-            boxes = np.concatenate((boxes.min(1), boxes.max(1)), -1)
-        return (boxes[:, 0] + 2 * boxes[:, 3] / np.median(boxes[:, 3] - boxes[:, 1])).argsort(), boxes
-
-    def _resolve_sub_lines(self, boxes: np.ndarray, word_idcs: List[int]) -> List[List[int]]:
-        """Split a line in sub_lines
-        Args:
-            boxes: bounding boxes of shape (N, 4)
-            word_idcs: list of indexes for the words of the line
-        Returns:
-            A list of (sub-)lines computed from the original line (words)
-        """
-        lines = []
-        # Sort words horizontally
-        word_idcs = [word_idcs[idx]
-                     for idx in boxes[word_idcs, 0].argsort().tolist()]
-
-        # Eventually split line horizontally
-        if len(word_idcs) < 2:
-            lines.append(word_idcs)
-        else:
-            sub_line = [word_idcs[0]]
-            for i in word_idcs[1:]:
-                horiz_break = True
-
-                prev_box = boxes[sub_line[-1]]
-                # Compute distance between boxes
-                dist = boxes[i, 0] - prev_box[2]
-                # If distance between boxes is lower than paragraph break, same sub-line
-                if dist < self.paragraph_break:
-                    horiz_break = False
-
-                if horiz_break:
-                    lines.append(sub_line)
-                    sub_line = []
-
-                sub_line.append(i)
-            lines.append(sub_line)
-
-        return lines
-
-    def _resolve_lines(self, boxes: np.ndarray) -> List[List[int]]:
-        """Order boxes to group them in lines
-        Args:
-            boxes: bounding boxes of shape (N, 4) or (N, 4, 2) in case of rotated bbox
-        Returns:
-            nested list of box indices
-        """
-
-        # Sort boxes, and straighten the boxes if they are rotated
-        idxs, boxes = self._sort_boxes(boxes)
-
-        # Compute median for boxes heights
-        y_med = np.median(boxes[:, 3] - boxes[:, 1])
-
-        lines = []
-        words = [idxs[0]]  # Assign the top-left word to the first line
-        # Define a mean y-center for the line
-        y_center_sum = boxes[idxs[0]][[1, 3]].mean()
-
-        for idx in idxs[1:]:
-            vert_break = True
-
-            # Compute y_dist
-            y_dist = abs(boxes[idx][[1, 3]].mean() - y_center_sum / len(words))
-            # If y-center of the box is close enough to mean y-center of the line, same line
-            if y_dist < y_med / 2:
-                vert_break = False
-
-            if vert_break:
-                # Compute sub-lines (horizontal split)
-                lines.extend(self._resolve_sub_lines(boxes, words))
-                words = []
-                y_center_sum = 0
-
-            words.append(idx)
-            y_center_sum += boxes[idx][[1, 3]].mean()
-
-        # Use the remaining words to form the last(s) line(s)
-        if len(words) > 0:
-            # Compute sub-lines (horizontal split)
-            lines.extend(self._resolve_sub_lines(boxes, words))
-
-        return lines
-
-    @staticmethod
-    def _resolve_blocks(boxes: np.ndarray, lines: List[List[int]]) -> List[List[List[int]]]:
-        """Order lines to group them in blocks
-        Args:
-            boxes: bounding boxes of shape (N, 4) or (N, 4, 2)
-            lines: list of lines, each line is a list of idx
-        Returns:
-            nested list of box indices
-        """
-        # Resolve enclosing boxes of lines
-        if boxes.ndim == 3:
-            box_lines = np.asarray([
-                resolve_enclosing_rbbox(
-                    [tuple(boxes[idx, :, :]) for idx in line])
-                for line in lines  # type: ignore[misc]
-            ])
-        else:
-            _box_lines = [
-                resolve_enclosing_bbox([
-                    # type: ignore[misc]
-                    (tuple(boxes[idx, :2]), tuple(boxes[idx, 2:])) for idx in line
-                ])
-                for line in lines
-            ]
-            box_lines = np.asarray([(x1, y1, x2, y2)
-                                   for ((x1, y1), (x2, y2)) in _box_lines])
-
-        # Compute geometrical features of lines to clusterize
-        # Clusterizing only with box centers yield to poor results for complex documents
-        if boxes.ndim == 3:
-            box_features = np.stack(
-                (
-                    (box_lines[:, 0, 0] + box_lines[:, 0, 1]) / 2,
-                    (box_lines[:, 0, 0] + box_lines[:, 2, 0]) / 2,
-                    (box_lines[:, 0, 0] + box_lines[:, 2, 1]) / 2,
-                    (box_lines[:, 0, 1] + box_lines[:, 2, 1]) / 2,
-                    (box_lines[:, 0, 1] + box_lines[:, 2, 0]) / 2,
-                    (box_lines[:, 2, 0] + box_lines[:, 2, 1]) / 2,
-                ), axis=-1
-            )
-        else:
-            box_features = np.stack(
-                (
-                    (box_lines[:, 0] + box_lines[:, 3]) / 2,
-                    (box_lines[:, 1] + box_lines[:, 2]) / 2,
-                    (box_lines[:, 0] + box_lines[:, 2]) / 2,
-                    (box_lines[:, 1] + box_lines[:, 3]) / 2,
-                    box_lines[:, 0],
-                    box_lines[:, 1],
-                ), axis=-1
-            )
-        # Compute clusters
-        clusters = fclusterdata(
-            box_features, t=0.1, depth=4, criterion='distance', metric='euclidean')
-
-        _blocks: Dict[int, List[int]] = {}
-        # Form clusters
-        for line_idx, cluster_idx in enumerate(clusters):
-            if cluster_idx in _blocks.keys():
-                _blocks[cluster_idx].append(line_idx)
-            else:
-                _blocks[cluster_idx] = [line_idx]
-
-        # Retrieve word-box level to return a fully nested structure
-        blocks = [[lines[idx] for idx in block] for block in _blocks.values()]
-
-        return blocks
-
-    def _build_blocks(self, boxes: np.ndarray, word_preds: List[Tuple[str, float]], page_shapes: List[Tuple[int, int]]) -> Any:
-        """Gather independent words in structured blocks
-        Args:
-            boxes: bounding boxes of all detected words of the page, of shape (N, 5) or (N, 4, 2)
-            word_preds: list of all detected words of the page, of shape N
-        Returns:
-            list of block elements
-        """
-
-        if boxes.shape[0] != len(word_preds):
-            raise ValueError(
-                f"Incompatible argument lengths: {boxes.shape[0]}, {len(word_preds)}")
-
-        if boxes.shape[0] == 0:
-            return []
-
-        # Decide whether we try to form lines
-        _boxes = boxes
-        if self.resolve_lines:
-            lines = self._resolve_lines(
-                _boxes if _boxes.ndim == 3 else _boxes[:, :4])
-            # Decide whether we try to form blocks
-            if self.resolve_blocks and len(lines) > 1:
-                _blocks = self._resolve_blocks(
-                    _boxes if _boxes.ndim == 3 else _boxes[:, :4], lines)
-            else:
-                _blocks = [lines]
-        else:
-            # Sort bounding boxes, one line for all boxes, one block for the line
-            lines = [self._sort_boxes(
-                _boxes if _boxes.ndim == 3 else _boxes[:, :4])[0]]
-            _blocks = [lines]
-
-        rows = []
-        for block_idx, lines in enumerate(_blocks):
-            for line_idx, line in enumerate(lines):
-                for i,idx in enumerate(line):
-                    h, w = page_shapes
-                    row = (
-                        block_idx, line_idx, i, word_preds[idx],
-                        int(round(boxes[idx, 0]*w)
-                            ), int(round(boxes[idx, 1]*h)),
-                        int(round(boxes[idx, 2]*w)
-                            ), int(round(boxes[idx, 3]*h)),
-                        int(round(boxes[idx, 4]*100))
-                    )
-                    rows.append(row)
-
-        return rows
-
-    def extra_repr(self) -> str:
-        return (f"resolve_lines={self.resolve_lines}, resolve_blocks={self.resolve_blocks}, "
-                f"paragraph_break={self.paragraph_break}, "
-                f"export_as_straight_boxes={self.export_as_straight_boxes}")
-
-    def __call__(
-        self,
-        boxes: List[np.ndarray],
-        text_preds: List[List[Tuple[str, float]]],
-        page_shapes: List[Tuple[int, int]]
-    ) -> pd.DataFrame:
-        """Re-arrange detected words into structured blocks
-        Args:
-            boxes: list of N elements, where each element represents the localization predictions, of shape (*, 5)
-                or (*, 6) for all words for a given page
-            text_preds: list of N elements, where each element is the list of all word prediction (text + confidence)
-            page_shape: shape of each page, of size N
-        Returns:
-            document object
-        """
-        if len(boxes) != len(text_preds) or len(boxes) != len(page_shapes):
-            raise ValueError(
-                "All arguments are expected to be lists of the same size")
-
-        if self.export_as_straight_boxes and len(boxes) > 0:
-            # If boxes are already straight OK, else fit a bounding rect
-            if boxes[0].ndim == 3:
-                straight_boxes = []
-                # Iterate over pages
-                for p_boxes in boxes:
-                    # Iterate over boxes of the pages
-                    straight_boxes.append(np.concatenate(
-                        (p_boxes.min(1), p_boxes.max(1)), 1))
-                boxes = straight_boxes
-
-        _pages = [
-            pd.DataFrame.from_records(self._build_blocks(page_boxes, word_preds, shape), columns=[
-                "block_num", "line_num", "word_num" ,"word", "xmin", "ymin", "xmax", "ymax", "confidence_score"
-            ])
-            for _idx, shape, page_boxes, word_preds in zip(range(len(boxes)), page_shapes, boxes, text_preds)
-        ]
-
-        return _pages

spaces/Alpaca233/SadTalker/src/face3d/data/flist_dataset.py

@@ -1,125 +0,0 @@
-"""This script defines the custom dataset for Deep3DFaceRecon_pytorch
-"""
-
-import os.path
-from data.base_dataset import BaseDataset, get_transform, get_affine_mat, apply_img_affine, apply_lm_affine
-from data.image_folder import make_dataset
-from PIL import Image
-import random
-import util.util as util
-import numpy as np
-import json
-import torch
-from scipy.io import loadmat, savemat
-import pickle
-from util.preprocess import align_img, estimate_norm
-from util.load_mats import load_lm3d
-
-
-def default_flist_reader(flist):
-    """
-    flist format: impath label\nimpath label\n ...(same to caffe's filelist)
-    """
-    imlist = []
-    with open(flist, 'r') as rf:
-        for line in rf.readlines():
-            impath = line.strip()
-            imlist.append(impath)
-
-    return imlist
-
-def jason_flist_reader(flist):
-    with open(flist, 'r') as fp:
-        info = json.load(fp)
-    return info
-
-def parse_label(label):
-    return torch.tensor(np.array(label).astype(np.float32))
-
-
-class FlistDataset(BaseDataset):
-    """
-    It requires one directories to host training images '/path/to/data/train'
-    You can train the model with the dataset flag '--dataroot /path/to/data'.
-    """
-
-    def __init__(self, opt):
-        """Initialize this dataset class.
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        """
-        BaseDataset.__init__(self, opt)
-        
-        self.lm3d_std = load_lm3d(opt.bfm_folder)
-        
-        msk_names = default_flist_reader(opt.flist)
-        self.msk_paths = [os.path.join(opt.data_root, i) for i in msk_names]
-
-        self.size = len(self.msk_paths) 
-        self.opt = opt
-        
-        self.name = 'train' if opt.isTrain else 'val'
-        if '_' in opt.flist:
-            self.name += '_' + opt.flist.split(os.sep)[-1].split('_')[0]
-        
-
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index (int)      -- a random integer for data indexing
-
-        Returns a dictionary that contains A, B, A_paths and B_paths
-            img (tensor)       -- an image in the input domain
-            msk (tensor)       -- its corresponding attention mask
-            lm  (tensor)       -- its corresponding 3d landmarks
-            im_paths (str)     -- image paths
-            aug_flag (bool)    -- a flag used to tell whether its raw or augmented
-        """
-        msk_path = self.msk_paths[index % self.size]  # make sure index is within then range
-        img_path = msk_path.replace('mask/', '')
-        lm_path = '.'.join(msk_path.replace('mask', 'landmarks').split('.')[:-1]) + '.txt'
-
-        raw_img = Image.open(img_path).convert('RGB')
-        raw_msk = Image.open(msk_path).convert('RGB')
-        raw_lm = np.loadtxt(lm_path).astype(np.float32)
-
-        _, img, lm, msk = align_img(raw_img, raw_lm, self.lm3d_std, raw_msk)
-        
-        aug_flag = self.opt.use_aug and self.opt.isTrain
-        if aug_flag:
-            img, lm, msk = self._augmentation(img, lm, self.opt, msk)
-        
-        _, H = img.size
-        M = estimate_norm(lm, H)
-        transform = get_transform()
-        img_tensor = transform(img)
-        msk_tensor = transform(msk)[:1, ...]
-        lm_tensor = parse_label(lm)
-        M_tensor = parse_label(M)
-
-
-        return {'imgs': img_tensor, 
-                'lms': lm_tensor, 
-                'msks': msk_tensor, 
-                'M': M_tensor,
-                'im_paths': img_path, 
-                'aug_flag': aug_flag,
-                'dataset': self.name}
-
-    def _augmentation(self, img, lm, opt, msk=None):
-        affine, affine_inv, flip = get_affine_mat(opt, img.size)
-        img = apply_img_affine(img, affine_inv)
-        lm = apply_lm_affine(lm, affine, flip, img.size)
-        if msk is not None:
-            msk = apply_img_affine(msk, affine_inv, method=Image.BILINEAR)
-        return img, lm, msk
-    
-
-
-
-    def __len__(self):
-        """Return the total number of images in the dataset.
-        """
-        return self.size

spaces/Alycer/VITS-Umamusume-voice-synthesizer/monotonic_align/__init__.py

@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
-  """ Cython optimized version.
-  neg_cent: [b, t_t, t_s]
-  mask: [b, t_t, t_s]
-  """
-  device = neg_cent.device
-  dtype = neg_cent.dtype
-  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
-  path = np.zeros(neg_cent.shape, dtype=np.int32)
-
-  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
-  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
-  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
-  return torch.from_numpy(path).to(device=device, dtype=dtype)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/value_guided_sampling.md

@@ -1,32 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Value-guided planning
-
-<Tip warning={true}>
-
-🧪 This is an experimental pipeline for reinforcement learning!
-
-</Tip>
-
-This pipeline is based on the [Planning with Diffusion for Flexible Behavior Synthesis](https://huggingface.co/papers/2205.09991) paper by Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine.
-
-The abstract from the paper is:
-
-*Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple, this combination has a number of empirical shortcomings, suggesting that learned models may not be well-suited to standard trajectory optimization. In this paper, we consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem, such that sampling from the model and planning with it become nearly identical. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories. We show how classifier-guided sampling and image inpainting can be reinterpreted as coherent planning strategies, explore the unusual and useful properties of diffusion-based planning methods, and demonstrate the effectiveness of our framework in control settings that emphasize long-horizon decision-making and test-time flexibility*.
-
-You can find additional information about the model on the [project page](https://diffusion-planning.github.io/), the [original codebase](https://github.com/jannerm/diffuser), or try it out in a demo [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb). 
-
-The script to run the model is available [here](https://github.com/huggingface/diffusers/tree/main/examples/reinforcement_learning).
-
-## ValueGuidedRLPipeline
-[[autodoc]] diffusers.experimental.ValueGuidedRLPipeline

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

@@ -1,94 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from ..utils import deprecate
-from .attention_processor import (  # noqa: F401
-    Attention,
-    AttentionProcessor,
-    AttnAddedKVProcessor,
-    AttnProcessor2_0,
-    LoRAAttnProcessor,
-    LoRALinearLayer,
-    LoRAXFormersAttnProcessor,
-    SlicedAttnAddedKVProcessor,
-    SlicedAttnProcessor,
-    XFormersAttnProcessor,
-)
-from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401
-
-
-deprecate(
-    "cross_attention",
-    "0.20.0",
-    "Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.",
-    standard_warn=False,
-)
-
-
-AttnProcessor = AttentionProcessor
-
-
-class CrossAttention(Attention):
-    def __init__(self, *args, **kwargs):
-        deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead."
-        deprecate("cross_attention", "0.20.0", deprecation_message, standard_warn=False)
-        super().__init__(*args, **kwargs)
-
-
-class CrossAttnProcessor(AttnProcessorRename):
-    def __init__(self, *args, **kwargs):
-        deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead."
-        deprecate("cross_attention", "0.20.0", deprecation_message, standard_warn=False)
-        super().__init__(*args, **kwargs)
-
-
-class LoRACrossAttnProcessor(LoRAAttnProcessor):
-    def __init__(self, *args, **kwargs):
-        deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead."
-        deprecate("cross_attention", "0.20.0", deprecation_message, standard_warn=False)
-        super().__init__(*args, **kwargs)
-
-
-class CrossAttnAddedKVProcessor(AttnAddedKVProcessor):
-    def __init__(self, *args, **kwargs):
-        deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead."
-        deprecate("cross_attention", "0.20.0", deprecation_message, standard_warn=False)
-        super().__init__(*args, **kwargs)
-
-
-class XFormersCrossAttnProcessor(XFormersAttnProcessor):
-    def __init__(self, *args, **kwargs):
-        deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead."
-        deprecate("cross_attention", "0.20.0", deprecation_message, standard_warn=False)
-        super().__init__(*args, **kwargs)
-
-
-class LoRAXFormersCrossAttnProcessor(LoRAXFormersAttnProcessor):
-    def __init__(self, *args, **kwargs):
-        deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead."
-        deprecate("cross_attention", "0.20.0", deprecation_message, standard_warn=False)
-        super().__init__(*args, **kwargs)
-
-
-class SlicedCrossAttnProcessor(SlicedAttnProcessor):
-    def __init__(self, *args, **kwargs):
-        deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead."
-        deprecate("cross_attention", "0.20.0", deprecation_message, standard_warn=False)
-        super().__init__(*args, **kwargs)
-
-
-class SlicedCrossAttnAddedKVProcessor(SlicedAttnAddedKVProcessor):
-    def __init__(self, *args, **kwargs):
-        deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead."
-        deprecate("cross_attention", "0.20.0", deprecation_message, standard_warn=False)
-        super().__init__(*args, **kwargs)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py

@@ -1,373 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from PIL import Image
-
-from ...models import UNet2DConditionModel, VQModel
-from ...schedulers import DDPMScheduler
-from ...utils import (
-    is_accelerate_available,
-    is_accelerate_version,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
-        >>> from diffusers.utils import load_image
-        >>> import torch
-
-        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
-        ... )
-        >>> pipe_prior.to("cuda")
-
-        >>> prompt = "A red cartoon frog, 4k"
-        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)
-
-        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
-        ... )
-        >>> pipe.to("cuda")
-
-        >>> init_image = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/frog.png"
-        ... )
-
-        >>> image = pipe(
-        ...     image=init_image,
-        ...     image_embeds=image_emb,
-        ...     negative_image_embeds=zero_image_emb,
-        ...     height=768,
-        ...     width=768,
-        ...     num_inference_steps=100,
-        ...     strength=0.2,
-        ... ).images
-
-        >>> image[0].save("red_frog.png")
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width
-def downscale_height_and_width(height, width, scale_factor=8):
-    new_height = height // scale_factor**2
-    if height % scale_factor**2 != 0:
-        new_height += 1
-    new_width = width // scale_factor**2
-    if width % scale_factor**2 != 0:
-        new_width += 1
-    return new_height * scale_factor, new_width * scale_factor
-
-
-# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.prepare_image
-def prepare_image(pil_image, w=512, h=512):
-    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
-    arr = np.array(pil_image.convert("RGB"))
-    arr = arr.astype(np.float32) / 127.5 - 1
-    arr = np.transpose(arr, [2, 0, 1])
-    image = torch.from_numpy(arr).unsqueeze(0)
-    return image
-
-
-class KandinskyV22Img2ImgPipeline(DiffusionPipeline):
-    """
-    Pipeline for image-to-image generation using Kandinsky
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        scheduler ([`DDIMScheduler`]):
-            A scheduler to be used in combination with `unet` to generate image latents.
-        unet ([`UNet2DConditionModel`]):
-            Conditional U-Net architecture to denoise the image embedding.
-        movq ([`VQModel`]):
-            MoVQ Decoder to generate the image from the latents.
-    """
-
-    def __init__(
-        self,
-        unet: UNet2DConditionModel,
-        scheduler: DDPMScheduler,
-        movq: VQModel,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            unet=unet,
-            scheduler=scheduler,
-            movq=movq,
-        )
-        self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
-
-    # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
-        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
-            raise ValueError(
-                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
-            )
-
-        image = image.to(device=device, dtype=dtype)
-
-        batch_size = batch_size * num_images_per_prompt
-
-        if image.shape[1] == 4:
-            init_latents = image
-
-        else:
-            if isinstance(generator, list) and len(generator) != batch_size:
-                raise ValueError(
-                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-                )
-
-            elif isinstance(generator, list):
-                init_latents = [
-                    self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
-                ]
-                init_latents = torch.cat(init_latents, dim=0)
-            else:
-                init_latents = self.movq.encode(image).latent_dist.sample(generator)
-
-            init_latents = self.movq.config.scaling_factor * init_latents
-
-        init_latents = torch.cat([init_latents], dim=0)
-
-        shape = init_latents.shape
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        # get latents
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-
-        latents = init_latents
-
-        return latents
-
-    # Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.KandinskyV22Pipeline.enable_model_cpu_offload
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.unet, self.movq]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
-        image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],
-        negative_image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 100,
-        guidance_scale: float = 4.0,
-        strength: float = 0.3,
-        num_images_per_prompt: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        output_type: Optional[str] = "pil",
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for text prompt, that will be used to condition the image generation.
-            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process. Can also accpet image latents as `image`, if passing latents directly, it will not be encoded
-                again.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for negative text prompt, will be used to condition the image generation.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 100):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
-                (`np.array`) or `"pt"` (`torch.Tensor`).
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`
-        """
-        device = self._execution_device
-
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        if isinstance(image_embeds, list):
-            image_embeds = torch.cat(image_embeds, dim=0)
-        batch_size = image_embeds.shape[0]
-        if isinstance(negative_image_embeds, list):
-            negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
-
-        if do_classifier_free_guidance:
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-
-            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
-                dtype=self.unet.dtype, device=device
-            )
-
-        if not isinstance(image, list):
-            image = [image]
-        if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):
-            raise ValueError(
-                f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and pytorch tensor"
-            )
-
-        image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
-        image = image.to(dtype=image_embeds.dtype, device=device)
-
-        latents = self.movq.encode(image)["latents"]
-        latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-        height, width = downscale_height_and_width(height, width, self.movq_scale_factor)
-        latents = self.prepare_latents(
-            latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator
-        )
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-            added_cond_kwargs = {"image_embeds": image_embeds}
-            noise_pred = self.unet(
-                sample=latent_model_input,
-                timestep=t,
-                encoder_hidden_states=None,
-                added_cond_kwargs=added_cond_kwargs,
-                return_dict=False,
-            )[0]
-
-            if do_classifier_free_guidance:
-                noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                _, variance_pred_text = variance_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
-
-            if not (
-                hasattr(self.scheduler.config, "variance_type")
-                and self.scheduler.config.variance_type in ["learned", "learned_range"]
-            ):
-                noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(
-                noise_pred,
-                t,
-                latents,
-                generator=generator,
-            )[0]
-
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        # post-processing
-        image = self.movq.decode(latents, force_not_quantize=True)["sample"]
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if output_type not in ["pt", "np", "pil"]:
-            raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
-
-        if output_type in ["np", "pil"]:
-            image = image * 0.5 + 0.5
-            image = image.clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py

@@ -1,237 +0,0 @@
-# Copyright 2023 NVIDIA and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import flax
-import jax.numpy as jnp
-from jax import random
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
-from .scheduling_utils_flax import FlaxSchedulerMixin
-
-
-@flax.struct.dataclass
-class KarrasVeSchedulerState:
-    # setable values
-    num_inference_steps: Optional[int] = None
-    timesteps: Optional[jnp.ndarray] = None
-    schedule: Optional[jnp.ndarray] = None  # sigma(t_i)
-
-    @classmethod
-    def create(cls):
-        return cls()
-
-
-@dataclass
-class FlaxKarrasVeOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        derivative (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
-            Derivative of predicted original image sample (x_0).
-        state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class.
-    """
-
-    prev_sample: jnp.ndarray
-    derivative: jnp.ndarray
-    state: KarrasVeSchedulerState
-
-
-class FlaxKarrasVeScheduler(FlaxSchedulerMixin, ConfigMixin):
-    """
-    Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
-    the VE column of Table 1 from [1] for reference.
-
-    [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
-    https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
-    differential equations." https://arxiv.org/abs/2011.13456
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of
-    Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the
-    optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper.
-
-    Args:
-        sigma_min (`float`): minimum noise magnitude
-        sigma_max (`float`): maximum noise magnitude
-        s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling.
-            A reasonable range is [1.000, 1.011].
-        s_churn (`float`): the parameter controlling the overall amount of stochasticity.
-            A reasonable range is [0, 100].
-        s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity).
-            A reasonable range is [0, 10].
-        s_max (`float`): the end value of the sigma range where we add noise.
-            A reasonable range is [0.2, 80].
-    """
-
-    @property
-    def has_state(self):
-        return True
-
-    @register_to_config
-    def __init__(
-        self,
-        sigma_min: float = 0.02,
-        sigma_max: float = 100,
-        s_noise: float = 1.007,
-        s_churn: float = 80,
-        s_min: float = 0.05,
-        s_max: float = 50,
-    ):
-        pass
-
-    def create_state(self):
-        return KarrasVeSchedulerState.create()
-
-    def set_timesteps(
-        self, state: KarrasVeSchedulerState, num_inference_steps: int, shape: Tuple = ()
-    ) -> KarrasVeSchedulerState:
-        """
-        Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            state (`KarrasVeSchedulerState`):
-                the `FlaxKarrasVeScheduler` state data class.
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-
-        """
-        timesteps = jnp.arange(0, num_inference_steps)[::-1].copy()
-        schedule = [
-            (
-                self.config.sigma_max**2
-                * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
-            )
-            for i in timesteps
-        ]
-
-        return state.replace(
-            num_inference_steps=num_inference_steps,
-            schedule=jnp.array(schedule, dtype=jnp.float32),
-            timesteps=timesteps,
-        )
-
-    def add_noise_to_input(
-        self,
-        state: KarrasVeSchedulerState,
-        sample: jnp.ndarray,
-        sigma: float,
-        key: random.KeyArray,
-    ) -> Tuple[jnp.ndarray, float]:
-        """
-        Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a
-        higher noise level sigma_hat = sigma_i + gamma_i*sigma_i.
-
-        TODO Args:
-        """
-        if self.config.s_min <= sigma <= self.config.s_max:
-            gamma = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1)
-        else:
-            gamma = 0
-
-        # sample eps ~ N(0, S_noise^2 * I)
-        key = random.split(key, num=1)
-        eps = self.config.s_noise * random.normal(key=key, shape=sample.shape)
-        sigma_hat = sigma + gamma * sigma
-        sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
-
-        return sample_hat, sigma_hat
-
-    def step(
-        self,
-        state: KarrasVeSchedulerState,
-        model_output: jnp.ndarray,
-        sigma_hat: float,
-        sigma_prev: float,
-        sample_hat: jnp.ndarray,
-        return_dict: bool = True,
-    ) -> Union[FlaxKarrasVeOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class.
-            model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
-            sigma_hat (`float`): TODO
-            sigma_prev (`float`): TODO
-            sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO
-            return_dict (`bool`): option for returning tuple rather than FlaxKarrasVeOutput class
-
-        Returns:
-            [`~schedulers.scheduling_karras_ve_flax.FlaxKarrasVeOutput`] or `tuple`: Updated sample in the diffusion
-            chain and derivative. [`~schedulers.scheduling_karras_ve_flax.FlaxKarrasVeOutput`] if `return_dict` is
-            True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
-        """
-
-        pred_original_sample = sample_hat + sigma_hat * model_output
-        derivative = (sample_hat - pred_original_sample) / sigma_hat
-        sample_prev = sample_hat + (sigma_prev - sigma_hat) * derivative
-
-        if not return_dict:
-            return (sample_prev, derivative, state)
-
-        return FlaxKarrasVeOutput(prev_sample=sample_prev, derivative=derivative, state=state)
-
-    def step_correct(
-        self,
-        state: KarrasVeSchedulerState,
-        model_output: jnp.ndarray,
-        sigma_hat: float,
-        sigma_prev: float,
-        sample_hat: jnp.ndarray,
-        sample_prev: jnp.ndarray,
-        derivative: jnp.ndarray,
-        return_dict: bool = True,
-    ) -> Union[FlaxKarrasVeOutput, Tuple]:
-        """
-        Correct the predicted sample based on the output model_output of the network. TODO complete description
-
-        Args:
-            state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class.
-            model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
-            sigma_hat (`float`): TODO
-            sigma_prev (`float`): TODO
-            sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO
-            sample_prev (`torch.FloatTensor` or `np.ndarray`): TODO
-            derivative (`torch.FloatTensor` or `np.ndarray`): TODO
-            return_dict (`bool`): option for returning tuple rather than FlaxKarrasVeOutput class
-
-        Returns:
-            prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO
-
-        """
-        pred_original_sample = sample_prev + sigma_prev * model_output
-        derivative_corr = (sample_prev - pred_original_sample) / sigma_prev
-        sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
-
-        if not return_dict:
-            return (sample_prev, derivative, state)
-
-        return FlaxKarrasVeOutput(prev_sample=sample_prev, derivative=derivative, state=state)
-
-    def add_noise(self, state: KarrasVeSchedulerState, original_samples, noise, timesteps):
-        raise NotImplementedError()

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/deepfloyd_if/test_if_inpainting_superresolution.py

@@ -1,90 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import random
-import unittest
-
-import torch
-
-from diffusers import IFInpaintingSuperResolutionPipeline
-from diffusers.utils import floats_tensor
-from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import skip_mps, torch_device
-
-from ..pipeline_params import (
-    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
-    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
-)
-from ..test_pipelines_common import PipelineTesterMixin
-from . import IFPipelineTesterMixin
-
-
-@skip_mps
-class IFInpaintingSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):
-    pipeline_class = IFInpaintingSuperResolutionPipeline
-    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
-    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"})
-    required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
-
-    def get_dummy_components(self):
-        return self._get_superresolution_dummy_components()
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        image = floats_tensor((1, 3, 16, 16), rng=random.Random(seed)).to(device)
-        original_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
-        mask_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
-
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "image": image,
-            "original_image": original_image,
-            "mask_image": mask_image,
-            "generator": generator,
-            "num_inference_steps": 2,
-            "output_type": "numpy",
-        }
-
-        return inputs
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
-
-    def test_save_load_optional_components(self):
-        self._test_save_load_optional_components()
-
-    @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
-    def test_save_load_float16(self):
-        # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
-        super().test_save_load_float16(expected_max_diff=1e-1)
-
-    def test_attention_slicing_forward_pass(self):
-        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)
-
-    def test_save_load_local(self):
-        self._test_save_load_local()
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(
-            expected_max_diff=1e-2,
-        )

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

@@ -1,48 +0,0 @@
-from fastai.layers import *
-from fastai.torch_core import *
-from torch.nn.parameter import Parameter
-from torch.autograd import Variable
-
-
-# The code below is meant to be merged into fastaiv1 ideally
-
-
-def custom_conv_layer(
-    ni: int,
-    nf: int,
-    ks: int = 3,
-    stride: int = 1,
-    padding: int = None,
-    bias: bool = None,
-    is_1d: bool = False,
-    norm_type: Optional[NormType] = NormType.Batch,
-    use_activ: bool = True,
-    leaky: float = None,
-    transpose: bool = False,
-    init: Callable = nn.init.kaiming_normal_,
-    self_attention: bool = False,
-    extra_bn: bool = False,
-):
-    "Create a sequence of convolutional (`ni` to `nf`), ReLU (if `use_activ`) and batchnorm (if `bn`) layers."
-    if padding is None:
-        padding = (ks - 1) // 2 if not transpose else 0
-    bn = norm_type in (NormType.Batch, NormType.BatchZero) or extra_bn == True
-    if bias is None:
-        bias = not bn
-    conv_func = nn.ConvTranspose2d if transpose else nn.Conv1d if is_1d else nn.Conv2d
-    conv = init_default(
-        conv_func(ni, nf, kernel_size=ks, bias=bias, stride=stride, padding=padding),
-        init,
-    )
-    if norm_type == NormType.Weight:
-        conv = weight_norm(conv)
-    elif norm_type == NormType.Spectral:
-        conv = spectral_norm(conv)
-    layers = [conv]
-    if use_activ:
-        layers.append(relu(True, leaky=leaky))
-    if bn:
-        layers.append((nn.BatchNorm1d if is_1d else nn.BatchNorm2d)(nf))
-    if self_attention:
-        layers.append(SelfAttention(nf))
-    return nn.Sequential(*layers)

spaces/Aristo/trafficsign/app.py

@@ -1,39 +0,0 @@
-import gradio as gr
-import PIL
-import numpy
-import matplotlib.pyplot as plt
-#load the trained model to classify sign
-from keras.models import load_model
-model = load_model('traffic_classifier.h5')
-#dictionary to label all traffic signs class.
-classes = { 1:'Speed limit (20km/h)',
-            2:'Speed limit (30km/h)', 
-            3:'Speed limit (50km/h)', 
-            4:'Speed limit (60km/h)', 
-            5:'Speed limit (70km/h)', 
-            6:'Speed limit (80km/h)', 
-            7:'End of speed limit (80km/h)', 
-            8:'Speed limit (100km/h)', 
-            9:'Speed limit (120km/h)', 
-            10:'Veh > 3.5 tons prohibited', 
-            11:'Bumpy road',
-            12:'Slippery road', 
-            13:'Road narrows on the right', 
-            14:'Road work', 
-            15:'Pedestrians', 
-            16:'Turn right ahead', 
-            17:'Turn left ahead', 
-            18:'Ahead only', 
-            19:'Go straight or right', 
-            20:'Go straight or left', 
-            21:'Keep right', 
-            22:'Keep left', 
-            23:'Roundabout mandatory'}
-#initialise GUI
-def predict(img):
-  img = numpy.expand_dims(img, axis=0)
-  predict_x = model.predict(img)
-  pred = numpy.argmax(predict_x, axis = 1)
-  sign = classes[pred[0]+1]
-  return sign
-gr.Interface(fn= predict, inputs = gr.inputs.Image(shape = (30,30)), outputs= "textbox" ).launch(share=True, debug = True)

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

@@ -1,642 +0,0 @@
-import railroad
-import pyparsing
-import typing
-from typing import (
-    List,
-    NamedTuple,
-    Generic,
-    TypeVar,
-    Dict,
-    Callable,
-    Set,
-    Iterable,
-)
-from jinja2 import Template
-from io import StringIO
-import inspect
-
-
-jinja2_template_source = """\
-<!DOCTYPE html>
-<html>
-<head>
-    {% if not head %}
-        <style type="text/css">
-            .railroad-heading {
-                font-family: monospace;
-            }
-        </style>
-    {% else %}
-        {{ head | safe }}
-    {% endif %}
-</head>
-<body>
-{{ body | safe }}
-{% for diagram in diagrams %}
-    <div class="railroad-group">
-        <h1 class="railroad-heading">{{ diagram.title }}</h1>
-        <div class="railroad-description">{{ diagram.text }}</div>
-        <div class="railroad-svg">
-            {{ diagram.svg }}
-        </div>
-    </div>
-{% endfor %}
-</body>
-</html>
-"""
-
-template = Template(jinja2_template_source)
-
-# Note: ideally this would be a dataclass, but we're supporting Python 3.5+ so we can't do this yet
-NamedDiagram = NamedTuple(
-    "NamedDiagram",
-    [("name", str), ("diagram", typing.Optional[railroad.DiagramItem]), ("index", int)],
-)
-"""
-A simple structure for associating a name with a railroad diagram
-"""
-
-T = TypeVar("T")
-
-
-class EachItem(railroad.Group):
-    """
-    Custom railroad item to compose a:
-    - Group containing a
-      - OneOrMore containing a
-        - Choice of the elements in the Each
-    with the group label indicating that all must be matched
-    """
-
-    all_label = "[ALL]"
-
-    def __init__(self, *items):
-        choice_item = railroad.Choice(len(items) - 1, *items)
-        one_or_more_item = railroad.OneOrMore(item=choice_item)
-        super().__init__(one_or_more_item, label=self.all_label)
-
-
-class AnnotatedItem(railroad.Group):
-    """
-    Simple subclass of Group that creates an annotation label
-    """
-
-    def __init__(self, label: str, item):
-        super().__init__(item=item, label="[{}]".format(label) if label else label)
-
-
-class EditablePartial(Generic[T]):
-    """
-    Acts like a functools.partial, but can be edited. In other words, it represents a type that hasn't yet been
-    constructed.
-    """
-
-    # We need this here because the railroad constructors actually transform the data, so can't be called until the
-    # entire tree is assembled
-
-    def __init__(self, func: Callable[..., T], args: list, kwargs: dict):
-        self.func = func
-        self.args = args
-        self.kwargs = kwargs
-
-    @classmethod
-    def from_call(cls, func: Callable[..., T], *args, **kwargs) -> "EditablePartial[T]":
-        """
-        If you call this function in the same way that you would call the constructor, it will store the arguments
-        as you expect. For example EditablePartial.from_call(Fraction, 1, 3)() == Fraction(1, 3)
-        """
-        return EditablePartial(func=func, args=list(args), kwargs=kwargs)
-
-    @property
-    def name(self):
-        return self.kwargs["name"]
-
-    def __call__(self) -> T:
-        """
-        Evaluate the partial and return the result
-        """
-        args = self.args.copy()
-        kwargs = self.kwargs.copy()
-
-        # This is a helpful hack to allow you to specify varargs parameters (e.g. *args) as keyword args (e.g.
-        # args=['list', 'of', 'things'])
-        arg_spec = inspect.getfullargspec(self.func)
-        if arg_spec.varargs in self.kwargs:
-            args += kwargs.pop(arg_spec.varargs)
-
-        return self.func(*args, **kwargs)
-
-
-def railroad_to_html(diagrams: List[NamedDiagram], **kwargs) -> str:
-    """
-    Given a list of NamedDiagram, produce a single HTML string that visualises those diagrams
-    :params kwargs: kwargs to be passed in to the template
-    """
-    data = []
-    for diagram in diagrams:
-        if diagram.diagram is None:
-            continue
-        io = StringIO()
-        diagram.diagram.writeSvg(io.write)
-        title = diagram.name
-        if diagram.index == 0:
-            title += " (root)"
-        data.append({"title": title, "text": "", "svg": io.getvalue()})
-
-    return template.render(diagrams=data, **kwargs)
-
-
-def resolve_partial(partial: "EditablePartial[T]") -> T:
-    """
-    Recursively resolves a collection of Partials into whatever type they are
-    """
-    if isinstance(partial, EditablePartial):
-        partial.args = resolve_partial(partial.args)
-        partial.kwargs = resolve_partial(partial.kwargs)
-        return partial()
-    elif isinstance(partial, list):
-        return [resolve_partial(x) for x in partial]
-    elif isinstance(partial, dict):
-        return {key: resolve_partial(x) for key, x in partial.items()}
-    else:
-        return partial
-
-
-def to_railroad(
-    element: pyparsing.ParserElement,
-    diagram_kwargs: typing.Optional[dict] = None,
-    vertical: int = 3,
-    show_results_names: bool = False,
-    show_groups: bool = False,
-) -> List[NamedDiagram]:
-    """
-    Convert a pyparsing element tree into a list of diagrams. This is the recommended entrypoint to diagram
-    creation if you want to access the Railroad tree before it is converted to HTML
-    :param element: base element of the parser being diagrammed
-    :param diagram_kwargs: kwargs to pass to the Diagram() constructor
-    :param vertical: (optional) - int - limit at which number of alternatives should be
-       shown vertically instead of horizontally
-    :param show_results_names - bool to indicate whether results name annotations should be
-       included in the diagram
-    :param show_groups - bool to indicate whether groups should be highlighted with an unlabeled
-       surrounding box
-    """
-    # Convert the whole tree underneath the root
-    lookup = ConverterState(diagram_kwargs=diagram_kwargs or {})
-    _to_diagram_element(
-        element,
-        lookup=lookup,
-        parent=None,
-        vertical=vertical,
-        show_results_names=show_results_names,
-        show_groups=show_groups,
-    )
-
-    root_id = id(element)
-    # Convert the root if it hasn't been already
-    if root_id in lookup:
-        if not element.customName:
-            lookup[root_id].name = ""
-        lookup[root_id].mark_for_extraction(root_id, lookup, force=True)
-
-    # Now that we're finished, we can convert from intermediate structures into Railroad elements
-    diags = list(lookup.diagrams.values())
-    if len(diags) > 1:
-        # collapse out duplicate diags with the same name
-        seen = set()
-        deduped_diags = []
-        for d in diags:
-            # don't extract SkipTo elements, they are uninformative as subdiagrams
-            if d.name == "...":
-                continue
-            if d.name is not None and d.name not in seen:
-                seen.add(d.name)
-                deduped_diags.append(d)
-        resolved = [resolve_partial(partial) for partial in deduped_diags]
-    else:
-        # special case - if just one diagram, always display it, even if
-        # it has no name
-        resolved = [resolve_partial(partial) for partial in diags]
-    return sorted(resolved, key=lambda diag: diag.index)
-
-
-def _should_vertical(
-    specification: int, exprs: Iterable[pyparsing.ParserElement]
-) -> bool:
-    """
-    Returns true if we should return a vertical list of elements
-    """
-    if specification is None:
-        return False
-    else:
-        return len(_visible_exprs(exprs)) >= specification
-
-
-class ElementState:
-    """
-    State recorded for an individual pyparsing Element
-    """
-
-    # Note: this should be a dataclass, but we have to support Python 3.5
-    def __init__(
-        self,
-        element: pyparsing.ParserElement,
-        converted: EditablePartial,
-        parent: EditablePartial,
-        number: int,
-        name: str = None,
-        parent_index: typing.Optional[int] = None,
-    ):
-        #: The pyparsing element that this represents
-        self.element: pyparsing.ParserElement = element
-        #: The name of the element
-        self.name: typing.Optional[str] = name
-        #: The output Railroad element in an unconverted state
-        self.converted: EditablePartial = converted
-        #: The parent Railroad element, which we store so that we can extract this if it's duplicated
-        self.parent: EditablePartial = parent
-        #: The order in which we found this element, used for sorting diagrams if this is extracted into a diagram
-        self.number: int = number
-        #: The index of this inside its parent
-        self.parent_index: typing.Optional[int] = parent_index
-        #: If true, we should extract this out into a subdiagram
-        self.extract: bool = False
-        #: If true, all of this element's children have been filled out
-        self.complete: bool = False
-
-    def mark_for_extraction(
-        self, el_id: int, state: "ConverterState", name: str = None, force: bool = False
-    ):
-        """
-        Called when this instance has been seen twice, and thus should eventually be extracted into a sub-diagram
-        :param el_id: id of the element
-        :param state: element/diagram state tracker
-        :param name: name to use for this element's text
-        :param force: If true, force extraction now, regardless of the state of this. Only useful for extracting the
-        root element when we know we're finished
-        """
-        self.extract = True
-
-        # Set the name
-        if not self.name:
-            if name:
-                # Allow forcing a custom name
-                self.name = name
-            elif self.element.customName:
-                self.name = self.element.customName
-            else:
-                self.name = ""
-
-        # Just because this is marked for extraction doesn't mean we can do it yet. We may have to wait for children
-        # to be added
-        # Also, if this is just a string literal etc, don't bother extracting it
-        if force or (self.complete and _worth_extracting(self.element)):
-            state.extract_into_diagram(el_id)
-
-
-class ConverterState:
-    """
-    Stores some state that persists between recursions into the element tree
-    """
-
-    def __init__(self, diagram_kwargs: typing.Optional[dict] = None):
-        #: A dictionary mapping ParserElements to state relating to them
-        self._element_diagram_states: Dict[int, ElementState] = {}
-        #: A dictionary mapping ParserElement IDs to subdiagrams generated from them
-        self.diagrams: Dict[int, EditablePartial[NamedDiagram]] = {}
-        #: The index of the next unnamed element
-        self.unnamed_index: int = 1
-        #: The index of the next element. This is used for sorting
-        self.index: int = 0
-        #: Shared kwargs that are used to customize the construction of diagrams
-        self.diagram_kwargs: dict = diagram_kwargs or {}
-        self.extracted_diagram_names: Set[str] = set()
-
-    def __setitem__(self, key: int, value: ElementState):
-        self._element_diagram_states[key] = value
-
-    def __getitem__(self, key: int) -> ElementState:
-        return self._element_diagram_states[key]
-
-    def __delitem__(self, key: int):
-        del self._element_diagram_states[key]
-
-    def __contains__(self, key: int):
-        return key in self._element_diagram_states
-
-    def generate_unnamed(self) -> int:
-        """
-        Generate a number used in the name of an otherwise unnamed diagram
-        """
-        self.unnamed_index += 1
-        return self.unnamed_index
-
-    def generate_index(self) -> int:
-        """
-        Generate a number used to index a diagram
-        """
-        self.index += 1
-        return self.index
-
-    def extract_into_diagram(self, el_id: int):
-        """
-        Used when we encounter the same token twice in the same tree. When this
-        happens, we replace all instances of that token with a terminal, and
-        create a new subdiagram for the token
-        """
-        position = self[el_id]
-
-        # Replace the original definition of this element with a regular block
-        if position.parent:
-            ret = EditablePartial.from_call(railroad.NonTerminal, text=position.name)
-            if "item" in position.parent.kwargs:
-                position.parent.kwargs["item"] = ret
-            elif "items" in position.parent.kwargs:
-                position.parent.kwargs["items"][position.parent_index] = ret
-
-        # If the element we're extracting is a group, skip to its content but keep the title
-        if position.converted.func == railroad.Group:
-            content = position.converted.kwargs["item"]
-        else:
-            content = position.converted
-
-        self.diagrams[el_id] = EditablePartial.from_call(
-            NamedDiagram,
-            name=position.name,
-            diagram=EditablePartial.from_call(
-                railroad.Diagram, content, **self.diagram_kwargs
-            ),
-            index=position.number,
-        )
-
-        del self[el_id]
-
-
-def _worth_extracting(element: pyparsing.ParserElement) -> bool:
-    """
-    Returns true if this element is worth having its own sub-diagram. Simply, if any of its children
-    themselves have children, then its complex enough to extract
-    """
-    children = element.recurse()
-    return any(child.recurse() for child in children)
-
-
-def _apply_diagram_item_enhancements(fn):
-    """
-    decorator to ensure enhancements to a diagram item (such as results name annotations)
-    get applied on return from _to_diagram_element (we do this since there are several
-    returns in _to_diagram_element)
-    """
-
-    def _inner(
-        element: pyparsing.ParserElement,
-        parent: typing.Optional[EditablePartial],
-        lookup: ConverterState = None,
-        vertical: int = None,
-        index: int = 0,
-        name_hint: str = None,
-        show_results_names: bool = False,
-        show_groups: bool = False,
-    ) -> typing.Optional[EditablePartial]:
-
-        ret = fn(
-            element,
-            parent,
-            lookup,
-            vertical,
-            index,
-            name_hint,
-            show_results_names,
-            show_groups,
-        )
-
-        # apply annotation for results name, if present
-        if show_results_names and ret is not None:
-            element_results_name = element.resultsName
-            if element_results_name:
-                # add "*" to indicate if this is a "list all results" name
-                element_results_name += "" if element.modalResults else "*"
-                ret = EditablePartial.from_call(
-                    railroad.Group, item=ret, label=element_results_name
-                )
-
-        return ret
-
-    return _inner
-
-
-def _visible_exprs(exprs: Iterable[pyparsing.ParserElement]):
-    non_diagramming_exprs = (
-        pyparsing.ParseElementEnhance,
-        pyparsing.PositionToken,
-        pyparsing.And._ErrorStop,
-    )
-    return [
-        e
-        for e in exprs
-        if not (e.customName or e.resultsName or isinstance(e, non_diagramming_exprs))
-    ]
-
-
-@_apply_diagram_item_enhancements
-def _to_diagram_element(
-    element: pyparsing.ParserElement,
-    parent: typing.Optional[EditablePartial],
-    lookup: ConverterState = None,
-    vertical: int = None,
-    index: int = 0,
-    name_hint: str = None,
-    show_results_names: bool = False,
-    show_groups: bool = False,
-) -> typing.Optional[EditablePartial]:
-    """
-    Recursively converts a PyParsing Element to a railroad Element
-    :param lookup: The shared converter state that keeps track of useful things
-    :param index: The index of this element within the parent
-    :param parent: The parent of this element in the output tree
-    :param vertical: Controls at what point we make a list of elements vertical. If this is an integer (the default),
-    it sets the threshold of the number of items before we go vertical. If True, always go vertical, if False, never
-    do so
-    :param name_hint: If provided, this will override the generated name
-    :param show_results_names: bool flag indicating whether to add annotations for results names
-    :returns: The converted version of the input element, but as a Partial that hasn't yet been constructed
-    :param show_groups: bool flag indicating whether to show groups using bounding box
-    """
-    exprs = element.recurse()
-    name = name_hint or element.customName or element.__class__.__name__
-
-    # Python's id() is used to provide a unique identifier for elements
-    el_id = id(element)
-
-    element_results_name = element.resultsName
-
-    # Here we basically bypass processing certain wrapper elements if they contribute nothing to the diagram
-    if not element.customName:
-        if isinstance(
-            element,
-            (
-                # pyparsing.TokenConverter,
-                # pyparsing.Forward,
-                pyparsing.Located,
-            ),
-        ):
-            # However, if this element has a useful custom name, and its child does not, we can pass it on to the child
-            if exprs:
-                if not exprs[0].customName:
-                    propagated_name = name
-                else:
-                    propagated_name = None
-
-                return _to_diagram_element(
-                    element.expr,
-                    parent=parent,
-                    lookup=lookup,
-                    vertical=vertical,
-                    index=index,
-                    name_hint=propagated_name,
-                    show_results_names=show_results_names,
-                    show_groups=show_groups,
-                )
-
-    # If the element isn't worth extracting, we always treat it as the first time we say it
-    if _worth_extracting(element):
-        if el_id in lookup:
-            # If we've seen this element exactly once before, we are only just now finding out that it's a duplicate,
-            # so we have to extract it into a new diagram.
-            looked_up = lookup[el_id]
-            looked_up.mark_for_extraction(el_id, lookup, name=name_hint)
-            ret = EditablePartial.from_call(railroad.NonTerminal, text=looked_up.name)
-            return ret
-
-        elif el_id in lookup.diagrams:
-            # If we have seen the element at least twice before, and have already extracted it into a subdiagram, we
-            # just put in a marker element that refers to the sub-diagram
-            ret = EditablePartial.from_call(
-                railroad.NonTerminal, text=lookup.diagrams[el_id].kwargs["name"]
-            )
-            return ret
-
-    # Recursively convert child elements
-    # Here we find the most relevant Railroad element for matching pyparsing Element
-    # We use ``items=[]`` here to hold the place for where the child elements will go once created
-    if isinstance(element, pyparsing.And):
-        # detect And's created with ``expr*N`` notation - for these use a OneOrMore with a repeat
-        # (all will have the same name, and resultsName)
-        if not exprs:
-            return None
-        if len(set((e.name, e.resultsName) for e in exprs)) == 1:
-            ret = EditablePartial.from_call(
-                railroad.OneOrMore, item="", repeat=str(len(exprs))
-            )
-        elif _should_vertical(vertical, exprs):
-            ret = EditablePartial.from_call(railroad.Stack, items=[])
-        else:
-            ret = EditablePartial.from_call(railroad.Sequence, items=[])
-    elif isinstance(element, (pyparsing.Or, pyparsing.MatchFirst)):
-        if not exprs:
-            return None
-        if _should_vertical(vertical, exprs):
-            ret = EditablePartial.from_call(railroad.Choice, 0, items=[])
-        else:
-            ret = EditablePartial.from_call(railroad.HorizontalChoice, items=[])
-    elif isinstance(element, pyparsing.Each):
-        if not exprs:
-            return None
-        ret = EditablePartial.from_call(EachItem, items=[])
-    elif isinstance(element, pyparsing.NotAny):
-        ret = EditablePartial.from_call(AnnotatedItem, label="NOT", item="")
-    elif isinstance(element, pyparsing.FollowedBy):
-        ret = EditablePartial.from_call(AnnotatedItem, label="LOOKAHEAD", item="")
-    elif isinstance(element, pyparsing.PrecededBy):
-        ret = EditablePartial.from_call(AnnotatedItem, label="LOOKBEHIND", item="")
-    elif isinstance(element, pyparsing.Group):
-        if show_groups:
-            ret = EditablePartial.from_call(AnnotatedItem, label="", item="")
-        else:
-            ret = EditablePartial.from_call(railroad.Group, label="", item="")
-    elif isinstance(element, pyparsing.TokenConverter):
-        ret = EditablePartial.from_call(
-            AnnotatedItem, label=type(element).__name__.lower(), item=""
-        )
-    elif isinstance(element, pyparsing.Opt):
-        ret = EditablePartial.from_call(railroad.Optional, item="")
-    elif isinstance(element, pyparsing.OneOrMore):
-        ret = EditablePartial.from_call(railroad.OneOrMore, item="")
-    elif isinstance(element, pyparsing.ZeroOrMore):
-        ret = EditablePartial.from_call(railroad.ZeroOrMore, item="")
-    elif isinstance(element, pyparsing.Group):
-        ret = EditablePartial.from_call(
-            railroad.Group, item=None, label=element_results_name
-        )
-    elif isinstance(element, pyparsing.Empty) and not element.customName:
-        # Skip unnamed "Empty" elements
-        ret = None
-    elif len(exprs) > 1:
-        ret = EditablePartial.from_call(railroad.Sequence, items=[])
-    elif len(exprs) > 0 and not element_results_name:
-        ret = EditablePartial.from_call(railroad.Group, item="", label=name)
-    else:
-        terminal = EditablePartial.from_call(railroad.Terminal, element.defaultName)
-        ret = terminal
-
-    if ret is None:
-        return
-
-    # Indicate this element's position in the tree so we can extract it if necessary
-    lookup[el_id] = ElementState(
-        element=element,
-        converted=ret,
-        parent=parent,
-        parent_index=index,
-        number=lookup.generate_index(),
-    )
-    if element.customName:
-        lookup[el_id].mark_for_extraction(el_id, lookup, element.customName)
-
-    i = 0
-    for expr in exprs:
-        # Add a placeholder index in case we have to extract the child before we even add it to the parent
-        if "items" in ret.kwargs:
-            ret.kwargs["items"].insert(i, None)
-
-        item = _to_diagram_element(
-            expr,
-            parent=ret,
-            lookup=lookup,
-            vertical=vertical,
-            index=i,
-            show_results_names=show_results_names,
-            show_groups=show_groups,
-        )
-
-        # Some elements don't need to be shown in the diagram
-        if item is not None:
-            if "item" in ret.kwargs:
-                ret.kwargs["item"] = item
-            elif "items" in ret.kwargs:
-                # If we've already extracted the child, don't touch this index, since it's occupied by a nonterminal
-                ret.kwargs["items"][i] = item
-                i += 1
-        elif "items" in ret.kwargs:
-            # If we're supposed to skip this element, remove it from the parent
-            del ret.kwargs["items"][i]
-
-    # If all this items children are none, skip this item
-    if ret and (
-        ("items" in ret.kwargs and len(ret.kwargs["items"]) == 0)
-        or ("item" in ret.kwargs and ret.kwargs["item"] is None)
-    ):
-        ret = EditablePartial.from_call(railroad.Terminal, name)
-
-    # Mark this element as "complete", ie it has all of its children
-    if el_id in lookup:
-        lookup[el_id].complete = True
-
-    if el_id in lookup and lookup[el_id].extract and lookup[el_id].complete:
-        lookup.extract_into_diagram(el_id)
-        if ret is not None:
-            ret = EditablePartial.from_call(
-                railroad.NonTerminal, text=lookup.diagrams[el_id].kwargs["name"]
-            )
-
-    return ret

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/utils/registry.py

@@ -1,60 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from typing import Any
-import pydoc
-from fvcore.common.registry import Registry  # for backward compatibility.
-
-"""
-``Registry`` and `locate` provide ways to map a string (typically found
-in config files) to callable objects.
-"""
-
-__all__ = ["Registry", "locate"]
-
-
-def _convert_target_to_string(t: Any) -> str:
-    """
-    Inverse of ``locate()``.
-
-    Args:
-        t: any object with ``__module__`` and ``__qualname__``
-    """
-    module, qualname = t.__module__, t.__qualname__
-
-    # Compress the path to this object, e.g. ``module.submodule._impl.class``
-    # may become ``module.submodule.class``, if the later also resolves to the same
-    # object. This simplifies the string, and also is less affected by moving the
-    # class implementation.
-    module_parts = module.split(".")
-    for k in range(1, len(module_parts)):
-        prefix = ".".join(module_parts[:k])
-        candidate = f"{prefix}.{qualname}"
-        try:
-            if locate(candidate) is t:
-                return candidate
-        except ImportError:
-            pass
-    return f"{module}.{qualname}"
-
-
-def locate(name: str) -> Any:
-    """
-    Locate and return an object ``x`` using an input string ``{x.__module__}.{x.__qualname__}``,
-    such as "module.submodule.class_name".
-
-    Raise Exception if it cannot be found.
-    """
-    obj = pydoc.locate(name)
-
-    # Some cases (e.g. torch.optim.sgd.SGD) not handled correctly
-    # by pydoc.locate. Try a private function from hydra.
-    if obj is None:
-        try:
-            # from hydra.utils import get_method - will print many errors
-            from hydra.utils import _locate
-        except ImportError as e:
-            raise ImportError(f"Cannot dynamically locate object {name}!") from e
-        else:
-            obj = _locate(name)  # it raises if fails
-
-    return obj

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/modeling/test_rpn.py

@@ -1,262 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-import torch
-
-from detectron2.config import get_cfg
-from detectron2.export import scripting_with_instances
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.backbone import build_backbone
-from detectron2.modeling.proposal_generator import RPN, build_proposal_generator
-from detectron2.modeling.proposal_generator.proposal_utils import (
-    add_ground_truth_to_proposals,
-    find_top_rpn_proposals,
-)
-from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
-from detectron2.utils.events import EventStorage
-
-logger = logging.getLogger(__name__)
-
-
-class RPNTest(unittest.TestCase):
-    def get_gt_and_features(self):
-        num_images = 2
-        images_tensor = torch.rand(num_images, 20, 30)
-        image_sizes = [(10, 10), (20, 30)]
-        images = ImageList(images_tensor, image_sizes)
-        image_shape = (15, 15)
-        num_channels = 1024
-        features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
-        gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
-        gt_instances = Instances(image_shape)
-        gt_instances.gt_boxes = Boxes(gt_boxes)
-        return (gt_instances, features, images, image_sizes)
-
-    def test_rpn(self):
-        torch.manual_seed(121)
-        cfg = get_cfg()
-        backbone = build_backbone(cfg)
-        proposal_generator = RPN(cfg, backbone.output_shape())
-        (gt_instances, features, images, image_sizes) = self.get_gt_and_features()
-        with EventStorage():  # capture events in a new storage to discard them
-            proposals, proposal_losses = proposal_generator(
-                images, features, [gt_instances[0], gt_instances[1]]
-            )
-
-        expected_losses = {
-            "loss_rpn_cls": torch.tensor(0.08011703193),
-            "loss_rpn_loc": torch.tensor(0.101470276),
-        }
-        for name in expected_losses.keys():
-            err_msg = "proposal_losses[{}] = {}, expected losses = {}".format(
-                name, proposal_losses[name], expected_losses[name]
-            )
-            self.assertTrue(torch.allclose(proposal_losses[name], expected_losses[name]), err_msg)
-
-        self.assertEqual(len(proposals), len(image_sizes))
-        for proposal, im_size in zip(proposals, image_sizes):
-            self.assertEqual(proposal.image_size, im_size)
-
-        expected_proposal_box = torch.tensor([[0, 0, 10, 10], [7.2702, 0, 10, 10]])
-        expected_objectness_logit = torch.tensor([0.1596, -0.0007])
-        self.assertTrue(
-            torch.allclose(proposals[0].proposal_boxes.tensor, expected_proposal_box, atol=1e-4)
-        )
-        self.assertTrue(
-            torch.allclose(proposals[0].objectness_logits, expected_objectness_logit, atol=1e-4)
-        )
-
-    def verify_rpn(self, conv_dims, expected_conv_dims):
-        torch.manual_seed(121)
-        cfg = get_cfg()
-        cfg.MODEL.RPN.CONV_DIMS = conv_dims
-        backbone = build_backbone(cfg)
-        proposal_generator = RPN(cfg, backbone.output_shape())
-        for k, conv in enumerate(proposal_generator.rpn_head.conv):
-            self.assertEqual(expected_conv_dims[k], conv.out_channels)
-        return proposal_generator
-
-    def test_rpn_larger_num_convs(self):
-        conv_dims = [64, 64, 64, 64, 64]
-        proposal_generator = self.verify_rpn(conv_dims, conv_dims)
-        (gt_instances, features, images, image_sizes) = self.get_gt_and_features()
-        with EventStorage():  # capture events in a new storage to discard them
-            proposals, proposal_losses = proposal_generator(
-                images, features, [gt_instances[0], gt_instances[1]]
-            )
-        expected_losses = {
-            "loss_rpn_cls": torch.tensor(0.08122821152),
-            "loss_rpn_loc": torch.tensor(0.10064548254),
-        }
-        for name in expected_losses.keys():
-            err_msg = "proposal_losses[{}] = {}, expected losses = {}".format(
-                name, proposal_losses[name], expected_losses[name]
-            )
-            self.assertTrue(torch.allclose(proposal_losses[name], expected_losses[name]), err_msg)
-
-    def test_rpn_conv_dims_not_set(self):
-        conv_dims = [-1, -1, -1]
-        expected_conv_dims = [1024, 1024, 1024]
-        self.verify_rpn(conv_dims, expected_conv_dims)
-
-    def test_rpn_scriptability(self):
-        cfg = get_cfg()
-        proposal_generator = RPN(cfg, {"res4": ShapeSpec(channels=1024, stride=16)}).eval()
-        num_images = 2
-        images_tensor = torch.rand(num_images, 30, 40)
-        image_sizes = [(32, 32), (30, 40)]
-        images = ImageList(images_tensor, image_sizes)
-        features = {"res4": torch.rand(num_images, 1024, 1, 2)}
-
-        fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor}
-        proposal_generator_ts = scripting_with_instances(proposal_generator, fields)
-
-        proposals, _ = proposal_generator(images, features)
-        proposals_ts, _ = proposal_generator_ts(images, features)
-
-        for proposal, proposal_ts in zip(proposals, proposals_ts):
-            self.assertEqual(proposal.image_size, proposal_ts.image_size)
-            self.assertTrue(
-                torch.equal(proposal.proposal_boxes.tensor, proposal_ts.proposal_boxes.tensor)
-            )
-            self.assertTrue(torch.equal(proposal.objectness_logits, proposal_ts.objectness_logits))
-
-    def test_rrpn(self):
-        torch.manual_seed(121)
-        cfg = get_cfg()
-        cfg.MODEL.PROPOSAL_GENERATOR.NAME = "RRPN"
-        cfg.MODEL.ANCHOR_GENERATOR.NAME = "RotatedAnchorGenerator"
-        cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]]
-        cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1]]
-        cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [[0, 60]]
-        cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1)
-        cfg.MODEL.RPN.HEAD_NAME = "StandardRPNHead"
-        backbone = build_backbone(cfg)
-        proposal_generator = build_proposal_generator(cfg, backbone.output_shape())
-        num_images = 2
-        images_tensor = torch.rand(num_images, 20, 30)
-        image_sizes = [(10, 10), (20, 30)]
-        images = ImageList(images_tensor, image_sizes)
-        image_shape = (15, 15)
-        num_channels = 1024
-        features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
-        gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32)
-        gt_instances = Instances(image_shape)
-        gt_instances.gt_boxes = RotatedBoxes(gt_boxes)
-        with EventStorage():  # capture events in a new storage to discard them
-            proposals, proposal_losses = proposal_generator(
-                images, features, [gt_instances[0], gt_instances[1]]
-            )
-
-        expected_losses = {
-            "loss_rpn_cls": torch.tensor(0.04291602224),
-            "loss_rpn_loc": torch.tensor(0.145077362),
-        }
-        for name in expected_losses.keys():
-            err_msg = "proposal_losses[{}] = {}, expected losses = {}".format(
-                name, proposal_losses[name], expected_losses[name]
-            )
-            self.assertTrue(torch.allclose(proposal_losses[name], expected_losses[name]), err_msg)
-
-        expected_proposal_box = torch.tensor(
-            [
-                [-1.77999556, 0.78155339, 68.04367828, 14.78156471, 60.59333801],
-                [13.82740974, -1.50282836, 34.67269897, 29.19676590, -3.81942749],
-                [8.10392570, -0.99071521, 145.39100647, 32.13126373, 3.67242432],
-                [5.00000000, 4.57370186, 10.00000000, 9.14740372, 0.89196777],
-            ]
-        )
-
-        expected_objectness_logit = torch.tensor([0.10924313, 0.09881870, 0.07649877, 0.05858029])
-
-        torch.set_printoptions(precision=8, sci_mode=False)
-
-        self.assertEqual(len(proposals), len(image_sizes))
-
-        proposal = proposals[0]
-        # It seems that there's some randomness in the result across different machines:
-        # This test can be run on a local machine for 100 times with exactly the same result,
-        # However, a different machine might produce slightly different results,
-        # thus the atol here.
-        err_msg = "computed proposal boxes = {}, expected {}".format(
-            proposal.proposal_boxes.tensor, expected_proposal_box
-        )
-        self.assertTrue(
-            torch.allclose(proposal.proposal_boxes.tensor[:4], expected_proposal_box, atol=1e-5),
-            err_msg,
-        )
-
-        err_msg = "computed objectness logits = {}, expected {}".format(
-            proposal.objectness_logits, expected_objectness_logit
-        )
-        self.assertTrue(
-            torch.allclose(proposal.objectness_logits[:4], expected_objectness_logit, atol=1e-5),
-            err_msg,
-        )
-
-    def test_find_rpn_proposals_inf(self):
-        N, Hi, Wi, A = 3, 3, 3, 3
-        proposals = [torch.rand(N, Hi * Wi * A, 4)]
-        pred_logits = [torch.rand(N, Hi * Wi * A)]
-        pred_logits[0][1][3:5].fill_(float("inf"))
-        find_top_rpn_proposals(proposals, pred_logits, [(10, 10)], 0.5, 1000, 1000, 0, False)
-
-    def test_find_rpn_proposals_tracing(self):
-        N, Hi, Wi, A = 3, 50, 50, 9
-        proposal = torch.rand(N, Hi * Wi * A, 4)
-        pred_logit = torch.rand(N, Hi * Wi * A)
-
-        def func(proposal, logit, image_size):
-            r = find_top_rpn_proposals(
-                [proposal], [logit], [image_size], 0.7, 1000, 1000, 0, False
-            )[0]
-            size = r.image_size
-            if not isinstance(size, torch.Tensor):
-                size = torch.tensor(size)
-            return (size, r.proposal_boxes.tensor, r.objectness_logits)
-
-        other_inputs = []
-        # test that it generalizes to other shapes
-        for Hi, Wi, shp in [(30, 30, 60), (10, 10, 800)]:
-            other_inputs.append(
-                (
-                    torch.rand(N, Hi * Wi * A, 4),
-                    torch.rand(N, Hi * Wi * A),
-                    torch.tensor([shp, shp]),
-                )
-            )
-        torch.jit.trace(
-            func, (proposal, pred_logit, torch.tensor([100, 100])), check_inputs=other_inputs
-        )
-
-    def test_append_gt_to_proposal(self):
-        proposals = Instances(
-            (10, 10),
-            **{
-                "proposal_boxes": Boxes(torch.empty((0, 4))),
-                "objectness_logits": torch.tensor([]),
-                "custom_attribute": torch.tensor([]),
-            }
-        )
-        gt_boxes = Boxes(torch.tensor([[0, 0, 1, 1]]))
-
-        self.assertRaises(AssertionError, add_ground_truth_to_proposals, [gt_boxes], [proposals])
-
-        gt_instances = Instances((10, 10))
-        gt_instances.gt_boxes = gt_boxes
-
-        self.assertRaises(
-            AssertionError, add_ground_truth_to_proposals, [gt_instances], [proposals]
-        )
-
-        gt_instances.custom_attribute = torch.tensor([1])
-        gt_instances.custom_attribute2 = torch.tensor([1])
-        new_proposals = add_ground_truth_to_proposals([gt_instances], [proposals])[0]
-
-        self.assertEqual(new_proposals.custom_attribute[0], 1)
-        # new proposals should only include the attributes in proposals
-        self.assertRaises(AttributeError, lambda: new_proposals.custom_attribute2)
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/AzumaSeren100/XuanShen-Bert-VITS2/bert_gen.py

@@ -1,54 +0,0 @@
-import torch
-from torch.utils.data import DataLoader
-from multiprocessing import Pool
-import commons
-import utils
-from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
-from tqdm import tqdm
-import warnings
-
-from text import cleaned_text_to_sequence, get_bert
-
-config_path = 'configs/config.json'
-hps = utils.get_hparams_from_file(config_path)
-
-def process_line(line):
-    _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|")
-    phone = phones.split(" ")
-    tone = [int(i) for i in tone.split(" ")]
-    word2ph = [int(i) for i in word2ph.split(" ")]
-    w2pho = [i for i in word2ph]
-    word2ph = [i for i in word2ph]
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    wav_path = f'{_id}'
-
-    bert_path = wav_path.replace(".wav", ".bert.pt")
-
-    try:
-        bert = torch.load(bert_path)
-        assert bert.shape[-1] == len(phone)
-    except:
-        bert = get_bert(text, word2ph, language_str)
-        assert bert.shape[-1] == len(phone)
-        torch.save(bert, bert_path)
-
-
-if __name__ == '__main__':
-    lines = []
-    with open(hps.data.training_files, encoding='utf-8' ) as f:
-        lines.extend(f.readlines())
-
-    with open(hps.data.validation_files, encoding='utf-8' ) as f:
-        lines.extend(f.readlines())
-
-    with Pool(processes=6) as pool: #P40 24GB suitable config,if coom,please decrease the processess number.
-        for _ in tqdm(pool.imap_unordered(process_line, lines)):
-            pass

spaces/Bajr/softly/README.md

@@ -1,11 +0,0 @@
----
-title: Yummy Research
-emoji: 🍦
-colorFrom: red
-colorTo: blue
-sdk: docker
-pinned: false
-duplicated_from: Bajr/soft
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Benson/text-generation/Examples/Caramelo Crush Soda Saga Juego Gratis Para Pc.md

@@ -1,103 +0,0 @@
-
-<h1>Candy Crush Soda Saga: Cómo descargar y jugar este divertido juego de puzzle en su PC</h1>
-<p>Si te gusta combinar dulces y resolver puzzles, es posible que haya oído hablar de Candy Crush Soda Saga, uno de los juegos más populares del mundo. Este juego es una secuela de la legendaria saga Candy Crush, y ofrece más diversión y desafíos con nuevos dulces, modos y características. En este artículo, te mostraremos cómo descargar y jugar este juego en tu PC de forma gratuita, utilizando la Epic Games Store. También le diremos por qué jugar juegos de puzzle en su PC es bueno para su cerebro y su estado de ánimo, y le daremos algunos consejos y trucos para aprovechar al máximo su experiencia de juego. </p>
- <h2>¿Qué es Candy Crush Soda Saga? </h2>
-<p>Candy Crush Soda Saga es un juego de puzzle match-3 desarrollado por King, una empresa líder en juegos casuales. El juego fue lanzado en 2014 como un spin-off de Candy Crush Saga, que tiene más de mil millones de descargas en todo el mundo. El juego sigue las aventuras de Kimmy, que está buscando a su hermana Tiffi en un mundo lleno de dulces. En el camino, se encuentra con nuevos personajes y se enfrenta a nuevos desafíos. </p>
-<h2>caramelo crush soda saga juego gratis para pc</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733; <a href="https://bltlly.com/2v6J0M">https://bltlly.com/2v6J0M</a></b></p><br /><br />
-<p>El juego tiene más de 10.000 niveles, cada uno con un objetivo y diseño diferentes. Tienes que combinar tres o más caramelos del mismo color para eliminarlos del tablero y crear dulces especiales que tienen efectos adicionales. También tienes que lidiar con varios obstáculos, como hielo, panal, mermelada, chocolate y botellas de refresco. El juego tiene diferentes modos, como Soda, Frosting, Honeycomb, Jam, Bubblegum y más. Cada modo tiene sus propias reglas y estrategias. </p>
-<p>El juego también tiene muchas características que lo hacen más divertido y atractivo. Puedes jugar con tus amigos en línea y competir por puntuaciones altas. También puedes unirte a equipos y cooperar con otros jugadores en eventos y desafíos. También puedes ganar recompensas y refuerzos que te ayudan en niveles difíciles. El juego también tiene actualizaciones mensuales que traen nuevo contenido y sorpresas. </p>
- <h2>¿Por qué jugar Candy Crush Soda Saga en su PC? </h2>
-
-<ul>
-<li> Puedes disfrutar de mejores gráficos y calidad de sonido en una pantalla más grande. </li>
-<li>Puedes usar el ratón y el teclado para controlar el juego más fácilmente. </li>
-<li> Puede ahorrar su vida útil de la batería y el uso de datos en su teléfono o tableta. </li>
-<li>Puedes evitar distracciones de notificaciones y llamadas mientras juegas. </li>
-</ul>
-<p>Una de las mejores maneras de jugar Candy Crush Soda Saga en tu PC es usar la Epic Games Store, una plataforma de distribución digital que te permite descargar juegos a tu PC a través del Lanzador de Epic Games. La Epic Games Store tiene muchas ventajas, como:</p>
-<ul>
-<li>Puedes acceder a cientos de juegos de varios géneros y categorías. </li>
-<li>Puedes obtener juegos gratis cada semana. </li>
-<li>Puedes disfrutar de ofertas y descuentos exclusivos. </li>
-<li>Puedes apoyar a los desarrolladores dándoles una mayor proporción de los ingresos. </li>
-<li>Puedes conectarte con tus amigos y otros jugadores a través de chat y funciones sociales. </li>
-</ul>
- <h2>¿Cómo descargar y jugar Candy Crush Soda Saga en su PC? </h2>
-<p>Descargar y jugar Candy Crush Soda Saga en tu PC es muy fácil. Solo sigue estos pasos:</p>
-<ol>
-<li>Instalar el lanzador de juegos épicos. Puede descargarlo desde [5](https:// epicgames.com/en-US/download) y ejecutar el instalador. Necesitarás crear una cuenta o iniciar sesión con la existente. </li>
-<li>Buscar Candy Crush Soda Saga en la Epic Games Store. Puedes encontrarlo en la sección de Juegos Gratis o usar la barra de búsqueda. </li>
-<li>Haga clic en el botón Obtener y confirme su pedido. El juego se agregará a su biblioteca. </li>
-<li>Vaya a su biblioteca y haga clic en el botón Instalar junto al juego. Elija una ubicación para los archivos del juego y espere a que se complete la descarga y la instalación. </li>
-<li>Inicie el juego desde su biblioteca o desde el acceso directo del escritorio. También puede ajustar la configuración y las preferencias del juego desde el lanzador. </li>
-</ol>
-
- <tabla>
-<tr>
-<th>Requisitos del sistema</th>
-<th>Mínimo</th>
-<th>Recomendado</th>
-</tr>
-<tr>
-<td>Sistema operativo</td>
-<td>Windows 7 o superior</td>
-<td>Windows 10</td>
-</tr>
-<tr>
-<td>Procesador</td>
-<td>Intel Core i3 o equivalente</td>
-<td>Intel Core i5 o equivalente</td>
-</tr>
-<tr>
-<td>Memoria</td>
-<td>4 GB de RAM</td>
-<td>8 GB de RAM</td>
-</tr>
-<tr>
-<td>Gráficos</td>
-<td>Intel HD Graphics 4000 o superior</td>
-<td>NVIDIA GeForce GTX 660 o superior</td>
-</tr>
-<tr>
-<td>Almacenamiento</td>
-<td>500 MB de espacio disponible</td>
-<td>1 GB de espacio disponible</td>
-</tr>
-<tr>
-<td>Conexión a Internet</td>
-<td>Conexión a Internet de banda ancha</td>
-<td>Conexión a Internet de banda ancha</td>
-</tr>
- <h2>Consejos y trucos para disfrutar de Candy Crush Soda Saga más</h2>
- <p>Candy Crush Soda Saga es un juego divertido y adictivo, pero también puede ser desafiante y frustrante a veces. Aquí hay algunos consejos y trucos para ayudarle a disfrutar del juego más:</p>
-<p></p>
- <ul>
- <li>Planifique sus movimientos. Trate de buscar partidos que pueden crear dulces especiales, como rayas, envueltos, pescado o caramelos para colorear. Estos pueden ayudarte a eliminar más caramelos y obstáculos en un solo movimiento. </li>
- <li>Use boosters sabiamente. Los boosters son elementos que pueden darle una ventaja en el juego, como movimientos adicionales, martillos de piruleta, interruptores libres y más. Puedes ganarlos completando niveles, eventos o desafíos, o comprándolos con dinero real. Sin embargo, no confíes demasiado en ellos, ya que son limitados y pueden agotarse rápidamente. </li>
- <li>Juega con amigos. Jugar con amigos puede hacer el juego más divertido y social. Puedes invitar a tus amigos a unirse a tu equipo, enviar y recibir vidas, chatear con ellos y competir por puntuaciones altas. También puedes pedirles ayuda cuando estás atascado en un nivel. </li>
-
- <li>Diviértete. No dejes que el juego te estrese o te frustre. Recuerda que es solo un juego, y el propósito principal es divertirse. Disfrute de los gráficos coloridos, la música pegadiza, y los personajes lindos. No tengas miedo de experimentar con diferentes estrategias y ver lo que funciona para ti. </li>
- <h2>Conclusión</h2>
- <p>Candy Crush Soda Saga es un gran juego para jugar en tu PC, especialmente si te gustan los juegos de puzzle y dulces. Puedes descargarlo gratis desde la Epic Games Store y disfrutar de sus características y beneficios. También puedes seguir nuestros consejos y trucos para aprovechar al máximo tu experiencia de juego. ¿Qué estás esperando? ¡Descarga Candy Crush Soda Saga hoy y únete a Kimmy en su dulce aventura! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas más frecuentes sobre Candy Crush Soda Saga:</p>
- <h3>P: ¿Cuántos niveles hay en Candy Crush Soda Saga? </h3>
- <p>A: Hay más de 10,000 niveles en Candy Crush Soda Saga a partir de junio de 2023, y se agregan más cada mes. </p>
- <h3>Q: ¿Cómo puedo sincronizar mi progreso a través de dispositivos? </h3>
- <p>A: Puedes sincronizar tu progreso entre dispositivos conectando tu juego a Facebook o King.com. Esto también le permitirá acceder a sus boosters y vidas salvadas. </p>
- <h3>Q: ¿Cómo consigo más vidas? </h3>
- <p>A: Tienes cinco vidas en Candy Crush Soda Saga, y pierdes una cada vez que fallas un nivel. Puedes obtener más vidas esperando a que se llenen (una vida cada 30 minutos), pidiendo a tus amigos que te envíen algunas, comprándolas con barras de oro o jugando las misiones diarias. </p>
- <h3>P: ¿Qué son las barras de oro y cómo las consigo? </h3>
- <p>A: Las barras de oro son la moneda premium en Candy Crush Soda Saga. Puedes usarlas para comprar potenciadores, vidas, movimientos y otros artículos. Puedes obtener barras de oro completando niveles, eventos o desafíos, o comprándolas con dinero real. </p>
- <h3>P: ¿Cuáles son los diferentes tipos de dulces especiales y cómo los hago? </h3>
-
-<ul>
-<li>Caramelo a rayas: Combina cuatro caramelos del mismo color en una fila o columna. Esto creará un caramelo a rayas que limpiará toda una fila o columna cuando coincida. </li>
-<li>Caramelo envuelto: Combina cinco caramelos del mismo color en forma de L o T. Esto creará un caramelo envuelto que explotará dos veces cuando coincida, despejando un área de 3x3 cada vez. </li>
-<li>Caramelo de pescado: Combina cuatro caramelos del mismo color en un cuadrado. Esto creará un caramelo de pescado que nadará a un caramelo o obstáculo al azar y lo despejará cuando coincida. </li>
-<li>Caramelo para colorear: combina seis o más caramelos del mismo color. Esto creará un caramelo para colorear que cambiará el color de todos los dulces que coincidan con su color cuando coincida. </li>
-<li>Bomba de color: Combina cinco caramelos del mismo color en una fila o columna. Esto creará una bomba de color que borrará todos los caramelos del mismo color con el que se intercambia. </li>
-<li>Pescado sueco: Este es un caramelo especial que solo se puede obtener mediante el uso de refuerzos o jugando ciertos niveles. Actúa como un caramelo de pescado, pero puede apuntar a dulces específicos u obstáculos que se necesitan para completar el nivel. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Cmo Descargar Blockman Ir En El PC Gratis.md

@@ -1,57 +0,0 @@
-
-<h1>Cómo descargar Blockman Go en PC gratis</h1>
-<p>Blockman Go es un popular juego que combina elementos de sandbox, aventura, acción y juegos sociales. Puedes jugar varios minijuegos estilo bloque, chatear y hacer amigos con otros jugadores, y personalizar tu avatar y hogar con diferentes decoraciones. Pero ¿sabías que también puedes jugar Blockman Go en tu PC de forma gratuita? En este artículo, te mostraremos cómo descargar e instalar Blockman Go en tu PC con BlueStacks, un potente emulador de Android que te permite ejecutar aplicaciones y juegos de Android en tu ordenador o portátil. </p>
-<h2>cómo descargar blockman ir en el PC gratis</h2><br /><p><b><b>Download</b> &#9658;&#9658;&#9658; <a href="https://bltlly.com/2v6INR">https://bltlly.com/2v6INR</a></b></p><br /><br />
- <h2>¿Qué es Blockman Go? </h2>
-<p>Blockman Go es una aplicación gratuita desarrollada por Blockman GO Studio. Es un juego sandbox que te permite jugar, crear y compartir tus experiencias divertidas con tus amigos. Puedes elegir entre un amplio catálogo de minijuegos, que se actualizan continuamente para mantener las cosas frescas y divertidas. Algunos de los minijuegos populares son Bed Wars, Egg War, Sky Block, Free City RP, Anime Fighting Simulator y más. Puedes unirte a cualquier juego con un clic y ganar recompensas por jugar. </p>
-<p>Blockman Go es también una plataforma social donde puedes chatear y hacer amigos con otros jugadores. Puede unirse o crear fiestas, enviar mensajes, chat de voz e interactuar con otros de varias maneras. También puedes unirte a la creciente comunidad de desarrolladores y compartir tus creaciones con el mundo. </p>
-<p>Blockman Go es también una herramienta creativa que te permite personalizar tu avatar y hogar con diferentes accesorios, disfraces y decoraciones. Puedes expresar tu estilo y personalidad únicos con cientos de opciones disponibles. También puedes usar el Blockman Editor para crear tus propias experiencias de sandbox y minijuegos. </p>
- <h2>¿Por qué jugar Blockman ir en el PC? </h2>
-<p>Mientras que Blockman Go está diseñado para dispositivos móviles, también se puede jugar en su PC de forma gratuita con BlueStacks. Hay muchas ventajas de jugar Blockman Go en PC, tales como:</p>
-<ul>
-
-<li>Puedes usar el teclado y el ratón para controles más precisos, lo que te dará una ventaja en minijuegos competitivos. </li>
-<li>Puede acceder a miles de aplicaciones y herramientas de productividad con BlueStacks, que le ayudarán a trabajar de manera más eficiente y conveniente en su PC.</li>
-</ul>
- <h2>Cómo descargar e instalar Blockman Ir en el PC con BlueStacks</h2>
-<p>Para jugar Blockman Ir en el PC de forma gratuita, es necesario descargar e instalar BlueStacks en su PC primero. BlueStacks es un emulador de Android que te permite ejecutar aplicaciones y juegos de Android en tu ordenador o portátil. Estos son los pasos para descargar e instalar Blockman Ir en el PC con BlueStacks:</p>
-<p></p>
-<ol>
-<li>Descargue e instale BlueStacks en su PC desde <a href="( 5 )">este enlace</a>. </li>
-<li> Iniciar sesión completo en Google para acceder a Play Store, o hacerlo más tarde. </li>
-<li>Buscar Blockman Ir en el centro de aplicaciones o la barra de búsqueda en la esquina superior derecha. </li>
-<li>Haga clic para instalar Blockman Go desde los resultados de búsqueda. </li>
-<li>Haga clic en el icono de Blockman Go en la pantalla de inicio para comenzar a jugar. </li>
-</ol>
- <h2>Conclusión</h2>
-<p>Blockman Go es un juego divertido y versátil que ofrece mucho entretenimiento y creatividad. Puedes jugar varios minijuegos, chatear y hacer amigos, y personalizar tu avatar y hogar. También puedes jugar a Blockman Go en tu PC gratis con BlueStacks, un emulador de Android que te permite ejecutar aplicaciones y juegos de Android en tu ordenador o portátil. Al jugar Blockman Go en PC, puedes disfrutar de una pantalla más grande, mejores gráficos, controles de teclado y ratón, y acceso a miles de aplicaciones y herramientas de productividad. Para descargar e instalar Blockman Ir en el PC con BlueStacks, solo tiene que seguir unos sencillos pasos. Esperamos que este artículo te haya ayudado a aprender a descargar Blockman Go en PC gratis. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Blockman Go y BlueStacks:</p>
-<tabla>
-<tr>
-<th>Pregunta</th>
-<th>Respuesta</th>
-</tr>
-<tr>
-<td>Es Blockman Go libre para jugar? </td>
-
-</tr>
-<tr>
-<td>¿Es seguro jugar a Blockman? </td>
-<td>Sí, Blockman Go es seguro para jugar. Tiene una calificación de 4.3 en la Google Play Store y una calificación de 4.6 en la App Store. También tiene controles parentales y sistemas anti-trucos para garantizar un entorno de juego justo y seguro. </td>
-</tr>
-<tr>
-<td>¿BlueStacks es de uso gratuito? </td>
-<td>Sí, BlueStacks es de uso gratuito. Puede descargarlo desde <a href="">este enlace</a>. También puede actualizar a BlueStacks Premium para obtener más características y beneficios. </td>
-</tr>
-<tr>
-<td>¿Es seguro usar BlueStacks? </td>
-<td>Sí, BlueStacks es seguro de usar. Es el emulador de Android más confiable y popular en el mundo, con más de 500 millones de usuarios. También tiene características avanzadas de seguridad y protección antivirus para garantizar su seguridad y privacidad. </td>
-</tr>
-<tr>
-<td>¿Cómo puedo contactar con el soporte de Blockman Go o BlueStacks? </td>
-<td>Si tiene algún problema o pregunta sobre Blockman Go o BlueStacks, puede ponerse en contacto con sus equipos de soporte a través de sus sitios web oficiales o canales de redes sociales. También puede consultar sus preguntas frecuentes y foros para obtener más información y soluciones. </td>
-</tr>
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />

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

@@ -1,315 +0,0 @@
-# Copyright 2012-2013 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-#     http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-from html.parser import HTMLParser
-from itertools import zip_longest
-
-PRIORITY_PARENT_TAGS = ('code', 'a')
-OMIT_NESTED_TAGS = ('span', 'i', 'code', 'a')
-OMIT_SELF_TAGS = ('i', 'b')
-HTML_BLOCK_DISPLAY_TAGS = ('p', 'note', 'ul', 'li')
-
-
-class DocStringParser(HTMLParser):
-    """
-    A simple HTML parser.  Focused on converting the subset of HTML
-    that appears in the documentation strings of the JSON models into
-    simple ReST format.
-    """
-
-    def __init__(self, doc):
-        self.tree = None
-        self.doc = doc
-        super().__init__()
-
-    def reset(self):
-        HTMLParser.reset(self)
-        self.tree = HTMLTree(self.doc)
-
-    def feed(self, data):
-        super().feed(data)
-        self.tree.write()
-        self.tree = HTMLTree(self.doc)
-
-    def close(self):
-        super().close()
-        # Write if there is anything remaining.
-        self.tree.write()
-        self.tree = HTMLTree(self.doc)
-
-    def handle_starttag(self, tag, attrs):
-        self.tree.add_tag(tag, attrs=attrs)
-
-    def handle_endtag(self, tag):
-        self.tree.add_tag(tag, is_start=False)
-
-    def handle_data(self, data):
-        self.tree.add_data(data)
-
-
-class HTMLTree:
-    """
-    A tree which handles HTML nodes. Designed to work with a python HTML parser,
-    meaning that the current_node will be the most recently opened tag. When
-    a tag is closed, the current_node moves up to the parent node.
-    """
-
-    def __init__(self, doc):
-        self.doc = doc
-        self.head = StemNode()
-        self.current_node = self.head
-        self.unhandled_tags = []
-
-    def add_tag(self, tag, attrs=None, is_start=True):
-        if not self._doc_has_handler(tag, is_start):
-            self.unhandled_tags.append(tag)
-            return
-
-        if is_start:
-            node = TagNode(tag, attrs)
-            self.current_node.add_child(node)
-            self.current_node = node
-        else:
-            self.current_node = self.current_node.parent
-
-    def _doc_has_handler(self, tag, is_start):
-        if is_start:
-            handler_name = 'start_%s' % tag
-        else:
-            handler_name = 'end_%s' % tag
-
-        return hasattr(self.doc.style, handler_name)
-
-    def add_data(self, data):
-        self.current_node.add_child(DataNode(data))
-
-    def write(self):
-        self.head.write(self.doc)
-
-
-class Node:
-    def __init__(self, parent=None):
-        self.parent = parent
-
-    def write(self, doc):
-        raise NotImplementedError
-
-
-class StemNode(Node):
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.children = []
-
-    def add_child(self, child):
-        child.parent = self
-        self.children.append(child)
-
-    def write(self, doc):
-        self.collapse_whitespace()
-        self._write_children(doc)
-
-    def _write_children(self, doc):
-        for child, next_child in zip_longest(self.children, self.children[1:]):
-            if isinstance(child, TagNode) and next_child is not None:
-                child.write(doc, next_child)
-            else:
-                child.write(doc)
-
-    def is_whitespace(self):
-        return all(child.is_whitespace() for child in self.children)
-
-    def startswith_whitespace(self):
-        return self.children and self.children[0].startswith_whitespace()
-
-    def endswith_whitespace(self):
-        return self.children and self.children[-1].endswith_whitespace()
-
-    def lstrip(self):
-        while self.children and self.children[0].is_whitespace():
-            self.children = self.children[1:]
-        if self.children:
-            self.children[0].lstrip()
-
-    def rstrip(self):
-        while self.children and self.children[-1].is_whitespace():
-            self.children = self.children[:-1]
-        if self.children:
-            self.children[-1].rstrip()
-
-    def collapse_whitespace(self):
-        """Remove collapsible white-space from HTML.
-
-        HTML in docstrings often contains extraneous white-space around tags,
-        for readability. Browsers would collapse this white-space before
-        rendering. If not removed before conversion to RST where white-space is
-        part of the syntax, for example for indentation, it can result in
-        incorrect output.
-        """
-        self.lstrip()
-        self.rstrip()
-        for child in self.children:
-            child.collapse_whitespace()
-
-
-class TagNode(StemNode):
-    """
-    A generic Tag node. It will verify that handlers exist before writing.
-    """
-
-    def __init__(self, tag, attrs=None, parent=None):
-        super().__init__(parent)
-        self.attrs = attrs
-        self.tag = tag
-
-    def _has_nested_tags(self):
-        # Returns True if any children are TagNodes and False otherwise.
-        return any(isinstance(child, TagNode) for child in self.children)
-
-    def write(self, doc, next_child=None):
-        prioritize_nested_tags = (
-            self.tag in OMIT_SELF_TAGS and self._has_nested_tags()
-        )
-        prioritize_parent_tag = (
-            isinstance(self.parent, TagNode)
-            and self.parent.tag in PRIORITY_PARENT_TAGS
-            and self.tag in OMIT_NESTED_TAGS
-        )
-        if prioritize_nested_tags or prioritize_parent_tag:
-            self._write_children(doc)
-            return
-
-        self._write_start(doc)
-        self._write_children(doc)
-        self._write_end(doc, next_child)
-
-    def collapse_whitespace(self):
-        """Remove collapsible white-space.
-
-        All tags collapse internal whitespace. Block-display HTML tags also
-        strip all leading and trailing whitespace.
-
-        Approximately follows the specification used in browsers:
-        https://www.w3.org/TR/css-text-3/#white-space-rules
-        https://developer.mozilla.org/en-US/docs/Web/API/Document_Object_Model/Whitespace
-        """
-        if self.tag in HTML_BLOCK_DISPLAY_TAGS:
-            self.lstrip()
-            self.rstrip()
-        # Collapse whitespace in situations like ``</b> <i> foo</i>`` into
-        # ``</b><i> foo</i>``.
-        for prev, cur in zip(self.children[:-1], self.children[1:]):
-            if (
-                isinstance(prev, DataNode)
-                and prev.endswith_whitespace()
-                and cur.startswith_whitespace()
-            ):
-                cur.lstrip()
-        # Same logic, but for situations like ``<b>bar </b> <i>``:
-        for cur, nxt in zip(self.children[:-1], self.children[1:]):
-            if (
-                isinstance(nxt, DataNode)
-                and cur.endswith_whitespace()
-                and nxt.startswith_whitespace()
-            ):
-                cur.rstrip()
-        # Recurse into children
-        for child in self.children:
-            child.collapse_whitespace()
-
-    def _write_start(self, doc):
-        handler_name = 'start_%s' % self.tag
-        if hasattr(doc.style, handler_name):
-            getattr(doc.style, handler_name)(self.attrs)
-
-    def _write_end(self, doc, next_child):
-        handler_name = 'end_%s' % self.tag
-        if hasattr(doc.style, handler_name):
-            if handler_name == 'end_a':
-                # We use lookahead to determine if a space is needed after a link node
-                getattr(doc.style, handler_name)(next_child)
-            else:
-                getattr(doc.style, handler_name)()
-
-
-class DataNode(Node):
-    """
-    A Node that contains only string data.
-    """
-
-    def __init__(self, data, parent=None):
-        super().__init__(parent)
-        if not isinstance(data, str):
-            raise ValueError("Expecting string type, %s given." % type(data))
-        self._leading_whitespace = ''
-        self._trailing_whitespace = ''
-        self._stripped_data = ''
-        if data == '':
-            return
-        if data.isspace():
-            self._trailing_whitespace = data
-            return
-        first_non_space = next(
-            idx for idx, ch in enumerate(data) if not ch.isspace()
-        )
-        last_non_space = len(data) - next(
-            idx for idx, ch in enumerate(reversed(data)) if not ch.isspace()
-        )
-        self._leading_whitespace = data[:first_non_space]
-        self._trailing_whitespace = data[last_non_space:]
-        self._stripped_data = data[first_non_space:last_non_space]
-
-    @property
-    def data(self):
-        return (
-            f'{self._leading_whitespace}{self._stripped_data}'
-            f'{self._trailing_whitespace}'
-        )
-
-    def is_whitespace(self):
-        return self._stripped_data == '' and (
-            self._leading_whitespace != '' or self._trailing_whitespace != ''
-        )
-
-    def startswith_whitespace(self):
-        return self._leading_whitespace != '' or (
-            self._stripped_data == '' and self._trailing_whitespace != ''
-        )
-
-    def endswith_whitespace(self):
-        return self._trailing_whitespace != '' or (
-            self._stripped_data == '' and self._leading_whitespace != ''
-        )
-
-    def lstrip(self):
-        if self._leading_whitespace != '':
-            self._leading_whitespace = ''
-        elif self._stripped_data == '':
-            self.rstrip()
-
-    def rstrip(self):
-        if self._trailing_whitespace != '':
-            self._trailing_whitespace = ''
-        elif self._stripped_data == '':
-            self.lstrip()
-
-    def collapse_whitespace(self):
-        """Noop, ``DataNode.write`` always collapses whitespace"""
-        return
-
-    def write(self, doc):
-        words = doc.translate_words(self._stripped_data.split())
-        str_data = (
-            f'{self._leading_whitespace}{" ".join(words)}'
-            f'{self._trailing_whitespace}'
-        )
-        if str_data != '':
-            doc.handle_data(str_data)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/themes.py

@@ -1,5 +0,0 @@
-from .default_styles import DEFAULT_STYLES
-from .theme import Theme
-
-
-DEFAULT = Theme(DEFAULT_STYLES)

spaces/Billyosoro/ESRGAN/tests/test_discriminator_arch.py

@@ -1,19 +0,0 @@
-import torch
-
-from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
-
-
-def test_unetdiscriminatorsn():
-    """Test arch: UNetDiscriminatorSN."""
-
-    # model init and forward (cpu)
-    net = UNetDiscriminatorSN(num_in_ch=3, num_feat=4, skip_connection=True)
-    img = torch.rand((1, 3, 32, 32), dtype=torch.float32)
-    output = net(img)
-    assert output.shape == (1, 1, 32, 32)
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net.cuda()
-        output = net(img.cuda())
-        assert output.shape == (1, 1, 32, 32)

spaces/Callimethee/Imagine-CR/app.py

@@ -1,29 +0,0 @@
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-import torch
-import gradio as gr
-
-tokenizer = GPT2Tokenizer.from_pretrained("./")
-model = GPT2LMHeadModel.from_pretrained("./")
-
-
-def generator(input_string):
-    input_string = "<|startoftext|>" + " " * (input_string != "") + input_string
-    prompt = torch.tensor(tokenizer.encode(input_string)).unsqueeze(0)
-
-    generated = model.generate(
-        prompt,
-        do_sample=True,
-        top_k=50,
-        max_length=1024,
-        top_p=0.95,
-        num_return_sequences=5,
-    )
-    out = ""
-    for tirade in generated:
-        out += tokenizer.decode(tirade, skip_special_tokens=True) + "\n\n"
-    return out
-
-desc = "> Artificial Intelligence, Eh? Sounds fancy - but it'll never replace geniuses such as myself.\n\n - *Huron Stahlmast, Exiled Hupperdook Engineer*\n\n\nThis generator allows you to generate your own transcripts from an imaginary episode of Critical Role! Input the start of a tirade (or nothing!), and let the magic of machine learning do the rest!\n\nFor the curious among you, this uses a fine-tuned version of GPT2."
-
-demo = gr.Interface(fn=generator, inputs="textbox", outputs="textbox", title="Critical Role Text Generator", examples=[["MATT:"], ["LAURA: I cast"]], description=desc)
-demo.launch()

spaces/Caoyunkang/Segment-Any-Anomaly/SAM/CODE_OF_CONDUCT.md

@@ -1,80 +0,0 @@
-# Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-  advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-  address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-
-This Code of Conduct also applies outside the project spaces when there is a
-reasonable belief that an individual's behavior may have a negative impact on
-the project or its community.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at <[email protected]>. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq

spaces/CognitiveLabs/Research-Assistant/config/config.py

@@ -1,82 +0,0 @@
-"""Configuration class to store the state of bools for different scripts access."""
-import os
-
-import openai
-from colorama import Fore
-from dotenv import load_dotenv
-
-from config.singleton import Singleton
-
-load_dotenv(verbose=True)
-
-
-class Config(metaclass=Singleton):
-    """
-    Configuration class to store the state of bools for different scripts access.
-    """
-
-    def __init__(self) -> None:
-        """Initialize the Config class"""
-        self.debug_mode = False
-        self.allow_downloads = False
-
-        self.selenium_web_browser = os.getenv("USE_WEB_BROWSER", "chrome")
-        self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo")
-        self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4")
-        self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 8000))
-        self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000))
-        self.browse_chunk_max_length = int(os.getenv("BROWSE_CHUNK_MAX_LENGTH", 8192))
-
-        self.openai_api_key = os.getenv("OPENAI_API_KEY")
-        self.openai_api_base = os.getenv("OPENAI_API_BASE", openai.api_base)
-        self.temperature = float(os.getenv("TEMPERATURE", "1"))
-
-        self.user_agent = os.getenv(
-            "USER_AGENT",
-            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36"
-            " (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",
-        )
-
-        self.memory_backend = os.getenv("MEMORY_BACKEND", "local")
-        # Initialize the OpenAI API client
-        openai.api_key = self.openai_api_key
-
-    def set_fast_llm_model(self, value: str) -> None:
-        """Set the fast LLM model value."""
-        self.fast_llm_model = value
-
-    def set_smart_llm_model(self, value: str) -> None:
-        """Set the smart LLM model value."""
-        self.smart_llm_model = value
-
-    def set_fast_token_limit(self, value: int) -> None:
-        """Set the fast token limit value."""
-        self.fast_token_limit = value
-
-    def set_smart_token_limit(self, value: int) -> None:
-        """Set the smart token limit value."""
-        self.smart_token_limit = value
-
-    def set_browse_chunk_max_length(self, value: int) -> None:
-        """Set the browse_website command chunk max length value."""
-        self.browse_chunk_max_length = value
-
-    def set_openai_api_key(self, value: str) -> None:
-        """Set the OpenAI API key value."""
-        self.openai_api_key = value
-
-    def set_debug_mode(self, value: bool) -> None:
-        """Set the debug mode value."""
-        self.debug_mode = value
-
-
-def check_openai_api_key() -> None:
-    """Check if the OpenAI API key is set in config.py or as an environment variable."""
-    cfg = Config()
-    if not cfg.openai_api_key:
-        print(
-            Fore.RED
-            + "Please set your OpenAI API key in .env or as an environment variable."
-        )
-        print("You can get your key from https://platform.openai.com/account/api-keys")
-        exit(1)

spaces/CoreyMorris/MMLU-by-task-Leaderboard/test_paths.py

@@ -1,19 +0,0 @@
-import unittest
-import os
-
-class TestPaths(unittest.TestCase):
-    def test_path_exists(self):
-        # test that the path results exists
-        self.assertTrue(os.path.exists('results'))
-
-    def test_results_directory_is_not_empty(self):
-        # test that the results directory is not empty
-        self.assertGreater(len(os.listdir('results')), 0)
-
-    def test_results_contain_json_files(self):
-        # test that the results director contains json files in the sub directores
-        # get a list of all the subdirectories
-        subdirectories = [x[0] for x in os.walk('results')]
-        # check if the subdirectories contain json files. only check one subdirectory
-        subdirectory = subdirectories[1]
-        self.assertGreater(len(os.listdir(subdirectory)), 0)

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/data/util.py

@@ -1,24 +0,0 @@
-import torch
-
-from ldm.modules.midas.api import load_midas_transform
-
-
-class AddMiDaS(object):
-    def __init__(self, model_type):
-        super().__init__()
-        self.transform = load_midas_transform(model_type)
-
-    def pt2np(self, x):
-        x = ((x + 1.0) * .5).detach().cpu().numpy()
-        return x
-
-    def np2pt(self, x):
-        x = torch.from_numpy(x) * 2 - 1.
-        return x
-
-    def __call__(self, sample):
-        # sample['jpg'] is tensor hwc in [-1, 1] at this point
-        x = self.pt2np(sample['jpg'])
-        x = self.transform({"image": x})["image"]
-        sample['midas_in'] = x
-        return sample

spaces/Cvandi/remake/realesrgan/archs/discriminator_arch.py

@@ -1,67 +0,0 @@
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn as nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm
-
-
-@ARCH_REGISTRY.register()
-class UNetDiscriminatorSN(nn.Module):
-    """Defines a U-Net discriminator with spectral normalization (SN)
-
-    It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    Arg:
-        num_in_ch (int): Channel number of inputs. Default: 3.
-        num_feat (int): Channel number of base intermediate features. Default: 64.
-        skip_connection (bool): Whether to use skip connections between U-Net. Default: True.
-    """
-
-    def __init__(self, num_in_ch, num_feat=64, skip_connection=True):
-        super(UNetDiscriminatorSN, self).__init__()
-        self.skip_connection = skip_connection
-        norm = spectral_norm
-        # the first convolution
-        self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1)
-        # downsample
-        self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False))
-        self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False))
-        self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False))
-        # upsample
-        self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False))
-        self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False))
-        self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False))
-        # extra convolutions
-        self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
-        self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
-        self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1)
-
-    def forward(self, x):
-        # downsample
-        x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True)
-        x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True)
-        x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True)
-        x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True)
-
-        # upsample
-        x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False)
-        x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True)
-
-        if self.skip_connection:
-            x4 = x4 + x2
-        x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False)
-        x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True)
-
-        if self.skip_connection:
-            x5 = x5 + x1
-        x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False)
-        x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True)
-
-        if self.skip_connection:
-            x6 = x6 + x0
-
-        # extra convolutions
-        out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True)
-        out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True)
-        out = self.conv9(out)
-
-        return out

spaces/DAMO-NLP-SG/Video-LLaMA/README.md

@@ -1,14 +0,0 @@
----
-title: Video LLaMA
-emoji: 🚀
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-

spaces/DHEIVER/timeseries-anomaly-detection-autoencoders/README.md

@@ -1,13 +0,0 @@
----
-title: Timeseries Anomaly Detection
-emoji: 🌍
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.1
-app_file: app.py
-pinned: false
-duplicated_from: keras-io/timeseries-anomaly-detection-autoencoders
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/charset_normalizer/utils.py

@@ -1,414 +0,0 @@
-import importlib
-import logging
-import unicodedata
-from codecs import IncrementalDecoder
-from encodings.aliases import aliases
-from functools import lru_cache
-from re import findall
-from typing import Generator, List, Optional, Set, Tuple, Union
-
-from _multibytecodec import MultibyteIncrementalDecoder
-
-from .constant import (
-    ENCODING_MARKS,
-    IANA_SUPPORTED_SIMILAR,
-    RE_POSSIBLE_ENCODING_INDICATION,
-    UNICODE_RANGES_COMBINED,
-    UNICODE_SECONDARY_RANGE_KEYWORD,
-    UTF8_MAXIMAL_ALLOCATION,
-)
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_accentuated(character: str) -> bool:
-    try:
-        description: str = unicodedata.name(character)
-    except ValueError:
-        return False
-    return (
-        "WITH GRAVE" in description
-        or "WITH ACUTE" in description
-        or "WITH CEDILLA" in description
-        or "WITH DIAERESIS" in description
-        or "WITH CIRCUMFLEX" in description
-        or "WITH TILDE" in description
-    )
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def remove_accent(character: str) -> str:
-    decomposed: str = unicodedata.decomposition(character)
-    if not decomposed:
-        return character
-
-    codes: List[str] = decomposed.split(" ")
-
-    return chr(int(codes[0], 16))
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def unicode_range(character: str) -> Optional[str]:
-    """
-    Retrieve the Unicode range official name from a single character.
-    """
-    character_ord: int = ord(character)
-
-    for range_name, ord_range in UNICODE_RANGES_COMBINED.items():
-        if character_ord in ord_range:
-            return range_name
-
-    return None
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_latin(character: str) -> bool:
-    try:
-        description: str = unicodedata.name(character)
-    except ValueError:
-        return False
-    return "LATIN" in description
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_ascii(character: str) -> bool:
-    try:
-        character.encode("ascii")
-    except UnicodeEncodeError:
-        return False
-    return True
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_punctuation(character: str) -> bool:
-    character_category: str = unicodedata.category(character)
-
-    if "P" in character_category:
-        return True
-
-    character_range: Optional[str] = unicode_range(character)
-
-    if character_range is None:
-        return False
-
-    return "Punctuation" in character_range
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_symbol(character: str) -> bool:
-    character_category: str = unicodedata.category(character)
-
-    if "S" in character_category or "N" in character_category:
-        return True
-
-    character_range: Optional[str] = unicode_range(character)
-
-    if character_range is None:
-        return False
-
-    return "Forms" in character_range
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_emoticon(character: str) -> bool:
-    character_range: Optional[str] = unicode_range(character)
-
-    if character_range is None:
-        return False
-
-    return "Emoticons" in character_range
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_separator(character: str) -> bool:
-    if character.isspace() or character in {"｜", "+", "<", ">"}:
-        return True
-
-    character_category: str = unicodedata.category(character)
-
-    return "Z" in character_category or character_category in {"Po", "Pd", "Pc"}
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_case_variable(character: str) -> bool:
-    return character.islower() != character.isupper()
-
-
-def is_private_use_only(character: str) -> bool:
-    character_category: str = unicodedata.category(character)
-
-    return character_category == "Co"
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_cjk(character: str) -> bool:
-    try:
-        character_name = unicodedata.name(character)
-    except ValueError:
-        return False
-
-    return "CJK" in character_name
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_hiragana(character: str) -> bool:
-    try:
-        character_name = unicodedata.name(character)
-    except ValueError:
-        return False
-
-    return "HIRAGANA" in character_name
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_katakana(character: str) -> bool:
-    try:
-        character_name = unicodedata.name(character)
-    except ValueError:
-        return False
-
-    return "KATAKANA" in character_name
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_hangul(character: str) -> bool:
-    try:
-        character_name = unicodedata.name(character)
-    except ValueError:
-        return False
-
-    return "HANGUL" in character_name
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_thai(character: str) -> bool:
-    try:
-        character_name = unicodedata.name(character)
-    except ValueError:
-        return False
-
-    return "THAI" in character_name
-
-
-@lru_cache(maxsize=len(UNICODE_RANGES_COMBINED))
-def is_unicode_range_secondary(range_name: str) -> bool:
-    return any(keyword in range_name for keyword in UNICODE_SECONDARY_RANGE_KEYWORD)
-
-
-@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION)
-def is_unprintable(character: str) -> bool:
-    return (
-        character.isspace() is False  # includes \n \t \r \v
-        and character.isprintable() is False
-        and character != "\x1A"  # Why? Its the ASCII substitute character.
-        and character != "\ufeff"  # bug discovered in Python,
-        # Zero Width No-Break Space located in 	Arabic Presentation Forms-B, Unicode 1.1 not acknowledged as space.
-    )
-
-
-def any_specified_encoding(sequence: bytes, search_zone: int = 4096) -> Optional[str]:
-    """
-    Extract using ASCII-only decoder any specified encoding in the first n-bytes.
-    """
-    if not isinstance(sequence, bytes):
-        raise TypeError
-
-    seq_len: int = len(sequence)
-
-    results: List[str] = findall(
-        RE_POSSIBLE_ENCODING_INDICATION,
-        sequence[: min(seq_len, search_zone)].decode("ascii", errors="ignore"),
-    )
-
-    if len(results) == 0:
-        return None
-
-    for specified_encoding in results:
-        specified_encoding = specified_encoding.lower().replace("-", "_")
-
-        encoding_alias: str
-        encoding_iana: str
-
-        for encoding_alias, encoding_iana in aliases.items():
-            if encoding_alias == specified_encoding:
-                return encoding_iana
-            if encoding_iana == specified_encoding:
-                return encoding_iana
-
-    return None
-
-
-@lru_cache(maxsize=128)
-def is_multi_byte_encoding(name: str) -> bool:
-    """
-    Verify is a specific encoding is a multi byte one based on it IANA name
-    """
-    return name in {
-        "utf_8",
-        "utf_8_sig",
-        "utf_16",
-        "utf_16_be",
-        "utf_16_le",
-        "utf_32",
-        "utf_32_le",
-        "utf_32_be",
-        "utf_7",
-    } or issubclass(
-        importlib.import_module("encodings.{}".format(name)).IncrementalDecoder,
-        MultibyteIncrementalDecoder,
-    )
-
-
-def identify_sig_or_bom(sequence: bytes) -> Tuple[Optional[str], bytes]:
-    """
-    Identify and extract SIG/BOM in given sequence.
-    """
-
-    for iana_encoding in ENCODING_MARKS:
-        marks: Union[bytes, List[bytes]] = ENCODING_MARKS[iana_encoding]
-
-        if isinstance(marks, bytes):
-            marks = [marks]
-
-        for mark in marks:
-            if sequence.startswith(mark):
-                return iana_encoding, mark
-
-    return None, b""
-
-
-def should_strip_sig_or_bom(iana_encoding: str) -> bool:
-    return iana_encoding not in {"utf_16", "utf_32"}
-
-
-def iana_name(cp_name: str, strict: bool = True) -> str:
-    cp_name = cp_name.lower().replace("-", "_")
-
-    encoding_alias: str
-    encoding_iana: str
-
-    for encoding_alias, encoding_iana in aliases.items():
-        if cp_name in [encoding_alias, encoding_iana]:
-            return encoding_iana
-
-    if strict:
-        raise ValueError("Unable to retrieve IANA for '{}'".format(cp_name))
-
-    return cp_name
-
-
-def range_scan(decoded_sequence: str) -> List[str]:
-    ranges: Set[str] = set()
-
-    for character in decoded_sequence:
-        character_range: Optional[str] = unicode_range(character)
-
-        if character_range is None:
-            continue
-
-        ranges.add(character_range)
-
-    return list(ranges)
-
-
-def cp_similarity(iana_name_a: str, iana_name_b: str) -> float:
-    if is_multi_byte_encoding(iana_name_a) or is_multi_byte_encoding(iana_name_b):
-        return 0.0
-
-    decoder_a = importlib.import_module(
-        "encodings.{}".format(iana_name_a)
-    ).IncrementalDecoder
-    decoder_b = importlib.import_module(
-        "encodings.{}".format(iana_name_b)
-    ).IncrementalDecoder
-
-    id_a: IncrementalDecoder = decoder_a(errors="ignore")
-    id_b: IncrementalDecoder = decoder_b(errors="ignore")
-
-    character_match_count: int = 0
-
-    for i in range(255):
-        to_be_decoded: bytes = bytes([i])
-        if id_a.decode(to_be_decoded) == id_b.decode(to_be_decoded):
-            character_match_count += 1
-
-    return character_match_count / 254
-
-
-def is_cp_similar(iana_name_a: str, iana_name_b: str) -> bool:
-    """
-    Determine if two code page are at least 80% similar. IANA_SUPPORTED_SIMILAR dict was generated using
-    the function cp_similarity.
-    """
-    return (
-        iana_name_a in IANA_SUPPORTED_SIMILAR
-        and iana_name_b in IANA_SUPPORTED_SIMILAR[iana_name_a]
-    )
-
-
-def set_logging_handler(
-    name: str = "charset_normalizer",
-    level: int = logging.INFO,
-    format_string: str = "%(asctime)s | %(levelname)s | %(message)s",
-) -> None:
-    logger = logging.getLogger(name)
-    logger.setLevel(level)
-
-    handler = logging.StreamHandler()
-    handler.setFormatter(logging.Formatter(format_string))
-    logger.addHandler(handler)
-
-
-def cut_sequence_chunks(
-    sequences: bytes,
-    encoding_iana: str,
-    offsets: range,
-    chunk_size: int,
-    bom_or_sig_available: bool,
-    strip_sig_or_bom: bool,
-    sig_payload: bytes,
-    is_multi_byte_decoder: bool,
-    decoded_payload: Optional[str] = None,
-) -> Generator[str, None, None]:
-    if decoded_payload and is_multi_byte_decoder is False:
-        for i in offsets:
-            chunk = decoded_payload[i : i + chunk_size]
-            if not chunk:
-                break
-            yield chunk
-    else:
-        for i in offsets:
-            chunk_end = i + chunk_size
-            if chunk_end > len(sequences) + 8:
-                continue
-
-            cut_sequence = sequences[i : i + chunk_size]
-
-            if bom_or_sig_available and strip_sig_or_bom is False:
-                cut_sequence = sig_payload + cut_sequence
-
-            chunk = cut_sequence.decode(
-                encoding_iana,
-                errors="ignore" if is_multi_byte_decoder else "strict",
-            )
-
-            # multi-byte bad cutting detector and adjustment
-            # not the cleanest way to perform that fix but clever enough for now.
-            if is_multi_byte_decoder and i > 0:
-                chunk_partial_size_chk: int = min(chunk_size, 16)
-
-                if (
-                    decoded_payload
-                    and chunk[:chunk_partial_size_chk] not in decoded_payload
-                ):
-                    for j in range(i, i - 4, -1):
-                        cut_sequence = sequences[j:chunk_end]
-
-                        if bom_or_sig_available and strip_sig_or_bom is False:
-                            cut_sequence = sig_payload + cut_sequence
-
-                        chunk = cut_sequence.decode(encoding_iana, errors="ignore")
-
-                        if chunk[:chunk_partial_size_chk] in decoded_payload:
-                            break
-
-            yield chunk

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/utils.py

@@ -1,228 +0,0 @@
-import re
-import warnings
-from dataclasses import is_dataclass
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    MutableMapping,
-    Optional,
-    Set,
-    Type,
-    Union,
-    cast,
-)
-from weakref import WeakKeyDictionary
-
-import fastapi
-from fastapi._compat import (
-    PYDANTIC_V2,
-    BaseConfig,
-    ModelField,
-    PydanticSchemaGenerationError,
-    Undefined,
-    UndefinedType,
-    Validator,
-    lenient_issubclass,
-)
-from fastapi.datastructures import DefaultPlaceholder, DefaultType
-from pydantic import BaseModel, create_model
-from pydantic.fields import FieldInfo
-from typing_extensions import Literal
-
-if TYPE_CHECKING:  # pragma: nocover
-    from .routing import APIRoute
-
-# Cache for `create_cloned_field`
-_CLONED_TYPES_CACHE: MutableMapping[
-    Type[BaseModel], Type[BaseModel]
-] = WeakKeyDictionary()
-
-
-def is_body_allowed_for_status_code(status_code: Union[int, str, None]) -> bool:
-    if status_code is None:
-        return True
-    # Ref: https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.1.0.md#patterned-fields-1
-    if status_code in {
-        "default",
-        "1XX",
-        "2XX",
-        "3XX",
-        "4XX",
-        "5XX",
-    }:
-        return True
-    current_status_code = int(status_code)
-    return not (current_status_code < 200 or current_status_code in {204, 304})
-
-
-def get_path_param_names(path: str) -> Set[str]:
-    return set(re.findall("{(.*?)}", path))
-
-
-def create_response_field(
-    name: str,
-    type_: Type[Any],
-    class_validators: Optional[Dict[str, Validator]] = None,
-    default: Optional[Any] = Undefined,
-    required: Union[bool, UndefinedType] = Undefined,
-    model_config: Type[BaseConfig] = BaseConfig,
-    field_info: Optional[FieldInfo] = None,
-    alias: Optional[str] = None,
-    mode: Literal["validation", "serialization"] = "validation",
-) -> ModelField:
-    """
-    Create a new response field. Raises if type_ is invalid.
-    """
-    class_validators = class_validators or {}
-    if PYDANTIC_V2:
-        field_info = field_info or FieldInfo(
-            annotation=type_, default=default, alias=alias
-        )
-    else:
-        field_info = field_info or FieldInfo()
-    kwargs = {"name": name, "field_info": field_info}
-    if PYDANTIC_V2:
-        kwargs.update({"mode": mode})
-    else:
-        kwargs.update(
-            {
-                "type_": type_,
-                "class_validators": class_validators,
-                "default": default,
-                "required": required,
-                "model_config": model_config,
-                "alias": alias,
-            }
-        )
-    try:
-        return ModelField(**kwargs)  # type: ignore[arg-type]
-    except (RuntimeError, PydanticSchemaGenerationError):
-        raise fastapi.exceptions.FastAPIError(
-            "Invalid args for response field! Hint: "
-            f"check that {type_} is a valid Pydantic field type. "
-            "If you are using a return type annotation that is not a valid Pydantic "
-            "field (e.g. Union[Response, dict, None]) you can disable generating the "
-            "response model from the type annotation with the path operation decorator "
-            "parameter response_model=None. Read more: "
-            "https://fastapi.tiangolo.com/tutorial/response-model/"
-        ) from None
-
-
-def create_cloned_field(
-    field: ModelField,
-    *,
-    cloned_types: Optional[MutableMapping[Type[BaseModel], Type[BaseModel]]] = None,
-) -> ModelField:
-    if PYDANTIC_V2:
-        return field
-    # cloned_types caches already cloned types to support recursive models and improve
-    # performance by avoiding unecessary cloning
-    if cloned_types is None:
-        cloned_types = _CLONED_TYPES_CACHE
-
-    original_type = field.type_
-    if is_dataclass(original_type) and hasattr(original_type, "__pydantic_model__"):
-        original_type = original_type.__pydantic_model__
-    use_type = original_type
-    if lenient_issubclass(original_type, BaseModel):
-        original_type = cast(Type[BaseModel], original_type)
-        use_type = cloned_types.get(original_type)
-        if use_type is None:
-            use_type = create_model(original_type.__name__, __base__=original_type)
-            cloned_types[original_type] = use_type
-            for f in original_type.__fields__.values():
-                use_type.__fields__[f.name] = create_cloned_field(
-                    f, cloned_types=cloned_types
-                )
-    new_field = create_response_field(name=field.name, type_=use_type)
-    new_field.has_alias = field.has_alias  # type: ignore[attr-defined]
-    new_field.alias = field.alias  # type: ignore[misc]
-    new_field.class_validators = field.class_validators  # type: ignore[attr-defined]
-    new_field.default = field.default  # type: ignore[misc]
-    new_field.required = field.required  # type: ignore[misc]
-    new_field.model_config = field.model_config  # type: ignore[attr-defined]
-    new_field.field_info = field.field_info
-    new_field.allow_none = field.allow_none  # type: ignore[attr-defined]
-    new_field.validate_always = field.validate_always  # type: ignore[attr-defined]
-    if field.sub_fields:  # type: ignore[attr-defined]
-        new_field.sub_fields = [  # type: ignore[attr-defined]
-            create_cloned_field(sub_field, cloned_types=cloned_types)
-            for sub_field in field.sub_fields  # type: ignore[attr-defined]
-        ]
-    if field.key_field:  # type: ignore[attr-defined]
-        new_field.key_field = create_cloned_field(  # type: ignore[attr-defined]
-            field.key_field, cloned_types=cloned_types  # type: ignore[attr-defined]
-        )
-    new_field.validators = field.validators  # type: ignore[attr-defined]
-    new_field.pre_validators = field.pre_validators  # type: ignore[attr-defined]
-    new_field.post_validators = field.post_validators  # type: ignore[attr-defined]
-    new_field.parse_json = field.parse_json  # type: ignore[attr-defined]
-    new_field.shape = field.shape  # type: ignore[attr-defined]
-    new_field.populate_validators()  # type: ignore[attr-defined]
-    return new_field
-
-
-def generate_operation_id_for_path(
-    *, name: str, path: str, method: str
-) -> str:  # pragma: nocover
-    warnings.warn(
-        "fastapi.utils.generate_operation_id_for_path() was deprecated, "
-        "it is not used internally, and will be removed soon",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    operation_id = name + path
-    operation_id = re.sub(r"\W", "_", operation_id)
-    operation_id = operation_id + "_" + method.lower()
-    return operation_id
-
-
-def generate_unique_id(route: "APIRoute") -> str:
-    operation_id = route.name + route.path_format
-    operation_id = re.sub(r"\W", "_", operation_id)
-    assert route.methods
-    operation_id = operation_id + "_" + list(route.methods)[0].lower()
-    return operation_id
-
-
-def deep_dict_update(main_dict: Dict[Any, Any], update_dict: Dict[Any, Any]) -> None:
-    for key, value in update_dict.items():
-        if (
-            key in main_dict
-            and isinstance(main_dict[key], dict)
-            and isinstance(value, dict)
-        ):
-            deep_dict_update(main_dict[key], value)
-        elif (
-            key in main_dict
-            and isinstance(main_dict[key], list)
-            and isinstance(update_dict[key], list)
-        ):
-            main_dict[key] = main_dict[key] + update_dict[key]
-        else:
-            main_dict[key] = value
-
-
-def get_value_or_default(
-    first_item: Union[DefaultPlaceholder, DefaultType],
-    *extra_items: Union[DefaultPlaceholder, DefaultType],
-) -> Union[DefaultPlaceholder, DefaultType]:
-    """
-    Pass items or `DefaultPlaceholder`s by descending priority.
-
-    The first one to _not_ be a `DefaultPlaceholder` will be returned.
-
-    Otherwise, the first item (a `DefaultPlaceholder`) will be returned.
-    """
-    items = (first_item,) + extra_items
-    for item in items:
-        if not isinstance(item, DefaultPlaceholder):
-            return item
-    return first_item
-
-
-def match_pydantic_error_url(error_type: str) -> Any:
-    from dirty_equals import IsStr
-
-    return IsStr(regex=rf"^https://errors\.pydantic\.dev/.*/v/{error_type}")

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/_util.py

@@ -1,135 +0,0 @@
-from typing import Any, Dict, NoReturn, Pattern, Tuple, Type, TypeVar, Union
-
-__all__ = [
-    "ProtocolError",
-    "LocalProtocolError",
-    "RemoteProtocolError",
-    "validate",
-    "bytesify",
-]
-
-
-class ProtocolError(Exception):
-    """Exception indicating a violation of the HTTP/1.1 protocol.
-
-    This as an abstract base class, with two concrete base classes:
-    :exc:`LocalProtocolError`, which indicates that you tried to do something
-    that HTTP/1.1 says is illegal, and :exc:`RemoteProtocolError`, which
-    indicates that the remote peer tried to do something that HTTP/1.1 says is
-    illegal. See :ref:`error-handling` for details.
-
-    In addition to the normal :exc:`Exception` features, it has one attribute:
-
-    .. attribute:: error_status_hint
-
-       This gives a suggestion as to what status code a server might use if
-       this error occurred as part of a request.
-
-       For a :exc:`RemoteProtocolError`, this is useful as a suggestion for
-       how you might want to respond to a misbehaving peer, if you're
-       implementing a server.
-
-       For a :exc:`LocalProtocolError`, this can be taken as a suggestion for
-       how your peer might have responded to *you* if h11 had allowed you to
-       continue.
-
-       The default is 400 Bad Request, a generic catch-all for protocol
-       violations.
-
-    """
-
-    def __init__(self, msg: str, error_status_hint: int = 400) -> None:
-        if type(self) is ProtocolError:
-            raise TypeError("tried to directly instantiate ProtocolError")
-        Exception.__init__(self, msg)
-        self.error_status_hint = error_status_hint
-
-
-# Strategy: there are a number of public APIs where a LocalProtocolError can
-# be raised (send(), all the different event constructors, ...), and only one
-# public API where RemoteProtocolError can be raised
-# (receive_data()). Therefore we always raise LocalProtocolError internally,
-# and then receive_data will translate this into a RemoteProtocolError.
-#
-# Internally:
-#   LocalProtocolError is the generic "ProtocolError".
-# Externally:
-#   LocalProtocolError is for local errors and RemoteProtocolError is for
-#   remote errors.
-class LocalProtocolError(ProtocolError):
-    def _reraise_as_remote_protocol_error(self) -> NoReturn:
-        # After catching a LocalProtocolError, use this method to re-raise it
-        # as a RemoteProtocolError. This method must be called from inside an
-        # except: block.
-        #
-        # An easy way to get an equivalent RemoteProtocolError is just to
-        # modify 'self' in place.
-        self.__class__ = RemoteProtocolError  # type: ignore
-        # But the re-raising is somewhat non-trivial -- you might think that
-        # now that we've modified the in-flight exception object, that just
-        # doing 'raise' to re-raise it would be enough. But it turns out that
-        # this doesn't work, because Python tracks the exception type
-        # (exc_info[0]) separately from the exception object (exc_info[1]),
-        # and we only modified the latter. So we really do need to re-raise
-        # the new type explicitly.
-        # On py3, the traceback is part of the exception object, so our
-        # in-place modification preserved it and we can just re-raise:
-        raise self
-
-
-class RemoteProtocolError(ProtocolError):
-    pass
-
-
-def validate(
-    regex: Pattern[bytes], data: bytes, msg: str = "malformed data", *format_args: Any
-) -> Dict[str, bytes]:
-    match = regex.fullmatch(data)
-    if not match:
-        if format_args:
-            msg = msg.format(*format_args)
-        raise LocalProtocolError(msg)
-    return match.groupdict()
-
-
-# Sentinel values
-#
-# - Inherit identity-based comparison and hashing from object
-# - Have a nice repr
-# - Have a *bonus property*: type(sentinel) is sentinel
-#
-# The bonus property is useful if you want to take the return value from
-# next_event() and do some sort of dispatch based on type(event).
-
-_T_Sentinel = TypeVar("_T_Sentinel", bound="Sentinel")
-
-
-class Sentinel(type):
-    def __new__(
-        cls: Type[_T_Sentinel],
-        name: str,
-        bases: Tuple[type, ...],
-        namespace: Dict[str, Any],
-        **kwds: Any
-    ) -> _T_Sentinel:
-        assert bases == (Sentinel,)
-        v = super().__new__(cls, name, bases, namespace, **kwds)
-        v.__class__ = v  # type: ignore
-        return v
-
-    def __repr__(self) -> str:
-        return self.__name__
-
-
-# Used for methods, request targets, HTTP versions, header names, and header
-# values. Accepts ascii-strings, or bytes/bytearray/memoryview/..., and always
-# returns bytes.
-def bytesify(s: Union[bytes, bytearray, memoryview, int, str]) -> bytes:
-    # Fast-path:
-    if type(s) is bytes:
-        return s
-    if isinstance(s, str):
-        s = s.encode("ascii")
-    if isinstance(s, int):
-        raise TypeError("expected bytes-like object, not int")
-    return bytes(s)

spaces/Datasculptor/MusicGen/tests/data/test_audio_utils.py

@@ -1,110 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import julius
-import torch
-import pytest
-
-from audiocraft.data.audio_utils import (
-    _clip_wav,
-    convert_audio_channels,
-    convert_audio,
-    normalize_audio
-)
-from ..common_utils import get_batch_white_noise
-
-
-class TestConvertAudioChannels:
-
-    def test_convert_audio_channels_downmix(self):
-        b, c, t = 2, 3, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=2)
-        assert list(mixed.shape) == [b, 2, t]
-
-    def test_convert_audio_channels_nochange(self):
-        b, c, t = 2, 3, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=c)
-        assert list(mixed.shape) == list(audio.shape)
-
-    def test_convert_audio_channels_upmix(self):
-        b, c, t = 2, 1, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=3)
-        assert list(mixed.shape) == [b, 3, t]
-
-    def test_convert_audio_channels_upmix_error(self):
-        b, c, t = 2, 2, 100
-        audio = get_batch_white_noise(b, c, t)
-        with pytest.raises(ValueError):
-            convert_audio_channels(audio, channels=3)
-
-
-class TestConvertAudio:
-
-    def test_convert_audio_channels_downmix(self):
-        b, c, dur = 2, 3, 4.
-        sr = 128
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=2)
-        assert list(out.shape) == [audio.shape[0], 2, audio.shape[-1]]
-
-    def test_convert_audio_channels_upmix(self):
-        b, c, dur = 2, 1, 4.
-        sr = 128
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=3)
-        assert list(out.shape) == [audio.shape[0], 3, audio.shape[-1]]
-
-    def test_convert_audio_upsample(self):
-        b, c, dur = 2, 1, 4.
-        sr = 2
-        new_sr = 3
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c)
-        out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr)
-        assert torch.allclose(out, out_j)
-
-    def test_convert_audio_resample(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        new_sr = 2
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c)
-        out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr)
-        assert torch.allclose(out, out_j)
-
-
-class TestNormalizeAudio:
-
-    def test_clip_wav(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        _clip_wav(audio)
-        assert audio.abs().max() <= 1
-
-    def test_normalize_audio_clip(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='clip')
-        assert norm_audio.abs().max() <= 1
-
-    def test_normalize_audio_rms(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='rms')
-        assert norm_audio.abs().max() <= 1
-
-    def test_normalize_audio_peak(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='peak')
-        assert norm_audio.abs().max() <= 1

spaces/Datasculptor/StyleGAN-NADA/op/fused_act_cpu.py

@@ -1,41 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.nn import functional as F
-
-
-module_path = os.path.dirname(__file__)
-
-
-class FusedLeakyReLU(nn.Module):
-    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
-        super().__init__()
-
-        self.bias = nn.Parameter(torch.zeros(channel))
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
-    if input.device.type == "cpu":
-        if bias is not None:
-            rest_dim = [1] * (input.ndim - bias.ndim - 1)
-            return (
-                F.leaky_relu(
-                    input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
-                )
-                * scale
-            )
-
-        else:
-            return F.leaky_relu(input, negative_slope=0.2) * scale
-
-    else:
-        return FusedLeakyReLUFunction.apply(
-            input.contiguous(), bias, negative_slope, scale
-        )
-